技术领域Technical Field
本发明涉及光电传感器技术领域，尤其涉及应用于动态检测的高精度光电传感器。The present invention relates to the technical field of photoelectric sensors, and in particular to a high-precision photoelectric sensor used for dynamic detection.
背景技术Background technique
光电传感器技术是研究光电效应和光电器件的原理、设计和应用的领域。它涵盖了使用光电效应将光信号转换为电信号的原理和技术，以及如何设计和应用光电器件来实现各种光学检测和测量的技术。光电传感器是一种能够将光信号转换为电信号的传感器。它利用光电效应原理，将光能转换为电信号，从而实现对光的检测和测量。光电传感器可以包括多种类型的光敏元件，如光电二极管、光电三极管、光电二极管阵列等。光电传感器的目的是检测和测量光的各种特性，如光强、光谱、光强分布、光强变化等。通过光电传感器，可以实现对环境光照度的测量、光源的识别和定位、物体的检测和识别等应用。Photoelectric sensor technology is a field that studies the principles, design and application of the photoelectric effect and photoelectric devices. It covers the principles and techniques of converting light signals into electrical signals using the photoelectric effect, as well as how to design and apply photoelectric devices to achieve various optical detection and measurement technologies. A photoelectric sensor is a sensor that can convert light signals into electrical signals. It uses the principle of the photoelectric effect to convert light energy into electrical signals, thereby achieving the detection and measurement of light. Photoelectric sensors can include various types of photosensitive elements, such as photodiodes, phototransistors, photodiode arrays, etc. The purpose of photoelectric sensors is to detect and measure various characteristics of light, such as light intensity, spectrum, light intensity distribution, light intensity changes, etc. Through photoelectric sensors, applications such as measurement of ambient light illumination, identification and positioning of light sources, and detection and identification of objects can be achieved.
在现有的光电传感器中，大部分现有光电传感器未采用高速信号处理技术，导致其对动态光信号的实时响应能力受限。现有光电传感器在多通道数据融合方面的技术还较为初级，可能导致数据的遗失或误差。未能进行充分的光学校准，从而影响到传感器的检测精度。很多传统光电传感器还在使用传统的算法进行数据处理，没有充分利用深度学习等先进技术进行智能优化。对于强光或其他干扰的处理能力不足，容易受到外界因素的影响，导致光电传感器失稳或输出错误数据。未使用超分辨率等先进成像技术，导致成像效果与高分辨率成像模块相比有所下降。没有有效地利用微纳加工技术，可能导致信号传输的损失或延迟。Among the existing photoelectric sensors, most of them do not use high-speed signal processing technology, which limits their real-time response capabilities to dynamic light signals. The technology of existing photoelectric sensors in multi-channel data fusion is still relatively rudimentary, which may lead to data loss or errors. Failure to perform adequate optical calibration affects the detection accuracy of the sensor. Many traditional photoelectric sensors are still using traditional algorithms for data processing, and have not fully utilized advanced technologies such as deep learning for intelligent optimization. The processing capabilities for strong light or other interference are insufficient, and they are easily affected by external factors, causing the photoelectric sensor to become unstable or output erroneous data. Advanced imaging technologies such as super-resolution are not used, resulting in a decrease in imaging effects compared to high-resolution imaging modules. Failure to effectively utilize micro-nano processing technology may lead to loss or delay in signal transmission.
发明内容Summary of the invention
本发明的目的是解决现有技术中存在的缺点，而提出的应用于动态检测的高精度光电传感器。The purpose of the present invention is to solve the shortcomings in the prior art and to propose a high-precision photoelectric sensor for dynamic detection.
为了实现上述目的，本发明采用了如下技术方案：应用于动态检测的高精度光电传感器是由高速信号处理模块、多通道融合模块、光学校准模块、智能算法优化模块、自适应光学系统模块、强光抑制模块、高分辨率成像模块、光学波导耦合模块组成；In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: the high-precision photoelectric sensor used for dynamic detection is composed of a high-speed signal processing module, a multi-channel fusion module, an optical calibration module, an intelligent algorithm optimization module, an adaptive optical system module, a strong light suppression module, a high-resolution imaging module, and an optical waveguide coupling module;
所述高速信号处理模块采用FFT和小波变换算法对高速采样的动态光信号进行实时处理和特征提取，生成高速特征分析结果；The high-speed signal processing module uses FFT and wavelet transform algorithms to perform real-time processing and feature extraction on high-speed sampled dynamic optical signals to generate high-speed feature analysis results;
所述多通道融合模块基于高速特征分析结果，采用卡尔曼滤波和神经网络算法进行多通道数据的融合处理，生成多通道融合结果；The multi-channel fusion module uses Kalman filtering and neural network algorithms to perform fusion processing of multi-channel data based on the high-speed feature analysis results to generate multi-channel fusion results;
所述光学校准模块利用光学标定和校准算法，对所述基于多通道融合结果的光电传感器进行精度校准，生成精度校准结果；The optical calibration module uses optical calibration and calibration algorithms to perform precision calibration on the photoelectric sensor based on the multi-channel fusion result to generate a precision calibration result;
所述智能算法优化模块通过深度学习和卷积神经网络技术，对精度校准结果进行模型训练和特征优化，生成优化特征结果；The intelligent algorithm optimization module performs model training and feature optimization on the precision calibration results through deep learning and convolutional neural network technology to generate optimized feature results;
所述自适应光学系统模块根据优化特征结果，利用自适应光学元件完成对光信号强度、波长、方向的自动调节和优化，生成光信号优化结果；The adaptive optical system module uses adaptive optical elements to automatically adjust and optimize the intensity, wavelength, and direction of the optical signal according to the optimization feature results, and generates an optical signal optimization result;
所述强光抑制模块根据光信号优化结果，通过滤波和自适应增益控制，对强光区域的干扰进行抑制，生成强光抑制结果；The strong light suppression module suppresses interference in the strong light area through filtering and adaptive gain control according to the optical signal optimization result, and generates a strong light suppression result;
所述高分辨率成像模块基于强光抑制结果，采用超分辨率算法和多帧图像融合方法进行高分辨率成像，生成高分辨率影像；The high-resolution imaging module uses a super-resolution algorithm and a multi-frame image fusion method to perform high-resolution imaging based on the strong light suppression result to generate a high-resolution image;
所述光学波导耦合模块利用微纳加工技术，将光敏元件与基于所述高分辨率影像的光学系统耦合，实现高效信号传输，生成光信号传输结果。The optical waveguide coupling module utilizes micro-nano processing technology to couple the photosensitive element with the optical system based on the high-resolution image, thereby achieving efficient signal transmission and generating an optical signal transmission result.
