A new energy station wireless transmission method and system based on RIS

Abstract

The application discloses a new energy station wireless transmission method and system based on RIS, relates to the technical field of wireless communication, and comprises the following steps: deploying an RIS system and a sensor network at a new energy station and collecting station data in real time; the RIS system comprises a plurality of RIS units; a theoretical model of the RIS unit is constructed according to the station data, and a regulation and control algorithm is designed based on the theoretical model; a multi-objective optimization model is constructed according to the output of the station data and the theoretical model, and the RIS unit is dynamically adjusted by using the regulation and control algorithm according to the multi-objective optimization result, so that optimal wireless transmission is realized. By using advanced interface electromagnetism principles and carefully designed subwavelength periodic structures, the application realizes accurate control of electromagnetic waves, and greatly improves the electromagnetic response characteristics and regulation and control capacity of the system.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a wireless transmission method and system for new energy power stations based on RIS. Background Technology

[0002] With the booming development of the new energy industry, the scale of new energy power plants such as wind farms and photovoltaic power stations is constantly expanding, and their communication needs are becoming increasingly complex. Traditional wireless communication technologies face many challenges in these scenarios. On the one hand, new energy power plants are usually widely distributed and have complex terrain, making it difficult for traditional base stations to cover them and resulting in unstable signal quality. On the other hand, the numerous large metal structures within the power plants, such as wind turbine generators and photovoltaic panel arrays, cause severe multipath effects and signal attenuation. In addition, the dynamic characteristics of new energy power plants are significant, such as wind turbine rotation and photovoltaic panel angle adjustments, making the wireless channel exhibit highly time-varying characteristics. These factors make it difficult for traditional wireless communication systems to achieve stable and efficient data transmission in new energy power plants, restricting the intelligent operation and maintenance and efficient management of these power plants.

[0003] In recent years, Reconfigurable Intelligent Surface (RIS) technology has attracted much attention due to its ability to actively regulate the electromagnetic wave propagation environment. RIS can optimize wireless channels by precisely controlling the electromagnetic properties of its surface units to achieve directional reflection, refraction, or scattering of incident electromagnetic waves. However, existing RIS technology still faces several limitations in applications at renewable energy power plants: First, there is a lack of precise theoretical models for the complex electromagnetic environment of renewable energy power plants, making it difficult to fully realize the performance potential of RIS; second, existing RIS control algorithms are mostly statically optimized, making it difficult to adapt to the highly dynamic communication needs of renewable energy power plants; and third, optimizing a single performance indicator is insufficient to meet the diverse communication needs of renewable energy power plants, such as the trade-offs between coverage, energy efficiency, and data throughput.

[0004] Existing wireless communication technologies for new energy power plants face problems such as limited coverage, unstable signal quality, and low energy efficiency. This invention proposes a RIS-based wireless transmission method for new energy power plants to address these issues. By combining RIS technology with a multi-objective optimization algorithm, it achieves accurate modeling and dynamic optimization control of the complex electromagnetic environment of new energy power plants. Summary of the Invention

[0005] In view of the problems existing in the wireless communication technology of new energy power stations, this invention is proposed.

[0006] Therefore, the problem that this invention aims to solve is that existing wireless communication technologies for new energy power stations face issues such as limited coverage, unstable signal quality, and low energy efficiency.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, embodiments of the present invention provide a wireless transmission method for new energy power stations based on RIS, comprising,

[0009] A RIS system and sensor network are deployed at the new energy power station to collect station data in real time; the RIS system includes multiple RIS units.

[0010] A theoretical model of the RIS unit is constructed based on the field data, and a control algorithm is designed based on the theoretical model.

[0011] Based on the field data and the output of the theoretical model, a multi-objective optimization model is constructed. Based on the multi-objective optimization results, the control algorithm is used to dynamically adjust the RIS unit to achieve optimal wireless transmission.

