Photovoltaic module subfissure early warning system and method based on multi-mode sensing and wind vibration coupling analysis

Abstract

The invention relates to a photovoltaic module subfissure early warning system and method based on multi-modal sensing and wind vibration coupling analysis, and the system comprises a multi-modal vibration test bed which is based on a six-degree-of-freedom vibration table technology and a wind vibration simulation device and is used for simulating wind-induced overall vibration and torsional vibration, integrating a wind speed simulation device and reproducing natural wind load spectrum characteristics; the multi-sensor fusion module is used for capturing crack propagation sound wave signals of the battery piece, detecting abnormal temperature rise of a hidden crack area and monitoring local deformation of a photovoltaic module and a bracket to obtain wind vibration, vibration and module hidden crack characteristic data; the wind vibration simulation data coupling module is used for establishing a wind vibration-vibration-subfissure associated database through finite element modeling in combination with wind vibration, vibration and component subfissure feature data; and the intelligent early warning module is used for constructing a hidden crack risk dynamic evaluation model based on a deep learning algorithm by fusing the wind vibration-vibration-hidden crack association database, and realizing hidden crack grade classification and residual life prediction. The defects in hidden crack detection of an existing photovoltaic module are overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic power generation, and in particular relates to a photovoltaic component hidden crack early warning system and method based on multi-modal sensing and wind-vibration coupling analysis. Background Art

[0002] Hidden cracks are tiny, imperceptible cracks in photovoltaic modules, typically found within or on the surface of the cells. Crack morphology can be categorized as tree-like cracks, diagonal cracks, and cracks parallel or perpendicular to the busbars. Hidden cracks are primarily caused by thermal stress, mechanical stress, material aging, or manufacturing defects. For example, uneven shrinkage of cells due to improper high or low temperature handling during production, improper mounting or external impact during installation, and prolonged exposure to severe weather (such as sandstorms and hail) can all contribute to hidden cracks.

[0003] Currently, detection of hidden cracks in photovoltaic modules relies primarily on manual inspections or offline electroluminescence (EL) testing, which suffers from low efficiency, inability to monitor in real time, and difficulty correlating with environmental loads (such as wind vibration). Traditional vibration test benches are primarily designed for single-mode simulation, unable to replicate the spectral characteristics of natural wind loads and lacking quantitative correlation models between hidden crack characteristics and vibration parameters. Furthermore, existing technologies often use acoustic emission and infrared thermal imaging independently, without integrating dynamic wind vibration data to comprehensively assess hidden crack risk. Summary of the Invention

[0004] The present invention provides a photovoltaic module hidden crack early warning system and method based on multi-modal sensing and wind vibration coupling analysis, aiming to overcome the deficiencies in existing photovoltaic module hidden crack detection.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: The photovoltaic module hidden crack early warning system based on multi-modal sensing and wind-vibration coupling analysis includes: Multimodal vibration test bench, based on six-degree-of-freedom vibration table technology and wind vibration simulator, is used to simulate wind-induced overall vibration and torsional vibration. It integrates a wind speed simulator to reproduce the spectrum characteristics of natural wind loads. The multi-sensor fusion module deploys acoustic emission sensors, infrared thermal imagers, and strain sensors on the surface of photovoltaic modules and key nodes of the brackets to capture the acoustic wave signals of crack propagation in the cell, detect abnormal temperature rise in the hidden crack area, and monitor the local deformation of the photovoltaic modules and brackets to obtain wind vibration, vibration, and module hidden crack characteristic data; The wind-induced vibration simulation data coupling module combines wind-induced vibration, vibration, and component hidden crack characteristic data through finite element modeling to establish a wind-induced vibration-vibration-hidden crack correlation database; The intelligent early warning module, based on a deep learning algorithm, integrates the wind-induced vibration-hidden crack correlation database to build a dynamic assessment model for hidden crack risks, thereby achieving hidden crack level classification and remaining life prediction.

[0006] A further improvement of the present invention is that the multimodal vibration test bench includes a six-degree-of-freedom vibration table and a wind tunnel simulation device; the six-degree-of-freedom vibration table is used to simulate a vibration environment with six degrees of freedom in space; and the wind tunnel simulation device is used to simulate a real air flow environment by controlling airflow.

