Reflection surface assisted unmanned aerial vehicle inland river communication channel model construction method and system

Abstract

The invention belongs to the technical field of 6G communication, and discloses a reflection surface assisted unmanned aerial vehicle inland river communication channel model construction method and system.The method comprises the steps that firstly, a geometric model is used for simulating an inland river scene, the influence of water surface fluctuation is considered, and the influence of the waveguide effect is considered; and the binary Markov process is utilized to simulate the birth and death process of the multipath component. The establishment of the model can provide a reference value for deployment and evaluation of an intelligent reflection surface assisted unmanned aerial vehicle ship inland river communication system, and provides a reference value for the deployment position, deployment angle and size of the intelligent reflection surface. The method has certain evaluation significance on the deployment of the intelligent reflecting surface and the influence of the number of units on the channel in the scene.

Description

Technical Field

[0001] The present invention belongs to the field of 6G communication technology, and in particular relates to a method and system for constructing a reflective surface-assisted UAV ship inland river communication channel model. Background Art

[0002] With the development of 6G technology, spectrum scarcity and the demand for high transmission volume are increasing, but the base station signal is easily blocked by obstacles such as walls due to poor signal diffraction. Effective deployment of smart reflective surfaces as controllable surfaces can enhance network capacity and coverage, while also creating opportunities for scenarios such as green communications, auxiliary electromagnetic wave environmental perception, and high-precision positioning.

[0003] The main advantage of using smart reflective surfaces is that they are so-called passive components, which are extremely low-power electronic devices. They represent an environmentally friendly and low-cost solution that can be deployed on a variety of objects and will be used as reconfigurable reflectors or transceivers to achieve large-scale transmission operations.

[0004] At present, most of the research on smart reflective surfaces is focused on assisting vehicle-to-vehicle communication and drone-to-vehicle communication systems. However, as the future 6G technology requires the construction of air-ground integration, there are still many potential scenarios for smart reflective surfaces to be explored. In addition, although drone communication is currently widely used, research on drone-to-ship communication systems in inland environments is still scarce.

[0005] Through the above analysis, the problems and defects of the prior art are as follows:

[0006] Although drone communications are now widely used, research on drone-ship communication systems in inland environments is still scarce. Summary of the invention

[0007] In view of the problems existing in the prior art, the present invention provides a method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface.

[0008] The present invention is implemented as follows: a method for constructing a reflective surface-assisted UAV ship inland river communication channel model comprises:

[0009] In step 1, smart reflective surfaces will be deployed on buildings and movable floating platforms to dynamically adjust the reflection angle according to the location of drones and ships;

[0010] Step 2: Optimize the configuration of the MIMO antenna and make adaptive adjustments based on real-time environmental conditions;

[0011] Step 3: Introduce a dynamic multipath tracking algorithm to analyze and track the changes of various multipath signals from the transmitter to the receiver in real time, and use machine learning technology to optimize;

[0012] Step 4: Install environmental perception sensors on the drone and ship respectively to monitor the surrounding obstacles and reflective environment in real time, adjust the angle of the smart reflective surface and the configuration of the MIMO antenna, and optimize the signal path;

[0013] Step 5: Use the improved channel model to simulate and predict signal transmission conditions by considering complex environmental factors such as wind speed, water surface fluctuations, and reflection paths between tall buildings; and calibrate using actual measurement data.

[0014] Furthermore, the model comprehensively considers various multipath components, including water surface reflection, scattering from distant scatterers, scattering from local scatterers, and reflection from smart reflective surfaces.

[0015] Furthermore, a scattering model is constructed with the ship where the receiver is deployed in the inland river as the center;

[0016] A UAV transmitter equipped with a multiple-input multiple-output antenna flies in the urban area and communicates with a ship receiver equipped with a multiple-input multiple-output antenna in the inland river.

[0017] Furthermore, an intelligent reflective surface is installed on the buildings near the UAV; the far scattering environment of the tall buildings on both sides is simulated as a large cylinder centered on the receiver.

