Low-altitude intelligent car networking energy efficiency and safety rate joint optimization method and system

Abstract

The invention discloses a low-altitude intelligent car networking energy efficiency and safety rate joint optimization method and system, and belongs to the technical field of car networking communication. The method comprises the following steps: constructing an air-ground integrated cooperation model comprising a far-end base station, an unmanned aerial vehicle relay and multiple vehicles, depicting fast time-varying characteristics based on a geometric random channel model, and constructing a secure communication model according to the Shannon confidentiality theorem, a multi-constraint optimization problem with the sum of the minimum total energy consumption of the unmanned aerial vehicle and the maximum vehicle safety rate as the target is formed; designing an alternative optimization solution framework, and decoupling an original problem into three sub-problems of base station transmitting power control, unmanned aerial vehicle transmitting power control and unmanned aerial vehicle track optimization; and in each iteration, other variables are fixed, the three sub-problems are solved by adopting a Newton iteration method, a model prediction control method, a targeting method and a gradient descent method in sequence, and the corresponding variables are updated until convergence. According to the method, the flight path and the communication power of the unmanned aerial vehicle are jointly optimized, so that the problem of cooperative improvement of safety communication and system energy efficiency under a fast time-varying channel is effectively solved.

Description

Technical Field

[0001] This invention belongs to the field of low-altitude intelligent vehicle networking technology, specifically relating to a method and system for joint optimization of energy efficiency and safety rate in low-altitude intelligent vehicle networking. Background Technology

[0002] With the deep integration of Intelligent Transportation Systems (ITS) and 5G / 6G technologies, Vehicle-to-Everything (VNets) have evolved into core infrastructure supporting autonomous driving and vehicle-road cooperation, improving traffic efficiency and reducing accident rates through real-time V2V, V2I, and V2P communication. However, traditional VNets rely on ground-based macro base stations, facing coverage blind spots and signal attenuation issues in high-speed scenarios. Furthermore, the rapid time-varying channel characteristics (such as rapid channel changes caused by Doppler shift) severely impact communication reliability and data rates, especially in 5G millimeter-wave communication where channel coherence time is extremely short. Traditional orthogonal frequency division multiplexing (oOFDM) technology struggles to effectively address rapid time-varying fading. To address this, Low-Altitude Intelligent Vehicle-to-Everything (LAVNets) have emerged. By introducing drones as relay nodes, they construct a three-layer link of "ground base station - low-altitude platform - vehicle," compensating for insufficient coverage by macro base stations and utilizing the mobility of drones to maintain line-of-sight links, thus reducing signal attenuation.

[0003] However, the application of low-altitude intelligent vehicle networks still faces challenges such as secure communication and insufficient endurance of low-altitude platforms. The information carried by vehicle networks has extremely high privacy requirements, and traditional upper-layer encryption schemes (such as TLS, RSA / ECC, and AES) have significant limitations in vehicle network scenarios, including low key distribution efficiency, high computational overhead, and weak resistance to attacks. With the development of quantum computing, traditional public-key encryption algorithms are easily cracked in a short time. Meanwhile, the energy consumption of drones, acting as relay nodes, directly affects the system's continuous operating capacity. Therefore, how to improve the overall energy efficiency of low-altitude intelligent vehicle networks while ensuring secure communication, by optimizing parameters such as drone flight paths, base station and drone transmission power, has become a critical issue that urgently needs to be addressed. Therefore, implementing a joint optimization framework based on alternating optimization can significantly improve the overall energy efficiency of the system while ensuring secure communication in low-altitude intelligent vehicle networks, providing a new and efficient solution for intelligent transportation systems.

[0004] Existing technology 1 proposes a secure communication method for vehicle-to-everything (V2X) networks based on upper-layer encryption protocols. Addressing the security requirements of data transmission in V2X networks, this technology employs Transport Layer Security (TLS) protocol combined with RSA / ECC asymmetric encryption and AES symmetric encryption. Specifically, when establishing a secure connection between a vehicle and a base station or other vehicles, a session key is first negotiated using an asymmetric encryption algorithm (such as RSA or ECC). This process leverages the public-key encryption and private-key decryption features to ensure the security of key exchange. Subsequently, a symmetric encryption algorithm (such as AES) is used to encrypt the transmitted data, utilizing the session key for efficient data protection. This method, through a layered encryption mechanism, constructs a dual security protection system at the application and transport layers, and is widely applicable to V2X communication scenarios in vehicle-to-everything (V2X) networks.

[0005] Existing technology two proposes a physical layer secure communication method based on static channel characteristics. Addressing the eavesdropping threat in open wireless channels of vehicular networks, existing technology two utilizes channel uniqueness and reciprocity to construct a secure communication link through beamforming and artificial noise injection techniques. Specifically, this method assumes that the channel state remains unchanged during the communication period. It optimizes the direction of the transmitted beam towards the legitimate receiver (such as a vehicle or base station) while injecting artificial noise in the eavesdropping direction to suppress illegal eavesdropping. Beamforming technology adjusts the phase and amplitude of the antenna array to form a main lobe in the legitimate direction and a null in the eavesdropping direction; artificial noise injection reduces the signal-to-noise ratio at the eavesdropping end by superimposing interference noise into the transmitted signal. This method achieves basic security protection without relying on upper-layer encryption through physical layer signal processing, and is suitable for static or low-speed moving vehicular network scenarios.

[0006] Existing technology three proposes a low-altitude vehicle-to-everything (V2X) communication method that separates and optimizes UAV trajectory and power allocation. Addressing the communication requirements of UAV relays in low-altitude intelligent V2X systems, this method decouples trajectory optimization from power control. Specifically, it first employs Model Predictive Control (MPC) to optimize the UAV's three-dimensional flight trajectory to maximize coverage or minimize flight energy consumption. MPC uses a rolling optimization strategy to calculate the optimal trajectory within each control cycle based on the current state and the predictive model, while considering obstacle avoidance and flight constraints. Subsequently, convex optimization techniques are used to adjust the transmission power of the base station and the UAV to improve system safety. The power allocation algorithm, based on Channel State Information (CSI), determines the optimal power allocation ratio by solving a convex optimization problem. This method reduces problem complexity through separation of optimization and is suitable for UAV-assisted V2X communication scenarios.

[0007] However, the problem with existing technologies is: First, existing technologies face a trade-off between computational complexity and real-time performance in large-scale low-altitude intelligent vehicle-to-everything (V2X) scenarios. These layered encryption-based schemes need to simultaneously handle multiple parameters, including key distribution, edge computing resource allocation, and dynamic security protocol negotiation, while also meeting the millisecond-level real-time decision-making requirements of high-speed V2X scenarios. However, the computational complexity of traditional asymmetric encryption algorithms (such as RSA / ECC) increases exponentially with key length, causing key update delays exceeding channel coherence time when vehicles are moving at high speeds, leading to security vulnerabilities. Furthermore, layered encryption systems require cross-layer collaboration across the application, transport, and physical layers, further exacerbating computational resource consumption and making it difficult to meet the demands of low-altitude intelligent V2X for efficient, real-time, and secure communication.

[0008] Secondly, existing technology two has shortcomings in adaptability to fast-time-varying channels. This technology is based on a static channel model and uses beamforming and artificial noise injection techniques to construct a secure communication link. However, in low-altitude intelligent vehicle networks, the high-speed relative motion between the drone and the vehicle causes the channel to exhibit strong non-stationary characteristics, and the Doppler shift significantly shortens the channel coherence time. The channel state information (CSI) estimation error, on which traditional beamforming algorithms rely, increases sharply with speed, leading to large fluctuations in the secure rate, narrowing the signal-to-noise ratio difference between the eavesdropping channel and the legitimate channel, and making it difficult to effectively maintain the physical layer security mechanism. At the same time, artificial noise injection technology cannot track channel changes in real time in fast-time-varying scenarios, instead introducing additional energy consumption and reducing the overall energy efficiency of the system.

