Adaptive control method and system for millimeter wave based drones

Abstract

The application discloses an unmanned aerial vehicle adaptive control method and system based on millimeter waves, and relates to the technical field of millimeter waves.A plurality of attenuation nodes are determined based on the path form of an attenuation path, the up-and-down activity amount of the unmanned aerial vehicle at the current position, and the multiple environment types, and millimeter wave attenuation data of the plurality of attenuation nodes is marked, thereby improving the detection accuracy of the plurality of attenuation nodes.Therefore, a millimeter wave correction mechanism is determined based on the data transmission state of the unmanned aerial vehicle and the millimeter wave attenuation data of the plurality of attenuation nodes, the working power regulation range of a ground millimeter wave base station is determined based on the millimeter wave attenuation data of the plurality of attenuation nodes and the current position of the unmanned aerial vehicle, and the adaptive control strategy of the unmanned aerial vehicle at the millimeter wave transmission level is determined according to the working power regulation range of the ground millimeter wave base station and the millimeter wave correction measure, thereby improving the accuracy of the adaptive control strategy of the unmanned aerial vehicle at the millimeter wave transmission level.

Description

Technical Field

[0001] This invention relates to the field of millimeter wave technology, and more particularly to an adaptive control method and system for a millimeter wave-based unmanned aerial vehicle (UAV). Background Technology

[0002] With the development of technology, drones are being used in people's lives. Drones communicate with ground millimeter-wave base stations and transmit corresponding millimeter-wave data. In the current technology, as the drone moves away from the ground millimeter-wave base station, the millimeter-wave data will be affected by some environmental factors, resulting in a large amount of attenuation in the transmission of millimeter-wave data. There is no control over the attenuation of millimeter-wave data, and it is impossible to achieve adaptive control of drones at the millimeter-wave transmission level. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides an adaptive control method and system for unmanned aerial vehicles based on millimeter waves.

[0004] This invention provides an adaptive control method for a millimeter-wave unmanned aerial vehicle (UAV), comprising: determining the millimeter-wave transmission space based on the UAV's position and the position of a ground millimeter-wave base station; determining multiple environmental parameters based on environmental detection of the millimeter-wave transmission space; determining multiple environmental types of the millimeter-wave transmission space based on the multiple environmental parameters; determining a millimeter-wave attenuation path based on the multiple environmental types, the spatial morphology of the millimeter-wave transmission space, and the millimeter-wave transmission data; determining multiple attenuation nodes in the millimeter-wave attenuation path based on the path morphology of the attenuation path, the vertical movement of the UAV at its current position, and the multiple environmental types, and marking the millimeter-wave attenuation data of the multiple attenuation nodes; determining a millimeter-wave correction mechanism based on the UAV's data transmission status and the millimeter-wave attenuation data of the multiple attenuation nodes, and triggering millimeter-wave correction during the UAV's flight; determining the operating power adjustment range of the ground millimeter-wave base station based on the millimeter-wave attenuation data of the multiple attenuation nodes and the UAV's current position; and determining an adaptive control strategy for the UAV at the millimeter-wave transmission layer based on the operating power adjustment range of the ground millimeter-wave base station and the millimeter-wave correction measures.

[0005] This invention provides an adaptive control system for a millimeter-wave-based unmanned aerial vehicle (UAV). This millimeter-wave-based UAV adaptive control system is applied to the aforementioned adaptive control method for millimeter-wave-based UAVs. The adaptive control system for the millimeter-wave-based UAV includes:

[0006] The environmental parameter module is used to determine the millimeter-wave transmission space based on the location of the UAV and the location of the ground millimeter-wave base station; and to determine multiple environmental parameters based on environmental detection of the millimeter-wave transmission space.

[0007] The attenuation path module is used to determine multiple environmental types of the millimeter wave transmission space based on multiple environmental parameters, and to determine the attenuation path of the millimeter wave based on the multiple environmental types, the spatial shape of the millimeter wave transmission space, and the millimeter wave transmission data.

[0008] The attenuation node module is used to determine multiple attenuation nodes in the attenuation path of the millimeter wave based on the path shape of the attenuation path, the vertical movement of the UAV at the current position, and multiple environmental types, and to mark the millimeter wave attenuation data of multiple attenuation nodes.

[0009] The millimeter-wave correction mechanism module is used to determine the millimeter-wave correction mechanism based on the data transmission status of the UAV and the millimeter-wave attenuation data of multiple attenuation nodes, and to trigger the millimeter-wave correction of the UAV during flight.

[0010] The adaptive control module is used to determine the operating power adjustment range of the ground millimeter-wave base station based on millimeter-wave attenuation data from multiple attenuation nodes and the current position of the UAV, and to determine the adaptive control strategy of the UAV at the millimeter-wave transmission layer based on the operating power adjustment range of the ground millimeter-wave base station and millimeter-wave correction measures.

[0011] Compared with the prior art, the beneficial effects of the present invention are:

[0012] In this embodiment of the invention, the method described herein determines multiple attenuation nodes in the millimeter wave attenuation path based on the path shape of the attenuation path, the vertical movement of the UAV at its current position, and multiple environmental types. The millimeter wave attenuation data of the multiple attenuation nodes are then marked. This introduces a millimeter wave attenuation path, which takes into account the overall consideration of the path shape of the attenuation path, the vertical movement of the UAV at its current position, and multiple environmental types. This improves the detection accuracy of multiple attenuation nodes and manages the millimeter wave attenuation data of multiple attenuation nodes.

[0013] Therefore, a millimeter-wave correction mechanism is determined based on the data transmission status of the UAV and the millimeter-wave attenuation data of multiple attenuation nodes, and the millimeter-wave correction is triggered during the flight of the UAV. The operating power control range of the ground millimeter-wave base station is determined based on the millimeter-wave attenuation data of multiple attenuation nodes and the current position of the UAV. Based on the operating power control range of the ground millimeter-wave base station and the millimeter-wave correction measures, an adaptive control strategy for the UAV at the millimeter-wave transmission layer is determined. The introduction of millimeter-wave correction measures during the flight of the UAV realizes the overall consideration of the operating power control range of the ground millimeter-wave base station and the millimeter-wave correction measures, and improves the accuracy of the adaptive control strategy for the UAV at the millimeter-wave transmission layer. Attached Figure Description

[0014] Figure 1This is a flowchart illustrating the adaptive control method for a millimeter-wave-based unmanned aerial vehicle (UAV) according to an embodiment of the present invention.

[0015] Figure 2 This is a flowchart illustrating step S11 in the adaptive control method for a millimeter-wave-based unmanned aerial vehicle (UAV) according to an embodiment of the present invention.

[0016] Figure 3 This is a flowchart illustrating step S12 in the adaptive control method for a millimeter-wave-based unmanned aerial vehicle (UAV) according to an embodiment of the present invention.

[0017] Figure 4 This is a flowchart illustrating step S13 in the adaptive control method for a millimeter-wave-based unmanned aerial vehicle (UAV) according to an embodiment of the present invention.

[0018] Figure 5 This is a flowchart illustrating step S14 in the adaptive control method for a millimeter-wave-based unmanned aerial vehicle (UAV) according to an embodiment of the present invention.

[0019] Figure 6 This is a flowchart illustrating step S15 in the adaptive control method for a millimeter-wave-based unmanned aerial vehicle (UAV) according to an embodiment of the present invention.

[0020] Figure 7 This is a schematic diagram of the structural composition of an adaptive control system for a millimeter-wave-based unmanned aerial vehicle (UAV) according to an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0022] Please see Figures 1 to 7 An adaptive control method for millimeter-wave-based unmanned aerial vehicles (UAVs) is proposed, applied to adaptive control scenarios for millimeter-wave-based UAVs. The adaptive control method for millimeter-wave-based UAVs includes:

[0023] Step S11: Determine the millimeter-wave transmission space based on the location of the UAV and the location of the ground millimeter-wave base station; determine multiple environmental parameters based on environmental detection of the millimeter-wave transmission space;

[0024] Step S12: Determine the multiple environmental types of the millimeter wave transmission space based on multiple environmental parameters, and determine the attenuation path of the millimeter wave based on the multiple environmental types, the spatial morphology of the millimeter wave transmission space, and the millimeter wave transmission data.

[0025] Step S13: In the attenuation path of the millimeter wave, multiple attenuation nodes are determined based on the path shape of the attenuation path, the vertical movement of the UAV at the current position, and multiple environmental types, and the millimeter wave attenuation data of the multiple attenuation nodes are marked.

[0026] Step S14: Determine the millimeter-wave correction mechanism based on the data transmission status of the UAV and the millimeter-wave attenuation data of multiple attenuation nodes, and trigger the millimeter-wave correction of the UAV during flight.

[0027] Step S15: Determine the operating power adjustment range of the ground millimeter-wave base station based on the millimeter-wave attenuation data of multiple attenuation nodes and the current position of the UAV; determine the adaptive control strategy of the UAV at the millimeter-wave transmission layer based on the operating power adjustment range of the ground millimeter-wave base station and the millimeter-wave correction measures.

[0028] refer to Figure 2 In step S11, the specific steps are as follows:

[0029] S111: During the flight of the UAV, the UAV establishes a communication connection with the ground millimeter-wave base station and transmits the corresponding millimeter-wave data. At the same time, it collects the position of the UAV and the position of the ground millimeter-wave base station, and determines the current flight attitude of the UAV based on multiple attitude parameters of the UAV.

[0030] S112: Construct a millimeter-wave transmission system based on the communication between the UAV and the ground millimeter-wave base station, and determine the transmission space of the millimeter wave according to the millimeter-wave transmission system, the location of the UAV and the location of the ground millimeter-wave base station;

[0031] S113: Determine the corresponding environmental area based on the spatial morphology of the millimeter wave transmission space, the current flight attitude of the UAV, and the relative position between the UAV and the millimeter wave base station. Determine multiple environmental parameters based on environmental detection of the environmental area. These multiple environmental parameters are distributed in different locations within the environmental area.

