Sound barrier photovoltaic facade intelligent optimization layout method and system based on light-heat coupling

Abstract

The present application relates to the field of construction engineering, and discloses a sound barrier gradient photovoltaic facade intelligent optimization layout method based on light-heat coupling, comprising the following steps: S1, collecting light intensity, temperature, humidity and wind speed data; S2, calculating the current light intensity of the photovoltaic panel based on the dynamic optimization algorithm of the light-heat coupling model, and adjusting the inclination and spacing of the photovoltaic panel according to the light 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; S4, real-time adjusting the layout of the photovoltaic panel and continuously feeding back and optimizing. In the present application, by introducing the light-heat coupling dynamic optimization algorithm, the inclination, spacing and arrangement mode of the photovoltaic panel can be adjusted in real time, so that the photovoltaic panel can dynamically adapt to factors such as environmental light intensity and temperature, thereby ensuring that the photovoltaic panel is always in the best light condition.

Description

Technical Field

[0001] This invention relates to the field of building engineering, and in particular to a method and system for intelligent optimization layout of photovoltaic facades for sound barriers based on photothermal coupling. Background Technology

[0002] With the accelerating pace of urbanization and the rapid expansion of transportation infrastructure, traffic noise pollution has become an increasingly serious problem, significantly impacting residents' quality of life and the urban environment. To effectively reduce noise interference, sound barriers, as a widely used physical sound insulation facility, have been deployed on a large scale in scenarios such as highways, overpasses, and urban main roads.

[0003] In recent years, with the development of renewable energy technologies, some sound barriers have begun to integrate solar photovoltaic modules, striving to achieve localized energy production and self-sufficiency while improving the overall energy efficiency and green value of the facilities, in addition to sound insulation and noise reduction. However, existing integration methods mostly adopt fixed tilt angles and parallel photovoltaic arrangement structures, failing to fully consider the dynamic impact of external environmental conditions (such as light intensity, solar incidence angle, temperature changes, etc.) on photovoltaic power generation efficiency, resulting in unstable power generation performance in different time periods and seasons, and overall low power generation efficiency. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides a method and system for intelligent optimization layout of photovoltaic facades for sound barriers based on photothermal coupling, aiming to improve the problems of poor flexibility and low power generation efficiency of existing photovoltaic sound barriers.

[0005] In a first aspect, the present invention provides the following technical solution: a method for intelligent optimization layout of a gradient photovoltaic facade for sound barriers based on photothermal coupling, the method comprising the following steps:

[0006] S1. Collect data on light intensity, temperature, humidity, and wind speed;

[0007] S2. The dynamic optimization algorithm based on the photothermal coupling model calculates the current light intensity of the photovoltaic panel and adjusts the tilt angle and spacing of the photovoltaic panel according to the light intensity.

[0008] S3. Establish a coupling model between the sound barrier's sound insulation performance and the photovoltaic layout. Through acoustic analysis, evaluate the impact of the photovoltaic layout on the sound barrier's sound insulation effect and simultaneously optimize the layout design.

[0009] S4. Adjust the layout of the photovoltaic panels in real time and continuously optimize based on feedback.

[0010] Preferably, the adjustment process in step S2 is achieved through the following formula:

[0011] θ opt=θ0+k1·ln(I)-k2·T;

[0012] Where θ0 is the reference tilt angle, k1 and k2 are regression coefficients, I is the current light intensity, T is the current temperature, and θ opt The optimal tilt angle;

[0013] As the light intensity I increases, the algorithm adjusts the tilt angle α and spacing d of the photovoltaic panels according to the following formula:

[0014] α new =α current +Δα·sign(θ opt -α current );

[0015] d new =d current -Δd·H(II thr );

[0016] Where α and d are the tilt angle and spacing, respectively, Δα and Δd are the adjustment step sizes for the tilt angle and spacing, 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.

[0017] Preferably, the dynamic optimization algorithm is further adjusted based on the power generation efficiency η feedback, which is calculated using the following formula:

[0018] η = η ref ·(1-β·(TT ref ))·cos(θ opt -α)

[0019] Where, η ref Let β be the photovoltaic power generation efficiency at the reference temperature, and T be the temperature sensitivity coefficient. ref For reference temperature, θ opt This is the optimal tilt angle.

