Rural base station planning method and system, electronic equipment and storage medium

Abstract

The invention discloses a rural base station planning method and system, electronic equipment and a storage medium. The method comprises the steps that a to-be-recognized rural remote sensing image is acquired; performing identification analysis on the to-be-identified rural remote sensing image through a preset building identification model to obtain preset building data; performing clustering analysis on the preset building data through a preset clustering algorithm to obtain preset centralized region data; and carrying out site planning according to the preset centralized area data and the current network base station information, and generating an expected base station planning site. According to the embodiment of the invention, automatic planning of the rural base station can be realized, and the site identification accuracy and planning efficiency are effectively improved. The method can be widely applied to the technical field of wireless communication planning.

Description

Technical Field

[0001] The present application relates to the technical field of wireless communication planning, and in particular to a rural base station planning method, system, electronic device and storage medium. Background Art

[0002] In order to improve the wireless network coverage in rural areas, it is necessary to plan and build base stations in the blank coverage areas in rural areas. In the related technologies, due to the large area of ​​rural areas, scattered residents, and the lack of concentrated MR grid positioning data like urban areas, manual point selection is time-consuming and laborious, and easy to miss. In addition, the current base station planning method is mainly aimed at urban areas, indoor areas and weak coverage areas. The planning of hollow areas is relatively simple, and the site selection is rough, making it difficult to achieve accurate planning of blank coverage areas in rural areas.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a rural base station planning method, system, electronic device and storage medium, which can realize automatic planning of rural base stations and effectively improve the accuracy of site identification and planning efficiency.

[0005] To achieve the above object, an embodiment of the present application provides a rural base station planning method, the method comprising the following steps:

[0006] Acquire remote sensing images of rural areas to be identified;

[0007] The preset building recognition model is used to identify and analyze the rural remote sensing image to be identified, so as to obtain preset building data;

[0008] Performing cluster analysis on the preset building data by using a preset clustering algorithm to obtain preset concentrated area data;

[0009] Site planning is performed based on the preset centralized area data and existing network base station information to generate expected base station planning sites.

[0010] In some embodiments, before performing the identification analysis of the to-be-identified rural remote sensing image using a preset building identification model to obtain preset building data, the method further includes:

[0011] Acquire a remote sensing image data set, and perform a first preprocessing on the remote sensing image data set to obtain a model training data set; wherein the first preprocessing includes size adjustment, image standardization, and data enhancement;

[0012] Constructing a preset training model; wherein the preset training model includes a visual converter model;

[0013] The model training data set is input into the visual converter model to perform model training through a back propagation algorithm and a preset loss function to obtain the preset building recognition model.

[0014] In some embodiments, the identifying and analyzing the to-be-identified rural remote sensing image by using a preset building identification model to obtain preset building data includes:

[0015] Performing a second preprocessing on the rural remote sensing image to be identified to obtain an image block to be identified; wherein the second preprocessing includes image segmentation and image standardization;

[0016] Inputting the image block to be identified into the preset building recognition model to extract features and generate a feature vector;

[0017] Performing mask prediction according to the feature vector to obtain an image segmentation prediction mask;

[0018] Mapping and transforming the image segmentation prediction mask to obtain a preset mask grid map;

[0019] The preset mask grid image is converted into a preset geographic information system data format to obtain the preset building data.

[0020] In some embodiments, performing cluster analysis on the preset building data by using a preset clustering algorithm to obtain preset concentrated area data includes:

[0021] Constructing preset clustering parameters; wherein the preset clustering parameters include a preset neighborhood radius and a neighborhood sample threshold;

[0022] According to the preset clustering parameters, a density-based spatial clustering algorithm is used to identify areas where buildings are clustered in the preset building data to obtain the preset concentrated area data.

[0023] In some embodiments, the identifying, according to the preset clustering parameters, an area where buildings are clustered in the preset building data by a density-based spatial clustering algorithm to obtain the preset concentrated area data includes:

[0024] Detecting an unmarked first building in the preset building data to determine whether the number of second buildings contained in the preset neighborhood radius of the first building is less than the neighborhood sample threshold;

[0025] When it is determined that the number of the second buildings contained in the preset neighborhood radius of the first building is greater than the neighborhood sample threshold, constructing a preset residential area, and adding the second building to a preset candidate set;

[0026] Determine whether the number of the unmarked third buildings in the preset candidate set that include the fourth buildings within the preset neighborhood radius is greater than the neighborhood sample threshold;

[0027] When it is determined that the number of fourth buildings contained in the preset neighborhood radius of the third building is greater than the neighborhood sample threshold, adding the fourth building to the preset candidate set;

[0028] When it is determined that the third building is not marked in a residential area, the third building is added to the preset residential area, and the step of determining whether the number of the fourth buildings included in the preset neighborhood radius of the unmarked third building in the preset candidate set is greater than the neighborhood sample threshold is returned until the preset candidate set is traversed;

[0029] When it is determined that the traversal of the preset candidate set is completed, return to the step of detecting the unmarked first building in the preset building data to determine whether the number of second buildings contained in the preset neighborhood radius of the first building is less than the neighborhood sample threshold, until each of the first buildings in the preset building data is marked to obtain the preset concentrated area data.

