Flight path planning method and device, storage medium and computer program product

Abstract

The invention provides a flight path planning method and device, a storage medium and a computer program product. The method comprises the following steps: acquiring a signal quality assessment parameter and an environmental risk assessment parameter; determining a flight path cost value based on the signal quality assessment parameter and the environmental risk assessment parameter; and the flight path is planned based on the flight path cost value, so that the data and image transmission quality of the unmanned aerial vehicle can be improved.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a flight path planning method, device, storage medium, and computer program product. Background Technology

[0002] In related technologies, when drone devices are connected to cellular networks, they can achieve beyond-line-of-sight flight and long-distance high-definition image transmission. However, the drone flight path planning schemes in these technologies focus on the drone's obstacle avoidance capabilities. This can easily lead to issues such as poor network coverage affecting the quality of data and images transmitted by the drone during missions. Summary of the Invention

[0003] In view of this, this application aims to provide a flight path planning method, device, storage medium, and computer program product that can improve the quality of data and images transmitted by UAVs.

[0004] The technical solution of this application is implemented as follows:

[0005] In a first aspect, this application provides a flight path planning method, the method comprising:

[0006] Obtain signal quality assessment parameters and environmental risk assessment parameters;

[0007] The cost of the flight path is determined based on signal quality assessment parameters and environmental risk assessment parameters.

[0008] Flight paths are planned based on the cost-effectiveness of flight paths.

[0009] Secondly, this application provides a flight path planning device, the device comprising:

[0010] The acquisition unit is used to acquire signal quality assessment parameters and environmental risk assessment parameters;

[0011] The determination unit is used to determine the flight path cost based on signal quality assessment parameters and environmental risk assessment parameters;

[0012] Planning unit, used to plan flight paths based on flight path cost.

[0013] Thirdly, this application provides a flight path planning device, the device comprising: a processor and a memory; the processor implements the above-described flight path planning method when executing the running program stored in the memory.

[0014] Fourthly, this application provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described flight path planning method.

[0015] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described flight path planning method.

[0016] This application provides a flight path planning method, device, storage medium, and computer program product. The method includes: acquiring signal quality assessment parameters and environmental risk assessment parameters; determining the flight path cost based on the signal quality assessment parameters and environmental risk assessment parameters; and planning the flight path based on the flight path cost. Using the above implementation scheme, during UAV flight, the path cost of the UAV flight path is determined by combining the network signal quality score and environmental risk score. By incorporating the network quality score into the flight path planning process, the determined flight path is a path with relatively good network quality. Therefore, when the UAV uses this optimized flight path during flight, the quality of transmitted data and images will be significantly improved. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of a flight path planning method provided in an embodiment of this application;

[0018] Figure 2 This application provides an implementation architecture diagram of a flight path planning method.

[0019] Figure 3 A schematic diagram of the composition structure of a flight path planning device provided in this application embodiment. Figure 1 ;

[0020] Figure 4 A schematic diagram of the composition structure of a flight path planning device provided in this application embodiment. Figure 2 . Detailed Implementation

[0021] To gain a more detailed understanding of the features and technical content of the embodiments of this application, the technical solution of this application will be further described in detail below with reference to the accompanying drawings and specific embodiments. The accompanying drawings are for reference only and are not intended to limit the embodiments of this application.

[0022] Unless otherwise defined, all technical and scientific terms used in the embodiments of this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the embodiments of this application is for the purpose of describing the embodiments of this application only and is not intended to limit this application.

[0023] In the following description, references to "some embodiments" refer to a subset of all possible embodiments. It is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. It should also be noted that the terms "first / second / third" used in the embodiments of this application are merely for distinguishing similar objects and do not represent a specific ordering of objects. It is understood that "first / second / third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein.

[0024] In related technologies, when a drone is not connected to a cellular network, its data transmission (such as data and images) is typically carried out via its own dedicated link. In this mode, the drone cannot perceive network quality, and therefore the quality of data and image transmission cannot be guaranteed. Consequently, when planning its flight path, the primary goal for a drone is obstacle avoidance, rather than optimizing communication quality.

[0025] When drones connect to cellular networks, they can achieve beyond-line-of-sight flight and long-distance high-definition image transmission. However, the drone flight path planning methods in related technologies fail to fully consider network quality requirements, focusing primarily on obstacle avoidance. This cannot ensure network quality along the drone's flight path, which limits the drone's capabilities in data and image transmission, especially in image transmission scenarios where higher network quality is required.

[0026] The main technical shortcomings of the above-mentioned technical solutions can be summarized as follows:

[0027] (1) Network quality is not included in flight path planning: In relevant technical solutions, the flight path planning of UAVs mainly focuses on obstacle avoidance functions, without considering network quality as an important factor in path planning. This may cause UAVs to fly into areas with poor network coverage when performing missions, thereby affecting the quality of data transmission and image transmission.