[0007] A further improvement of the present invention is that the six-degree-of-freedom vibration table includes a motion platform and six hydraulic actuators, a hydraulic cylinder drive system, a universal joint and a motion control system arranged on the motion platform; the six-degree-of-freedom vibration table is coordinated by the six hydraulic actuators to simulate the vibration of the three translational degrees of freedom and three rotational degrees of freedom of the photovoltaic module in three-dimensional space; the six hydraulic actuators are connected to the hydraulic cylinder drive system through universal joints, each hydraulic actuator is driven by the hydraulic cylinder drive system, and independently controls the movement of one degree of freedom. The hydraulic cylinder drive system converts hydraulic energy into mechanical energy to drive the hydraulic actuator to extend and retract, and the motion control system is used to control the hydraulic cylinder drive system to make the six hydraulic actuators move.

[0008] A further improvement of the present invention is that the wind tunnel simulation device includes a bracket, a fan, two recirculation channels and a closed experimental test system arranged on the bracket; the wind tunnel simulation device simulates the aerodynamic performance of photovoltaic modules in a real environment by artificially generating controllable airflow; the closed experimental test system generates high-pressure airflow by controlling the fan to simulate wind conditions of different intensities and angles, and the two recirculation channels can form a closed loop to allow the airflow to circulate back.

[0009] A further improvement of the present invention is that the multi-sensor fusion module includes: The multimodal feature extraction unit uses acoustic emission sensors, infrared thermal imagers, and strain sensors to extract wind vibration, vibration, and component hidden crack feature data; The feature data transmission unit is connected to the multimodal feature extraction unit and is used to transmit the wind vibration, vibration and component hidden crack feature data to the wind vibration simulation data coupling module.

[0010] A further improvement of the present invention is that the wind-induced vibration simulation data coupling module includes: Wind-induced vibration feature extraction unit, used to extract wind-induced vibration, vibration and component hidden crack feature data and fuse them to obtain comprehensive wind-induced vibration features; The multimodal data fusion unit uses finite element modeling to combine comprehensive wind-induced vibration characteristics into an enhanced wind-induced vibration-hidden crack correlation database.

[0011] A further improvement of the present invention is that the dynamic assessment model of hidden crack risk is based on the wind vibration-vibration-hidden crack correlation database and uses a deep learning algorithm to continuously optimize and verify the hidden crack risk of photovoltaic modules. The dynamic assessment model of hidden crack risk is obtained through learning, thereby realizing the hidden crack level classification and remaining life prediction.

[0012] A further improvement of the present invention is that the deep learning algorithm adopts a convolutional neural network CNN.

[0013] The photovoltaic module hidden crack early warning method based on multimodal sensing and wind-induced vibration coupling analysis includes: The multimodal vibration test bench is based on six-degree-of-freedom vibration table technology and a wind vibration simulator to simulate wind-induced overall vibration and torsional vibration. It integrates a wind speed simulator to reproduce the spectrum characteristics of natural wind loads. The multi-sensor fusion module deploys acoustic emission sensors, infrared thermal imagers, and strain sensors on the surface of photovoltaic modules and key nodes of the bracket to capture the acoustic wave signals of crack propagation in the cell, detect abnormal temperature rise in the hidden crack area, and monitor the local deformation of the photovoltaic modules and brackets to obtain wind vibration, vibration, and module hidden crack characteristic data; The wind-induced vibration simulation data coupling module combines wind-induced vibration, vibration, and component hidden crack characteristic data through finite element modeling to establish a wind-induced vibration-vibration-hidden crack correlation database; The intelligent early warning module is based on a deep learning algorithm and integrates the correlation database of wind-induced vibration and hidden cracks to build a dynamic assessment model for hidden crack risks, thereby realizing hidden crack level classification and remaining life prediction.

[0014] A further improvement of the present invention is that the multimodal vibration test bench simulates a six-degree-of-freedom vibration environment in space by using a six-degree-of-freedom vibration table, and controls the airflow by using a wind tunnel simulation device to simulate a real air flow environment.