[0018] Furthermore, the local scattering environment composed of other ships near the receiver is simulated as a small cylinder with the receiver as the center.

[0019] Furthermore, a total of six multipath components are considered in the model: the multipath component transmitted from the transmitter and reflected by the water surface to reach the receiving end; the multipath component transmitted from the transmitter and scattered by far scatterers to reach the receiving end; the multipath component transmitted from the transmitter and scattered by local scatterers to reach the receiving end; the multipath component transmitted from the transmitter and reflected by the smart reflecting surface to reach the receiving end; the multipath component transmitted from the transmitter, reflected by the smart reflecting surface, scattered by far scatterers and then reached the receiving end; the multipath component transmitted from the transmitter, reflected by the smart reflecting surface, scattered by local scatterers and then reached the receiving end; the component transmitted from the transmitter, reflected by the smart reflecting surface and then reflected by the water surface to reach the receiving end.

[0020] Another object of the present invention is to provide a reflective surface-assisted UAV ship inland river communication channel model construction system comprising:

[0021] A deployment module that deploys smart reflective surfaces on buildings and movable floating platforms, dynamically adjusting the reflective angle based on the location of drones and ships;

[0022] An optimization module is used to optimize the configuration of MIMO antennas and make adaptive adjustments based on real-time environmental conditions;

[0023] Tracking module, which is used to introduce dynamic multipath tracking algorithm, analyze and track the changes of various multipath signals from transmitter to receiver in real time, and optimize them using machine learning technology;

[0024] Monitoring module, which is used to install environmental perception sensors on drones and ships respectively, to monitor surrounding obstacles and reflective environments in real time, adjust the angle of the smart reflective surface and the configuration of the MIMO antenna, and optimize the signal path;

[0025] The simulation prediction module is used to use an improved channel model to simulate and predict signal transmission conditions by taking into account complex environmental factors such as wind speed, water surface fluctuations, and reflection paths between high-rise buildings; and to calibrate using actual measurement data.

[0026] Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface.

[0027] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface.

[0028] Another object of the present invention is to provide an information data processing terminal, which is used to implement the reflective surface-assisted drone-ship inland river communication channel model construction system.

[0029] Another object of the present invention is to provide a dynamic multipath channel modeling method and system suitable for low-altitude communication environment, characterized in that the method and system include the following steps and modules:

[0030] Water surface fluctuation simulation module: Assume the horizontal position coordinates of the ship at a specific time point, and simulate the changes in the three-dimensional coordinates of the ship caused by water surface fluctuations. By setting parameters such as the fluctuation amplitude, the number of water waves, and the random offset angle of water waves, the ship position is updated in real time to affect the propagation path of the multipath component.

[0031] Waveguide effect simulation module: In low-altitude communications, the waveguide effect is generated during signal propagation due to the atmospheric refraction effect. The propagation channel between the smart reflective surface and the scatterer is abstracted as a rectangular cross-section waveguide model. Especially when the water vapor concentration decreases or the temperature increases, multiple reflections in the vertical direction are considered to simulate the changes in the propagation path caused by signal refraction.

[0032] Multipath channel dynamic characteristics simulation module: Use the two-state time Markov process to simulate the dynamic transformation of multipath components. For the non-stationary channel model caused by the movement of drones, ships and scatterers, establish a survival probability model of multipath components, and dynamically simulate the change process of multipath components through far scatterers, smart reflective surfaces and water surface reflections. This module combines factors such as the speed of the moving body, the position of the scatterer and the smart reflective surface to predict the birth and death process of the multipath channel and enhance the robustness and stability of the communication system.

[0033] This method and system comprehensively consider the water surface fluctuations, waveguide effects and dynamic characteristics of multipath channels, and are suitable for modeling low-altitude communication systems in complex environments. In particular, under the condition of relative motion between ships and drones, the method and system can dynamically calculate and adjust the propagation path and signal strength of multipath channels.