[0032] In the embodiments of this application, after takeoff or entering the work area, the UAV actively or passively searches for and connects to a preset or available ground millimeter-wave base station. Because millimeter-wave signals are sensitive to obstruction, the UAV may experience signal strength fluctuations or even brief interruptions during flight. The data transmitted via the millimeter-wave link is diverse, including: control commands, status feedback, and service data; different data have different real-time requirements. For example, control commands and status feedback typically require low-latency transmission.

[0033] Drones typically use a combination of technologies to obtain precise locations; in this case, GPS / BeiDou / GLONASS / Galileo satellite navigation systems provide basic position, velocity, and time (PVT) information; base station ranging / positioning uses the time of propagation (TOA), angle of arrival (AOA), or time difference of arrival (TDOA) of signals from ground millimeter-wave base stations or other base stations to assist or supplement GPS positioning.

[0034] The location of the ground millimeter-wave base station is usually known and configured in advance. It collects multiple attitude parameters of the UAV, which typically include pitch angle, roll angle, and yaw angle. The collected raw attitude parameters (usually the angular velocity of the gyroscope and the gravity vector of the accelerometer) are converted into stable attitude descriptions without cumulative error (such as Euler angles and quaternions) through specific algorithms (such as complementary filtering, Kalman filtering, and quaternion method). The final flight attitude can be represented by Euler angles (Pitch, Roll, Yaw) or quaternions.

[0035] Furthermore, the drone and the ground millimeter-wave base station have established a basic communication link through the communication connection in S111. On this basis, it is necessary to further "build" a more detailed transmission system model. This is not just about knowing that they are "communicating", but about understanding the technical details of this communication link. This includes: modulation and demodulation methods, bandwidth allocation, antenna configuration, beamforming state, signal strength and quality. Integrating the above parameters forms a digital transmission system model, which is used to describe how the signal propagates from the transmitter to the receiver.

[0036] By combining the acquired UAV location (latitude, longitude, altitude, or 3D coordinates relative to the base station) and the base station location, along with the constructed transmission system model (especially antenna orientation and beam characteristics), the main propagation area of ​​the millimeter-wave signal in space is determined. Direct path: Calculate the straight-line distance and azimuth / elevation angle between the UAV and the base station; this is the main path of signal propagation, especially under line-of-sight conditions. Beam coverage area: If beamforming is used, the beam will form a specific spatial area, typically a cone or fan shape, the size and shape of which are determined by the beamwidth, antenna gain, and distance. The specific location of this area in 3D space needs to be determined based on the UAV's position and beam orientation. Multipath effect area: Millimeter-wave signals are easily affected by reflection, diffraction, and scattering in the environment, forming multiple propagation paths. Although accurately calculating all multipath paths is complex, areas with significant reflection or diffraction can be roughly estimated based on environmental type (urban, suburban, indoor, etc.) and basic geometric relationships. Based on the above information, the "transmission space" is defined as a three-dimensional spatial region with the UAV and base station as endpoints, including the direct path, beam coverage area, and multipath influence area. This region is the basis for subsequent environmental detection and analysis of signal attenuation, and can be visualized as a complex spatial volume.

[0037] Therefore, the spatial morphology of the millimeter-wave transmission space, the current flight attitude of the UAV, and the relative position between the UAV and the millimeter-wave base station are introduced. The spatial morphology of the transmission space describes the geometry of the main propagation of the millimeter-wave signal, including: direct path, reflection area, diffraction area, shadow area, and antenna beam coverage area. Based on the UAV's attitude and antenna characteristics, the beam coverage area of ​​the current main transmitted / received signal is determined. For example, if the UAV uses a directional antenna and its current attitude causes its antenna main lobe to point towards the ground base station, then the beam coverage area is the key area of ​​concern.

[0038] Based on the relative position of the drone and the base station and the shape of the transmission space, determine whether there are obstacles in the direct path (by judging the shadow area in the spatial shape) and identify the main reflective surface (by judging the reflection area in the spatial shape).

[0039] Based on the above analysis, the transmission space is divided into several key environmental areas: Direct path area: the path of the direct wave, focusing on whether there are any obstructions; Main reflection area: the surface area of ​​objects expected to generate strong reflected signals; Potential interference area: the area where other wireless signal sources or strong multipath effects exist; Edge coverage area: the edge of the beam coverage range, where the signal is weaker. Determining these environmental areas is to conduct targeted environmental detection later, rather than uniformly casting a wide net across the entire transmission space, thereby improving detection efficiency and the relevance of information.

[0040] Within a defined environmental area, data reflecting the environmental impact on millimeter-wave signals needs to be collected. Detection methods may include: analyzing the characteristic changes of millimeter-wave signals transmitted between the UAV and the base station; Signal Strength Index (RSSI): measuring signal strength changes in different directions or locations (simulated via antenna scanning or diversity reception) to infer the presence of obstacles or reflectors; Signal-to-Noise Ratio (SNR) / Signal-to-Interference Ratio (SINR): measuring the signal strength relative to noise and interference, reflecting channel quality; Channel State Information (CSI): more detailed measurements, including amplitude, phase, delay spread, and angular spread, accurately reflecting multipath effects and angular spread; Angle of Arrival (AoA) / Angle of Transmission (AoD): measuring the arrival / transmission angle of the signal via an antenna array to locate reflection or interference sources. Based on the detected data, specific environmental parameters are determined. These parameters should quantify the environmental impact on millimeter-wave transmission: obstacle information, reflector information, multipath component information, location, type, intensity of interference sources, and atmospheric parameters.

[0041] refer to Figure 3 In step S12, the specific steps are as follows:

[0042] S121: Collect multiple environmental parameters and determine multiple combinations of environmental parameters based on the multiple environmental parameters and the spatial morphology of the millimeter wave transmission space. Each combination of environmental parameters contains at least three environmental parameters, and the matching coefficient of multiple environmental parameters in each combination of environmental parameters is greater than a preset matching coefficient threshold.

[0043] S122: Based on the identification of multiple environmental parameter combinations, the corresponding environmental characteristics are determined. Based on the morphology, distribution location, and spatial size of the millimeter wave transmission space of the multiple environmental characteristics, the multiple environmental types are determined, and the multiple environmental types contain multiple environmental types.

[0044] S123: Mark the distribution location of multiple environmental types in multiple environmental types, monitor the millimeter wave transmission process of the UAV in real time, collect millimeter wave transmission data, and determine the attenuation path of millimeter waves based on the distribution location of multiple environmental types, the spatial shape of the millimeter wave transmission space, and the millimeter wave transmission data.

[0045] In the embodiments of this application, multiple environmental parameters are collected. In S113, several environmental regions are determined based on the transmission space morphology, UAV attitude, and relative position, and environmental parameters are collected within these regions. The transmission space morphology of millimeter waves (such as the cone, fan, or other shapes determined in S112) determines which environmental parameters are spatially adjacent or related. For example, if the transmission space is a cone, parameters located on the same side of the cone and close to each other have superimposed or related effects on the signal. The collected environmental parameters are clustered or grouped according to the spatial morphology (e.g., based on distance, angle, whether they are in the same beam area, etc.) to form "environmental parameter combinations". Each combination represents a comprehensive description of a local area or a local environmental state within the transmission space. Each combination contains at least three environmental parameters. This requirement is to ensure that the combination has a certain amount of information and representativeness, and to avoid misjudgment due to the randomness of a single or two parameters.

[0046] The matching coefficient is used to measure the consistency or correlation among the parameters within a combination of environmental parameters; it determines whether these parameters describe a unified environmental phenomenon or whether their effects are synergistic to some extent; the calculation method of the matching coefficient can be designed according to specific applications, for example: based on type and effect: if the parameters in the combination all belong to the same type (such as all being obstacles) or all have similar types of effects on the signal (such as all causing signal attenuation), then the coefficient is higher.

[0047] The preset threshold is a pre-set threshold value (e.g., 0.7 or 0.8). Only when the matching coefficient of the internal parameters of a combination exceeds this threshold is the combination considered "valid" and able to represent a meaningful environmental state; combinations below the threshold are considered noise or too scattered and are temporarily not adopted.

[0048] Furthermore, pattern recognition and abstraction are performed on the multiple combinations of environmental parameters generated by S121. The system needs to analyze which parameter types are included in each combination, and the physical meaning represented by these parameter combinations together. An "environmental feature" is a general description of a set of related environmental parameter combinations. It usually corresponds to a typical physical scene or a phenomenon that has a significant impact on millimeter wave propagation. For example: strong obstruction feature: composed of parameter combinations such as "large buildings" and "high-density vegetation"; strong reflection feature: composed of parameter combinations such as "smooth walls", "large water surfaces", and "metal structures"; high loss feature: composed of parameter combinations such as "high humidity air", "rainy and snowy weather", and "a large amount of dust"; multipath feature: composed of parameter combinations such as "multiple reflecting surfaces" and "complex geometric structures".

[0049] The system needs to establish a mapping rule from combinations of environmental parameters to environmental features. This can be based on a predefined rule base. For example, if the combination of "building-reflection-humidity" is detected, the system can identify it as "partial shading with strong reflection features".

[0050] By introducing the morphology, distribution location, and spatial size of millimeter wave transmission space of multiple environmental features, the transmission space is divided into several main, macroscopic environmental types. This requires considering: the coverage of features: whether a feature occupies the entire space or only a small part; the spatial distribution of features: whether features are concentrated or dispersed; whether features are independent or mutually influential; the overall size of the space: what the size and distribution of features mean relative to the size of the space; a large obstruction occupying 10% of the space is a "severe obstruction environment" in a small space, but a "partial obstruction environment" in a huge space; the combination of features: whether multiple features coexist in the space; and how they are combined.

