Base station parameter determination method and device, nonvolatile storage medium and electronic equipment

Abstract

The invention discloses a base station parameter determination method and device, a nonvolatile storage medium and electronic equipment. The method comprises the steps that initial base station data of a target base station are acquired through an unmanned aerial vehicle, and the initial base station data comprise base station image data of the target base station; an image mask corresponding to an antenna of the target base station is determined in the base station image data, feature points of the antenna are determined according to the image mask, and the feature points comprise end points of the antenna; engineering parameters of the target base station are determined according to the feature points of the antenna and camera parameters of the unmanned aerial vehicle, and the engineering parameters comprise angle information of the antenna; and verifying the engineering parameters according to the historical data and the design parameters of the target base station. According to the invention, the technical problems of low acquisition efficiency and safety risk caused by determining the engineering parameters of the base station by adopting a manual tower climbing measurement mode in the related technology are solved.

Description

Technical Field

[0001] The present application relates to the field of computer vision technology, and in particular to a method, device, non-volatile storage medium and electronic device for determining base station parameters. Background Art

[0002] In the related technologies, when determining the engineering parameters of a base station, it is usually necessary to reach the collection point by ground vehicles or on foot, and tower workers climb the tower to measure the angle. The specific steps include collecting GPS positioning information, measuring antenna height, measuring antenna azimuth and inclination, etc. However, these methods face many challenges in some complex terrains and dense urban environments, and have high collection costs, low efficiency, high manpower requirements, and personnel safety risks.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a method, device, non-volatile storage medium and electronic device for determining base station parameters, so as to at least solve the technical problem that the related technology requires the use of manual tower climbing measurement to determine the engineering parameters of the base station, resulting in low collection efficiency and safety risks.

[0005] According to one aspect of an embodiment of the present application, a method for determining base station parameters is provided, comprising: obtaining initial base station data of a target base station through a drone, wherein the initial base station data includes base station image data of the target base station; determining an image mask corresponding to an antenna of the target base station in the base station image data, and determining feature points of the antenna based on the image mask, wherein the feature points include endpoints of the antenna; determining engineering parameters of the target base station based on the feature points of the antenna and camera parameters of the drone, wherein the engineering parameters include angle information of the antenna; and verifying the engineering parameters based on historical data and design parameters of the target base station.

[0006] Optionally, determining an image mask corresponding to an antenna of the target base station in the base station image data includes: detecting an antenna image area corresponding to the antenna in the base station image data; determining a bounding box of the antenna image area; and generating an image mask based on the bounding box.

[0007] Optionally, determining the feature points of the antenna based on the image mask includes: determining a target outline corresponding to the antenna in the image mask; determining a direction reference line corresponding to the antenna in the target outline; and determining feature points in the target outline based on direction information of the direction reference line, wherein the feature points include the top endpoint and the bottom endpoint of the antenna.

[0008] Optionally, determining the target contour corresponding to the antenna in the image mask includes: converting the image mask into a grayscale image; determining an alternative contour in the grayscale image; and when there are multiple alternative contours, determining the alternative contour with the longest circumference among the multiple alternative contours as the target contour.

[0009] Optionally, determining the direction reference line corresponding to the antenna in the target contour includes: performing Hough transform on the target contour to determine a straight line in the target contour; when multiple straight lines are determined, determining the longest straight line among the multiple straight lines as the direction reference line.

[0010] Optionally, the direction information of the direction reference line includes the slope of the direction reference line; determining the feature point in the target contour based on the direction information of the direction reference line includes: determining the slope of the direction reference line; when the absolute value of the slope is less than a preset slope threshold, determining the ordinate value of each point in the target contour in the target plane rectangular coordinate system, and determining the point with the largest ordinate value as the top endpoint, and determining the point with the smallest ordinate value as the bottom endpoint, wherein the target plane rectangular coordinate system is a plane rectangular coordinate system established in the plane where the image mask is located, and the positive direction of the longitudinal axis of the target plane rectangular coordinate system is from the bottom of the image mask to the top of the image mask; when the slope is not less than a preset slope threshold, determining the intersection of the extension line of the direction reference line and the target contour, and determining the intersection with the largest ordinate value as the top endpoint, and determining the intersection with the smallest ordinate value as the bottom endpoint.

[0011] Optionally, the camera parameters include intrinsic camera parameters and extrinsic camera parameters; determining the engineering parameters of the target base station based on the feature points of the antenna and the camera parameters of the drone includes: calibrating the camera of the drone to obtain the intrinsic camera parameters of the camera, wherein the intrinsic camera parameters include the focal length, principal point position and distortion coefficient of the camera; determining the extrinsic camera parameters of the camera, wherein the extrinsic camera parameters include position information and attitude information of the camera relative to a world coordinate system, and the world coordinate system is a three-dimensional rectangular coordinate system; determining the coordinates of the feature points in the camera coordinate system of the camera based on the intrinsic camera parameters and the extrinsic camera parameters, wherein the camera coordinate system is a three-dimensional rectangular coordinate system determined based on the position and orientation of the camera; determining the attitude angle of the drone, and determining the angle information of the antenna in the world coordinate system based on the attitude angle of the drone and the coordinates of the feature points in the camera coordinate system.