[0020] Preferably, in step S3, the coupling model is calculated using an LSTM model, wherein the LSTM neural network receives photovoltaic module data as input and outputs a predicted acoustic performance value including insertion loss or total sound insulation.

[0021] Preferably, the data received by the LSTM neural network includes the arrangement, tilt angle, spacing, light intensity, temperature, and wind speed.

[0022] Preferably, the predicted acoustic performance values ​​are used to evaluate the insertion loss or sound insulation effect of the sound barrier under the current layout, serving as a basis for synchronous optimization of the layout design.

[0023] Secondly, the present invention provides the following technical solution: a sound barrier gradient photovoltaic facade intelligent optimization layout system based on photothermal coupling, the system comprising:

[0024] The data acquisition module is used to collect environmental data such as light intensity, temperature, humidity, and wind speed.

[0025] The photothermal coupling dynamic optimization module is used to calculate the light intensity of the photovoltaic panel based on the photothermal coupling model, and to adjust the tilt angle and spacing of the photovoltaic panel.

[0026] The sound barrier sound insulation performance evaluation module is used to establish a coupling model between photovoltaic layout and sound barrier sound insulation performance, and to evaluate its impact.

[0027] The LSTM neural network module is used to receive data from photovoltaic modules, perform calculations through the LSTM neural network, and output predicted values ​​of acoustic performance.

[0028] The intelligent adjustment module is used to receive optimization results and adjust the layout of photovoltaic panels in real time, and output instructions to the drive device for layout adjustment.

[0029] Preferably, the data acquisition module includes:

[0030] Light intensity sensor, used to measure the light intensity in the environment in real time;

[0031] Temperature sensors are used to monitor temperature changes on the surface of photovoltaic panels or in the surrounding environment.

[0032] Humidity sensor, used to measure air humidity;

[0033] Wind speed sensor, used to measure wind speed;

[0034] Tilt sensor, used to measure the tilt angle of photovoltaic panels.

[0035] Thirdly, the invention provides the following technical solution: a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned intelligent optimization layout method for a sound barrier gradient photovoltaic facade based on photothermal coupling.

[0036] Fourthly, the present invention provides the following technical solution: a readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the above-mentioned intelligent optimization layout method for a sound barrier gradient photovoltaic facade based on photothermal coupling.

[0037] The present invention has the following beneficial effects:

[0038] 1. In this invention, by introducing a photothermal coupling dynamic optimization algorithm, the tilt angle, spacing, and arrangement of photovoltaic panels can be adjusted in real time. This allows the photovoltaic panels to dynamically adapt to factors such as ambient light intensity and temperature, ensuring that they are always under optimal lighting conditions. This optimization mechanism significantly improves photovoltaic power generation efficiency, fully utilizes solar energy resources, and avoids the problem of low power generation efficiency under traditional fixed layouts.

[0039] 2. In this invention, by combining a sound barrier sound insulation performance evaluation and optimization model, it is ensured that dynamic adjustments to the photovoltaic panel layout will not significantly affect the sound insulation effect of the sound barrier. By rationally designing the tilt angle, spacing, and arrangement of the photovoltaic panels, the layout of the photovoltaic panels can minimize interference with the propagation path of sound waves and maintain the effective sound insulation function of the sound barrier.

[0040] 3. In this invention, by integrating a real-time environmental data acquisition module, a central processing unit, and an automatic drive device, the automatic adjustment of the photovoltaic panel layout is achieved. The system can automatically calculate the optimal layout scheme based on real-time collected data such as light intensity and temperature, and adjust the position of the photovoltaic panels through the drive device. This intelligent and automated adjustment system avoids manual intervention, improves the system's stability and sustainability, and reduces manual maintenance costs. Attached Figure Description

[0041] Figure 1 This is a flowchart of the intelligent optimization layout method for a gradient photovoltaic facade based on photothermal coupling for sound barriers proposed in this invention.

