Self-adaptive control method and system of unmanned aerial vehicle based on millimeter waves

Abstract

The invention discloses an adaptive control method and system for an unmanned aerial vehicle based on millimeter waves, and relates to the technical field of millimeter waves, and the method comprises the steps: determining a plurality of attenuation nodes based on the path form of an attenuation path, the vertical movement amount of the unmanned aerial vehicle at the current position and multiple environment types, and marking the millimeter wave attenuation data of the plurality of attenuation nodes; and the detection accuracy of the plurality of attenuation nodes is improved. 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, and the working power regulation and control range of the 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. The adaptive control strategy of the unmanned aerial vehicle at the millimeter wave transmission layer is determined according to the working power regulation and control range of the ground millimeter wave base station and the millimeter wave correction measures, and the accuracy of the adaptive control strategy of the unmanned aerial vehicle at the millimeter wave transmission layer is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of millimeter waves, and in particular to an adaptive control method and system for a millimeter-wave-based unmanned aerial vehicle. Background Art

[0002] With the development of science and technology, drones are used in people's lives. Drones communicate with ground millimeter-wave base stations and transmit corresponding millimeter-wave data. In existing technologies, as drones move away from ground millimeter-wave base stations, 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 the present invention is to overcome the deficiencies of the prior art. The present invention provides an adaptive control method and system for a millimeter-wave-based UAV.

[0004] An embodiment of the present invention provides an adaptive control method for a millimeter-wave-based unmanned aerial vehicle, comprising: determining a millimeter-wave transmission space based on the position of the unmanned aerial vehicle 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 according to the multiple environmental parameters, and determining a millimeter-wave attenuation path based on the multiple environmental types, the spatial morphology of the millimeter-wave transmission space, and the transmission data of the millimeter wave; in the attenuation path of the millimeter wave, determining multiple attenuation nodes based on the path morphology of the attenuation path, the up and down movement of the unmanned aerial vehicle at the 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 data transmission status of the unmanned aerial vehicle and the millimeter-wave attenuation data of the multiple attenuation nodes, and triggering the millimeter-wave correction of the unmanned aerial vehicle during flight; determining an operating power control range of the ground millimeter-wave base station based on the millimeter-wave attenuation data of the multiple attenuation nodes and the current position of the unmanned aerial vehicle, and determining an adaptive control strategy of the unmanned aerial vehicle at the millimeter-wave transmission level according to the operating power control range of the ground millimeter-wave base station and the millimeter-wave correction measures.

[0005] An embodiment of the present invention provides an adaptive control system for a millimeter-wave-based UAV. The adaptive control system for a millimeter-wave-based UAV is applied to the above-mentioned adaptive control method for a millimeter-wave-based UAV. The adaptive control system for a millimeter-wave-based UAV includes:

[0006] An 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 the environmental detection of the millimeter wave transmission space;

[0007] an attenuation path module, configured to determine multiple environmental types of the millimeter wave transmission space according to multiple environmental parameters, and determine the millimeter wave attenuation path based on the multiple environmental types, the spatial form of the millimeter wave transmission space, and the millimeter wave transmission data;

[0008] An attenuation node module is used to determine multiple attenuation nodes in the millimeter wave attenuation path based on the path shape of the attenuation path, the up and down movement of the UAV at the current location, and multiple environmental types, and mark the millimeter wave attenuation data of the multiple attenuation nodes;

[0009] The millimeter wave correction measure 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 trigger the millimeter wave correction of the UAV during flight;

[0010] The adaptive control module is used to determine the operating power control 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, and to determine the adaptive control strategy of the UAV at the millimeter wave transmission level according to the operating power control range of the ground millimeter wave base station and the millimeter wave correction measures.

[0011] Compared with the prior art, the present invention has the following beneficial effects:

[0012] In an embodiment of the present invention, through the method in the embodiment of the present invention, in the attenuation path of the millimeter wave, multiple attenuation nodes are determined based on the path shape of the attenuation path, the up and down activity of the UAV at the current position and multiple environmental types, and the millimeter wave attenuation data of the multiple attenuation nodes are marked. The attenuation path of the millimeter wave is introduced, which is compatible with the overall consideration of the path shape of the attenuation path, the up and down activity of the UAV at the current position and multiple environmental types, improves the detection accuracy of multiple attenuation nodes, and manages the millimeter wave attenuation data of multiple attenuation nodes.

[0013] Therefore, 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; the working 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, and the adaptive control strategy of the UAV at the millimeter wave transmission level is determined according to the working power control range of the ground millimeter wave base station and the millimeter wave correction measures. The millimeter wave correction measures of the UAV during flight are introduced, and the overall consideration of the working power control range and millimeter wave correction measures of the ground millimeter wave base station is realized, thereby improving the accuracy of the adaptive control strategy of the UAV at the millimeter wave transmission level. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 11 is a flow chart of an adaptive control method for a millimeter-wave-based UAV in an embodiment of the present invention;

[0015] Figure 2 1 is a flow chart of step S11 in the adaptive control method for a millimeter-wave-based UAV in an embodiment of the present invention;

[0016] Figure 3 1 is a flow chart of step S12 in the adaptive control method for a millimeter-wave-based UAV in an embodiment of the present invention;

[0017] Figure 4 1 is a flow chart of step S13 in the adaptive control method for a millimeter-wave-based UAV in an embodiment of the present invention;

[0018] Figure 5 1 is a flow chart of step S14 in the adaptive control method for a millimeter-wave-based UAV in an embodiment of the present invention;

[0019] Figure 6 1 is a flow chart of step S15 in the adaptive control method for a millimeter-wave-based UAV in an embodiment of the present invention;

[0020] Figure 7 Schematic diagram of the structure of the adaptive control system of the millimeter wave-based UAV in an embodiment of the present invention. DETAILED DESCRIPTION

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

[0022] See also Figures 1 to 7 , an adaptive control method for a millimeter wave-based UAV is applied to the adaptive control scenario of a millimeter wave-based UAV; the adaptive control method for a millimeter wave-based UAV includes:

[0023] Step S11: determining a millimeter wave transmission space based on the location of the UAV and the location of the ground millimeter wave base station; and determining a plurality of environmental parameters based on environmental detection of the millimeter wave transmission space;

[0024] Step S12: determining multiple environmental types of the millimeter wave transmission space according to multiple environmental parameters, and determining the millimeter wave attenuation path based on the multiple environmental types, the spatial shape of the millimeter wave transmission space, and the millimeter wave transmission data;

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

[0026] Step S14: determining a millimeter wave correction mechanism based on the data transmission status of the UAV and the millimeter wave attenuation data of multiple attenuation nodes, and triggering the millimeter wave correction of the UAV during flight;

[0027] Step S15: Determine the operating power control 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, and determine the adaptive control strategy of the UAV at the millimeter wave transmission level according to the operating power control 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:

[0029] S111: During the flight of the UAV, the UAV communicates with the ground millimeter wave base station and transmits corresponding millimeter wave data. At the same time, the position of the UAV and the position of the ground millimeter wave base station are collected, and the current flight attitude of the UAV is determined based on multiple attitude parameters of the UAV.

[0030] S112: Building a millimeter wave transmission system based on communication between the UAV and the ground millimeter wave base station, and determining a millimeter wave transmission space 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 form of the millimeter wave transmission space, the current flight posture of the UAV, and the relative position between the UAV and the millimeter wave base station, and determine multiple environmental parameters based on environmental detection of the environmental area, and the multiple environmental parameters are distributed at different positions in the environmental area.