作为本发明的进一步方案：所述高速信号处理模块包括光信号采集子模块、FFT处理子模块、小波变换子模块；As a further solution of the present invention: the high-speed signal processing module includes an optical signal acquisition submodule, an FFT processing submodule, and a wavelet transform submodule;
所述多通道融合模块包括数据同步子模块、卡尔曼滤波子模块、神经网络融合子模块；The multi-channel fusion module includes a data synchronization submodule, a Kalman filter submodule, and a neural network fusion submodule;
所述光学校准模块包括传感器定位子模块、角度校准子模块、视场调整子模块；The optical calibration module includes a sensor positioning submodule, an angle calibration submodule, and a field of view adjustment submodule;
所述智能算法优化模块包括大数据训练子模块、深度学习算法子模块、卷积神经网络优化子模块；The intelligent algorithm optimization module includes a big data training submodule, a deep learning algorithm submodule, and a convolutional neural network optimization submodule;
所述自适应光学系统模块包括光强自适应调节子模块、波长自适应优化子模块、方向自动校正子模块；The adaptive optical system module includes a light intensity adaptive adjustment submodule, a wavelength adaptive optimization submodule, and a direction automatic correction submodule;
所述强光抑制模块包括滤波子模块、自适应增益控制子模块、动态范围扩展子模块；The strong light suppression module includes a filtering submodule, an adaptive gain control submodule, and a dynamic range extension submodule;
所述高分辨率成像模块包括超分辨率算法子模块、多帧图像融合子模块、图像细节捕捉子模块；The high-resolution imaging module includes a super-resolution algorithm submodule, a multi-frame image fusion submodule, and an image detail capture submodule;
所述光学波导耦合模块包括波导结构制备子模块、光信号定向捕捉子模块、集成化处理子模块。The optical waveguide coupling module includes a waveguide structure preparation submodule, an optical signal directional capture submodule, and an integrated processing submodule.
作为本发明的进一步方案：所述光信号采集子模块运用高精度光电探测技术来捕捉动态光信号，生成光信号原始数据；As a further solution of the present invention: the optical signal acquisition submodule uses high-precision photoelectric detection technology to capture dynamic optical signals and generate optical signal raw data;
所述FFT处理子模块基于光信号原始数据，利用快速傅里叶变换算法进行频域分析，实现信号的分解和重构，生成FFT分析结果；The FFT processing submodule performs frequency domain analysis based on the original data of the optical signal using a fast Fourier transform algorithm to achieve signal decomposition and reconstruction and generate FFT analysis results;
所述小波变换子模块借助FFT分析结果，运用小波变换进行时频域的联合分析，捕捉信号中的瞬时变化特征，生成高速特征分析结果。The wavelet transform submodule uses the FFT analysis result to perform a joint analysis of the time and frequency domains using wavelet transform, captures the instantaneous change characteristics in the signal, and generates a high-speed feature analysis result.
作为本发明的进一步方案：所述数据同步子模块根据高速特征分析结果，确保传感器采集的数据在时间上的同步，生成同步数据结果；As a further solution of the present invention: the data synchronization submodule ensures the synchronization of the data collected by the sensor in time according to the high-speed feature analysis result, and generates a synchronization data result;
所述卡尔曼滤波子模块基于同步数据结果，应用卡尔曼滤波算法来去除数据中的噪声并进行预测性数据融合，生成卡尔曼滤波结果；The Kalman filter submodule applies a Kalman filter algorithm to remove noise from the data and perform predictive data fusion based on the synchronized data results to generate a Kalman filter result;
所述神经网络融合子模块利用卡尔曼滤波结果，通过神经网络算法来实现多通道数据的深度融合，生成多通道融合结果。The neural network fusion submodule uses the Kalman filter result to achieve deep fusion of multi-channel data through a neural network algorithm to generate a multi-channel fusion result.
作为本发明的进一步方案：所述传感器定位子模块采用光学标定技术和算法，依据所述多通道融合结果对传感器进行精确定位，生成传感器定位结果；As a further solution of the present invention: the sensor positioning submodule adopts optical calibration technology and algorithm to accurately position the sensor according to the multi-channel fusion result to generate a sensor positioning result;
所述角度校准子模块依赖传感器定位结果，进行角度校准来确保光学系统的正确指向和捕捉，生成角度校准结果；The angle calibration submodule relies on the sensor positioning result to perform angle calibration to ensure the correct pointing and capturing of the optical system and generate an angle calibration result;
所述视场调整子模块基于角度校准结果，进行视场调整以优化图像的捕捉区域和角度，生成精度校准结果。The field of view adjustment submodule performs field of view adjustment based on the angle calibration result to optimize the image capture area and angle and generate an accuracy calibration result.