[0042] Figure 2 This is a diagram of the intelligent optimized layout system for a gradient photovoltaic facade based on photothermal coupling for sound barriers proposed in this invention. Detailed Implementation

[0043] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Example 1:

[0045] Reference Figure 1 In the first embodiment of the present invention, the present invention provides a method for intelligent optimization layout of a gradient photovoltaic facade for sound barriers based on photothermal coupling, the method comprising the following steps:

[0046] S1. Collect data on light intensity, temperature, humidity, and wind speed;

[0047] Specifically, in this embodiment, the data acquisition module collects real-time data on light intensity, temperature, humidity, and wind speed using multiple sensors. The main function of this module is to provide the necessary environmental data for the subsequent photothermal coupling dynamic optimization algorithm, enabling more accurate calculation of the light intensity of the photovoltaic panels. This allows for adjustment of the tilt angle and spacing of the photovoltaic panels, thereby optimizing the power generation efficiency of the photovoltaic system and the sound insulation performance of the sound barrier.

[0048] In the specific implementation process, the data acquisition module collects the following environmental data by connecting to the sensor: In this embodiment, the light intensity is measured using a light intensity sensor (such as a photovoltaic sensor, photodiode, etc.). The light intensity sensor monitors changes in light intensity in the environment in real time, providing data support for the subsequent photothermal coupling dynamic optimization module.

[0049] Specifically, light intensity data can be expressed using the following formula:

[0050] I current =k·I ambient ·cos(θ);

[0051] Among them, I current I represents the current light intensity received by the photovoltaic panel. ambient θ represents the ambient light intensity, θ represents the illumination angle, and k represents the calibration coefficient of the light sensor.

[0052] Temperature data is acquired in real time using temperature sensors (such as thermocouples and thermistors). These sensors are installed on the surface of the photovoltaic panel or in the surrounding environment to measure changes in ambient temperature. Based on these temperature changes, the tilt angle and spacing of the photovoltaic panels are dynamically adjusted to optimize 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));

[0053] Where 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, and T is the reference temperature.

[0054] Humidity sensors are used to measure humidity in the air to determine its impact on photovoltaic (PV) systems. Changes in humidity can affect the performance of PV panels, especially in high-humidity environments, leading to condensation or corrosion on the panel surface and thus affecting their photoelectric conversion efficiency. Humidity data can be collected by humidity sensors (such as capacitive and conductive types) and considered in conjunction with temperature data to adjust the layout of the PV panels.

[0055] Wind speed sensors monitor ambient wind speed, which affects the cooling effect of photovoltaic (PV) panels and consequently their operating efficiency. Wind speed sensors can measure wind speed using methods such as ultrasonic waves or cup anemometers, providing real-time wind speed data for dynamic optimization algorithms. Based on the wind speed, the system can further adjust the spacing between PV panels to optimize the overall performance of the PV system.

[0056] During data acquisition, data from all sensors is transmitted to the central processing unit (CPU) via the communication module for real-time processing. Based on the data acquired by the data acquisition module, the CPU executes the corresponding photothermal coupling dynamic optimization algorithm and adjusts the layout of the photovoltaic panels. Throughout this process, the sensor data undergoes filtering and processing to ensure its accuracy and validity.

[0057] For example, light intensity may be correlated with factors such as temperature, humidity, and wind speed. The data acquisition module should adaptively adjust according to changes in real-time data to ensure that the collected environmental data has high timeliness and accuracy.

[0058] In addition, in this embodiment, the data acquisition module may also include other auxiliary sensors, such as a tilt sensor, for measuring the actual tilt 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.

[0059] S2. The dynamic optimization algorithm based on the photothermal coupling model calculates the current light intensity of the photovoltaic panel and adjusts the tilt angle and spacing of the photovoltaic panel according to the light intensity.

[0060] The S2 step adjustment process is achieved through the following formula:

[0061] θ opt =θ0+k1·ln(I)-k2·T;

[0062] Where θ0 is the reference tilt angle, k1 and k2 are regression coefficients, I is the current light intensity, T is the current temperature, and θ opt The optimal tilt angle;

[0063] As the light intensity I increases, the algorithm adjusts the tilt angle α and spacing d of the photovoltaic panels according to the following formula:

[0064] α new =α current +Δα·sign(θ opt -α current );

[0065] d new =d current -Δd·H(II thr );

[0066] Where α and d are the tilt angle and spacing, respectively, Δα and Δd are the adjustment step sizes for the tilt angle and spacing, 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.