[0030] In some embodiments, the existing network base station information includes existing network base station coverage area data;

[0031] The performing site planning according to the preset centralized area data and the existing network base station information to generate the expected base station planning site includes:

[0032] Acquire preset engineering parameter data, so as to calculate the coverage area data of the existing base station through the preset engineering parameter data; wherein the preset engineering parameter data includes a base station identifier, base station coordinate data, base station azimuth, base station frequency band, and base station transmission power;

[0033] Screening the preset centralized area data for buildings according to the existing network base station coverage area data to obtain a number of candidate buildings;

[0034] The candidate building with the largest number of neighboring buildings among the candidate buildings is taken as a target building, so as to generate the expected planned base station site according to the target building.

[0035] In some embodiments, the screening of buildings in the preset centralized area data according to the existing network base station coverage area data to obtain several candidate buildings includes:

[0036] Determine the coverage radius of the existing network base station according to the preset engineering parameter data;

[0037] Eliminate the covered buildings in the preset centralized area data according to the coverage radius of the existing network base station to obtain uncovered buildings;

[0038] Counting the number of buildings within a preset coverage radius of the uncovered building to obtain the number of neighborhood buildings; wherein the preset coverage radius includes an expected coverage radius of a preset planned base station;

[0039] Eliminate the uncovered buildings whose number of neighborhood buildings is less than a preset number threshold to obtain dense buildings;

[0040] The base station distance between the preset existing base station and the preset planned base station is calculated according to the coverage radius of the existing base station and the preset coverage radius, so as to determine the planned base station range through the base station distance, and eliminate the dense buildings located in the planned base station range to obtain a number of candidate buildings.

[0041] To achieve the above object, another aspect of the embodiment of the present application provides a rural base station planning system, the system comprising:

[0042] The first module is used to obtain the rural remote sensing images to be identified;

[0043] The second module is used to identify and analyze the rural remote sensing image to be identified by using a preset building recognition model to obtain preset building data;

[0044] The third module is used to perform cluster analysis on the preset building data by using a preset clustering algorithm to obtain preset concentrated area data;

[0045] The fourth module is used to perform site planning based on the preset centralized area data and the existing network base station information to generate the expected base station planning site.

[0046] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising:

[0047] at least one processor;

[0048] at least one memory for storing at least one program;

[0049] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0050] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0051] The embodiments of the present application include at least the following beneficial effects: The present application provides a rural base station planning method, system, electronic device and storage medium. The scheme obtains the rural remote sensing image to be identified, and identifies and analyzes the rural remote sensing image to be identified through a preset building recognition model to obtain preset building data. Then, the embodiment of the present invention performs cluster analysis on the preset building data through a preset clustering algorithm to obtain preset concentrated area data, and then performs site planning based on the preset concentrated area and the existing network base station information, generates the expected base station planning site, and realizes automatic planning of rural base stations. It is easy to understand that the embodiment of the present invention extracts rural building information through the integration of remote sensing images and deep learning technology, obtains preset building data, and supplements rural building data to provide a basis for rural base station deployment value assessment. At the same time, the rural concentrated area, that is, the preset concentrated area data, is obtained through a preset clustering algorithm, and then the rural base station planning is carried out in combination with the existing network base station information, thereby effectively improving the site identification accuracy and planning efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flowchart of the steps of the rural base station planning method provided by an embodiment of the present invention;

[0053] Figure 2 is a schematic diagram of a sample of a rural remote sensing image to be identified provided by an embodiment of the present invention;

[0054] Figure 3 is a flowchart of the steps of training a preset building recognition model provided by an embodiment of the present invention;

[0055] Figure 4 is a sample schematic diagram of a remote sensing image data set provided by an embodiment of the present invention;

[0056] Figure 5 The embodiment of the present invention provides a flowchart of steps for obtaining preset building data by performing recognition analysis on a rural remote sensing image to be recognized through a preset building recognition model;

[0057] Figure 6 is a schematic diagram of building recognition provided by an embodiment of the present invention;

[0058] Figure 7 is a schematic diagram of base station planning provided by an embodiment of the present invention;

[0059] Figure 8 It is a schematic diagram of the overall technical architecture of the rural base station planning method provided by an embodiment of the present invention;

[0060] Fig. 9 It is a structural diagram of a rural base station planning system provided by an embodiment of the present invention;

[0061] Fig.10It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0063] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0064] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0066] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0067] Geographic Information System (GIS): A system for capturing, storing, managing, analyzing, and displaying geographic spatial data. GIS combines maps, databases, and analysis tools.

[0068] Vision Transformer (ViT): is a deep learning model based on the Transformer architecture for computer vision tasks. Correspondingly, traditional computer vision tasks usually use convolutional neural networks (CNNs), but ViT applies the Transformer model to visual data.

[0069] Convolutional Neural Network (CNN): is a deep learning model used to process data with a grid structure, such as images. Accordingly, a convolutional neural network is a feed-forward neural network that can be applied to image recognition, object detection, image segmentation, or other visual tasks.

[0070] U-Net neural network: It is a convolutional neural network with an encoding-decoding structure that can be used for image segmentation. It can effectively utilize contextual information and accurately locate targets.

[0071] SegNet neural network: is a deep convolutional neural network for image segmentation that can improve segmentation accuracy while maintaining computational efficiency.

[0072] Dice loss: It is a set similarity metric function used to evaluate model performance in image segmentation tasks. It is based on the Dice coefficient, which is often used to measure the similarity between two samples.

[0073] DBSCAN (Density-Based Spatial Clustering of Applications with Noise): It is a density-based clustering algorithm with noise, which can find clusters of arbitrary shapes without specifying the number of clusters in advance, and can effectively handle noisy data.

