Sound barrier photovoltaic facade intelligent optimization layout method and system based on photo-thermal coupling

Abstract

The invention relates to the field of constructional engineering, and discloses a sound barrier gradual change type photovoltaic facade intelligent optimization layout method based on photo-thermal coupling, and the method comprises the following steps: S1, collecting illumination intensity, temperature, humidity and wind speed data; s2, calculating the illumination intensity of the current photovoltaic panel based on a dynamic optimization algorithm of a photo-thermal coupling model, and adjusting the inclination angle and the spacing of the photovoltaic panel according to the illumination intensity; s3, establishing a coupling model between the sound insulation performance of the sound barrier and the photovoltaic layout, evaluating the influence of the photovoltaic layout on the sound insulation effect of the sound barrier through acoustic analysis, and synchronously optimizing the layout design; and S4, adjusting the layout of the photovoltaic panel in real time and performing continuous feedback optimization. According to the invention, by introducing the photo-thermal coupling dynamic optimization algorithm, the inclination angle, the spacing and the arrangement mode of the photovoltaic panels can be adjusted in real time, so that the photovoltaic panels can dynamically adapt according to factors such as environment illumination intensity and temperature, thereby ensuring that the photovoltaic panels are always in the optimal illumination condition.

Description

Technical Field

[0001] The present invention relates to the field of construction engineering, and particularly to an intelligent optimization layout method and system for a photovoltaic facade of a sound barrier based on photothermal coupling. Background Art

[0002] With the continuous acceleration of the urbanization process, the rapid expansion of transportation infrastructure has led to an increasingly serious problem of traffic noise pollution, which has become an important factor affecting the quality of residents' lives and the level of the urban environment. To effectively reduce noise interference, as a widely used physical sound insulation facility, sound barriers have been widely deployed in scenarios such as highways, viaducts, and urban arterial roads.

[0003] In recent years, with the development of renewable energy technologies, some sound barriers have begun to integrate solar photovoltaic modules, aiming to achieve local energy production and self-sufficiency on the basis of sound insulation and noise reduction, and improve the overall energy efficiency and green value of the facilities. However, the existing integration methods mostly adopt a photovoltaic layout structure with a fixed inclination angle and parallel arrangement, and do not fully consider the dynamic influence of external environmental conditions (such as light intensity, solar incidence angle, temperature change, etc.) on the photovoltaic power generation efficiency, resulting in unstable power generation performance in different time periods and seasons and relatively low overall power generation efficiency. Summary of the Invention

[0004] To make up for the above deficiencies, the present invention provides an intelligent optimization layout method and system for a gradient photovoltaic facade of a sound barrier based on photothermal coupling, aiming to improve the problems of poor flexibility and low power generation efficiency of existing sound barrier photovoltaics.

[0005] In a first aspect, the present invention provides the following technical solution, an intelligent optimization layout method for a gradient photovoltaic facade of a sound barrier based on photothermal coupling, the method comprising the following steps: S1. Collect data on light intensity, temperature, humidity, and wind speed; S2. Calculate the light intensity of the current photovoltaic panel through a dynamic optimization algorithm based on a photothermal coupling model, and adjust the inclination angle and spacing of the photovoltaic panel according to the light intensity; S3. Establish a coupling model between the sound insulation performance of the sound barrier and the photovoltaic layout, and evaluate the influence of the photovoltaic layout on the sound insulation effect of the sound barrier through acoustic analysis, and synchronously optimize the layout design; S4. Real-time adjust the layout of the photovoltaic panel and perform continuous feedback optimization.

[0006] Preferably, the adjustment process in step S2 is achieved through the following formula: θ opt = θ0 + k1·ln(I) - k2·T; where θ0 is the reference inclination angle, k1 and k2 are regression coefficients, I is the current light intensity, T is the current temperature, and θopt is the optimal inclination angle; When the light intensity I increases, the algorithm adjusts the inclination angle α and the spacing d of the photovoltaic panel according to the following formula: α new = α current + Δα·sign(θ opt - α current ); d new = d current - Δd·H(I - I thr ); Wherein, α and d are the inclination angle and the spacing respectively, Δα and Δd are the adjustment steps of the inclination angle and the spacing respectively, and H is the Heaviside function, which is used to judge whether the light intensity threshold I is exceeded thr , if the light intensity I exceeds the threshold, the spacing d is reduced to maximize the light absorption area.

[0007] Preferably, the dynamic optimization algorithm is further adjusted based on the feedback of the power generation efficiency η, and the power generation efficiency η is calculated by the following formula: η = η ref ·(1 - β·(T - T ref ))·cos(θ opt - α) Wherein, η ref is the photovoltaic power generation efficiency at the reference temperature, β is the temperature sensitivity coefficient, T ref is the reference temperature, θ opt is the optimal inclination angle.