[0067] The dynamic optimization algorithm further adjusts based on feedback of power generation efficiency η, which is calculated using the following formula: η = η ref ·(1-β·(TT ref ))·cos(θ opt -α);

[0068] Where, η ref Let β be the photovoltaic power generation efficiency at the reference temperature, and T be the temperature sensitivity coefficient. ref For reference temperature, θ opt This is the optimal tilt angle.

[0069] Specifically, in this embodiment, after the data acquisition module completes the collection of data such as light intensity, temperature, humidity, and wind speed, the photothermal coupling dynamic optimization module performs calculations based on this real-time data using a photothermal coupling model. The main purpose of this module is to optimize the layout of the photovoltaic panels according to the current ambient light intensity, that is, to adjust the tilt angle and spacing of the photovoltaic panels to achieve higher photovoltaic power generation efficiency.

[0070] By using a photothermal coupling model, the system can take into account the impact of environmental factors such as temperature, humidity, and wind speed on the photovoltaic panels, thereby calculating the optimal light intensity. Subsequently, the system further adjusts the tilt angle and spacing of the photovoltaic panels based on the calculation results to improve the overall power generation efficiency of the system.

[0071] Specifically, there is a functional relationship between light intensity and the tilt angle and spacing of photovoltaic panels. Changes in light intensity directly affect the energy received by the photovoltaic panels; therefore, the angle and spacing of the photovoltaic panels need to be dynamically adjusted to ensure maximum absorption of solar radiation. For this purpose, this embodiment uses the following formula to describe the relationship between light intensity and the angle of the photovoltaic panels:

[0072] I current =k·I ambient ·cos(θ);

[0073] Among them, I current I represents the current light intensity received by the photovoltaic panel. ambient θ represents the ambient light intensity, θ represents the illumination angle, and k represents the calibration coefficient of the light sensor.

[0074] Generally, when sunlight intensity increases, the tilt angle of photovoltaic panels will be appropriately reduced to avoid excessive heat generation caused by excessive sunlight, which would reduce the efficiency of the photovoltaic panels. At the same time, the spacing will also be adjusted to adapt to changes in sunlight intensity, ensuring that the photovoltaic system can absorb more light energy.

[0075] Alternatively, this embodiment adjusts the tilt angle and spacing of the photovoltaic panels using the following formula:

[0076] α new =α base -β·ΔI light ;

[0077] d new =d base -γ·ΔI light ;

[0078] Where, α new For the adjusted tilt angle of the photovoltaic panel, α base Let ΔI be the initial tilt angle. light d represents the change in light intensity, β and γ are adjustment coefficients, and d new and d base These are the adjusted spacing and the initial spacing, respectively.

[0079] When the light intensity exceeds a certain threshold, the system reduces the spacing between the photovoltaic panels to maximize the light absorption area. At this point, the spacing between the photovoltaic panels can be adjusted according to the following formula:

[0080] Δd=H(I current -I threshold )·Δd max ;

[0081] Where Δd is the spacing adjustment amount, and H is the Heaviside function, indicating that spacing adjustment will only be performed when the light intensity exceeds a threshold. max This is for the maximum adjustment step size.

[0082] Specifically, when the light intensity reaches or exceeds a set threshold, the system automatically reduces the spacing between the photovoltaic panels to ensure that the system can absorb energy from the sun to the maximum extent. At this time, the tilt angle of the photovoltaic panels will also be adjusted appropriately to adapt to the new layout and avoid heat generation problems caused by excessive sunlight. In some embodiments, the photothermal coupling model will further optimize the tilt angle and spacing of the photovoltaic panels based on factors such as wind speed and humidity.

[0083] For example, when wind speeds are high, the system may increase the spacing between photovoltaic panels to reduce wind resistance and improve system stability. Through the above optimization process, the photothermal coupling dynamic optimization module can adjust the layout of photovoltaic panels in real time according to changes in environmental conditions, achieving efficient operation of the photovoltaic system.

[0084] S3. Establish a coupling model between the sound barrier's sound insulation performance and the photovoltaic (PV) layout. Through acoustic analysis, evaluate the impact of the PV layout on the sound barrier's sound insulation effect and simultaneously optimize the layout design. The coupling model is calculated using an LSTM (Laser-Based Memory) model. The LSTM neural network receives PV module data as input and outputs predicted acoustic performance values, including insertion loss or total sound insulation. The data received by the LSTM neural network includes arrangement, tilt angle, spacing, light intensity, temperature, and wind speed. The predicted acoustic performance values ​​are used to evaluate the sound barrier's insertion loss or sound insulation effect under the current layout, serving as a basis for simultaneous optimization of the layout design.

