Method and device for tuning base station antenna and electronic equipment

Abstract

The invention discloses a base station antenna tuning method and apparatus, and an electronic device. The method comprises the following steps: acquiring multiple types of acquisition data in an antenna feeder system; creating grids according to the distribution area of the measurement data in the multiple types of collection data, and matching the measurement data with the grids; determining grids meeting a first preset condition from the grids as problem grids, and determining a problem area according to the problem grids; determining a to-be-adjusted and optimized cell corresponding to the problem area, and determining a target adjusting and optimizing scheme of the to-be-adjusted and optimized cell; and an adjusting and optimizing instruction is generated according to the target adjusting and optimizing scheme, the adjusting and optimizing instruction is sent to the base station, and the base station adjusts the angle of the base station antenna according to the adjusting and optimizing instruction. The technical problems of low efficiency and poor accuracy due to the fact that the optimization method of the antenna feeder problem in the prior art is mainly completed manually are solved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, device and electronic equipment for tuning a base station antenna. Background Art

[0002] In the current mobile communication network, unreasonable antenna and feeder parameter settings will lead to a series of problems such as weak or no coverage in the user distribution area, increased interference, uneven traffic load, deterioration of performance KPI indicators, and reduced user experience.

[0003] In related technologies, the discovery, analysis, location, and optimization of antenna feeder problems are mainly done manually, which is inefficient, costly, and has potential safety risks. Antenna feeder optimization solutions rely on engineers' personal experience and intuition, have no scientific data support, have poor accuracy, and require repeated on-site optimization and verification.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a method, device and electronic device for tuning a base station antenna, so as to at least solve the technical problems that the optimization method for antenna feed problem in the related art is mainly completed manually, which has low efficiency and poor accuracy.

[0006] According to one aspect of an embodiment of the present application, a method for tuning a base station antenna is provided, comprising: acquiring multiple types of collected data in an antenna feed 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; determining a grid that meets a first preset condition from the grid as a problem grid, and determining a problem area based on the problem grid; determining a cell to be tuned corresponding to the problem area, and determining a target tuning scheme for the cell to be tuned; generating a tuning instruction based on the target tuning scheme, and sending the tuning instruction to a base station, wherein the base station adjusts the angle of the base station antenna based on the tuning instruction.

[0007] Optionally, creating a grid based on the distribution area of ​​measurement data in multiple types of collected data includes: obtaining size information of the grid, 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 maximum latitude, minimum latitude, maximum longitude and minimum longitude; determining the number of grids based on the boundary information and the size information; and creating a two-dimensional array to represent the grid based on the number of grids.

[0008] Optionally, matching the measurement data with the grid includes: obtaining longitude and latitude information of a 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; determining the row and column numbers of the target grid to which the first sampling point belongs in the two-dimensional array based on the longitude and latitude 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 cell to be optimized corresponding to the problem area includes: acquiring the cell associated with the sampling point in the problem area to obtain the cell to be optimized; sorting the cells to be optimized in descending order according to the number of sampling points contained in the cell to be optimized, and determining the priority of the sorted cell to be optimized.

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

[0011] Optionally, the first optimal solution for each cell to be optimized is scored separately, including: determining a first score of the first optimal solution for each cell to be optimized in the 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 the antenna adjustment; determining a second score of the first optimal solution for each cell to be optimized in the second dimension, wherein the second dimension is used to represent the path loss, and the path loss includes multipath loss and free space path loss; determining a third score of the first optimal solution for each cell to be optimized in the third dimension, wherein the third dimension is used to represent the signal interference intensity, and the signal interference intensity is determined by the ratio of the power of the interference signal to the power of the expected signal; determining a fourth score of the first optimal solution for each cell to be optimized in the fourth dimension, wherein the fourth dimension is used to represent the optimal multipath effect; and determining a total score of the first optimal solution for each cell to be optimized based on the first score, the second score, the third score and the fourth score.

[0012] Optionally, determining a first optimal scheme among multiple tuning schemes includes: obtaining relevant data of a GIS three-dimensional map, a problem area, and a first cell to be optimized; constructing a three-dimensional environmental model based on the relevant data, the GIS three-dimensional map, and the problem area; taking the antenna of the first cell to be optimized as the starting point, emitting rays to the problem area according to the antenna parameters in a first tuning scheme among multiple tuning schemes, and determining the propagation path of the rays in the three-dimensional environmental model, wherein the first tuning scheme is any one of the multiple tuning schemes; evaluating the rank of the channel based on the number of propagation paths, the number of transmitting antennas of the rays, and the number of receiving antennas of the rays to obtain a first evaluation result; when the first evaluation result does not meet the second preset condition, iteratively adjusting the antenna parameters in the next tuning scheme until the evaluation result of the adjusted tuning scheme meets the second preset condition and stopping the iteration; determining the last tuning scheme as the first tuning scheme.