[0028] (2) Limited data and image transmission capabilities: Since the flight path planning does not take into account the optimization of network coverage, the data transmission and image transmission capabilities of UAVs are limited when performing tasks that require high-quality network connections (such as remote monitoring and high-definition image transmission).

[0029] (3) Lack of adaptability to environmental changes: The relevant technical solutions do not take into account the impact of environmental factors (such as weather changes, terrain features, etc.) on network quality, thus they cannot dynamically adjust the flight path to adapt to these environmental changes, thereby affecting the operating efficiency and safety of the UAV.

[0030] Correspondingly, the main technical problems solved by the embodiments of this application can be summarized as follows:

[0031] (1) Flight path optimization based on network quality: The embodiments of this application aim to solve the problem of neglecting network quality in related technical solutions. By incorporating network signal strength and distribution into flight path planning, the flight path of the UAV is optimized to meet the high-quality requirements of data and image transmission.

[0032] (2) Enhance data and image transmission capabilities: By optimizing the flight path to ensure good network coverage, the embodiments of this application will significantly improve the data and image transmission capabilities of the UAV when performing tasks, especially in application scenarios with high network quality requirements.

[0033] (3) Dynamic flight path planning to adapt to environmental changes: Considering the impact of environmental factors on network quality, this application embodiment will use advanced path planning algorithms to dynamically adjust the flight path of the UAV to adapt to environmental changes such as weather and terrain, and improve operational efficiency and safety.

[0034] To address the aforementioned technical problems, embodiments of this application provide a flight path planning method, such as... Figure 1 As shown, the method may include:

[0035] S101. Obtain signal quality assessment parameters and environmental risk assessment parameters.

[0036] In this embodiment of the application, the signal quality evaluation parameter can be a signal quality evaluation value.

[0037] In this embodiment of the application, the environmental risk assessment parameter can be an environmental risk score.

[0038] In this embodiment of the application, signal quality evaluation parameters can be obtained in the following ways:

[0039] Obtain the first environmental data for the first time period and the first signal quality data for the second time period; construct a signal quality barometer based on the first signal quality data and the first environmental data; and obtain the signal quality score from the signal quality barometer.

[0040] In this embodiment of the application, the first future time period can be any upcoming time period.

[0041] In this embodiment of the application, the second time period can be the time period during which the UAV collects the first signal quality data in real time.

[0042] In this embodiment of the application, the first environmental data may be temperature, humidity, air pressure, etc., which can be understood as upcoming weather forecast data.

[0043] In this embodiment of the application, the first signal quality data may be received signal strength indication (RSSI), signal-to-noise ratio (SNR), etc., acquired in real time.

[0044] In this embodiment of the application, a signal quality barometer is constructed based on the first signal quality data and the first environmental data, which can be achieved in the following ways:

[0045] Acquire Geographic Information System (GIS) data; input the first signal quality data and the first environmental data into the pre-trained first model to obtain the second signal quality data for the first time period in the future; determine the signal quality distribution data based on the second signal quality data and the GIS data; and generate a signal quality barometer based on the signal quality distribution data.

[0046] In this embodiment of the application, the second signal quality data is future RSSI, SNR, etc.

[0047] In this embodiment of the application, the signal quality distribution data is a signal quality distribution map.

[0048] In this embodiment of the application, the process of generating a signal quality barometer first requires pre-training the first model, which specifically includes the following steps:

[0049] (1) Data collection and data preprocessing:

[0050] Data collection includes: Historical signal quality data collection: collecting RSSI data (i.e., data representing the signal strength of 5G), SNR data, signal coverage, and other data over a period of time.

[0051] Historical environmental data (such as meteorological data) collection: Acquire historical meteorological data, including temperature, humidity, air pressure, wind speed, rainfall, etc.

[0052] Data preprocessing includes standardizing and normalizing the collected historical signal quality data and historical environmental data, and removing outliers. This ensures the quality of the collected historical signal quality data and historical environmental data.

[0053] (2) Feature extraction:

[0054] Signal quality feature extraction: Extract key features from signal quality data, such as average signal strength, rate of change of signal strength, maximum and minimum signal strength, etc.

[0055] Environmental data feature extraction: Extract key features from environmental data, such as average temperature, temperature change rate, and rainfall.

[0056] Based on the two features mentioned above, we can add time features: by adding time features such as seasons and time periods, we can capture the impact of time changes on signal quality.

[0057] (3) Model selection and training:

[0058] Model selection: Choose a suitable machine learning model for predicting signal quality data. Commonly used models include random forest, gradient boosting decision tree (GBDT), and support vector machine (SVM).

[0059] Model training: The model is trained using preprocessed historical signal quality data and historical environmental data. Cross-validation can be used to evaluate the model's performance and select the optimal model and parameters.