[0085] Specifically, this invention proposes a coupled model based on an LSTM neural network to evaluate the impact of photovoltaic module layout on the sound insulation performance of sound barriers and optimize the layout design. This coupled model between sound barrier sound insulation performance and photovoltaic layout aims to optimize the sound insulation effect of sound barriers through acoustic analysis and artificial intelligence technology.

[0086] Generally, the sound insulation performance of sound barriers is affected by various factors, including the material and structural design of the sound barrier itself, as well as the surrounding environment. With the rapid development of the photovoltaic industry, more and more photovoltaic modules are being placed on or around sound barriers, which may have a certain impact on the sound insulation effect of the sound barriers. Therefore, this invention proposes an innovative method to predict and optimize the effect of photovoltaic layout on the sound insulation performance of sound barriers by establishing a coupling model between the sound insulation performance of sound barriers and photovoltaic layout, combined with an LSTM neural network.

[0087] Alternatively, in this embodiment, the input to the LSTM model is relevant data about the photovoltaic modules, including their arrangement, angle, size, and surface material. Using this input data, the LSTM model can be trained and computed, outputting predicted acoustic performance values, specifically insertion loss or total sound insulation. These acoustic performance indicators reflect the sound insulation capability of the sound barrier under different photovoltaic layouts, thus providing a scientific basis for optimizing photovoltaic module layout.

[0088] Specifically, in this embodiment, acoustic analysis was used to evaluate the impact of different photovoltaic (PV) layouts on the sound insulation performance of the sound barrier. The acoustic analysis model is mainly based on acoustic propagation theory, combined with the specific PV module arrangement, and considers the influence of sound wave propagation paths, reflection, diffraction, and other factors on the sound barrier's sound insulation effect. By introducing an LSTM neural network, this model can be trained under multiple possible layout schemes, thereby improving the accuracy and reliability of the model's predictions.

[0089] In one possible implementation, the training process of the coupled model includes the following steps: First, acoustic data under different photovoltaic layouts are collected, including the layout characteristics of photovoltaic modules and the sound insulation performance data of sound barriers; then, LSTM models are trained using these data, and the neural network parameters are optimized so that the model can accurately predict the sound insulation effect of sound barriers under different layout conditions; finally, the trained LSTM model is used to predict the insertion loss or total sound insulation under different photovoltaic layouts, and further layout optimization is performed.

[0090] In addition, other influencing factors, such as meteorological conditions (wind speed, temperature, etc.) and terrain features, need to be considered in this embodiment. These factors may indirectly affect the sound insulation effect of the sound barrier in the photovoltaic layout, so appropriate supplementation and adjustment are required in the coupled model. For example, certain meteorological conditions may cause changes in the speed of sound wave propagation, thereby affecting the sound insulation effect of the sound barrier.

[0091] In some embodiments, the model's output is not limited to insertion loss or total sound insulation; it can be extended to other acoustic parameters such as frequency response and reflection coefficient. Depending on the specific needs, these results can be used to further optimize the design of sound barriers or provide a reference for the layout of photovoltaic modules. In this case, the output of the LSTM model can provide guidance for acoustic engineers, enabling sound barriers and photovoltaic layouts to achieve a better balance between sound insulation performance and economic benefits.

[0092] S4. Adjust the layout of the photovoltaic panels in real time and continuously optimize based on feedback.

[0093] Specifically, in this embodiment, by combining the aforementioned coupled model of photovoltaic layout and sound barrier sound insulation performance, a real-time data input and continuous feedback mechanism is used to dynamically adjust the layout of the photovoltaic panels. Specifically, during implementation, the layout characteristics of the photovoltaic modules, the sound insulation performance data of the sound barrier, and external environmental factors (such as meteorological conditions and noise source intensity) will be used as real-time input data, calculated and optimized through an LSTM neural network model. This input data includes, but is not limited to, the arrangement method, angle, size, surface material of the photovoltaic panels, and meteorological data.