[0051] Common "environment types" include: Free space: almost no interference, only free space path loss; Partially occluded environment: one or more obstructions exist, but some line-of-sight or non-line-of-sight (NLoS) paths are still available; Strong reflection environment: multiple strong reflective surfaces exist, leading to severe multipath effects; High loss environment: atmospheric conditions (such as rain, fog, high humidity) cause severe signal attenuation; Complex mixed environment: multiple complex factors such as obstruction, reflection, and high loss exist simultaneously; "Multiple environment types" means that the transmission space is divided into two or more environment types. For example, the first half of the transmission space is "free space" and the second half is "partially occluded environment"; the system needs to clearly record the distribution area of ​​each environment type in the space.

[0052] Therefore, by marking the distribution locations of multiple environmental types, monitoring the millimeter-wave transmission process of the UAV in real time, and collecting millimeter-wave transmission data, the attenuation path of the millimeter wave is determined based on the distribution locations of multiple environmental types, the spatial morphology of the millimeter-wave transmission space, and the millimeter-wave transmission data. This approach takes into account the overall consideration of the distribution locations of multiple environmental types, the spatial morphology of the millimeter-wave transmission space, and the millimeter-wave transmission data, ensuring the accuracy of the millimeter-wave attenuation path.

[0053] At this point, it is necessary to draw the specific geographical distribution area of ​​each environmental type in space. This usually requires combining information such as the real-time location of the drone, the status of the base station, and the distribution location. Now it is necessary to accurately "draw" it on the spatial map.

[0054] Labeling methods: Geographic Information System (GIS) or simple 2D / 3D coordinate system can be used for labeling. For example, Type A (complex obstacle environment in front) is labeled as a fan-shaped area 30 degrees to the right of the current position of the UAV, at a distance of 50-200 meters; Type B (low-loss environment behind / below) is labeled as a broad area behind and below the UAV; Type C (generally moderate atmospheric loss background) is labeled as covering the entire transmission space, but with gradient changes in the intensity of the influence. This provides spatial reference for subsequent real-time monitoring and data analysis. The system needs to know which environmental type(s) the UAV is currently in when it moves or the signal changes, and which areas the signal is traversing.

[0055] Real-time monitoring of the millimeter-wave communication link status between the UAV and the ground base station includes, but is not limited to: signal received power (RxPower): signal strength; signal quality metrics (such as bit error rate (BER), signal-to-noise ratio (SNR): signal cleanliness; channel state information (CSI): including amplitude, phase, frequency response, etc., which can reflect multipath effects, delay spread, etc.; beam tracking status: whether the beam is aligned; degree of misalignment; transmission rate and throughput: current communication efficiency; these data are continuously collected through the communication module and signal processing unit on the UAV; the data needs to be timestamped and preferably associated with the UAV's current geographical location and attitude information for subsequent analysis; obtaining the real-time status of the current communication link serves as direct evidence for determining whether the signal is interfered with or attenuated.

[0056] Inference is performed by combining spatial information with real-time signal data; the communication link between the UAV and the base station (usually line-of-sight or near-line-of-sight) is analyzed to see which environmental types marked in S123 are traversed. For example, the current link traverses type A (complex obstacle environment ahead) and type C (generally moderate atmospheric loss background); the changes in transmission data before and after traversing different types of regions are compared. For example, before entering type A region, the signal RxPower is -60dBm and SNR is 20dB; after traversing type A region, RxPower drops to -75dBm, SNR drops to 10dB, and CSI shows obvious multipath components and phase jumps; by combining the definition of environmental types and the changes in transmission data, the cause and path of attenuation are inferred.

[0057] Category A defines strong reflection and building obstruction. The data changes correspond precisely to a decrease in signal strength and an increase in multipath effects. Therefore, it can be determined that the signal undergoes significant attenuation when passing through the Category A region. This region and the subsequent path are the main attenuation paths. The spatial morphology of the transmission space (such as whether it is an open space, a tunnel, an urban canyon, etc.) also affects the determination of the attenuation path. For example, in an urban canyon, the signal is reflected and obstructed by buildings multiple times, making the attenuation path more complex and requiring consideration of multiple reflections and diffraction. The final output is a specific description of the attenuation path, including: the main area where attenuation occurs (which environmental category it corresponds to, and its specific location); the main type of attenuation (such as obstruction, strong reflection, atmospheric absorption, etc.); the degree of attenuation (such as how many dB the power decreases); and the existing multipath path information.

[0058] refer to Figure 4 In step S13, the specific steps are as follows:

[0059] S131: Real-time monitoring of the attenuation path of millimeter waves, and determination of the path shape of the attenuation path based on the morphology detection of the attenuation path. At the same time, the drone's current position is collected, and the vertical movement of the drone at the current position is determined based on the activity monitoring of the drone at the current position.

[0060] S132: Determine the first attenuation distribution based on the path shape of the attenuation path and the distribution location of multiple environmental types among the multiple environmental types; determine the second attenuation distribution based on the vertical movement of the UAV at the current location and the distribution location of multiple environmental types among the multiple environmental types.

[0061] S133: Based on the first attenuation distribution, the second attenuation distribution, and the attenuation path, multiple attenuation regions are determined. These multiple attenuation regions are distributed in a stepped manner. The corresponding attenuation nodes are determined based on the synchronous detection of the multiple attenuation regions. Multiple attenuation nodes are collected, and the corresponding millimeter-wave attenuation data is determined based on the real-time monitoring of the multiple attenuation nodes.

[0062] In the embodiments of this application, the attenuation path of millimeter waves is monitored in real time. The receiver on the drone continuously measures key indicators such as signal strength (RSSI), signal-to-noise ratio (SNR), bit error rate (BER), and signal phase from the ground millimeter wave base station. The system compares these indicators with preset thresholds to determine which known attenuation paths the signal performs poorly on and records the changes of these indicators over time.

[0063] Morphological detection is performed on the attenuation path. Signal data collected along the attenuation path is used to identify specific patterns or shapes of attenuation. Based on the detected patterns, the attenuation path is classified into a specific morphology. Common morphologies include: uniform attenuation: the signal strength decreases uniformly throughout the path; shadow fading: the signal drops sharply due to obstruction by large obstacles (such as buildings), usually with drastic changes at the edge of the obstacle; multipath fading: the signal strength fluctuates rapidly (fast fading) due to interference between signals arriving at the receiver via different paths (reflection, diffraction); and mixed morphology: a combination of the above.

[0064] The system analyzes parameters such as signal strength variation over time or space (if the UAV is moving and spatial sampling is possible), signal phase variation, and multipath delay spread to determine the main characteristics of attenuation. For example, by analyzing rapidly changing signal strength and phase, it can determine whether there is a significant multipath effect.

[0065] Collecting the drone's current location is crucial for linking previously detected attenuation path patterns and subsequent activity measurements to this precise spatial point. This is essential for understanding the relationship between attenuation and the environment, as well as the drone's attitude. Sensors on the drone (primarily an IMU, including accelerometers and gyroscopes) are used to monitor its vertical motion. The drone's displacement or velocity changes in the vertical direction (Z-axis) are calculated or estimated. This can be an instantaneous value (e.g., current vertical velocity) or a statistical value over a period of time (e.g., amplitude or frequency of vertical displacement). The drone's vertical movement (e.g., ascent, descent, turbulence) alters its geometric relationship with the ground station, thus affecting the millimeter-wave signal propagation path, line-of-sight status, and multipath components. Understanding this activity helps distinguish between environmentally induced attenuation and signal changes caused by the drone's own motion.

[0066] Furthermore, the first attenuation distribution is determined based on the path shape of the attenuation path and the distribution of multiple environmental types among the multiple environmental types. The second attenuation distribution is determined based on the vertical movement of the UAV at the current location and the distribution of multiple environmental types among the multiple environmental types. This comprehensive consideration of both the vertical movement of the UAV at the current location and the distribution of multiple environmental types among the multiple environmental types ensures the accuracy of the second attenuation distribution.

[0067] At this point, the path shape of the attenuation path and the distribution location of multiple environmental types in the multiple environmental types are introduced. The distribution location of multiple environmental types in the multiple environmental types indicates the specific location of different types of environments (such as: type A - complex obstacle environment in front, type B - open environment to the side and rear, type C - high humidity area below, etc.) in the space around the UAV.

[0068] Determine the first attenuation distribution: This explains how the environment itself causes signal attenuation along a specific path. Specifically, it involves correlating the shape of the attenuation path with the distribution of environmental types. For example, if an attenuation path is a "multipath fading" pattern and this path passes through a "Type A - Complex Obstacle Environment" (including buildings), then the first attenuation distribution can be inferred to be: "Multipath reflections and blockages caused by buildings cause signal attenuation when passing through this area." This process involves the simplified application of signal propagation models (such as ray tracing and statistical models) to map environmental types to their contribution to signal attenuation.

[0069] The system software analyzes the path morphology information (such as "multipath fading") provided by S131 and the environmental distribution map provided by S122; it finds which environmental types of areas the attenuation path passes through; then, according to predefined rules or databases (e.g., "building areas usually cause high multipath fading and shadow fading", "high humidity areas cause increased path loss"), it assigns an attenuation influence factor or pattern to each environmental type of area; finally, it integrates this information to generate a descriptive or graphical "first attenuation distribution", indicating which environmental areas play a major role in signal attenuation and the type of role (e.g., occlusion, reflection, absorption).