[0009] Finally, the existing technology three has theoretical limitations in multivariate coupled optimization. While the scheme of separating the optimization of UAV trajectory and power control reduces the complexity of the problem, it ignores the dynamic impact of trajectory changes on channel power gain, as well as the constraint of power allocation on UAV energy consumption. In fact, adjusting the UAV trajectory will change the path loss of the base station-UAV link and the UAV-vehicle link, thus requiring the recalculation of the optimal power allocation; and the adjustment of power allocation will limit the UAV's endurance, thereby constraining the range of trajectory selection. This strong coupling effect makes the safe rate of the separate optimization scheme significantly lower than that of the joint optimization scheme. In addition, the separate optimization framework lacks convergence theory guarantees in high-dimensional non-convex problems, the algorithm is prone to getting trapped in local optima, and the optimization results are unstable, failing to meet the dual high standards of safe communication performance and energy efficiency required by low-altitude intelligent vehicle networks.

[0010] The difficulty in solving the above technical problems: First, constructing a comprehensive and accurate multi-UAV collaborative optimization model in the complex scenario of low-altitude intelligent vehicle-to-everything (V2X) networks faces multiple challenges. This model needs to deeply integrate three-dimensional spatial constraints and simultaneously quantify the dynamic impact of communication security mechanisms (such as signal encryption and anti-eavesdropping technologies) on communication quality. Due to the high-speed relative motion between UAVs and vehicles, the channel exhibits rapidly time-varying characteristics, making it difficult for traditional static channel models to accurately describe such dynamic fading processes. Furthermore, the model needs to consider energy efficiency optimization, minimizing system energy consumption by jointly adjusting UAV trajectories and power allocation strategies. However, existing research often focuses on single-variable optimization, failing to fully consider the strong coupling relationship between trajectory changes and power adjustments, leading to deviations between optimization results and actual scenarios and hindering applicability in complex environments.

[0011] Secondly, designing efficient solution algorithms requires overcoming the optimization bottleneck under high-dimensional non-convex constraints. Multi-UAV collaborative optimization involves strongly coupled variables such as base station power allocation, trajectory planning, and transmit power control. Traditional algorithms are prone to getting trapped in local optima during global optimization, while separate optimization frameworks suffer performance losses due to neglecting the correlation between variables. Simultaneously, the dynamic changes in the low-altitude environment require algorithms to be adaptable in real time, capable of quickly sensing sudden changes in channel state information and adjusting optimization strategies. However, existing algorithms struggle to balance computational complexity and convergence speed, making it difficult to meet the real-time communication scheduling requirements of fast-changing scenarios. Summary of the Invention

[0012] The technical problem to be solved by the present invention is to provide a method and system for joint optimization of energy efficiency and safety rate of low-altitude intelligent vehicle network, which addresses the shortcomings of the prior art. This method and system is used to solve the technical problem of how to coordinately optimize the three-dimensional flight trajectory of UAV, base station transmission power and UAV transmission power in the fast time-varying channel environment of low-altitude intelligent vehicle network, so as to maximize the overall energy efficiency of the system while meeting the strict physical layer security communication requirements.

[0013] The present invention adopts the following technical solution: A method for jointly optimizing energy efficiency and safety speed in low-altitude intelligent vehicle-to-everything (V2X) networks includes the following steps: An integrated air-ground collaboration model is constructed, establishing a three-layer communication architecture that includes a remote ground base station, a drone acting as a relay node, and multiple intelligent connected vehicles. The characteristics of fast time-varying channels are characterized based on a geometric random channel model, and a secure communication model is constructed based on Shannon's secrecy theorem. With the goal of minimizing the total energy consumption of the drone and maximizing the sum of the secure speeds of all intelligent connected vehicles, a multi-constraint optimization problem is established, in which the optimization variables include the transmission power of the base station, the transmission power of the drone, and the three-dimensional flight trajectory of the drone. An alternating optimization solution framework is designed to decouple the multi-constraint optimization problem into a base station transmit power control subproblem, a UAV transmit power control subproblem, and a UAV trajectory optimization subproblem, and to iteratively solve them until the convergence condition is met. In each iteration, perform the following optimizations: With the UAV's transmission power and flight trajectory fixed, the base station's transmission power control subproblem is solved using the Newton-Raphson iteration method, and the base station's transmission power is updated. With the base station's transmission power and the UAV's flight trajectory fixed, the UAV's transmission power control subproblem is solved using the model predictive control method, and the UAV's transmission power is updated. With the base station's transmission power and the UAV's transmission power fixed, the UAV's flight trajectory optimization subproblem is solved using a combination of the target shooting method and the gradient descent method, and the UAV's flight trajectory is updated.

[0014] Preferably, the construction of the integrated air-ground cooperation model specifically includes: A two-hop relay transmission mode of base station → drone → vehicle is adopted, and the drone uses an amplification and forwarding protocol to relay signals from the base station. For the first-hop base station → UAV link and the second-hop UAV → vehicle link, the air-to-ground channel is modeled using the Rician distribution, and the channel state information is estimated from the received signal using the minimum mean square error estimation method. Based on the channel estimation results, the legitimate communication rate and the eavesdropping rate are calculated, and the secure communication rate is defined as the difference between the legitimate communication rate and the eavesdropping rate.

[0015] Preferably, the constraints of the multi-constraint optimization problem include: The transmission power of base stations and drones shall not exceed their respective maximum transmission power; The drone's flight speed shall not exceed its maximum flight speed; The drone's flight location is within a pre-defined flight area; The channel estimation error is bounded.

[0016] Preferably, the nonlinear equations of the base station transmit power control subproblem are solved using Newton's method.

[0017] Preferably, the model predictive control method includes: Predict the channel state information in the future within a finite time domain; based on the predicted channel state information, construct and solve a finite-time optimal control problem to obtain the UAV's transmit power control strategy at the current moment.

[0018] Preferably, the UAV trajectory optimization subproblem is transformed into a continuous-time optimal control problem, and the solution is obtained based on Pontryagin's maximum value principle.

[0019] Preferably, the target shooting method combined with the gradient descent method is used to solve the costate equation based on the Pontryagin maximum principle in order to obtain the optimal UAV trajectory.

[0020] Preferably, the intelligent connected vehicle travels at a constant speed along a one-way highway, and its position is a function of its initial position, speed, and time; the drone flies in the stratosphere at a preset altitude, and its flight area is a preset three-dimensional spatial range.

[0021] Preferably, during the iterative solution process, convergence is determined by judging whether the change in the objective function value between two consecutive iterations is less than a preset threshold.

[0022] Secondly, embodiments of the present invention provide a low-altitude intelligent vehicle-to-everything (V2X) energy efficiency and safety rate joint optimization system, comprising: The module is used to build an integrated air-ground collaboration model, establishing a three-layer communication architecture that includes a remote ground base station, drones acting as relay nodes, and multiple intelligent connected vehicles. It characterizes the fast time-varying channel characteristics based on a geometric random channel model and constructs a secure communication model based on Shannon's secrecy theorem. With the goal of minimizing the total energy consumption of drones and maximizing the sum of the secure speeds of all intelligent connected vehicles, a multi-constraint optimization problem is established, where the optimization variables include the base station's transmission power, the drone's transmission power, and the drone's three-dimensional flight trajectory. The solution module is used to design an alternating optimization solution framework, decoupling the multi-constraint optimization problem into a base station transmit power control subproblem, a UAV transmit power control subproblem, and a UAV trajectory optimization subproblem, and iteratively solving them until the convergence condition is met; wherein, in each iteration, the following is executed: With the UAV's transmission power and flight trajectory fixed, the base station's transmission power control subproblem is solved using the Newton-Raphson iteration method, and the base station's transmission power is updated. With the base station's transmission power and the UAV's flight trajectory fixed, the UAV's transmission power control subproblem is solved using the model predictive control method, and the UAV's transmission power is updated. With the base station's transmission power and the UAV's transmission power fixed, the UAV's flight trajectory optimization subproblem is solved using a combination of the target shooting method and the gradient descent method, and the UAV's flight trajectory is updated.

[0023] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for joint optimization of energy efficiency and safety rate in low-altitude intelligent vehicle networking.

[0024] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described method for jointly optimizing energy efficiency and safety speed in low-altitude intelligent vehicle networking.

[0025] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for joint optimization of energy efficiency and safety rate in low-altitude intelligent vehicle networking.

[0026] In a sixth aspect, embodiments of the present invention provide an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-described method for jointly optimizing energy efficiency and safety rate in low-altitude intelligent vehicle networking.