[0012] As a preferred embodiment of the RIS-based wireless transmission method for new energy power stations described in this invention, the theoretical model can not only predict the response of a single RIS unit, but also simulate the collective behavior of the entire array as shown in the following equation:

[0013]

[0014] Where, θ i Let θ be the angle of incidence. r Let E be the reflection angle, k0 be the wave number, and E be the wave number. i For the incident electric field, E r For the reflected electric field, H i For the incident magnetic field, H r To reflect the magnetic field, E t For the total electric field, H t Where is the total magnetic field, and N is the number of RIS units.

[0015] As a preferred embodiment of the RIS-based wireless transmission method for new energy power stations described in this invention, the control algorithm determines the phase and amplitude of the RIS unit by setting the beam intensity or multiple beams in the desired direction. The objective function is as follows:

[0016]

[0017] Among them, A n and φ n These represent the amplitude and phase of the nth RIS unit, respectively. w represents the desired phase adjustment amount. i is the weighting coefficient, M is the number of desired directions, and N is the number of RIS units.

[0018] As a preferred embodiment of the RIS-based wireless transmission method for new energy power stations described in this invention, the control algorithm dynamically adjusts the RIS unit, including:

[0019] Based on the collected field data, the optimal beam direction and shape are determined according to the theoretical model. The required phase and amplitude adjustment values ​​for each RIS unit are determined according to the control algorithm, and the adjustment values ​​are converted into specific control signals. The control signals are converted into analog voltage signals through a high-speed DAC. The diodes in each RIS unit change their state according to the driving signal. The change in the diode state causes the electromagnetic characteristics of the RIS unit to change, thereby changing the phase and amplitude of the incident electromagnetic wave. The synergistic effect of all RIS units forms the desired beam direction and shape.

[0020] As a preferred embodiment of the RIS-based wireless transmission method for new energy power stations described in this invention, the multi-objective optimization model includes signal-to-noise ratio, data throughput, coverage area, and energy efficiency. Optimization is achieved by minimizing the weighted sum of multiple objective functions, as shown in the following equation:

[0021]

[0022] Among them, Ψ RIS Given the optimization objective function of the RIS system, we seek to minimize the weighted sum of multiple objective functions, where θ is the phase adjustment vector of the RIS unit, α is the amplitude adjustment vector of the RIS unit, N is the number of optimization objectives, and w i f is the weight of the i-th objective. i Let be the i-th objective function.

[0023] As a preferred embodiment of the RIS-based wireless transmission method for new energy power stations described in this invention, the multi-objective optimization model further includes setting constraints for each objective.

[0024] The constraints include phase constraints, amplitude constraints, and total power constraints.

[0025] As a preferred embodiment of the RIS-based wireless transmission method for new energy power stations described in this invention, the optimal wireless transmission includes:

[0026] Achieve the predetermined minimum signal-to-noise ratio within the target coverage area;

[0027] The specified data throughput threshold has been reached;

[0028] Maximize the effective coverage area;

[0029] While meeting the above conditions, minimize system energy consumption.

[0030] Secondly, embodiments of the present invention provide a RIS-based wireless transmission system for new energy power stations, comprising:

[0031] The data acquisition and deployment module is used to deploy RIS systems and sensor networks at new energy power plants and collect data from the power plants in real time.

[0032] The model building module is used to build a theoretical model of the RIS unit based on the field data, and to build a multi-objective optimization model based on the field data and the output of the theoretical model.

[0033] The dynamic control module is used to design a control algorithm based on the above theoretical model, and dynamically adjust the RIS unit according to the multi-objective optimization results to achieve optimal wireless transmission.

[0034] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the above-described RIS-based wireless transmission method for new energy power stations.

[0035] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the above-described RIS-based wireless transmission method for new energy power stations.