[0060] Model formula: Signal quality = f(signal quality characteristics, environmental data characteristics, time characteristics).

[0061] In the embodiments of this application, after obtaining the first model through the above training, the obtained first model can be used for further prediction and simulation calculations.

[0062] (4) Prediction and simulation calculations:

[0063] Input data: the first environmental data acquired in the first time period of the future and the first signal quality data collected in real time.

[0064] It should be noted that the first environmental data and the first signal quality data can also be preprocessed and their features extracted before being input into the first model obtained through training.

[0065] Prediction results: The first model outputs the prediction results of the second signal quality data within the first time period in the future, which may include the prediction results of RSSI, SNR, etc.

[0066] Simulation Calculation: After obtaining the second signal quality prediction result, spatial simulation is performed on the prediction result using the acquired GIS data to generate a signal quality distribution map covering the entire flight area. The generated signal quality distribution map shows the distribution area of ​​the signal quality.

[0067] (5) Generate a signal quality barometer based on the signal quality distribution map:

[0068] Signal quality barometer structure: The signal quality barometer includes signal strength and signal quality scores for different time periods and regions in the future.

[0069] In this embodiment of the application, the signal quality score can be set according to different scoring standards based on signal strength and signal-to-noise ratio, and the signal quality score level can be divided into excellent, good, medium, poor, etc.

[0070] In this embodiment of the application, after the signal quality barometer is constructed, since the signal quality barometer contains signal quality scores, the signal quality scores for different time periods and different areas can be obtained from the constructed signal quality barometer when planning flight paths.

[0071] In this embodiment of the application, the generation of the signal quality barometer is performed by the artificial intelligence (AI) system of the data processing center. The purpose is to predict the signal quality distribution map based on long-term data analysis, weather forecasts (i.e. future first environmental data) and finally combined with the simulation results of GIS data, and generate the signal quality barometer accordingly.

[0072] It should be noted that the process of generating the signal quality barometer is carried out regularly, especially before any anticipated major weather changes, to ensure the accuracy and real-time nature of the information during the drone's flight.

[0073] In this embodiment, an artificial intelligence algorithm is used in conjunction with long-term data analysis and weather forecasting to predict signal quality distribution maps and generate a signal quality barometer. Compared with related technologies, this process enables UAV flight planning to be adjusted according to predicted network conditions, thereby optimizing flight efficiency and safety.

[0074] In this embodiment of the application, the generated signal quality barometer can be updated in real time. Specifically, the signal quality barometer can be updated in real time based on the latest environmental data (such as weather forecast data) and signal quality data to ensure the accuracy and real-time performance of the final prediction result. As for the pre-trained first model, the parameters of the first model can be continuously optimized and adjusted using new data collected during flight to improve prediction accuracy and system robustness.

[0075] In this embodiment, the environmental risk score can be used to assess risk based on environmental data, such as whether there is strong wind or thunderstorms. The specific method for classifying environmental risk values ​​can be selected according to the actual situation, and this embodiment does not impose any specific limitations.

[0076] S102. Determine the flight path cost based on signal quality assessment parameters and environmental risk assessment parameters.

[0077] In this embodiment of the application, based on the signal quality assessment results, the flight path of the UAV is dynamically adjusted using the A* algorithm or the Dijkstra algorithm to ensure optimal signal coverage.

[0078] In this embodiment of the application, the cost of the flight path is specifically reflected. The cost of the flight path can be calculated using formula (1), which is as follows:

[0079] Flight path cost = ∑(signal quality score + environmental risk score) (1)

[0080] In this embodiment of the application, during the flight of the UAV, the UAV flight control system continuously monitors signal quality data and environmental data, and can dynamically adjust the flight path of the UAV based on the evaluation result of the signal quality score.

[0081] S103. Based on the flight path cost, plan the flight path.

[0082] In this embodiment of the application, the flight route is determined based on the flight path cost value, such as using a route with a lower flight path cost value.

[0083] In this embodiment of the application, the UAV flight control system is executed by the flight control unit inside the UAV, whose main responsibility is to guide the UAV to perform flight tasks according to the flight path provided by the flight path planning algorithm.

[0084] In this embodiment, the UAV flight control system receives and processes flight path data in real time, uses advanced flight control algorithms to precisely control the UAV, and provides system feedback, which includes data feedback and model updates.

[0085] Data Feedback: After the flight, all collected data is uploaded to the central server for further optimization of the model and algorithm.

[0086] Model updates: Regularly update model parameters and algorithms to adapt to new environments and signal conditions.

[0087] It should be noted that this integrated control system enables drones to maintain optimal operational performance in complex environments while responding to potential emergencies, thereby optimizing flight paths and data transmission efficiency while ensuring safety.