[0008] Preferably, in the S3 step, the coupling model is calculated by the LSTM model, and the LSTM neural network receives the photovoltaic module data as input, and the output result after calculation is the acoustic performance prediction value including the insertion loss or the total sound insulation quantity.

[0009] Preferably, the data received by the LSTM neural network includes the arrangement mode, the inclination angle, the spacing, the light intensity, the temperature, and the wind speed.

[0010] Preferably, the acoustic performance prediction value is used to evaluate the insertion loss or the sound insulation effect of the sound barrier under the current layout, and is used as the basis for synchronously optimizing the layout design.

[0011] In the second aspect, the present invention provides the following technical solution, a sound barrier gradient photovoltaic facade intelligent optimization layout system based on photo-thermal coupling, the system includes: A data acquisition module, which is used to acquire environmental data such as light intensity, temperature, humidity, and wind speed; A photo-thermal coupling dynamic optimization module, which is used to calculate the light intensity of the photovoltaic panel based on the photo-thermal coupling model, and adjust the inclination angle and the spacing of the photovoltaic panel; A sound barrier sound insulation performance evaluation module, which is used to establish a coupling model between the photovoltaic layout and the sound barrier sound insulation performance and evaluate its influence; An LSTM neural network module, which is used to receive photovoltaic module data, calculate through the LSTM neural network and output the predicted value of acoustic performance; An intelligent adjustment module, which is used to receive the optimization result and adjust the layout of the photovoltaic panels in real time, and output instructions to the driving device for layout adjustment.

[0012] Preferably, the data acquisition module includes: A light intensity sensor, which is used to measure the light intensity in the environment in real time; A temperature sensor, which is used to monitor the temperature change of the photovoltaic panel surface or the surrounding environment; A humidity sensor, which is used to measure the air humidity; An anemometer, which is used to measure the wind speed; An inclination sensor, which is used to measure the inclination angle of the photovoltaic panel.

[0013] In a third aspect, the present invention provides the following technical solution. A computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned intelligent optimization layout method for the sound barrier gradient photovoltaic facade based on photo-thermal coupling is realized.

[0014] In a fourth aspect, the present invention provides the following technical solution. A readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned intelligent optimization layout method for the sound barrier gradient photovoltaic facade based on photo-thermal coupling is realized.

[0015] The present invention has the following beneficial effects: 1. In the present invention, by introducing a photo-thermal coupling dynamic optimization algorithm, the present invention can adjust the inclination angle, spacing and arrangement mode of the photovoltaic panels in real time, so that the photovoltaic panels can dynamically adapt according to factors such as environmental light intensity and temperature, thereby ensuring that the photovoltaic panels are always in the best lighting conditions. This optimization mechanism significantly improves the photovoltaic power generation efficiency, can make full use of solar energy resources, and avoids the problem of low power generation efficiency under the traditional fixed layout.

[0016] 2. In the present invention, by combining the sound barrier sound insulation performance evaluation and optimization model, it is ensured that the dynamic adjustment of the photovoltaic panel layout will not significantly affect the sound insulation effect of the sound barrier. By reasonably designing the inclination angle, spacing and arrangement mode of the photovoltaic panels, the layout of the photovoltaic panels can avoid interfering with the sound wave propagation path to the greatest extent and maintain the effective sound insulation function of the sound barrier.

[0017] 3. In the present invention, by integrating a real-time environmental data acquisition module, a central processor, and an automatic driving device, the automatic adjustment of the photovoltaic panel layout is realized. The system can automatically calculate the optimal layout scheme according to the real-time collected data such as light intensity and temperature, and adjust the position of the photovoltaic panel through the driving device. This intelligent and automatic adjustment system avoids manual intervention, improves the stability and sustainability of the system, and reduces the manual maintenance cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of the intelligent optimization layout method for the gradient photovoltaic facade based on photo-thermal coupling proposed by the present invention; Figure 2 It is a system diagram of the intelligent optimization layout system for the gradient photovoltaic facade based on photo-thermal coupling proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Embodiment 1: Referring to Figure 1 , in the first embodiment of the present invention, the present invention provides an intelligent optimization layout method for the gradient photovoltaic facade based on photo-thermal coupling. The method includes the following steps: S1. Collect data of light intensity, temperature, humidity, and wind speed; Specifically, in this embodiment, the data acquisition module collects data of light intensity, temperature, humidity, and wind speed in real time through multiple sensors. The main function of this module is to provide necessary environmental data for the subsequent photo-thermal coupling dynamic optimization algorithm, so as to calculate the light intensity of the photovoltaic panel more accurately, and then adjust the inclination angle and spacing of the photovoltaic panel, thereby optimizing the power generation efficiency of the photovoltaic system and the sound insulation performance of the sound barrier.