[0070] Specifically, the system software analyzes the path morphology information (such as "multipath fading") provided by S131 and the environmental distribution map provided by S122; it identifies which environmental types of areas the attenuation path traverses; then, based on predefined rules or a database (e.g., "building areas typically cause high multipath fading and shadow fading," "high humidity areas cause increased path loss"), it assigns an attenuation influence factor or pattern to each environmental type of area. Finally, it synthesizes this information to generate a descriptive or graphical "first attenuation distribution," indicating which environmental areas play a major role in signal attenuation and the type of role (e.g., blocking, reflection, absorption); Path morphology: "multipath fading"; Environmental distribution: Path A traverses "Type A" area, and "Type C" area is below the path.

[0071] System analysis: "Type A - Complex obstacle environment in front" (buildings) is the main factor causing "multipath fading" because buildings reflect signals, creating multiple propagation paths, leading to coherent superposition or cancellation; "Type C - High humidity area below" is also near the signal propagation path, but its direct impact on path A (such as absorption loss) is less than the reflection effect of buildings, or its impact is more reflected in the overall path loss; "First attenuation distribution" is determined as: "The main attenuation source is the building area in front of the UAV (Type A), causing severe multipath fading; the high humidity area below (Type C) also has some absorption loss impact on the signal, but it is relatively minor." This description clearly points out the contribution distribution of environmental factors to signal attenuation.

[0072] The study introduces the vertical movement of the drone at its current location and the distribution of multiple environmental types in multiple environmental categories. The vertical movement of the drone at its current location describes the drone's motion state in the vertical direction, such as whether it is hovering smoothly, slowly ascending / descending, or violently turbulent.

[0073] Determine the second attenuation distribution: Analyze how the drone's own motion interacts with the surrounding environment, thereby affecting signal attenuation; specifically, assess whether the drone's vertical motion causes its position relative to certain environmental features (such as reflective surfaces or obstructions) to change, thus altering signal propagation conditions. For example, if the drone moves up and down near a reflective surface (such as water or a smooth wall), it will change the reflection angle and the length of the reflection path, causing changes in signal strength or phase; if the drone moves up and down near the edge of a shadow, it will frequently enter and exit the shadow area, causing rapid fluctuations in signal strength.

[0074] The system software analyzes the "vertical movement" (such as vertical velocity and vertical displacement range) provided by S131 and the environmental distribution map provided by S122. It identifies environmental areas where the UAV's current movement is significant (e.g., near a reflector, near a shadow edge, or in a high multipath region). Then, based on physical principles or empirical models, it evaluates the signal changes caused by this vertical movement. For example, "when near a reflector, vertical movement causes changes in the reflection angle, resulting in periodic fluctuations in signal strength" or "near a shadow edge, vertical movement rapidly switches between signal obstruction and signal occlusion." Finally, it generates a descriptive or graphical "secondary attenuation distribution" that illustrates in which areas and in what ways the UAV's movement exacerbates or alters signal attenuation.

[0075] Specifically, vertical movement: slight turbulence (±0.25m); environmental distribution: directly below the drone is a "Type C" area, and in front is a "Type A" area; system analysis: the "slight vertical turbulence" occurs directly above the "Type C - high humidity area"; the high humidity area itself has signal absorption loss; although a vertical displacement of 0.5 meters is not small relative to the signal wavelength (millimeter wave), the "slight turbulence" is not enough to significantly change the path length or angle of the signal when passing through the humidity layer, so the impact on signal attenuation is limited; the drone is also near the reflection area of ​​the "Type A - building"; the vertical turbulence will slightly change the height and angle of the drone relative to the building's reflective surface, which causes a small change in the path length and phase of the reflected signal, thus causing a small fluctuation or phase jitter in the received signal strength; however, because the turbulence amplitude is small (0.5 meters), this effect is not as severe as when the drone moves significantly at the edge of the shadow.

[0076] The “second attenuation distribution” was defined as follows: “Slight vertical turbulence of the drone, above the high humidity area below, causes a small change in signal absorption loss; when approaching the reflection area of ​​the building in front, vertical turbulence will slightly change the reflection path, resulting in a small fluctuation in the received signal strength.” This description points out the impact of drone movement and environmental interaction on signal attenuation.

[0077] The system analyzes the signal attenuation distribution from two dimensions: the first attenuation distribution reveals how the inherent characteristics of the environment (such as building obstruction and reflection, and humidity absorption) cause signal attenuation along a specific path; the second attenuation distribution reveals how the UAV's own motion state interacts with these environmental characteristics, further affecting or changing the signal attenuation pattern. These two distributions together constitute a more comprehensive and dynamic understanding of the current attenuation status of millimeter-wave communication links, providing a basis for the next step (S133) to identify specific attenuation areas and nodes. For example, by combining the first distribution (buildings causing multipath) and the second distribution (slight bumps causing small fluctuations), the system can more accurately locate the area with the most severe attenuation (the area near buildings) and the main factors affecting attenuation (environmental structure).

[0078] Therefore, multiple attenuation regions are determined based on the first attenuation distribution, the second attenuation distribution, and the attenuation path. These attenuation regions are distributed in a tiered manner, and corresponding attenuation nodes are determined based on the synchronous detection of these regions. This allows for the collection of data from multiple attenuation nodes, and the determination of corresponding millimeter-wave attenuation data based on real-time monitoring of these nodes. This approach considers the overall real-time monitoring of multiple attenuation nodes, ensuring the accuracy of the corresponding millimeter-wave attenuation data. Furthermore, it takes into account the path shape of the attenuation path, the vertical movement of the UAV at its current location, and various environmental factors, thereby improving the detection accuracy of multiple attenuation nodes and managing the millimeter-wave attenuation data from these nodes.

[0079] At this point, the system introduces a first attenuation distribution, a second attenuation distribution, and an attenuation path. The first attenuation distribution is the attenuation pattern caused by inherent environmental characteristics, such as building obstruction and reflection, and humidity absorption below in example S132. The second attenuation distribution is the attenuation change caused by the interaction between the drone's movement and the environment, such as the slight fluctuations caused by minor bumps in example S132. The attenuation path is the main route where signal attenuation occurs, such as the path identified in example S131 that is close to buildings and the humid area below. Based on the above information, the system spatially divides areas with different levels of signal attenuation. Typically, these areas are distributed along the attenuation path. The attenuation areas are not uniform but are divided into several levels according to the severity of attenuation, such as mild attenuation areas, moderate attenuation areas, and severe attenuation areas. This tiered distribution helps the system understand the gradual attenuation process and the most dangerous areas.

[0080] The system needs to monitor the signal status in these different areas simultaneously or nearly simultaneously. This is usually achieved through multiple antenna elements or beamforming capabilities on the UAV, which can simultaneously sense the signal quality from different directions or distances. In each attenuation area, one or more representative points are selected as "attenuation nodes". These nodes are the locations where detailed signal quality measurements are performed. When selecting nodes, the locations where the signal characteristics change most drastically or are most typical in the area are usually considered.

[0081] Specifically, the system performs synchronous detection to determine the following attenuation nodes: Node A (slightly attenuated area): located in the center of the slightly attenuated area, 30 meters away from the building; the signal strength, signal-to-noise ratio (SNR), and bit error rate (BER) at this point are detected; Node B (moderately attenuated area): located in the center of the moderately attenuated area, 10 meters away from the building; the signal strength, signal-to-noise ratio, bit error rate, and multipath delay spread at this point are detected; Node C (severely attenuated area): located in the center of the severely attenuated area, close to the edge of the building; the signal strength, signal-to-noise ratio, bit error rate, and signal phase change at this point are detected. These nodes represent the typical signal attenuation characteristics within their respective attenuation areas.

[0082] At designated attenuation nodes (such as nodes A, B, and C), the system uses corresponding sensors (such as receiving antennas and signal processing units) to collect signal data. The system continuously or periodically performs measurements at each node to obtain real-time signal quality indicators. The collected real-time data is analyzed and processed to quantify the degree of attenuation at each node. This includes calculating the decibel (dB) decrease in signal strength, the specific value of the signal-to-noise ratio (SNR), the percentage of the bit error rate (BER), and the magnitude of the phase change.

[0083] Specifically, the system performs real-time monitoring and data collection at nodes A, B, and C: Node A: Real-time monitoring shows a signal strength of -85dBm, an SNR of 20dB, and a BER of 10^-6; corresponding millimeter-wave attenuation data: relative to free space, the signal strength attenuates by approximately 10dB; Node B: Real-time monitoring shows a rapid drop in signal strength to -95dBm, an SNR drop to 8dB, and a BER rise to 10^-4, with significant multipath delay spread detected (approximately 0.3 microseconds); corresponding millimeter-wave attenuation data: relative to free space, the signal strength attenuates by approximately 20dB, with significant multipath effects; Node C: Real-time monitoring shows a sharp drop in signal strength to -110dBm, an SNR drop to 2dB, and a BER rise sharply to 10^-2, with rapid signal phase jitter; corresponding millimeter-wave attenuation data: relative to free space, the signal strength attenuates by approximately 35dB, with extremely poor communication quality, approaching interruption.

[0084] By combining attenuation distribution and attenuation path, the system successfully divided the space into tiered attenuation regions and identified key attenuation nodes within these regions. Through real-time monitoring and data acquisition of these nodes, the system obtained accurate, regional millimeter-wave attenuation data. This information is an indispensable input for subsequent steps (such as adaptive control strategy formulation), enabling the system to adjust communication parameters (such as power, beam direction, modulation method, etc.) in a targeted manner to maintain or restore the stability and reliability of the communication link. For example, based on the severe attenuation data of nodes B and C, the system can determine whether to take immediate action (such as increasing transmission power, switching to anti-shadow / anti-multipath beam mode, or even temporarily adjusting the flight path).

[0085] refer to Figure 5 In step S14, the specific steps are as follows:

[0086] S141: Real-time monitoring of the communication between the UAV and the ground millimeter-wave base station, and collection of multiple communication coefficients of the UAV, and determination of the data transmission status of the UAV based on the multiple communication coefficients and data transmission efficiency.