[0074] In order to improve the wireless network coverage in rural areas, it is necessary to plan and build base stations in rural blank coverage areas. In related technologies, rural areas are large in area, residents are scattered, and there is a lack of MR grid positioning data similar to that concentrated in urban areas. Manual point selection is time-consuming and labor-intensive, and easy to miss. The current automatic planning methods for base stations are mainly aimed at urban areas, indoor areas and weak coverage areas. Some automatic planning algorithms for void areas are simple, do not consider user aggregation, and the site selection is rough. There is a lack of accurate automatic planning for rural blank coverage areas. In addition, building layer data mainly comes from traditional map vendors and is obtained from open source data. A large number of buildings in rural areas are missing, which cannot meet the needs of rural planning and network analysis.

[0075] In view of this, a rural base station planning method, system, electronic device and storage medium are provided in the embodiments of the present invention. The scheme obtains a rural remote sensing image to be identified, and identifies and analyzes the rural remote sensing image to be identified through a preset building recognition model to obtain preset building data. Then, the embodiment of the present invention performs cluster analysis on the preset building data through a preset clustering algorithm to obtain preset concentrated area data, and then performs site planning based on the preset concentrated area and the existing network base station information, generates the expected base station planning site, realizes automatic planning of rural base stations, and effectively improves the site identification accuracy and planning efficiency.

[0076] The rural base station planning method provided in the embodiment of the present application relates to the field of wireless communication planning technology. The rural base station planning method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The cloud server, the server can also be a node server in a blockchain network; the software can be an application that implements the rural base station planning method, etc., but is not limited to the above forms.

[0077] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0078] Figure 1 is an optional flowchart of the rural base station planning method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S110 to S140.

[0079] Step S110: Acquire the rural remote sensing image to be identified.

[0080] Step S120: performing recognition analysis on the rural remote sensing image to be recognized by using a preset building recognition model to obtain preset building data.

[0081] Step S130: performing cluster analysis on the preset building data by using a preset clustering algorithm to obtain preset concentrated area data.

[0082] Step S140: Perform site planning based on the preset centralized area data and the existing network base station information to generate the expected base station planning site.

[0083] In the working process of this specific embodiment, the embodiment of the present invention first obtains the rural remote sensing image to be identified, and then uses the preset building recognition model to identify and analyze the rural remote sensing image to be identified, and obtains the preset building data. Specifically, the rural remote sensing image to be identified in the embodiment of the present invention refers to the remote sensing image of the rural area where base station planning and deployment is required, such as Figure 2 As shown. Among them, the rural remote sensing image to be identified includes the image of the rural area to be identified and the corresponding geographical location information, so as to identify the outline of the rural building. At the same time, the preset building recognition model in the embodiment of the present invention is a deep learning image segmentation recognition model obtained by pre-training, such as a ViT neural network model, a CNN neural network model, a U-Net model or a SegNet model. In the embodiment of the present invention, the rural remote sensing image to be identified is input into the trained preset building recognition model to identify the outline of the rural building in the image, so as to obtain the preset building data. Further, the embodiment of the present invention performs cluster analysis on the preset building data through a preset clustering algorithm to obtain the preset concentrated area data. Specifically, after identifying the location of the rural building, the embodiment of the present invention uses a preset clustering algorithm to divide the building into different residential areas according to its spatial density, that is, to label the buildings that are relatively close in position with the same label, and identify the area where the building is concentrated, that is, the residential area, so as to obtain the preset concentrated area data. Finally, the embodiment of the present invention performs site planning according to the preset concentrated area data and the existing network base station information to generate the expected base station planning site. Specifically, the existing network base station information in the embodiment of the present invention refers to the parameter information of each base station in the existing network, such as the base station location, base station identification, coverage and performance indicators. After identifying the rural residential area, it is necessary to select appropriate locations to plan sites in the corresponding area to cover as many buildings as possible. Therefore, the embodiment of the present invention screens buildings in the preset centralized area data according to the deployment status and corresponding parameter information of each base station in the existing network, that is, the existing network base station information, and then selects the corresponding building location to generate the expected base station planning site to maximize the coverage of the base station while reducing unnecessary overlapping coverage.

[0084] Reference Figure 3 In some embodiments of the present invention, before performing recognition analysis on the rural remote sensing image to be recognized by using a preset building recognition model to obtain preset building data, the rural base station planning method provided by the embodiment of the present invention further includes but is not limited to the following steps:

[0085] Step S210: Acquire a remote sensing image data set, and perform a first preprocessing on the remote sensing image data set to obtain a model training data set, wherein the first preprocessing includes size adjustment, image standardization, and data enhancement.

[0086] Step S220: constructing a preset training model, wherein the preset training model includes a visual converter model.

[0087] Step S230: Input the model training data set into the visual converter model to perform model training through a back propagation algorithm and a preset loss function to obtain a preset building recognition model.