[0015] Compared with the prior art, the present invention has at least the following beneficial technical effects: The present invention provides a photovoltaic module hidden crack early warning system and method that combines multimodal sensing and wind-vibration coupling analysis. The six-degree-of-freedom vibration table technology can simulate the multi-dimensional vibration of photovoltaic modules in complex wind fields, which is closer to actual working conditions and improves the reliability of test results. By reproducing the spectrum characteristics of natural wind loads (such as turbulence and gusts), the anti-cracking performance of the module under different wind-vibration conditions can be verified, providing a basis for design optimization. Wind speed and vibration coupling simulation can analyze the impact of wind pressure distribution on module stress and is particularly suitable for module reliability assessment in high wind speed areas. Acoustic emission sensors can monitor the acoustic wave signals of crack propagation inside the cell in real time. Their sensitivity is higher than traditional visual inspection and can detect early microcracks. Infrared thermal imagers can quickly locate hot spot effects by detecting local temperature rise anomalies in the hidden crack area, avoiding fire risks. Strain sensors monitor the local deformation of brackets and components, quantify stress concentration areas caused by wind vibration, and provide data support for structural optimization. The wind-induced vibration simulation data coupling module integrates test bench data (wind-induced vibration) with sensor data (hidden crack characteristics) to establish a "wind-induced vibration-hidden crack" correlation database, overcoming the limitations of a single physical model. The intelligent early warning module, based on a deep learning-based dynamic assessment model, automatically identifies crack propagation stages (e.g., initial stage, expansion stage, critical stage) and predicts the remaining life of components.

[0016] In summary, the present invention uses a six-degree-of-freedom vibration test bench to reproduce natural wind loads, combines acoustic emission, infrared thermal imaging, and wind-induced vibration simulation data, and constructs a deep learning-driven hidden crack risk assessment model to achieve real-time monitoring and accurate early warning of hidden cracks in photovoltaic modules. The present invention is a solution that integrates technologies from multiple industries and can significantly improve the efficiency and safety of photovoltaic power station operation and maintenance. The present invention can monitor the initiation and expansion of hidden cracks in photovoltaic modules in real time, with an early warning response time of ≤10 seconds; the hidden crack identification accuracy is ≥95%, and the life prediction error is ≤8%; it supports deployment in harsh outdoor environments, and the sensor protection level reaches IP67. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is the layout diagram of the multimodal sensor for photovoltaic modules; Figure 2 This is a schematic diagram of the structure of the multi-modal vibration test bench; Figure 3 This is a flow chart of the algorithm for assessing the risk of hidden cracks in photovoltaic modules. DETAILED DESCRIPTION

[0019] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and description are to be considered as illustrative in nature and not restrictive.

[0020] In the description of the present invention, it should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0021] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0023] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0024] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0025] Example 1 like Figures 1 to 3 As shown, the photovoltaic module hidden crack early warning system provided by the present invention with multi-modal sensing and wind vibration coupling analysis includes: Multimodal vibration test bench, based on six-degree-of-freedom vibration table technology and wind vibration simulator, is used to simulate wind-induced overall vibration and torsional vibration. It integrates a wind speed simulator to reproduce the spectrum characteristics of natural wind loads. The multi-sensor fusion module deploys acoustic emission sensors, infrared thermal imagers, and strain sensors on the surface of photovoltaic modules and key nodes of the brackets to capture the acoustic wave signals of crack propagation in the cell, detect abnormal temperature rise in the hidden crack area, and monitor the local deformation of the photovoltaic modules and brackets to obtain wind vibration, vibration, and module hidden crack characteristic data; The wind-induced vibration simulation data coupling module combines wind-induced vibration, vibration, and component hidden crack characteristic data through finite element modeling to establish a wind-induced vibration-vibration-hidden crack correlation database; The intelligent early warning module, based on a deep learning algorithm, integrates the wind-induced vibration-hidden crack correlation database to build a dynamic assessment model for hidden crack risks, thereby achieving hidden crack level classification and remaining life prediction.

[0026] In this embodiment, the multimodal vibration test bench includes a six-degree-of-freedom vibration table and a wind tunnel simulation device; the six-degree-of-freedom vibration table is used to simulate a vibration environment with six degrees of freedom in space; and the wind tunnel simulation device is used to simulate a real air flow environment by controlling airflow.

[0027] In this embodiment, the six-degree-of-freedom vibration table includes a motion platform and six hydraulic actuators, a hydraulic cylinder drive system, a universal joint and a motion control system arranged on the motion platform; the six-degree-of-freedom vibration table is coordinated by the six hydraulic actuators to simulate the vibration of the three translational degrees of freedom (X / Y / Z) and three rotational degrees of freedom (Roll / Pitch / Yaw) of the photovoltaic module in three-dimensional space; the six hydraulic actuators are connected to the hydraulic cylinder drive system through universal joints, and each hydraulic actuator is driven by the hydraulic cylinder drive system to independently control the movement of one degree of freedom. The hydraulic cylinder drive system converts hydraulic energy into mechanical energy to drive the hydraulic actuator to extend and retract, and the motion control system is used to control the hydraulic cylinder drive system to move the six hydraulic actuators.