[0012] Optionally, determining the angle information of the antenna in the world coordinate system based on the attitude angle of the drone and the coordinates of the feature point in the camera coordinate system includes: determining a first pointing baseline vector of the antenna in the camera coordinate system based on the coordinates of the feature point in the camera coordinate system; determining a rotation matrix between the camera coordinate system and the world coordinate system based on the attitude angle, wherein the rotation matrix is ​​used to determine the coordinate transformation relationship between the camera coordinate system and the world coordinate system; determining a second pointing baseline vector of the antenna in the world coordinate system based on the rotation matrix and the first pointing baseline vector; and determining the angle information of the antenna in the world coordinate system based on the second pointing baseline vector, wherein the angle information includes the azimuth and downtilt angle of the antenna.

[0013] According to another aspect of an embodiment of the present application, a base station parameter determination device is also provided, including: a first processing module, used to obtain initial base station data of a target base station through a drone, wherein the initial base station data includes base station image data of the target base station; a second processing module, used to determine an image mask corresponding to an antenna of the target base station in the base station image data, and determine feature points of the antenna based on the image mask, wherein the feature points include endpoints of the antenna; a third processing module, used to determine engineering parameters of the target base station based on the feature points of the antenna and camera parameters of the drone, wherein the engineering parameters include angle information of the antenna; a fourth processing module, used to verify the engineering parameters based on historical data and design parameters of the target base station.

[0014] According to another aspect of an embodiment of the present application, a non-volatile storage medium is further provided, in which a program is stored, wherein when the program is running, a device where the non-volatile storage medium is located is controlled to execute a base station parameter determination method.

[0015] According to another aspect of an embodiment of the present application, an electronic device is further provided, including: a memory and a processor, the processor being configured to run a program stored in the memory, wherein the base station parameter determination method is executed when the program is run.

[0016] According to another aspect of an embodiment of the present application, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the steps of the base station parameter determination method are implemented.

[0017] In an embodiment of the present application, an initial base station data of a target base station is obtained by using an unmanned aerial vehicle, wherein the initial base station data includes base station image data of the target base station; an image mask corresponding to the antenna of the target base station is determined in the base station image data, and feature points of the antenna are determined based on the image mask, wherein the feature points include end points of the antenna; engineering parameters of the target base station are determined based on the feature points of the antenna and camera parameters of the unmanned aerial vehicle, wherein the engineering parameters include angle information of the antenna; and the engineering parameters are verified based on historical data and design parameters of the target base station. An image of the base station is collected by using an unmanned aerial vehicle, and an image mask corresponding to the antenna is extracted from the image to determine feature points of the antenna in the image, and then the engineering parameters of the base station are determined based on the feature points, thereby achieving the purpose of automatically determining the engineering parameters of the base station by using an unmanned aerial vehicle, thereby achieving the technical effect of not requiring manual tower climbing measurement in the process of determining the engineering parameters, thereby solving the technical problem of low collection efficiency and safety risks caused by the need to use manual tower climbing measurement in the related technology to determine the engineering parameters of the base station. 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 structural schematic diagram of a computer terminal (mobile terminal) provided according to an embodiment of the present application;

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

[0021] Figure 3 It is a flowchart of a data analysis process provided according to an embodiment of the present application;

[0022] Figure 4 It is a flow chart of a base station parameter determination process provided according to an embodiment of the present application;

[0023] Figure 5 It is a structural diagram of a base station parameter determination device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] 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.

[0025] 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.

[0026] In order to better understand the embodiments of the present application, the technical terms involved in the embodiments of the present application are explained as follows:

[0027] UAV (Unmanned Aerial Vehicle): An aircraft used for aerial photography and data collection.

[0028] BSEP (Base Station Engineering Parameters): Base station engineering parameters, including the location information of the base station (such as longitude and latitude), antenna height, antenna downtilt angle, sector direction angle, etc., are crucial for network planning, optimization, and troubleshooting.

[0029] YOLO (You Only Look Once): is an open source target detection model that has a good balance between accuracy and speed.

[0030] findContours: is a method for finding the contours of objects in computer vision and image processing.

[0031] In wireless communication networks, the collection of base station engineering parameters (engineering parameters) is a key step in optimizing network coverage, capacity, and quality. The related technical engineering parameter collection methods mainly use ground vehicles or manual walking to reach the collection point, and tower workers climb the tower to measure the angle. The specific steps include GPS positioning information collection, antenna height measurement, antenna azimuth and inclination measurement, etc. However, these methods face many challenges in some complex terrains and dense urban environments, and the collection cost is high, the efficiency is low, the manpower demand is high, and there are personnel safety risks.

