Signal coverage quality optimization method and device, electronic equipment and storage medium

Abstract

The invention discloses a signal coverage quality optimization method and apparatus. The method comprises the steps of collecting real-time flight trajectory data of a flight object in a flight process and real-time signal quality data of one or more road test devices; a base station is arranged on the flying object; matching the real-time flight trajectory data with the real-time signal quality data of one or more road test devices to obtain a data matching result; determining a flight parameter of a flight object in a future specified time length by using a data matching result and a preset service parameter threshold, and adjusting a flight path of the flight object in the future specified time length based on the flight parameter so as to optimize signal coverage quality; the preset service parameter threshold value comprises a threshold value of each parameter in one or more parameters representing the signal coverage quality. According to the technical scheme, the signal coverage area of the base station carried by the flying object in the flying process of the flying object can meet the preset signal coverage quality.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of communications, and in particular to a signal coverage quality optimization method and device, electronic equipment, and storage medium. Background Art

[0002] In many scenarios, it is necessary to use drones equipped with communication equipment to provide wireless network coverage on the ground. Due to the limitations of factors such as the drone's payload capacity, flight time, base station transmission power, and path loss, the area where wireless network coverage is achieved on the ground when the drone is equipped with communication equipment will be constrained. In order to achieve ground communication coverage, it is necessary to select a reasonable and efficient solution to adjust the flight trajectory of the drone during the flight of the communication equipment to optimize the network coverage of the communication equipment carried by the drone. Summary of the Invention

[0003] To solve the above technical problems, the embodiments of the present application provide a signal coverage quality optimization method and device, an electronic device, and a storage medium.

[0004] In a first aspect, an embodiment of the present application provides a method for optimizing signal coverage quality, the method comprising:

[0005] Collecting real-time flight trajectory data of a flying object and real-time signal quality data of one or more drive test devices during flight; wherein the flying object is provided with a base station;

[0006] Matching the real-time flight trajectory data with the real-time signal quality data of the one or more drive test devices to obtain a data matching result;

[0007] The data matching results and the preset business parameter thresholds are used to determine the flight parameters of the flying object for a specified period of time in the future, and the flight trajectory of the flying object for the specified period of time in the future is adjusted based on the flight parameters to optimize the signal coverage quality; the preset business parameter thresholds include thresholds for each parameter in one or more parameters that characterize the signal coverage quality.

[0008] In a second aspect, an embodiment of the present application provides a signal coverage quality optimization device, the device comprising:

[0009] A collection unit, configured to collect real-time flight trajectory data of a flying object during flight and real-time signal quality data of one or more drive test devices; wherein the flying object is provided with a base station;

[0010] a matching unit, configured to match the real-time flight trajectory data with the real-time signal quality data of the one or more drive test devices to obtain a data matching result;

[0011] A determination unit is used to determine the flight parameters of the flying object for a specified period of time in the future using the data matching results and a preset business parameter threshold, and adjust the flight trajectory of the flying object for the specified period of time in the future based on the flight parameters to optimize the signal coverage quality; the preset business parameter threshold includes a threshold for each parameter of one or more parameters that characterize the signal coverage quality.

[0012] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory and a processor, wherein the memory stores computer-executable instructions, and when the processor runs the computer-executable instructions on the memory, the signal coverage quality optimization method described in the embodiment of the first aspect can be implemented.

[0013] In a fourth aspect, an embodiment of the present application provides a computer storage medium having executable instructions stored thereon, which, when executed by a processor, implements the signal coverage quality optimization method described in the embodiment of the first aspect above.

[0014] In a fifth aspect, an embodiment of the present application provides a computer storage medium on which a computer program is stored. When the computer program is executed by a processor, the signal coverage quality optimization method described in the embodiment of the first aspect is implemented.

[0015] The technical solution of the embodiment of the present application collects real-time flight trajectory data of a flying object during flight and real-time signal quality data of one or more drive test devices; wherein the flying object is provided with a base station; matches the real-time flight trajectory data with the real-time signal quality data of the one or more drive test devices to obtain a data matching result; uses the data matching result and a preset service parameter threshold to determine the flight parameters of the flying object for a specified future time period; and adjusts the flight trajectory of the flying object for the specified future time period based on the flight parameters to optimize signal coverage quality; the preset service parameter threshold includes a threshold value for each of one or more parameters representing signal coverage quality. In this way, the trajectory data of the flying object carrying the base station during actual flight and the drive test data can be used to adjust the flight parameters of the flying object during flight, so that the signal coverage area of ​​the base station carried by the flying object during flight meets the preset signal coverage quality, thereby better providing communication services to ground terminals. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flight scenario for a flying object;

[0017] Figure 2 Schematic diagram of the signal coverage quality optimization method provided in the embodiment of the present application Figure 1 ;

[0018] Figure 3 Schematic diagram of the signal coverage quality optimization method provided in the embodiment of the present application Figure 2 ;

[0019] Figure 4 Schematic diagram of the signal coverage quality optimization method provided in the embodiment of the present application Figure 3 ;

[0020] Figure 5 Schematic diagram of the signal coverage quality optimization method provided in the embodiment of the present application Figure 4 ;

[0021] Figure 6 A schematic diagram of the structure of the signal coverage quality optimization device provided in an embodiment of the present application;

[0022] Figure 7 A schematic diagram of the structural composition of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions 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 a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0024] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not necessarily need to be defined or explained in subsequent drawings.

[0025] The term "and / or" herein simply describes an association relationship, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, and the existence of B alone. In addition, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0026] Figure 1 A schematic diagram of a flight scene of a flying object provided in an embodiment of the present application is shown in FIG. Figure 1 As shown in the figure, the drone is equipped with a core network server, an indoor baseband processing unit (BBU, Building Baseband Unit) and a wireless base station antenna unit (AAU, Active Antenna Unit) to form an aerial mobile base station, providing signal coverage for various types of terminals on the ground. Figure 1 In the simulation, the drone flies in a circle around a set center. In actual applications, the flight path of the drone is not limited to circular flight. Various types of flight paths can also be set according to scene requirements.