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

[0033] Drones typically use a fusion of multiple technologies to obtain precise positioning. In this case, GPS / Beidou / Glonass / Galileo: satellite navigation systems provide basic position, velocity, and time (PVT) information; base station ranging / positioning: uses signal propagation time (TOA), angle of arrival (AOA), or time difference of arrival (TDOA) with 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. Multiple attitude parameters of the drone are collected, usually including: 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, error-free attitude descriptions (such as Euler angles and quaternions) through specific algorithms (such as complementary filtering, Kalman filtering, and quaternion method). The final flight attitude can be expressed using 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 also about understanding the specific technical details of this communication link, including: modulation and demodulation methods, bandwidth allocation, antenna configuration, beamforming status, signal strength and quality. These parameters are integrated to form a digital transmission system model, which is used to describe how the signal propagates from the transmitter to the receiver.

[0036] Combined with the obtained UAV position (latitude and longitude, altitude or three-dimensional coordinates relative to the base station) and the base station position, as well as the constructed transmission system model (especially the antenna direction and beam characteristics), the main area where the millimeter wave signal propagates in space is determined; direct path: calculate the straight-line distance and azimuth / pitch angle between the UAV and the base station, which is the main path for signal propagation, especially under line-of-sight conditions; beam coverage area: if beamforming is used, the beam will form a specific spatial area, which is usually a cone or fan, and its size and shape are determined by the beam width, antenna gain and distance; the specific position of this area in three-dimensional space needs to be determined based on the position of the UAV and the beam direction; multipath effect area: millimeter wave signals are easily affected by reflection, diffraction and scattering in the environment, forming multiple propagation paths; although it is complicated to accurately calculate all multipath paths, the area where there is significant reflection or diffraction can be roughly estimated based on the environment type (urban, suburban, indoor, etc.) and basic geometric relationships. Based on the above information, the "transmission space" is defined as a three-dimensional spatial area with the drone and base station as endpoints, including the direct path, beam coverage area, and multipath impact area. This area 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 form of the millimeter wave transmission space, the current flight posture of the UAV, and the relative position between the UAV and the millimeter wave base station are introduced. The spatial form of the transmission space describes the geometric shape of the main propagation of the millimeter wave signal, including: direct path, reflection area, diffraction area, shadow area, and antenna beam coverage area; according to the posture and antenna characteristics of the UAV, the beam coverage range of the current main transmission / reception signal is determined. For example, if the UAV uses a directional antenna and its current posture makes its antenna main lobe point to the ground base station, then the beam coverage area is the key area of ​​concern.

[0038] According to the relative position of the UAV and the base station and the transmission space form, determine whether there are obstacles on the direct path (judged by the shadow area in the space form) and identify the main reflecting surface (judged by the reflecting area in the space form).

[0039] Based on the above analysis, the transmission space is divided into several key environmental areas: direct path area: the path along which the direct wave passes, paying attention to whether there is any obstruction; main reflection area: the surface area of ​​objects where strong reflection signals are expected; 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; these environmental areas are determined in order to conduct targeted environmental detection in the future, rather than casting a uniform net across the entire transmission space, to improve detection efficiency and information relevance.

[0040] Within a defined environmental area, data reflecting the environmental impact on millimeter-wave signals needs to be collected. Detection methods may include analyzing changes in the characteristics of millimeter-wave signals transmitted between the drone and the base station. Signal strength (RSSI): Measuring changes in signal strength in different directions or locations (via antenna scanning or diversity reception simulation) can infer the presence of obstacles or reflectors. Signal-to-noise ratio (SNR) / signal-to-interference-and-noise ratio (SINR): Measuring the strength of the signal relative to noise and interference, reflecting channel quality. Channel state information (CSI): More detailed measurements, including amplitude, phase, delay spread, and angular spread, can accurately reflect multipath effects and angular spread. Angle of arrival (AoA) / angle of departure (AoD): Measuring the signal's arrival / departure angle using an antenna array can 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, reflecting surface information, multipath component information, interference source location, type, and strength, and atmospheric parameters.

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

[0042] S121: Collect multiple environmental parameters, and determine multiple environmental parameter combinations based on the multiple environmental parameters and the spatial form of the millimeter wave transmission space, where each environmental parameter combination includes at least three environmental parameters, and matching coefficients of multiple environmental parameters in each environmental parameter combination are greater than a preset matching coefficient threshold;

[0043] S122: determining corresponding environmental features based on the identification of a combination of multiple environmental parameters, and determining multiple environmental categories of the millimeter wave transmission space based on the shapes and distribution positions of the multiple environmental features and the size of the millimeter wave transmission space, where the multiple environmental categories include multiple environmental categories;

[0044] S123: Marking the distribution locations of multiple environment types among the multiple environment types, monitoring the millimeter wave transmission process of the UAV in real time, collecting millimeter wave transmission data, and determining the millimeter wave attenuation path based on the distribution locations of the multiple environment types, the spatial form of the millimeter wave transmission space, and the millimeter wave transmission data;

[0045] In an embodiment of the present application, multiple environmental parameters are collected, and S113 has determined several environmental areas based on the transmission space form, drone posture and relative position, and collected environmental parameters in these areas; the transmission space form of the millimeter wave (such as the cone, fan or other shape determined in S112) determines which environmental parameters are spatially adjacent or interrelated. For example, if the transmission space is a cone, then the parameters located on the same side of the cone and at a similar distance have superimposed or correlated effects on the signal; the collected environmental parameters are clustered or grouped according to the spatial form (for example, based on distance, angle, whether in the same beam area, etc.) to form an "environmental parameter combination"; each combination represents a comprehensive description of a local area or a local environmental state in 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 between the parameters within an environmental parameter combination; it determines whether these parameters are describing 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 the specific application, for example: based on type and impact: if the parameters in the combination are all of the same type (such as obstacles) or have similar types of effects on the signal (such as causing signal attenuation), the coefficient is higher.

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

[0048] Furthermore, pattern recognition and abstraction are performed on the multiple environmental parameter combinations generated by S121; the system needs to analyze the parameter types contained in each combination and the physical meaning represented by these parameter combinations; an "environmental feature" is a general description of a group 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 "large amounts 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 environmental parameter combinations to environmental features, which 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 occlusion with strong reflection features."

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

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

[0052] Therefore, the distribution positions of multiple environmental types among multiple environmental types are marked, the millimeter wave transmission process of the drone is monitored in real time, and the millimeter wave transmission data is collected. The attenuation path of the millimeter wave is determined according to the distribution positions of multiple environmental types, the spatial form of the millimeter wave transmission space and the millimeter wave transmission data. This is compatible with the overall consideration of the distribution positions of multiple environmental types, the spatial form 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, the specific geographical distribution area of ​​each environmental type is mapped in space. This usually requires combining the real-time location of the drone, the status of the base station, and the "distribution location" information, and now it needs to be accurately "drawn" on the spatial map.

[0054] Marking method: Geographic Information System (GIS) or simple 2D / 3D coordinate system can be used for marking. For example, Category A (complex obstacle environment ahead) is marked as a fan-shaped area 30 degrees to the right of the current position of the UAV and 50-200 meters away; Category B (low-loss environment behind / below) is marked as a wide area behind and below the UAV; Category C (overall medium atmospheric loss background) is marked to cover the entire transmission space, but the impact intensity has a gradient change; providing a spatial reference for subsequent real-time monitoring and data analysis; the system needs to know, when the UAV moves or the signal changes, which environment type or types of areas it is currently in, and which areas the signal is passing through.