[0034] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0035] First, this solution aims to improve the communication effect between drones and ships in inland rivers by introducing intelligent reflective surfaces, optimizing MIMO antenna configuration, applying dynamic multipath tracking algorithms, deploying environmental perception sensors, and adopting improved channel models to build an efficient and reliable scattering communication model. This model will fully consider multipath components to ensure stable signal transmission in various complex environments.

[0036] First, the invention deploys intelligent reflective surfaces on buildings and movable floating platforms, enabling them to dynamically adjust the reflection angle according to the location of drones and ships to enhance signal transmission. At the same time, the configuration of MIMO antennas is optimized so that they can be adaptively adjusted according to real-time environmental conditions to improve the efficiency of signal reception and transmission.

[0037] In order to further improve the reliability and transmission speed of the signal, the present invention will introduce a dynamic multipath tracking algorithm to analyze and track the changes of various multipath signals from the transmitter to the receiver in real time, and use machine learning technology to optimize them. In addition, the present invention will also install environmental perception sensors on drones and ships to monitor the surrounding obstacles and reflective environment in real time, so as to adjust the angle of the intelligent reflective surface and the configuration of the MIMO antenna in time to optimize the signal path.

[0038] Finally, the present invention will adopt an improved channel model to consider complex environmental factors such as wind speed, water surface fluctuations, and reflection paths between high-rise buildings to more accurately simulate and predict signal transmission conditions. Calibration will be performed using actual measurement data. At the same time, the model of the present invention will fully consider a variety of multipath components, including water surface reflection, scattering from far scatterers, scattering from local scatterers, and reflection from smart reflective surfaces, to ensure stable and efficient communication in various complex environments.

[0039] Second, the existing low-altitude communication systems, especially in the ocean or offshore environment, often cause complex and unstable multipath effects of the channel due to water surface reflection, changing meteorological conditions and atmospheric refraction, which leads to problems such as signal attenuation, increased interference and communication interruption. This poses a major challenge to the reliability of communication between drones and ships, especially in dynamic environments.

[0040] 2) Lack of dynamic adaptability to complex environments:

[0041] Traditional communication systems usually do not take into account the dynamic impact of water surface fluctuations and changes in atmospheric conditions, and lack the ability to model and respond to multipath effects in complex environments in real time. This causes the system to perform poorly in a changing environment and is unable to ensure the stability and effectiveness of low-altitude communications.

[0042] Significant technological advancements:

[0043] 1) Accuracy and adaptability of dynamic multipath channel modeling:

[0044] The dynamic multipath channel modeling method proposed in the present invention can simulate the dynamic changes of water surface fluctuations, atmospheric refraction effects and multipath channels in real time, and accurately model the birth and death of multipath components through a two-state time Markov process. Compared with traditional methods, the present invention can more accurately predict and adjust communication paths, improve the stability and reliability of communication signals, especially in complex and changeable marine and offshore environments.

[0045] 2) Enhanced system robustness and anti-interference capabilities:

[0046] The present invention improves the robustness of low-altitude communication systems in complex environments by simulating the dynamic changes of waveguide effects and multipath components. The system can effectively cope with channel changes caused by the movement of ships, drones, and local scatterers, reducing signal interference and interruption. Compared with the prior art, the present invention significantly improves the anti-interference ability of communication in a dynamic environment, ensuring the continuity and accuracy of data transmission.

[0047] 3) Broad industrial application prospects:

[0048] The technical solution of the present invention is not only suitable for maritime search and rescue, offshore wind power stations and other fields, but can also be extended to other scenarios that require high-stability low-altitude communications, such as offshore oil platform monitoring, marine environment monitoring, drone logistics and other fields, and has broad industrial application value.

[0049] In summary, the present invention solves the problems of channel instability and insufficient adaptability in traditional low-altitude communication systems, thereby improving communication stability and enhancing system robustness in complex dynamic environments, and significantly promoting the application and development of low-altitude communication technology in actual industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface provided in an embodiment of the present invention.

[0051] Figure 2 It is a structural block diagram of a reflective surface-assisted UAV-ship inland river communication channel model building system provided in an embodiment of the present invention.