[0027] Currently, when using drones equipped with communication equipment to provide wireless network coverage on the ground, a ground measurement-based approach is used to optimize the signal quality of mobile base stations, or a modeling-based approach is used to optimize the signal quality of mobile base stations.

[0028] Optimizing mobile base station signal quality using ground-based measurement requires setting up a certain number of fixed measurement points on the ground. Using specialized testing equipment, the drone's transmitted signals are measured and analyzed to obtain signal coverage and quality data. Signal optimization is then performed based on this data, adjusting the drone's flight trajectory and signal transmission parameters to improve signal coverage and quality. This approach has the following drawbacks:

[0029] 1. Coverage is limited by terrain and obstacles: The results of ground measurements will be affected by terrain and obstacles, such as mountains and tall buildings, which will make it impossible to accurately measure the signal coverage, thus affecting the optimization effect.

[0030] 2. High measurement cost: Ground measurement requires manual setting of measurement points on the ground, which consumes a lot of time, manpower and material resources. It also requires the use of professional measurement equipment, which increases the measurement cost and difficulty.

[0031] 3. Insufficient measurement accuracy: The accuracy of ground measurement is affected by many factors, such as environmental noise, signal interference, equipment failure, etc., which may lead to inaccurate measurement results and thus affect the optimization effect.

[0032] 4. Poor adaptability: Ground measurement requires the arrangement of measurement points in advance and cannot adapt to changes in the environment. For example, if new buildings or obstacles appear, re-measurement and optimization are required.

[0033] When using a modeling-based approach to optimize mobile base station signal quality, it's necessary to first model the drone signal propagation characteristics and consider the impact of factors such as the drone's altitude, speed, and direction on the signal. Then, based on the modeling results, the drone's signal propagation path is optimized to improve signal coverage and quality. This approach has the following drawbacks:

[0034] 1. Model accuracy: Modeling relies on certain data, such as topography, drone equipment parameters, etc. If these data are inaccurate or missing, the accuracy of the model will be affected, thereby affecting the optimization effect.

[0035] 2. High complexity: Modeling requires a lot of computing and model building technology, and needs to process a large amount of data and complex algorithms, which increases the complexity and computing cost of the model.

[0036] 3. Difficulty in data acquisition: Modeling relies on a large amount of data, such as flight trajectory data and terrain data. The cost of acquiring this data is high and it is also affected by various factors, such as weather conditions and equipment failures. This may lead to missing or insufficient data, thus affecting the accuracy of modeling and optimization effects.

[0037] 4. Difficulty in adapting to environmental changes: Modeling requires building the model in advance and cannot adapt to environmental changes in real time. For example, if new buildings or obstacles appear, the model needs to be rebuilt and optimized.

[0038] To address the problems of the above two solutions, the present embodiment proposes a method for adjusting the flight trajectory of the drone during flight using the flight trajectory and fixed-point drive test data, so that the signal coverage area of ​​the flying object during flight meets the preset signal coverage quality, and better communication services are provided to the ground terminal. The solution of the embodiment of the present application has the following advantages:

[0039] 1. Coverage Issue: Mobile base station signal quality optimization solutions based on ground measurements may be affected by factors such as terrain and obstacles, while those based on modeling may not accurately reflect actual signal propagation conditions. However, optimization solutions based on flight trajectory and fixed-point drive test data can utilize data collected during actual flight to calculate signal coverage, thereby more accurately reflecting actual conditions.

[0040] 2. Robustness: Mobile base station signal quality optimization solutions based on ground measurements may be affected by factors such as the selection and number of drive test points, while those based on modeling may be affected by factors such as model accuracy and real-time performance. However, optimization solutions based on flight trajectory and fixed-point drive test data can improve robustness and accuracy through extensive data collection and support from machine learning algorithms.

[0041] 3. Real-time performance: Optimization solutions based on ground measurements require setting up test points on the ground for data collection and processing, while optimization solutions based on modeling require model training and optimization, which takes time. However, optimization solutions based on flight trajectory and fixed-point test data can achieve better real-time performance through real-time data collection and algorithm calculations from drones.

[0042] Below, Figure 1 The signal coverage quality optimization method of the embodiment of the present application is introduced by taking the flight scenario shown as an example.

[0043] Figure 2 Schematic diagram of the signal coverage quality optimization method provided in the embodiment of the present application Figure 1 ,like Figure 1 As shown, the following steps are included:

[0044] S201: Collecting real-time flight trajectory data of a flying object and real-time signal quality data of one or more drive test devices during flight.

[0045] S202: Match the real-time flight trajectory data with the real-time signal quality data of the one or more drive test devices to obtain a data matching result.

[0046] S203: Determine the flight parameters of the flying object for a specified period of time in the future using the data matching result and the preset service parameter threshold, and adjust the flight trajectory of the flying object for the specified period of time in the future based on the flight parameters to optimize the signal coverage quality.

[0047] In the embodiment of the present application, the base station is set on the flying object and transmits communication signals during the flying object's flight to achieve communication coverage for the ground terminal. Figure 1 The drone shown can also be other devices with flight capabilities. When the base station is mounted on a flying object, it can be called a mobile base station.

[0048] In an embodiment of the present application, in order to achieve real-time optimization of the signal quality of a mobile base station, it is necessary to install location information and signal quality data acquisition equipment on the sensor carried by the drone. The signal quality data acquisition equipment can collect the flight trajectory data of the drone, including: Global Positioning System (GPS) time, longitude, latitude, speed, and altitude. The value of the data acquisition interval T can be flexibly set, for example, the value of T is 1 second. A fixed-point road test device is set on the ground to collect corresponding signal quality data, including: User Equipment (UE) time, longitude, latitude, signal strength, signal quality, upload speed, download speed, and modulation mode. The data is collected in real time according to the data collection interval and uploaded to the signal quality data acquisition equipment carried by the drone in real time. Optionally, the collected data needs to be preprocessed, including data cleaning, denoising, missing value filling and other processing to ensure the accuracy and completeness of the data.