[0088] It should be noted that through the above-described dynamic flight path optimization, future signal quality can be predicted based on real-time signal quality and predicted environmental changes, and a signal quality score can be obtained accordingly to dynamically adjust the flight path. This flexibility and adaptability are crucial for ensuring the continuity and safety of UAV flight. Because of the optimized flight path, the UAV's flight control system is also optimized to better adapt to intelligent analysis-based flight path planning.

[0089] It is understood that the flight path planning method provided in this application determines the path cost of the UAV flight path by combining the network signal quality score and the environmental risk score during the UAV flight. By incorporating the network quality score into the flight path planning process, the determined flight path is a path with relatively good network quality. Thus, when the UAV uses this optimized flight path during flight, the quality of transmitted data and images will be significantly improved.

[0090] In one embodiment of this application, the signal quality of the flight area can be optimized before obtaining the signal quality assessment parameters and environmental risk assessment parameters.

[0091] In this embodiment of the application, historical signal quality data and historical environmental data are acquired; based on the historical signal quality data and historical environmental data, third signal quality data for a future third time period is predicted; based on the third signal quality data, weak signal coverage areas are determined; and the signal quality of the weak signal coverage areas is adjusted.

[0092] In this embodiment of the application, when determining a weak coverage area, the first step is to acquire data, which may be historical signal quality data and historical environmental data, or real-time acquired signal quality data and environmental data.

[0093] Real-time signal quality data and environmental data can be obtained through the following methods:

[0094] Real-time signal quality data detection: Using the 5G signal receiving module on the drone, parameters such as RSSI and SNR are collected in real time to record the signal coverage of various locations.

[0095] Environmental data acquisition: Real-time monitoring of meteorological conditions within the flight area using temperature, humidity, and barometric pressure sensors.

[0096] In this embodiment of the application, the historical signal quality data and historical environmental data are further preprocessed, specifically:

[0097] Historical signal quality data preprocessing: The collected historical signal quality data is filtered and denoised to remove outliers and noise.

[0098] Historical environmental data preprocessing: The historical environmental data corresponding to the environmental sensors are standardized and normalized to ensure that different types of data are analyzed at a uniform scale.

[0099] In this embodiment of the application, based on historical signal data and historical environmental data, the prediction of the third signal quality data for a future third time period can be achieved in the following ways:

[0100] By using preprocessed signal quality data as the dependent variable and environmental data (temperature, humidity, and air pressure) as the independent variables, a multiple linear regression model is established. This model can predict signal strength under different environmental conditions.

[0101] The multiple linear regression model can be represented as follows:

[0102] RSSI = β1 × Temperature + β2 × Humidity + β3 × Air Pressure + ε

[0103] Where β1, β2, and β3 are the influencing factors of environmental data, and ε is a constant.

[0104] When training the above multiple linear regression model, historical signal quality data and historical environmental data are used to train the model, and cross-validation is used to evaluate the model's prediction accuracy.

[0105] In this embodiment, a Long Short-Term Memory (LSTM) model is used to model time-series data (such as historical signal quality data and historical environmental data). This model can be used to predict signal strength changes over a future period. The model is established as follows:

[0106] LSTM(Xt) = f(W·Xt + b)

[0107] Where W represents the weight matrix, Xt represents the time series data of the input vector (such as historical signal quality data and historical environmental data), and b represents the bias term.

[0108] The model takes as input historical signal quality data (such as signal strength) and historical environmental data sequences.

[0109] The model outputs the signal strength change over a future period of time.

[0110] In this embodiment of the application, the prediction results of the multiple linear regression model and the LSTM model are combined to predict the third signal quality data over a future period of time.

[0111] In this embodiment of the application, after predicting the third signal quality data, such as RSSI data, the region with a weak signal can be determined based on the RSSI data. For example, if the RSSI data of a certain region is small, then that region is determined to be a region with a weak signal.

[0112] In this embodiment of the application, after determining the weak signal coverage area, it is necessary to further adjust the signal quality of the weak signal coverage area to ensure the signal quality of the area where the UAV flies.

[0113] In this embodiment of the application, by analyzing the third signal quality data, it is also possible to identify potential sources of interference, such as areas with low signal quality data that may be caused by interference sources such as building obstruction or weather conditions.

[0114] It should be noted that if real-time data is used to predict the quality of the third signal, the above implementation process can be referred to, and will not be repeated here.

[0115] It should be noted that, if real-time signal quality and environmental data are used, this collaborative data collection network is responsible for collecting 5G signal strength and environmental data (such as temperature, humidity, and air pressure) within the flight area, and identifying low-quality signal areas in real time through intelligent analysis technology. The entire data collection and analysis process covers the entire flight cycle of the drone, from initial data collection before takeoff, real-time data updates during flight, to subsequent analysis after flight. By employing advanced signal detection technology and artificial intelligence algorithms, the system can collect and transmit 5G signal strength (such as RSSI) and environmental data (such as temperature, humidity, and air pressure) within the flight area in real time.