[0055] Real-time monitoring of the millimeter wave communication link status between the drone and the ground base station, including but not limited to: signal received power (RxPower): how strong the signal is; signal quality indicators (such as bit error rate BER, signal-to-noise ratio SNR): how clean the signal is; 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; the degree of misalignment; transmission rate and throughput: how efficient the current communication is; continuously collect these data through the communication module and signal processing unit on the drone; the data needs to be timestamped, and it is best to be associated with the drone's current geographic location and attitude information for subsequent analysis; obtain the real-time status of the current communication link as direct evidence to determine whether the signal is interfered with or attenuated.

[0056] Combine spatial information with real-time signal data for inference. Analyze which environmental categories (marked in S123) the communication link between the current drone and the base station (usually line of sight or near line of sight) passes through. For example, the current link passes through category A (complex obstacle environment ahead) and category C (overall moderate atmospheric loss background). Compare changes in transmission data before and after the link passes through different types of areas. For example, before entering category A, the signal's RxPower is -60dBm and SNR is 20dB. After passing through category A, the RxPower drops to -75dBm, the SNR drops to 10dB, and the CSI shows obvious multipath components and phase jumps. Combined with the definition of environmental categories and changes in transmission data, infer the cause and path of attenuation.

[0057] Category A defines strong reflections and building obstructions. The data changes correspond exactly to the decrease in signal strength and the increase in multipath effects. Therefore, it can be determined that the signal has undergone significant attenuation when passing through the Category A area. This area and the path thereafter are the main attenuation paths. The spatial form of the transmission space (such as whether it is an open space, a tunnel, an urban canyon, etc.) will also affect the judgment of the attenuation path. For example, in an urban canyon, the signal is reflected and obstructed by buildings multiple times, and the attenuation path will be more complex. Multiple reflections and diffraction need to be considered. The final output is a specific attenuation path description, including: the main area where attenuation occurs (corresponding to which environment type and 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 drops); and the existence of multipath path information.

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

[0059] S131: monitoring the attenuation path of the millimeter wave in real time, and determining the path shape of the attenuation path based on the shape detection of the attenuation path. At the same time, collecting the current position of the drone, and determining the up and down movement amount of the drone at the current position based on the activity monitoring of the drone at the current position;

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

[0061] S133: Based on the first attenuation distribution, the second attenuation distribution and the attenuation path, multiple attenuation areas are determined, and the multiple attenuation areas are distributed in a stepped manner. Corresponding attenuation nodes are determined based on synchronous detection of the multiple attenuation areas, so as to collect multiple attenuation nodes and determine corresponding millimeter wave attenuation data based on real-time monitoring of the multiple attenuation nodes.

[0062] In an embodiment of the present application, the attenuation path of the millimeter wave is monitored in real time, and the receiver on the drone continuously measures key indicators such as the 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 in these indicators over time.

[0063] Morphological detection is performed on the attenuation path, and the signal data collected on the attenuation path is used to identify the specific pattern or shape of attenuation. Based on the detected pattern, the attenuation path is classified into a specific form. Common forms include: uniform attenuation: the signal strength decreases evenly along the entire path; shadow fading: the signal drops sharply due to obstruction by large obstacles (such as buildings), usually changing dramatically at the edge of the obstacle; multipath fading: because the signal reaches the receiver through different paths (reflection, diffraction), mutual interference causes rapid fluctuations in signal strength (fast fading); mixed form: includes multiple of the above situations at the same time.

[0064] The system determines the main characteristics of attenuation by analyzing the curve of signal strength changes over time or space (if the drone is moving and spatial sampling is possible), changes in signal phase, and multipath delay spread and other parameters. For example, the rapid changes in signal strength and phase can be used to determine whether there is a significant multipath effect.

[0065] The purpose of collecting data on the drone's current location and knowing its specific location is to associate the previously detected attenuation path morphology and the activity to be measured later with this exact spatial point. This is crucial for understanding the relationship between attenuation, the environment, and the drone's attitude. The sensors on the drone (primarily the IMU, including accelerometers and gyroscopes) are used to monitor the drone's vertical motion state. The displacement or velocity change of the drone in the vertical direction (Z-axis) is calculated or estimated. This can be an instantaneous value (such as the current vertical velocity) or a statistical value within a time period (such as the amplitude and frequency of the vertical displacement). The vertical movement of the drone (such as ascent, descent, and bumps) will change the geometric relationship between it and the ground base station, thereby affecting the propagation path, line-of-sight state, and multipath components of the millimeter-wave signal. Understanding this activity helps to distinguish between attenuation caused by the environment 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 positions of multiple environmental types among the multiple environmental types, and the second attenuation distribution is determined based on the up and down activity of the UAV at the current position and the distribution positions of multiple environmental types among the multiple environmental types. This is compatible with the overall consideration of the up and down activity of the UAV at the current position and the distribution positions of multiple environmental types among the multiple environmental types, ensuring the accuracy of the second attenuation distribution.

[0067] At this time, the path shape of the attenuation path and the distribution positions of multiple environmental types in the multiple environmental types are introduced. The distribution positions of multiple environmental types in the multiple environmental types indicate the specific positions of different types of environments (such as: Type A-complex obstacle environment in front, Type B-open environment on the sides and rear, Type C-high humidity area below, etc.) in the space around the drone.

[0068] Determine the first attenuation distribution: Explain how the environment itself causes the signal to attenuate along a specific path. Specifically, the attenuation path's morphology is correlated with the distribution of environmental types. For example, if an attenuation path exhibits "multipath fading" and passes through "Type A - Complex Obstacle Environment Ahead" (including buildings), then the first attenuation distribution can be inferred to be: "Due to multipath reflections and obstructions caused by buildings, the signal attenuates 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 contributions 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, based on predefined rules or databases (for example, "building areas generally lead to high multipath fading and shadow fading", "high humidity areas lead to increased path loss"), it assigns an attenuation impact factor or pattern to each environmental type of area. Finally, it combines this information to generate a descriptive or graphical "first attenuation distribution situation", which shows which environmental areas play a major role in signal attenuation and the type of effect (such as obstruction, reflection, absorption).

[0070] Specifically, the system software will analyze the path morphology information (such as "multipath fading") provided by S131 and the environmental distribution map provided by S122; it will find which environmental types of areas the attenuation path passes through; then, based on predefined rules or databases (for example, "building areas usually lead to high multipath fading and shadow fading", "high humidity areas lead to increased path loss"), it assigns an attenuation impact factor or pattern to each environmental type area. Finally, based on this information, it generates a descriptive or graphical "first attenuation distribution situation" to explain which environmental areas play a major role in signal attenuation and the type of effect (such as obstruction, reflection, absorption); path morphology: "multipath fading"; environmental distribution: path A passes through the "Type A" area, and the "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 will reflect signals, generate multiple propagation paths, and lead 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 smaller than the reflection effect of the building, 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 drone (Type A), which leads to severe multipath fading; the high humidity area below (Type C) also has a certain absorption loss effect on the signal, but it is relatively minor." This description clearly points out the contribution distribution of environmental factors to signal attenuation.