作为本发明的进一步方案：所述大数据训练子模块依据精度校准结果，使用大数据算法来训练和优化模型，生成大数据训练结果；As a further solution of the present invention: the big data training submodule uses a big data algorithm to train and optimize the model based on the accuracy calibration result to generate a big data training result;
所述深度学习算法子模块采纳大数据训练结果为基础，应用深度学习算法进行模型的深度训练和优化，生成深度学习优化结果；The deep learning algorithm submodule adopts the big data training results as a basis, applies the deep learning algorithm to perform deep training and optimization of the model, and generates deep learning optimization results;
所述卷积神经网络优化子模块根据深度学习优化结果，利用卷积神经网络技术对模型进行优化，生成优化特征结果。The convolutional neural network optimization submodule optimizes the model based on the deep learning optimization results using convolutional neural network technology to generate optimized feature results.
作为本发明的进一步方案：所述光强自适应调节子模块基于贝叶斯优化算法，利用光强度传感器实现对光信号强度的实时监测与自适应调节，生成自适应光强调整结果；As a further solution of the present invention: the light intensity adaptive adjustment submodule is based on the Bayesian optimization algorithm, using the light intensity sensor to achieve real-time monitoring and adaptive adjustment of the light signal intensity, and generate an adaptive light intensity adjustment result;
所述波长自适应优化子模块依据自适应光强调整结果，运用神经网络算法对光信号的波长进行深度学习和自适应优化，生成波长优化结果；The wavelength adaptive optimization submodule uses a neural network algorithm to perform deep learning and adaptive optimization on the wavelength of the optical signal according to the adaptive light intensity adjustment result to generate a wavelength optimization result;
所述方向自动校正子模块结合波长优化结果，利用卡尔曼滤波器进行光信号方向的精确校正和调节，生成光信号优化结果。The automatic direction correction submodule combines the wavelength optimization result and uses the Kalman filter to accurately correct and adjust the direction of the optical signal to generate the optical signal optimization result.
作为本发明的进一步方案：所述滤波子模块根据光信号优化结果，应用傅里叶变换与高斯滤波联合进行复杂光信号的滤波处理，生成滤波处理结果；As a further solution of the present invention: the filtering submodule applies Fourier transform and Gaussian filtering to filter the complex optical signal according to the optical signal optimization result to generate a filtering result;
所述自适应增益控制子模块基于滤波处理结果，通过线性增益控制与递归最小二乘法进行自适应增益的调控，生成增益调节结果；The adaptive gain control submodule adjusts the adaptive gain based on the filtering processing result through linear gain control and recursive least square method to generate a gain adjustment result;
所述动态范围扩展子模块基于增益调节结果，利用波形失真分析实施强光区域的动态范围扩展和抑制，生成强光抑制结果。The dynamic range extension submodule implements dynamic range expansion and suppression of the strong light area based on the gain adjustment result by using waveform distortion analysis to generate a strong light suppression result.
作为本发明的进一步方案：所述超分辨率算法子模块根据强光抑制结果，运用深度学习超分辨率算法进行高分辨率图像的重建，生成超分辨率重构结果；As a further solution of the present invention: the super-resolution algorithm submodule uses a deep learning super-resolution algorithm to reconstruct a high-resolution image based on the strong light suppression result to generate a super-resolution reconstruction result;
所述多帧图像融合子模块基于超分辨率重构结果，利用卷积神经网络与图像对齐技术合作完成多帧图像的融合，生成多帧融合结果；The multi-frame image fusion submodule uses a convolutional neural network and image alignment technology to complete the fusion of multi-frame images based on the super-resolution reconstruction result to generate a multi-frame fusion result;
所述图像细节捕捉子模块基于多帧融合结果，采用边缘检测和图像锐化算法细化捕捉图像的高频细节，生成高分辨率影像。The image detail capture submodule uses edge detection and image sharpening algorithms based on multi-frame fusion results to refine the high-frequency details of the captured image and generate a high-resolution image.
作为本发明的进一步方案：所述波导结构制备子模块应用纳米刻蚀技术，以微纳加工技术创建波导结构结果；As a further solution of the present invention: the waveguide structure preparation submodule applies nano-etching technology to create a waveguide structure result by micro-nano processing technology;
所述光信号定向捕捉子模块结合波导结构结果和高分辨率影像，利用光场捕捉和光束形成技术进行光信号的定向捕捉和引导，生成定向捕捉结果；The optical signal directional capture submodule combines the waveguide structure results and high-resolution images, uses light field capture and beam forming technology to directional capture and guide the optical signal, and generates directional capture results;
所述集成化处理子模块基于定向捕捉结果，运用光子集成电路技术和数字信号处理实现光信号传输，生成光信号传输结果。The integrated processing submodule realizes optical signal transmission based on the directional capture result by using photonic integrated circuit technology and digital signal processing to generate an optical signal transmission result.