[0013] According to another aspect of an embodiment of the present application, a tuning device for a base station antenna is also provided, including: an acquisition module, used to acquire multiple types of collected data in an antenna feed system; a matching module, used to create a grid based on the distribution area of ​​measurement data in the multiple types of collected data, and match the measurement data with the grid; a first determination module, used to determine a grid that meets a first preset condition from the grid as a problem grid, and determine the problem area based on the problem grid; a second determination module, used to determine a cell to be tuned corresponding to the problem area, and determine a target tuning scheme for the cell to be tuned; a sending module, used to generate a tuning instruction based on the target tuning scheme, and send the tuning instruction to the base station, wherein the base station adjusts the angle of the base station antenna based on the tuning instruction.

[0014] According to another aspect of the embodiment of the present application, an electronic device is also provided, including: a memory for storing program instructions; a processor, connected to the memory, for executing program instructions to implement the following functions: obtaining multiple types of collected data in the antenna feed system; creating a grid based on the distribution area of ​​the measurement data in the multiple types of collected data, and matching the measurement data with the grid; determining a grid that meets a first preset condition from the grid as a problem grid, and determining a problem area based on the problem grid; determining a cell to be optimized corresponding to the problem area, and determining a target tuning plan for the cell to be optimized; generating a tuning instruction based on the target tuning plan, and sending the tuning instruction to a base station, wherein the base station adjusts the angle of the base station antenna based on the tuning instruction.

[0015] According to another aspect of the embodiments of the present application, a non-volatile storage medium is provided, which includes 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 the present application, a computer program product is provided, including computer instructions, which implement the above-mentioned base station antenna tuning method when executed by a processor.

[0017] In an embodiment of the present application, multiple types of collected data in an antenna feed system are obtained; 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; a grid that meets a first preset condition is determined as a problem grid from the grid, and a problem area is determined based on the problem grid; a cell to be optimized corresponding to the problem area is determined, and a target tuning scheme for the cell to be optimized is determined; a tuning instruction is generated based on the target tuning scheme, and the tuning instruction is sent to a base station, wherein the base station adjusts the angle of the base station antenna based on the tuning instruction, thereby achieving the purpose of automatically identifying and locating network problems in the antenna feed system, thereby achieving the technical effect of intelligent optimization of antenna feed parameters, and further solving the technical problem that the optimization method of the antenna feed problem in the related art is mainly completed manually, with low efficiency and poor accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal for implementing a method for tuning a base station antenna according to an embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for tuning a base station antenna according to an embodiment of the present application;

[0021] Figure 3 It is a structural diagram of a base station antenna tuning device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work 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 and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] First, some nouns or terms that appear in the process of explaining the embodiments of the present application are subject to the following explanations:

[0025] Northbound interface: (North bound Interface) is an interface for manufacturers or operators to access and manage the network, that is, an interface provided upward, which allows upper-layer systems or third-party systems to access and manage the network. The northbound interface plays a key role in the network management architecture, especially in operator networks and NGN (next generation converged networks). Management is implemented in layers, and this network management can be divided into three layers: application layer, data processing layer, and data management layer. The data interaction between the application layer and the data processing layer defines the northbound interface. Since the application layer is located above the data processing layer, this interface is called the northbound interface.

[0026] Density clustering: is a cluster analysis method that identifies clusters based on the density distribution of points in the data space. In density clustering, clusters are defined as consisting of high-density points separated from each other by low-density areas. This clustering method does not require the number of clusters to be specified in advance, nor is it restricted by the geometric shape of the clusters. It can discover clusters of arbitrary shapes and is robust to noise and outliers.

[0027] Ray tracing technology: It is to predict the coverage and quality of signals by simulating the propagation of radio waves in three-dimensional space, including direct radiation, reflection, diffraction and transmission. It can provide more accurate prediction results than traditional empirical models because it takes into account the impact 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). The higher the RSRP value, the stronger the signal, which usually means better network coverage and potentially better communication quality.