[0094] Alternatively, the system can assess the impact of the current photovoltaic (PV) panel layout on the sound barrier's noise insulation performance based on real-time input data. Through calculations using an LSTM neural network, the model can predict the sound insulation performance of the PV panel layout under real-time changing environmental conditions. Specifically, the LSTM model can predict the insertion loss (IL) or total sound insulation (SI) under the PV layout and provide real-time feedback to optimize the layout. The model's output reflects the sound insulation effect of the PV layout under specific environmental conditions and provides decision support for subsequent layout adjustments.

[0095] In one possible implementation, the position, angle, and arrangement of photovoltaic panels are adjusted in real time by monitoring the intensity of noise sources and other influencing factors in the environment. For example, if the noise source changes, the system can automatically adjust the layout of the photovoltaic panels through a continuous feedback mechanism to ensure optimal sound insulation performance of the sound barrier. In this approach, real-time data input, LSTM model calculation, and feedback adjustment are combined to form a closed-loop optimization process.

[0096] To achieve continuous feedback optimization, this embodiment also introduces various optimization algorithms, such as genetic algorithms and particle swarm optimization, to optimize the photovoltaic layout after each adjustment. The optimization algorithms continuously adjust the layout parameters of the photovoltaic panels based on the prediction results provided by the LSTM model, thereby optimizing the sound insulation effect of the sound barrier. This optimization process considers multiple factors, including the power generation efficiency and economics of the photovoltaic modules, as well as the sound insulation capability of the sound barrier, to achieve the optimal design of the overall system.

[0097] Specifically, in a practical application scenario, the training process of the LSTM neural network will include the following steps: First, collect acoustic data and environmental data under different photovoltaic layouts, including the layout characteristics of photovoltaic modules and the sound insulation performance data of sound barriers; then, use this data to train the LSTM model, and optimize the neural network parameters so that the model can 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 real-time data and perform feedback optimization.

[0098] Example 2:

[0099] Reference Figure 2 In a second embodiment of the present invention, the present invention provides a smart optimization layout system for a sound barrier with a gradient photovoltaic facade based on photothermal coupling, the system comprising:

[0100] The data acquisition module is used to collect environmental data such as light intensity, temperature, humidity, and wind speed.

[0101] The data acquisition module includes:

[0102] Light intensity sensor, used to measure the light intensity in the environment in real time;

[0103] Temperature sensors are used to monitor temperature changes on the surface of photovoltaic panels or in the surrounding environment.

[0104] Humidity sensor, used to measure air humidity;

[0105] Wind speed sensor, used to measure wind speed;

[0106] Tilt sensor, used to measure the tilt angle of photovoltaic panels.

[0107] The photothermal coupling dynamic optimization module is used to calculate the light intensity of the photovoltaic panel based on the photothermal coupling model, and to adjust the tilt angle and spacing of the photovoltaic panel.

[0108] The sound barrier sound insulation performance evaluation module is used to establish a coupling model between photovoltaic layout and sound barrier sound insulation performance, and to evaluate its impact.

[0109] The LSTM neural network module is used to receive data from photovoltaic modules, perform calculations through the LSTM neural network, and output predicted values ​​of acoustic performance.

[0110] The intelligent adjustment module is used to receive optimization results and adjust the layout of photovoltaic panels in real time, and output instructions to the drive device for layout adjustment.

[0111] Specifically, in this embodiment, the data acquisition module collects real-time data through multiple sensors. A light intensity sensor measures the ambient light intensity, a temperature sensor monitors temperature changes on the photovoltaic panel surface or in the surrounding environment, a humidity sensor measures air humidity, a wind speed sensor measures ambient wind speed, and a tilt sensor monitors the actual tilt angle of the photovoltaic panel. These sensors transmit the data in real-time to the central processing unit via a communication module, providing input data for the dynamic optimization algorithm.

[0112] In this embodiment, the photothermal coupling dynamic optimization algorithm calculates the optimal layout of the photovoltaic panels based on real-time data. Initially, the tilt angle of the photovoltaic panels is set to α, the spacing to d, and the arrangement to be parallel. When the light intensity changes, the photothermal coupling model dynamically adjusts the tilt angle θ of the photovoltaic panels by calculating the current light intensity and temperature. opt and spacing d opt This maximizes the area for light absorption. For example, when the light intensity increases, the algorithm adjusts the tilt angle of the photovoltaic panel to the optimal angle θ. opt And appropriately reduce the spacing between photovoltaic panels to ensure optimal light absorption efficiency.