[0049] In order to optimize the signal quality of mobile base stations, it is necessary to match the collected flight trajectory data with the fixed-point drive test data.

[0050] In some implementations, matching the collected flight trajectory data with the fixed-point drive test data may be achieved by the following steps:

[0051] a1. Sort the real-time flight trajectory data and the signal quality data of the one or more drive test devices respectively according to timestamps to obtain N+1 groups of data; wherein one group of data in the N+1 groups of data is the real-time flight trajectory data sorted according to timestamps, and the other N groups of data in the N+1 groups of data are N groups of signal quality data sorted according to timestamps; each group of signal quality data in the N groups of signal quality data corresponds to one drive test device;

[0052] a2. Select any one set of data from the N+1 sets of data, divide the set of data into blocks according to time windows, and record the start timestamp and end timestamp of the data blocks in each divided time window;

[0053] a3. For the N+1 groups of data except the arbitrary group of data, divide the other N groups of data into data blocks according to the same time window as the arbitrary group of data;

[0054] a4. For each time window of any one group of data, search for a target data block that intersects with the start timestamp and end timestamp of the time window from the other N groups of data, and search for data that matches the time window from the target data block to obtain matching data corresponding to the time window of any one group of data from the other N groups of data.

[0055] Specifically, during data matching, we use timestamps to maximize the matching of flight trajectory data and fixed-point road test data at the same time. During data matching, since both drone flight trajectory data and road test data have timestamps and the data volume is relatively large, we use a timestamp-based sorting and block method to maximize the matching of data at the same time. The specific steps are as follows:

[0056] b1. Sort the two sets of data by timestamp to ensure that data at the same time are arranged in adjacent positions.

[0057] b2. Divide the data into chunks, and set the timestamp range of each chunk as a time window. Divide the first set of data into chunks based on the time window, and record the start and end timestamps of each time window. Divide the second set of data into chunks based on the same time window.

[0058] b3. Traverse each time window of the first set of data and find the data blocks in the second set of data whose start and end timestamps of the time window intersect with the time window. Find the data in these data blocks that completely matches the time window.

[0059] b4. Repeat step b3 until all time windows of the first set of data are traversed, and finally the matching data at the same time in the two sets of data are obtained.

[0060] By matching the real-time flight trajectory data and the real-time signal quality data of the road test equipment according to the timestamp, the flight trajectory data corresponding to the same moment and the signal quality data of each road test equipment can be matched, thereby facilitating further obtaining the signal quality data corresponding to each position of the flying object in the current flight trajectory.

[0061] In the embodiments of the present application, the preset service parameter thresholds include thresholds for each of one or more parameters characterizing signal coverage quality. For example, parameters including but not limited to uplink and downlink rate thresholds, maximum latency, and maximum bit error rate can be set based on service requirements to characterize signal coverage quality. By setting thresholds for these parameters, signal coverage quality within the signal coverage area can be ensured.

[0062] In an embodiment of the present application, after the flight trajectory data corresponding to the same moment and the signal quality data of each path test device are matched, the signal quality data corresponding to each position of the flying object at the current flight trajectory can be used to determine the signal coverage range of the flying object's current flight trajectory and whether the current signal coverage meets the preset business conditions, that is, whether the preset business parameter threshold is met. If not, the flight trajectory of the flying object is adjusted so that the adjusted flight trajectory meets the preset business parameter threshold.

[0063] The technical solution of the embodiment of the present application can use the trajectory data and road test data of the flying object equipped with the base station during the actual flight process to adjust the flight parameters of the flying object during the flight process, so that the signal coverage area of ​​the base station equipped with the flying object during the flight process of the flying object meets the preset signal coverage quality, and better provides communication services for the ground terminal.

[0064] Figure 3 Schematic diagram of the signal coverage quality optimization method provided in the embodiment of the present application Figure 2 ,like Figure 3 As shown, the following steps are included:

[0065] S301: Collecting real-time flight trajectory data of a flying object and real-time signal quality data of one or more drive test devices during flight;

[0066] S302: Matching the real-time flight trajectory data with the real-time signal quality data of the one or more drive test devices to obtain a data matching result;

[0067] S303: Obtaining signal quality data of each position of the flying object in the current flight trajectory according to the data matching result;

[0068] S304: If the current flight area of ​​the flying object is not a new flight area, input the signal quality data of the flying object at each position of the current flight trajectory into a prediction model to obtain predicted values ​​of the service parameters and signal coverage of the flying object for a specified time period in the future;

[0069] S305: Determine whether the predicted value of the service parameter meets the preset service parameter threshold;

[0070] S306: When the predicted value of the service parameter does not meet the preset service parameter threshold, calculate the first flight parameter of the flight object for a specified time period in the future.

[0071] In the embodiment of the present application, the implementation of steps S301 and S302 can be understood with reference to the above-mentioned steps S201 and S202.

[0072] By matching the real-time flight trajectory data and the real-time signal quality data of the road test equipment according to the timestamp, the flight trajectory data corresponding to the same moment and the signal quality data of each road test equipment can be matched, thereby facilitating further obtaining the signal quality data corresponding to each position of the flying object in the current flight trajectory.

[0073] In the embodiment of the present application, the signal quality data of the flying object at each position of the current flight trajectory can be obtained based on the matching results of the real-time flight trajectory data and the real-time signal quality data of one or more road test devices.

[0074] In the embodiment of the present application, the flight scene type of the current flight area of ​​the flying object can be determined in two ways.

[0075] In one embodiment, the flight scenario type can be pre-set. For example, the flight scenario type can be pre-set to a non-new area or a new area. For example, if the flight scenario type is a new area, the corresponding flight scenario identifier can be set to 1, and if the flight scenario type is an old area, the corresponding flight scenario identifier can be set to 2. Here, a flight scenario can also be referred to as a business scenario.