[0029] Signal-to-Interference-plus-Noise Ratio (SINR): The signal-to-interference-plus-noise ratio is an indicator of signal quality. It not only takes into account the strength of the signal, but also the background noise and interference from other signal sources.

[0030] Channel Quality Indicator (CQI): It 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 the MIMO system (Multiple-Input Multiple-Output), it reflects the size of the channel spatial freedom. In a MIMO system, the RI value indicates the number of independent data streams that the UE can receive simultaneously.

[0032] In related technologies, the discovery, analysis, location, and optimization of antenna feeder problems are mainly done manually, which is inefficient, costly, and has potential safety risks. Antenna feeder optimization solutions rely on engineers' personal experience and intuition, have no scientific data support, have poor accuracy, and require repeated on-site optimization and verification.

[0033] In addition, with the evolution of the network, the demand for service experience rate is getting higher and higher, and the antenna capability is also developing from ordinary antennas to multi-frequency antennas, MIMO antennas and large-scale array antennas (such as Massive MIMO antennas). Antenna feed optimization not only solves the coverage requirements, but also pays more attention to performance tuning (such as improving the perception rate and throughput rate). Especially for 5G, 5G-A and future millimeter wave scenarios, the performance tuning of Massive MIMO antennas (such as creating multipath, improving RANK flow, improving rate and throughput rate, etc.) is particularly important. However, in the face of complex wireless environments, it is difficult to complete antenna performance tuning based on manual experience.

[0034] In order to solve the problems existing in the related art, the present application embodiment provides a method for tuning a base station antenna, which can be run on Figure 1In the computer terminal shown, the computer terminal is explained below.

[0035] The base station antenna tuning method embodiment provided in the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a method for tuning a base station antenna. Figure 1 As shown, the computer terminal 10 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions connected via a wired and / or wireless network. 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 can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0036] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[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 tuning method of the base station antenna in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the above-mentioned tuning method of the base station antenna is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0038] The transmission module 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

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

[0040] It should be noted that, in some optional embodiments, the above Figure 1 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0041] In the above operating environment, an embodiment of the present application provides an embodiment of a method for tuning 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, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0042] Figure 2 is a flow chart of a method for tuning a base station antenna according to an embodiment of the present application, such as Figure 2As shown, the method comprises the following steps:

[0043] Step S202: Acquire multiple types of collected data in the antenna feeder system.

[0044] In the above step S202, data collection mainly includes two aspects: network management collection and external import. First, determine the collection data type and field, mainly including north MR data (measurement report, including RSRP, SINR and other signal quality information), network management configuration data (such as antenna power setting, neighboring area configuration), network management KPI indicators, base station engineering parameters (such as base station location, antenna height, etc.), antenna data (including antenna gain, polarization mode and other information), GIS three-dimensional map data (including terrain, building distribution, vegetation and other environmental information) and other types of data. The 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 network management systems, API interfaces or direct database connection technology are used to instantly obtain MR data (including MR longitude and latitude, primary cell RSRP / SINR, neighboring cell RSRP / SINR, CQI, RANK, etc.), network management configuration parameters (including power configuration data, neighboring cell configuration data), and network management indicators (including user perceived rate, throughput, interference level, PRB utilization, etc.).

[0048] For external data import, base station engineering parameters (including station longitude and latitude, station name, cell name, CGI, frequency, PCI, azimuth, longitude and latitude, mechanical downtilt angle, electronic downtilt angle, antenna hanging height), antenna data (including antenna type, gain, polarization mode, electrical parameters, etc.), GIS data (GIS map data disclosed by a third party), etc. are imported through the external interface.

[0049] After acquiring multiple types of collected data, data cleaning is completed through data cleaning algorithms to remove abnormal, duplicate, and erroneous data, correct data formats, and normalize KPI data (such as PRB utilization). The data cleaning rules are as follows:

[0050] (1) Outliers: Let X be a data set, x be any data in the data set, μ be the mean of the data set, σ be the standard deviation, outlier x 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 is the i-th element in the dataset.

[0054] (2) Duplicate values: Let X be a data set. Duplicate value x detection should satisfy: Duplicate item = {x∈X|X.count(x)>1}.

[0055] (3) Error value: Define a reasonable value range for the data. If the data exceeds the range, it is judged to be wrong. If x is a data point, min_val and max_val are the predefined minimum and maximum values. The error value detection rule is: x<min_val∨x> max_valx, x is wrong.

[0056] (4) Check the format: For each data item d in the data set, use the corresponding validation function V to check its format: is_valid(d) = V(d). If is_valid(d) is True, the data item d is in the correct format; otherwise, it is in an incorrect format.