[0088] In this specific embodiment, the embodiment of the present invention first obtains a remote sensing image data set, and performs a first preprocessing on the remote sensing image data set to obtain a model training data set. Specifically, the remote sensing image data set in the embodiment of the present invention includes remote sensing images and labeled building contours (pixels belonging to buildings in the image are marked as 1, and other pixels are marked as 0) for training building segmentation and recognition models, such as Figure 4 As shown. Accordingly, the embodiment of the present invention performs a first preprocessing on the images in the remote sensing image data set to increase the robustness of the model. Among them, the embodiment of the present invention adjusts the images of different sizes to the same size by adjusting the image size (resizing), such as cropping the training pictures to 1024*1024 format, so as to alleviate the deviation problem caused by the difference in image size and facilitate input into the model. At the same time, the embodiment of the present invention performs standardization on the pixel values, such as scaling the pixel values ​​to the range of [0,1] or [-1,1], by subtracting the mean and dividing by the standard deviation, so as to alleviate the impact of different eigenvalue scales. In addition, the embodiment of the present invention generates additional training samples by performing various random transformations on the original image, such as random cropping, flipping, rotation and scaling, so as to achieve data enhancement and improve the robustness of the model.

[0089] Next, the embodiment of the present invention constructs a preset training model. Specifically, the preset training model in the embodiment of the present invention includes a visual converter (ViT) model. For example, the embodiment of the present invention uses the vit-base-patch16-224 visual large model as the preset training model. Then, the embodiment of the present invention inputs the model training data set into the visual converter model to perform model training through the back propagation algorithm and the preset loss function to obtain a preset building recognition model. Specifically, the preset loss function in the embodiment of the present invention includes a pixel-level cross entropy loss or a Dice loss to measure the difference between the model prediction and the true annotation. For example, the embodiment of the present invention inputs a model training data set, uses Adam as the optimizer for training, and the learning rate is set to 2e 10-5 , using the Dice loss function, and selecting 64-core / RTX 4080 hardware resources for model training. In addition, the embodiment of the present invention monitors the performance of the model through the corresponding validation data set, and performs hyperparameter adjustment to improve the model performance, thereby completing the training of the preset building recognition model.

[0090] Reference Figure 5 In some embodiments of the present invention, the preset building recognition model is used to perform recognition analysis on the rural remote sensing image to be recognized to obtain the preset building data, including but not limited to the following steps:

[0091] Step S310: performing a second preprocessing on the rural remote sensing image to be identified to obtain an image block to be identified, wherein the second preprocessing includes image segmentation and image standardization.

[0092] Step S320: input the image block to be identified into a preset building recognition model for feature extraction to generate a feature vector.

[0093] Step S330: performing mask prediction according to the feature vector to obtain an image segmentation prediction mask.

[0094] Step S340: mapping and transforming the image segmentation prediction mask to obtain a preset mask grid image.

[0095] Step S350: converting the preset mask raster image into a preset geographic information system data format to obtain preset building data.

[0096] In this specific embodiment, the embodiment of the present invention first performs a second preprocessing on the rural remote sensing image to be identified to obtain the image block to be identified. Specifically, the embodiment of the present invention first performs a second preprocessing on the acquired rural remote sensing image to be identified before performing the identification analysis, including image segmentation and image standardization. Accordingly, the embodiment of the present invention first divides the image into blocks of fixed size, for example, divides the image into blocks of 1024*1024, and these blocks are usually evenly divided. Then, each block is converted into a vector as the input of the ViT model. At the same time, the embodiment of the present invention also performs image standardization to make the image data suitable for the preset building recognition model. Then, the embodiment of the present invention inputs the image block to be identified into the preset building recognition model library for feature extraction and generates a feature vector. Specifically, the embodiment of the present invention sends the preprocessed image block to be identified into the input layer of the ViT model (preset building recognition model), and the model will generate a feature vector for each image block to be identified. Among them, these feature vectors are processed by the multi-layer self-attention mechanism of the model to capture the contextual information in the image. Furthermore, the embodiment of the present invention performs mask prediction based on the feature vector to obtain an image segmentation prediction mask, and then maps and transforms the image segmentation prediction mask to obtain a preset mask grid map, and converts the preset mask grid map into a preset geographic information system data format, thereby obtaining preset building data. Specifically, a decoder is attached to the top of the ViT model in the embodiment of the present invention to generate an image segmentation mask, that is, a mask of the same size as the input image, in which each pixel corresponds to a predicted semantic category, for example, the pixel of the building is marked as 1, and the others are 0. Accordingly, this decoder is composed of a series of convolutional layers, and the output of the last convolutional layer is the prediction of the mask, that is, the image segmentation prediction mask. For example, the embodiment of the present invention uses a trained image segmentation model for inference and outputs an image segmentation prediction mask of the same size as the input image, wherein the hyperparameters use box_threshold=0.19 and text_threshold=0.1. Next, the embodiment of the present invention maps and transforms the image segmentation prediction mask to convert the predicted mask data into a grid in pixels, i.e., a pixel-level classification mask raster map (preset mask raster map), so as to represent the classification information of each pixel. Finally, the embodiment of the present invention converts the pixel-level classification mask raster map into a preset geographic information system data format, such as a vector shapefile format, to facilitate the storage, display and analysis of building data. It is easy to understand that the embodiment of the present invention automatically identifies and locates the position of rural buildings in remote sensing images by using a deep learning image algorithm, thereby realizing the intelligent and rapid acquisition of rural building layers, and providing more comprehensive and accurate basic data for base station planning.

[0097] In some embodiments of the present invention, cluster analysis is performed on the preset building data by using a preset clustering algorithm to obtain the preset concentrated area data, including but not limited to the following steps:

[0098] Construct preset clustering parameters, wherein the preset clustering parameters include a preset neighborhood radius and a neighborhood sample threshold.

[0099] According to the preset clustering parameters, a density-based spatial clustering algorithm is used to identify areas where buildings are clustered in the preset building data, so as to obtain preset concentrated area data.