[0087] S142: Collect the spatial location of multiple attenuation nodes and their corresponding millimeter-wave data, perform attenuation control sequentially along the order of multiple attenuation nodes, and determine the corresponding millimeter-wave attenuation data based on the spatial location of multiple attenuation nodes and their corresponding millimeter-wave data.

[0088] S143: A millimeter-wave correction mechanism is constructed based on the data transmission status of the UAV, the spatial location of multiple attenuation nodes, and the multiple synthesis of millimeter-wave attenuation data. The millimeter-wave correction mechanism presents the attenuation process of millimeter waves and dynamically adjusts the UAV or ground millimeter-wave base station based on the complex feedback of millimeter-wave attenuation data to dynamically correct the millimeter-wave data, thereby triggering millimeter-wave correction of the UAV during flight.

[0089] In the embodiments of this application, the communication between the UAV and the ground millimeter-wave base station is monitored in real time. The system extracts a series of key performance indicators (KPIs) from the communication link. These indicators reflect the quality and efficiency of the current communication. These coefficients include: signal-to-noise ratio (SNR), bit error rate (BER), packet loss rate (PLR), round-trip time (RTT), throughput, and modulation and coding scheme (MCS).

[0090] The system comprehensively judges the current communication status based on the collected communication coefficients and the actual measured data transmission efficiency (e.g., the ratio of throughput to the theoretical maximum throughput). This status can be a classification result, such as: Good status: high SNR, low BER, low PLR, and throughput close to the theoretical value; Average status: medium SNR, slightly increased BER, increased PLR, and decreased throughput; Poor status / outage risk: low SNR, high BER, significantly increased PLR, and a significant decrease in throughput, or even a brief outage.

[0091] Furthermore, the spatial locations of multiple attenuation nodes and their corresponding millimeter-wave data are collected, and attenuation control is performed sequentially along the order of the multiple attenuation nodes. The corresponding millimeter-wave attenuation data is determined based on the spatial locations of the multiple attenuation nodes and their corresponding millimeter-wave data. This comprehensive approach takes into account the spatial locations of multiple attenuation nodes and their corresponding millimeter-wave data, ensuring the accuracy of the corresponding millimeter-wave attenuation data.

[0092] At this point, the system acquires the spatial coordinates (e.g., distance and angle relative to the UAV, or absolute three-dimensional coordinates) of the multiple attenuation nodes previously determined in step S133, as well as the millimeter-wave data (e.g., signal strength, phase, delay, multipath components, etc.) monitored in real time at these nodes.

[0093] The system processes these attenuation nodes in a predetermined order (e.g., from the drone to the base station, or from the lightest to the heaviest attenuation). This does not mean taking physical "control" actions at each node, but rather that the system logically analyzes the contribution of each node to the overall attenuation in this order, preparing for subsequent correction mechanisms. Based on the spatial location of the nodes and the monitored millimeter-wave data, the system more accurately quantifies the attenuation characteristics at each node, including: path loss: the theoretical or measured loss of signal propagation from the drone to the node location; shadowing fading: the decrease in signal strength due to obstruction; multipath effect: the amplitude, phase changes, and time spread caused by the signal arriving through multiple paths; and specific attenuation value: for example, the amount of signal strength at a certain node attenuated relative to free space propagation (dB).

[0094] Therefore, a millimeter-wave correction mechanism is constructed based on the data transmission status of the UAV, the spatial location of multiple attenuation nodes, and the multiple synthesis of millimeter-wave attenuation data. The millimeter-wave correction mechanism presents the attenuation process of millimeter waves and dynamically adjusts the UAV or ground millimeter-wave base station according to the complex feedback of millimeter-wave attenuation data to dynamically correct the millimeter-wave data, thereby triggering millimeter-wave correction of the UAV during flight. It takes into account the overall consideration of the data transmission status of the UAV, the spatial location of multiple attenuation nodes, and the multiple synthesis of millimeter-wave attenuation data, ensuring the accuracy of the millimeter-wave correction mechanism.

[0095] At this point, the system combines the data transmission status determined in S141 (e.g., "general status"), the spatial locations of each attenuation node determined in S142, and the specific attenuation data (e.g., 20dB shadow fading at node B and 100ns multipath delay). This combination (multi-synthesis) enables the system to construct a dynamic "millimeter-wave correction mechanism" that matches the current flight environment and communication conditions. This mechanism not only describes the current attenuation process (which area has severe attenuation and what the cause is), but also includes a series of preset or calculated coping strategies. The correction mechanism contains a simulation or description of the attenuation process, enabling the system to "understand" how the signal originates from the UAV, passes through each attenuation node, and finally reaches the base station. This helps in selecting the most effective correction strategy.

[0096] The system uses continuous feedback information (complex feedback, i.e., comprehensive information from multiple nodes and link status) to dynamically determine how to adjust; the adjustment object can be the UAV, the ground base station, or both simultaneously; the control measures include: UAV adjustment: adjusting the transmission power: increasing or decreasing the UAV's transmission power to compensate for path loss; changing flight attitude / altitude: fine-tuning the UAV's elevation angle or altitude to avoid areas with strong obstruction; switching antenna mode / beamforming: using an antenna mode with stronger anti-interference capabilities, or adjusting the beam direction to focus energy on a better propagation path.

[0097] Once the control decision is made and executed, it is a "dynamic correction" of millimeter-wave data transmission. This process is continuous. Whenever the system detects a change in state or new attenuation information, it will re-evaluate and trigger a new correction action, which ensures the continuous optimization of the communication link during the flight of the UAV.

[0098] Specifically, the system combined the "general condition" (throughput 50Mbps, lower than expected), the location and attenuation data of nodes A / B / C (especially the 20dB shadow fading and 100ns multipath delay of node B are the main problems) to construct a correction mechanism; the mechanism judged that the main communication bottleneck was the building blockage and multipath effect near node B; the internal model of the mechanism showed that the signal strength dropped sharply at node B due to the building and generated obvious multipath components.

[0099] The system decides to adopt a combined strategy: first, it attempts to slightly increase the drone's flight altitude (e.g., from 110m to 115m, bypassing some obstructions) while simultaneously reducing the drone's MCS order (e.g., from 256QAM to 64QAM) to enhance its resistance to multipath and shadowing; upon receiving instructions, the drone performs a fine-tuning of its altitude; its communication module also switches to the new MCS setting; after increasing the altitude, the shadowing fading of node B is reduced to 15dB, and the multipath latency is reduced to 50ns; after reducing the MCS, although the theoretical rate decreases, the tolerance for BER increases; the system continuously monitors the new communication coefficients and attenuation node data to evaluate the effect of the correction; if the effect is good (e.g., throughput recovers to 60Mbps, BER decreases), the current settings are maintained; if the effect is poor, further analysis is conducted and other measures are taken (such as further adjusting altitude or power).

[0100] refer to Figure 6 In step S15, the specific steps are as follows:

[0101] S151: Collect millimeter-wave attenuation data from multiple attenuation nodes and the current position of the UAV, and determine the corresponding millimeter-wave correction amount based on the current position of the UAV, the millimeter-wave correction mechanism, and the millimeter-wave attenuation data from multiple attenuation nodes. Determine the operating power adjustment range of the ground millimeter-wave base station based on the millimeter-wave correction amount, the current operating power of the ground millimeter-wave base station, and the operating power mapping relationship.

[0102] S152: Collect the millimeter-wave correction measures corresponding to the millimeter-wave correction mechanism, determine the first adaptive coefficient based on the millimeter-wave correction measures and the working power adjustment range of the ground millimeter-wave base station, and determine the second adaptive coefficient based on the millimeter-wave correction measures and the current working state of the UAV.

[0103] S153: Determine the adaptive control strategy of the UAV at the millimeter-wave transmission layer based on the first adaptive coefficient, the second adaptive coefficient, and the adaptive mapping relationship.

[0104] In the embodiments of this application, millimeter-wave attenuation data from multiple attenuation nodes and the current position of the UAV are collected. Based on the current position of the UAV, the millimeter-wave correction mechanism, and the millimeter-wave attenuation data from multiple attenuation nodes, the corresponding millimeter-wave correction amount is determined. Based on the millimeter-wave correction amount, the current operating power of the ground millimeter-wave base station, and the operating power mapping relationship, the operating power adjustment range of the ground millimeter-wave base station is determined. This approach takes into account the overall considerations of the millimeter-wave correction amount, the current operating power of the ground millimeter-wave base station, and the operating power mapping relationship, ensuring the accuracy of the operating power adjustment range of the ground millimeter-wave base station.

[0105] At this point, millimeter-wave attenuation data from multiple attenuation nodes and the current position of the UAV are collected. Millimeter-wave attenuation data typically includes, but is not limited to: signal strength attenuation (dB): indicating how much the signal strength weakens when passing through the node area; signal-to-noise ratio (SNR) or signal-to-interference-plus-noise ratio (SINR): indicating how much the signal quality deteriorates after passing through the node area; phase change: millimeter waves are sensitive to phase changes, and multipath effects can cause phase drift; time delay spread: multipath signals arrive at different times, causing the signal to broaden in time; attenuation characteristics at specific frequencies: millimeter waves have a wide frequency band, and different frequency components have different attenuation characteristics.

[0106] The current location of a drone is usually obtained through the drone's own GPS, RTK or other positioning system, accurate to latitude, longitude and altitude. This location information is crucial because, combined with the spatial location of attenuation nodes, it can determine which attenuation area(s) the drone is currently passing through, or which attenuation area it is closest to.