[0036] The beneficial effects of this invention are as follows: By employing advanced interface electromagnetics principles and a meticulously designed subwavelength-scale periodic structure, precise manipulation of electromagnetic waves is achieved, significantly improving the system's electromagnetic response characteristics and control capabilities. Secondly, the innovative RIS unit design and array layout effectively reduce inter-unit coupling effects, significantly improving the overall system efficiency. The scheme supports flexible implementation of planar and curved surface structures, greatly enhancing the system's adaptability in complex new energy power plant environments. Furthermore, the precise electromagnetic field theory model established based on Maxwell's equations and Floquet theory provides a reliable theoretical foundation for the system, making the prediction of the behavior of the RIS units and the entire array more accurate. The innovative phase and amplitude control algorithms, combined with gradient descent and genetic algorithms, not only achieve precise control of a single beam but also support simultaneous multi-beam shaping, significantly improving the system's adaptability in complex communication scenarios. The design using diodes instead of traditional T / R components significantly reduces system costs while ensuring performance through precise control strategies. The introduction of a distributed sensor network enables the system to perceive environmental changes in real time, and combined with a multi-objective optimization framework, comprehensive optimization of multiple key performance indicators such as signal-to-noise ratio, data throughput, coverage area, and energy efficiency is achieved. This dynamic adaptive optimization method enables the system to maximize coverage and optimize energy efficiency while ensuring communication quality, effectively addressing the wireless communication challenges of new energy power plants in complex and dynamic environments. Overall, this solution demonstrates significant advantages in improving communication quality, expanding coverage, optimizing energy utilization, and adapting to complex environments, providing strong technical support for the intelligent operation and maintenance and efficient management of new energy power plants. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0038] Figure 1 This is a flowchart of a RIS-based wireless transmission method for new energy power stations.

[0039] Figure 2 This is a diagram of a traditional phased array antenna architecture for a RIS-based wireless transmission method in new energy power stations.

[0040] Figure 3 This is a diagram of the RIS phased array antenna architecture for a RIS-based wireless transmission method in new energy power stations. Detailed Implementation

[0041] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0042] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0043] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0044] This invention is described in detail with reference to the schematic diagrams. When describing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0045] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0046] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0047] Example 1

[0048] Reference Figures 1-3This is the first embodiment of the present invention, which provides a RIS-based wireless transmission method for new energy power stations, including:

[0049] The novel RIS phased array antenna architecture employs advanced interface electromagnetics principles, achieving precise manipulation of electromagnetic waves through a meticulously designed subwavelength-scale periodic structure. Each RIS element undergoes geometric optimization, employing a square or hexagonal design to obtain optimal electromagnetic response characteristics. These elements integrate tunable components, including varactor diodes or PIN diodes, enabling each element to independently adjust its phase and amplitude response to incident electromagnetic waves.

[0050] When constructing the RIS array, the array size and number of cells were precisely calculated based on specific application requirements. Through innovative array layout design, the mutual coupling effect between cells was effectively reduced, improving the overall system efficiency. Furthermore, this architecture supports flexible implementation of planar and curved structures, enabling it to adapt to various complex installation environments, including wind turbine towers or solar panel array surfaces.

[0051] Furthermore, establishing an accurate electromagnetic field theoretical model is fundamental to achieving efficient control. This model considers the physical structure, material properties, and interaction of the RIS cells with incident electromagnetic waves. Using Maxwell's equations as a foundation, combined with boundary conditions and an equivalent surface impedance model, it accurately describes the modulation effect of the RIS cells on electromagnetic waves. This theoretical model can not only predict the response of individual RIS cells but also simulate the collective behavior of the entire array, providing a reliable theoretical basis for subsequent algorithm design.

[0052] For example, in one feasible embodiment, the modulation effect of the RIS unit on electromagnetic waves is as follows:

[0053] First, considering the physical structure and material properties of the RIS cells, the equivalent permittivity and permeability of anisotropic materials are introduced. Second, combining the periodic arrangement of the RIS cells, Floquet theory is used to describe the collective behavior of the entire array. The following are the improved Maxwell equations:

[0054]

[0055] Where D = εE, ε=ε r ε0,μ=μ r μ0, E is the electric field strength, B is the magnetic induction intensity, D is the electric displacement vector, H is the magnetic field strength, ρ is the charge density, ε0 ​​is the free space permittivity, μ0 is the free space permeability, ε r μ is the relative permittivity. r ρ is the relative permeability. fJ is the free charge density. f This represents the free current density.