[0100] In this specific embodiment, the embodiment of the present invention sets preset clustering parameters, so as to perform cluster analysis on the preset building data through a density-based spatial clustering algorithm according to the preset clustering parameters, and obtain corresponding preset concentrated area data. Specifically, the embodiment of the present invention performs cluster analysis through a density-based spatial clustering algorithm (DBSCAN algorithm). Among them, the preset clustering parameters include a preset neighborhood radius and a neighborhood sample threshold, such as setting the preset neighborhood radius to 500 meters and the neighborhood sample threshold to 30. Accordingly, in the embodiment of the present invention, the preset neighborhood radius refers to the neighborhood radius scanned when dividing the area of ​​the building, and the neighborhood sample threshold refers to the minimum point count threshold (minPts) of the number of buildings within the preset radius neighborhood. Then, according to the preset neighborhood radius and the neighborhood, the embodiment of the present invention divides the buildings into different residential areas in a density-connected manner through the DBSCAN algorithm, and identifies noise points, thereby obtaining areas where buildings are concentrated, that is, preset concentrated area data. Among them, as Figure 6 As shown, the orange blocks in the figure are the identified building areas.

[0101] In some embodiments of the present invention, identifying the area where buildings are concentrated in the preset building data by using a density-based spatial clustering algorithm according to preset clustering parameters to obtain preset concentrated area data includes but is not limited to the following steps:

[0102] An unmarked first building in the preset building data is detected to determine whether the number of second buildings contained in a preset neighborhood radius of the first building is less than a neighborhood sample threshold.

[0103] When it is determined that the number of second buildings contained in the preset neighborhood radius of the first building is greater than the neighborhood sample threshold, a preset residential area is constructed, and the second building is added to the preset candidate set.

[0104] It is determined whether the number of fourth buildings included in the preset neighborhood radius of the unmarked third buildings in the preset candidate set is greater than the neighborhood sample threshold.

[0105] When it is determined that the number of fourth buildings included in the preset neighborhood radius of the third building is greater than the neighborhood sample threshold, the fourth building is added to the preset candidate set.

[0106] When it is determined that the third building is not marked in the residential area, the third building is added to the preset residential area, and the step of determining whether the number of fourth buildings contained in the preset neighborhood radius of the unmarked third building in the preset candidate set is greater than the neighborhood sample threshold is returned until the preset candidate set is traversed.

[0107] When it is determined that the traversal of the preset candidate set is completed, return to the step of detecting the unmarked first building in the preset building data to determine whether the number of second buildings contained in the preset neighborhood radius of the first building is less than the neighborhood sample threshold, until all the first buildings in the preset building data are marked to obtain the preset concentrated area data.

[0108] In this specific embodiment, the present invention first detects the unlabeled first building in the preset building data to determine whether the number of the second building contained in the preset neighborhood radius of the first building is less than the neighborhood sample threshold. When it is determined that the number of the second building contained in the preset neighborhood radius of the first building is greater than the neighborhood sample threshold, a preset residential area is constructed, and the second building is added to the preset candidate set. Specifically, the embodiment of the present invention first detects the unlabeled building x i (first building), if the number of second buildings contained in its radius e is not less than minPts (neighborhood sample threshold), then a residential area C (preset residential area) is established, and all buildings in its neighborhood are added to the candidate set N (preset candidate set). In this embodiment of the present invention, the first building refers to a building that is not classified into any residential area or is not identified as noise. Accordingly, the embodiment of the present invention detects this building x i The number of buildings contained in the neighborhood of radius e. When this number is not less than the neighborhood sample threshold, the building x is considered i is a core point, and a new residential area C is established with it as the center. Then, the building x i All buildings in the neighborhood (including building x i itself) into a candidate set N. These buildings can be core points or edge points (i.e., the number of points in the neighborhood is less than minPts but belongs to the neighborhood of a core point).

[0109] Next, the embodiment of the present invention determines whether the number of the fourth building included in the preset neighborhood radius of the unmarked third building in the preset candidate set is greater than the neighborhood sample threshold, and when the number of the fourth building included in the preset neighborhood radius of the third building is greater than the neighborhood sample threshold, the embodiment of the present invention adds the fourth building to the preset candidate set. Specifically, the embodiment of the present invention detects the unmarked building x in the candidate set N (preset candidate set) j (third building), when the third building has more than minPts buildings within the preset neighborhood radius, these buildings (fourth building) are added to the candidate set N. Accordingly, when it is determined that the third building is not marked in the residential area, the embodiment of the present invention adds the third building to the preset residential area, and returns to the step of determining whether the number of unmarked third buildings in the preset candidate set that contain the fourth building within the preset neighborhood radius is greater than the neighborhood sample threshold, until the preset candidate set is traversed. Specifically, after adding the fourth building to the preset candidate set, the embodiment of the present invention determines whether the third building is classified into the corresponding residential area. Accordingly, if the building x j (The third building) is not included in any residential area, that is, it has not been marked as any residential area, then the third building is added to the residential area C (preset residential area) to include more buildings in the current residential area, and at the same time, new core points are discovered and the candidate set is expanded. At the same time, the embodiment of the present invention continues to detect other unmarked buildings in the preset candidate set until the preset candidate set is empty, that is, the marking of each building in the preset candidate set is completed, and no more buildings can be added to the boundary of the current residential area C by density connection, and the iteration is completed.