[0107] Specifically, assuming the system previously identified three attenuation nodes (NodeA, NodeB, NodeC) and collected their data: NodeA: located 300 meters directly below the drone, with a signal strength attenuation of approximately -15dB and an SNR of 10dB; NodeB: located 500 meters in front of the drone and 100 meters to its right, with a signal strength attenuation of approximately -8dB and an SNR of 18dB; NodeC: located 200 meters behind the drone and 50 meters to its left, with a signal strength attenuation of approximately -12dB and an SNR of 12dB; at this point, the drone uses GPS positioning to determine its current location as: longitude 116.391 degrees, latitude 39.907 degrees, and altitude 110 meters.

[0108] The system needs to compare the drone's current location with the spatial locations of all attenuation nodes; determine which attenuation area the drone is currently in, or which attenuation area it is closest to and has the greatest impact on, for example, the drone is passing through the influence area of ​​Node A, or is approaching Node B; the millimeter-wave correction mechanism (from S143) describes how to understand and respond to the attenuation process; it contains some rules or algorithms, such as: "When the drone enters an area where the signal strength attenuation exceeds -10dB, it needs to compensate for at least 50% of the attenuation value" or "Calculate the power that needs to be increased based on how much the SNR value is below a certain threshold (such as 15dB)"; based on the above information, the system calculates a specific "correction amount", which represents the value of adjusting system parameters (mainly power) to overcome the current major attenuation effect; it is an absolute value (such as needing to increase power by 3dBm) and a relative value (such as needing to compensate for 40% of the current attenuation value), and this amount is the basis for subsequent adjustments to the base station power.

[0109] Specifically, the drone's current position (110 meters high) is relatively far from Node A (300 meters below), but is at the upper boundary of Node A's influence; it is closest to Node B (600 meters in front, 100 meters to the right), and is about to enter its influence range; it is at a moderate distance from Node C (200 meters behind, 50 meters to the left); the correction mechanism stipulates that "when the drone is less than 500 meters away from a certain attenuation node in the horizontal distance, and the attenuation value of that node is greater than -10dB, it needs to compensate 30% of the attenuation value of that node as a correction amount."

[0110] Determine the correction amount: Node A: Horizontal distance approximately sqrt(300^2+0^2) = 300 meters < 500 meters, attenuation -15dB > -10dB; Correction amount = -15dB30% = -4.5dB, which means that 4.5dB of signal loss needs to be compensated; Node B: Horizontal distance approximately sqrt(500^2+100^2) ≈ 509.9 meters > 500 meters, which does not meet the condition; Node C: Horizontal distance approximately sqrt(200^2+50^2) ≈ 206.2 meters < 500 meters, attenuation -12dB > -10dB; Correction amount = -12dB30% = -3.6dB.

[0111] Although NodeC also meets the conditions, NodeA has a more severe attenuation (-15dB vs -12dB) and is closer (300m vs 206m; although NodeC is closer, NodeA's attenuation is more critical, and here we assume the algorithm prioritizes the more severe attenuation). The system chooses to use the correction amount of NodeA as the main factor, or take the larger correction amount of the two (4.5dB); we assume that the final determined correction amount is 4.5dB.

[0112] The system needs to know the actual power at which the terrestrial millimeter-wave base station is currently transmitting. This is usually a configurable parameter that can be queried through the network management interface, for example, the current power is 10dBm. The reference operating power mapping relationship is a key data table or rule set that defines the constraints and limits of base station power adjustment. This relationship includes: power adjustment step size: for example, only 1dBm or 2dBm can be increased or decreased at a time; power limits: the minimum transmit power (e.g., 5dBm) and maximum transmit power (e.g., 20dBm) of the base station; the relationship between power and performance / energy consumption / heat dissipation: some power points are unavailable due to hardware limitations or efficiency reasons, or performance improvement is not significant but energy consumption increases sharply after exceeding a certain power; safety constraints: comply with radio management regulations and avoid harmful interference to other frequency bands.

[0113] Based on the calculated correction amount (e.g., needing to increase signal strength by 4.5dB), the current power (e.g., 10dBm), and the power mapping relationship, the system calculates the specific range within which the base station power can be adjusted. This range is usually an interval, indicating which interval the base station power should be adjusted to in order to effectively compensate for attenuation. For example, if the correction amount is +4.5dB and the current power is 10dBm, theoretically it needs to reach 10dBm + 4.5dB = 14.5dBm; however, considering that the power adjustment step size is 1dBm and the power limit is 5-20dBm, the adjustment range is [14dBm, 15dBm].

[0114] Specifically, assuming the current operating power of the ground millimeter-wave base station is 10dBm; referencing the power mapping relationship: assuming the base station has a minimum power of 5dBm and a maximum power of 20dBm; adjustment step size: 1dBm (only integer dBm adjustments are allowed); calculating the target power: current power 10dBm + correction 4.5dB ≈ 14.5dBm; adjusting according to the step size: the adjustable power points closest to 14.5dBm are 14dBm and 15dBm; checking power limits: both 14dBm and 15dBm are within the range of [5dBm, 20dBm]; therefore, the operating power adjustment range of the ground millimeter-wave base station is determined to be [14dBm, 15dBm]. This means the system recommends adjusting the base station power to 14dBm or 15dBm to compensate for the 4.5dB signal attenuation at the current location of the UAV (affected by Node A).

[0115] Furthermore, the millimeter-wave correction measures corresponding to the millimeter-wave correction mechanism are collected. A first adaptive coefficient is determined based on the millimeter-wave correction measures and the operating power adjustment range of the ground millimeter-wave base station. A second adaptive coefficient is determined based on the millimeter-wave correction measures and the current operating state of the UAV. This approach takes into account both the millimeter-wave correction measures and the current operating state of the UAV, ensuring the accuracy of the second adaptive coefficient.

[0116] At this point, the millimeter-wave correction mechanism is a comprehensive strategy library or rule set that describes the correction methods that can be taken under different attenuation scenarios. In step S143, the system has preliminarily determined the types of correction measures that need to be taken based on the attenuation situation. Now, the task of S152 is to explicitly extract the specific measures that are currently applicable from this mechanism. These correction measures include: power adjustment: This is the most direct method, that is, adjusting the transmission power of the transmitter (ground base station or UAV) to compensate for path loss or shadow fading, for example, "increasing the transmission power" or "decreasing the transmission power"; beamforming / wave... Beam switching: Millimeter-wave communication typically uses directional antennas. By adjusting the antenna's beam direction or shape, obstacles can be avoided or signals in a specific direction can be enhanced. For example, "switching the beam to direction B" or "enhancing the focus of the current beam"; Modulation and coding scheme (MCS) adjustment: Different modulation schemes (such as QPSK, 16QAM, 64QAM) and coding rates are selected based on channel quality. When the channel is poor, a more robust but lower-rate scheme is selected, while when the channel is good, a high-rate scheme that requires high channel quality is selected. For example, "downgrading the MCS from 64QAM3 / 4 to 16QAM1 / 2".

[0117] Diversity techniques: using spatial, frequency, or time diversity to combat fading, for example, "enable spatial diversity"; frequency adjustment / frequency hopping: if there is interference or attenuation at a specific frequency, you can try to switch to an alternative frequency, for example, "switch to alternative band X"; UAV maneuver commands: although mainly ground base station adjustments, in some cases, it also includes minor UAV maneuver commands, such as "small ascent / descent / translation", to improve visibility or avoid areas of strong local attenuation.

[0118] Specifically, the drone is at an altitude of 110 meters and is affected by NodeA. The correction mechanism suggests taking measures to "increase the transmission power of the ground base station" and supplement it with "downgrading the MCS from 64QAM3 / 4 to 16QAM1 / 2" to increase link reliability. Therefore, the millimeter wave correction measures collected by S152 are: "increase the power of the ground base station" and "reduce the MCS level".

[0119] By combining the specific measure of “increasing power” with the previously calculated base station power regulation range (e.g., [14dBm, 15dBm]), a quantified coefficient is generated that reflects the priority, magnitude, or some trade-off of power adjustment under the current measure.

[0120] The process of determining the first adaptive coefficient involves: if "increasing power" is the highest priority measure, then the coefficient reflects a high willingness to adjust; the coefficient indicates whether it tends to use the upper limit, lower limit, or middle of the control range, for example, if the attenuation is very severe and persistent, the coefficient tends to point to the upper limit; if it is only a temporary fluctuation, it points to the middle or lower limit; if the correction measure is not just "increasing power" but has a specific increase recommendation (e.g., "increase at least 3dB"), then the coefficient can reflect the relationship between this increase and the control range, for example, if a 3dB increase is required, and the current power is 10dBm, and the control range is [14,15]dBm (i.e., a maximum increase of 4dB), the coefficient indicates "increase is allowed, and there is sufficient room"; the specific calculation of the coefficient is a simple ratio, a rule-based (IF-THEN) value, or a more complex function output, for example, coefficient A = (required correction amount / control range width) priority weight.

[0121] Specifically, the corrective measure is to "increase the power of the ground base station," with a power adjustment range of [14dBm, 15dBm]. System analysis suggests that although a power increase is needed (correction amount of 4.5dB), the adjustment range provides sufficient adjustment space (up to 5dB), and "increasing power" is currently the most effective measure. Therefore, the first adaptive coefficient (denoted as coefficient A) is determined to be a relatively high value, such as 0.8. This 0.8 can be understood as: within the current adjustable power range, there is a strong tendency to adjust the power to near the upper limit (i.e., around 15dBm) to compensate for attenuation to the greatest extent. If the coefficient is 0.3, it means that only a small adjustment is needed or the urgency of adjustment is not high.

[0122] Assess the drone's current state, including remaining battery power, flight mode, load status, current flight stability, and mission priority. Evaluate the fit between current corrective actions (including drone maneuver commands) and the drone's state. For example, if an action includes "slight increase" but the drone's battery is low, the fit is low. The coefficient B is also calculated based on rules, proportions, or functions; for example, coefficient B = (state fit) * (weight of drone maneuver involved in the action). If the action does not involve drone maneuver at all, this coefficient is primarily used to assess the potential impact of the overall strategy on the drone's state.