[0057] (5) Data normalization: The PRB utilization index is normalized according to the minimum-maximum method to linearly convert the original data into the range of [0,1], and other data are not processed. The formula is as follows:

[0058]

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

[0060] Step S204: creating a grid according to the distribution area of ​​the measurement data in the multiple types of collected data, and matching the measurement data with the grid.

[0061] In the above step S204, by creating a grid, the measurement data can be mapped to the geographic space to form a gridded data distribution. In this way, the problem area, that is, the area with poor signal quality, insufficient coverage or excessive interference, can be more intuitively identified. Each grid represents the signal quality data within a certain geographic range. By matching the MR data with the grid, the distribution of the signal quality measurement value in each grid can be statistically calculated.

[0062] Step S206: determine a grid that meets the first preset condition from the grids as a problem grid, and determine a problem area based on the problem grid.

[0063] In the above step S206, according to the first preset condition, the problem grid can be automatically identified, and by aggregating multiple adjacent problem grids into a problem area, the target area of ​​network optimization can be more accurately determined, providing a basis for subsequent antenna feeder parameter adjustment.

[0064] Step S208, determining the cell to be optimized corresponding to the problem area, and determining the target optimization solution for the cell to be optimized.

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

[0066] Step S210: Generate a tuning instruction according to the target tuning solution, and send the tuning instruction to the base station, wherein the base station adjusts the angle of the base station antenna according to the tuning instruction.

[0067] In the above step S210, based on the determined target tuning solution, the system automatically generates a tuning instruction and sends it to the base station through the network management system. After receiving the tuning instruction, the base station will automatically adjust the angle of its antenna to implement the optimization solution. Automatic execution of the tuning instruction avoids the inefficiency and inaccuracy of manual on-site adjustment.

[0068] In step S204 of the above-mentioned base station antenna tuning method, a grid is created based on the distribution area of ​​the measurement data in the multiple types of collected data, including: obtaining size information of the grid, 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, the minimum latitude, the maximum longitude and the minimum longitude; determining the number of grids based on the boundary information and the 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 the measurement data with the grid includes: obtaining longitude and latitude information of a 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; determining the row and column numbers of the target grid to which the first sampling point belongs in the two-dimensional array according to the longitude and latitude 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 the embodiment of the present application, the grids corresponding to the measurement data domain are matched by the following steps:

[0071] (1) Determine the grid size: Set the size information of the grid, which includes the length and width of the grid. If the grid is a square, determine the side length of the grid. For example, if the grid is a square, assume that its side length is A meters.

[0072] (2) Determine the first boundary information of the MR data (ie, measurement data) distribution area: the minimum longitude λmin, the maximum longitude λmax, the minimum latitude φmin, and the 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 initially has no MR sampling points.

[0077] (5) Calculate grid boundaries: For each grid, calculate its longitude and latitude boundaries. For the grid in row i and column j, its longitude boundary and It can be calculated as follows: Latitude Boundary and It can be calculated as follows: Among them, φavg is the average value of the latitude of the grid, which can be the average of φmin and φmax.

[0078] (6) Matching MR sampling points to grids: For each MR sampling point, such as the first sampling point mentioned above, whose longitude and latitude 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 grid.

[0079] (7) Ensure that each grid has at least one MR sampling point: If there is no MR sampling point in a grid, delete the grid.

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

[0081] After matching the measurement data and sampling points to the corresponding grid, 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 with the serving cell level > E (e.g., < -103 dBm) and the level difference between the serving cell and F (e.g., 3) neighboring cells < 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 if it meets any problem condition defined in Conditions;

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

[0093] Among them: Any() function checks whether at least one of its parameters is true. Condition() function returns the proportion of sampling points that meet the condition according to a specific threshold value. ThresholdPercentage is the threshold percentage used to determine whether the problem is true;

[0094] (6) Specific condition function:

[0095] ① Weak cover:

[0096]

[0097] ②Poor quality conditions:

[0098] ③ Overlapping coverage conditions:

[0099]

[0100] ④Low CQI conditions:

[0101] ⑤Low Rank Conditions:

[0102] Use the density clustering algorithm (DBSCAN) to aggregate the problem grids into problem areas. The detailed process of the algorithm is as follows:

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

[0104] ① Core Point: If a point has at least min_samples neighbors within the specified radius ε, the point is called a core point.