[0056] The RIS unit modulation effect, combining boundary conditions and the equivalent surface impedance model, is as follows:

[0057]

[0058] Where, θ i Let θ be the angle of incidence. r Let E be the reflection angle, k0 be the wave number, and E be the wave number. i For the incident electric field, E r For the reflected electric field, H i For the incident magnetic field, H r To reflect the magnetic field, E t For the total electric field, H t Where is the total magnetic field, and N is the number of RIS units.

[0059] Furthermore, based on the established theoretical model, advanced phase and amplitude modulation algorithms are designed. The core objective of this algorithm is to achieve precise beamforming and direction control. Specifically, the algorithm optimizes the phase and amplitude response of each RIS unit so that reflected or transmitted electromagnetic waves can form a beam with maximum intensity in the desired direction. A combination of gradient descent and genetic algorithms is used to quickly converge to the optimal solution. This method not only enables precise control of a single beam but also supports simultaneous multi-beam forming, meeting the needs of complex communication scenarios.

[0060] For example, in a feasible embodiment, the phase and amplitude modulation algorithm is as follows:

[0061] To combine gradient descent and genetic algorithms and achieve precise control over the phase and amplitude of each RIS unit, the objective function is set to maximize beam strength in the desired direction, while considering the formation of multiple beams. The objective function is shown in the following equation:

[0062]

[0063] Among them, A n and φ n These represent the amplitude and phase of the nth RIS unit, respectively. w represents the desired phase adjustment amount. i is the weighting coefficient, M is the number of desired directions, and N is the number of RIS units.

[0064] The optimization process combining gradient descent and genetic algorithm is as follows:

[0065]

[0066] Where, θk Let L be the parameter vector at step k, a be the learning rate, and ▽L(θ) k ) is the objective function with respect to θ k The gradient.

[0067] Based on the established theoretical model, the designed phase and amplitude modulation algorithms can not only achieve precise control of a single beam, but also support the simultaneous shaping of multiple beams, meeting the needs of complex communication scenarios.

[0068] Furthermore, inexpensive diodes are used to replace traditional T / R components, and dynamic beam adjustment is achieved based on the diode control circuit, as follows:

[0069] The system first collects environmental data, including channel state information (CSI), user location, and interference sources, through a distributed sensor network. This data is then sent to the central control unit.

[0070] The central control unit runs phase and amplitude modulation algorithms to calculate the optimal beam direction and shape based on the collected data. The algorithm outputs the required phase and amplitude adjustment values ​​for each RIS unit. The control unit converts the algorithm output into specific diode control signals. The control signals are converted into analog voltage signals by a high-speed DAC. The drive circuit receives the analog signals, and the diodes in each RIS unit change their states according to the drive signals. The change in the diode states causes changes in the electromagnetic characteristics of the RIS unit, thereby changing the phase and amplitude of the incident electromagnetic wave. The coordinated action of all RIS units forms the desired beam direction and shape.

[0071] It should be noted that each RIS unit typically contains multiple diodes, and the control signal determines the switching state and bias voltage of each diode.

[0072] The drive circuit receives analog signals and generates precise bias voltages and switching timings. For example, for a PIN diode, a forward bias of 1V turns it on, and a reverse bias of -5V turns it off.

[0073] Furthermore, assess the specific environment of the new energy power station, including the layout of wind turbine towers and solar panel arrays, as well as the area and distribution of the station. Determine data transmission requirements, including the type of data to be transmitted (such as monitoring data and control signals), transmission distance, coverage area, and bandwidth requirements.

[0074] Based on the specific environment of the site, the RIS phased array antenna architecture is determined using the above-mentioned RIS theoretical model, including array size, number of elements and layout, and each RIS element is optimized.

[0075] A distributed sensor network is deployed within the site to collect environmental data in real time, including channel state information (CSI), user location, and interference sources. A wireless transmission link is established to achieve efficient data transmission.