[0027] Compared with the prior art, the present invention has at least the following beneficial effects: A joint optimization method for energy efficiency and safe speed in low-altitude intelligent vehicle-to-everything (V2X) networks is proposed. First, by constructing a multi-objective optimization problem that integrates fast time-varying channel characteristics and a secure communication model, it for the first time balances the energy consumption of UAVs with the sum of the safe speeds of multiple vehicles within the same framework. Second, it creatively employs an alternating optimization (AO) strategy to decompose the complex high-dimensional non-convex coupled problem into three sequentially solvable subproblems, theoretically ensuring the feasibility of the problem-solving process. Finally, it precisely matches efficient numerical solution algorithms (Newton's method, MPC, and a target-shooting method combined with gradient descent) to each subproblem, ensuring the real-time solution capability and convergence of the entire framework in dynamic environments, fundamentally solving the problems of "performance loss due to separation optimization" and "insufficient real-time performance of algorithms" in the background technologies.

[0028] Furthermore, a two-hop AF relay mode of base station → UAV → vehicle is adopted, clarifying the core relay role of the UAV. Using the Rican distribution and MMSE estimation to model and estimate the air-to-ground channel enables a more realistic portrayal of the low-altitude, fast-time-varying channel characteristics, including line-of-sight and multipath components, providing relatively accurate channel state information input for subsequent optimization. Based on Shannon's secrecy theorem, a secure rate is constructed, quantifying physical layer security into an optimizable objective function. This allows for improvements in security performance to be directly achieved by adjusting physical parameters such as transmit power and UAV position, avoiding complex upper-layer encryption overhead and responding to the background technology's demand for "efficient, real-time, and secure communication."

[0029] Furthermore, by constraining the upper limits of the transmission power of base stations and drones, the hardware limitations and regulatory requirements of communication equipment are met. Constraints on drone flight speed and area ensure flight safety and airspace compliance. In particular, the introduction of the constraint of bounded channel estimation error acknowledges the imperfections in CSI acquisition under rapidly changing environments and incorporates this uncertainty into the optimization model. This makes the optimized power and trajectory strategies inherently robust to estimation errors, effectively addressing the challenge in the background technology where "CSI estimation errors lead to the failure of security mechanisms."

[0030] Furthermore, the Newton-Raphson iteration method possesses a quadratic convergence speed, enabling rapid solution of the nonlinear optimal equation concerning base station transmit power. It calculates the base station power value that maximizes the sum of the system's safe rates under a given UAV state in the current iteration round. This locally fast convergence characteristic ensures the solution efficiency of this sub-module within the alternating optimization framework, making it a crucial element in enabling the entire framework to achieve real-time or near-real-time optimization.

[0031] Furthermore, MPC, through rolling optimization, not only considers the current channel state but also predicts the channel change trend within a finite time domain in the future, and solves a finite-time optimal control problem accordingly. This makes the UAV's transmit power allocation no longer static or reactive, but forward-looking and adaptive, enabling it to better track fast time-varying channels, smooth power adjustments, and thus maintain a high safe rate more stably in dynamic environments, and potentially avoid unnecessary power fluctuations to save energy.

[0032] Furthermore, by transforming trajectory optimization into a continuous-time optimal control problem and applying the Pontryagin Maximum Principle (PMP), the necessary conditions for the optimal trajectory can be derived from a variational perspective. This provides clear theoretical guidance and mathematical form for finding the optimal trajectory, transforming the trajectory search problem, which is difficult to handle directly, into a problem of solving a set of differential equations. This forms the theoretical basis for achieving joint optimization of trajectory and communication performance.

[0033] Furthermore, the target-shooting method is an effective numerical solution to boundary value problems, matching terminal conditions by iteratively guessing initial costate values. Combining this with gradient descent to correct the guesses enables efficient and numerically stable calculation of the UAV flight trajectory that satisfies the optimality conditions. This combination of "theoretical principles + numerical algorithms" ensures that even under complex coupled objective functions, a near-optimal UAV trajectory can still be calculated, which is a key step in achieving the joint optimization objective of this invention.

[0034] Furthermore, it is clarified that intelligent connected vehicles travel at a constant speed on a one-way highway, and their position model is simple and clear, making it easy to substitute as known time-varying parameters in the optimization model. It is stipulated that the UAV flies at stratospheric altitude and within a preset three-dimensional airspace, conforming to the typical operating mode and air traffic control requirements of low-altitude UAVs. This specification not only enhances the understandability and feasibility of the solution but also limits the specific scenarios in which the technical advantages of this invention can be best utilized.

[0035] Furthermore, by monitoring the change in the overall objective function value between two consecutive iterations and comparing it with a preset small threshold, it is possible to reliably determine whether the optimization process has reached a local optimum or a satisfactory solution. This criterion is simple and effective, easy to implement on computing devices, ensures that the algorithm does not loop infinitely, and can output definite optimization results, thus improving the engineering practicality and reliability of the entire method system.

[0036] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0037] In summary, this invention constructs a joint optimization problem integrating fast time-varying channel and security models, and designs an efficient solution strategy based on an alternating optimization framework, achieving coordinated dynamic adjustment of UAV trajectory, base station, and UAV power. Its core advantages lie in its theoretically unified handling of the multivariable, strongly coupled problem, and in engineering, its implementation of real-time optimization through a matching algorithm. Ultimately, in fast time-varying vehicular network scenarios, it significantly improves both the physical layer secure communication rate and the overall energy efficiency of the UAV relay system.

[0038] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0039] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of a low-altitude intelligent vehicle-to-everything (V2X) scenario provided in an embodiment of the present invention. Figure 3 This paper compares the performance of the embodiments of the present invention with those of comparative schemes 1, 2, 3, and 4 in terms of total safe rate (sub-figure a) and average safe rate (sub-figure b) under different numbers of intelligent connected vehicles. Figure 4 This document compares the performance of the embodiments of the present invention with comparative schemes 1, 2, 3, and 4 in terms of energy consumption (mean and variance) per unit safe rate. Figure 5 The performance of the embodiments of the present invention and comparative schemes 1, 2, 3, and 4 in terms of the sum of safe rates is compared under different maximum transmission power conditions (base station and drone). Figure 6 The performance of the embodiments of the present invention compared with comparative schemes 1, 2, 3, and 4 in terms of the sum of safe speeds under different vehicle travel time conditions; Figure 7This document compares the performance of the embodiments of the present invention with those of Comparative Scheme 1, Comparative Scheme 2, Comparative Scheme 3, and Comparative Scheme 4 in terms of the sum of safe rates under different channel error conditions. Figure 8 The performance of the embodiments of the present invention compared with comparative schemes 1, 2, 3, and 4 in terms of the sum of safe speeds under different speed conditions of intelligent connected vehicles. Figure 9 The present invention compares the performance of its embodiments with comparative schemes 1, 2, 3, and 4 in terms of the sum of safe speeds under different drone speed conditions. Figure 10 The performance changes of the sum of safety rates during the convergence process of the objective function in this embodiment of the invention are shown for different initial numbers of smart terminals. Figure 11 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 12 This is a block diagram of a chip provided according to an embodiment of the present invention.

[0040] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0041] This invention provides a joint optimization method for energy efficiency and safety rate in low-altitude intelligent vehicle-to-everything (V2X) networks. By constructing a multi-objective optimization model, it comprehensively considers fast time-varying channel characteristics, three-dimensional trajectory constraints, and the secure communication requirements of multiple vehicles, achieving joint optimization of UAV flight paths, base station, and UAV transmit power. Based on this, a geometrical stochastic channel model is used to accurately characterize channel variations, and a secure communication model is constructed using Shannon's secrecy theorem, aiming to minimize UAV energy consumption while maximizing secure communication rate. Through an alternating optimization framework and efficient solution algorithm, the method effectively improves system energy efficiency and safety performance, providing important technical support for the practical deployment of low-altitude intelligent V2X networks.

[0042] This invention achieves a synergistic improvement in secure communication rate and system energy efficiency through a closed-loop design system comprising dynamic channel modeling, multivariate joint optimization, theoretical performance analysis, and simulation verification. It not only overcomes the limitations of traditional methods in handling fast time-varying channels and multivariate coupling problems at the theoretical level, but also provides key technical support for the practical deployment of low-altitude intelligent vehicle networks in complex dynamic environments through quantifiable optimization parameters and adaptive strategies, significantly promoting the evolution of intelligent transportation systems towards higher security and lower latency.