与现有技术相比，本发明的优点和积极效果在于：Compared with the prior art, the advantages and positive effects of the present invention are:
本发明中，利用FFT和小波变换算法对动态光信号进行高速且实时的处理，确保数据的实时性和准确性。通过卡尔曼滤波与神经网络算法的精确融合，对多通道数据进行精准的融合处理，实现了数据的完整性和一致性。在光电传感器上进行了高精度校准，利用光学标定和校准算法对其进行了准确性的验证。应用深度学习与卷积神经网络技术来进行智能优化，增强了其自我学习和适应的能力。根据实时数据自动调节光信号的强度、波长和方向，实现自适应调整，从而提供更为稳定和优质的光信号输出。通过对强光区域进行干扰抑制，降低强光干扰。结合超分辨率算法和多帧图像融合，实现高清成像效果，提供成像结果。通过微纳加工技术，确保光敏元件与光学系统的完美耦合，提高了信号的传输效率。In the present invention, the FFT and wavelet transform algorithms are used to process the dynamic optical signal at high speed and in real time to ensure the real-time and accuracy of the data. Through the precise fusion of Kalman filtering and neural network algorithms, the multi-channel data is accurately fused and processed to achieve data integrity and consistency. High-precision calibration is performed on the photoelectric sensor, and its accuracy is verified using optical calibration and calibration algorithms. Deep learning and convolutional neural network technology are used for intelligent optimization to enhance its self-learning and adaptation capabilities. The intensity, wavelength and direction of the optical signal are automatically adjusted according to real-time data to achieve adaptive adjustment, thereby providing a more stable and high-quality optical signal output. By suppressing interference in the strong light area, strong light interference is reduced. Combined with super-resolution algorithm and multi-frame image fusion, high-definition imaging effect is achieved and imaging results are provided. Through micro-nano processing technology, the perfect coupling of photosensitive elements and optical systems is ensured, and the transmission efficiency of signals is improved.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明的传感器流程图；FIG1 is a flow chart of a sensor according to the present invention;
图2为本发明的传感器模块框图；FIG2 is a block diagram of a sensor module of the present invention;
图3为本发明的高速信号处理模块流程图；FIG3 is a flow chart of a high-speed signal processing module of the present invention;
图4为本发明的多通道融合模块流程图；FIG4 is a flow chart of a multi-channel fusion module of the present invention;
图5为本发明的光学校准模块流程图；FIG5 is a flow chart of an optical calibration module of the present invention;
图6为本发明的智能算法优化模块流程图；FIG6 is a flow chart of an intelligent algorithm optimization module of the present invention;
图7为本发明的自适应光学系统模块流程图；FIG7 is a flowchart of an adaptive optical system module of the present invention;
图8为本发明的强光抑制模块流程图；FIG8 is a flowchart of a strong light suppression module of the present invention;
图9为本发明的高分辨率成像模块流程图；FIG9 is a flow chart of a high-resolution imaging module of the present invention;
图10为本发明的光学波导耦合模块流程图。FIG. 10 is a flow chart of the optical waveguide coupling module of the present invention.
具体实施方式Detailed ways
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.
在本发明的描述中，需要理解的是，术语“长度”、“宽度”、“上”、“下”、“前”、“后”、“左”、“右”、“竖直”、“水平”、“顶”、“底”、“内”、“外”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本发明的限制。此外，在本发明的描述中，“多个”的含义是两个或两个以上，除非另有明确具体的限定。In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "plurality" means two or more, unless otherwise clearly and specifically defined.
实施例一Embodiment 1
请参阅图1，应用于动态检测的高精度光电传感器是由高速信号处理模块、多通道融合模块、光学校准模块、智能算法优化模块、自适应光学系统模块、强光抑制模块、高分辨率成像模块、光学波导耦合模块组成；Please refer to Figure 1. The high-precision photoelectric sensor used for dynamic detection is composed of a high-speed signal processing module, a multi-channel fusion module, an optical calibration module, an intelligent algorithm optimization module, an adaptive optical system module, a strong light suppression module, a high-resolution imaging module, and an optical waveguide coupling module.
高速信号处理模块采用FFT和小波变换算法对高速采样的动态光信号进行实时处理和特征提取，生成高速特征分析结果；The high-speed signal processing module uses FFT and wavelet transform algorithms to perform real-time processing and feature extraction on high-speed sampled dynamic optical signals to generate high-speed feature analysis results;
多通道融合模块基于高速特征分析结果，采用卡尔曼滤波和神经网络算法进行多通道数据的融合处理，生成多通道融合结果；The multi-channel fusion module uses Kalman filtering and neural network algorithms to perform fusion processing of multi-channel data based on high-speed feature analysis results to generate multi-channel fusion results;
光学校准模块利用光学标定和校准算法，对基于多通道融合结果的光电传感器进行精度校准，生成精度校准结果；The optical calibration module uses optical calibration and calibration algorithms to perform precision calibration on the photoelectric sensor based on multi-channel fusion results and generate precision calibration results;
智能算法优化模块通过深度学习和卷积神经网络技术，对精度校准结果进行模型训练和特征优化，生成优化特征结果；The intelligent algorithm optimization module uses deep learning and convolutional neural network technology to perform model training and feature optimization on the precision calibration results to generate optimized feature results;
自适应光学系统模块根据优化特征结果，利用自适应光学元件完成对光信号强度、波长、方向的自动调节和优化，生成光信号优化结果；The adaptive optical system module uses adaptive optical elements to automatically adjust and optimize the intensity, wavelength, and direction of the optical signal according to the optimization feature results, and generates the optical signal optimization results;
强光抑制模块根据光信号优化结果，通过滤波和自适应增益控制，对强光区域的干扰进行抑制，生成强光抑制结果；The strong light suppression module suppresses the interference in the strong light area through filtering and adaptive gain control according to the optical signal optimization result, and generates the strong light suppression result;
高分辨率成像模块基于强光抑制结果，采用超分辨率算法和多帧图像融合方法进行高分辨率成像，生成高分辨率影像；The high-resolution imaging module uses super-resolution algorithms and multi-frame image fusion methods to generate high-resolution images based on the results of strong light suppression;
光学波导耦合模块利用微纳加工技术，将光敏元件与基于高分辨率影像的光学系统耦合，实现高效信号传输，生成光信号传输结果。The optical waveguide coupling module uses micro-nano processing technology to couple photosensitive elements with optical systems based on high-resolution imaging to achieve efficient signal transmission and generate optical signal transmission results.