[0072] The up-and-down movement of the drone at the current location and the distribution of multiple environmental types in multiple environmental types are introduced. The up-and-down movement of the drone at the current location describes the vertical motion state of the drone, such as whether it is hovering smoothly, slowly rising / falling, or violently bumping.

[0073] Determine the second attenuation distribution: Analyze how the drone's own movement interacts with the surrounding environment, thereby affecting signal attenuation. Specifically, evaluate whether the drone's vertical movement causes its position relative to certain environmental features (such as reflective surfaces or obstructions) to change, thereby changing the signal propagation conditions. For example, if the drone moves up and down when near a reflective surface (such as water or a smooth wall), it will change the reflection angle and the length of the reflection path, resulting in 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 will analyze the "up and down activity" (such as vertical speed, vertical displacement range) provided by S131 and the environmental distribution map provided by S122; it will search for environmental areas where the current activity of the drone is significant (for example, near a reflecting surface, near the edge of a shadow, in a high multipath area); then, based on physical principles or empirical models, it will evaluate the signal changes caused by this vertical movement, for example, "when close to a reflecting surface, vertical movement will cause the reflection angle to change, thereby causing periodic fluctuations in signal strength" or "near the edge of a shadow, vertical movement will quickly switch the state of whether the signal is blocked." Finally, it will generate a descriptive or graphical "second attenuation distribution" to explain in which areas and in what way the movement of the drone has aggravated or changed the signal attenuation.

[0075] Specifically, the amount of up and down movement: slight bumps (±0.25m); environmental distribution: the "Type C" area is directly below the drone, and the "Type A" area is in front; system analysis: "Slight vertical bumps" occur directly above the "Type C-high humidity area"; the high humidity area itself has absorption loss on the signal; although the vertical displacement of 0.5 meters is not small relative to the signal wavelength (millimeter wave), "slight bumps" are 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 "Type A-building" reflection area; vertical bumps will slightly change the height and angle of the drone relative to the reflecting surface of the building, which causes slight changes in the path length and phase of the reflected signal, thereby causing slight fluctuations in the received signal strength or phase jitter; but due to the small amplitude of the bumps (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 determined as follows: "The slight vertical turbulence of the drone causes a small change in signal absorption loss above the high humidity area below; when approaching the reflection area of ​​the building in front, the vertical turbulence will slightly change the reflection path, resulting in small fluctuations in the received signal strength." This description points out the impact of the interaction between the drone's movement and the environment on signal attenuation.

[0077] The system analyzes the distribution of signal attenuation from two dimensions: the first attenuation distribution reveals how the inherent characteristics of the environment itself (such as obstruction and reflection from buildings, and absorption from humidity) cause the signal to attenuate on 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 the millimeter wave communication link, providing a basis for the next step (S133) of identifying specific attenuation areas and nodes. For example, by combining the first distribution (buildings causing multipath) and the second distribution (minor bumps causing small fluctuations), the system can more accurately locate the area with the most severe attenuation (areas close to buildings) and the main factors affecting attenuation (environmental structure).

[0078] Therefore, based on the first attenuation distribution, the second attenuation distribution and the attenuation path, multiple attenuation areas are determined, and the multiple attenuation areas are distributed in a stepped manner. The corresponding attenuation nodes are determined based on the synchronous detection of the multiple attenuation areas to collect multiple attenuation nodes. The corresponding millimeter-wave attenuation data is determined based on the real-time monitoring of the multiple attenuation nodes. This is compatible with the overall consideration of the real-time monitoring of multiple attenuation nodes, ensuring the accuracy of the corresponding millimeter-wave attenuation data. At the same time, it is compatible with the overall consideration of the path shape of the attenuation path, the up and down activity of the drone at the current position and multiple environmental types, thereby improving the detection accuracy of multiple attenuation nodes and managing the millimeter-wave attenuation data of multiple attenuation nodes.

[0079] At this time, the first attenuation distribution, the second attenuation distribution and the attenuation path are introduced. The first attenuation distribution is the attenuation pattern caused by the inherent characteristics of the environment, such as the building obstruction and reflection, and the humidity absorption below in the S132 example; the second attenuation distribution is the attenuation change caused by the interaction between the drone movement and the environment, such as the slight fluctuation caused by slight bumps in the S132 example; the attenuation path is the main route where signal attenuation occurs, such as the path close to the building and the humidity area below identified in the S131 example; based on the above information, the system divides the space into areas with different degrees of signal attenuation; usually, these areas are distributed along the attenuation path; the attenuation area is not uniform, but is divided into several levels according to the severity of the attenuation, such as the mild attenuation area, the moderate attenuation area and the severe attenuation area; the stepped distribution helps the system understand the gradual process of attenuation and the most dangerous area.

[0080] The system needs to monitor the signal status in these different areas simultaneously or nearly simultaneously. This is usually achieved through multiple antenna units or beamforming capabilities on the drone, which can simultaneously perceive the signal quality in 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 in the area change most dramatically or are most typical are usually considered.

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

[0082] The system uses corresponding sensors (such as receiving antennas and signal processing units) to collect signal data at determined attenuation nodes (such as nodes A, B, and C). 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, including calculating the decibel (dB) drop in signal strength, the specific value of the signal-to-noise ratio (SNR), the percentage of the bit error rate (BER), the magnitude of the phase change, etc.

[0083] Specifically, the system performs real-time monitoring and collects data at nodes A, B, and C: Node A: Real-time monitoring shows that the signal strength is -85dBm, the SNR is 20dB, and the BER is 10^-6; the corresponding millimeter wave attenuation data: relative to free space, the signal strength attenuates by about 10dB; Node B: Real-time monitoring shows that the signal strength drops rapidly to -95dBm, the SNR drops to 8dB, the BER rises to 10^-4, and obvious multipath delay spread (about 0.3 microseconds) is detected; the corresponding millimeter wave attenuation data: relative to free space, the signal strength attenuates by about 20dB, and the multipath effect is significant; Node C: Real-time monitoring shows that the signal strength drops sharply to -110dBm, the SNR drops to 2dB, the BER rises sharply to 10^-2, and the signal phase jitters rapidly; the corresponding millimeter wave attenuation data: relative to free space, the signal strength attenuates by about 35dB, the communication quality is extremely poor, and it is close to being interrupted.

[0084] By combining the attenuation distribution and attenuation path, the system successfully spatially demarcated stepped attenuation regions and identified key attenuation nodes within these regions. Through real-time monitoring and data acquisition of these nodes, the system obtained precise, regionalized millimeter-wave attenuation data. This information is an indispensable input for subsequent steps (such as the formulation of adaptive control strategies), enabling the system to specifically adjust communication parameters (such as power, beam direction, modulation mode, etc.) to maintain or restore the stability and reliability of the communication link. For example, based on the severe attenuation data from nodes B and C, the system can determine the need for immediate action (such as increasing transmit power, switching to an 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:

[0086] S141: monitoring the communication of the UAV with respect to the ground millimeter wave base station in real time, collecting multiple communication coefficients of the UAV, and determining the data transmission status of the UAV based on the multiple communication coefficients of the UAV and the data transmission efficiency;

[0087] S142: collecting spatial positions and corresponding millimeter wave data of multiple attenuation nodes, performing attenuation control in sequence along the multiple attenuation nodes, and determining corresponding millimeter wave attenuation data according to the spatial positions of the multiple attenuation nodes and the corresponding millimeter wave data;

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

[0089] In an embodiment of the present application, the communication between the UAV and the ground millimeter wave base station is monitored in real time, and 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 coefficient and the actual measured data transmission efficiency (for example, the ratio of throughput to the theoretical maximum throughput). This status can be a classification result, for example: good status: high SNR, low BER, low PLR, and throughput close to the theoretical value; fair status: medium SNR, slightly increased BER, increased PLR, and decreased throughput; poor status / interruption risk: low SNR, high BER, significantly increased PLR, a significant decrease in throughput, and even a brief interruption.