[0110] Furthermore, after traversing the preset candidate set, the embodiment of the present invention returns to the step of detecting the unmarked first building in the preset building data to determine whether the number of second buildings contained in the first building within the preset neighborhood radius is less than the neighborhood sample threshold, until each first building in the preset building data is marked to obtain the preset concentrated area data. Specifically, after marking each building in the preset candidate set and completing the construction of a preset residential area, the embodiment of the present invention returns to the step of detecting the unmarked first building in the preset building data to select the next building that has not been marked to start the construction process of a new residential area. Accordingly, this process is repeated until all buildings are classified into a certain residential area or marked as noise. Here, noise refers to isolated points that neither belong to any residential area nor meet the core point conditions.

[0111] In some embodiments of the present invention, the existing network base station information includes the existing network base station coverage area data. Accordingly, in the embodiments of the present invention, site planning is performed based on the preset centralized area data and the existing network base station information to generate the expected base station planning site, including but not limited to the following steps:

[0112] Acquire preset engineering parameter data to calculate the coverage area data of the existing base station through the preset engineering parameter data. The preset engineering parameter data includes base station identification, base station coordinate data, base station azimuth, base station frequency band and base station transmission power.

[0113] According to the existing network base station coverage area data, the preset centralized area data is screened for buildings to obtain several candidate buildings.

[0114] A candidate building with the largest number of neighboring buildings among several candidate buildings is taken as a target building, so as to generate an expected base station planning site according to the target building.

[0115] In this specific embodiment, the embodiment of the present invention first obtains preset engineering parameter data, so as to calculate the existing network container coverage area data through the preset engineering parameter data, and then screens the preset centralized area data for buildings according to the existing network base station coverage area data to obtain several candidate buildings. Specifically, the preset engineering parameter data in the embodiment of the present invention includes base station identification (base station ID), base station coordinate data (base station latitude and longitude), base station azimuth, base station frequency band and base station transmission power. Correspondingly, the embodiment of the present invention calculates the existing network base station coverage area data, that is, the existing base station coverage area, according to the preset engineering parameter data of each base station in the existing network. The embodiment of the present invention screens each building in the preset centralized area data by combining the coverage area data of each base station in the existing network to propose buildings that do not meet the requirements and obtain several candidate buildings. Finally, the embodiment of the present invention uses the candidate building with the largest number of neighboring buildings among several candidate buildings as the target building to generate the expected base station planning site according to the target building. Specifically, after screening, the remaining buildings (candidate buildings) are all potential base station site candidate targets. Accordingly, the embodiment of the present invention compares the number of neighboring buildings within a preset distance threshold range, such as a radius of 1.5*r (r is a coverage radius defined according to the frequency band of the planned station) of each candidate building, so as to select the building with the largest number of neighboring buildings as the final planned site, that is, the expected base station planning site, such as Figure 7 As shown, it can cover the most buildings and achieve better coverage effect. Figure 7 This is a schematic diagram of base station planning provided by an embodiment of the present invention. In the figure, orange represents identified building data, green circles represent 1.5km buffer zones of existing base stations, and red represents base station planning points.

[0116] In some embodiments of the present invention, the preset centralized area data is screened for buildings according to the existing network base station coverage area data to obtain several candidate buildings, including but not limited to the following steps:

[0117] Determine the coverage radius of the existing base station based on the preset engineering parameter data.

[0118] According to the coverage radius of the existing base stations, the covered buildings in the preset centralized area data are eliminated to obtain uncovered buildings.

[0119] The number of buildings within the preset coverage radius of the uncovered building is counted to obtain the number of neighboring buildings, wherein the preset coverage radius includes the expected coverage radius of the preset planned base station.

[0120] The uncovered buildings whose number of neighborhood buildings is less than the preset threshold are removed to obtain dense buildings.

[0121] The base station distance between the preset existing base station and the preset planned base station is calculated according to the coverage radius of the existing base station and the preset coverage radius, so as to determine the planned base station range through the base station distance, and eliminate the dense buildings within the planned base station range to obtain several candidate buildings.