[0032] In recent years, with the development of drone technology, base station engineering parameter collection methods based on drone platforms have begun to attract attention. Compared with traditional collection methods, drones can flexibly and maneuverably reach locations that are difficult to reach with traditional methods, thereby providing more accurate and efficient data collection capabilities. Existing drone base station engineering parameter collection systems mainly include drone platforms, communication modules, GPS receivers, attitude sensors, and image / video capture devices. These systems can automatically fly to a specified location, capture the physical environment information of the base station, and transmit the data back to the ground control station via a wireless link.

[0033] At present, the relevant technology for collecting engineering parameters of base stations based on drones mainly uses laser radar (LiDAR) scanning. The LiDAR device carried by the drone creates a three-dimensional model of the base station and its surrounding environment by emitting laser pulses and measuring the reflection time. The azimuth angle, downtilt angle and other parameters are calculated through the three-dimensional model, but this method has some defects:

[0034] 1. LiDAR equipment is expensive, and professional software and computing resources for 3D modeling require a lot of initial construction and procurement costs.

[0035] 2. In dense urban areas or complex terrain, LiDAR has limited penetration capabilities and may not be able to fully penetrate vegetation or other obstacles.

[0036] In order to solve the above problems, relevant solutions are provided in the embodiments of the present application, which are described in detail below.

[0037] According to an embodiment of the present application, a method embodiment of a base station parameter determination method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although 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.

[0038] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1FIG. 1 shows a hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for determining base station parameters. Figure 1 As shown, the computer terminal 10 (or mobile device 10) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 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. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art 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.

[0039] It should be noted that the one or more processors 102 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 (or mobile device). 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).

[0040] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the base station parameter determination method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the above-mentioned base station parameter determination method 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 examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, 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.

[0041] The transmission device 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 device 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 device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0042] 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 (or mobile device).

[0043] In the above operating environment, the embodiment of the present application provides a method for determining base station parameters, which is applicable to an electronic device or ground station that has a communication connection with a drone. Figure 2 As shown, the method comprises the following steps:

[0044] Step S202, obtaining initial base station data of the target base station through the drone, wherein the initial base station data includes base station image data of the target base station;

[0045] Before the technical solution provided in step S202, it is necessary to plan the flight range of the drone according to the collection requirements of the target base station and the location information of the base station, such as the approximate location of the latitude and longitude, including setting the parameters such as the flight altitude and speed of the drone. After that, the drone can automatically take off and fly to the designated area according to the planned flight range and parameters, and use sensors such as GPU and IMU for positioning and attitude control during the flight to ensure the smooth flight of the drone.

[0046] After the drone flies to the designated area, it can use the integrated sensors (HD camera, RF receiver) to automatically collect the base station engineering parameter data during the flight, including but not limited to the base station latitude and longitude, signal strength, antenna video, pictures, etc. After collecting the relevant data, the drone can send the collected data to the electronic equipment or ground station that has a communication connection with the drone, and then the electronic equipment or ground station will perform preliminary quality inspection and preprocessing on the data sent by the drone to ensure data integrity.

[0047] Step S204, determining an image mask corresponding to the antenna of the target base station in the base station image data, and determining feature points of the antenna according to the image mask, wherein the feature points include the end points of the antenna;

[0048] In the technical solution provided in step S204, the step of determining the image mask corresponding to the antenna of the target base station in the base station image data includes: detecting the antenna image area corresponding to the antenna in the base station image data; determining the bounding box of the antenna image area; and generating the image mask based on the bounding box.

[0049] As an optional implementation, a YOLO-based target detection algorithm can be used to generate a mask for the antenna area. YOLOv5 is a version of the open source target detection model of the YOLO (You Only Look Once) series. It has good speed and high recognition accuracy in image recognition. It achieves target detection capabilities by training 20,000 antenna photos. The trained model path is saved as path / to / yolov5s.pt, and then the mask of the area is generated based on the model. The specific generation process is as follows:

[0050] The first step is to load the YOLO5 model;

[0051] The second step is to load the image;

[0052] The third step is to perform target detection;

[0053] Step 4: Extract the detection results and obtain the bounding box of the detected antenna;

[0054] In the fifth step, based on the detected bounding box, the class label of the antenna is set to 0, and then a mask is created and visualized.

[0055] Step S206, determining the engineering parameters of the target base station according to the characteristic points of the antenna and the camera parameters of the drone, wherein the engineering parameters include the angle information of the antenna;

[0056] In the technical solution provided in step S206, the step of determining the feature points of the antenna based on the image mask includes: determining the target contour corresponding to the antenna in the image mask; determining the direction reference line corresponding to the antenna in the target contour; determining the feature points in the target contour based on the direction information of the direction reference line, wherein the feature points include the top endpoint and the bottom endpoint of the antenna.

[0057] In some embodiments of the present application, the step of determining the target contour corresponding to the antenna in the image mask includes: converting the image mask into a grayscale image; determining the candidate contour in the grayscale image; and, if there are multiple candidate contours, determining the candidate contour with the longest perimeter among the multiple candidate contours as the target contour. When determining the contour, the antenna contour can be determined by using findContours.