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

[0106] ③Noise 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., grid) in the dataset corresponding to the problem grid as the starting point.

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

[0110] ③ Density connection: If there is a point such that both points are density-reachable from this point, then the two points are considered to be density-connected.

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

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

[0113] The DBSCAN algorithm can aggregate geographically adjacent grids with common problem characteristics into problem areas based on the density relationship between grids. In this way, it is possible to determine which areas in the network have coverage or signal quality problems and need further optimization. On the other hand, an important feature of the DBSCAN algorithm is that it can identify and exclude isolated points (i.e., noise points), avoiding the inclusion of isolated problem grids that do not affect the overall network performance in the analysis of problem areas. This helps to focus on continuous problem areas that truly affect user experience and network performance, and improve the pertinence and efficiency of optimization solutions. Through the DBSCAN algorithm, the system can accurately identify areas with serious overlapping coverage problems or generally poor signal quality. These areas may be caused by signal overlap from multiple cells or sectors, and need to be optimized by adjusting antenna feed parameters (such as azimuth and downtilt).

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

[0115] In some embodiments of the present application, the system identifies the cells associated with the MR (Measurement Report) sampling points in the problem area by analyzing the sampling points of the MR (Measurement Report) sampling points. These cells are the cells to be optimized. The system will sort them according to the number of sampling points contained in these cells. The more sampling points there are, the more serious the problems of the cell in terms of network coverage and signal quality may be, so they need to be optimized first. By sorting from large to small, the system can determine the priority of the cells to be optimized, and give priority to the cells with the most prominent problems, thereby improving the optimization efficiency and pertinence.

[0116] In step S208 of the above-mentioned method for tuning the base station antenna, determining a target tuning scheme for the cell to be tuned includes: obtaining 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; 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 a different antenna parameter adjustment; scoring the first optimal scheme of each cell to be tuned respectively, 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 separately, including: determining a first score of the first optimal solution for each cell to be optimized in the 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 the antenna adjustment; determining a second score of the first optimal solution for each cell to be optimized in the second dimension, wherein the second dimension is used to represent the path loss, and the path loss includes multipath loss and free space path loss; determining a third score of the first optimal solution for each cell to be optimized in the third dimension, wherein the third dimension is used to represent the signal interference strength, and the signal interference strength is determined by the ratio of the power of the interference signal to the power of the expected signal; determining a fourth score of the first optimal solution for each cell to be optimized in the fourth dimension, wherein the fourth dimension is used to represent the optimal multipath effect; and determining a total score of the first optimal solution for each cell to be optimized based on the first score, the second score, the third score and the fourth score.

[0118] In some embodiments of the present application, for each cell to be optimized, the system first obtains the data of the cell, including the current position, downtilt angle, azimuth angle and other parameters of the antenna, as well as environmental information related to the cell, such as GIS three-dimensional map data. The system generates multiple tuning schemes for each cell to be optimized. Each scheme corresponds to different antenna parameter adjustments, such as adjusting the azimuth angle, downtilt angle, etc. of the antenna, in an attempt to improve signal coverage and reduce interference. For each cell, the system evaluates and determines the first optimal scheme from these tuning 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 to reduce the potential impact on other cells. The score for calculating the antenna adjustment amplitude is 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 the path loss under each tuning scheme, including diffraction, reflection and direct radiation of the signal, to ensure that the signal can effectively reach the target area. Signal interference strength (third dimension): The signal interference strength is determined by calculating the power ratio of the interference signal to the desired signal. The goal is to find a solution that can minimize interference, thereby improving signal clarity and user communication quality. Multipath effect (fourth dimension): The evaluation of multipath effect is to optimize the performance of the MIMO (Multiple-Input Multiple-Output) system. The system evaluates the multipath signal under the scheme through ray tracing technology, with the goal of maximizing the rank (RANK) of the channel, that is, increasing the number of independent data streams, thereby improving data transmission rate and network performance. The system will comprehensively consider the scores of these four dimensions and calculate a total score for each optimal solution. The solution with the highest total score will be determined as the target tuning solution for the cell.

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

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

[0121] (2) Path loss (L): A 3D ray tracing model is used to calculate the path loss in the actual environment, 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): The interference impact can be evaluated by analyzing the signal interference of adjacent sectors. The calculation formula is: I = Pinterfering / Pdesired, where PinterferingPinterfering is the power of the interfering signal and PdesiredPdesired is the power of the desired signal.