首先，采用高速信号处理模块实现了对动态光信号的实时处理和特征提取，确保了数据的实时性和准确性。其次，通过多通道融合模块对多通道数据进行精确融合，提高了数据的完整性和一致性。光学校准模块进行精度校准，确保了传感器的检测精度和稳定性。智能算法优化模块利用深度学习和卷积神经网络等技术对数据进行智能优化，提高了系统的性能和自适应能力。自适应光学系统模块能够根据实时数据自动调节光信号优化结果，提供稳定和优质的光信号输出。强光抑制模块减轻了强光干扰，提高了系统的稳定性和可靠性。应用超分辨率算法和多帧图像融合的高分辨率成像模块提供了清晰、详细的成像效果。最后，光学波导耦合模块利用微纳加工技术提高了光信号的传输效率和稳定性。First, the high-speed signal processing module is used to realize the real-time processing and feature extraction of dynamic optical signals, ensuring the real-time and accuracy of the data. Secondly, the multi-channel data is accurately fused through the multi-channel fusion module to improve the integrity and consistency of the data. The optical calibration module performs precision calibration to ensure the detection accuracy and stability of the sensor. The intelligent algorithm optimization module uses technologies such as deep learning and convolutional neural networks to intelligently optimize the data, improving the performance and adaptability of the system. The adaptive optical system module can automatically adjust the optical signal optimization results according to real-time data to provide stable and high-quality optical signal output. The strong light suppression module reduces strong light interference and improves the stability and reliability of the system. The high-resolution imaging module that applies super-resolution algorithm and multi-frame image fusion provides clear and detailed imaging effects. Finally, the optical waveguide coupling module uses micro-nano processing technology to improve the transmission efficiency and stability of optical signals.
请参阅图2，高速信号处理模块包括光信号采集子模块、FFT处理子模块、小波变换子模块；Please refer to FIG2 , the high-speed signal processing module includes an optical signal acquisition submodule, an FFT processing submodule, and a wavelet transform submodule;
多通道融合模块包括数据同步子模块、卡尔曼滤波子模块、神经网络融合子模块；The multi-channel fusion module includes a data synchronization submodule, a Kalman filter submodule, and a neural network fusion submodule;
光学校准模块包括传感器定位子模块、角度校准子模块、视场调整子模块；The optical calibration module includes a sensor positioning submodule, an angle calibration submodule, and a field of view adjustment submodule;
智能算法优化模块包括大数据训练子模块、深度学习算法子模块、卷积神经网络优化子模块；The intelligent algorithm optimization module includes a big data training submodule, a deep learning algorithm submodule, and a convolutional neural network optimization submodule;
自适应光学系统模块包括光强自适应调节子模块、波长自适应优化子模块、方向自动校正子模块；The adaptive optical system module includes a light intensity adaptive adjustment submodule, a wavelength adaptive optimization submodule, and a direction automatic correction submodule;
强光抑制模块包括滤波子模块、自适应增益控制子模块、动态范围扩展子模块；The strong light suppression module includes a filtering submodule, an adaptive gain control submodule, and a dynamic range extension submodule;
高分辨率成像模块包括超分辨率算法子模块、多帧图像融合子模块、图像细节捕捉子模块；The high-resolution imaging module includes a super-resolution algorithm submodule, a multi-frame image fusion submodule, and an image detail capture submodule;
光学波导耦合模块包括波导结构制备子模块、光信号定向捕捉子模块、集成化处理子模块。The optical waveguide coupling module includes a waveguide structure preparation submodule, an optical signal directional capture submodule, and an integrated processing submodule.
首先，高速信号处理模块通过光信号采集子模块、FFT处理子模块和小波变换子模块实现了对动态光信号的高速处理和特征提取，从而实现快速响应和高精度的信号分析。First, the high-speed signal processing module realizes high-speed processing and feature extraction of dynamic optical signals through the optical signal acquisition submodule, FFT processing submodule and wavelet transform submodule, thereby achieving fast response and high-precision signal analysis.
多通道融合模块通过数据同步子模块、卡尔曼滤波子模块和神经网络融合子模块，能够实现各个通道数据的同步融合，提高数据的完整性和一致性，从而获得更准确和全面的信息。The multi-channel fusion module can realize the synchronous fusion of data from each channel through the data synchronization submodule, Kalman filter submodule and neural network fusion submodule, improve the integrity and consistency of the data, and thus obtain more accurate and comprehensive information.
光学校准模块通过传感器定位子模块、角度校准子模块和视场调整子模块，能够精确校准传感器的位置和角度，调整视场，保证检测结果的准确性和稳定性。The optical calibration module can accurately calibrate the position and angle of the sensor, adjust the field of view, and ensure the accuracy and stability of the detection results through the sensor positioning submodule, angle calibration submodule and field of view adjustment submodule.
智能算法优化模块通过大数据训练子模块、深度学习算法子模块和卷积神经网络优化子模块，能够利用先进的智能算法对数据进行优化和分析，提高系统的性能和自适应能力，从而获得更精确和可靠的结果。The intelligent algorithm optimization module can optimize and analyze data using advanced intelligent algorithms through the big data training sub-module, deep learning algorithm sub-module and convolutional neural network optimization sub-module, thereby improving the performance and adaptability of the system and obtaining more accurate and reliable results.
自适应光学系统模块通过光强自适应调节子模块、波长自适应优化子模块和方向自动校正子模块，能够根据优化特征结果自动调节和优化光信号的强度、波长和方向，提供优质的光信号输出。The adaptive optical system module can automatically adjust and optimize the intensity, wavelength and direction of the optical signal according to the optimization feature results through the light intensity adaptive adjustment submodule, the wavelength adaptive optimization submodule and the direction automatic correction submodule, and provide high-quality optical signal output.
强光抑制模块通过滤波子模块、自适应增益控制子模块和动态范围扩展子模块，能够消除强光干扰，提高系统的稳定性和准确性，获得更可靠的检测结果。The strong light suppression module can eliminate strong light interference, improve the stability and accuracy of the system, and obtain more reliable detection results through the filtering submodule, adaptive gain control submodule and dynamic range extension submodule.