[0021] In the specific implementation process, the data acquisition module is connected to the sensors to collect the following environmental data: In this embodiment, the light intensity is measured by a light intensity sensor (such as a photovoltaic sensor, a photodiode, etc.). The light intensity sensor monitors the light changes in the environment in real time and provides data support for the subsequent photo-thermal coupling dynamic optimization module.

[0022] Specifically, the data of light intensity can be expressed by the following formula: I current = k·I ambient ·cos(θ); Among them, I current represents the light intensity received by the current photovoltaic panel, I ambient is the ambient light intensity, θ is the light angle, and k is the calibration coefficient of the light sensor.

[0023] Temperature data is collected in real time through temperature sensors (such as thermocouples, thermistors, etc.). The temperature sensors are installed on the surface of the photovoltaic panel or in the surrounding environment to measure the temperature changes of the current environment. According to the temperature changes, the inclination angle and spacing of the photovoltaic panel are dynamically adjusted to optimize the power generation efficiency. The relationship between temperature and photovoltaic power generation efficiency can be expressed by the following formula: η = η0·(1 - β·(T - T0)); Among them, η is the photovoltaic power generation efficiency at the current temperature, η0 is the photovoltaic power generation efficiency at the reference temperature, β is the temperature sensitivity coefficient, T is the current temperature, and T0 is the reference temperature.

[0024] The humidity sensor is used to measure the humidity in the air to determine its impact on the photovoltaic system. Changes in humidity may affect the performance of the photovoltaic panel, especially in high-humidity environments, resulting in condensation or corrosion on the surface of the photovoltaic panel, thereby affecting its photoelectric conversion efficiency. Humidity data can be collected through humidity sensors (such as capacitive, conductive, etc.) and considered together with temperature data for adjusting the layout of the photovoltaic panel.

[0025] The wind speed sensor is used to monitor the wind speed of the environment. The wind speed affects the cooling effect of the photovoltaic panel, and thus affects the working efficiency of the photovoltaic panel. The wind speed sensor can be measured by ultrasonic, cup anemometer, etc., and provides real-time wind speed data for the dynamic optimization algorithm. According to the wind speed, the system can further adjust the spacing of the photovoltaic panel to optimize the overall performance of the photovoltaic system.

[0026] During the data collection process, the data of all sensors are sent to the central processor through the communication module for real-time processing. The central processor executes the corresponding optothermal coupling dynamic optimization algorithm based on the data obtained by the data collection module and adjusts the layout of the photovoltaic panel. During this process, the data of the sensors need to be filtered and processed to ensure the accuracy and effectiveness of the data.

[0027] For example, there may be a certain correlation between light intensity and factors such as temperature, humidity, and wind speed. The data collection module should make adaptive adjustments according to the changes in real-time data to ensure that the collected environmental data has high timeliness and accuracy.

[0028] In addition, in this embodiment, the data collection module may further include other auxiliary sensors, such as an inclination sensor, for measuring the actual inclination angle of the photovoltaic panel. This data will be further used in the optimization algorithm to accurately adjust the actual angle and layout of the photovoltaic panel.

[0029] S2. Calculate the current light intensity of the photovoltaic panel based on the photothermal coupling model, and adjust the inclination angle and spacing of the photovoltaic panel according to the light intensity; The adjustment process in step S2 is implemented through the following formula: θ opt = θ0 + k1·ln(I) - k2·T; where θ0 is the reference inclination angle, k1 and k2 are regression coefficients, I is the current light intensity, T is the current temperature, and θ opt is the optimal inclination angle; When the light intensity I increases, the algorithm adjusts the inclination angle α and spacing d of the photovoltaic panel according to the following formula: α new = α current + Δα·sign(θ opt - α current ); d new = d current - Δd·H(I - I thr ); where α and d are the inclination angle and spacing respectively, Δα and Δd are the adjustment steps of the inclination angle and spacing respectively, and H is the Heaviside function used to determine whether the light intensity threshold I thr is exceeded. If the light intensity I exceeds the threshold, the spacing d is reduced to maximize the light absorption area.

[0030] The dynamic optimization algorithm is further adjusted based on the feedback of the power generation efficiency η. The power generation efficiency η is calculated by the following formula: η = η ref ·(1 - β·(T - T ref ))·cos(θ opt - α); where η ref is the photovoltaic power generation efficiency at the reference temperature, β is the temperature sensitivity coefficient, T ref is the reference temperature, and θ opt is the optimal inclination angle.