[0058] As an optional implementation, the step of determining a directional reference line corresponding to an antenna in a target contour includes: performing a Hough transform on the target contour to determine a straight line in the target contour; when multiple straight lines are determined, determining the longest straight line among the multiple straight lines as the directional reference line.

[0059] In some embodiments of the present application, the direction information of the direction reference line includes the slope of the direction reference line; the step of determining the feature point in the target contour based on the direction information of the direction reference line includes: determining the slope of the direction reference line; when the absolute value of the slope is less than a preset slope threshold, determining the ordinate value of each point in the target contour in the target plane rectangular coordinate system, and determining the point with the largest ordinate value as the top endpoint, and determining the point with the smallest ordinate value as the bottom endpoint, wherein the target plane rectangular coordinate system is a plane rectangular coordinate system established in the plane where the image mask is located, and the positive direction of the longitudinal axis of the target plane rectangular coordinate system is from the bottom of the image mask to the top of the image mask; when the slope is not less than the preset slope threshold, determining the intersection of the extension line of the direction reference line and the target contour, and determining the intersection with the largest ordinate value as the top endpoint, and determining the intersection with the smallest ordinate value as the bottom endpoint.

[0060] Specifically, you can use the findContours method of opencv to implement contour detection, by fitting a straight line to the point set of the antenna contour, and then determine the "top" and "bottom" of the antenna based on the direction of the straight line. Use Hough Transform to detect the straight line, and then determine the feature points based on the slope of the straight line. The process of determining the feature points is as follows:

[0061] The first step is to convert the mask into a grayscale image to ensure the consistency of subsequent operations;

[0062] The second step is to find the contour in the grayscale image, and when multiple contours are found, determine the largest contour as the antenna contour;

[0063] The third step is to use Hough transform to find the straight lines on the contour, and determine the longest straight line from the found straight lines as the direction reference line of the antenna;

[0064] The fourth step is to calculate the slope of the direction reference line, and determine the feature point according to the comparison result between the slope of the direction reference line and the preset slope threshold;

[0065] Specifically, when the absolute value of the slope is less than a preset slope threshold, the ordinate values ​​of each point in the target contour in the target plane rectangular coordinate system are determined, and the point with the largest ordinate value is determined as the top endpoint, and the point with the smallest ordinate value is determined as the bottom endpoint, wherein the target plane rectangular coordinate system is a plane rectangular coordinate system established in the plane where the image mask is located, and the positive direction of the longitudinal axis of the target plane rectangular coordinate system is from the bottom of the image mask to the top of the image mask; when the slope is not less than a preset slope threshold, the intersection of the extension line of the direction reference line and the target contour is determined, and the intersection with the largest ordinate value is determined as the top endpoint, and the intersection with the smallest ordinate value is determined as the bottom endpoint.

[0066] In addition, when calculating the slope, you can choose any two points on the direction reference line to calculate. In order to avoid the divisor being zero, you can add a small value of 1e-5 to the divisor.

[0067] Step 5: Output the coordinates of the feature points.

[0068] As an optional implementation, the camera parameters include intrinsic camera parameters and extrinsic camera parameters; the step of determining the engineering parameters of the target base station based on the feature points of the antenna and the camera parameters of the drone includes: calibrating the camera of the drone to obtain the intrinsic camera parameters of the camera, wherein the intrinsic camera parameters include the focal length, principal point position and distortion coefficient of the camera; determining the extrinsic camera parameters of the camera, wherein the extrinsic camera parameters include the position information and attitude information of the camera relative to a world coordinate system, and the world coordinate system is a three-dimensional rectangular coordinate system; determining the coordinates of the feature points in the camera coordinate system of the camera based on the intrinsic camera parameters and the extrinsic camera parameters, wherein the camera coordinate system is a three-dimensional rectangular coordinate system determined based on the position and orientation of the camera; determining the attitude angle of the drone, and determining the angle information of the antenna in the world coordinate system based on the attitude angle of the drone and the coordinates of the feature points in the camera coordinate system.

[0069] Optionally, the camera's intrinsic parameter matrix is ​​usually expressed as follows:

[0070]

[0071] Among them, f x and f y is the focal length of the camera in the x-axis and y-axis directions, c x and c y is the position of the image center in the pixel coordinate system.

[0072] The camera's external parameters are usually expressed as the first rotation transformation matrix R and the translation vector T. These parameters can be estimated using SLAM (Simultaneous Localization and Mapping) technology or the PnP (Perspective-n-Point) algorithm in multi-view geometry.

[0073] After determining the intrinsic and extrinsic parameters, back-projection can be used to map the 2D image coordinates to 3D space. Assume that the pixel coordinates of the feature point in the image are u = (u x ,u y ) T , its three-dimensional coordinate X in the camera coordinate system c =(X,Y,Z) T It can be calculated by the following formula:

[0074]

[0075] Where s is the scale factor, which can be ignored in most cases. Since this is a nonlinear system of equations, the least squares method or an iterative algorithm is usually used to solve X, Y, and Z.