[0123] Specifically, the main corrective measures are "increasing the power of the ground base station" and "reducing the MCS level," which do not directly involve drone maneuvers. However, the system checks the drone's status: sufficient battery (80%), in autopilot mode, stable flight, and mission-permitted communication priority. Although drone maneuvers are not currently required, the system assessment considers the drone capable of cooperating with more complex corrective strategies (if necessary). Therefore, the second adaptive coefficient (denoted as coefficient B) is set to a relatively high value, such as 0.9. This 0.9 indicates that the drone is currently in a good state, very suitable for implementing corrective measures (even if mainly base station adjustments), and capable of cooperating if more complex corrections (such as minor maneuvers) are needed in the future. If the drone's battery is only 15% and it is under manual control, coefficient B will be very low (e.g., 0.2), indicating that the drone's state is not suitable for any additional corrective actions.

[0124] The drone is flying at an altitude of 110 meters and is affected by NodeA, requiring correction. S152 first collects the current millimeter-wave correction measures, which are "increasing the power of the ground base station" and "reducing the MCS level". Then, based on the measure of "increasing power" and the previously calculated base station power adjustment range [14dBm, 15dBm], the system determines that a significant increase in power is needed to compensate for the attenuation, and the base station is capable of doing so. Therefore, the first adaptive coefficient A is determined to be 0.8, indicating a strong tendency to adjust the power to near the upper limit. Next, the system evaluates the drone's status: sufficient battery, auto-cruise, stable flight, and mission permitted. Although the current correction does not involve maneuvering, the drone is in good condition and capable of cooperating with more complex corrections. Therefore, the second adaptive coefficient B is determined to be 0.9. These two coefficients (A = 0.8, B = 0.9) will be used for the next step (S153) comprehensive decision-making, ultimately deciding whether to adjust the base station power to 14dBm or 15dBm, and whether to maintain the MCS degradation.

[0125] Therefore, the adaptive control strategy of the UAV at the millimeter-wave transmission layer is determined based on the first adaptive coefficient, the second adaptive coefficient, and the adaptive mapping relationship. This approach takes into account the overall consideration of the first adaptive coefficient, the second adaptive coefficient, and the adaptive mapping relationship, ensuring the accuracy of the adaptive control strategy of the UAV at the millimeter-wave transmission layer. At the same time, millimeter-wave correction measures are introduced during the flight of the UAV, realizing the overall consideration of the working power adjustment range of the ground millimeter-wave base station and the millimeter-wave correction measures, thereby improving the accuracy of the adaptive control strategy of the UAV at the millimeter-wave transmission layer.

[0126] At this point, the system receives two adaptive coefficients from S152—the first adaptive coefficient (coefficient A) and the second adaptive coefficient (coefficient B); coefficient A reflects the tendency of the power adjustment based on the base station power control range and correction measures, and coefficient B reflects the suitability of the UAV's current state for implementing correction measures (including cooperating with base station adjustments or self-adjustment); at the same time, the system also needs to refer to the millimeter-wave correction measures determined in previous steps (such as S142, S151).

[0127] The adaptive mapping relationship defines how to select the final control strategy based on the combination of values ​​of coefficient A and coefficient B. This relationship is based on the following considerations: a high coefficient A (e.g., >0.7) means that there is a strong need to adjust the base station power to compensate for the attenuation; a high coefficient B (e.g., >0.7) means that the UAV is in good condition and can cooperate to perform corrections, and the overall system state allows for active adjustment; the combination of coefficient A and coefficient B can be mapped to different strategy priorities and specific actions.

[0128] For example: A high and B high: Prioritize base station power adjustment and implement other corrective measures (such as MCS adjustment) to maintain a stable drone state; A high but B low: Base station power adjustment is necessary, but the drone's condition is poor (e.g., low battery, mission critical), the strategy focuses on base station adjustment and temporarily refrains from implementing corrections requiring drone cooperation, or limits the drone's own adjustment range; A low but B high: Base station power adjustment range is small or not the optimal solution, the strategy tends to allow the drone to perform minor maneuvers (if the correction mechanism allows and the drone's condition is good) or wait for environmental changes; A low and B low: A more conservative strategy is needed, such as only adjusting the MCS, or temporarily accepting lower communication quality.

[0129] The system inputs the values ​​of coefficients A and B into the adaptive mapping relationship; the mapping relationship outputs one or more specific control commands based on the value range and combination of these two coefficients. These commands explicitly specify the parameters that need to be adjusted and their target values ​​or ranges, ultimately determining the "adaptive control strategy of the UAV at the millimeter-wave transmission layer". This strategy is a composite command.

[0130] Specifically, the system obtains A=0.8, B=0.9, and corrective measures (increase power, reduce MCS); the adaptive mapping relationship is a decision table or algorithm; it sees A=0.8 (high) and B=0.9 (high), and the corrective measures include "increase power" and "reduce MCS"; according to preset rules, the combination of "high A high B" usually means that the corrective measures can be implemented actively and comprehensively.

[0131] Regarding "increasing power": the mapping relationship indicates that, given the high A value and the allowable range of base station adjustment [14dBm, 15dBm], the highest value within this range should be selected to compensate for attenuation to the greatest extent. Simultaneously, a high B value indicates that the UAV is in good condition and can withstand stronger signals (if increasing power leads to an excessively strong signal, the UAV needs fine-tuning, but here the main adjustment is by the base station). Regarding "reducing MCS": the mapping relationship indicates that, given the high A value (power adjustment is the primary means) and the high B value (good UAV condition and stable communication link), reducing MCS is a reasonable supplementary measure to improve data transmission reliability, especially when power adjustment has not yet fully covered all attenuation. The strategy is clearly defined as follows: instructing the ground millimeter-wave base station to adjust its operating power from the current 10dBm to 15dBm; simultaneously, instructing the UAV to adjust its data transmission modulation and coding scheme (MCS) from the previous 64QAM / 3 / 4 to 16QAM / 1 / 2; and maintaining the current flight status of 110 meters altitude and cruising speed.

[0132] In another embodiment of this application, the real-time acquisition system module obtains the device's position, attitude, and information on the surrounding environment such as temperature, humidity, clouds, rain, fog, and dust from temperature and humidity sensors, and uses this information as one of the inputs to the UAV millimeter-wave attenuation model system. To improve the system's robustness, for ultra-long distances, the temperature, humidity, clouds, rain, fog, and dust information of the central node of the propagation path are initially input into the UAV millimeter-wave attenuation system as a static prior configuration.

[0133] The UAV millimeter-wave attenuation system integrates real-time and prior information on frequency, distance, attitude, and atmospheric environment to calculate the total attenuation value. This value includes free-space path attenuation, wind-induced UAV vibration attenuation, and atmospheric particle attenuation. The adaptive power transmission system calculates the total transmit power based on the expected received power and the attenuation value, then initiates step detection. Subsequently, it corrects the attenuation value and transmit power based on feedback channel quality. The receiving system establishes attenuation error correction models and service feedback correction models based on the combined actual received power, expected received power, and actual transmit power.

[0134] The attenuation error is estimated using a normal distribution between the receiving and transmitting systems, and the optimal signal-to-noise ratio is calculated using linear regression. The compensation and correction coefficients are fed back to the transmitting end to compensate for the error in the attenuation model of the UAV millimeter-wave channel and determine the optimal transmission power.

[0135] Please see Figure 7 , Figure 7 This is a schematic diagram of the structural composition of an adaptive control system for a millimeter-wave-based unmanned aerial vehicle (UAV) according to an embodiment of the present invention; the adaptive control system for the millimeter-wave-based UAV includes:

[0136] The environmental parameter module 21 is used to determine the millimeter wave transmission space based on the location of the UAV and the location of the ground millimeter wave base station; and to determine multiple environmental parameters based on environmental detection of the millimeter wave transmission space.

[0137] The attenuation path module 22 is used to determine multiple environmental types of the millimeter wave transmission space based on multiple environmental parameters, and to determine the attenuation path of the millimeter wave based on the multiple environmental types, the spatial shape of the millimeter wave transmission space and the transmission data of the millimeter wave.

[0138] The attenuation node module 23 is used to determine multiple attenuation nodes in the attenuation path of the millimeter wave based on the path shape of the attenuation path, the vertical movement of the UAV at the current position, and multiple environmental types, and to mark the millimeter wave attenuation data of the multiple attenuation nodes.

[0139] The millimeter-wave correction measure module 24 is used to determine the millimeter-wave correction mechanism based on the data transmission status of the UAV and the millimeter-wave attenuation data of multiple attenuation nodes, and to trigger the millimeter-wave correction of the UAV during flight.

[0140] The adaptive control module 25 is used to determine the operating power adjustment range of the ground millimeter-wave base station based on millimeter-wave attenuation data from multiple attenuation nodes and the current position of the UAV, and to determine the adaptive control strategy of the UAV at the millimeter-wave transmission layer based on the operating power adjustment range of the ground millimeter-wave base station and millimeter-wave correction measures.