[0031] Specifically, in this embodiment, after the data acquisition module completes the acquisition of data such as light intensity, temperature, humidity, and wind speed, the photothermal coupling dynamic optimization module calculates based on these real-time data through the photothermal coupling model. The main purpose of this module is to optimize the layout of the photovoltaic panel according to the current ambient light intensity, that is, to adjust the inclination angle and spacing of the photovoltaic panel to achieve higher photovoltaic power generation efficiency.

[0032] Through the photothermal coupling model, the system can consider the influence of environmental factors such as temperature, humidity, and wind speed on the photovoltaic panel, and thus calculate the optimal light intensity. Subsequently, the system further adjusts the inclination angle and spacing of the photovoltaic panel according to the calculation results to improve the overall power generation efficiency of the system.

[0033] Specifically, there is a certain functional relationship between the light intensity and the inclination angle and spacing of the photovoltaic panels. The change in light intensity directly affects the energy received by the photovoltaic panels. Therefore, the angles and spacing of the photovoltaic panels need to be dynamically adjusted to ensure that they can maximize the absorption of solar radiation. For this purpose, the following formula is used in this embodiment to describe the relationship between the light intensity and the angle of the photovoltaic panels: I current = k·I ambient ·cos(θ); Where, I current is the light intensity received by the current photovoltaic panel, I ambient is the ambient light intensity, θ is the light angle, and k is the calibration coefficient of the light sensor.

[0034] Generally, when the light intensity increases, the inclination angle of the photovoltaic panel will be appropriately reduced to avoid overheating caused by too strong light and reduce the efficiency of the photovoltaic panel. At the same time, the spacing will also be adjusted to adapt to the change in light intensity to ensure that the photovoltaic system can absorb more light energy.

[0035] As an option, in this embodiment, the inclination angle and spacing of the photovoltaic panels are adjusted by the following formula: α new = α base - β·ΔI light ; d new = d base - γ·ΔI light ; Where, α new is the inclination angle of the adjusted photovoltaic panel, α base is the initial inclination angle, ΔI light is the change in light intensity, β and γ are adjustment coefficients, d new and d base are the adjusted spacing and the initial spacing respectively.

[0036] When the light intensity exceeds a certain threshold, the system will reduce the spacing between the photovoltaic panels to maximize the light absorption area. At this time, the spacing between the photovoltaic panels can be adjusted according to the following formula: Δd = H(I current - I threshold )·Δd max ; Where, Δd is the adjustment amount of the spacing, H is the Heaviside function, indicating that the spacing adjustment will only be carried out when the light intensity exceeds the threshold, and Δd max is the maximum adjustment step.

[0037] Specifically, when the light intensity reaches or exceeds the set threshold, the system automatically reduces the spacing between the photovoltaic panels to ensure that the system can absorb the maximum amount of energy from the sun. At this time, the tilt angle of the photovoltaic panels will also be adjusted appropriately to adapt to the new layout and avoid overheating problems caused by excessive light intensity. In some embodiments, the photovoltaic-thermal coupling model further optimizes the tilt angle and spacing of the photovoltaic panels according to factors such as wind speed and humidity.

[0038] For example, when the wind speed is high, the system may increase the spacing between the photovoltaic panels to reduce wind resistance and improve the stability of the system. Through the above optimization process, the photovoltaic-thermal coupling dynamic optimization module can adjust the layout of the photovoltaic panels in real time according to the changes in environmental conditions, realizing the efficient operation of the photovoltaic system.

[0039] S3. Establish a coupling model between the sound insulation performance of the sound barrier and the photovoltaic layout. Through acoustic analysis, evaluate the impact of the photovoltaic layout on the sound insulation effect of the sound barrier, and synchronously optimize the layout design; the coupling model is calculated through the LSTM model, where the LSTM neural network receives the photovoltaic module data as input, and the calculation output result is the predicted value of the acoustic performance including the insertion loss or the total sound insulation amount. The data received by the LSTM neural network includes the arrangement method, tilt angle, spacing, light intensity, temperature, and wind speed. The predicted value of the acoustic performance is used to evaluate the insertion loss or sound insulation effect of the sound barrier under the current layout, and serves as the basis for synchronously optimizing the layout design.

[0040] Specifically, in the present invention, a coupling model based on the LSTM neural network is proposed to evaluate the impact of the photovoltaic module layout on the sound insulation performance of the sound barrier and optimize the layout design. The coupling model between the sound insulation performance of the sound barrier and the photovoltaic layout aims to optimize the sound insulation effect of the sound barrier through acoustic analysis and artificial intelligence technology.