[0076] Through the above steps, although the image collected by the drone is a two-dimensional image, the three-dimensional coordinates of the antenna in the drone camera coordinate system can still be determined by the back projection method. Specifically, the three-dimensional coordinates of the antenna in the drone camera coordinate system can be referred to by determining the coordinates of the feature points in the camera coordinate system.

[0077] In some embodiments of the present application, the step of determining the angle information of the antenna in the world coordinate system based on the attitude angle of the drone and the coordinates of the feature point in the camera coordinate system includes: determining the first pointing baseline vector of the antenna in the camera coordinate system based on the coordinates of the feature point in the camera coordinate system; determining the rotation matrix between the camera coordinate system and the world coordinate system based on the attitude angle, wherein the rotation matrix is ​​used to determine the coordinate transformation relationship between the camera coordinate system and the world coordinate system; determining the second pointing baseline vector of the antenna in the world coordinate system based on the rotation matrix and the first pointing baseline vector; determining the angle information of the antenna in the world coordinate system based on the second pointing baseline vector, wherein the angle information includes the azimuth angle and downtilt angle of the antenna.

[0078] Specifically, assume that the coordinates of the top and bottom endpoints of the antenna in the drone camera coordinate system are P1(x1, y1, z1) and P2(x2, y2, z2) respectively. To simplify the calculation, we assume that the drone's camera is pointing straight ahead, and its coordinate system is consistent with the z-axis (vertically upward) and y-axis (pointing to the right) of the world coordinate system.

[0079] Then the first pointing baseline vector of the antenna v=P2-P1=(x2-x1, y2-y1, z2-z1).

[0080] After that, the conversion relationship between the camera coordinate system of the drone and the world coordinate system can be determined. Specifically, the IMU provides the attitude angle of the drone, including the yaw angle (yaw), pitch angle (pitch) and roll angle (roll). Suppose the yaw angle of the drone is θ y , the pitch angle is θ p , the roll angle is θ r The antenna pointing baseline v is transformed into the world coordinate system by the rotation matrix R, which can be obtained by the product of the following three basic rotation matrices:

[0081] R=R z (θ y )·R x (θ p )·R y (θ r )

[0082] in,

[0083]

[0084] The first pointing baseline vector can be transformed by the rotation matrix using the following formula to obtain the second pointing baseline vector v′:

[0085] v′=R·v

[0086] After obtaining the second pointing baseline vector, the antenna's azimuth and downtilt angle and other engineering parameters can be further determined. Specifically, the azimuth angle α is the angle between the projection of the antenna pointing vector on the horizontal plane and the true north direction. Assuming that the north direction is 0° and the clockwise direction is the positive direction, the azimuth angle can be calculated using the following formula:

[0087] α=arctan2(v′ y ,v′ x )

[0088] Among them, v′ x and v′ y are the components of the v′ vector on the x-axis and y-axis in the world coordinate system respectively.

[0089] The downtilt angle β is the angle between the antenna pointing vector and the horizontal plane. It can be calculated using the following formula:

[0090]

[0091] Among them, v′ z is the component of the v′ vector on the z-axis in the world coordinate system.

[0092] In summary, the processing flow of the drone data collection in the embodiment of the present application is as follows: Figure 3 As shown in the figure, it is divided into several steps, including feature intrinsic parameter acquisition, camera parameter determination, 3D mapping reconstruction and engineering parameter calculation. The feature intrinsic parameter acquisition step includes YOLO antenna detection, antenna mask generation, findContours and feature point coordinate acquisition. Camera parameter determination includes camera calibration and external parameter calculation.

[0093] Step S208: verifying the engineering parameters based on the historical data and design parameters of the target base station.

[0094] In the technical solution provided in step S208, historical data and design parameters can be compared to verify the accuracy of the determined engineering parameters, and a detailed base station engineering parameter report can be generated to facilitate subsequent network optimization.

[0095] According to the embodiment of the present application, there is also provided a Figure 4 The base station parameter determination process shown includes the following steps:

[0096] Step S402, mission planning and UAV flight preparation;

[0097] Step S404, setting the flight parameters and flight range of the drone, and letting the drone automatically navigate to fly to the designated area;

[0098] Step S406, collecting data of the target base station;

[0099] Step S408, the drone transmits back the collected data, including the latitude and longitude of the base station, altitude information, and base station image video;

[0100] Step S410, the ground station or electronic device determines the engineering parameters of the antenna based on the returned base station image video, including the azimuth angle and downtilt angle of the antenna;

[0101] Step S412, output engineering parameters and archive the data.