[0122] In this specific embodiment, the embodiment of the present invention first determines the coverage radius of the existing network base station according to the preset working parameter data, so as to eliminate the covered buildings in the preset concentrated area data according to the existing base station coverage radius to obtain uncovered buildings. Specifically, the coverage radius of the existing network base station in the embodiment of the present invention refers to the coverage radius R of the base station of the existing network. Since different working parameter configurations of the base station will lead to different coverage areas, such as frequency bands, power, etc., R in the embodiment of the present invention can be set and calculated according to the working parameters of the base station (i.e., preset working parameter data), such as the base station coverage radius R=1km. Accordingly, the covered building in the embodiment of the present invention refers to a building that has been covered by an existing base station (preset existing network base station). The embodiment of the present invention screens each building in the preset concentrated area data by the coverage radius of the existing network base station to accurately identify the buildings covered by the existing base station, thereby screening out uncovered buildings. Next, the embodiment of the present invention counts the number of buildings within the preset coverage radius of the uncovered building to obtain the number of neighborhood buildings, and then eliminates the uncovered buildings whose number of neighborhood buildings is less than the preset number threshold to obtain dense buildings. Specifically, the preset coverage radius in the embodiment of the present invention refers to the coverage radius r of the planned base station, and its coverage radius r is defined according to the frequency band of the planned base station, such as r=1km. After determining the frequency band of the planned base station and the corresponding coverage radius r, for each building in the area, the embodiment of the present invention queries all other buildings within its coverage radius r, and these buildings together constitute a neighborhood. Then, the embodiment of the present invention counts how many other buildings are in the neighborhood of each uncovered building, that is, the number of neighborhood buildings is obtained by counting, and this number reflects the potential coverage effect of the location as a base station site. Accordingly, in order to ensure that there are enough buildings around the selected base station site to improve the utilization efficiency and coverage effect of the base station, the embodiment of the present invention removes uncovered buildings whose number of buildings in the neighborhood is less than a preset number threshold S (such as S=30) to obtain dense buildings, thereby avoiding the construction of base stations in too sparse areas and effectively saving resources. Finally, the embodiment of the present invention calculates the base station distance between the preset existing network base station and the preset planned base station according to the coverage radius of the existing network base station and the preset coverage radius, so as to determine the range of the planned base station by the base station distance, so as to remove the dense buildings within the range of the planned base station, and obtain several candidate buildings. Specifically, in order to avoid overlapping coverage between the newly planned base station and the existing base station, resulting in resource waste and possible signal interference, the embodiment of the present invention removes buildings within the range (base station distance) of the sum of the existing base station coverage radius R and the newly planned base station coverage radius r (planned base station range) so that a certain distance is maintained between the newly planned base station and the existing base station, thereby optimizing the layout of the entire network. Accordingly, the planned base station range refers to a range determined with the preset planned base station as the center and the base station distance between the preset existing network base station and the preset planned base station as the radius.Among them, in the embodiment of the present invention, the planned base station range D=the existing network base station coverage radius R+the planned station coverage radius r.

[0123] It is easy to understand that the embodiment of the present invention obtains the rural building clustering area through a clustering algorithm, combines the base station coverage radius and the number of neighborhood buildings, generates planned sites through building elimination and site selection shrinkage, and realizes automatic and accurate identification of high-value rural sites, thereby reducing manual planning costs and improving planning efficiency.

[0124] Next, in conjunction with a specific rural base station planning scenario, the solution of the embodiment of the present invention is described in detail and explained:

[0125] For example, Figure 8 As shown, the embodiment of the present invention first trains the preset training model through the remote sensing image data set to obtain an image segmentation and recognition model, that is, a preset building recognition model. Then, the embodiment of the present invention obtains the rural remote sensing image to be identified, and performs image segmentation and recognition processing on the rural remote sensing image to be identified through the preset building recognition model to obtain rural building vectors, that is, preset building data. Furthermore, the embodiment of the present invention performs cluster analysis on the rural building vectors through the DBSCAN clustering algorithm to obtain rural residential areas, that is, preset concentrated area data. Finally, the embodiment of the present invention performs site planning based on the existing network base station information and the rural concentrated residential area, thereby generating the expected base station planning site.

[0126] It is easy to understand that the embodiment of the present invention realizes the extraction of rural building outlines through the integration of remote sensing images and AI technology, and applies it to rural base station planning. Among them, the embodiment of the present invention obtains high-resolution remote sensing image maps through geographical location, performs data annotation and image processing, and trains remote sensing image building recognition models, that is, preset building recognition models, through image segmentation and image recognition technology in deep learning. Then, the extraction of rural buildings is realized according to model reasoning, and the rural building layer is output through integration processing, and then the clustering algorithm in machine learning is used to obtain the rural concentrated residential area, and the buildings and scattered buildings covered by the existing network base station in the residential area are eliminated. Then, the site selection is shrunk by the base station coverage radius, and the site is selected on the building with the largest number of surrounding buildings to complete the base station planning generation in the rural area. The embodiment of the present invention is suitable for base station planning in rural blank coverage areas. Through the integration of GIS and AI image processing technology, rural building data is intelligently supplemented to provide a basis for rural value assessment. At the same time, the planning site generation method is efficient and accurate, and the wireless network planning cost is significantly saved.

[0127] See also Fig. 9 The embodiment of the present application also provides a rural base station planning system, which can implement the above rural base station planning method, and the system includes:

[0128] The first module 410 is used to obtain a remote sensing image of a rural area to be identified.

[0129] The second module 420 is used to identify and analyze the rural remote sensing image to be identified by using a preset building recognition model to obtain preset building data.

[0130] The third module 430 is used to perform cluster analysis on the preset building data by using a preset clustering algorithm to obtain preset concentrated area data.

[0131] The fourth module 440 is used to perform site planning according to the preset centralized area data and the existing network base station information to generate the expected base station planning site.

[0132] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0133] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above rural base station planning method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0134] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0135] See also Fig.10 , Fig.10 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0136] The processor 510 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0137] The memory 520 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 520 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 520, and the processor 510 calls and executes the rural base station planning method of the embodiment of this application;

[0138] Input / output interface 530, used to implement information input and output;

[0139] Communication interface 540, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0140] bus 550 , which transmits information between the various components of the device (e.g., processor 510 , memory 520 , input / output interface 530 , and communication interface 540 );

[0141] The processor 510 , the memory 520 , the input / output interface 530 and the communication interface 540 are connected to each other in communication within the device via the bus 550 .

[0142] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the rural base station planning method is implemented.

[0143] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0144] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor 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.

[0145] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0146] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0147] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0149] The terms "first", "second", "third", "fourth", etc. (if any) in the specification 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.

[0150] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0151] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as 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 devices or units, which can be electrical, mechanical or other forms.

[0152] The units described above 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 network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0153] 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.