[0052] Figure 3 It is a geometric model diagram of a channel model from a UAV to a ship assisted by an intelligent reflective surface provided in an embodiment of the present invention.

[0053] Figure 4 This is a waveguide effect diagram provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] like Figure 1 As shown, a method for constructing a reflective surface-assisted UAV-ship inland river communication channel model provided by an embodiment of the present invention includes the following steps:

[0056] S101, which will deploy smart reflective surfaces on buildings and movable floating platforms, dynamically adjusting the reflection angle based on the location of drones and ships;

[0057] S102, optimizing the configuration of the MIMO antenna and performing adaptive adjustment according to real-time environmental conditions;

[0058] S103, introduces a dynamic multipath tracking algorithm to analyze and track the changes of various multipath signals from transmitter to receiver in real time, and uses machine learning technology for optimization;

[0059] S104, installing environmental perception sensors on the drone and the ship respectively, for real-time monitoring of surrounding obstacles and reflective environment, adjusting the angle of the smart reflective surface and the configuration of the MIMO antenna, and optimizing the signal path;

[0060] S105 uses an improved channel model to simulate and predict signal transmission conditions by taking into account complex environmental factors such as wind speed, water surface fluctuations, and reflection paths between tall buildings; and calibrates using actual measurement data.

[0061] The model provided by the embodiment of the present invention comprehensively considers various multipath components, including water surface reflection, scattering by far scatterers, scattering by local scatterers, and reflection by smart reflective surfaces.

[0062] The embodiment of the present invention provides a scattering model constructed with a ship on which a receiver is deployed in an inland river as the center;

[0063] A UAV transmitter equipped with a multiple-input multiple-output antenna flies in the urban area and communicates with a ship receiver equipped with a multiple-input multiple-output antenna in the inland river.

[0064] The embodiment of the present invention provides a method of installing an intelligent reflective surface on a building near a UAV; the far scattering environment of the tall buildings on both sides of the river is simulated as a large cylinder centered on a receiver.

[0065] The local scattering environment formed by other ships near the receiver provided in the embodiment of the present invention is simulated as a small cylinder with the receiver as the center.

[0066] A total of six multipath components are considered in the model provided by the embodiment of the present invention: a multipath component transmitted from the transmitter and reflected by the water surface to reach the receiving end; a multipath component transmitted from the transmitter and scattered by far scatterers to reach the receiving end; a multipath component transmitted from the transmitter and scattered by local scatterers to reach the receiving end; a multipath component transmitted from the transmitter and reflected by an intelligent reflecting surface to reach the receiving end; a multipath component transmitted from the transmitter and reflected by the intelligent reflecting surface and then scattered by far scatterers to reach the receiving end; a multipath component transmitted from the transmitter and reflected by the intelligent reflecting surface and then scattered by local scatterers to reach the receiving end; a component transmitted from the transmitter and reflected by the intelligent reflecting surface and then reflected by the water surface to reach the receiving end.

[0067] like Figure 2 As shown, a reflective surface-assisted UAV ship inland river communication channel model construction system provided by an embodiment of the present invention includes:

[0068] A deployment module that deploys smart reflective surfaces on buildings and movable floating platforms, dynamically adjusting the reflective angle based on the location of drones and ships;

[0069] An optimization module is used to optimize the configuration of MIMO antennas and make adaptive adjustments based on real-time environmental conditions;

[0070] Tracking module, which is used to introduce dynamic multipath tracking algorithm, analyze and track the changes of various multipath signals from transmitter to receiver in real time, and optimize them using machine learning technology;

[0071] Monitoring module, which is used to install environmental perception sensors on drones and ships respectively, to monitor surrounding obstacles and reflective environments in real time, adjust the angle of the smart reflective surface and the configuration of the MIMO antenna, and optimize the signal path;

[0072] The simulation prediction module is used to use an improved channel model to simulate and predict signal transmission conditions by taking into account complex environmental factors such as wind speed, water surface fluctuations, and reflection paths between high-rise buildings; and to calibrate using actual measurement data.