[0091] Furthermore, the spatial positions of multiple attenuation nodes and the corresponding millimeter-wave data are collected, attenuation control is performed in sequence along the multiple attenuation nodes, and the corresponding millimeter-wave attenuation data is determined based on the spatial positions of the multiple attenuation nodes and the corresponding millimeter-wave data, which is compatible with the overall consideration of the spatial positions of multiple attenuation nodes and the corresponding millimeter-wave data, and ensures the accuracy of the corresponding millimeter-wave attenuation data.

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

[0093] The system processes these attenuation nodes in a predetermined order (for example, from drone to base station, or from lightest to heaviest attenuation). This does not mean that physical "control" actions are taken at each node, but that the system logically analyzes the contribution of each node to the overall attenuation in this order to prepare for subsequent correction mechanisms; based on the spatial position of the node 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 the signal propagating from the drone to the node position; shadow fading: the decrease in signal strength due to obstruction; multipath effect: the amplitude, phase change and time expansion caused by the signal arriving through multiple paths; specific attenuation value: for example, the attenuation of the signal strength at a certain node 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 positions of multiple attenuation nodes, and the multiple synthesis of millimeter-wave attenuation data. The millimeter-wave attenuation process is presented in the millimeter-wave correction mechanism, and the UAV or ground millimeter-wave base station is dynamically controlled according to the complex feedback of the millimeter-wave attenuation data to dynamically correct the millimeter-wave data, thereby triggering the millimeter-wave correction of the UAV during flight. It is compatible with the overall consideration of the data transmission status of the UAV, the spatial positions of multiple attenuation nodes, and the multiple synthesis of millimeter-wave attenuation data, thereby ensuring the accuracy of the millimeter-wave correction mechanism.

[0095] At this time, the system combines the data transmission status determined by S141 (such as "general status"), the spatial position of each attenuation node determined by S142, and the specific attenuation data (such as the shadow attenuation of node B is 20dB, and the multipath delay is 100ns). This combination (multiple synthesis) enables the system to build 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 is severely attenuated and what is the reason), but also includes a series of preset or calculated response strategies; the correction mechanism includes a simulation or description of the attenuation process, allowing the system to "understand" how the signal starts from the drone, passes through each attenuation node, and finally reaches the base station, which helps to select the most effective correction strategy.

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

[0097] Once the control decision is made and executed, it is a "dynamic correction" of the millimeter wave data transmission. This process is continuous. As long as 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 of the drone during flight.

[0098] Specifically, the system constructed a correction mechanism based on the "general status" (throughput of 50Mbps, lower than expected), the location and attenuation data of nodes A, B, and C (especially the 20dB shadow fading and 100ns multipath delay of node B were the main problems). The mechanism determined that the main communication bottleneck was the obstruction of buildings and multipath effects near node B. The internal model of the mechanism showed that the signal strength at node B dropped sharply due to the building and produced a significant multipath component.

[0099] The system decided to adopt a combined strategy: first, it tried to slightly increase the drone's flight altitude (for example, from 110m to 115m to bypass some obstructions) while reducing the drone's MCS order (for example, from 256QAM to 64QAM) to enhance its ability to resist multipath and shadowing; the drone received the command and performed altitude fine-tuning; its communication module also switched to the new MCS setting; after the altitude was increased, the shadow fading of node B was reduced to 15dB and the multipath delay was reduced to 50ns; after lowering the MCS, although the theoretical rate was reduced, the tolerance to BER was improved; the system continuously monitored the new communication coefficient and attenuation node data to evaluate the correction effect; if the effect was good (for example, the throughput returned to 60Mbps and the BER was reduced), the current setting was maintained; if the effect was not good, further analysis was continued and other measures were taken (such as further adjustment of altitude or power).

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

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

[0102] S152: Collecting millimeter wave correction measures corresponding to the millimeter wave correction mechanism, determining a first adaptive coefficient based on the millimeter wave correction measures and an operating power control range of a ground millimeter wave base station, and determining a second adaptive coefficient based on the millimeter wave correction measures and a current operating state of the UAV;

[0103] S153: Determine an adaptive control strategy of the UAV at the millimeter wave transmission level according to the first adaptive coefficient, the second adaptive coefficient, and the adaptive mapping relationship.

[0104] In an embodiment of the present application, millimeter-wave attenuation data of multiple attenuation nodes and the current position of the UAV are collected, and the corresponding millimeter-wave correction amount is determined based on the current position of the UAV, the millimeter-wave correction mechanism and the millimeter-wave attenuation data of multiple attenuation nodes. The working power control range of the ground millimeter-wave base station is determined based on the millimeter-wave correction amount, the current working power of the ground millimeter-wave base station and the working power mapping relationship, which is compatible with the overall consideration of the millimeter-wave correction amount, the current working power of the ground millimeter-wave base station and the working power mapping relationship, thereby ensuring the accuracy of the working power control range of the ground millimeter-wave base station.

[0105] At this time, the millimeter wave attenuation data of multiple attenuation nodes and the current position of the drone are collected. The millimeter wave attenuation data usually includes but is not limited to: signal strength attenuation value (dB): indicates how much the signal strength is weakened when passing through the node area; signal-to-noise ratio (SNR) or signal-to-interference and noise ratio (SINR): indicates how poor the signal quality is after passing through the node area; phase change: millimeter waves are sensitive to phase changes, and multipath effects can cause phase drift; delay spread: multipath signals arrive at different times, causing the signal to be broadened in time; attenuation characteristics of specific frequencies: the millimeter wave frequency band is wide, and different frequency components attenuate differently.

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

[0107] Specifically, assume that the system has 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 the 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 the left, with a signal strength attenuation of approximately -12dB and an SNR of 12dB; at this time, the drone uses GPS positioning to determine its current position: longitude 116.391 degrees, latitude 39.907 degrees, and altitude 110 meters.

[0108] The system needs to compare the drone's current position with the spatial locations of all attenuation nodes and determine which attenuation area the drone is currently located in, or which attenuation area is closest to and has the greatest impact. For example, the drone is passing through the influence area of ​​NodeA or approaching NodeB. 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 with signal strength attenuation exceeding -10dB, it is necessary to compensate for at least 50% of the attenuation value" or "Calculate the required power increase based on the degree to which the SNR value falls below a certain threshold (such as 15dB)." Based on this information, the system calculates a specific "correction value," which represents the numerical value of the system parameters (primarily power) required to overcome the current main attenuation impact. This value is an absolute value (such as the need to increase power by 3dBm) or a relative value (such as the need to compensate for 40% of the current attenuation value). This value serves as the basis for subsequent adjustments to the base station power.