[0043] Please see Figure 1 This invention discloses a method for jointly optimizing energy efficiency and safety speed in low-altitude intelligent vehicle networking, comprising the following steps: S1. Construct an integrated air-ground cooperation model, designing a three-layer architecture of "remote base station - UAV relay - multiple vehicles," integrating a geometrical random channel model to characterize fast time-varying characteristics, decomposing the channel between UAVs and vehicles, and between the UAV and the base station into line-of-sight and multipath components, and quantifying channel attenuation and fluctuation. Combining Shannon's secrecy theorem, construct a secure communication model that integrates channel estimation error and eavesdropping links, forming a multi-constraint optimization problem with the objectives of minimizing UAV energy consumption and maximizing security rate; S101, Relay Transmission Model Figure 1 The low-altitude intelligent vehicular networks (LAVNets) under consideration include one remote ground base station (BS), one relay unmanned aerial vehicle (UAV), and... V Intelligent connected vehicles (ICVs) traveling on a one-way highway. The set of intelligent connected vehicles is defined as follows: Ground base stations are fixedly deployed at the ground coordinate origin. Drones act as relay nodes at varying altitudes. H Stratospheric flight, its three-dimensional position is represented as ,in and respectively drones in t The x and y coordinates of time. It is the transpose matrix. This is the communication cycle. Furthermore, the permitted flight area for drones is defined as... . V Intelligent connected vehicles travel along the highway (set as x (Axis) travels at a constant speed, the first v vehicle The location of intelligent connected vehicles is ,in For the firstv Intelligent connected vehicles t The x-axis coordinate at time t is calculated as ,in and The first v The initial position and speed of the intelligent connected vehicle.

[0044] The communication link adopts a two-hop relay mode of "base station → drone → vehicle", with no direct link between the base station and the vehicle. The drone uses the amplify-and-forward (AF) protocol to relay signals from the base station. In the first hop (i.e., the "base station → drone" link), the base station transmits confidential information... Modulated into a signal , is represented as: (1) in, This refers to the base station's transmission power. It is additive white Gaussian noise. ; This refers to the noise power of the base station. The received signal from the drone after passing through the air-to-ground (ATG) channel from the base station to the drone is... It is represented as: (2) in, This represents the air-to-ground channel gain related to multipath in the first-hop link. For multipath delay; This represents the total gain of the air-to-ground channel in the first hop link; For the receiving noise of the drone, ; Let be the received noise power of the UAV. In equation (2), This is the noise term.

[0045] In the second hop (i.e., the "drone → vehicle" link), the drone amplifies and forwards the received signal. Amplification and forwarding gain. Power constraints must be met ,in This is the maximum transmit power of the drone. In addition, it will... Defined as the instantaneous transmit power of the UAV. After the relayed signal passes through the air-to-ground channel from the UAV to the vehicle, the... v Signal received by intelligent connected vehicles It is represented as: (3) in, This refers to the air-to-ground channel gain related to multipath in the second-hop link. For the first v The received noise of intelligent connected vehicles ; For the first v The received noise power of a smart connected vehicle.

[0046] S102, Fast Time-Variating Channel Model Considering the high-speed movement of both drones and vehicles, the channel exhibits rapidly changing time-varying characteristics. Therefore, it is difficult to obtain channel state information (CSI) accurately in real time. Pilot signaling is used to estimate the channel. First, the base station transmits pilot signals. The channel estimation gain of the UAV in the first hop link is... , can be obtained ,in This represents the estimation error of the first-hop link. Similarly, the drone transmits pilot signals. . No. v The channel estimation gain of a smart connected vehicle in the second-hop link is , can be obtained ,in This represents the estimation error of the second-hop link. The covariance of the two links is defined as follows: and In the above process, the minimum mean square error (MMSE) method is used to estimate the channel from the received signal.

[0047] Furthermore, a Rician distribution is used to model the air-to-ground channel. Since the base station is located at a far distance, the first-hop link includes both line-of-sight (LoS) and non-line-of-sight (NLoS) components. Further expressed as: (4) in, The Rician factor for the first hop link; The total number of NLoS paths in the first hop link is defined as follows: ; For the first l strip Gain of the NLoS path; For the first l The delay of the NLoS path. Furthermore, and The steering vector of the UAV antenna array and the Doppler frequency shift of the first hop link, respectively, are expressed as: (5) and (6) in, The spacing between array elements; Angle of incidence; The carrier wavelength; This refers to the number of antennas. For the speed of the drone; The relative positions of the drone and the base station; c It is the speed of light.

[0048] Subsequently, thanks to the drone's altitude advantage, the second-hop link was dominated by the Loss of Speed ​​(LoS) component. It is further expressed as: (7) in, The Rician factor for the second-hop link; Define the total number of NLoS paths for the second-hop link. ; For the first v The steering vector of the antenna array of a smart connected vehicle; This is the Doppler frequency shift of the second-hop link.

[0049] S103, Secure Communication Model Legal communication rate Calculated as: (8) in, and The channel estimation gains are given for "base station → UAV" and "UAV → vehicle", respectively. Furthermore, in equation (8), the amplified forwarding gain is... .

[0050] Suppose there exists a passive eavesdropper (defined as Eve), whose received signal includes two links: base station → eavesdropper and drone → eavesdropper. The eavesdropping rate... Calculated as: (9) in, Estimating channel gain for the "drone → eavesdropper" link. Estimate the channel gain for the "base station → eavesdropper" link. The noise power of the eavesdropper.

[0051] Secure communication rate For legal communication rates With eavesdropping rate The difference is calculated as: (10) S104, Problem Modeling The objective of this invention is to minimize the total energy consumption of the drone (including launch and mobility energy consumption) while maximizing V The sum of the safe speeds of all intelligent connected vehicles. Specifically, the total energy consumption of the drone. It is represented as: (11) in, Energy consumption coefficient per unit speed. Sum of safe speeds. for: (12) By optimizing the base station's transmission power, the drone's transmission power, and the drone's trajectory, the multi-objective optimization problem is modeled as follows: (13) in, This refers to the maximum transmit power of the base station; and These are two constants; This is a weighting coefficient used to balance energy consumption and safe rate. Let be the trace of the covariance matrix. In P1, constraints C1 and C2 define the transmit power ranges of the base station and the UAV, respectively. Constraints C3 and C4 limit the flight speed and flight area of ​​the UAV, respectively. Constraints C5 and C6 indicate that the channel estimation error is bounded. The modeled multi-objective optimization problem P1 optimizes the network parameters. , and Secure and energy-efficient communication for low-altitude intelligent vehicle networking was achieved under fast time-varying channel conditions.

[0052] S2. An alternating optimization framework is designed to address the high-dimensional coupled optimization problem by decoupling the problem into three sub-problems: base station transmit power control, UAV transmit power control, and UAV trajectory optimization. Newton's iterative method is used to optimize base station power, model predictive control enables real-time power allocation for the UAV, and a target-shooting method combined with gradient descent optimizes the trajectory, ensuring that each sub-problem converges to a local optimum within the feasible region. S201, Base Station Transmit Power Control At this stage, given and ,optimization To maximize the sum of safe rates It should be noted that in this case, It has been determined that the original objective is equivalent to maximizing According to equation (8), the first vSignal-to-interference-plus-noise ratio (SINR) of an intelligent connected vehicle for: (14) Will Substitute into equation (14). It was further rewritten as: (15) in, Then, the legal communication rate. It was further rewritten as: (16) In addition, Substitute into formula (9), eavesdropping rate It was further rewritten as: (17) According to equation (10), the secure communication rate It was further rewritten as: (18) After merging constant terms, the secure communication rate Simplified to: (19) in, , , , .

[0053] Therefore, the sum of safe rates for: (20) To maximize ,right Seeking information about Taking the first derivative of and setting it to zero, we get: (twenty one) Let the nonlinear equation be: Because the objective function In the interval The solution is continuously differentiable and bounded on the above surface, therefore Newton's method is used: (twenty two) in, for The derivative of . Repeat the above process until convergence.