[0116] In one embodiment of this application, adjusting the signal quality in areas with weak signal coverage can be achieved in the following way:

[0117] Based on a pre-built digital twin model, signal adjustment parameters are determined; based on these parameters, the signal quality in areas with weak signal coverage is adjusted.

[0118] In this embodiment of the application, determining the signal adjustment parameters first requires the construction of a digital twin model. Specifically, this involves: acquiring base station location information and GIS data; and constructing a digital twin model based on historical signal quality data, historical environmental data, base station location information, and GIS data.

[0119] In this embodiment, the base station location information and GIS data can be obtained from a third-party platform. Specifically, the selection can be made according to the actual situation, and no specific limitation is made in this embodiment.

[0120] In this embodiment, the construction of the digital twin model is performed by the professional modeling module of the data processing center. Its main purpose is to construct a digital twin model covering the flight area based on collected historical signal quality data, historical environmental data, base station location information, and GIS data. This digital twin model can reflect in detail elements such as terrain, buildings, weather conditions, and 5G signal coverage. A detailed model of the flight area is constructed using digital twin technology, and finally, flight path planning is performed by combining relevant signal quality data scores from 5G signal quality data. This contrasts sharply with the simple obstacle avoidance planning of related technologies, providing a more comprehensive and efficient flight strategy.

[0121] In this embodiment, the construction of the digital twin model begins in the flight planning phase and is continuously updated throughout the flight path optimization process to ensure the model's real-time performance and accuracy. All modeling activities are conducted at the data processing center, combining on-site collected data with advanced simulation technology to create a dynamically updated, high-precision 3D environment model that reflects changes in the physical world in real time.

[0122] In the embodiments of this application, during the construction of the digital twin model, the optimal base station adjustment parameters can be output based on the data required during the construction process, providing a scientific basis for base station configuration, thereby assisting in the optimization and adjustment of the base station and ensuring the optimization of signal quality.

[0123] In this embodiment, after constructing the aforementioned digital twin model, the network operator and signal management system are responsible for executing the base station planning and parameter adjustment. This mainly involves dynamically adjusting the base station planning or parameters using the simulation calculation results of the digital twin model. This includes not only optimizing for low-quality signal areas identified within the flight area, but also adjusting the base station parameters based on real-time and predicted data collected during the UAV's flight to adapt to changes in signal demand.

[0124] It should be noted that signal adjustment begins immediately after identifying areas with weak signal coverage and continues throughout the drone's flight, based on real-time feedback from the digital twin model. The digital twin model analyzes the causes of insufficient signal coverage, such as terrain obstruction and building reflections, and provides specific adjustment methods, including but not limited to the following:

[0125] (1) Transmit power adjustment: Based on the weak signal areas identified by the model, the transmit power of the base station is appropriately increased or decreased to optimize signal coverage.

[0126] (2) Antenna directivity adjustment: Adjust the direction and tilt angle of the base station antenna to optimize the signal propagation path and reduce the impact of reflection and blockage.

[0127] (3) Base station location planning: Optimize base station locations or increase base station density based on signal coverage to ensure stable signal quality.

[0128] (4) Frequency bandwidth adjustment: Adjust the frequency bandwidth and time slot configuration of the base station to improve signal quality and transmission rate.

[0129] Furthermore, the system can be adjusted according to specific adjustment methods.

[0130] It should be noted that by intelligently analyzing the 5G signal quality data collected by drones and supplementing it with information from ground base stations, the method of identifying and improving low-quality signal areas not only improves the communication quality of drones but also optimizes the coverage efficiency of the entire 5G network compared to related technologies.

[0131] In the embodiments of this application, after adjustments are made based on one or more of the above adjustment methods, the effectiveness of the adjustments can be verified by simulation or field testing, that is, to determine whether the signal quality of the weak coverage area has been improved.

[0132] It should be noted that after adjusting and optimizing the signal quality in areas with weak signal coverage, the signal quality needs to be monitored in real time to dynamically adjust the signal adjustment parameters and ensure the stability of the signal quality during the drone's flight.

[0133] Based on the above embodiments, this application integrates signal quality data, digital twin technology, artificial intelligence, and flight path planning algorithms to optimize the flight path of the UAV, thereby achieving low-latency, efficient, and stable communication capabilities. This implementation not only considers the current network quality but also aims to improve network coverage through long-term data collection and analysis, and predict future network conditions to provide more accurate flight planning for the UAV.