[0041] Generally, the sound insulation performance of the sound barrier is affected by various factors, including the material and structural design of the sound barrier itself and the influence of the surrounding environment. With the rapid development of the photovoltaic industry, more and more photovoltaic modules are arranged on or around the sound barrier, which may have a certain impact on the sound insulation effect of the sound barrier. Therefore, the present invention proposes an innovative method to realize the prediction and optimization of the impact of the photovoltaic layout on the sound insulation effect of the sound barrier by establishing a coupling model between the sound insulation performance of the sound barrier and the photovoltaic layout and combining the LSTM neural network.

[0042] As an option, in this embodiment, the input of the LSTM model is the relevant data of the photovoltaic module, including information such as the layout mode, angle, size, and surface material of the photovoltaic module. Through these input data, the LSTM model can be trained and calculated, and the output result is the predicted value of the acoustic performance, specifically including the insertion loss or the total sound insulation. These acoustic performance indicators reflect the sound insulation ability of the sound barrier under different photovoltaic layouts, thus providing a scientific basis for optimizing the photovoltaic module layout.

[0043] Specifically, in this embodiment, the influence of different photovoltaic layouts on the sound insulation performance of the sound barrier is evaluated through acoustic analysis. The acoustic analysis model is mainly based on the acoustic propagation theory, combined with the specific layout of the photovoltaic module, and considers the influence of factors such as the sound wave propagation path, reflection, and diffraction on the sound insulation effect of the sound barrier. By introducing the LSTM neural network, this model can be trained under multiple possible layout schemes, thereby improving the accuracy and reliability of the model prediction.

[0044] In a possible implementation manner, the training process of the coupling model includes the following steps: First, collect the acoustic data under different photovoltaic layouts, including the layout characteristics of the photovoltaic module and the sound insulation performance data of the sound barrier; then, use these data to train the LSTM model, and by optimizing the neural network parameters, make the model accurately predict the sound insulation effect of the sound barrier under different layout conditions; finally, use the trained LSTM model to predict the insertion loss or the total sound insulation under different photovoltaic layouts, and further optimize the layout.

[0045] In addition, in this embodiment, other influencing factors need to be considered, such as meteorological conditions (wind speed, temperature, etc.) and terrain features. These factors may indirectly affect the sound insulation effect of the photovoltaic layout, so appropriate supplementation and adjustment are required in the coupling model. For example, certain meteorological conditions may cause changes in the sound wave propagation speed, thereby affecting the sound insulation effect of the sound barrier.

[0046] In some embodiments, the output results of the model are not limited to the insertion loss or the total sound insulation, but can be further extended to other acoustic indicators, such as frequency response, reflection coefficient, etc. According to actual needs, these results can be used to further optimize the design of the sound barrier or provide a reference basis for the layout of the photovoltaic module. At this time, the output of the LSTM model can provide guidance for acoustic engineers, enabling the sound barrier and the photovoltaic layout to achieve a better balance between sound insulation effect and economic benefits.

[0047] S4. Adjust the layout of the photovoltaic panel in real time and perform continuous feedback optimization.

[0048] Specifically, in this embodiment, by combining the aforementioned coupling model of photovoltaic layout and sound barrier sound insulation performance, a dynamic adjustment of the photovoltaic panel layout is carried out by using a real-time data input and continuous feedback mechanism. Specifically, during the implementation process, the layout characteristics of photovoltaic modules, the sound insulation performance data of the sound barrier, and external environmental factors (such as meteorological conditions, noise source intensity, etc.) will be used as real-time input data and calculated and optimized through the LSTM neural network model. These input data include but are not limited to the arrangement method, angle, size, surface material of the photovoltaic panels, and meteorological data, etc.

[0049] As an option, the system will evaluate the impact of the current photovoltaic panel layout on the sound insulation effect of the sound barrier according to the real-time input data. Through the calculation of the LSTM neural network, the model can predict the sound insulation performance of the photovoltaic panel layout under real-time changing environmental conditions. Specifically, the LSTM model can predict the insertion loss (IL) or the total sound insulation (SI) under the photovoltaic layout and provide real-time feedback to optimize the layout. The output result of the model can reflect the sound insulation effect of the photovoltaic layout under specific environmental conditions and provide decision-making support for subsequent layout adjustments.

[0050] In a possible implementation manner, by monitoring the noise source intensity and other influencing factors in the environment, the position, angle, and arrangement method of the photovoltaic panels are adjusted in real time. For example, if the noise source changes, the system can automatically adjust the photovoltaic panel layout through the continuous feedback mechanism to ensure the optimization of the sound insulation effect of the sound barrier. In this way, the combination of real-time data input, LSTM model calculation, and feedback adjustment forms a closed-loop optimization process.