[0054] Finally, the optimal base station transmit power control can be obtained. .

[0055] S202, UAV Transmission Power Control Similarly, given and ,optimization To maximize the sum of safe rates For ease of processing, let , , , It is represented as: (twenty three) Then, the eavesdropping rate Can be rewritten as In this form. Based on this, It is represented as: (twenty four) right The first derivative is calculated and set to zero, then solved using Newton's method. Furthermore, to address the real-time requirements of fast time-varying channels, model predictive control (MPC) is employed for online optimization. (Predicting the future...) Channel state over time (i.e.) and ),in For time intervals, N To predict the time domain length, we then solve the following finite-time optimal control problem.

[0056] (25) in, and These are the weighting coefficients for the channel estimation error. The optimal power strategy is obtained by solving this problem using a convex optimization solver (such as CVX). Furthermore, the power at the initial moment is expressed as... The prediction and optimization process is repeated in the next moment to achieve rolling adjustments.

[0057] S203, UAV trajectory optimization Given and Optimize drone flight paths and satisfy speed constraints and regional constraints As mentioned above, the objective function It is further expressed as: (26) Equation (26) is essentially The implicit function, and the channel gain and Coupling. The trajectory optimization is transformed into a continuous-time optimal control problem, with the state vector defined as follows: The control input (i.e., the velocity component) is The following state equations must be satisfied: (27) in, for The derivative. Due to the initial position. Given and terminal position Since it is free (without a specified endpoint), the objective function can be rewritten as: (28) in, This is the corresponding Lagrangian function. Then, the Pontryagin maximal principle (PMP) is used to solve the optimal control problem. The Hamiltonian function is constructed. ,have to: (29) in, Let be the adjoint variables. According to Pontryagin's maximum principle, the adjoint variables must satisfy the following adjoint equation: (30) in, for right The gradient. Therefore, the costate equation is: (31) and (32) Due to the terminal location According to Pontryagin's maximum principle, the free terminal costate variable must satisfy the following: (33) The key step in the above process is calculation. .However, The expression contains higher-order terms and a random phase. Directly solving the costate equation analytically is extremely difficult. Therefore, a shooting method combined with gradient descent is used to obtain the optimal UAV trajectory optimization strategy. .

[0058] S3. Verify algorithm performance. This invention theoretically analyzes the computational complexity and convergence of the algorithm, proves its scalability in large-scale scenarios, and ensures stable communication performance in complex environments.

[0059] S301, Computational Complexity A single iteration of the algorithm comprises three sub-problems: base station power optimization, UAV power optimization, and trajectory optimization. The complexity analysis of each sub-problem is as follows: 1) Base station transmit power control: The nonlinear equation (i.e., equation (21)) is solved using the Newton-Raphson iteration method. Each iteration requires calculating the first and second derivatives of the objective function, which involves... V The channel parameters of the vehicle. The complexity of calculating the derivative for a single intelligent connected vehicle is... Therefore, the complexity of a single Newton iteration is . Suppose that Newton's method requires... In the next iteration, the complexity of the base station transmit power control problem is: .

[0060] 2) UAV transmit power control: Model predictive control (MPC) is used to solve for optimal control in the finite-time domain. The prediction time domain length is... N Each time step needs to process N Step state transition and V The safe speed of the vehicle is calculated. Since this invention uses a convex optimization solver to handle constrained optimization problems, the complexity of the UAV launch power control problem is... .

[0061] 3) UAV trajectory optimization: Optimal control is solved using a target practice method combined with gradient descent. Each target practice requires forward integration of the costate equation and backward gradient calculation, with a complexity of O(n log n). The number of iterations for gradient descent is set to... The complexity of the UAV trajectory optimization problem is then... .

[0062] In summary, the total complexity of a single iteration is... The sum of the subproblems is represented as: (34) S302, Convergence The original problem is a high-dimensional nonconvex optimization problem, and the convergence of alternating optimization depends on the monotonicity and boundedness of the objective function. We define the Lyapunov function as the iterative objective function value. This invention needs to prove that it is monotonically decreasing and has a lower bound, eventually converging to the KKT point.

[0063] 1) The objective function is monotonically decreasing: First, fix... and ,optimization achievable Newton's law guarantees yes The local optimum, therefore Secondly, fixed and ,optimization achievable Similarly, we can obtain Based on this, fix and ,optimization achievable The shooting method and gradient descent make... In conclusion, due to It can be deduced that the objective function is monotonically decreasing.

[0064] 2) The objective function has a lower bound: In P1, the objective function consists of two parts: energy consumption and the safe rate. On the one hand, regarding energy consumption, we can obtain... The reason is that, On the other hand, for the safe rate, we can obtain... ,in This represents the upper limit of the channel capacity. Therefore, the objective function has a lower bound.

[0065] 3) Convergence to the KKT point: A monotonically decreasing sequence with a lower bound will converge. According to the alternation optimization theory, if each subproblem converges to a local optimum, the iteration will eventually converge to the KKT point (the point that satisfies the objective function gradient being zero and the constraint conditions). For non-convex problems, this point is a local optimum or a saddle point, but in practice, through proper initialization (such as choosing an initial trajectory and power close to the optimum), it usually converges to a local optimum.

[0066] In another embodiment of the present invention, a joint optimization system for energy efficiency and safety rate of low-altitude intelligent vehicle network is provided. This system can be used to implement the above-mentioned joint optimization method for energy efficiency and safety rate of low-altitude intelligent vehicle network. Specifically, the joint optimization system for energy efficiency and safety rate of low-altitude intelligent vehicle network includes a construction module and a solution module.

[0067] The module is used to build an integrated air-ground collaboration model, establish a three-layer communication architecture including a remote ground base station, a drone as a relay node, and multiple intelligent connected vehicles. It characterizes the fast time-varying channel characteristics based on a geometric random channel model and builds a secure communication model based on Shannon's secrecy theorem. With the goal of minimizing the total energy consumption of the drone and maximizing the sum of the secure rates of all intelligent connected vehicles, a multi-constraint optimization problem is established, in which the optimization variables include the base station's transmission power, the drone's transmission power, and the drone's three-dimensional flight trajectory. The solution module is used to design an alternating optimization solution framework, decoupling the multi-constraint optimization problem into a base station transmit power control subproblem, a UAV transmit power control subproblem, and a UAV trajectory optimization subproblem, and iteratively solving them until the convergence condition is met; wherein, in each iteration, the following is executed: With the UAV's transmission power and flight trajectory fixed, the base station's transmission power control subproblem is solved using the Newton-Raphson iteration method, and the base station's transmission power is updated. With the base station's transmission power and the UAV's flight trajectory fixed, the UAV's transmission power control subproblem is solved using the model predictive control method, and the UAV's transmission power is updated. With the base station's transmission power and the UAV's transmission power fixed, the UAV's flight trajectory optimization subproblem is solved using a combination of the target shooting method and the gradient descent method, and the UAV's flight trajectory is updated.

[0068] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment can be used in the operation of a joint optimization method for energy efficiency and safety speed in low-altitude intelligent vehicle networking, including: An integrated air-ground collaboration model is constructed, establishing a three-layer communication architecture comprising a remote ground base station, UAVs acting as relay nodes, and multiple intelligent connected vehicles. A geometrical stochastic channel model characterizes the fast time-varying channel characteristics, and a secure communication model is built based on Shannon's secrecy theorem. With the objectives of minimizing the total energy consumption of the UAVs and maximizing the sum of the secure speeds of all intelligent connected vehicles, a multi-constraint optimization problem is established. The optimization variables include the base station's transmit power, the UAV's transmit power, and the UAV's three-dimensional flight trajectory. An alternating optimization solution framework is designed to decouple the multi-constraint optimization problem into a base station transmit power control subproblem, a UAV transmit power control subproblem, and a UAV trajectory optimization subproblem, and these are iteratively solved until the convergence condition is met. In each iteration, the following optimizations are performed: With the UAV's transmit power and flight trajectory fixed, the base station transmit power control subproblem is solved using the Newton-Raphson iteration method, and the base station transmit power is updated; with the base station transmit power and UAV flight trajectory fixed, the UAV transmit power control subproblem is solved using model predictive control, and the UAV transmit power is updated; with the base station transmit power and UAV transmit power fixed, the UAV trajectory optimization subproblem is solved using a combination of target practice and gradient descent methods, and the UAV flight trajectory is updated.