[0076] In one embodiment, the flight scenario type can be determined by signal coverage. For example, after obtaining a match result between real-time flight data and real-time signal quality data from one or more drive test devices, the matched data can be further used to calculate the current signal coverage of the flight object. Based on the signal coverage, it can be determined whether the current flight area is a new area or an old area.

[0077] In an embodiment of the present application, the parameters in the data matching results include drone location information, terminal location information, flight speed, acceleration, altitude, signal strength, signal-to-noise ratio, modulation mode, uplink rate, downlink rate, timestamp, etc. These parameters can be used to calculate the coverage range of the mobile base station signal, including stable coverage and the farthest coverage distance.

[0078] The distance calculation formula between the drone and the terminal is as follows:

[0079] d=2*R*arcsin(sqrt(sin((lat2-lat1) / 2)^2+cos(lat1)*cos(lat2)*sin((lon2-lon1) / 2)^2))

[0080] Where d is the distance between two points (i.e., the flying object and the terminal), lat1 and lon1 are the latitude and longitude of the first point (i.e., the flying object), lat2 and lon2 are the latitude and longitude of the second point (i.e., the terminal), and R is the radius of the Earth.

[0081] For new areas, drones are deployed with core network base stations for their first test flights. For scenarios like emergency rescue, search and rescue, and emergency communications, drones equipped with core base stations are deployed for the first time to provide signal coverage. In these scenarios, real-time flight trajectory adjustments are used to improve signal coverage.

[0082] In older areas, drones carrying core network base stations are performing non-initial flight missions. For scenarios such as coverage signal experiments and service demonstrations, multiple test flights are required to test the performance of the core network base stations and provide a theoretical and experimental basis for subsequent operational deployments. In these scenarios, due to the large amount of historical data, model prediction methods can be used to predict the service data at the next moment, and use this predicted data to adjust the flight trajectory in real time.

[0083] In the embodiment of the present application, the prediction model is a model trained using historical data for predicting the service parameters and signal coverage of the flight object for a specified period of time in the future; the historical data is historical flight trajectory data and historical signal quality data recorded when the flight object flew in the same area as the current flight area in the past;

[0084] In an embodiment of the present application, a machine learning algorithm is used to utilize historical data to train a model to predict the signal coverage range and service parameters of the next task gap.

[0085] In one embodiment, the above step S306 includes:

[0086] With the goal of maximizing the predicted value of the signal coverage range and with the constraint that the predicted value of the service parameter meets the preset service parameter threshold, the first flight parameter of the flying object for a specified time period in the future is determined.

[0087] After using the prediction model to output the predicted values ​​of the service parameters of the flying object and the predicted values ​​of the signal coverage range for a specified period of time in the future, the feasible flight trajectory for the next mission gap is further calculated based on the prediction results and the current position of the drone to maximize the signal coverage range and meet the preset service parameter thresholds (such as uplink and downlink rate thresholds). The drone is controlled based on the feasible flight trajectory to optimize the signal coverage quality of the aerial base station carried by the drone.

[0088] In an embodiment of the present application, the calculation of the flight trajectory of the next mission gap can specifically be the calculation of the flight parameters of the next mission gap. Here, the flight parameters include but are not limited to the following parameters: flight altitude, flight radius, antenna installation angle, and flight roll angle.

[0089] In an embodiment of the present application, if the above step S305 determines that the predicted value of the business parameter meets the preset business parameter threshold, the current flight parameters are maintained and the flight trajectory of the next task gap is temporarily not adjusted, that is, the current flight parameters of the drone are not adjusted.

[0090] The technical solution of the embodiment of the present application can, when the flight scene is an old area, utilize the trajectory data and road test data of the flying object during the actual flight and combine with a pre-trained prediction model to adjust the flight trajectory of the flying object during the flight, so that the signal coverage area of ​​the aerial base station carried by the flying object during the flight of the flying object meets the preset signal coverage quality, thereby better providing communication services for the ground terminal.

[0091] Figure 4 Schematic diagram of the signal coverage quality optimization method provided in the embodiment of the present application Figure 3 ,like Figure 4 As shown, the following steps are included:

[0092] S401: Collecting real-time flight trajectory data of a flying object and real-time signal quality data of one or more drive test devices during flight;

[0093] S402: Matching the real-time flight trajectory data with the real-time signal quality data of the one or more drive test devices to obtain a data matching result;

[0094] S403: Obtaining signal quality data of each position of the flying object in the current flight trajectory according to the data matching result;

[0095] S404: When the current flight area of ​​the flying object is a new flight area, calculating the service parameters of the flying object in the current flight trajectory according to the signal quality data of each position of the flying object in the current flight trajectory;

[0096] S405: Determine whether the service parameters of the current flight trajectory meet a preset service parameter threshold;

[0097] S406: When the service parameter of the current flight trajectory does not meet the preset service parameter threshold, calculate a second flight parameter of the flying object for a specified time period in the future.

[0098] In the embodiment of the present application, the implementation of steps S401 to S403 can be understood with reference to the above-mentioned steps S301 to S303.

[0099] For new areas, drones are deployed with core network base stations for their first test flights. For scenarios like emergency rescue, search and rescue, and emergency communications, drones equipped with core base stations are deployed for the first time to provide signal coverage. In these scenarios, real-time flight trajectory adjustments are used to improve signal coverage.

[0100] In an embodiment of the present application, the flight scene can be judged as a new area by a pre-set flight scene type, or the flight scene can be judged as a new area by calculating the signal coverage range using the collected real-time trajectory data and the signal quality data of one or more road test devices.

[0101] In an embodiment of the present application, when it is determined that the current flight area of ​​the flying object is a new flight area, the signal quality data of the flying object at each position of the current flight trajectory obtained in step S403 can be used to further calculate the service parameters of the flying object in the current flight trajectory (such as uplink and downlink rates, delay, bit error rate, etc.), and by comparing the calculated service parameters with the preset service parameter threshold, it is determined whether the current flight trajectory of the flying object meets the service conditions (i.e., whether the preset service parameters are met).