[0076] Furthermore, the completed RIS-based new energy power station faces a trade-off between several conflicting performance objectives in wireless communication. In order to realize a system that can adaptively adjust in a complex and dynamic communication environment to meet the diverse needs of different users and application scenarios, a multi-objective optimization framework needs to be constructed.

[0077] For example, in a feasible implementation, the multi-objective optimization framework is as follows:

[0078] First, based on the theoretical model of the RIS unit established above, several key performance indicators are defined, such as signal-to-noise ratio (SNR), data throughput, coverage area, and energy efficiency. An advanced multi-objective optimization algorithm is adopted to quickly calculate a set of Pareto optimal solutions. Each solution represents a configuration scheme of the RIS unit. The system selects the most suitable configuration from this set of solutions according to the current priority strategy, and dynamically adjusts the phase and amplitude of the RIS unit through control signals. In this way, the RIS phased array system can maximize the coverage area and optimize energy efficiency while ensuring communication quality.

[0079] For example, in a feasible implementation, the multi-objective optimization algorithm is as follows:

[0080]

[0081] Among them, Ψ RIS Given the optimization objective function of the RIS system, we seek to minimize the weighted sum of multiple objective functions, where θ is the phase adjustment vector of the RIS unit, α is the amplitude adjustment vector of the RIS unit, N is the number of optimization objectives, and w i f is the weight of the i-th objective. i Let be the i-th objective function.

[0082] Preferably, the selection of individual goals is as follows:

[0083]

[0084] Here, SNR stands for Signal-to-Noise Ratio. Throughput directly reflects system performance. Coverage represents the effective coverage area, reflecting the system's spatial performance. Energy Efficiency considers the balance between throughput and power consumption, reflecting the system's sustainability.

[0085] Specifically, the SNR performance metric is calculated as follows:

[0086]

[0087] Where M is the number of RIS units, h j Let σ be the channel coefficient from the j-th RIS unit to the user. 2 This represents noise power.

[0088] Throughput is calculated as follows:

[0089] Throughput(θ,a)=Blog(1+SNR(θ,a))

[0090] Where B is the system bandwidth.

[0091] The effective coverage area is calculated as follows:

[0092] Coverage(θ,a)=∫∫ A 1(SNR(θ,a,x,y)>SNR th )dxdy

[0093] Among them, SNR th The SNR threshold for coverage determination is derived from experiments, and A represents the service area.

[0094] The balance between throughput and power consumption is shown in the following equation:

[0095]

[0096] Among them, P c This is for fixed circuit power consumption.

[0097] Furthermore, setting relevant constraints, including phase and amplitude constraints, ensures the physical feasibility of the optimization results, while the total power constraint considers the actual limitations of the system. As follows:

[0098]

[0099] Where Pmax is the system's maximum power constraint.

[0100] It should be noted that the weights of the objectives allow the system to dynamically adjust the importance of each objective based on its current priority, as shown in the following formula:

[0101]

[0102] Among them, PriorityScore i Reflects real-time system requirements, λ i This controls the sensitivity to changes in weights.

[0103] In multi-user scenarios, the system can rationally allocate communication resources based on optimization results, balancing the service quality for each user. While ensuring communication quality, the system can intelligently adjust power allocation to maximize energy efficiency. Specifically, achieving optimal wireless transmission requires achieving a predetermined minimum signal-to-noise ratio, reaching a specified data throughput threshold, maximizing the effective coverage area within the target coverage area, and minimizing system energy consumption after satisfying the aforementioned conditions.

[0104] Furthermore, this embodiment also provides a RIS-based wireless transmission system for new energy power stations, including:

[0105] The data acquisition and deployment module is used to deploy RIS systems and sensor networks at new energy power plants and collect data from the power plants in real time.

[0106] The model building module is used to build a theoretical model of the RIS unit based on the field data, and to build a multi-objective optimization model based on the field data and the output of the theoretical model.

[0107] The dynamic control module is used to design a control algorithm based on the above theoretical model, and dynamically adjust the RIS unit according to the multi-objective optimization results to achieve optimal wireless transmission.