[0073] Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface.

[0074] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface.

[0075] Another object of the present invention is to provide an information data processing terminal, which is used to implement the reflective surface-assisted drone-ship inland river communication channel model construction system.

[0076] This scheme aims to improve the communication effect between drones and ships in inland rivers. By introducing intelligent reflective surfaces, optimizing MIMO antenna configuration, applying dynamic multipath tracking algorithms, deploying environmental perception sensors, and adopting improved channel models, an efficient and reliable scattering communication model is constructed. This model will fully consider multipath components to ensure stable transmission of signals in various complex environments.

[0077] First, the invention deploys intelligent reflective surfaces on buildings and movable floating platforms, enabling them to dynamically adjust the reflection angle according to the location of drones and ships to enhance signal transmission. At the same time, the configuration of MIMO antennas is optimized so that they can be adaptively adjusted according to real-time environmental conditions to improve the efficiency of signal reception and transmission.

[0078] In order to further improve the reliability and transmission speed of the signal, the present invention will introduce a dynamic multipath tracking algorithm to analyze and track the changes of various multipath signals from the transmitter to the receiver in real time, and use machine learning technology to optimize them. In addition, the present invention will also install environmental perception sensors on drones and ships to monitor the surrounding obstacles and reflective environment in real time, so as to adjust the angle of the intelligent reflective surface and the configuration of the MIMO antenna in time to optimize the signal path.

[0079] Finally, the present invention will adopt an improved channel model to consider complex environmental factors such as wind speed, water surface fluctuations, and reflection paths between high-rise buildings to more accurately simulate and predict signal transmission conditions. Calibration will be performed using actual measurement data. At the same time, the model of the present invention will fully consider a variety of multipath components, including water surface reflection, scattering from far scatterers, scattering from local scatterers, and reflection from smart reflective surfaces, to ensure stable and efficient communication in various complex environments.

[0080] The scattering model is constructed with the ship with receiver deployed in the inland river as the center. The UAV transmitter with MIMO antenna deployed flies in the urban area and communicates with the ship receiver with MIMO antenna deployed in the inland river. Since the line-of-sight signal of the UAV will be blocked by high-rise buildings during its flight in the urban area. An intelligent reflective surface is installed on the building near the UAV. The far scattering environment of the tall buildings on both sides is simulated as a large cylinder with the receiver as the center. The local scattering environment composed of other ships near the receiver is simulated as a small cylinder with the receiver as the center. Since the position of the high-rise building is fixed, the large cylinder remains stationary in the coordinate system. Due to the non-uniform random motion of the nearby ships, the small cylinder maintains a fixed combined velocity motion. A total of six multipath components are considered in this model: the multipath component transmitted from the transmitter, reflected by the water surface and reaching the receiving end; the multipath component transmitted from the transmitter, scattered by distant scatterers and reaching the receiving end; the multipath component transmitted from the transmitter, scattered by local scatterers and reaching the receiving end; the multipath component transmitted from the transmitter, reflected by the smart reflecting surface and reaching the receiving end; the multipath component transmitted from the transmitter, reflected by the smart reflecting surface, scattered by distant scatterers and reaching the receiving end; the multipath component transmitted from the transmitter, reflected by the smart reflecting surface, scattered by local scatterers and reaching the receiving end, and the component transmitted from the transmitter, reflected by the smart reflecting surface and then reflected by the water surface and reaching the receiving end.

[0081] like Figure 3 As shown, where p is the pth transmitting antenna, q is the qth receiving antenna, is the nth far scatterer, is the nth local scatterer, S ref is the water surface reflection point, S IRS-refis the reflection point after being reflected by the smart reflective surface and then by the water surface. The speed of the drone is v T , the speed of the ship is v R .