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

[0110] Determine the correction amount: NodeA: Horizontal distance is approximately sqrt(300^2+0^2)=300 meters, which is less than 500 meters. Attenuation is -15dB, which is greater than -10dB. Correction amount = -15dB30% = -4.5dB, which means that a 4.5dB signal loss needs to be compensated. NodeB: Horizontal distance is approximately sqrt(500^2+100^2)≈509.9 meters, which is greater than 500 meters. This condition is not met. NodeC: Horizontal distance is approximately sqrt(200^2+50^2)≈206.2 meters, which is less than 500 meters. Attenuation is -12dB, which is greater than -10dB. Correction amount = -12dB30% = -3.6dB.

[0111] Although Node C also meets the requirements, Node A has more severe attenuation (-15 dB vs. -12 dB) and is closer (300 m vs. 206 m. Although Node C is closer, Node A's attenuation is more critical. Here, the algorithm prioritizes the more severe attenuation. The system chooses to prioritize the correction for Node A or the larger of the two corrections (4.5 dB). We assume that the final correction is 4.5 dB.

[0112] The system needs to know the actual power at which the ground 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 working power mapping relationship is a key data table or rule set that defines the constraints and characteristics 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 limit: the minimum transmit power (such as 5dBm) and maximum transmit power (such as 20dBm) of the base station; the relationship between power and performance / energy consumption / heat dissipation: some power points are not available due to hardware limitations or efficiency reasons, or after exceeding a certain power, the performance improvement is not obvious but the energy consumption increases sharply; safety constraints: comply with radio management regulations to avoid harmful interference to other frequency bands.

[0113] Based on the calculated correction amount (for example, the signal strength needs to be increased by 4.5dB), the current power (for example, 10dBm), and the power mapping relationship, the system calculates the specific range in which the base station power can be adjusted. This range is usually an interval, indicating the interval to which the base station power should be adjusted 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; but considering that the power adjustment step is 1dBm and the power limit is 5-20dBm, the control range is [14dBm, 15dBm].

[0114] Specifically, assume that the current operating power of the ground millimeter wave base station is 10dBm; reference power mapping relationship: assuming that the base station: minimum power: 5dBm, maximum power: 20dBm; adjustment step: 1dBm (can only be adjusted in integer dBm); calculate the target power: current power 10dBm + correction amount 4.5dB ≈ 14.5dBm; adjust according to the step size: the adjustable power points closest to 14.5dBm are 14dBm and 15dBm; check the power limit: 14dBm and 15dBm are both within the range of [5dBm, 20dBm]; therefore, the operating power control range of the ground millimeter wave base station is determined to be [14dBm, 15dBm], which means that the system recommends adjusting the base station power to 14dBm or 15dBm to compensate for the 4.5dB signal attenuation at the current position of the drone (affected by NodeA).

[0115] Furthermore, millimeter wave correction measures corresponding to the millimeter wave correction mechanism are collected, and a first adaptive coefficient is determined based on the millimeter wave correction measures and the operating power control range of the ground millimeter wave base station. A second adaptive coefficient is determined based on the millimeter wave correction measures and the current working status of the UAV. This takes into account the overall consideration of the millimeter wave correction measures and the current working status of the UAV, ensuring the accuracy of the second adaptive coefficient.

[0116] At this point, the millimeter wave correction mechanism is a comprehensive policy 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 type of correction measures that need to be taken based on the attenuation situation; now, the task of S152 is to clearly 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 drone) to compensate for path loss or shadow fading, for example, "increase transmission power" or "reduce transmission power"; beamforming / wave Beam switching: Millimeter-wave communications typically use directional antennas. By adjusting the direction or shape of the antenna's beam, obstacles can be avoided or signals in a specific direction can be enhanced. For example, "switch the beam to direction B" or "enhance the focus of the current beam." Modulation and Coding Scheme (MCS) adjustment: Different modulation methods (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. When the channel is good, a high-rate scheme but with high channel quality requirements is selected. For example, "downgrade MCS from 64QAM3 / 4 to 16QAM1 / 2."

[0117] Diversity technology: Use space, frequency, or time diversity to combat fading, for example, "Enable space diversity"; Frequency adjustment / hopping: If there is interference or attenuation at a specific frequency, you can try switching to an alternative frequency, for example, "Switch to alternative frequency band X"; UAV maneuvering instructions: Although mainly ground base station adjustments, in some cases, it also includes small UAV maneuvering instructions, such as "small ascent / descent / translation" to improve the line of sight or avoid local strong attenuation areas.

[0118] Specifically, the drone is at an altitude of 110 meters and is being affected by NodeA. The correction mechanism recommends increasing the ground base station's transmit power and downgrading the MCS from 64QAM3 / 4 to 16QAM1 / 2 to increase link reliability. Therefore, the millimeter wave correction measures collected by S152 are increasing the ground base station's power and reducing the MCS level.

[0119] The specific measure of "increasing power" is combined with the previously calculated base station power control range (for example, [14dBm, 15dBm]) to generate a quantitative coefficient 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 higher willingness to adjust; the coefficient indicates whether to tend to use the upper limit, lower limit or middle value of the control range, for example, if the attenuation is very severe and continuous, the coefficient tends to point to the upper limit; if it is only a temporary fluctuation, point to the middle or lower limit; if the corrective measure is not just "increasing power", but there is a specific increase amplitude recommendation (for example, "increase by at least 3dB"), then the coefficient can reflect the relationship between this amplitude 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 (that is, a maximum increase of 4dB), the coefficient indicates "increase is allowed, and there is enough 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," and the power control range is [14dBm, 15dBm]. System analysis shows that although the power needs to be increased (correction amount 4.5dB), the control range provides sufficient adjustment space (up to 5dB), and "increasing the power" is currently the most effective measure. Therefore, the first adaptive coefficient (denoted as coefficient A) is determined to be a higher value, such as 0.8. This 0.8 can be understood as: within the current power adjustable range, there is a strong tendency to adjust the power to an area close to the upper limit (i.e., around 15dBm) to maximize compensation for attenuation. If the coefficient is 0.3, it means that only a small adjustment is required or the urgency of the adjustment is not high.

[0122] Evaluate the current state of the drone, including remaining battery power, flight mode, load, current flight stability, and mission priority. Evaluate the degree of match between the current corrective measures (including drone maneuvering instructions) and the drone's state. For example, if the measure includes a "slight increase" but the drone's battery is low, the match is low. The calculation of coefficient B is also based on rules, proportions, or functions. For example, coefficient B = (state matching) * (weight of the measure involving drone maneuvers). If the measure does not involve drone maneuvers at all, this coefficient is mainly used to evaluate 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 "lowering the MCS level", which do not directly involve UAV maneuvers; but the system checks the status of the UAV: ​​sufficient power (80%), in automatic cruise mode, stable flight, and the mission allows communication priority; although the UAV maneuvers are not required at present, the system assesses that the UAV is capable of cooperating with more complex correction strategies (if necessary); therefore, the second adaptive coefficient (denoted as coefficient B) is determined to be a higher value, such as 0.9. This 0.9 indicates that the UAV is in good condition and is very suitable for executing corrective measures (even if it is mainly base station adjustments), and is capable of cooperating if more complex corrections (such as minor maneuvers) are needed in the future; if the UAV has only 15% power and is under manual control, the coefficient B will be very low (such as 0.2), indicating that the UAV state is not suitable for any additional corrective actions.