[0134] Furthermore, the improvements in this embodiment include base station optimization, predictive flight planning, and dynamic adjustment and environmental adaptation. Base station optimization intelligently identifies low-quality signal areas and dynamically adjusts base station parameters to optimize signal coverage. Predictive flight planning combines signal quality barometers and environmental data, using predictive algorithms to accurately plan flight paths, improving the UAV's adaptability to changing network conditions. Dynamic adjustment and environmental adaptation adjust flight plans using real-time data and predictive models, enhancing the UAV's operational flexibility and efficiency in variable environments, ensuring it can complete missions efficiently and safely under any circumstances. These improvements significantly enhance the overall system performance and reliability, enabling the UAV to perform complex tasks in various environments, and the solution is relatively easy to implement.

[0135] This application embodiment demonstrates a comprehensive and highly practical technical implementation scheme by detailing each step from 5G signal quality data acquisition, environmental data acquisition, intelligent analysis, base station optimization to flight path planning and UAV control. This method significantly improves the performance of UAVs in terms of flight efficiency, safety, and data transmission quality, and is particularly suitable for applications in dynamic and complex environments. Whether conducting urban surveillance, agricultural surveys, or emergency rescue missions, this scheme ensures that UAVs complete their tasks with maximum efficiency and safety.

[0136] Based on the above embodiments, this application also provides an architecture diagram for implementing a flight path planning method, such as... Figure 2 As shown, Figure 2 The specific implementation methods for each step have been described in the foregoing embodiments; here, we will focus on... Figure 2 Here is a brief introduction:

[0137] This solution begins with real-time data collection of 5G signal quality data and environmental data from the drone's flight. Using sensors and a 5G signal receiving module onboard the drone, data such as signal strength, temperature, humidity, and air pressure within the flight area are collected. After data collection, the system uses intelligent analysis technology to process this information to identify low-quality areas of signal coverage.

[0138] Furthermore, after obtaining this crucial information, the system will construct a digital twin model, creating a precise three-dimensional simulation of the flight area by fusing collected 5G signal quality data and environmental data. This digital twin model can reflect the drone's flight environment in real time and predict signal quality changes, providing decision support for subsequent steps.

[0139] Furthermore, the system will adjust base station planning and related parameters based on the output of the digital twin model. This adjustment is to optimize 5G signal coverage, especially after identifying low-quality signal areas in the previous analysis. This process may involve changing the location of base stations, adjusting transmit power, or modifying other network configuration settings.

[0140] Furthermore, the system can also generate a signal quality barometer, which is a signal quality map derived from historical and predictive data analysis. This barometer can predict the distribution of signal quality under different weather and environmental conditions, providing a reference for UAV flight planning.

[0141] Finally, the system integrates the output results of the signal quality barometer and the digital twin model, and calculates the optimal flight route for the drone using a flight path planning algorithm. This route takes into account various factors such as signal quality, obstacles, and weather conditions to ensure the drone's flight efficiency and safety.

[0142] The entire operation is monitored in real time by the drone's flight control system, ensuring the execution of flight path planning and precise control of flight maneuvers, achieving low-latency and high-efficiency drone flight operations.

[0143] Compared with related technologies, the embodiments of this application have at least the following technical advantages:

[0144] 1. Comprehensive environmental adaptability:

[0145] Advantages: The embodiments of this application can dynamically adjust the flight path based on environmental changes (such as weather and terrain) and real-time 5G signal quality data. The corresponding technical means adopted is to use a digital twin model combined with AI algorithms to achieve real-time response and prediction of environmental factors and signal quality, thereby maintaining an optimized flight path in complex environments.

[0146] 2. Improve flight efficiency and safety:

[0147] Advantages: Compared to related technologies, the embodiments of this application provide higher flight efficiency and safety. The technical means employed are a precise environmental model constructed using digital twin technology and an intelligent flight path planning algorithm, ensuring that the drone effectively avoids obstacles while maintaining optimal signal coverage, thereby reducing flight risks.

[0148] 3. More precise flight path planning:

[0149] Advantages: The embodiments of this application can generate more accurate and optimized flight paths. The technical means employed is to combine the output results of digital twin models and signal quality barometers, and use advanced algorithms to accurately plan flight paths, taking into account not only obstacle avoidance but also network connectivity optimization.

[0150] 4. Enhance data transmission quality:

[0151] Advantages: Provides more stable and efficient data transmission capabilities. The technology employed optimizes the flight path to remain within the best signal coverage area, ensuring high signal quality and low latency during data transmission.

[0152] The implementation of the embodiments in this application has certain commercial value, mainly reflected in the following aspects:

[0153] 1. Market demand and application breadth:

[0154] Drone technology has developed rapidly in recent years and is widely used in agriculture, surveying, logistics, monitoring, film and television production, and other fields. With the popularization and maturity of 5G technology, drones combined with 5G networks will have stronger data transmission capabilities and broader application prospects. The technical solution of this application can significantly improve the practicality and versatility of drones by optimizing their flight paths and improving their communication stability in complex environments, thus meeting the market demand for efficient and reliable drone operation.