[0082] (2) Simulation of water surface fluctuations. Assume that the horizontal position coordinates of the ship at time t are (x ship ,y ship ), due to the influence of water surface fluctuations, the three-dimensional (x ship ,y ship ,z ship ) coordinates satisfy:

[0083]

[0084] Among them, a i , k and ε i Respectively represent the amplitude of the wave, the amount of water in the water wave, and the random deviation angle of the water wave. i Represents the angle of incidence in a specific direction.

[0085] (3) Simulation of waveguide effect. As the water vapor concentration decreases or the temperature increases, the signal will undergo atmospheric refraction during propagation, thus producing a waveguide effect. Due to low-altitude communications, the evaporation pipeline occurs in the atmosphere above the inland river. For the components of multiple scattering, the probability of multiple refractions during propagation is greater. Therefore, the propagation channel between the smart reflective surface and the scatterer can be abstracted as a rectangular cross-section waveguide, and the waveguide effect can be simplified as Figure 4 As shown. When only multiple reflections in the vertical direction are considered. a represents the width of the equivalent rectangle. The coordinates (x (m,n) ,y (m，n) ,z (m,n) ), the coordinates of the nth scatterer are expressed as After r reflections, the distance from the (m,n) reflection unit to the nth scatterer can be calculated as

[0086]

[0087] in,

[0088]

[0089] (4) Two-state time Markov process simulates the dynamic transformation of multipath components. The two-state time Markov process is suitable for simulating the dynamic characteristics of different multipath components in non-stationary channel models. Due to the dynamic changes of drones, ships and local scatterers, the propagation environment will continue to change over time. It has been proven that multipath components have a limited "lifespan", and each path passing through the scatterer is not always observed by all reflection units of the smart reflective surface. In addition, fixed reflection units are still at risk of being blocked by obstacles due to the movement of drones and ships. Based on the above analysis and assumptions, the two-state Markov process is used to simulate the birth and death process of multipath components. For the multipath component that is transmitted from the transmitter and scattered by distant scatterers and reaches the receiver; the multipath component that is transmitted from the transmitter and reflected by the smart reflective surface and reaches the receiver; the multipath component that is transmitted from the transmitter and reflected by the water surface and reaches the receiver, its survival probability after Δt is

[0090]

[0091] where λ B and λ D Represents the survival rate and mortality rate respectively. T and v R Represent the speed of the drone and the speed of the ship respectively.

[0092] For the multipath component transmitted from the transmitter and scattered by the local scatterer to the receiving end, its survival probability after Δt is

[0093]

[0094] where v S represents the combined velocity of the local scatterer.

[0095] For the multipath component transmitted from the transmitter and reflected by the smart reflective surface and scattered by the scatterer, the channel can be decomposed into two sub-channels. The first sub-channel is from the transmitter to the (m,n)th reflection unit. Since the sub-channel fluctuation factor is only affected by the movement of the drone, for the multipath component transmitted from the transmitter to the smart reflective surface, its survival probability after Δt is

[0096]

[0097] For the multipath component reaching the receiver from the far scatterer, and the multipath component reaching the receiving end after being reflected by the water surface, its survival probability after Δt is

[0098]

[0099] For the multipath component reaching the receiver from the local scatterer, its survival probability after Δt is

[0100]

[0101] The observation probability of the multipath component through the smart reflective surface and the scatterer is the product of the survival probabilities of the above two sub-channels, that is,

[0102]

[0103] The present invention has the following beneficial effects:

[0104] It provides reference value for the deployment and evaluation of the UAV ship inland river communication system assisted by the intelligent reflective surface, and provides reference value for the deployment position and deployment angle of the intelligent reflective surface. It has certain evaluation significance for the impact of the deployment of the intelligent reflective surface and the number of units on the channel in this scenario.

[0105] Example 1: Maritime search and rescue communication system

[0106] In maritime search and rescue operations, low-altitude communication between drones and rescue ships is crucial. Due to sea surface fluctuations and complex meteorological conditions, communication signals are easily interfered with, resulting in reduced communication reliability of search and rescue missions. By using the dynamic multipath channel modeling method in the present invention, the search and rescue system can simulate the impact of sea surface fluctuations and atmospheric waveguide effects on the channel in real time, and predict and adjust the propagation path of multipath components. In this way, rescuers can maintain stable communication and improve the success rate of search and rescue missions, especially under severe sea and meteorological conditions.