[0108] This embodiment also provides a computer device applicable to the RIS-based wireless transmission method for new energy power stations, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the RIS-based wireless transmission method for new energy power stations as proposed in the above embodiment.

[0109] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0110] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the RIS-based wireless transmission method for new energy power stations as proposed in the above embodiments.

[0111] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0112] Example 2

[0113] This is the second embodiment of the present invention, which provides a wireless transmission method for new energy power stations based on RIS. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0114] To verify the effectiveness of the RIS-based wireless transmission method for new energy power stations, a field test was conducted at a wind farm. The wind farm covers an area of ​​approximately 50 square kilometers and contains 100 wind turbines with an average height of 120 meters. A 25-square-kilometer area was selected as the test site, containing 50 wind turbines.

[0115] First, a RIS phased array antenna system was designed based on the specific environment of the wind farm. Each RIS element adopts a hexagonal design with a side length of λ / 4 (λ being the wavelength of the operating frequency), and integrates eight PIN diodes for phase and amplitude modulation. The RIS array consists of 1024 elements in a 16x64 rectangular layout, with a total area of ​​approximately 2 square meters. The array is mounted on a wind turbine tower located in the center of the test area, at a height of 80 meters.

[0116] Next, 50 distributed sensor nodes were deployed in the test area to collect environmental data in real time. These nodes were evenly distributed around the wind turbine generators and were able to measure local channel state information (CSI), meteorological conditions (such as wind speed and temperature), and potential sources of electromagnetic interference.

[0117] The experiment used 3.5GHz of the 5G NR band as the operating frequency, with a bandwidth of 100MHz. To comprehensively evaluate system performance, six test scenarios were set up to simulate different communication requirements and environmental conditions. Each scenario was tested continuously for 24 hours to fully account for day-night variations and weather effects.

[0118] In each test scenario, the central control unit continuously receives real-time data from the sensor network and runs a multi-objective optimization algorithm. This algorithm comprehensively considers four key indicators: signal-to-noise ratio (SNR), data throughput, coverage area, and energy efficiency, dynamically adjusting the weights of each indicator according to the priority of the current scenario. The optimization results are used to adjust the phase and amplitude of the RIS unit in real time to form the optimal beam direction and shape.

[0119] For comparative analysis, a traditional 5G base station system was also deployed as a control group under the same location and conditions. Both systems used the same transmit power and bandwidth resources to ensure a fair comparison. See the table below:

[0120] Table 1 Experimental Data

[0121]

[0122]

[0123] By analyzing the above experimental data, the following important conclusions can be drawn:

[0124] Signal quality improvement: In all test scenarios, the RIS system consistently achieved a higher average SNR. For example, in a standard weekday scenario, the RIS system's SNR was 6.2 dB higher than that of a traditional 5G system, representing a 27.8% improvement. This significant improvement in signal quality is mainly attributed to the RIS system's ability to precisely control the propagation direction of electromagnetic waves, effectively reducing multipath effects and signal attenuation.

[0125] Increased data throughput: The RIS system demonstrates higher data throughput across various scenarios. Particularly in multi-user scenarios, the RIS system achieves a throughput of 3.8Gbps, 40.7% higher than traditional 5G systems. This performance improvement stems from the RIS system's ability to simultaneously generate multiple directional beams, making more efficient use of space resources.

[0126] Extended Coverage: The RIS system performs exceptionally well in terms of effective coverage area. In long-distance coverage scenarios, the RIS system achieves a coverage area of ​​24.8 km. 2 This represents a 28.5% increase compared to traditional 5G systems. This result demonstrates the adaptability of the RIS system in complex terrain, effectively overcoming signal obstruction caused by large metal structures in wind farms.

[0127] Energy Efficiency Optimization: The RIS system demonstrates higher energy efficiency across all scenarios. Particularly in low-power mode, the RIS system achieves an energy efficiency of 1.73E+06 bits / J, 73% higher than traditional 5G systems. This significant efficiency improvement demonstrates the advantages of the RIS system in intelligent power allocation and beamforming.