[0141] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. An adaptive control method for a millimeter-wave-based unmanned aerial vehicle (UAV), characterized in that, include: The transmission space of millimeter waves is determined based on the location of the drone and the location of the ground millimeter wave base station; Multiple environmental parameters are determined by environmental detection in the transmission space based on millimeter waves; Multiple environmental types of millimeter wave transmission space are determined based on multiple environmental parameters, and the attenuation path of millimeter wave is determined based on multiple environmental types, spatial morphology of millimeter wave transmission space and millimeter wave transmission data. In the attenuation path of the millimeter wave, multiple attenuation nodes are determined based on the path shape of the attenuation path, the vertical movement of the UAV at its current location, and multiple environmental types, and the millimeter wave attenuation data of multiple attenuation nodes are marked. The millimeter-wave correction mechanism is determined based on the data transmission status of the UAV and the millimeter-wave attenuation data of multiple attenuation nodes, and the millimeter-wave correction of the UAV during flight is triggered. Based on millimeter-wave attenuation data from multiple attenuation nodes and the current location of the UAV, the operating power control range of the ground millimeter-wave base station is determined. Based on the operating power control range of the ground millimeter-wave base station and millimeter-wave correction measures, an adaptive control strategy for the UAV at the millimeter-wave transmission layer is determined.

2. The adaptive control method for a millimeter-wave-based unmanned aerial vehicle according to claim 1, characterized in that, The transmission space of millimeter waves is determined based on the location of the drone and the location of the ground millimeter wave base station; Multiple environmental parameters are determined based on environmental detection in the millimeter-wave transmission space, including: During the flight of the drone, the drone communicates with the ground millimeter-wave base station and transmits the corresponding millimeter-wave data. At the same time, it collects the position of the drone and the position of the ground millimeter-wave base station, and determines the current flight attitude of the drone based on multiple attitude parameters of the drone. A millimeter-wave transmission system is constructed based on the communication between the UAV and the ground millimeter-wave base station, and the transmission space of the millimeter wave is determined according to the millimeter-wave transmission system, the location of the UAV and the location of the ground millimeter-wave base station. The corresponding environmental area is determined based on the spatial morphology of the millimeter wave transmission space, the current flight attitude of the UAV, and the relative position between the UAV and the millimeter wave base station. Multiple environmental parameters are determined based on environmental detection of the environmental area, and these multiple environmental parameters are distributed in different locations within the environmental area.

3. The adaptive control method for a millimeter-wave-based unmanned aerial vehicle according to claim 1, characterized in that, The process of determining multiple environmental types of the millimeter wave transmission space based on multiple environmental parameters, and determining the millimeter wave attenuation path based on the multiple environmental types, the spatial morphology of the millimeter wave transmission space, and the millimeter wave transmission data, includes: Multiple environmental parameters are collected, and multiple combinations of environmental parameters are determined based on the multiple environmental parameters and the spatial morphology of the millimeter wave transmission space. Each combination of environmental parameters contains at least three environmental parameters, and the matching coefficient of multiple environmental parameters in each combination of environmental parameters is greater than a preset matching coefficient threshold. Based on the identification of multiple environmental parameter combinations, the corresponding environmental characteristics are determined. Based on the morphology, distribution location, and spatial size of the millimeter wave transmission space of the multiple environmental characteristics, the multiple environmental types of the millimeter wave transmission space are determined. The multiple environmental types contain multiple environmental types. The distribution locations of multiple environmental types are marked, the millimeter-wave transmission process of the UAV is monitored in real time, and the millimeter-wave transmission data is collected. The attenuation path of the millimeter wave is determined based on the distribution locations of multiple environmental types, the spatial morphology of the millimeter-wave transmission space, and the millimeter-wave transmission data.

4. The adaptive control method for a millimeter-wave-based unmanned aerial vehicle according to claim 1, characterized in that, In the attenuation path of the millimeter wave, multiple attenuation nodes are determined based on the path shape of the attenuation path, the vertical movement of the UAV at its current location, and multiple environmental types. The millimeter wave attenuation data of these multiple attenuation nodes are then marked, including: The attenuation path of millimeter waves is monitored in real time, and the path shape of the attenuation path is determined based on the morphology detection of the attenuation path. At the same time, the drone's current position is collected, and the vertical movement of the drone at the current position is determined based on the activity monitoring of the drone at the current position.

5. The adaptive control method for a millimeter-wave-based unmanned aerial vehicle according to claim 4, characterized in that, In the attenuation path of the millimeter wave, multiple attenuation nodes are determined based on the path shape of the attenuation path, the vertical movement of the UAV at its current location, and multiple environmental types, and the millimeter wave attenuation data of the multiple attenuation nodes are marked. The method also includes: The first attenuation distribution is determined based on the path shape of the attenuation path and the distribution of multiple environmental types among the multiple environmental types. The second attenuation distribution is determined based on the vertical movement of the UAV at the current location and the distribution of multiple environmental types among the multiple environmental types. Multiple attenuation regions are determined based on the first attenuation distribution, the second attenuation distribution, and the attenuation path. These attenuation regions are distributed in a stepped manner, and corresponding attenuation nodes are determined based on the synchronous detection of these multiple attenuation regions. Multiple attenuation nodes are collected, and the corresponding millimeter-wave attenuation data is determined based on the real-time monitoring of these multiple attenuation nodes.

6. The adaptive control method for a millimeter-wave-based unmanned aerial vehicle according to claim 1, characterized in that, The millimeter-wave correction mechanism is determined based on the data transmission status of the UAV and the millimeter-wave attenuation data of multiple attenuation nodes, and the millimeter-wave correction is triggered during the flight of the UAV, including: Real-time monitoring of the communication between the drone and the ground millimeter-wave base station, and collection of multiple communication coefficients of the drone, and determination of the data transmission status of the drone based on the multiple communication coefficients and data transmission efficiency. The spatial locations of multiple attenuation nodes and their corresponding millimeter-wave data are collected. Attenuation control is performed sequentially along the order of the multiple attenuation nodes, and the corresponding millimeter-wave attenuation data is determined based on the spatial locations of the multiple attenuation nodes and their corresponding millimeter-wave data.

7. The adaptive control method for a millimeter-wave-based unmanned aerial vehicle according to claim 6, characterized in that, The method for determining a millimeter-wave correction mechanism based on the data transmission status of the UAV and millimeter-wave attenuation data from multiple attenuation nodes, and triggering millimeter-wave correction of the UAV during flight, also includes: A millimeter-wave correction mechanism is constructed based on the data transmission status of the UAV, the spatial location of multiple attenuation nodes, and the multiple synthesis of millimeter-wave attenuation data. The millimeter-wave correction mechanism presents the attenuation process of millimeter waves and dynamically adjusts the UAV or ground millimeter-wave base station according to the complex feedback of millimeter-wave attenuation data to dynamically correct the millimeter-wave data, thereby triggering millimeter-wave correction of the UAV during flight.

8. The adaptive control method for a millimeter-wave-based unmanned aerial vehicle according to claim 1, characterized in that, The operating power adjustment range of the ground millimeter-wave base station is determined based on millimeter-wave attenuation data from multiple attenuation nodes and the current position of the UAV. Based on the operating power adjustment range of the ground millimeter-wave base station and millimeter-wave correction measures, an adaptive control strategy for the UAV at the millimeter-wave transmission layer is determined, including: The system collects millimeter-wave attenuation data from multiple attenuation nodes and the current location of the UAV. Based on the current location of the UAV, the millimeter-wave correction mechanism, and the millimeter-wave attenuation data from multiple attenuation nodes, the corresponding millimeter-wave correction amount is determined. Based on the millimeter-wave correction amount, the current operating power of the ground millimeter-wave base station, and the operating power mapping relationship, the operating power adjustment range of the ground millimeter-wave base station is determined.

9. The adaptive control method for a millimeter-wave-based unmanned aerial vehicle according to claim 8, characterized in that, The process of determining the operating power adjustment range of the ground millimeter-wave base station based on millimeter-wave attenuation data from multiple attenuation nodes and the current position of the UAV, and determining the adaptive control strategy of the UAV at the millimeter-wave transmission layer based on the operating power adjustment range of the ground millimeter-wave base station and millimeter-wave correction measures, further includes: The millimeter-wave correction measures corresponding to the millimeter-wave correction mechanism are collected. The first adaptive coefficient is determined based on the millimeter-wave correction measures and the working power adjustment range of the ground millimeter-wave base station. The second adaptive coefficient is determined based on the millimeter-wave correction measures and the current working state of the UAV. The adaptive control strategy for the UAV at the millimeter-wave transmission layer is determined based on the first adaptive coefficient, the second adaptive coefficient, and the adaptive mapping relationship.

10. An adaptive control system for a millimeter-wave unmanned aerial vehicle (UAV), characterized in that, The millimeter-wave-based UAV adaptive control system is applied to the adaptive control method for a millimeter-wave-based UAV as described in any one of claims 1-9, wherein the millimeter-wave-based UAV adaptive control system comprises: The environmental parameter module is used to determine the millimeter-wave transmission space based on the location of the UAV and the location of the ground millimeter-wave base station; and to determine multiple environmental parameters based on environmental detection of the millimeter-wave transmission space. The attenuation path module is used to determine multiple environmental types of the millimeter wave transmission space based on multiple environmental parameters, and to determine the attenuation path of the millimeter wave based on the multiple environmental types, the spatial shape of the millimeter wave transmission space, and the millimeter wave transmission data. The attenuation node module is used to determine multiple attenuation nodes in the attenuation path of the millimeter wave based on the path shape of the attenuation path, the vertical movement of the UAV at the current position, and multiple environmental types, and to mark the millimeter wave attenuation data of multiple attenuation nodes. The millimeter-wave correction mechanism module is used to determine the millimeter-wave correction mechanism based on the data transmission status of the UAV and the millimeter-wave attenuation data of multiple attenuation nodes, and to trigger the millimeter-wave correction of the UAV during flight. The adaptive control module is used to determine the operating power adjustment range of the ground millimeter-wave base station based on millimeter-wave attenuation data from multiple attenuation nodes and the current position of the UAV, and to determine the adaptive control strategy of the UAV at the millimeter-wave transmission layer based on the operating power adjustment range of the ground millimeter-wave base station and millimeter-wave correction measures.

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