[0102] If the service parameters of the current flight trajectory meet the preset service parameter threshold, the current flight parameters are maintained and the flight trajectory of the next mission gap is not adjusted temporarily, that is, the current flight parameters of the UAV are not adjusted.

[0103] In one embodiment, if it is determined that the service parameters of the current flight trajectory do not meet the preset service parameter threshold, a second flight parameter of the flight object for the future specified time period is determined with the goal of maximizing the predicted value of the service demand access amount and the predicted value of the signal coverage range of the flight object for the future specified time period, and subjecting the service parameters of the flight object for the future specified time period to meeting the preset service parameter threshold as a constraint condition;

[0104] Among them, the business demand access volume is a variable that characterizes the number of access terminals in the signal coverage area of ​​the flying object and the cumulative value of the transmission rate between the flying object and each terminal; the business demand access volume is determined according to the business demand access volume model.

[0105] In an embodiment of the present application, the business demand access volume model is the business demand access volume model of the flying object within a preset mission cycle; the business demand access volume model of the flying object within the preset mission cycle is determined based on the communication coverage variable model and transmission rate model of the flying object within the preset mission cycle; the communication coverage variable model is a binary variable model that characterizes whether each ground terminal can obtain the communication service provided by the flying object within each task time slot of the preset mission cycle; the transmission rate model is a model that characterizes the signal transmission rate between each ground terminal and the flying object within each task time slot of the preset mission cycle.

[0106] In one embodiment, in the above step S405, if it is determined that the business parameters of the current flight trajectory meet the preset business parameter threshold, the current flight parameters are maintained and the flight trajectory of the next mission gap is temporarily not adjusted, that is, the current flight parameters of the drone are not adjusted.

[0107] The following describes the data processing, modeling process, and trajectory adjustment scheme for changing the current flight area of ​​a flying object to a new flight area:

[0108] Step 1: Determine whether the signal coverage of the current flight trajectory meets the preset service parameter threshold;

[0109] In practical applications, it's necessary to clearly define service conditions, that is, to pre-set service parameter thresholds. These conditions include parameters such as uplink and downlink rate requirements, maximum latency, and maximum bit error rate, which are important indicators for evaluating mobile communication system performance. Given these defined service conditions, it's necessary to determine whether the signal coverage within the current flight trajectory of the flying object meets these requirements.

[0110] Specifically, drive test data can be used to obtain signal quality data at various points along the drone's flight trajectory. Based on this signal quality data, parameters such as uplink and downlink rates, latency, and bit error rate (BER) at different drone locations can be calculated. By comparing these parameters with preset service parameter thresholds, it is determined whether the current drone flight trajectory meets service requirements. If so, the calculation ends, maintaining the current flight trajectory. If not, the drone's flight trajectory is adjusted, and the signal quality data is recalculated based on the adjusted drone position.

[0111] In order to maximize coverage and meet business needs (i.e., meet business conditions), based on the principles of maximizing coverage and meeting business needs, the collected data is input into a pre-established model, and finally the drone flight path is trained and output, so as to plan the optimal route and realize real-time adjustment of the flight trajectory.

[0112] Step 2: Model building

[0113] First, we need to establish an environmental model. In a wireless communication network deployed with base station drones, one base station drone provides wireless communication services to multiple ground users, whose locations may constantly change. The number of base station drones is K, and the number of ground users is N. The base station drone can establish communication connections with external networks via communication satellites. Because the locations of ground users change over time, the wireless communication rate between the fixed base station drone and ground users may decrease. Therefore, it is necessary to plan the drone's flight path to track mobile ground users in real time and improve the efficiency of wireless communication between users and the base station drone.

[0114] Consider a mission in which a base station drone provides network communication services to ground users. This mission lasts for T time slots, with each time slot having the same duration. At the mission's initial moment, the base station drone takes off from a random location and flies at a fixed altitude, H. It then continuously adjusts its flight trajectory to maximize signal coverage and service requirements within the drone network over the T-slot mission period.

[0115] For the UAV flight path model, the UAV path planning needs to calculate the flight trajectory of the UAV within a period of time. The method of discretizing a period of time into multiple time slots is adopted. By calculating the UAV flight strategy (including flight direction angle and flight distance) for each discrete time slot, the UAV path planning is realized. In the communication task of T time slots, the flight direction angle and flight distance of the base station UAV in each time slot are calculated. Where k represents the kth UAV and t represents the tth time slot.

[0116] Given the flight direction angle and flight distance of the base station drone k in time slot t, the three-dimensional coordinates of the terminal position of the base station drone k in time slot t can be expressed as:

[0117]

[0118] in, represents the position of the base station UAV k at the initial moment of the mission, which is the take-off position randomly selected at the beginning of the mission.

[0119] For the ground user mobility model, the ground user's activities are dynamic and random. The ground user's movement direction is determined by an angle uniformly distributed between [0, 2π], and the user is assigned a random speed, which is in the range of [0, v max ],v max Indicates the maximum walking speed of an average pedestrian.

[0120] The moving direction angle and speed of ground user n are specified as (σ n ,v n ), under the premise that the time interval of time slot t is set sufficiently small and the moving speed v of the ground user is small, the two-dimensional coordinates of user n in time slot t are expressed as:

[0121]

[0122] in, represents the position of ground user n at the initial moment of the mission, and Δt is the time interval of a time slot. Combining formula (1) and formula (2), it can be concluded that the distance between user n and base station drone k in time slot t is:

[0123]

[0124] Regarding the communication coverage during the flight of the drone, the communication coverage of the base station drone is limited, and ground users can only obtain wireless communication services within the communication range of the base station drone. Using binary variables Indicates whether ground user n can obtain the communication service of the base station drone in time slot t,

[0125]

[0126] Where D represents the distance threshold at which the base station drone can establish a communication connection with the ground user. Indicates that user n can obtain wireless communication services in time slot t, This means that user n cannot obtain wireless communication services in time slot t.