[0123] ④Multipath effect (M): In antenna optimization, by adjusting the azimuth and downtilt angles of the base station and increasing the reflection of the ground and buildings, the number of independent and unrelated channels can be distinguished in space, the channel's space division multiplexing capability can be increased, and the number of space division multiplexing streams can be increased. 3D ray tracing technology can accurately predict the propagation path and characteristics of the signal by simulating the propagation of radio waves in the actual environment, including direct radiation, reflection, refraction and scattering.

[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] Among them, A(x) is the antenna feed adjustment amplitude score of solution x (i.e., the first optimal solution mentioned above), i.e., the first score mentioned above, L(x) is the path loss score of solution x, i.e., the second score mentioned above, I(x) is the interference impact score of solution x, i.e., the third score mentioned above, M(x) is the multipath effect score of solution x, i.e., the fourth score mentioned above, and w A , w L , w I , w M is the weight coefficient corresponding to each scoring dimension.

[0127] In the above steps, a first optimal solution among multiple tuning solutions is determined, including: obtaining relevant data of a GIS three-dimensional map, a problem area, and a first cell to be optimized; constructing a three-dimensional environmental model based on the relevant data, the GIS three-dimensional map, and the problem area; taking the antenna of the first cell to be optimized as the starting point, emitting rays to the problem area according to the antenna parameters in the first tuning solution among multiple tuning solutions, and determining the propagation path of the rays in the three-dimensional environmental model, wherein the first tuning solution is any one of the multiple tuning solutions; evaluating the rank of the channel based on the number of propagation paths, the number of antennas at the transmitting end of the rays, and the number of antennas at the receiving end of the rays to obtain a first evaluation result; when the first evaluation result does not meet the second preset condition, iteratively adjusting the antenna parameters in the next tuning solution until the evaluation result of the adjusted tuning solution meets the second preset condition and stopping the iteration; determining the last tuning solution as the first tuning solution.

[0128] In an embodiment of the present application, for the first optimal solution determined, the system will construct a three-dimensional environmental model, which includes GIS three-dimensional map data, characteristics of the problem area, and the antenna configuration of the first cell to be tuned. Then, the system will use the antenna as the starting point, emit rays according to the first tuning plan, and trace the propagation path of the rays in the three-dimensional model to evaluate the rank (RANK) of the channel. If the evaluation result (RANK value) does not meet the preset optimization conditions (ie, the second preset conditions), the system will iteratively adjust the antenna parameters to generate the next tuning plan. This process will be repeated until a tuning plan that meets the optimization conditions (such as the RANK value reaches the target and the signal coverage is improved) is found. The final solution is the first tuning plan, which is the best tuning plan for the cell.

[0129] In the above process, the description of the channel rank (RANK) is 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 number of independent paths is the number of independent paths identified by 3D ray tracing technology (i.e., the number of the above-mentioned propagation paths).

[0131] Finally, the system generates tuning instructions based on the target tuning plan for each cell to be tuned, and sends these instructions to the base station. After receiving the instructions, the base station adjusts the azimuth and downtilt angles of the antenna to execute the tuning plan. This process is automated, avoiding the inefficiency and inaccuracy of manual adjustments on the station. At the same time, the system also monitors the performance of the adjusted network to ensure the optimization effect.

[0132] Figure 3is a structural diagram of a base station antenna tuning device according to an embodiment of the present application, such as Figure 3 As shown, the device comprises:

[0133] An acquisition module 30 is used to acquire multiple types of collected data in the antenna feeder system;

[0134] A matching module 32, for creating a grid according to the distribution area of ​​the measurement data in the multiple types of collected data, and matching the measurement data with the grid;

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

[0136] A second determination module 36 is used to determine a cell to be optimized corresponding to the problem area, and to determine a target optimization solution for the cell to be optimized;

[0137] The sending module 38 is used to generate a tuning instruction according to the target tuning solution, and send the tuning instruction to the base station, wherein the base station adjusts the angle of the base station antenna according to the tuning instruction.

[0138] In the matching module in the tuning device of the above-mentioned base station antenna, the matching module is also used to obtain the size information of the grid, 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, the minimum latitude, the maximum longitude and the minimum longitude; determine the number of grids based on the boundary information and the size information; and create a two-dimensional array to represent the grid based on the number of grids.

[0139] In the matching module in the tuning device of the above-mentioned base station antenna, the matching module is also used to obtain the longitude and latitude information of a 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 measurement data; based on the longitude and latitude information and the first boundary information, determine the row and column numbers of the target grid to which the first sampling point belongs in the two-dimensional array; and add the first sampling point and the first measurement data corresponding to the first sampling point to the corresponding target grid.