[0128] Improved latency performance: The RIS system achieves lower average latency across various scenarios. For example, in peak load scenarios, the RIS system has an average latency of 2.7ms, which is 40% lower than traditional 5G systems. This latency improvement is crucial for real-time monitoring and control of wind farms.

[0129] In summary, these experimental data fully demonstrate the significant advantages of the RIS-based wireless transmission method for new energy power plants in terms of signal quality, data throughput, coverage, energy efficiency, and environmental adaptability. This method not only effectively addresses the communication challenges in the complex electromagnetic environment of wind farms but also provides strong technical support for the intelligent operation and maintenance and efficient management of new energy power plants.

[0130] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A wireless transmission method for new energy power stations based on RIS, characterized in that: include, A RIS system and sensor network are deployed at the new energy power station to collect station data in real time; the RIS system includes multiple RIS units. Based on the field data, a theoretical model of the RIS unit is constructed using Maxwell's equations and Floquet's theory. This theoretical model can not only predict the response of a single RIS unit but also simulate the collective behavior of the entire array. A control algorithm is designed based on this theoretical model. Based on the site data and the output of the theoretical model, a multi-objective optimization model is constructed. Based on the multi-objective optimization results, the control algorithm is used to dynamically adjust the RIS unit to achieve optimal wireless transmission. The control algorithm determines the phase and amplitude of the RIS unit by setting the beam intensity or multiple beams in the desired direction. The objective function is as follows: Among them, A n and These represent the amplitude and phase of the nth RIS unit, respectively. w represents the desired phase adjustment amount. i Here, M represents the weighting coefficient, M represents the number of desired directions, and N represents the number of RIS units; The control algorithm dynamically adjusts the RIS unit, including: Based on the collected field data, the optimal beam direction and shape are determined according to the theoretical model. The required phase and amplitude adjustment values ​​for each RIS unit are determined according to the control algorithm, and the adjustment values ​​are converted into specific control signals. The control signals are converted into analog voltage signals through a high-speed DAC. The diodes in each RIS unit change their state according to the driving signal. The change in the diode state causes the electromagnetic characteristics of the RIS unit to change, thereby changing the phase and amplitude of the incident electromagnetic wave. The synergistic effect of all RIS units forms the desired beam direction and shape. The multi-objective optimization model includes signal-to-noise ratio, data throughput, coverage area, and energy efficiency. It is optimized by minimizing the weighted sum of multiple objective functions, as shown in the following equation: Among them, Ψ RIS Given the optimization objective function of the RIS system, we seek to minimize the weighted sum of multiple objective functions, where θ is the phase adjustment vector of the RIS unit, α is the amplitude adjustment vector of the RIS unit, Q is the number of optimization objectives, and w k f is the weight of the k-th objective. k Let be the k-th objective function.

2. The RIS-based wireless transmission method for new energy power stations as described in claim 1, characterized in that: The multi-objective optimization model also includes setting constraints for each objective; The constraints include phase constraints, amplitude constraints, and total power constraints.

3. The RIS-based wireless transmission method for new energy power stations as described in claim 2, characterized in that: The optimal wireless transmission includes, Achieve the predetermined minimum signal-to-noise ratio within the target coverage area; The specified data throughput threshold has been reached; Maximize the effective coverage area; Minimize system energy consumption.

4. A wireless transmission system based on the RIS-based wireless transmission method for new energy power stations as described in any one of claims 1 to 3, characterized in that: include, The data acquisition and deployment module is used to deploy RIS systems and sensor networks at new energy power plants and collect data from the power plants in real time. The model building module is used to build a theoretical model of the RIS unit based on the field data, and to build a multi-objective optimization model based on the field data and the output of the theoretical model. The dynamic control module is used to design a control algorithm based on the above theoretical model, and dynamically adjust the RIS unit according to the multi-objective optimization results to achieve optimal wireless transmission.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the RIS-based wireless transmission method for new energy power stations as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the RIS-based wireless transmission method for new energy power stations as described in any one of claims 1 to 3.

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