[0127] The communication connection between the ground user and the base station drone can be regarded as an air-to-ground communication channel. The path loss of this channel is modeled as two propagation categories: line-of-sight (LoS) and non-line-of-sight (NLoS). The probability of establishing a LoS connection between user n and drone k is calculated as follows:

[0128]

[0129] The constant parameters α and β depend on the environment (such as urban environment or rural environment, etc.). represents the elevation angle between user n and drone k, and d can be calculated by formula (3). The average path loss can be expressed as:

[0130]

[0131] Among them, f c and c represent the carrier frequency and the speed of light respectively. The constant η LoS and η NLOS represents the additional loss of signal propagation in free space. In addition, the non-line-of-sight link probability P NoS =1-P LoS .

[0132] Calculation of channel rate:

[0133] According to Shannon's formula, the transmission rate between user n and drone k can be expressed as:

[0134]

[0135] Among them, the unit of R is bith / s, W n,k represents the communication channel bandwidth, SNR n,k is the signal-to-noise ratio, calculated as follows:

[0136] SNR n,k =p n,k L n,k / N gu (8)

[0137] Where p n,k represents the transmission power from drone k to user n, N gu is additive white Gaussian noise, L n,k It can be calculated by formula (6).

[0138] In the embodiment of the present application, the current signal coverage range is calculated based on the positions of the drone and the terminal.

[0139] Regarding the flight trajectory planning process, in order to improve the wireless communication efficiency between users and base station drones and enhance the base station drone network performance, our goal is to maximize the signal coverage range and the access volume C of business needs in the drone network during the mission cycle. sum :

[0140]

[0141] The communication coverage variable can be calculated using formula (4): The transmission rate R can be calculated using formula (7): n,t The value of is related to the distance between the ground user and the base station drone in each time slot. As can be seen from formula (3), the distance between the ground user and the base station drone is determined by their positions. The drone equipped with the base station periodically collects ground environment data (the location of the ground user), calculates the optimal flight action for each time slot based on the ground environment, and sends the action information to the base station drone that is providing wireless communication services through instructions. The drone receives the instructions and makes real-time adjustments to the flight trajectory.

[0142] By repeatedly executing steps 1 and 2 above, the flight parameters of the current flight trajectory of the UAV are adjusted until the signal coverage of the current flight trajectory meets the preset business conditions.

[0143] The technical solution of the embodiment of the present application can adjust the flight trajectory of the flying object during the flight by using the trajectory data of the flying object during the actual flight and the road test data when the flight scene is a new flight area, so that the signal coverage area of ​​the aerial base station carried by the flying object during the flight of the flying object meets the preset signal coverage quality, thereby better providing communication services for the ground terminal.

[0144] Figure 5 Schematic diagram of the signal coverage quality optimization method provided in the embodiment of the present application Figure 4 ,like Figure 5 As shown, the following steps are included:

[0145] S501: Real-time collection of flight trajectory data and fixed-point drive test data;

[0146] S502: Matching the collected real-time flight trajectory data with the fixed-point drive test data;

[0147] S503: Calculate signal coverage using matching data;

[0148] S504: Determine whether the current signal coverage area is a new area;

[0149] S505: If it is not a new area, the prediction model trained with historical data is used to predict the signal coverage and service parameters at the next moment, and whether the predicted service parameters meet the preset service parameters is determined;

[0150] S506, if not satisfied, the real-time flight trajectory is adjusted until the predicted business parameter parent case group preset business parameters;

[0151] S507: If it is a new area, calculate the current service parameters based on the matching data, and determine whether the current service parameters meet the preset service parameter threshold;

[0152] S508: If not, the real-time flight trajectory is adjusted until the service parameters corresponding to the current flight trajectory meet the preset service parameter threshold.

[0153] The above steps S501 and S502 can be understood with reference to the above steps S201 and S202.

[0154] In step S503, the distance between the drone and the terminal can be calculated using the distance calculation formula to calculate the current signal coverage of the drone. According to the signal coverage, it can be determined whether the current flight area of ​​the drone is a new area or an old area.

[0155] If it is determined that the current flight area of ​​the drone is an old area, steps S505 and S506 are used to adjust the flight trajectory of the drone; that is, the flight trajectory of the flying object during the actual flight process is adjusted by using the trajectory data of the flying object and the road test data in combination with the pre-trained prediction model.

[0156] If it is determined that the current flight area of ​​the drone is a new area, steps S507 and S508 are used to adjust the flight trajectory of the drone, that is, by judging whether the signal coverage quality of the aerial base station carried by the drone under the current flight trajectory meets the preset business conditions. If it does not meet the conditions, the flight parameters of the drone are adjusted using the established environmental model, ground user mobility model, etc., so that the signal coverage of the aerial base station under the flight trajectory corresponding to the adjusted flight parameters meets the preset signal coverage quality, thereby better providing communication services for the ground terminal.

[0157] Figure 6 A schematic diagram of the structure of the signal coverage quality optimization device provided in the embodiment of the present application is shown as follows: Figure 6 As shown, the device includes:

[0158] The acquisition unit 601 is configured to acquire real-time flight trajectory data of a flying object and real-time signal quality data of one or more drive test devices during flight; wherein the flying object is provided with a base station;

[0159] a matching unit 602, configured to match the real-time flight trajectory data with the real-time signal quality data of the one or more drive test devices to obtain a data matching result;

[0160] Determination unit 603 is used to determine the flight parameters of the flying object for a specified period of time in the future by using the data matching results and the preset business parameter thresholds, and adjust the flight trajectory of the flying object for the specified period of time in the future based on the flight parameters to optimize the signal coverage quality; the preset business parameter thresholds include thresholds for each parameter in one or more parameters that characterize the signal coverage quality.