[0140] In the second determination module in the above-mentioned base station antenna tuning device, the second determination module is also 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 order from large to small, and the priorities of the sorted cells to be tuned are determined.

[0141] In the second determination module in the above-mentioned base station antenna tuning device, the second determination module is also used to obtain 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 a first optimal scheme among the multiple tuning schemes, wherein each tuning scheme corresponds to a different antenna parameter adjustment; score the first optimal scheme for each cell to be tuned respectively, and determine the first optimal scheme with the highest score as the target tuning scheme.

[0142] In the second determination module in the tuning device of the above-mentioned base station antenna, the second determination module is also used to determine the first score of the first optimal solution for each cell to be optimized in the 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 the antenna adjustment; determine the second score of the first optimal solution for each cell to be optimized in the second dimension, wherein the second dimension is used to represent the path loss, and the path loss includes multipath loss and free space path loss; determine the third score of the first optimal solution for each cell to be optimized in the third dimension, wherein the third dimension is used to represent the signal interference strength, and the signal interference strength is determined by the ratio of the power of the interference signal to the power of the expected signal; determine the fourth score of the first optimal solution for each cell to be optimized in the fourth dimension, wherein the fourth dimension is used to represent the optimal multipath effect; determine the total score of the first optimal solution for each cell to be optimized based on the first score, the second score, the third score and the fourth score.

[0143] In the second determination module in the above-mentioned tuning device of the base station antenna, the second determination module is also used to obtain relevant data of the GIS three-dimensional map, the problem area and the first cell to be tuned; construct a three-dimensional environmental model based on the relevant data, the GIS three-dimensional map and the problem area; take the antenna of the first cell to be tuned as the starting point, emit rays to the problem area according to the antenna parameters in the first tuning scheme among multiple tuning schemes, and determine the propagation path of the rays in the three-dimensional environmental model, wherein the first tuning scheme is any one of the multiple tuning schemes; evaluate the rank of the channel based on the number of propagation paths, the number of antennas at the transmitting end of the rays, and the number of antennas at the receiving end of the rays to 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 tuning scheme until the evaluation result of the adjusted tuning scheme meets the second preset condition and stops the iteration; determine the last tuning scheme as the first tuning 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 of the base station antenna is shown, so the relevant explanations in the above-mentioned tuning method of the base station antenna are also applicable to the tuning device of the base station antenna, and will not be repeated here.

[0145] An embodiment of the present application also provides an electronic device, which includes a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory, and is used to execute the program instructions to implement the following functions: obtaining multiple types of collected data in the antenna feed system; creating a grid based on the distribution area of ​​the measurement data in the multiple types of collected data, and matching the measurement data with the grid; determining a grid that meets a first preset condition from the grid as a problem grid, and determining a problem area based on the problem grid; determining a cell to be optimized corresponding to the problem area, and determining a target tuning plan for the cell to be optimized; generating a tuning instruction based on the target tuning plan, and sending the tuning instruction to a base station, wherein the base station adjusts the angle of the base station antenna based on the tuning instruction.

[0146] It should be noted that the above electronic equipment is used to execute Figure 2 The tuning method of the base station antenna shown in the figure, therefore the relevant explanations and instructions in the above-mentioned tuning method of the base station antenna are also applicable to the electronic device, and will not be repeated here.

[0147] An embodiment of the present application further provides a non-volatile storage medium, which includes 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.

[0148] The embodiment of the present application further provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the base station antenna tuning method in each embodiment of the present application.

[0149] The embodiments of the present application also provide a computer program, which, when executed by a processor, implements the steps of the base station antenna tuning method in each embodiment of the present application.

[0150] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0151] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description 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. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

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

[0154] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0155] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present 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, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0156] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for tuning a base station antenna, characterized in that: include: Obtain multiple types of collected data in the antenna feeder system; Creating a grid according to the distribution area of ​​the measurement data in the multiple types of collected data, and matching the measurement data with the grid; Determining a grid that meets a first preset condition from the grids as a problem grid, and determining a problem area based on the problem grid; Determining a cell to be optimized corresponding to the problem area, and determining a target optimization solution for the cell to be optimized; Generate a tuning instruction according to the target tuning scheme, and send the tuning instruction to a base station, wherein the base station adjusts the angle of the base station antenna according to the tuning instruction.