[0028] In this embodiment, the wind tunnel simulation device includes a frame, a fan mounted on the frame, two recirculation channels, and a closed-circuit test system. The wind tunnel simulation device simulates the aerodynamic performance of photovoltaic modules in real environments by artificially generating controlled airflow, making it a key aerodynamic testing device. The closed-circuit test system controls the fan to generate high-pressure airflow, simulating wind conditions of varying intensities and angles. The two recirculation channels form a closed loop, allowing the airflow to circulate back.

[0029] In this embodiment, the multi-sensor fusion module includes: a multimodal feature extraction unit, which uses an acoustic emission sensor, an infrared thermal imager and a strain sensor to extract wind vibration, vibration and component hidden crack feature data respectively; a feature data transmission unit, which is connected to the multimodal feature extraction unit and is used to transmit the wind vibration, vibration and component hidden crack feature data to the wind vibration simulation data coupling module.

[0030] In this embodiment, the wind-vibration simulation data coupling module includes: a wind-vibration feature extraction unit, which is used to extract wind-vibration, vibration and component hidden crack feature data and fuse them to obtain comprehensive wind-vibration features; a multimodal data fusion unit, which uses finite element modeling to combine the comprehensive wind-vibration features into an enhanced wind-vibration-vibration-hidden crack correlation database.

[0031] In this embodiment, the dynamic assessment model for hidden crack risk is based on the wind vibration-vibration-hidden crack correlation database and uses a deep learning algorithm to continuously optimize and verify the hidden crack risk of photovoltaic modules. The dynamic assessment model for hidden crack risk is obtained through learning, thereby realizing the classification of hidden crack levels and the prediction of remaining life.

[0032] In this embodiment, the deep learning algorithm adopts convolutional neural network CNN.

[0033] Example 2 The core of this invention is to build a photovoltaic module hidden crack early warning system based on multi-modal sensing and wind-induced vibration coupling analysis, which includes the following modules: 1. Multimodal vibration test bench: Based on six-degree-of-freedom vibration table technology (inspired by automobile crash tests), it is designed to simulate wind-induced overall and torsional vibrations. It integrates a wind speed simulator to accurately reproduce the spectrum characteristics of natural wind loads.

[0034] 2. Multi-sensor fusion module: Deploy acoustic emission sensors (to capture acoustic wave signals of crack expansion), infrared thermal imagers (to detect abnormal temperature rise in hidden crack areas), and strain sensors (to monitor local deformation) on the surface of photovoltaic modules and key nodes of the bracket.

[0035] 3. Wind-induced vibration simulation data coupling module: The dynamic response data of the flexible bracket under natural wind loads is obtained through finite element modeling, and a wind-induced vibration-hidden crack correlation database is established.

[0036] 4. Intelligent early warning module: Based on deep learning algorithms (such as convolutional neural networks (CNN), it integrates the time-frequency characteristics of acoustic emission signals, infrared thermal image temperature gradients, and wind-induced vibration simulation data to build a dynamic assessment model for hidden crack risk, thereby achieving hidden crack level classification and remaining life prediction.

[0037] In this embodiment, it also includes: 1. The first domestic set: The six-degree-of-freedom vibration table technology used in the automotive field is introduced for the first time into photovoltaic module testing. It combines wind tunnel simulation with multi-modal vibration control to achieve high-precision reproduction of natural wind loads.

[0038] 2. Interdisciplinary sensor fusion: Innovatively combine acoustic emission (dynamic crack monitoring), infrared thermal imaging (static thermal anomaly detection), and wind vibration simulation (environmental load correlation) technologies to address the limitations of a single sensing method.

[0039] 3. Dynamic risk assessment algorithm: A hidden crack prediction algorithm based on the CNN-LSTM hybrid model is proposed, which uses wind-induced vibration simulation data to correct real-time monitoring results and improve early warning accuracy.

[0040] Example 3 1. Hardware deployment: Acoustic emission sensors (frequency range 20kHz-1MHz) and infrared thermal imagers (resolution 640×480) are mounted on the surface of photovoltaic modules, and strain sensors are installed at the bracket nodes; The test bench has a built-in six-degree-of-freedom hydraulic drive system that generates a variable wind speed of 0-30m / s through a wind tunnel simulation device, controlling the vibration amplitude (±50mm) and frequency (0.1-30Hz).