[0161] In some embodiments, the matching unit 602 is configured to sort the real-time flight trajectory data and the signal quality data of the one or more road test devices according to timestamps to obtain N+1 groups of data; wherein one group of data in the N+1 groups of data is the real-time flight trajectory data sorted according to timestamps, and the other N groups of data in the N+1 groups of data are N groups of signal quality data sorted according to timestamps; each group of signal quality data in the N groups of signal quality data corresponds to one road test device; any group of data is selected from the N+1 groups of data, and the group of data is sorted according to the time window The method comprises the steps of: dividing the N+1 groups of data into blocks, and recording the start timestamp and end timestamp of the data blocks of each divided time window; dividing the data blocks into blocks according to the same time window as the arbitrary group of data for the other N groups of data except for the arbitrary group of data; for each time window of the arbitrary group of data, searching the other N groups of data for a target data block that intersects with the start timestamp and end timestamp of the time window, and searching the target data block for data that matches the time window, to obtain the matching data corresponding to the time window of the arbitrary group of data in the other N groups of data.

[0162] In some embodiments, the determination unit 603 is used to obtain the signal quality data of the flying object at each position of the current flight trajectory based on the data matching result; when the current flight area of ​​the flying object is not a new flight area, the signal quality data of the flying object at each position of the current flight trajectory is input into the prediction model to obtain the predicted values ​​of the service parameters of the flying object for a specified period of time in the future and the predicted values ​​of the signal coverage range; the prediction model is a model trained using historical data for predicting the service parameters and signal coverage range of the flying object for a specified period of time in the future; the historical data is the historical flight trajectory data and historical signal quality data recorded when the flying object flew in the same area as the current flight area in the past; it is determined whether the predicted value of the service parameter meets the preset service parameter threshold; if so, the current flight parameter is maintained; if not, the first flight parameter of the flying object for a specified period of time in the future is calculated.

[0163] In some embodiments, the determination unit 603 is used to determine the first flight parameter of the flying object for a specified period of time in the future with the goal of maximizing the predicted value of the signal coverage range, and with the predicted value of the service parameter satisfying the preset service parameter threshold as a constraint condition.

[0164] In some embodiments, the determination unit 603 is used to obtain the signal quality data of the flying object at each position in the current flight trajectory based on the data matching result; when the current flight area of ​​the flying object is a new flight area, calculate the business parameters of the flying object in the current flight trajectory based on the signal quality data of each position in the current flight trajectory; determine whether the business parameters of the current flight trajectory meet the preset business parameter threshold; if so, maintain the current flight parameters; if not, calculate the second flight parameters of the flying object for a specified period of time in the future.

[0165] In some embodiments, the determination unit 603 is used to maximize the predicted value of the business demand access amount and the predicted value of the signal coverage range of the flying object in the future specified time period, and to determine the second flight parameter of the flying object in the future specified time period, with the constraint that the business parameters of the flying object in the future specified time period meet the preset business parameter threshold; wherein, the business demand access amount is a variable that characterizes the number of access terminals in the signal coverage area of ​​the flying object and the cumulative value of the transmission rate between the flying object and each terminal; the business demand access amount is determined according to a business demand access amount model.

[0166] In some embodiments, the business demand access volume model is the business demand access volume model of the flying object within a preset mission cycle; the business demand access volume model of the flying object within the preset mission cycle is determined based on the communication coverage variable model and transmission rate model of the flying object within the preset mission cycle; the communication coverage variable model is a binary variable model that characterizes whether each ground terminal can obtain the communication service provided by the flying object within each task time slot of the preset mission cycle; the transmission rate model is a model that characterizes the signal transmission rate between each ground terminal and the flying object within each task time slot of the preset mission cycle.

[0167] Those skilled in the art should understand that Figure 6 The implementation functions of each unit in the signal coverage quality optimization device shown can be understood by referring to the relevant description of the aforementioned signal coverage quality optimization method. Figure 6 The functions of the various units in the signal coverage quality optimization device shown can be implemented by a program running on a processor, or by a specific logic circuit.

[0168] An embodiment of the present application also provides an electronic device. Figure 7 This is a schematic diagram of the hardware structure of the electronic device according to the embodiment of the present application. Figure 7As shown, the electronic device includes: a communication component 703 for data transmission, one or more processors 701 and a memory 702 for storing computer programs that can be run on the processor 701. The various components in the terminal are coupled together through a bus system 704. It is understood that the bus system 704 is used to achieve connection and communication between these components. In addition to including a data bus, the bus system 704 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 7 Various buses are labeled as bus system 704 .

[0169] When the processor 701 executes the computer program, it at least performs Figures 1 to 5 The steps of any method shown in any one of the above.

[0170] It is understood that memory 702 can be volatile memory or non-volatile memory, or can include both volatile and non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disk, or compact disc read-only memory (CD-ROM); magnetic surface memory can be magnetic disk memory or tape memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM).The memory 702 described in the embodiments of the present application is intended to include, but is not limited to, these and any other suitable types of memories.

[0171] The methods disclosed in the above embodiments of the present application can be applied to or implemented by processor 701. Processor 701 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in processor 701 or by software instructions. The above processor 701 may be a general-purpose processor, a DSP, or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc. Processor 701 can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium located in memory 702. Processor 701 reads information from memory 702 and, in conjunction with its hardware, completes the steps of the aforementioned signal coverage quality optimization method.

[0172] In an exemplary embodiment, the electronic device can be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), FPGAs, general-purpose processors, controllers, MCUs, microprocessors, or other electronic components to perform the aforementioned signal coverage quality optimization method.

[0173] The present application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is characterized in that when the program is executed by a processor, it is used to perform at least Figures 2 to 5 The computer readable storage medium may be a memory. The memory may be as follows: Figure 7 Memory 702 is shown.

[0174] The present application also provides a computer program product, including a computer program, which can be Figure 7 The processor 701 in the electronic device executes to at least complete the above Figures 2 to 5 Each step of the signal coverage quality optimization method described in each embodiment.

[0175] The technical solutions described in the embodiments of this application can be combined arbitrarily unless there is any conflict.

[0176] In the several embodiments provided in this application, it should be understood that the disclosed methods and intelligent devices can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0177] The units described above as separate components may or may not be physically separated, and the components displayed 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.