[0124] The drone is flying at an altitude of 110 meters and is affected by Node A, requiring correction. S152 first detects that the current millimeter wave correction measures are "increasing ground base station power" and "reducing the MCS level." Then, based on the "increasing power" measure and the previously calculated base station power control range [14dBm, 15dBm], the system determines that a significant power increase is needed to compensate for attenuation, and that the base station is capable of doing so. Therefore, the first adaptive coefficient A is determined to be 0.8, indicating a strong preference for adjusting the power close to the upper limit. Next, the system evaluates the drone's status: sufficient battery, automatic 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 in the next step (S153) to make a comprehensive decision, ultimately determining whether to adjust the base station power to 14dBm or 15dBm and whether to maintain the MCS downgrade.

[0125] Therefore, the adaptive control strategy of the UAV at the millimeter wave transmission level is determined according to the first adaptive coefficient, the second adaptive coefficient and the adaptive mapping relationship, which is compatible with 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 level. At the same time, millimeter wave correction measures for the UAV during flight are introduced, realizing the overall consideration of the working power control range of the ground millimeter wave base station and the millimeter wave correction measures, and improving the accuracy of the adaptive control strategy of the UAV at the millimeter wave transmission level.

[0126] At this time, 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 strength of power adjustment based on the base station power control range and correction measures, and coefficient B reflects the suitability of executing correction measures (including cooperating with base station adjustments or self-adjustment) based on the current state of the drone; 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 coefficients A and 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 attenuation; a high coefficient B (e.g., >0.7) means that the drone is in good condition and can cooperate with the correction, and the overall system status allows for active adjustment; the combination of coefficients A and B can be mapped to different policy priorities and specific actions.

[0128] For example: A is high and B is high: base station power adjustment is prioritized and other corrective measures (such as MCS adjustment) are implemented in conjunction with it, and the drone remains in a stable state; A is high but B is low: base station power adjustment is necessary, but the drone is in poor condition (such as low battery or mission-critical). The strategy focuses on base station adjustment and temporarily does not implement corrections that require drone cooperation, or limits the adjustment range of the drone itself; A is low but B is high: the base station power adjustment is not large or is not the optimal solution. The strategy tends to allow the drone to perform small maneuvers (if the correction mechanism allows and the drone is in good condition) or wait for environmental changes; A is low and B is low: a more conservative strategy is needed, such as adjusting only MCS, or temporarily accepting lower communication quality.

[0129] The system inputs the values ​​of coefficients A and B into an adaptive mapping relationship; the mapping relationship outputs one or more specific control instructions based on the value range and combination of the two coefficients. These instructions clearly specify the parameters that need to be adjusted and their target values ​​or ranges. The final "adaptive control strategy for drones at the millimeter wave transmission level" is a composite instruction.

[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 the preset rules, the "high A high B" combination usually means that the corrective measures can be actively and comprehensively implemented.

[0131] For "Increase Power," the mapping relationship indicates that, due to the high A value and the base station's control range of [14dBm, 15dBm], the highest value within this range should be selected to maximize attenuation compensation. Furthermore, a high B value indicates that the drone is in good condition and can withstand a stronger signal. (If the power increase results in an overly strong signal, the drone will need to make fine adjustments, but here, the base station adjustment is primarily responsible.) For "Reduce MCS," the mapping relationship indicates that, due to the high A value (power adjustment is the primary measure) and the high B value (drone condition is good and the communication link is stable), reducing the MCS is a reasonable supplementary measure to improve data transmission reliability, especially if power adjustment has not yet fully compensated for all attenuation. The strategy is clear: instruct the ground millimeter wave base station to adjust its operating power from the current 10dBm to 15dBm; simultaneously, instruct the drone to adjust its data transmission modulation and coding scheme (MCS) from the previous 64QAM / 3 / 4 to 16QAM / 1 / 2; and the drone maintains its current flight altitude of 110 meters and cruising speed.

[0132] In another embodiment of the present application, the real-time data acquisition system module obtains information such as the device's location and posture, as well as the surrounding environment's temperature, humidity, cloud, rain, fog, and dust from temperature and humidity sensors, as one of the inputs to the drone's millimeter-wave attenuation model system. To enhance system robustness, for ultra-long distances, the temperature, humidity, cloud, rain, fog, and dust information at the central node of the propagation path is initially input into the drone's millimeter-wave attenuation system as a static prior configuration.

[0133] The drone's millimeter-wave attenuation system integrates real-time and a priori frequency, distance, attitude, and atmospheric environment information to calculate a total attenuation value. This value accounts for free-space path attenuation, wind-induced drone vibration attenuation, and atmospheric particle swarm attenuation. The adaptive power transmission system derives the total transmit power based on the expected received power and attenuation values. It then initiates step detection and subsequently adjusts the attenuation value and transmit power based on channel quality feedback. The receiving system establishes an attenuation error correction model and a service feedback correction model based on the combined actual received power, expected received power, and actual transmit power.

[0134] Normal distribution is used to estimate the attenuation error between the receiving system and the transmitting system, and linear regression is used to calculate the optimal signal-to-noise ratio. The compensation and correction coefficients are fed back to the transmitter to make up for the error of the UAV millimeter wave channel attenuation model and determine the optimal transmission power.

[0135] See also Figure 7 , Figure 7 : is a schematic diagram of the structural composition of an adaptive control system for a millimeter wave-based UAV in an embodiment of the present invention; the adaptive control system for a 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 determine multiple environmental parameters based on the environmental detection of the millimeter wave transmission space;

[0137] an attenuation path module 22 for determining multiple environmental types of the millimeter wave transmission space according to multiple environmental parameters, and determining the millimeter wave attenuation path based on the multiple environmental types, the spatial form of the millimeter wave transmission space, and the millimeter wave transmission data;

[0138] An attenuation node module 23 is configured to determine a plurality of attenuation nodes in the millimeter wave attenuation path based on the path shape of the attenuation path, the vertical movement of the UAV at the current location, and multiple environmental types, and mark the millimeter wave attenuation data of the plurality of 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 trigger the millimeter wave correction of the UAV during flight;

[0140] The adaptive control module 25 is used to determine the operating power control 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, and to determine the adaptive control strategy of the UAV at the millimeter wave transmission level according to the operating power control range of the ground millimeter wave base station and the millimeter wave correction measures.

[0141] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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 UAV, characterized in that: include: Determine the millimeter wave transmission space based on the location of the drone and the location of the ground millimeter wave base station; Determine multiple environmental parameters based on environmental detection of the millimeter wave transmission space; determining multiple environmental types of the millimeter wave transmission space according to multiple environmental parameters, and determining the millimeter wave attenuation path based on the multiple environmental types, the spatial shape of the millimeter wave transmission space, and the millimeter wave transmission data; In the millimeter wave attenuation path, multiple attenuation nodes are determined based on the path shape of the attenuation path, the up and down movement of the UAV at the current location, and multiple environmental types, and the millimeter wave attenuation data of the multiple attenuation nodes are marked; Determine the millimeter wave correction mechanism based on the UAV's data transmission status and the millimeter wave attenuation data of multiple attenuation nodes, and trigger the millimeter wave correction of the UAV during flight; Based on the millimeter-wave attenuation data of multiple attenuation nodes and the current position of the UAV, the operating power control range of the ground millimeter-wave base station is determined. According to the operating power control range of the ground millimeter-wave base station and the millimeter-wave correction measures, the adaptive control strategy of the UAV at the millimeter-wave transmission level is determined.