[0107] Example 2: Offshore wind power station monitoring system

[0108] Offshore wind power plants require continuous monitoring and maintenance, which is usually achieved through low-altitude communication between drones and monitoring ships. However, water surface reflection and atmospheric refraction effects in offshore environments can cause unstable signal transmission and affect the performance of the monitoring system. The method and system of the present invention can effectively simulate and cope with these environmental factors to ensure unimpeded communication between drones and monitoring ships. The system can dynamically adjust the communication channel to ensure high-quality data transmission, which is conducive to the efficient operation and timely maintenance of wind power plants. It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person skilled in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVDROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., and can also be implemented by software executed by various types of processors, or by a combination of the above hardware circuits and software, such as firmware.

[0109] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface, characterized in that: The following steps are involved: In step 1, smart reflective surfaces will be deployed on buildings and movable floating platforms to dynamically adjust the reflection angle according to the location of drones and ships; Step 2: Optimize the configuration of the MIMO antenna and make adaptive adjustments based on real-time environmental conditions; Step 3: Introduce a dynamic multipath tracking algorithm to analyze and track the changes of various multipath signals from the transmitter to the receiver in real time, and use machine learning technology to optimize; Step 4: Install environmental perception sensors on the drone and ship respectively to monitor the surrounding obstacles and reflective environment in real time, adjust the angle of the smart reflective surface and the configuration of the MIMO antenna, and optimize the signal path; Step 5: Use the improved channel model to simulate and predict signal transmission conditions by considering complex environmental factors such as wind speed, water surface fluctuations, and reflection paths between tall buildings; and calibrate using actual measurement data.

2. The method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface as claimed in claim 1, characterized in that: The model fully considers various multipath components, including water reflection, scattering from distant scatterers, scattering from local scatterers, and reflection from smart reflective surfaces.

3. The method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface as claimed in claim 1, characterized in that: The scattering model is constructed with the ship with receivers deployed in the inland river as the center; A UAV transmitter equipped with a multiple-input multiple-output antenna flies in the urban area and communicates with a ship receiver equipped with a multiple-input multiple-output antenna in the inland river.

4. The method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface as claimed in claim 3, characterized in that: An intelligent reflective surface is installed on the buildings near the UAV; the far scattering environment of the tall buildings on both sides is simulated as a large cylinder centered on the receiver.

5. The method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface as claimed in claim 4, characterized in that: The local scattering environment composed of other ships near the receiver is simulated as a small cylinder with the receiver as the center.

6. The method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface as claimed in claim 3, characterized in that: A total of six multipath components are considered in the model: the multipath component transmitted from the transmitter and reflected by the water surface to the receiver; the multipath component transmitted from the transmitter and scattered by distant scatterers to the receiver; The multipath component transmitted from the transmitter and scattered by the local scatterer reaches the receiving end; The multipath components transmitted from the transmitter and reflected by the smart reflective surface reach the receiving end; The multipath component that is emitted from the transmitter, reflected by the smart reflective surface, scattered by the distant scatterer, and reaches the receiving end; The multipath component that is emitted from the transmitter, reflected by the smart reflective surface, scattered by the local scatterer, and reaches the receiving end; The component transmitted from the transmitter, reflected by the smart reflecting surface, and then reflected by the water surface reaches the receiving end.