[0069] Please see Figure 11 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the energy efficiency and safety rate joint optimization method for low-altitude intelligent vehicle networking in this embodiment. To avoid repetition, details are omitted here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the low-altitude intelligent vehicle networking energy efficiency and safety rate joint optimization system of this embodiment. To avoid repetition, details are omitted here.

[0070] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 11 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0071] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0072] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on the computer device 60.

[0073] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0074] Please see Figure 12 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0075] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0076] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0077] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0078] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0079] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0080] Example 4 This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0081] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0082] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0083] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the energy efficiency and safety rate joint optimization method for low-altitude intelligent vehicle networking in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: An integrated air-ground collaboration model is constructed, establishing a three-layer communication architecture comprising a remote ground base station, UAVs acting as relay nodes, and multiple intelligent connected vehicles. A geometrical stochastic channel model characterizes the fast time-varying channel characteristics, and a secure communication model is built based on Shannon's secrecy theorem. With the objectives of minimizing the total energy consumption of the UAVs and maximizing the sum of the secure speeds of all intelligent connected vehicles, a multi-constraint optimization problem is established. The optimization variables include the base station's transmit power, the UAV's transmit power, and the UAV's three-dimensional flight trajectory. An alternating optimization solution framework is designed to decouple the multi-constraint optimization problem into a base station transmit power control subproblem, a UAV transmit power control subproblem, and a UAV trajectory optimization subproblem, and these are iteratively solved until the convergence condition is met. In each iteration, the following optimizations are performed: With the UAV's transmit power and flight trajectory fixed, the base station transmit power control subproblem is solved using the Newton-Raphson iteration method, and the base station transmit power is updated; with the base station transmit power and UAV flight trajectory fixed, the UAV transmit power control subproblem is solved using model predictive control, and the UAV transmit power is updated; with the base station transmit power and UAV transmit power fixed, the UAV trajectory optimization subproblem is solved using a combination of target practice and gradient descent methods, and the UAV flight trajectory is updated.

[0084] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0085] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0086] The technical effects of the present invention will be described in detail below with reference to simulation.

[0087] This experiment evaluates the effectiveness of the joint optimization method and system for energy efficiency and safety speed in low-altitude intelligent vehicle networking through simulation experiments. The specific steps are as follows: The specific simulation parameters are as follows: The simulation scenario is set as a one-way highway, with vehicles traveling at a constant speed along the X-axis. The drone acts as a relay at [altitude missing]. Stratospheric flight. The base station is fixed at the coordinate origin. The number of vehicles is The initial positions are evenly distributed in The section has a travel speed of The drone's initial position is Maximum flight speed is The noise power is respectively and A geometrical random channel model is used to describe the fast time-varying characteristics. The Rician factor of the base station-UAV (BS-UAV) link is... The Rician factor for the UAV-Vehicle link is The number of multipath scatterers are respectively and The maximum transmit power of the base station is The maximum transmission power of the drone is Suppose there exists a stationary eavesdropper whose location is... The noise power is In addition, the number of Monte Carlo simulations was [number missing]. This is to eliminate the influence of random channels.

[0088] The existing mechanism is as follows: a) Scheme 1: This scheme assumes the UAV is fixed in its initial position. The base station and UAV transmit power use statically optimized values ​​(i.e., the average of the optimal power over the entire time period calculated offline). This scheme ignores the UAV's dynamic adjustment capability and the time-varying characteristics of the channel, and only uses them as a lower bound reference for performance.

[0089] b) Scheme 2: This scheme is based on a static channel model (ignoring Doppler shift and channel estimation error) and uses model predictive control (MPC) to optimize the UAV trajectory, with the goal of maximizing the average safe rate.

[0090] c) Option 3: This option fixes the drone's trajectory and uses an alternating optimization framework to jointly optimize the base station and drone's transmission power. Power adjustment improves the safety rate.

[0091] d) Scheme 4: This scheme adopts a static Rician channel model. By jointly optimizing the UAV trajectory, base station, and UAV power, the safe transmission performance of low-altitude intelligent vehicle-to-everything (V2X) network is maximized.

[0092] Please see Figure 3 , Figure 3 (a) illustrates the trend of the sum of safe rates as a function of the number of vehicles under different schemes. It can be observed that as the number of vehicles increases, the sum of safe rates for all schemes except Scheme 1 continuously rises. Notably, the proposed safe and energy-efficient communication scheme consistently outperforms the other four advanced schemes in terms of the sum of safe rates. Furthermore, Figure 3 (b) The relationship between the average safe rate and the number of vehicles under different schemes was compared. The results show that when the number of vehicles increases from 10 to 100, the average safe rate of the proposed scheme decreases from 3.25 bps / Hz to 2.02 bps / Hz, a decrease of 38%.

[0093] In comparison, the performance reductions for Schemes 1, 2, 3, and 4 are as high as 99%, 56%, 53%, and 54%, respectively. The reasons for this performance difference are as follows.

[0094] In Option 1, because the drone is fixed in the initial position, it cannot dynamically adjust according to changes in vehicle density, resulting in a sharp drop in speed.

[0095] Furthermore, while Schemes 2, 3, and 4 optimize the multi-dimensional resource allocation of the network to some extent, they fail to achieve joint optimization of trajectory and power, thus failing to effectively suppress co-channel interference and consequently affecting system communication performance. In conclusion, the simulation results verify that in low-altitude intelligent vehicle-to-everything (V2X) networks, jointly optimizing the power control of UAVs and base stations, as well as the UAV trajectory design, can significantly improve the network's secure communication rate.

[0096] Please see Figure 4 , Figure 4(a) and Figure 4 (b) The mean and variance of the UAV's energy consumption per unit safe rate are shown for different schemes. As can be seen from the figure, the proposed secure and energy-efficient communication scheme is significantly superior to other comparative schemes in terms of energy consumption, with a mean energy consumption per unit safe rate of only 5.1 × 10⁻⁶. -3 The energy consumption per unit safe rate (J / (bps / Hz)) is reduced by 47% and 27% compared to Scheme 1 and Scheme 4, respectively. Theoretically, energy consumption per unit safe rate reflects the balance between system safety performance and energy consumption. The main reason why the proposed scheme achieves better energy consumption performance is that it simultaneously considers the joint optimization of UAV trajectory planning and power allocation. On the one hand, dynamically adjusting the UAV trajectory can avoid redundant flight paths, thereby reducing unnecessary energy consumption. On the other hand, optimizing power allocation allows the base station to undertake part of the transmission task in appropriate time slots, avoiding prolonged high-power operation of the UAV.

[0097] In contrast, Scheme 1, employing a fixed trajectory and constant high-power transmission strategy, exhibits significant ineffective energy consumption, resulting in low overall energy utilization efficiency. While Scheme 2 optimizes the UAV trajectory, it lacks adaptive power adjustment, forcing the UAV to compensate for channel loss through high-power transmission, leading to higher energy consumption. Scheme 3 outperforms Schemes 1 and 2 in power optimization, but lacks trajectory optimization, and power allocation cannot match channel conditions in real time, limiting its energy-saving potential. Scheme 4, although considering resource allocation to some extent, fails to adequately account for the impact of rapidly changing channels on system performance, resulting in energy waste. Therefore, the proposed secure and energy-efficient communication scheme achieves improved communication performance and energy efficiency through joint optimization of UAV trajectory and power control. The proposed scheme can adaptively adjust transmission power and flight path in dynamic channel environments, effectively avoiding energy loss caused by over-transmission and ineffective trajectories, fully validating the effectiveness of the joint optimization strategy in improving the energy efficiency of low-altitude intelligent vehicle-to-everything (V2X) networks.