[0102] The invention adopts a method of acquiring initial base station data of a target base station by using a drone, wherein the initial base station data includes base station image data of the target base station; determining an image mask corresponding to the antenna of the target base station in the base station image data, and determining feature points of the antenna based on the image mask, wherein the feature points include the end points of the antenna; determining engineering parameters of the target base station based on the feature points of the antenna and camera parameters of the drone, wherein the engineering parameters include angle information of the antenna; verifying the engineering parameters based on historical data and design parameters of the target base station, collecting images of the base station by using a drone, extracting an image mask corresponding to the antenna from the image to determine feature points of the antenna in the image, and then determining the engineering parameters of the base station based on the feature points, thereby achieving the purpose of automatically determining the engineering parameters of the base station by using a drone, thereby achieving the technical effect of not requiring manual tower climbing measurement in the process of determining the engineering parameters, thereby solving the technical problem of low collection efficiency and safety risks caused by the need to use manual tower climbing measurement in related technologies to determine the engineering parameters of the base station.

[0103] In addition, compared with the related technologies, the method provided in the embodiments of the present application provides an efficient, automated and intelligent base station engineering parameter collection solution by integrating drone flight technology, computer vision algorithms and wireless communication technology. It not only improves the efficiency and accuracy of data collection, but also significantly reduces costs and safety risks, and provides strong support for the planning, optimization and maintenance of wireless communication networks.

[0104] In addition, the YOLOv5 target detection model used in this application when generating image masks has been specially trained for antenna structures, and can quickly and accurately identify base station antennas from images taken by drones, forming a mask of the antenna area, laying the foundation for subsequent industrial parameter extraction. This model can have a faster processing speed while maintaining high recognition accuracy, greatly shortening the collection cycle of industrial parameter data. Moreover, the model can adapt to different lighting conditions and background environments, and can stably detect antennas even in complex scenes, improving the robustness of the system. Compared with the method of using LiDAR for 3D modeling in related technologies, the image-based target detection in the embodiment of this application significantly reduces hardware and software costs.

[0105] The method provided in the embodiment of the present application uses the findContours method to detect the antenna contour, then uses the Hough transform to detect the straight line, and determines the top and bottom feature points of the antenna, which can provide key information for calculating the antenna azimuth and downtilt angle. It can also accurately identify the geometric features of the antenna and improve the accuracy of the engineering parameter calculation. Even in the case of obstructed or partially damaged antennas, it can still be effectively identified, enhancing the practicality of the system. Moreover, by combining a variety of image processing techniques, it can reduce recognition errors caused by image blur or lighting changes.

[0106] The present application provides a base station parameter determination device. Figure 5 is a schematic diagram of the structure of the device. Figure 5 It can be seen that the device includes: a first processing module 50, which is used to obtain the initial base station data of the target base station through the drone, wherein the initial base station data includes the base station image data of the target base station; a second processing module 52, which is used to determine the image mask corresponding to the antenna of the target base station in the base station image data, and determine the feature points of the antenna based on the image mask, wherein the feature points include the end points of the antenna; a third processing module 54, which is used to determine the engineering parameters of the target base station based on the feature points of the antenna and the camera parameters of the drone, wherein the engineering parameters include the angle information of the antenna; a fourth processing module 56, which is used to verify the engineering parameters based on the historical data and design parameters of the target base station.

[0107] In some embodiments of the present application, the step in which the second processing module 52 determines an image mask corresponding to the antenna of the target base station in the base station image data includes: detecting an antenna image area corresponding to the antenna in the base station image data; determining a bounding box of the antenna image area; and generating an image mask based on the bounding box.

[0108] In some embodiments of the present application, the step in which the third processing module 54 determines the feature points of the antenna based on the image mask includes: determining a target contour corresponding to the antenna in the image mask; determining a direction reference line corresponding to the antenna in the target contour; and determining feature points in the target contour based on direction information of the direction reference line, wherein the feature points include the top endpoint and the bottom endpoint of the antenna.

[0109] In some embodiments of the present application, the step in which the third processing module 54 determines the target contour corresponding to the antenna in the image mask includes: converting the image mask into a grayscale image; determining an alternative contour in the grayscale image; and when there are multiple alternative contours, determining the alternative contour with the longest circumference among the multiple alternative contours as the target contour.

[0110] In some embodiments of the present application, the step in which the third processing module 54 determines the directional reference line corresponding to the antenna in the target contour includes: performing a Hough transform on the target contour to determine a straight line in the target contour; when determining multiple straight lines, determining the longest straight line among the multiple straight lines as the directional reference line.

[0111] In some embodiments of the present application, the direction information of the direction reference line includes the slope of the direction reference line; the step of the third processing module 54 determining the feature point in the target contour based on the direction information of the direction reference line includes: determining the slope of the direction reference line; when the absolute value of the slope is less than a preset slope threshold, determining the ordinate value of each point in the target contour in the target plane rectangular coordinate system, and determining the point with the largest ordinate value as the top endpoint, and determining the point with the smallest ordinate value as the bottom endpoint, wherein the target plane rectangular coordinate system is a plane rectangular coordinate system established in the plane where the image mask is located, and the positive direction of the longitudinal axis of the target plane rectangular coordinate system is from the bottom of the image mask to the top of the image mask; when the slope is not less than the preset slope threshold, determining the intersection of the extension line of the direction reference line and the target contour, and determining the intersection with the largest ordinate value as the top endpoint, and determining the intersection with the smallest ordinate value as the bottom endpoint.