[0154] 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, 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 multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0155] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A rural base station planning method, characterized in that: The method comprises the following steps: Acquire remote sensing images of rural areas to be identified; The preset building recognition model is used to identify and analyze the rural remote sensing image to be identified, so as to obtain preset building data; Performing cluster analysis on the preset building data by using a preset clustering algorithm to obtain preset concentrated area data; Site planning is performed based on the preset centralized area data and existing network base station information to generate expected base station planning sites.

2. The method according to claim 1, characterized in that: Before performing the identification analysis of the to-be-identified rural remote sensing image by using the preset building identification model to obtain the preset building data, the method further includes: Acquire a remote sensing image data set, and perform a first preprocessing on the remote sensing image data set to obtain a model training data set; wherein the first preprocessing includes size adjustment, image standardization, and data enhancement; Constructing a preset training model; wherein the preset training model includes a visual converter model; The model training data set is input into the visual converter model to perform model training through a back propagation algorithm and a preset loss function to obtain the preset building recognition model.

3. The method according to claim 2, characterized in that The method of identifying and analyzing the rural remote sensing image to be identified by using a preset building identification model to obtain preset building data includes: Performing a second preprocessing on the rural remote sensing image to be identified to obtain an image block to be identified; wherein the second preprocessing includes image segmentation and image standardization; Inputting the image block to be identified into the preset building recognition model to extract features and generate a feature vector; Performing mask prediction according to the feature vector to obtain an image segmentation prediction mask; Mapping and transforming the image segmentation prediction mask to obtain a preset mask grid map; The preset mask grid image is converted into a preset geographic information system data format to obtain the preset building data.

4. The method according to claim 1, characterized in that: The step of performing cluster analysis on the preset building data by using a preset clustering algorithm to obtain preset concentrated area data includes: Constructing preset clustering parameters; wherein the preset clustering parameters include a preset neighborhood radius and a neighborhood sample threshold; According to the preset clustering parameters, a density-based spatial clustering algorithm is used to identify areas where buildings are clustered in the preset building data to obtain the preset concentrated area data.

5. The method according to claim 4, characterized in that The step of identifying the area where buildings are clustered in the preset building data by using a density-based spatial clustering algorithm according to the preset clustering parameters to obtain the preset concentrated area data comprises: Detecting an unmarked first building in the preset building data to determine whether the number of second buildings contained in the preset neighborhood radius of the first building is less than the neighborhood sample threshold; When it is determined that the number of the second buildings contained in the preset neighborhood radius of the first building is greater than the neighborhood sample threshold, constructing a preset residential area, and adding the second building to a preset candidate set; Determine whether the number of the unmarked third buildings in the preset candidate set that include the fourth buildings within the preset neighborhood radius is greater than the neighborhood sample threshold; When it is determined that the number of fourth buildings contained in the preset neighborhood radius of the third building is greater than the neighborhood sample threshold, adding the fourth building to the preset candidate set; When it is determined that the third building is not marked in a residential area, the third building is added to the preset residential area, and the step of determining whether the number of the fourth buildings included in the preset neighborhood radius of the unmarked third building in the preset candidate set is greater than the neighborhood sample threshold is returned until the preset candidate set is traversed; When it is determined that the traversal of the preset candidate set is completed, return to the step of detecting the unmarked first building in the preset building data to determine whether the number of second buildings contained in the preset neighborhood radius of the first building is less than the neighborhood sample threshold, until each of the first buildings in the preset building data is marked to obtain the preset concentrated area data.

6. The method according to claim 1, characterized in that The existing network base station information includes existing network base station coverage area data; The performing site planning according to the preset centralized area data and the existing network base station information to generate the expected base station planning site includes: Acquire preset engineering parameter data, so as to calculate the coverage area data of the existing base station through the preset engineering parameter data; wherein the preset engineering parameter data includes a base station identifier, base station coordinate data, base station azimuth, base station frequency band, and base station transmission power; Screening the preset centralized area data for buildings according to the existing network base station coverage area data to obtain a number of candidate buildings; The candidate building with the largest number of neighboring buildings among the candidate buildings is taken as a target building, so as to generate the expected planned base station site according to the target building.

7. The method according to claim 6, characterized in that The method of screening the preset centralized area data for buildings according to the existing network base station coverage area data to obtain a number of candidate buildings includes: Determine the coverage radius of the existing network base station according to the preset engineering parameter data; Eliminate the covered buildings in the preset centralized area data according to the coverage radius of the existing network base station to obtain uncovered buildings; Counting the number of buildings within a preset coverage radius of the uncovered building to obtain the number of neighborhood buildings; wherein the preset coverage radius includes an expected coverage radius of a preset planned base station; Eliminate the uncovered buildings whose number of neighborhood buildings is less than a preset number threshold to obtain dense buildings; The base station distance between the preset existing base station and the preset planned base station is calculated according to the coverage radius of the existing base station and the preset coverage radius, so as to determine the planned base station range through the base station distance, and eliminate the dense buildings located in the planned base station range to obtain a number of candidate buildings.

8. A rural base station planning system, characterized in that: The system comprises: The first module is used to obtain the rural remote sensing images to be identified; The second module is used to identify and analyze the rural remote sensing image to be identified by using a preset building recognition model to obtain preset building data; The third module is used to perform cluster analysis on the preset building data by using a preset clustering algorithm to obtain preset concentrated area data; The fourth module is used to perform site planning based on the preset centralized area data and the existing network base station information to generate the expected base station planning site.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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