[0098] Please see Figure 5 , Figure 5 (a) and Figure 5(b) The impact of base station transmit power and UAV transmit power on the sum of safe rates under different schemes is shown. To verify the effectiveness of the power control mechanism, the maximum transmit power of the base station and UAV were adjusted in the simulation to observe the changes in the sum of safe rates. The results show that when the maximum transmit power of the base station is increased from 30dBm to 50dBm, the sum of safe rates of the proposed safe and energy-efficient communication scheme increases from 131bps / Hz to 150bps / Hz. This improvement is attributed to the fact that the enhanced base station power significantly improves the received signal-to-interference-plus-noise ratio (SINR) at the UAV end, thereby effectively improving the safe communication performance of the low-altitude intelligent vehicle network. Further analysis shows that the proposed scheme exhibits excellent adaptability in terms of base station and UAV power control. When the maximum transmit power of the base station or UAV gradually increases, the sum of safe rates of the low-altitude intelligent vehicle network increases significantly in the initial stage, but then tends to stabilize and remain at a stable level. This indicates that once the transmit power is increased to a level that can fully compensate for channel loss, further increasing the power is unlikely to bring about a significant improvement in safe rates, but will instead lead to energy waste. Unlike other schemes, the proposed secure and energy-efficient communication scheme does not independently optimize the power of the base station or the UAV, but rather achieves coordinated scheduling of the two through joint optimization. This strategy effectively suppresses redundant energy consumption while maximizing the safe data rate, thus achieving a good balance between performance and energy efficiency. As mentioned above, simulation results verify the advantages of optimized power control in improving the secure communication performance and energy utilization efficiency of low-altitude intelligent vehicle-to-everything (V2X) networks.

[0099] Please see Figure 6 , Figure 6The trend of the sum of secure rates under different schemes as simulation runtime is shown. As simulation runtime increases, the sum of secure rates of the proposed secure and energy-efficient communication scheme remains stable between 140 and 143 bps / Hz, significantly higher than other comparative schemes. In contrast, the sum of secure rates in Scheme 1 rapidly decreases from an initial 80 bps / Hz to 59.5 bps / Hz. This is because as the vehicle moves away from the initial position of the UAV, the line-of-sight link attenuation between the base station and the UAV, and between the UAV and the vehicle, gradually intensifies. Under these circumstances, static power allocation cannot effectively compensate for channel loss, leading to a continuous decrease in secure rates. Furthermore, Scheme 2, based on static power allocation, cannot adapt to dynamic changes in channel quality. When the UAV's position changes, if the power is insufficient to compensate for channel attenuation, the signal-to-interference-plus-noise ratio (SNR) of the legitimate link cannot be improved, thus maintaining the sum of secure rates only between 105 and 110.5 bps / Hz. Although Scheme 3 optimizes power allocation, due to its fixed trajectory, it fails to adjust channel quality in real time, resulting in a relatively slow rate increase. Finally, Scheme 4 uses a static channel model, which does not consider the Doppler frequency shift and channel estimation errors caused by vehicle movement. This results in a lower optimization score than the proposed secure and energy-efficient communication scheme. In contrast, the proposed scheme, through its designed alternating optimization framework, can adjust the UAV's trajectory and power allocation according to the real-time channel conditions, thereby maintaining a high and stable secure rate throughout the entire communication cycle.

[0100] Please see Figure 7 , Figure 7 The impact of channel estimation error on the sum of safe rates is illustrated in the figure. As the channel estimation error increases, the sum of safe rates of the proposed secure and energy-efficient communication scheme decreases by only about 2%, indicating that the proposed scheme has good robustness. This is because the proposed scheme considers channel uncertainty in both channel modeling and optimization algorithm design. Specifically, the geometrical random channel model, by introducing scatterer gain and Rician factor, can quantify the channel estimation error, thus more realistically reflecting the dynamic changes in channel state in the low-altitude intelligent vehicle-to-everything (V2X) environment. Simultaneously, in the alternating optimization framework, the gradient descent process incorporates the channel estimation error into the optimization constraint, enabling the algorithm to maintain a stable optimization direction even with estimation bias. Therefore, even under conditions of inaccurate channel estimation, the proposed scheme can still maintain a high sum of safe rates by dynamically adjusting the UAV trajectory and power allocation. It is worth noting that channel estimation error is unavoidable in high-speed moving V2X environments. The proposed secure and energy-efficient communication scheme maintains stable performance under such complex conditions, demonstrating superior robustness and practical value compared to the other four advanced schemes.

[0101] Please see Figure 8 , Figure 8The impact of vehicle speed on the sum of safe rates is demonstrated. It can be observed that the sum of safe rates for all schemes decreases as vehicle speed increases. This is mainly due to the increased estimation error of channel state information caused by high-speed vehicle movement. In low-altitude intelligent vehicle networks, the safe rate is highly dependent on the accurate estimation of channel state information. As vehicle speed increases, the instantaneous changes in the channel become more drastic, and its coherence time shortens significantly. The rapid fluctuations in channel characteristics within a short period cause lag or bias in the estimation of channel state information, leading to a decline in system performance. However, compared with other schemes, the proposed safe and energy-efficient communication scheme exhibits stronger robustness under high vehicle speed conditions. When the vehicle speed increases from 30 km / h to 120 km / h, the sum of safe rates of the proposed scheme decreases by only about 19%, significantly lower than the decreases of other schemes (Scheme 1: 65%, Scheme 2: 38%, Scheme 3: 31%, Scheme 4: 37%). This result verifies the superior performance of the proposed scheme in fast time-varying channel environments. Its fast time-varying channel modeling and track-power joint optimization mechanism can track channel changes in real time and dynamically adjust the UAV strategy. Under these circumstances, we can effectively mitigate the impact of channel fading and estimation errors caused by high-speed movement.

[0102] Please see Figure 9 , Figure 9The impact of UAV speed on the sum of safe rates is illustrated in the figure. As the UAV speed increases from 20 km / h to 120 km / h, the sum of safe rates of the proposed safe and energy-efficient communication scheme exhibits a trend of first increasing and then decreasing. At low speeds (e.g., 20 km / h), the UAV's maneuverability is insufficient. Its trajectory adjustment speed lags behind the vehicle's movement, making it difficult for the UAV to adapt to changes in channel conditions in a timely manner, thus affecting the safe rate of the low-altitude intelligent vehicle network. As the UAV speed increases, its position updates become more flexible, enabling it to better track vehicle trajectories, thereby improving the channel quality of the base station-UAV-vehicle link. The safe rate is thus improved. When the UAV speed reaches approximately 80 km / h, the safe communication performance of the low-altitude intelligent vehicle network reaches its optimal level. At this speed, the UAV possesses good tracking capabilities while maintaining efficient communication performance under controllable energy consumption. However, when the UAV speed continues to increase (above 80 km / h), the safe communication performance of the low-altitude intelligent vehicle network actually decreases. This is because excessively high flight speeds lead to frequent attitude adjustments and trajectory deviations. In this scenario, channel estimation errors and control energy consumption are exacerbated, ultimately leading to a decrease in the safe rate. Therefore, UAV speed has a significant nonlinear impact on the safe rate of low-altitude intelligent vehicle networks. Future research can further explore the dynamic balance between UAV speed, energy consumption, and safe rate. Establishing a speed adaptive control mechanism can achieve optimal speed allocation and trajectory planning for UAVs under different communication scenarios, further improving the safe communication performance and energy utilization efficiency of low-altitude intelligent vehicle networks.

[0103] Please see Figure 10 , Figure 10 The convergence process of the objective function under different initial point settings is demonstrated. It can be observed that regardless of the initial conditions, the proposed secure and energy-efficient communication scheme converges to a stable value after approximately 12-15 iterations. This result verifies the effectiveness and stability of the algorithm design and is consistent with the theoretical convergence analysis in Section 3.4.2. This scheme is based on an alternating optimization framework, decomposing the original high-dimensional coupled optimization problem into three interrelated but separable sub-problems: base station power optimization, UAV power optimization, and UAV trajectory planning. Each sub-problem employs a specific solution strategy tailored to its problem structure, enabling it to converge to a local optimum within its corresponding feasible region. Once the three sub-problems have converged, the alternating update process obtains a feasible solution to the overall optimization problem, ultimately achieving stable convergence at the system level. This indicates that the proposed algorithm not only has a strong convergence speed but also maintains consistent stability and generalization ability in multi-initial-point scenarios, thus ensuring good feasibility in complex and dynamic vehicular network environments. Future work could incorporate convex relaxation techniques to explore their potential for expansion in larger-scale systems or multi-drone collaborative scenarios.