[0113] The sound insulation performance evaluation module establishes a coupled model between the sound barrier's sound insulation performance and the photovoltaic panel layout to assess the impact of photovoltaic panel adjustments on the sound barrier's sound insulation effect. 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. In this case, the system will adjust the spacing and arrangement of the photovoltaic panels based on the sound barrier's sound insulation performance evaluation results to ensure that the sound insulation effect after the photovoltaic layout optimization does not significantly decrease.

[0114] The intelligent adjustment system comprises a central processing unit (CPU), a communication module, and a drive unit. The data acquisition module monitors environmental changes in real time. Based on the received data, the CPU executes a photothermal coupling optimization algorithm and a sound barrier sound insulation performance evaluation model to calculate the optimal photovoltaic panel layout. The CPU transmits adjustment commands to the drive unit via the communication module, and the drive unit adjusts the tilt angle and spacing of the photovoltaic panels in real time according to the commands. The intelligent adjustment system can adaptively adjust the photovoltaic panel layout based on changes in real-time data, ensuring an optimal balance between photovoltaic power generation efficiency and sound barrier sound insulation performance.

[0115] In this technology, to comprehensively consider the impact of photovoltaic panel layout on sound barriers, we propose a sound barrier sound insulation performance evaluation model that considers the influence of photovoltaic panel arrangement and spacing on the sound wave propagation path. Specifically, the tilt angle, arrangement, and spacing of the photovoltaic panels 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 through the following aspects:

[0116] The tilt angle of photovoltaic panels directly affects their interference with the propagation path of sound waves. Different tilt angles affect the reflection angle and attenuation effect of sound waves. To ensure effective sound wave blocking, the tilt angle of the photovoltaic panels needs to be optimized to prevent excessive sound wave reflection.

[0117] The spacing and arrangement of photovoltaic panels also need to be assessed regarding their structural impact on the sound barrier. For example, excessive spacing may cause sound waves to propagate through the gaps, thus reducing the effective sound insulation performance of the sound barrier. The spacing between photovoltaic panels should be rationally designed based on the height and structure of the sound barrier to maximize its sound insulation capacity.

[0118] Photovoltaic panel shading effect: The shading effect of photovoltaic panels directly affects the propagation path of sound waves. Optimizing the layout of photovoltaic panels so that they can ensure power generation efficiency without affecting the propagation path of sound waves is the key to this model.

[0119] In practical applications, sound insulation performance evaluation models can be developed using acoustic simulation and numerical calculation methods. Common sound insulation performance indicators include the transmission loss (TL) and acoustic transmission rate (ATR) of the sound barrier. The system optimizes the layout of the photovoltaic panels to minimize the impact of sound waves passing through gaps in the photovoltaic panels or interfering through reflection. During dynamic optimization, the sound insulation performance of the sound barrier needs to be balanced with the optimization goals of the photovoltaic panel layout.

[0120] Furthermore, the shading effect of photovoltaic panels has a significant impact on the sound transmission loss of the sound barrier. When the spacing between photovoltaic panels is too large or their arrangement is unreasonable, unfavorable sound wave propagation channels may be created. To avoid this problem, the system ensures that the photovoltaic panel layout does not have a significant negative impact on the sound transmission loss of the sound barrier by adjusting the spacing and arrangement of the photovoltaic panels in real time.

[0121] The intelligent adjustment system collects environmental data such as light intensity, temperature, humidity, and wind speed in real time, calculates the optimal photovoltaic panel layout based on the photothermal coupling optimization algorithm, and combines it with the sound barrier sound insulation performance evaluation model to ensure that every adjustment to the photovoltaic layout can improve the photovoltaic power generation efficiency without affecting the sound insulation effect of the sound barrier.

[0122] This system can assess the impact of 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 tilt angle of the photovoltaic panels. Ultimately, the system can automatically balance photovoltaic power generation efficiency and sound barrier sound insulation effect during dynamic adjustment, achieving the optimal balance.

[0123] Example 3:

[0124] In the third embodiment of the present invention, based on the same inventive concept, the present invention proposes a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the above-described embodiment of the intelligent optimization layout method for a sound barrier with a gradient photovoltaic facade based on photothermal coupling.