2. The adaptive control method for a millimeter-wave-based UAV according to claim 1, characterized in that: The millimeter wave transmission space is determined based on the position of the UAV and the position of the ground millimeter wave base station; Based on the environmental detection of the millimeter wave transmission space, multiple environmental parameters are determined, 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, the drone's position and the position of the ground millimeter wave base station are collected, and the current flight attitude of the drone is determined 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 millimeter wave transmission space 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 form of the millimeter wave transmission space, the current flight posture of the UAV, and the relative position between the UAV and the millimeter wave base station, and multiple environmental parameters are determined based on the environmental detection of the environmental area. The multiple environmental parameters are distributed in different positions of the environmental area.

3. The adaptive control method for a millimeter-wave-based UAV according to claim 1, characterized in that: The determining of multiple environmental types of the millimeter wave transmission space according to multiple environmental parameters, and determining the millimeter wave attenuation path based on the multiple environmental types, the spatial form of the millimeter wave transmission space, and the millimeter wave transmission data, includes: Collecting multiple environmental parameters and determining multiple environmental parameter combinations based on the multiple environmental parameters and the spatial morphology of the millimeter wave transmission space, each environmental parameter combination including at least three environmental parameters, and a matching coefficient of multiple environmental parameters in each environmental parameter combination being greater than a preset matching coefficient threshold; Determining corresponding environmental features based on the identification of a combination of multiple environmental parameters, and determining multiple environmental categories of the millimeter wave transmission space based on the shapes and distribution positions of the multiple environmental features and the spatial size of the millimeter wave transmission space, where the multiple environmental categories include multiple environmental categories; Mark the distribution locations of multiple environmental types, monitor the millimeter wave transmission process of the drone in real time, collect millimeter wave transmission data, and determine the millimeter wave attenuation path based on the distribution locations of multiple environmental types, the spatial form of the millimeter wave transmission space, and the millimeter wave transmission data.

4. The adaptive control method for a millimeter-wave-based UAV according to claim 1, characterized in that: In the millimeter wave attenuation path, multiple attenuation nodes are determined based on the path shape of the attenuation path, the up and down movement of the drone at the current location, and multiple environmental types, and millimeter wave attenuation data of the multiple attenuation nodes are marked, including: The attenuation path of the millimeter wave 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 up and down 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 UAV according to claim 4, characterized in that: In the millimeter wave attenuation path, multiple attenuation nodes are determined based on the path shape of the attenuation path, the up and down movement of the UAV at the current location, and multiple environmental types, and millimeter wave attenuation data of the multiple attenuation nodes are marked, further comprising: Determining a first attenuation distribution based on a path shape of the attenuation path and distribution positions of multiple environmental types among the multiple environmental types, and determining a second attenuation distribution based on an amount of up and down movement of the UAV at the current location and distribution positions of multiple environmental types among the multiple environmental types; Based on the first attenuation distribution, the second attenuation distribution and the attenuation path, multiple attenuation areas are determined, and the multiple attenuation areas are distributed in a stepped manner. Corresponding attenuation nodes are determined based on the synchronous detection of the multiple attenuation areas, so as to collect the multiple attenuation nodes and determine the corresponding millimeter wave attenuation data based on real-time monitoring of the multiple attenuation nodes.

6. The adaptive control method for a millimeter-wave-based UAV according to claim 1, characterized in that: The method of determining a millimeter wave correction mechanism based on the data transmission state of the drone and the millimeter wave attenuation data of multiple attenuation nodes, and triggering millimeter wave correction of the drone during flight, includes: Monitor the drone's communication with the ground millimeter wave base station in real time, collect multiple communication coefficients of the drone, and determine the drone's data transmission status based on the drone's multiple communication coefficients and data transmission efficiency; The spatial positions of multiple attenuation nodes and the corresponding millimeter wave data are collected, attenuation control is performed in sequence along the multiple attenuation nodes, and the corresponding millimeter wave attenuation data is determined according to the spatial positions of the multiple attenuation nodes and the corresponding millimeter wave data.

7. The adaptive control method for a millimeter-wave-based UAV according to claim 6, characterized in that: The method of determining a millimeter wave correction mechanism based on the data transmission state of the drone and the millimeter wave attenuation data of multiple attenuation nodes, and triggering the millimeter wave correction of the drone during flight, further includes: A millimeter-wave correction mechanism is constructed based on the data transmission status of the UAV, the spatial positions of multiple attenuation nodes, and the multiple synthesis of millimeter-wave attenuation data. The millimeter-wave attenuation process is presented in the millimeter-wave correction mechanism, and the UAV or ground millimeter-wave base station is dynamically controlled according to the complex feedback of the millimeter-wave attenuation data to dynamically correct the millimeter-wave data, thereby triggering the millimeter-wave correction of the UAV during flight.

8. The adaptive control method for a millimeter wave-based UAV according to claim 1, characterized in that: The method includes determining the operating power control 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, and determining the adaptive control strategy of the UAV at the millimeter wave transmission level according to the operating power control range of the ground millimeter wave base station and the millimeter wave correction measures, including: The millimeter-wave attenuation data of multiple attenuation nodes and the current position of the UAV are collected, and the corresponding millimeter-wave correction amount is determined based on the current position of the UAV, the millimeter-wave correction mechanism and the millimeter-wave attenuation data of multiple attenuation nodes. The working power control range of the ground millimeter-wave base station is determined based on the millimeter-wave correction amount, the current working power of the ground millimeter-wave base station and the working power mapping relationship.

9. The adaptive control method for a millimeter-wave-based UAV according to claim 8, characterized in that: The method further includes determining the operating power control 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, and determining the adaptive control strategy of the UAV at the millimeter wave transmission level according to the operating power control range of the ground millimeter wave base station and the millimeter wave correction measures. Collecting millimeter wave correction measures corresponding to the millimeter wave correction mechanism, determining a first adaptive coefficient based on the millimeter wave correction measures and an operating power control range of a ground millimeter wave base station, and determining a second adaptive coefficient based on the millimeter wave correction measures and a current operating state of the UAV; The adaptive control strategy of the UAV at the millimeter wave transmission level is determined according to the first adaptive coefficient, the second adaptive coefficient and the adaptive mapping relationship.

10. An adaptive control system for a UAV based on millimeter waves, characterized in that: The adaptive control system of the millimeter wave-based UAV is applied to the adaptive control method of the millimeter wave-based UAV according to any one of claims 1 to 9. The adaptive control system of the millimeter wave-based UAV includes: An 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 the environmental detection of the millimeter wave transmission space; an attenuation path module, configured to determine multiple environmental types of the millimeter wave transmission space according to multiple environmental parameters, and determine the millimeter wave attenuation path based on the multiple environmental types, the spatial form of the millimeter wave transmission space, and the millimeter wave transmission data; An attenuation node module is used to determine multiple attenuation nodes in the millimeter wave attenuation path based on the path shape of the attenuation path, the up and down movement of the UAV at the current location, and multiple environmental types, and mark the millimeter wave attenuation data of the multiple attenuation nodes; The millimeter wave correction measure 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 trigger the millimeter wave correction of the UAV during flight; The adaptive control module is used to determine the operating power control 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, and to determine the adaptive control strategy of the UAV at the millimeter wave transmission level according to the operating power control range of the ground millimeter wave base station and the millimeter wave correction measures.

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