[0112] In some embodiments of the present application, the camera parameters include intrinsic camera parameters and extrinsic camera parameters; the step of the third processing module 54 determining the engineering parameters of the target base station based on the feature points of the antenna and the camera parameters of the drone includes: calibrating the camera of the drone to obtain the intrinsic camera parameters of the camera, wherein the intrinsic camera parameters include the focal length, principal point position and distortion coefficient of the camera; determining the extrinsic camera parameters of the camera, wherein the extrinsic camera parameters include the position information and attitude information of the camera relative to the world coordinate system, and the world coordinate system is a three-dimensional rectangular coordinate system; determining the coordinates of the feature points in the camera coordinate system of the camera based on the intrinsic camera parameters and the extrinsic camera parameters, wherein the camera coordinate system is a three-dimensional rectangular coordinate system determined based on the position and orientation of the camera; determining the attitude angle of the drone, and determining the angle information of the antenna in the world coordinate system based on the attitude angle of the drone and the coordinates of the feature points in the camera coordinate system.

[0113] In some embodiments of the present application, the step in which the third processing module 54 determines the angle information of the antenna in the world coordinate system based on the attitude angle of the drone and the coordinates of the feature point in the camera coordinate system includes: determining the first pointing baseline vector of the antenna in the camera coordinate system based on the coordinates of the feature point in the camera coordinate system; determining the rotation matrix between the camera coordinate system and the world coordinate system based on the attitude angle, wherein the rotation matrix is ​​used to determine the coordinate conversion relationship between the camera coordinate system and the world coordinate system; determining the second pointing baseline vector of the antenna in the world coordinate system based on the rotation matrix and the first pointing baseline vector; determining the angle information of the antenna in the world coordinate system based on the second pointing baseline vector, wherein the angle information includes the azimuth angle and downtilt angle of the antenna.

[0114] It should be noted that the various modules in the above-mentioned base station parameter determination device can be program modules (for example, a set of program instructions that implement a certain specific function) or hardware modules. For the latter, it can be expressed in the following forms, but is not limited to this: the expression form of each of the above-mentioned modules is a processor, or the functions of each of the above-mentioned modules are implemented by a processor.

[0115] According to an embodiment of the present application, a non-volatile storage medium is also provided, in which a program is stored, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the following base station parameter determination method: obtaining initial base station data of the target base station through a drone, wherein the initial base station data includes base station image data of the target base station; determining an image mask corresponding to the antenna of the target base station in the base station image data, and determining the feature points of the antenna based on the image mask, wherein the feature points include the endpoints of the antenna; determining the engineering parameters of the target base station based on the feature points of the antenna and the camera parameters of the drone, wherein the engineering parameters include the angle information of the antenna; verifying the engineering parameters based on the historical data and design parameters of the target base station.

[0116] According to an embodiment of the present application, an electronic device is also provided, including: a memory and a processor, the processor being used to run a program stored in the memory, wherein the following base station parameter determination method is executed when the program is running: obtaining initial base station data of a target base station through a drone, wherein the initial base station data includes base station image data of the target base station; determining an image mask corresponding to an antenna of the target base station in the base station image data, and determining feature points of the antenna based on the image mask, wherein the feature points include endpoints of the antenna; determining engineering parameters of the target base station based on the feature points of the antenna and camera parameters of the drone, wherein the engineering parameters include angle information of the antenna; and verifying the engineering parameters based on historical data and design parameters of the target base station.

[0117] According to an embodiment of the present application, a computer program product is also provided, including a computer program, which implements the steps of the following base station parameter determination method when executed by a processor: obtaining initial base station data of a target base station through a drone, wherein the initial base station data includes base station image data of the target base station; determining an image mask corresponding to an antenna of the target base station in the base station image data, and determining feature points of the antenna based on the image mask, wherein the feature points include endpoints of the antenna; determining engineering parameters of the target base station based on the feature points of the antenna and camera parameters of the drone, wherein the engineering parameters include angle information of the antenna; and verifying the engineering parameters based on historical data and design parameters of the target base station.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 relevant technology or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for 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, etc. Various media that can store program codes.

[0123] 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 determining base station parameters, characterized in that: include: Acquire initial base station data of a target base station by using a drone, wherein the initial base station data includes base station image data of the target base station; Determine an image mask corresponding to the antenna of the target base station in the base station image data, and determine a feature point of the antenna according to the image mask, wherein the feature point includes an end point of the antenna; Determining the engineering parameters of the target base station according to the characteristic points of the antenna and the camera parameters of the drone, wherein the engineering parameters include angle information of the antenna; The engineering parameters are verified based on the historical data and design parameters of the target base station.