高分辨率成像模块通过超分辨率算法子模块、多帧图像融合子模块和图像细节捕捉子模块，能够实现高分辨率的成像效果，提供更清晰、更详细的图像信息。The high-resolution imaging module can achieve high-resolution imaging effects and provide clearer and more detailed image information through the super-resolution algorithm sub-module, multi-frame image fusion sub-module and image detail capture sub-module.
最后，光学波导耦合模块通过波导结构制备子模块、光信号定向捕捉子模块和集成化处理子模块，能够高效地传输光信号，保证信号的高效、稳定的传输。Finally, the optical waveguide coupling module can efficiently transmit optical signals through the waveguide structure preparation sub-module, the optical signal directional capture sub-module and the integrated processing sub-module, ensuring efficient and stable transmission of signals.
请参阅图3，光信号采集子模块运用高精度光电探测技术来捕捉动态光信号，生成光信号原始数据；Please refer to FIG3 , the optical signal acquisition submodule uses high-precision photoelectric detection technology to capture dynamic optical signals and generate optical signal raw data;
FFT处理子模块基于光信号原始数据，利用快速傅里叶变换算法进行频域分析，实现信号的分解和重构，生成FFT分析结果；The FFT processing submodule uses the fast Fourier transform algorithm to perform frequency domain analysis based on the original data of the optical signal, realize signal decomposition and reconstruction, and generate FFT analysis results;
小波变换子模块借助FFT分析结果，运用小波变换进行时频域的联合分析，捕捉信号中的瞬时变化特征，生成高速特征分析结果。The wavelet transform submodule uses the FFT analysis results to perform a joint analysis of the time and frequency domains using wavelet transform to capture the instantaneous change characteristics in the signal and generate high-speed feature analysis results.
首先，光信号采集子模块利用高精度光电探测技术，能够准确地捕捉动态光信号，生成光信号原始数据。这确保了系统对于光信号的准确采集和输入。First, the optical signal acquisition submodule uses high-precision photoelectric detection technology to accurately capture dynamic optical signals and generate optical signal raw data, which ensures the accurate acquisition and input of optical signals by the system.
其次，FFT处理子模块采用快速傅里叶变换算法对光信号原始数据进行频域分析。这能够将信号分解为不同频率成分，并进行频谱重构，生成FFT分析结果。这提供了对信号频域特性的详细分析。Secondly, the FFT processing submodule uses the fast Fourier transform algorithm to perform frequency domain analysis on the raw data of the optical signal. This can decompose the signal into different frequency components, reconstruct the spectrum, and generate FFT analysis results. This provides a detailed analysis of the frequency domain characteristics of the signal.
小波变换子模块在FFT分析结果的基础上，运用小波变换对信号进行时频域的联合分析。通过捕捉信号中的瞬时变化特征，生成高速特征分析结果。这使系统能够更准确地捕捉到信号的时序变化和瞬时特征。Based on the FFT analysis results, the wavelet transform submodule uses wavelet transform to perform a joint analysis of the signal in the time and frequency domains. By capturing the instantaneous change characteristics in the signal, a high-speed feature analysis result is generated. This enables the system to more accurately capture the timing changes and instantaneous characteristics of the signal.
请参阅图4，数据同步子模块根据高速特征分析结果，确保传感器采集的数据在时间上的同步，生成同步数据结果；Please refer to FIG4 , the data synchronization submodule ensures the synchronization of the data collected by the sensor in time according to the high-speed feature analysis result, and generates a synchronization data result;
卡尔曼滤波子模块基于同步数据结果，应用卡尔曼滤波算法来去除数据中的噪声并进行预测性数据融合，生成卡尔曼滤波结果；The Kalman filter submodule applies the Kalman filter algorithm to remove noise from the data and perform predictive data fusion based on the synchronized data results to generate the Kalman filter results;
神经网络融合子模块利用卡尔曼滤波结果，通过神经网络算法来实现多通道数据的深度融合，生成多通道融合结果。The neural network fusion submodule uses the Kalman filter results to achieve deep fusion of multi-channel data through a neural network algorithm to generate multi-channel fusion results.
首先，数据同步子模块根据高速特征分析结果，确保传感器采集的数据在时间上的同步。通过对数据进行时间校准和同步处理，生成同步数据结果。这确保了不同通道采集的数据具有相同的时间基准，为后续的融合处理提供了一致和可靠的数据源。First, the data synchronization submodule ensures the synchronization of the data collected by the sensor in time based on the high-speed feature analysis results. By performing time calibration and synchronization processing on the data, the synchronized data results are generated. This ensures that the data collected by different channels have the same time reference, providing a consistent and reliable data source for subsequent fusion processing.
其次，卡尔曼滤波子模块基于同步数据结果，应用卡尔曼滤波算法来去除数据中的噪声并进行预测性数据融合。卡尔曼滤波算法能够通过估计系统状态和参数，减少测量误差和噪声的影响，提高数据的准确性和稳定性。通过卡尔曼滤波，生成卡尔曼滤波结果，该结果包含了去除噪声和融合处理后的数据。Secondly, the Kalman filter submodule applies the Kalman filter algorithm to remove noise from the data and perform predictive data fusion based on the synchronized data results. The Kalman filter algorithm can reduce the impact of measurement errors and noise by estimating system states and parameters, and improve data accuracy and stability. Through Kalman filtering, a Kalman filter result is generated, which includes the data after noise removal and fusion processing.
神经网络融合子模块利用卡尔曼滤波结果，通过神经网络算法实现多通道数据的深度融合。神经网络算法可以学习和识别不同通道数据之间的关联和特征，通过多通道数据的综合处理，生成多通道融合结果。这样可以提高数据的全面性和综合性，从而获得更准确和全面的信息。The neural network fusion submodule uses the Kalman filter results to achieve deep fusion of multi-channel data through the neural network algorithm. The neural network algorithm can learn and identify the associations and features between different channel data, and generate multi-channel fusion results through comprehensive processing of multi-channel data. This can improve the comprehensiveness and comprehensiveness of the data, thereby obtaining more accurate and comprehensive information.