[0041] 2. Software implementation: Develop a wind-induced vibration-hidden crack coupling simulation module, use ANSYS to simulate the wind-induced vibration response of flexible supports, and output vibration spectrum characteristics; Construct a CNN-LSTM hybrid model: CNN extracts the spatial features of infrared thermal images, LSTM processes the temporal features of acoustic emission signals, and integrates wind-induced vibration data to output a hidden crack risk index. Design a visual early warning interface that supports risk level (low / medium / high) display and maintenance suggestion push.

[0042] Example 4 The photovoltaic module hidden crack early warning method based on multi-modal sensing and wind-vibration coupled analysis provided by the present invention includes: The multimodal vibration test bench is based on six-degree-of-freedom vibration table technology and a wind vibration simulator to simulate wind-induced overall vibration and torsional vibration. It integrates a wind speed simulator to reproduce the spectrum characteristics of natural wind loads. The multi-sensor fusion module deploys acoustic emission sensors, infrared thermal imagers, and strain sensors on the surface of photovoltaic modules and key nodes of the bracket to capture the acoustic wave signals of crack propagation in the cell, detect abnormal temperature rise in the hidden crack area, and monitor the local deformation of the photovoltaic modules and brackets to obtain wind vibration, vibration, and module hidden crack characteristic data; The wind-induced vibration simulation data coupling module combines wind-induced vibration, vibration, and component hidden crack characteristic data through finite element modeling to establish a wind-induced vibration-vibration-hidden crack correlation database; The intelligent early warning module is based on a deep learning algorithm and integrates the correlation database of wind-induced vibration and hidden cracks to build a dynamic assessment model for hidden crack risks, thereby realizing hidden crack level classification and remaining life prediction.

[0043] In this embodiment, the multimodal vibration test bench simulates a six-degree-of-freedom vibration environment in space by using a six-degree-of-freedom vibration table, and controls the airflow by using a wind tunnel simulation device to simulate a real air flow environment.

[0044] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from all points of view, the embodiments should be regarded as illustrative and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and range of equivalents of the claims are included in the present invention. Any reference signs in the claims should not be construed as limiting the claim to which they relate.

[0045] In addition, it should be understood that although this specification describes the embodiments, not every embodiment contains only one independent technical solution. This description is for clarity only. Those skilled in the art should consider the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for the purpose of illustrating the technical concept of the present invention and cannot be used to limit the scope of protection of the present invention. Any changes made based on the technical solution in accordance with the technical concept proposed by the present invention fall within the scope of protection of the claims of the present invention.

Claims

1. Photovoltaic module hidden crack early warning system based on multi-modal sensing and wind-vibration coupling analysis, characterized by: include: Multimodal vibration test bench, based on six-degree-of-freedom vibration table technology and wind vibration simulator, is used to simulate wind-induced overall vibration and torsional vibration. It integrates a wind speed simulator to reproduce the spectrum characteristics of natural wind loads. The multi-sensor fusion module deploys acoustic emission sensors, infrared thermal imagers, and strain sensors on the surface of photovoltaic modules and key nodes of the brackets to capture the acoustic wave signals of crack propagation in the cell, detect abnormal temperature rise in the hidden crack area, and monitor the local deformation of the photovoltaic modules and brackets to obtain wind vibration, vibration, and module hidden crack characteristic data; The wind-induced vibration simulation data coupling module combines wind-induced vibration, vibration, and component hidden crack characteristic data through finite element modeling to establish a wind-induced vibration-vibration-hidden crack correlation database; The intelligent early warning module, based on a deep learning algorithm, integrates the wind-induced vibration-hidden crack correlation database to build a dynamic assessment model for hidden crack risks, thereby achieving hidden crack level classification and remaining life prediction.

2. The photovoltaic module hidden crack early warning system based on multi-modal sensing and wind-vibration coupling analysis according to claim 1 is characterized in that: Multimodal vibration test bench: includes a six-degree-of-freedom vibration table and a wind tunnel simulation device; the six-degree-of-freedom vibration table is used to simulate the vibration environment of six degrees of freedom in space; the wind tunnel simulation device is used to simulate the real air flow environment by controlling the airflow.