[0125] Example 4:

[0126] In the fourth embodiment of the present invention, based on the same inventive concept, the present invention proposes a computer, which 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 intelligent optimization layout method for gradient photovoltaic facade based on photothermal coupling of the sound barrier in the above embodiment.

[0127] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0128] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended 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 make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent optimization layout of a sound barrier based on light-thermal coupling, characterized in that, The method comprises the following steps: S1, collecting light intensity, temperature, humidity and wind speed data; S2, calculating the light intensity of the current photovoltaic panel based on a dynamic optimization algorithm of a light-heat coupling model, and adjusting the inclination and spacing of the photovoltaic panel according to the light intensity; The S2 step adjustment process is realized by the following formula: ； wherein, is the reference tilt angle, , is the regression coefficient, is the current light intensity, T is the current temperature, is the optimal tilt angle; When the light intensity increases, the algorithm adjusts the inclination angle a and the spacing d of the photovoltaic panels according to the following formula: ； ； wherein, and are the tilt angle and the pitch, respectively, and Δd are the adjustment step of the tilt angle and the pitch, respectively, H is the Heaviside function, used to determine whether the light intensity threshold is exceeded , if the light intensity exceeds the threshold, the pitch is reduced to maximize the light absorption area; The dynamic optimization algorithm is further adjusted based on power generation efficiency η feedback, which is calculated by the following formula: ； wherein, is the photovoltaic power generation efficiency at a reference temperature, is a temperature sensitivity coefficient, is a reference temperature, is an optimal tilt angle; S3, establishing a coupling model between the sound barrier sound insulation performance 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; S4, real-time adjustment of the layout of the photovoltaic panel and continuous feedback optimization.

2. The method of claim 1, wherein the method is a method of intelligent optimization layout of a photo-thermal coupling-based acoustic barrier graded photovoltaic facade, characterized in that, The coupling model in the S3 step is calculated by an LSTM model, wherein the LSTM neural network receives photovoltaic component data as input and outputs acoustic performance prediction values including insertion loss or total sound insulation after calculation.

3. The method of claim 2, wherein the method is a method of intelligent optimization layout of a photo-thermal coupling-based acoustic barrier graded photovoltaic facade, characterized in that, The data accepted by the LSTM neural network includes arrangement mode, inclination, spacing, light intensity, temperature and wind speed.

4. The method of claim 2, wherein the method is a method of intelligent optimization layout of a photo-thermal coupling-based acoustic barrier graded photovoltaic facade, characterized in that, The acoustic performance prediction value is used to evaluate the sound barrier insertion loss or sound insulation effect under the current layout, and is used as the basis for synchronous optimization of the layout design.

5. The light-heat coupling based acoustic barrier gradient photovoltaic facade intelligent optimization layout system is characterized in that, The system for the light-heat coupling-based sound barrier gradient photovoltaic facade intelligent optimization layout method of any one of claims 1-4, the system comprising: a data acquisition module for acquiring light intensity, temperature, humidity and wind speed environmental data; a light-heat coupling dynamic optimization module for calculating the light intensity of the photovoltaic panel based on a light-heat coupling model and adjusting the inclination 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 barrier sound insulation performance and evaluating its influence; an LSTM neural network module for receiving photovoltaic component data, calculating through an LSTM neural network and outputting acoustic performance prediction values; an intelligent adjustment module for receiving optimization results and real-time adjustment of the layout of the photovoltaic panel, and outputting instructions to the driving device for layout adjustment.

6. The graded photovoltaic facade intelligent optimized layout system based on photo-thermal coupling acoustic barrier of claim 5, wherein, The data acquisition module comprises: a light intensity sensor for real-time measurement of the light intensity in the environment; a temperature sensor for monitoring the temperature change of the photovoltaic panel surface or the surrounding environment; a humidity sensor for measuring air humidity; a wind speed sensor for measuring wind speed; an inclination sensor for measuring the inclination angle of the photovoltaic panel.

7. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the light-heat coupling-based sound barrier gradient photovoltaic facade intelligent optimization layout method of any one of claims 1-4.

8. A readable storage medium, characterized by, The readable storage medium stores a computer program, and the computer program is executed by the processor to realize the light-heat coupling-based sound barrier gradient photovoltaic facade intelligent optimization layout method of any one of claims 1-4.

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