[0051] To achieve continuous feedback optimization, in this embodiment, a variety of optimization algorithms are also introduced, such as genetic algorithms, particle swarm optimization, etc., which are used to optimize the photovoltaic layout after each adjustment. The optimization algorithm will continuously adjust the layout parameters of the photovoltaic panels according to the prediction results provided by the LSTM model, so as to optimize the sound insulation effect of the sound barrier. In this optimization process, multiple factors such as the power generation efficiency, economy of the photovoltaic modules, and the sound insulation ability of the sound barrier are considered to achieve the optimal design of the overall system.

[0052] Specifically, in an actual application scenario, the training process of the LSTM neural network will include the following steps: First, collect the acoustic data and environmental data under different photovoltaic layouts, including the layout characteristics of photovoltaic modules and the sound insulation performance data of the sound barrier; then, use these data to train the LSTM model, and by optimizing the neural network parameters, make the model accurately predict the sound insulation effect of the sound barrier under real-time data input; finally, use the trained LSTM model to adjust the layout according to the real-time data and perform feedback optimization.

[0053] Embodiment 2: Refer toFigure 2 , in the second embodiment of the present invention, the present invention provides an intelligent optimization layout system for a variable - type photovoltaic facade of a sound barrier based on photo - thermal coupling. The system includes: A data acquisition module, which is used to collect environmental data such as light intensity, temperature, humidity, and wind speed; The data acquisition module includes: A light intensity sensor, which is used to measure the light intensity in the environment in real - time; A temperature sensor, which is used to monitor the temperature change of the surface or the surrounding environment of the photovoltaic panel; A humidity sensor, which is used to measure the air humidity; A wind speed sensor, which is used to measure the wind speed; An inclination sensor, which is used to measure the tilt angle of the photovoltaic panel.

[0054] A photo - thermal coupling dynamic optimization module, which is used to calculate the light intensity of the photovoltaic panel based on the photo - thermal coupling model and adjust the tilt angle and spacing of the photovoltaic panel; A sound barrier sound insulation performance evaluation module, which is used to establish a coupling model between the photovoltaic layout and the sound barrier sound insulation performance and evaluate its influence; An LSTM neural network module, which is used to receive photovoltaic component data, calculate through the LSTM neural network and output the predicted value of the acoustic performance; An intelligent adjustment module, which is used to receive the optimization result and adjust the layout of the photovoltaic panel in real - time, and output instructions to the driving device for layout adjustment.

[0055] Specifically, in this embodiment, the data acquisition module collects real - time data through a variety of sensors. The light intensity sensor measures the light intensity in the environment, the temperature sensor monitors the temperature change of the surface or the surrounding environment of the photovoltaic panel, the humidity sensor measures the air humidity, the wind speed sensor measures the environmental wind speed, and the inclination sensor is used to monitor the actual tilt angle of the photovoltaic panel. These sensors transmit data to the central processor in real - time through the communication module, providing input data for the dynamic optimization algorithm.

[0056] In this embodiment, the photo - thermal coupling dynamic optimization algorithm calculates the optimal layout of the photovoltaic panel according to the real - time data. Under the initial conditions, the tilt angle of the photovoltaic panel is set as α, the spacing is set as d, and the arrangement method is parallel. When the light intensity changes, the photo - thermal coupling model dynamically adjusts the tilt angle θ opt and the spacing d opt of the photovoltaic panel through calculating the current light intensity and temperature, so as to maximize the light absorption area. For example, when the light intensity increases, the algorithm will adjust the tilt angle of the photovoltaic panel to the optimal angle θ opt , and appropriately reduce the spacing of the photovoltaic panels to ensure the best light absorption efficiency.

[0057] The sound insulation performance evaluation module evaluates the impact of photovoltaic panel adjustment on the sound insulation effect of the sound barrier by establishing a coupling model between the sound insulation performance of the sound barrier and the photovoltaic panel layout. For example, if the spacing between photovoltaic panels is too large, it may cause changes in the sound wave propagation path, thereby reducing the sound insulation effect of the sound barrier. At this time, the system will adjust the spacing and arrangement of the photovoltaic panels according to the evaluation results of the sound insulation performance of the sound barrier to ensure that the sound insulation effect after photovoltaic layout optimization will not decrease significantly.