[0155] 2. Advantages brought about by technological innovation:

[0156] The technical solution presented in this application provides a more efficient and safer drone flight solution by integrating 5G signal quality analysis, intelligent flight path planning, and digital twin technology. This innovation makes drones more stable and efficient when performing tasks, especially in application scenarios requiring long-distance, high-quality data transmission.

[0157] 3. Potential market size:

[0158] The drone market is experiencing continuous growth, and drones incorporating 5G technology are expected to find applications across multiple industries. From commercial photography to agricultural monitoring, from express delivery and logistics to urban planning, the technology's application potential is enormous. With the further popularization of 5G networks and the increasing maturity of the technology, this drone solution integrating advanced communication technologies will have broad market appeal.

[0159] 4. Competitive Advantages:

[0160] This technological solution offers a significant competitive advantage over traditional drone flight solutions by providing more efficient flight planning and more stable data communication. It will be particularly favored by the market in fields requiring precise flight control and high-quality data transmission.

[0161] In conclusion, the embodiments of this application have promising market prospects. They not only meet the current market demand for efficient and safe drone operation but also align with the development trend of 5G technology. Through technological innovation, the embodiments of this application have the potential for widespread application in multiple industries, particularly in fields requiring high precision in drone operation and stable communication.

[0162] Based on the above embodiments, another embodiment of this application provides a flight path planning device 1, such as... Figure 3 As shown, the device 1 includes:

[0163] Acquisition unit 10 is used to acquire signal quality assessment parameters and environmental risk assessment parameters.

[0164] The determination unit 11 is used to determine the flight path cost based on signal quality assessment parameters and environmental risk assessment parameters.

[0165] Planning unit 12 is used to plan flight paths based on flight path cost.

[0166] In one embodiment, the signal quality assessment parameter is a signal quality score.

[0167] In one embodiment, the acquisition unit 10 is further configured to acquire first environmental data for a future first time period and first signal quality data for a second time period.

[0168] The determining unit 11 is also used to construct a signal quality barometer based on the first signal quality data and the first environmental data.

[0169] The acquisition unit 10 is also used to acquire a signal quality score from the signal quality barometer.

[0170] In one embodiment, the acquisition unit 10 is also used to acquire GIS data.

[0171] The determining unit 11 is also used to input the first signal quality data and the first environment data into the pre-trained first model to obtain the second signal quality data for the first time period in the future.

[0172] The determining unit 11 is also used to determine signal quality distribution data based on the second signal quality data and GIS data; and to generate a signal quality barometer based on the signal quality distribution data.

[0173] In one embodiment, the flight path planning device 1 may further include an adjustment unit.

[0174] The acquisition unit 10 is also used to acquire historical signal quality data and historical environmental data.

[0175] The determining unit 11 is also used to predict the third signal quality data for the third time period in the future based on historical signal data and historical environmental data; and to determine the weak signal coverage area based on the third signal quality data.

[0176] Adjustment unit, used to adjust signal quality in areas with weak signal coverage.

[0177] In one embodiment, the determining unit 11 is further configured to determine signal adjustment parameters based on a pre-built digital twin model.

[0178] The adjustment unit is also used to adjust the signal quality in areas with weak signal coverage based on signal adjustment parameters.

[0179] In one embodiment, the acquisition unit 10 is further configured to acquire base station location information and GIS data;

[0180] The determination unit 11 is also used to construct a digital twin model based on historical signal quality data, historical environmental data, base station location information and GIS data.

[0181] This application provides a flight path planning device that acquires signal quality assessment parameters and environmental risk assessment parameters; determines the flight path cost based on the signal quality assessment parameters and environmental risk assessment parameters; and plans the flight path based on the flight path cost. Therefore, the flight path planning device proposed in this application determines the path cost of the UAV flight path by combining the network signal quality score and environmental risk score during UAV flight. By incorporating the network quality score into the flight path planning process, the determined flight path is a path with relatively good network quality. Thus, when the UAV uses this optimized flight path during flight, the quality of transmitted data and images will be significantly improved.

[0182] Figure 4 This is a schematic diagram of the composition of a flight path planning device 1 provided in an embodiment of this application. In practical applications, based on the same disclosed concept of the above embodiments, such as... Figure 4 As shown, the flight path planning device 1 of this application embodiment includes a processor 13, a memory 14 and a communication bus 15.

[0183] In specific embodiments, the acquisition unit 10, determination unit 11, planning unit 12, and adjustment unit described above can be implemented by a processor 13 located on the flight path planning device 1. The processor 13 can be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), CPU, controller, microcontroller, and microprocessor. It is understood that for different devices, the electronic device used to implement the above processor functions can also be other types, and this application embodiment does not impose specific limitations.