请参阅图5，传感器定位子模块采用光学标定技术和算法，依据多通道融合结果对传感器进行精确定位，生成传感器定位结果；Please refer to Figure 5. The sensor positioning submodule uses optical calibration technology and algorithms to accurately position the sensor based on the multi-channel fusion results and generate sensor positioning results;
角度校准子模块依赖传感器定位结果，进行角度校准来确保光学系统的正确指向和捕捉，生成角度校准结果；The angle calibration submodule relies on the sensor positioning results to perform angle calibration to ensure the correct pointing and capture of the optical system and generate angle calibration results;
视场调整子模块基于角度校准结果，进行视场调整以优化图像的捕捉区域和角度，生成精度校准结果。The field of view adjustment submodule performs field of view adjustment based on the angle calibration results to optimize the image capture area and angle and generate precision calibration results.
首先，传感器定位子模块采用光学标定技术和算法，依据多通道融合结果对传感器进行精确定位。通过分析多通道融合结果，确定传感器的空间位置和姿态，生成传感器定位结果。这有助于确保光学系统对目标的准确捕捉和跟踪，提高检测的精度和稳定性。First, the sensor positioning submodule uses optical calibration technology and algorithms to accurately position the sensor based on the multi-channel fusion results. By analyzing the multi-channel fusion results, the spatial position and posture of the sensor are determined, and the sensor positioning results are generated. This helps ensure that the optical system accurately captures and tracks the target and improves the accuracy and stability of detection.
其次，角度校准子模块依赖传感器定位结果，进行角度校准，确保光学系统的正确指向和捕捉。通过校准传感器的旋转角度，使其与目标一致，消除角度偏差，生成角度校准结果。这有助于确保光学系统的准确定位和准确跟踪，提高检测的精度和可靠性。Secondly, the angle calibration submodule relies on the sensor positioning results to perform angle calibration to ensure the correct pointing and capture of the optical system. By calibrating the rotation angle of the sensor to make it consistent with the target, eliminating the angle deviation, and generating the angle calibration result. This helps to ensure the accurate positioning and accurate tracking of the optical system, and improve the accuracy and reliability of detection.
视场调整子模块基于角度校准结果，进行视场调整，优化图像的捕捉区域和角度。通过调整光学系统的焦距、视场范围或镜头角度，使图像捕捉更加准确和全面，生成精度校准结果。这有助于提高图像的分辨率和清晰度，优化图像的信息获取和分析能力。The field of view adjustment submodule adjusts the field of view based on the angle calibration results to optimize the image capture area and angle. By adjusting the focal length, field of view range or lens angle of the optical system, the image capture is more accurate and comprehensive, and the precision calibration results are generated. This helps to improve the resolution and clarity of the image and optimize the image information acquisition and analysis capabilities.
请参阅图6，大数据训练子模块依据精度校准结果，使用大数据算法来训练和优化模型，生成大数据训练结果；Please refer to FIG6 , the big data training submodule uses the big data algorithm to train and optimize the model based on the accuracy calibration result to generate the big data training result;
深度学习算法子模块采纳大数据训练结果为基础，应用深度学习算法进行模型的深度训练和优化，生成深度学习优化结果；The deep learning algorithm submodule adopts the big data training results as the basis, applies the deep learning algorithm to perform deep training and optimization of the model, and generates deep learning optimization results;
卷积神经网络优化子模块根据深度学习优化结果，利用卷积神经网络技术对模型进行优化，生成优化特征结果。The convolutional neural network optimization submodule optimizes the model based on the deep learning optimization results and uses convolutional neural network technology to generate optimized feature results.
首先，大数据训练子模块依据精度校准结果，利用大数据算法对模型进行训练和优化。通过分析准确校准的数据结果，利用大量的数据进行训练，提高模型的准确性和鲁棒性，生成大数据训练结果。这有助于提升模型的预测能力和泛化能力，使其更适应多样化的数据场景。First, the big data training submodule uses big data algorithms to train and optimize the model based on the precision calibration results. By analyzing the accurately calibrated data results and using a large amount of data for training, the accuracy and robustness of the model are improved, and big data training results are generated. This helps to improve the model's predictive and generalization capabilities, making it more adaptable to diverse data scenarios.
其次，深度学习算法子模块采纳大数据训练结果为基础，应用深度学习算法进行模型的深度训练和优化。深度学习算法可以通过多层神经网络模型进行特征学习和模式识别，从而进一步提升模型的性能和准确度。通过深度学习算法的训练和优化，生成深度学习优化结果，该结果包含了经过深度学习算法训练后的模型参数和权重。Secondly, the deep learning algorithm submodule adopts the big data training results as the basis and applies the deep learning algorithm to perform deep training and optimization of the model. The deep learning algorithm can perform feature learning and pattern recognition through a multi-layer neural network model, thereby further improving the performance and accuracy of the model. Through the training and optimization of the deep learning algorithm, a deep learning optimization result is generated, which includes the model parameters and weights after training with the deep learning algorithm.
卷积神经网络优化子模块根据深度学习优化结果，利用卷积神经网络技术对模型进行优化。卷积神经网络具有优秀的图像特征提取和处理能力，可以通过卷积、池化等操作对数据进行多尺度的处理和解析。通过卷积神经网络的优化，提取和强化模型的特征表达，生成优化特征结果。这有助于进一步提高模型的图像处理和分析能力，使其更适应复杂的数据情况，并提供更准确、更全面的特征分析结果。The convolutional neural network optimization submodule optimizes the model using convolutional neural network technology based on the deep learning optimization results. Convolutional neural networks have excellent image feature extraction and processing capabilities, and can process and analyze data at multiple scales through operations such as convolution and pooling. Through the optimization of convolutional neural networks, the feature expression of the model is extracted and strengthened, and optimized feature results are generated. This helps to further improve the image processing and analysis capabilities of the model, making it more adaptable to complex data situations, and providing more accurate and comprehensive feature analysis results.