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

3. The method according to claim 2, characterized in that Matching the measurement data with the grid includes: Acquire the latitude and longitude information of a 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; Determine the row and column numbers of the target grid to which the first sampling point belongs in the two-dimensional array according to the longitude and latitude information and the first boundary information; 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: Determining a cell to be optimized corresponding to the problem area includes: Acquire cells associated with the sampling points in the problem area to obtain cells to be optimized; According to the number of sampling points included in the cells to be optimized, the cells to be optimized are sorted in descending order, and the priorities of the sorted cells to be optimized are determined.

5. The method according to claim 1, characterized in that Determining a target optimization scheme for the cell to be optimized includes: Acquire a first cell to be optimized among the cells to be optimized, wherein the first cell to be optimized is any one of the cells to be optimized; Determine multiple tuning schemes for the first cell to be optimized, and determine a first optimal scheme among the multiple tuning schemes, wherein each tuning scheme corresponds to a different antenna parameter adjustment; The first optimal solution of each cell to be optimized is scored respectively, and the first optimal solution with the highest score is determined as the target optimization solution.

6. The method according to claim 5, characterized in that The first optimal solution of each cell to be optimized is scored separately, including: Determine a first score of a first optimal solution for each cell to be optimized in a first dimension, wherein the first dimension is used to represent an antenna adjustment amplitude, and the antenna adjustment amplitude is determined by parameters before and after the antenna is adjusted; Determine a second score of the first optimal solution of each cell to be optimized in the second dimension, wherein the second dimension is used to represent path loss, and the path loss includes multipath loss and free space path loss; Determine a third score of the first optimal solution of each cell to be optimized in a third dimension, wherein the third dimension is used to represent signal interference strength, and the signal interference strength is determined by a ratio of the power of the interference signal to the power of the desired signal; Determine a fourth score of the first optimal solution of each cell to be optimized in a fourth dimension, wherein the fourth dimension is used to indicate an optimal multipath effect; A total score of the first optimal solution for each cell to be optimized is determined according to the first score, the second score, the third score, and the fourth score.

7. The method according to claim 5, characterized in that Determining a first optimal solution among the multiple tuning solutions includes: Obtaining relevant data of the GIS three-dimensional map, the problem area, and the first cell to be optimized; Constructing a three-dimensional environmental model based on the relevant data, the GIS three-dimensional map and the problem area; Taking the antenna of the first cell to be optimized as a starting point, emitting rays to the problem area according to antenna parameters in a first tuning scheme among the multiple tuning schemes, and determining a propagation path of the rays in the three-dimensional environment model, wherein the first tuning scheme is any one of the multiple tuning schemes; According to the number of the propagation paths, the number of transmitting end antennas of the rays, and the number of receiving end antennas of the rays, the rank of the channel is evaluated to 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 tuning scheme until the evaluation result of the adjusted tuning scheme meets the second preset condition and stops iterating; The last optimization solution is determined as the first optimization solution.

8. A tuning device for a base station antenna, characterized in that: include: An acquisition module is used to acquire multiple types of collected data in the antenna feeder system; A matching module, used to create a grid according to the distribution area of ​​the measurement data in the multiple types of collected data, and match the measurement data with the grid; A first determination module, used to determine a grid that meets a first preset condition from the grids as a problem grid, and determine a problem area based on the problem grid; A second determination module is used to determine a cell to be optimized corresponding to the problem area, and determine a target optimization solution for the cell to be optimized; The sending module is used to generate a tuning instruction according to the target tuning scheme, and send the tuning instruction to the base station, wherein the base station adjusts the angle of the base station antenna according to the tuning instruction.

9. An electronic device, characterized in that: include: A memory for storing program instructions; A processor is connected to the memory and is used to execute program instructions to implement the following functions: acquiring multiple types of collected data in the antenna feed system; creating a grid according to the distribution area of ​​the measurement data in the multiple types of collected data, and matching the measurement data with the grid; determining a grid that meets a first preset condition from the grid as a problem grid, and determining a problem area according to the problem grid; Determining a cell to be optimized corresponding to the problem area, and determining a target optimization solution for the cell to be optimized; Generate a tuning instruction according to the target tuning scheme, and send the tuning instruction to a base station, wherein the base station adjusts the angle of the base station antenna according to the tuning instruction.

10. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the base station antenna tuning method according to any one of claims 1 to 7 by running the computer program.

11. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by a processor, the base station antenna tuning method described in any one of claims 1 to 7 is implemented.

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