2. The method for determining base station parameters according to claim 1, characterized in that: Determining an image mask corresponding to an antenna of the target base station in the base station image data includes: detecting an antenna image area corresponding to the antenna in the base station image data; determining a bounding box of the antenna image region; The image mask is generated according to the bounding box.

3. The method for determining base station parameters according to claim 1, characterized in that: Determining the characteristic points of the antenna according to the image mask includes: determining, in the image mask, a target contour corresponding to the antenna; Determining a directional reference line corresponding to the antenna in the target profile; The feature point is determined in the target contour according to the direction information of the direction reference line, wherein the feature point includes a top endpoint and a bottom endpoint of the antenna.

4. The method for determining base station parameters according to claim 3, characterized in that: Determining a target contour corresponding to the antenna in the image mask includes: Converting the image mask into a grayscale image; determining candidate contours in the grayscale image; When there are multiple candidate contours, the candidate contour with the longest perimeter among the multiple candidate contours is determined as the target contour.

5. The method for determining base station parameters according to claim 3, characterized in that: Determining a direction reference line corresponding to the antenna in the target profile includes: Performing Hough transform on the target contour to determine straight lines in the target contour; When multiple straight lines are determined, the longest straight line among the multiple straight lines is determined as the direction reference line.

6. The method for determining base station parameters according to claim 3, characterized in that: The direction information of the direction reference line includes the slope of the direction reference line; and determining the feature point in the target contour according to the direction information of the direction reference line includes: determining a slope of the directional reference line; In the case where the absolute value of the slope is less than a preset slope threshold, determining the ordinate values ​​of each point in the target contour in the target plane rectangular coordinate system, and determining the point with the largest ordinate value as the top endpoint, and determining the point with the smallest ordinate value as the bottom endpoint, wherein the target plane rectangular coordinate system is a plane rectangular coordinate system established in the plane where the image mask is located, and the positive direction of the longitudinal axis of the target plane rectangular coordinate system is from the bottom of the image mask to the top of the image mask; When the slope is not less than the preset slope threshold, determine the intersection of the extension line of the direction reference line and the target contour, and determine the intersection with the largest ordinate value as the top endpoint, and determine the intersection with the smallest ordinate value as the bottom endpoint.

7. The method for determining base station parameters according to claim 1, characterized in that: The camera parameters include camera internal parameters and camera external parameters; the engineering parameters of the target base station determined according to the characteristic points of the antenna and the camera parameters of the drone include: Calibrate the camera of the drone to obtain the camera intrinsic parameters of the camera, wherein the camera intrinsic parameters include the focal length, principal point position and distortion coefficient of the camera; Determine the camera extrinsic parameters of the camera, wherein the camera extrinsic parameters include position information and posture information of the camera relative to a world coordinate system, and the world coordinate system is a three-dimensional rectangular coordinate system; Determine the coordinates of the feature point in a camera coordinate system of the camera according to the camera intrinsic parameters and the camera extrinsic parameters, wherein the camera coordinate system is a three-dimensional rectangular coordinate system determined according to the position and orientation of the camera; The attitude angle of the drone is determined, and the angle information of the antenna in the world coordinate system is determined according to the attitude angle of the drone and the coordinates of the feature point in the camera coordinate system.

8. The method for determining base station parameters according to claim 7, characterized in that: Determining the angle information of the antenna in the world coordinate system according to the attitude angle of the drone and the coordinates of the feature point in the camera coordinate system includes: Determine a first pointing baseline vector of the antenna in the camera coordinate system according to the coordinates of the feature point in the camera coordinate system; Determining a rotation matrix between the camera coordinate system and the world coordinate system according to the attitude angle, wherein the rotation matrix is ​​used to determine a coordinate transformation relationship between the camera coordinate system and the world coordinate system; Determine a second pointing baseline vector of the antenna in the world coordinate system according to the rotation matrix and the first pointing baseline vector; The angle information of the antenna in the world coordinate system is determined according to the second pointing baseline vector, wherein the angle information includes the azimuth angle and downtilt angle of the antenna.

9. A base station parameter determination device, characterized in that: include: A first processing module is used to obtain initial base station data of a target base station through a drone, wherein the initial base station data includes base station image data of the target base station; A second processing module is used to determine an image mask corresponding to the antenna of the target base station in the base station image data, and determine a feature point of the antenna according to the image mask, wherein the feature point includes an end point of the antenna; A third processing module is used to determine the engineering parameters of the target base station according to the characteristic points of the antenna and the camera parameters of the drone, wherein the engineering parameters include the angle information of the antenna; The fourth processing module is used to verify the engineering parameters according to the historical data and design parameters of the target base station.

10. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the base station parameter determination method according to any one of claims 1 to 8.

11. An electronic device, characterized in that: include: A memory and a processor, wherein the processor is used to run a program stored in the memory, wherein the base station parameter determination method according to any one of claims 1 to 8 is executed when the program is run.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the base station parameter determination method according to any one of claims 1 to 8 are implemented.

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