3. The photovoltaic module hidden crack early warning system based on multi-modal sensing and wind-vibration coupled analysis according to claim 2 is characterized in that: The six-degree-of-freedom vibration table includes a motion platform and six hydraulic actuators, a hydraulic cylinder drive system, a universal joint and a motion control system arranged on the motion platform; the six-degree-of-freedom vibration table is coordinated through the control of six hydraulic actuators to simulate the vibration of three translational degrees of freedom and three rotational degrees of freedom of photovoltaic modules in three-dimensional space; the six hydraulic actuators are connected to the hydraulic cylinder drive system through universal joints, and each hydraulic actuator is driven by the hydraulic cylinder drive system to independently control the movement of one degree of freedom. The hydraulic cylinder drive system converts hydraulic energy into mechanical energy to drive the hydraulic actuator to extend and retract, and the motion control system is used to control the hydraulic cylinder drive system to make the six hydraulic actuators move.

4. The photovoltaic module hidden crack early warning system based on multi-modal sensing and wind-vibration coupled analysis according to claim 2 is characterized in that: The wind tunnel simulation device includes a bracket, a fan installed on the bracket, two recirculation channels and a closed experimental test system; the wind tunnel simulation device simulates the aerodynamic performance of photovoltaic modules in a real environment by artificially generating controllable airflow; the closed experimental test system generates high-pressure airflow by controlling the fan to simulate wind conditions of different intensities and angles, and the two recirculation channels can form a closed loop to circulate the airflow back.

5. The photovoltaic module hidden crack early warning system based on multi-modal sensing and wind-vibration coupling analysis according to claim 1 is characterized in that: The multi-sensor fusion module includes: The multimodal feature extraction unit uses acoustic emission sensors, infrared thermal imagers, and strain sensors to extract wind vibration, vibration, and component hidden crack feature data; The feature data transmission unit is connected to the multimodal feature extraction unit and is used to transmit the wind vibration, vibration and component hidden crack feature data to the wind vibration simulation data coupling module.

6. The photovoltaic module hidden crack early warning system based on multi-modal sensing and wind-vibration coupled analysis according to claim 1 is characterized in that: The wind-induced vibration simulation data coupling module includes: Wind-induced vibration feature extraction unit, used to extract wind-induced vibration, vibration and component hidden crack feature data and fuse them to obtain comprehensive wind-induced vibration features; The multimodal data fusion unit uses finite element modeling to combine comprehensive wind-induced vibration characteristics into an enhanced wind-induced vibration-hidden crack correlation database.

7. The photovoltaic module hidden crack early warning system based on multi-modal sensing and wind-vibration coupled analysis according to claim 1 is characterized in that: The dynamic assessment model for hidden crack risk is based on the wind-vibration-hidden crack correlation database and uses a deep learning algorithm to continuously optimize and verify the hidden crack risk of photovoltaic modules. Through learning, a dynamic assessment model for hidden crack risk is obtained, which in turn realizes the classification of hidden crack levels and the prediction of remaining life.

8. The photovoltaic module hidden crack early warning system based on multi-modal sensing and wind-vibration coupled analysis according to claim 1 is characterized in that: The deep learning algorithm uses convolutional neural network CNN.

9. A photovoltaic module hidden crack early warning method based on multimodal sensing and wind-vibration coupling analysis is characterized by: include: The multimodal vibration test bench is based on six-degree-of-freedom vibration table technology and a wind vibration simulator to simulate wind-induced overall vibration and torsional vibration. It integrates a wind speed simulator to reproduce the spectrum characteristics of natural wind loads. The multi-sensor fusion module deploys acoustic emission sensors, infrared thermal imagers, and strain sensors on the surface of photovoltaic modules and key nodes of the bracket to capture the acoustic wave signals of crack propagation in the cell, detect abnormal temperature rise in the hidden crack area, and monitor the local deformation of the photovoltaic modules and brackets to obtain wind vibration, vibration, and module hidden crack characteristic data; The wind-induced vibration simulation data coupling module combines wind-induced vibration, vibration, and component hidden crack characteristic data through finite element modeling to establish a wind-induced vibration-vibration-hidden crack correlation database; The intelligent early warning module is based on a deep learning algorithm and integrates the correlation database of wind-induced vibration and hidden cracks to build a dynamic assessment model for hidden crack risks, thereby realizing hidden crack level classification and remaining life prediction.

10. The photovoltaic module hidden crack early warning method based on multi-modal sensing and wind-vibration coupling analysis according to claim 9 is characterized in that: The multimodal vibration test bench simulates the six-degree-of-freedom vibration environment in space through a six-degree-of-freedom vibration table, and controls the airflow through a wind tunnel simulation device to simulate the real air flow environment.

Images