[0184] In this embodiment, the communication bus 15 is used to establish communication between the processor 13 and the memory 14; when the processor 13 executes the running program stored in the memory 14, it implements the following flight path planning method:

[0185] Obtain signal quality assessment parameters and environmental risk assessment parameters; determine the flight path cost based on the signal quality assessment parameters and environmental risk assessment parameters; and plan the flight path based on the flight path cost.

[0186] In one embodiment, the signal quality assessment parameter is a signal quality score.

[0187] In one embodiment, the processor 13 is further configured to acquire first environmental data for a future first time period and first signal quality data for a second time period; construct a signal quality barometer based on the first signal quality data and the first environmental data; and acquire a signal quality score from the signal quality barometer.

[0188] In one embodiment, the processor 13 is further configured to acquire GIS data; input first signal quality data and first environmental data into a pre-trained first model to obtain second signal quality data for a future first time period; determine signal quality distribution data based on the second signal quality data and GIS data; and generate a signal quality barometer based on the signal quality distribution data.

[0189] In one embodiment, the processor 13 is further configured to acquire historical signal quality data and historical environmental data; predict third signal quality data for a future third time period based on the historical signal data and historical environmental data; determine weak signal coverage areas based on the third signal quality data; and adjust the signal quality of the weak signal coverage areas.

[0190] In one embodiment, the processor 13 is further configured to determine signal adjustment parameters based on a pre-built digital twin model; and to adjust the signal quality of weak signal coverage areas based on the signal adjustment parameters.

[0191] In one embodiment, the processor 13 is further configured to acquire base station location information and GIS data; and to construct a digital twin model based on historical signal quality data, historical environmental data, base station location information, and GIS data.

[0192] Based on the above embodiments, this application provides a storage medium storing a computer program thereon. The computer-readable storage medium stores one or more programs, which can be executed by one or more processors and applied in a flight path planning device. The computer program implements the flight path planning method as described above.

[0193] Based on the above embodiments, this application provides a computer program product, including a computer program that can be executed by one or more processors and applied in a flight path planning device. The computer program implements the flight path planning method described above.

[0194] It should be noted that, in the embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0195] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause an image display device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the embodiments of this application.

[0196] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A flight path planning method, characterized in that, The method includes: Obtain signal quality assessment parameters and environmental risk assessment parameters; Based on the signal quality assessment parameters and the environmental risk assessment parameters, the flight path cost is determined; Based on the cost-effectiveness of the flight path, the flight path is planned.

2. The method according to claim 1, characterized in that, The signal quality assessment parameter is a signal quality score. Obtaining the signal quality assessment parameter includes: Acquire the first environmental data for the first time period and the first signal quality data for the second time period in the future; Based on the first signal quality data and the first environmental data, a signal quality barometer is constructed. The signal quality score is obtained from the signal quality barometer.

3. The method according to claim 2, characterized in that, The step of constructing a signal quality barometer based on the first signal quality data and the first environmental data includes: Acquire Geographic Information System (GIS) data; The first signal quality data and the first environmental data are input into the pre-trained first model to obtain the second signal quality data for the first time period in the future. Based on the second signal quality data and the GIS data, the signal quality distribution data is determined; Based on the signal quality distribution data, the signal quality barometer is generated.

4. The method according to claim 1, characterized in that, Before obtaining the signal quality assessment parameters and the environmental risk assessment parameters, the method further includes: Acquire historical signal quality data and historical environmental data; Based on the historical signal data and the historical environmental data, predict the third signal quality data for the third time period in the future; Based on the third signal quality data, areas with weak signal coverage are determined; Adjust the signal quality in the area with weak signal coverage.

5. The method according to claim 4, characterized in that, Adjusting the signal quality in the weak signal coverage area includes: Based on a pre-constructed digital twin model, the signal adjustment parameters are determined; Based on the signal adjustment parameters, the signal quality in the weak signal coverage area is adjusted.

6. The method according to claim 5, characterized in that, Before determining the adjustment parameters for adjusting the signal quality based on the pre-built digital twin model, the method further includes: Obtain base station location information and GIS data; The digital twin model is constructed based on historical signal quality data, historical environmental data, base station location information, and GIS data.

7. A flight path planning device, characterized in that, The device includes: The acquisition unit is used to acquire signal quality assessment parameters and environmental risk assessment parameters; The determining unit is used to determine the flight path cost based on the signal quality assessment parameters and the environmental risk assessment parameters; A planning unit is used to plan the flight path based on the flight path cost.

8. A flight path planning device, characterized in that, The device includes a processor and a memory; the processor, when executing a running program stored in the memory, implements the method as described in any one of claims 1 to 6.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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