7. The method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface as claimed in claim 1, characterized in that: A channel model construction method based on a dynamic multipath tracking algorithm and a two-state time Markov process is adopted, comprising the following steps: Multi-input multi-output antennas and environmental perception sensors are installed on drones and ships respectively to monitor surrounding obstacles and reflective environments in real time and dynamically adjust the angle of the intelligent reflective surface and antenna configuration; Apply dynamic multipath tracking algorithm to analyze and track the changes of multipath signals in real time, and optimize the signal transmission path through machine learning technology; Construct a channel model, taking into account six multipath components including water surface reflection, scattering from far scatterers, scattering from local scatterers, and reflection from smart reflective surfaces; The dynamic changes of multipath components are simulated by a two-state time Markov process, where the survival probability of multipath components is determined by factors such as the speed of the drone, the speed of the ship, and the combined speed of the scatterers. The water surface wave model is used to simulate the dynamic changes of the water surface reflection path, and the waveguide effect model is used to simulate the waveguide effect caused by multiple refractions.

8. The method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface as claimed in claim 7, characterized in that: Communication is achieved through multiple-input multiple-output antennas installed on drones and inland vessels, and signal transmission is enhanced using smart reflective surfaces; The far scattering environment between high-rise buildings in the urban area is simulated as a large cylinder with the receiver as the center, and the local scattering environment composed of other ships near the receiver is simulated as a small cylinder with the receiver as the center; Model the multipath components and consider various paths from the transmitter to the receiver, including the direct path, the water surface reflection path, the distant scatterer scattering path, the local scatterer scattering path, the smart reflective surface reflection path and their combined paths; The channel model calibrates the path through multiple reflections and scattering effects, and uses a two-state time Markov process to calculate the survival probability of multipath components, ensuring the high accuracy and stability of the model in complex environments.

9. The method for constructing a UAV-ship inland river communication channel model assisted by a reflective surface as claimed in claim 7, characterized in that: The method and system include the following steps and modules: Water surface fluctuation simulation module: Assume the horizontal position coordinates of the ship at a specific time point, and simulate the changes in the three-dimensional coordinates of the ship caused by water surface fluctuations. By setting parameters such as the fluctuation amplitude, the number of water waves, and the random offset angle of water waves, the ship position is updated in real time to affect the propagation path of the multipath component. Waveguide effect simulation module: In low-altitude communications, the waveguide effect is generated during signal propagation due to the atmospheric refraction effect. The propagation channel between the smart reflective surface and the scatterer is abstracted as a rectangular cross-section waveguide model. Especially when the water vapor concentration decreases or the temperature increases, multiple reflections in the vertical direction are considered to simulate the changes in the propagation path caused by signal refraction. Multipath channel dynamic characteristics simulation module: Use the two-state time Markov process to simulate the dynamic transformation of multipath components. For the non-stationary channel model caused by the movement of drones, ships and scatterers, establish a survival probability model of multipath components, and dynamically simulate the change process of multipath components through far scatterers, smart reflective surfaces and water surface reflections. This module combines factors such as the speed of the moving body, the position of the scatterer and the smart reflective surface to predict the birth and death process of the multipath channel and enhance the robustness and stability of the communication system.

10. A system for constructing a channel model for inland river communication of unmanned aerial vehicles (UAVs) assisted by a reflective surface, which implements the method for constructing a channel model for inland river communication of unmanned aerial vehicles (UAVs) assisted by a reflective surface as claimed in any one of claims 1 to 8, characterized in that: The reflective surface-assisted UAV ship inland river communication channel model construction system includes: A deployment module that deploys smart reflective surfaces on buildings and movable floating platforms, dynamically adjusting the reflective angle based on the location of drones and ships; An optimization module is used to optimize the configuration of MIMO antennas and make adaptive adjustments based on real-time environmental conditions; Tracking module, which is used to introduce dynamic multipath tracking algorithm, analyze and track the changes of various multipath signals from transmitter to receiver in real time, and optimize them using machine learning technology; Monitoring module, which is used to install environmental perception sensors on drones and ships respectively, to monitor surrounding obstacles and reflective environments in real time, adjust the angle of the smart reflective surface and the configuration of the MIMO antenna, and optimize the signal path; The simulation prediction module is used to use an improved channel model to simulate and predict signal transmission conditions by taking into account complex environmental factors such as wind speed, water surface fluctuations, and reflection paths between high-rise buildings; and to calibrate using actual measurement data.

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