[0178] In addition, all functional units in the embodiments of the present application can be integrated into a second processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the above-mentioned integrated units can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0179] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A signal coverage quality optimization method, characterized in that: The method comprises: Collecting real-time flight trajectory data of a flying object and real-time signal quality data of one or more drive test devices during flight; wherein the flying object is provided with a base station; Matching the real-time flight trajectory data with the real-time signal quality data of the one or more drive test devices to obtain a data matching result; The data matching results and the preset business parameter thresholds are used to determine the flight parameters of the flying object for a specified period of time in the future, and the flight trajectory of the flying object for the specified period of time in the future is adjusted based on the flight parameters to optimize the signal coverage quality; the preset business parameter thresholds include thresholds for each parameter in one or more parameters that characterize the signal coverage quality.

2. The method according to claim 1, characterized in that The matching of the real-time flight trajectory data and the real-time signal quality data of the one or more drive test devices to obtain a data matching result includes: sorting the real-time flight trajectory data and the signal quality data of the one or more drive test devices according to timestamps to obtain N+1 groups of data; wherein one group of data in the N+1 groups of data is the real-time flight trajectory data sorted according to timestamps, and the other N groups of data in the N+1 groups of data are N groups of signal quality data sorted according to timestamps; each group of signal quality data in the N groups of signal quality data corresponds to one drive test device; Select any one group of data from the N+1 groups of data, divide the group of data into blocks according to time windows, and record the start timestamp and end timestamp of the data blocks of each divided time window; For the other N groups of data in the N+1 groups of data except the arbitrary group of data, dividing the data blocks according to the same time window as the arbitrary group of data; For each time window of any one group of data, search for a target data block that intersects with the start timestamp and end timestamp of the time window from the other N groups of data, and search for data that matches the time window from the target data block to obtain matching data corresponding to the time window of any one group of data in the other N groups of data.

3. The method according to claim 1, characterized in that The determining of the flight parameters of the flight object for a specified period of time in the future by using the data matching result and a preset service parameter threshold includes: Obtaining signal quality data of the flying object at each position of the current flight trajectory according to the data matching result; If the current flight area of ​​the flying object is not a new flight area, inputting signal quality data of the flying object at various positions along the current flight trajectory into a prediction model to obtain predicted values ​​of service parameters and signal coverage of the flying object for a specified time period in the future; the prediction model is a model trained using historical data for predicting the service parameters and signal coverage of the flying object for a specified time period in the future; the historical data is historical flight trajectory data and historical signal quality data recorded when the flying object flew in the same area as the current flight area in the past; Determine whether the predicted value of the business parameter meets the preset business parameter threshold; if so, maintain the current flight parameter flight; if not, calculate the first flight parameter of the flight object for a specified time in the future.

4. The method according to claim 3, characterized in that Calculating the first flight parameter of the flying object for a specified time period in the future includes: With the goal of maximizing the predicted value of the signal coverage range and with the constraint that the predicted value of the service parameter meets the preset service parameter threshold, the first flight parameter of the flying object for a specified time period in the future is determined.

5. The method according to claim 1, wherein The determining of the flight parameters of the flight object for a specified period of time in the future by using the data matching result and a preset service parameter threshold includes: Obtaining signal quality data of the flying object at each position of the current flight trajectory according to the data matching result; When the current flight area of ​​the flying object is a new flight area, calculating the service parameters of the flying object in the current flight trajectory according to the signal quality data of each position of the flying object in the current flight trajectory; Determine whether the business parameters of the current flight trajectory meet a preset business parameter threshold; if so, maintain the current flight parameters; if not, calculate the second flight parameters of the flying object for a specified time in the future.

6. The method according to claim 5, characterized in that Calculating the second flight parameter of the flying object for a specified time period in the future includes: The goal is to maximize the predicted value of the business demand access volume and the predicted value of the signal coverage range of the flying object in the future specified time period, and the second flight parameter of the flying object in the future specified time period is determined based on the constraint condition that the business parameters of the flying object in the future specified time period meet the preset business parameter threshold; wherein, the business demand access volume is a variable that characterizes the number of access terminals in the signal coverage area of ​​the flying object and the cumulative value of the transmission rate between the flying object and each terminal; the business demand access volume is determined according to the business demand access volume model.

7. The method according to claim 6, characterized in that The business demand access volume model is the business demand access volume model of the flying object within the preset mission cycle; the business demand access volume model of the flying object within the preset mission cycle is determined based on the communication coverage variable model and transmission rate model of the flying object within the preset mission cycle; the communication coverage variable model is a binary variable model that characterizes whether each ground terminal can obtain the communication service provided by the flying object within each task time slot of the preset mission cycle; the transmission rate model is a model that characterizes the signal transmission rate between each ground terminal and the flying object within each task time slot of the preset mission cycle.

8. A signal coverage quality optimization device, characterized in that: The device comprises: A collection unit, configured to collect real-time flight trajectory data of a flying object during flight and real-time signal quality data of one or more drive test devices; wherein the flying object is provided with a base station; a matching unit, configured to match the real-time flight trajectory data with the real-time signal quality data of the one or more drive test devices to obtain a data matching result; A determination unit is used to determine the flight parameters of the flying object for a specified period of time in the future using the data matching results and a preset business parameter threshold, and adjust the flight trajectory of the flying object for the specified period of time in the future based on the flight parameters to optimize the signal coverage quality; the preset business parameter threshold includes a threshold for each parameter of one or more parameters that characterize the signal coverage quality.

9. An electronic device, characterized in that: The electronic device includes: a memory and a processor, wherein the memory stores computer-executable instructions, and the processor can implement any one of claims 1 to 7 when executing the computer-executable instructions on the memory.

10. A computer storage medium, characterized in that The storage medium stores executable instructions, which, when executed by a processor, implement the method according to any one of claims 1 to 7.

11. A computer program product comprising a computer program, characterized in that The computer program implements the method according to any one of claims 1 to 7 when executed by a processor.

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