[0104] This invention addresses the challenges of secure communication and energy efficiency optimization in low-altitude intelligent vehicle-to-everything (V2X) networks operating in high-speed and dynamic channel environments, proposing a joint power and trajectory optimization solution. By constructing a multi-objective optimization model and combining it with an alternating optimization framework, a significant improvement in system energy efficiency and safe speed is achieved while ensuring communication security and reliability, providing key technical support for the practical deployment of low-altitude intelligent V2X networks.

[0105] This invention achieves secure communication optimization in complex dynamic environments by constructing an integrated air-ground cooperation model. This model integrates a geometrical stochastic channel model and Shannon's secrecy theorem, incorporating UAV trajectory, base station, UAV power, and security rate into a unified optimization framework, characterizing the impact of rapidly time-varying channel characteristics on communication performance. By quantifying channel attenuation and fluctuations, a multi-constraint optimization problem is proposed, effectively balancing energy efficiency and security requirements, and providing a scientific and efficient scheduling strategy for practical scenarios.

[0106] The alternating optimization framework designed in this invention decouples a high-dimensional coupled problem into three separable subproblems, which are solved using Newton's iteration method, model predictive control, and target shooting method, respectively. This framework, by combining centralized optimization with distributed execution, significantly improves the algorithm's adaptability and real-time performance in dynamic channel environments. Within this framework, the agent can autonomously learn and adjust its strategy, achieving coordinated optimization of UAV trajectory and power, and greatly improving the system's stability and communication efficiency in complex scenarios.

[0107] This invention provides an in-depth theoretical analysis of the algorithm's computational complexity and convergence, proving its stability and scalability under different initial conditions. This analysis provides a theoretical basis for the large-scale application of the algorithm, ensuring the efficient operation of this invention in low-altitude intelligent vehicle networks.

[0108] In summary, this invention presents a method and system for joint optimization of energy efficiency and safe speed in low-altitude intelligent vehicle-to-everything (V2X) networks. It constructs an integrated air-ground collaborative architecture comprising a remote base station, a UAV relay, and multiple vehicles. This architecture accurately characterizes the rapidly changing time-varying characteristics using a geometrical random channel model, and employs an alternating optimization framework to iteratively solve for the UAV trajectory, base station, and UAV transmit power. Specifically, it implements base station power optimization based on Newton's iteration method, dynamic power allocation for UAVs under a model predictive control framework, and UAV trajectory planning combining a target-shooting method and gradient descent. The aim is to minimize the total energy consumption of UAVs while maximizing the sum of safe speeds for intelligent connected vehicles. This effectively solves the coverage blind spots and rapidly changing channel adaptation problems of traditional V2X networks in high-speed mobile scenarios, providing theoretical support and technical implementation solutions for safe and efficient communication in low-altitude intelligent V2X networks.

Claims

1. A low-altitude intelligent vehicle networking energy efficiency and safety rate joint optimization method, characterized in that, The method comprises the following steps: An air-ground integrated cooperation model is constructed, a three-layer communication architecture including a remote ground base station, a UAV as a relay node and multiple intelligent connected vehicles is established, a fast time-varying channel characteristic is described based on a geometric random channel model, a secure communication model is constructed according to a Shannon secrecy theorem, a multi-constraint optimization problem is established, in which optimization variables include a transmission power of the base station, a transmission power of the UAV and a three-dimensional flight trajectory of the UAV, and the sum of the secure rates of all the intelligent connected vehicles is maximized and the total energy consumption of the UAV is minimized as an objective; An alternating optimization solution framework is designed, the multi-constraint optimization problem is decoupled into a base station transmission power control sub-problem, a UAV transmission power control sub-problem and a UAV trajectory optimization sub-problem, and iterative solutions are performed until a convergence condition is met; In each iteration, the following optimizations are performed: The base station transmission power control sub-problem is solved by using a Newton iteration method to update the transmission power of the base station, the UAV transmission power control sub-problem is solved by using a model prediction control method to update the transmission power of the UAV, and the UAV trajectory optimization sub-problem is solved by combining a shooting method and a gradient descent method to update the flight trajectory of the UAV, with the transmission power of the base station and the transmission power of the UAV being fixed. 2.The low-altitude intelligent connected vehicle energy efficiency and safety rate joint optimization method according to claim 1, characterized in that, The air-ground integrated cooperation model specifically comprises: A two-hop relay transmission mode of base station-UAV-vehicle is adopted, and the UAV relays signals from the base station by using an amplify-and-forward protocol; For the first hop base station-UAV link and the second hop UAV-vehicle link, a Rician distribution is used to model the air-to-ground channel, and a minimum mean square error estimation method is used to estimate channel state information from received signals; Based on the channel estimation result, a legal communication rate and an eavesdropping rate are calculated, and a secure communication rate is defined as a difference between the legal communication rate and the eavesdropping rate. 3.The low-altitude intelligent connected vehicle energy efficiency and safety rate joint optimization method of claim 1, wherein, The constraint conditions of the multi-constraint optimization problem include: The transmission power of the base station and the transmission power of the UAV do not exceed their maximum transmission powers respectively; The flight speed of the UAV does not exceed its maximum flight speed; The flight position of the UAV is located in a preset flight region; Channel estimation errors are bounded. 4.The low-altitude intelligent connected vehicle energy efficiency and safety rate joint optimization method of claim 1, wherein, A non-linear equation of the base station transmission power control sub-problem is solved by using a Newton method.

5. The low-altitude intelligent connected vehicle energy efficiency and safety rate joint optimization method of claim 1, wherein, The model prediction control method comprises: Channel state information in a future finite time domain is predicted, and a finite time optimal control problem is constructed and solved based on the predicted channel state information to obtain a transmission power control strategy of the UAV at the current time. 6.The low-altitude intelligent connected vehicle energy efficiency and safety rate joint optimization method of claim 1, wherein, The UAV trajectory optimization sub-problem is converted into a continuous time optimal control problem and solved based on a Pontryagin maximum principle.

7. The low-altitude intelligent connected vehicle energy efficiency and safety rate joint optimization method according to claim 6, characterized in that, The co-state equation obtained based on the Pontryagin maximum principle is solved by using a shooting method combined with a gradient descent method to obtain an optimal UAV trajectory. 8.The low-altitude intelligent connected vehicle energy efficiency and safety rate joint optimization method of claim 1, wherein, The intelligent connected vehicles travel at a constant speed along a one-way highway, and their positions are functions of an initial position, a travel speed and time; the UAV flies at a preset height in the stratosphere, and a flight region is a preset three-dimensional space range.

9. The low-altitude intelligent connected vehicle energy efficiency and safety rate joint optimization method according to any one of claims 1 to 8, characterized in that, In the iterative solving process, convergence is determined by judging whether the change of the objective function value of two consecutive iterations is less than a preset threshold.

10. A low-altitude intelligent vehicle networking energy efficiency and safety rate joint optimization system, characterized in that, The method comprises the following steps: The construction module is configured to construct an integrated air-ground cooperation model, establish a three-layer communication architecture including a remote ground base station, a UAV serving as a relay node, and multiple intelligent connected vehicles, depict fast time-varying channel characteristics based on a geometric random channel model, and construct a secure communication model according to the Shannon secrecy theorem, so as to minimize the total energy consumption of the UAV and maximize the sum of the safety rates of all intelligent connected vehicles, and establish a multi-constraint optimization problem, wherein the optimization variables include the transmission power of the base station, the transmission power of the UAV, and the three-dimensional flight trajectory of the UAV. The solving module is configured to design an alternating optimization solving framework, decouple the multi-constraint optimization problem into a base station transmission power control sub-problem, a UAV transmission power control sub-problem, and a UAV trajectory optimization sub-problem, and iteratively solve the sub-problems until a convergence condition is met. In each iteration, the transmission power and flight trajectory of the UAV are fixed, the base station transmission power control sub-problem is solved by using a Newton iteration method to update the transmission power of the base station, the transmission power of the UAV is fixed and the UAV trajectory optimization sub-problem is solved by using a model predictive control method to update the transmission power of the UAV, and the transmission power of the base station and the transmission power of the UAV are fixed, and the UAV trajectory optimization sub-problem is solved by combining a shooting method and a gradient descent method to update the flight trajectory of the UAV.

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