[0058] The intelligent adjustment system includes a central processor, a communication module, and a driving device. The data acquisition module monitors environmental changes in real time. The central processor executes the optical-thermal coupling optimization algorithm and the sound barrier sound insulation performance evaluation model based on the received data, and calculates the optimal photovoltaic panel layout plan. The central processor transmits the adjustment instructions to the driving device through the communication module, and the driving device adjusts the tilt angle and spacing of the photovoltaic panels in real time according to the instructions. The intelligent adjustment system can adaptively adjust the photovoltaic panel layout according to the changes in real-time data to ensure the optimal balance between the photovoltaic power generation efficiency and the sound barrier sound insulation performance.

[0059] In this technology, in order to comprehensively consider the impact of the photovoltaic panel layout on the sound barrier, we propose a sound barrier sound insulation performance evaluation model that considers the impact of the photovoltaic panel arrangement and spacing on the sound wave propagation path. Specifically, the tilt angle, arrangement, and spacing of the photovoltaic panels will affect the sound wave propagation path and reflection, thereby changing the sound insulation effect of the sound barrier. The sound insulation performance of the sound barrier can be optimized from the following aspects: Tilt angle of the photovoltaic panel: The tilt angle of the photovoltaic panel directly affects its interference with the sound wave propagation path. Different tilt angles will affect the reflection angle of the sound wave and the attenuation effect of the sound wave. In order to ensure the effective blocking of the sound wave, it is necessary to optimize the tilt angle of the photovoltaic panel so that it will not generate excessive sound wave reflection.

[0060] Spacing and arrangement of the photovoltaic panels: The structural impact of the spacing and arrangement of the photovoltaic panels on the sound barrier also needs to be evaluated. For example, too large a spacing may cause the sound wave to propagate through the gap, thereby reducing the effective sound insulation effect of the sound barrier. The spacing between the photovoltaic panels should be reasonably designed according to the height and structure of the sound barrier to maximize its sound insulation ability.

[0061] Blocking effect of the photovoltaic panel: The blocking effect of the photovoltaic panel will directly affect the sound wave propagation path. Optimizing the layout of the photovoltaic panel so that it can ensure the power generation efficiency while not affecting the sound wave propagation path is the key to this model.

[0062] In practical applications, the sound insulation performance evaluation model can be carried out by means of acoustic simulation and numerical calculation. Common sound insulation performance indicators include the transmission loss (TL) of the sound barrier and the acoustic transmission rate (ATR) of the sound barrier. The system optimizes and adjusts the layout of the photovoltaic panels to minimize the influence of sound waves passing through the gaps between the photovoltaic panels or being interfered by reflection. During the dynamic optimization process, the sound insulation performance of the sound barrier needs to be balanced with the layout optimization goal of the photovoltaic panels.

[0063] In addition, the shading effect of the photovoltaic panels has a great influence on the transmission loss of the sound barrier. When the spacing between the photovoltaic panels is too large or the arrangement is unreasonable, adverse sound wave propagation channels may be generated. To avoid this problem, the system adjusts the spacing and arrangement of the photovoltaic panels in real time to ensure that the layout of the photovoltaic panels will not have a significant negative impact on the transmission loss of the sound barrier.

[0064] The intelligent adjustment system collects environmental data such as light intensity, temperature, humidity, and wind speed in real time, calculates the optimal layout of the photovoltaic panels according to the photothermal coupling optimization algorithm, and combines the sound insulation performance evaluation model of the sound barrier to ensure that each adjustment of the photovoltaic layout can improve the photovoltaic power generation efficiency without affecting the sound insulation effect of the sound barrier.

[0065] The system can evaluate the influence of the photovoltaic panel layout on sound wave propagation in real time, and optimize the sound insulation performance of the sound barrier by continuously adjusting the arrangement, spacing, and inclination angle of the photovoltaic panels. Finally, the system can automatically balance the photovoltaic power generation efficiency and the sound insulation effect of the sound barrier during the dynamic adjustment process to achieve the optimal balance.

[0066] Embodiment 3: In the third embodiment of the present invention, based on the same inventive concept, a computer-readable storage medium is proposed by the present invention. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for intelligent optimization layout of a sound barrier gradient photovoltaic facade based on photothermal coupling in the above embodiment are realized.

[0067] Embodiment 4: In the fourth embodiment of the present invention, based on the same inventive concept, a computer is proposed by the present invention. The computer includes: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory to execute the method for intelligent optimization layout of a sound barrier gradient photovoltaic facade based on photothermal coupling in the above embodiment.

[0068] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0069] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for intelligent optimization layout of a gradient photovoltaic facade of a sound barrier based on photo-thermal coupling, characterized in that, The method includes the following steps: S1. Collect data on light intensity, temperature, humidity, and wind speed; S2. Calculate the light intensity of the current photovoltaic panel based on the dynamic optimization algorithm of the photothermal coupling model, and adjust the inclination angle and spacing of the photovoltaic panel according to the light intensity; S3. Establish a coupling model between the sound insulation performance of the sound barrier and the photovoltaic layout, evaluate the impact of the photovoltaic layout on the sound insulation effect of the sound barrier through acoustic analysis, and synchronously optimize the layout design; S4. Adjust the layout of the photovoltaic panel in real time and perform continuous feedback optimization.

2. The intelligent optimization layout method of the gradient photovoltaic facade of the sound barrier based on photo-thermal coupling according to claim 1, wherein, The adjustment process in step S2 is achieved through the following formula: θ opt = θ0 + k1·ln(I) - k2·T; Among them, θ0 is the reference inclination angle, k1 and k2 are regression coefficients, I is the current light intensity, T is the current temperature, and θ opt is the optimal inclination angle; When the light intensity I increases, the algorithm adjusts the inclination angle α and spacing d of the photovoltaic panel according to the following formula: α new = α current + Δα·sign(θ opt - α current ); d new = d current - Δd·H(I - I thr ); where α and d are the inclination angle and the spacing respectively, Δα and Δd are the adjustment steps of the inclination angle and the spacing respectively, and H is the Heaviside function used to determine whether the light intensity threshold I is exceeded thr , if the light intensity I exceeds the threshold, the spacing d is reduced to maximize the light absorption area.

3. The intelligent optimization layout method of the gradient photovoltaic facade of the sound barrier based on photo-thermal coupling according to claim 2, wherein The dynamic optimization algorithm is further adjusted based on the feedback of the power generation efficiency η, and the power generation efficiency η is calculated through the following formula: η = η ref ·(1 - β·(T - T ref ))·cos(θ opt - α) Among them, η ref is the photovoltaic power generation efficiency at the reference temperature, β is the temperature sensitivity coefficient, T ref is the reference temperature, and θ opt is the optimal tilt angle.

4. The intelligent optimization layout method for the gradient photovoltaic facade of the sound barrier based on photo-thermal coupling according to claim 1, wherein In step S3, the coupling model is calculated through the LSTM model, where the LSTM neural network receives photovoltaic module data as input, and the output result after calculation is the predicted value of the acoustic performance including the insertion loss or total sound insulation amount.

5. The intelligent optimization layout method for the gradient photovoltaic facade of the sound barrier based on photo-thermal coupling according to claim 4, characterized in that, The data received by the LSTM neural network includes the arrangement method, inclination angle, spacing, light intensity, temperature, and wind speed.

6. The intelligent optimization layout method of the gradient photovoltaic facade of the sound barrier based on photo-thermal coupling according to claim 4, characterized in that The predicted value of the acoustic performance is used to evaluate the insertion loss or sound insulation effect of the sound barrier under the current layout, and is used as the basis for synchronously optimizing the layout design.

7. The intelligent optimization layout system for the gradient photovoltaic facade of the sound barrier based on photo-thermal coupling is characterized in that For the intelligent optimization layout method of the sound barrier gradient photovoltaic facade based on photothermal coupling according to any one of claims 1-6, the system includes: a data acquisition module for collecting environmental data on light intensity, temperature, humidity, and wind speed; A photothermal coupling dynamic optimization module for calculating the light intensity of the photovoltaic panel based on the photothermal coupling model and adjusting the inclination angle and spacing of the photovoltaic panel; A sound barrier sound insulation performance evaluation module for establishing a coupling model between the photovoltaic layout and the sound insulation performance of the sound barrier and evaluating its impact; An LSTM neural network module for receiving photovoltaic module data, calculating through the LSTM neural network, and outputting the predicted value of the acoustic performance; An intelligent adjustment module for receiving the optimization result and adjusting the layout of the photovoltaic panel in real time, and outputting an instruction to the driving device for layout adjustment.

8. The intelligent optimization layout system for the gradient photovoltaic facade of the sound barrier based on photo-thermal coupling according to claim 7, wherein, The data acquisition module includes: A light intensity sensor for measuring the light intensity in the environment in real time; A temperature sensor for monitoring the temperature change on the surface of the photovoltaic panel or the surrounding environment; A humidity sensor for measuring the air humidity; A wind speed sensor for measuring the wind speed; An inclination angle sensor for measuring the inclination angle of the photovoltaic panel.

9. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the intelligent optimization layout method of the sound barrier gradient photovoltaic facade based on photothermal coupling according to any one of claims 1 to 6.

10. A readable storage medium, characterized in that, A computer program is stored on the readable storage medium, and when the computer program is executed by the processor, it implements the intelligent optimization layout method of the sound barrier gradient photovoltaic facade based on photothermal coupling according to any one of claims 1 to 6.

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