请参阅图7，光强自适应调节子模块基于贝叶斯优化算法，利用光强度传感器实现对光信号强度的实时监测与自适应调节，生成自适应光强调整结果；Please refer to FIG7 , the light intensity adaptive adjustment submodule is based on the Bayesian optimization algorithm, and uses the light intensity sensor to realize the real-time monitoring and adaptive adjustment of the light signal intensity, and generates the adaptive light intensity adjustment result;
波长自适应优化子模块依据自适应光强调整结果，运用神经网络算法对光信号的波长进行深度学习和自适应优化，生成波长优化结果；The wavelength adaptive optimization submodule uses a neural network algorithm to perform deep learning and adaptive optimization on the wavelength of the optical signal based on the adaptive light intensity adjustment results to generate wavelength optimization results.
方向自动校正子模块结合波长优化结果，利用卡尔曼滤波器进行光信号方向的精确校正和调节，生成光信号优化结果。The automatic direction correction submodule combines the wavelength optimization results and uses the Kalman filter to accurately correct and adjust the direction of the optical signal to generate the optical signal optimization results.
首先，光强自适应调节子模块基于贝叶斯优化算法，利用光强度传感器实现对光信号强度的实时监测与自适应调节。通过不断地监测光强度传感器的输出，利用贝叶斯优化算法实现光强度的自适应调节，使得光信号强度始终保持在一个合适的范围内。这有助于避免光强度不足或过强所引发的问题，并生成自适应光强调整结果。First, the light intensity adaptive adjustment submodule uses the light intensity sensor to achieve real-time monitoring and adaptive adjustment of the light signal intensity based on the Bayesian optimization algorithm. By continuously monitoring the output of the light intensity sensor and using the Bayesian optimization algorithm to achieve adaptive adjustment of the light intensity, the light signal intensity is always kept within an appropriate range. This helps to avoid problems caused by insufficient or excessive light intensity and generate adaptive light intensity adjustment results.
其次，波长自适应优化子模块依据自适应光强调整结果，运用神经网络算法对光信号的波长进行深度学习和自适应优化。通过分析自适应光强调整结果，运用神经网络算法学习和优化光信号的波长特征，以适应不同的光学环境和应用需求。这有助于提高光信号的适应性和表达能力，生成波长优化结果。Secondly, the wavelength adaptive optimization submodule uses the neural network algorithm to perform deep learning and adaptive optimization on the wavelength of the optical signal based on the adaptive light intensity adjustment results. By analyzing the adaptive light intensity adjustment results, the neural network algorithm is used to learn and optimize the wavelength characteristics of the optical signal to adapt to different optical environments and application requirements. This helps to improve the adaptability and expression ability of the optical signal and generate wavelength optimization results.
方向自动校正子模块结合波长优化结果，利用卡尔曼滤波器进行光信号方向的精确校正和调节。通过分析波长优化结果，利用卡尔曼滤波器对光信号的方向进行校正，消除方向误差和偏差，使光信号的方向始终精确指向目标。这有助于提高光学系统的准确性和稳定性，生成光信号优化结果。The automatic direction correction submodule combines the wavelength optimization results and uses the Kalman filter to accurately correct and adjust the direction of the optical signal. By analyzing the wavelength optimization results, the Kalman filter is used to correct the direction of the optical signal, eliminate the direction error and deviation, and make the direction of the optical signal always accurately point to the target. This helps to improve the accuracy and stability of the optical system and generate the optical signal optimization results.
请参阅图8，滤波子模块根据光信号优化结果，应用傅里叶变换与高斯滤波联合进行复杂光信号的滤波处理，生成滤波处理结果；Please refer to FIG8 , the filtering submodule applies Fourier transform and Gaussian filtering to perform filtering processing on the complex optical signal according to the optical signal optimization result, and generates the filtering processing result;
自适应增益控制子模块基于滤波处理结果，通过线性增益控制与递归最小二乘法进行自适应增益的调控，生成增益调节结果；The adaptive gain control submodule adjusts the adaptive gain based on the filtering processing result through linear gain control and recursive least square method to generate the gain adjustment result;
动态范围扩展子模块基于增益调节结果，利用波形失真分析实施强光区域的动态范围扩展和抑制，生成强光抑制结果。The dynamic range extension submodule implements dynamic range expansion and suppression in the strong light area based on the gain adjustment result and utilizes waveform distortion analysis to generate a strong light suppression result.
首先，滤波子模块根据光信号优化结果，应用傅里叶变换与高斯滤波联合进行复杂光信号的滤波处理。通过傅里叶变换将光信号转换到频域，然后应用高斯滤波来去除噪声和不需要的频率成分。这有助于滤除干扰噪声、降低图像噪声和改善信号质量，生成滤波处理结果。First, the filter submodule applies Fourier transform and Gaussian filtering to filter the complex optical signal according to the optical signal optimization results. The optical signal is converted to the frequency domain through Fourier transform, and then Gaussian filtering is applied to remove noise and unnecessary frequency components. This helps to filter out interference noise, reduce image noise and improve signal quality, and generate filtering processing results.
其次，自适应增益控制子模块基于滤波处理结果，通过线性增益控制与递归最小二乘法进行自适应增益的调控。根据滤波处理后的信号特性，通过递归最小二乘法来自适应地调整信号的增益，以增强信号的可视化效果或满足特定要求，生成增益调节结果。这有助于优化图像亮度和对比度，提高图像的可视化效果和信息提取能力。Secondly, the adaptive gain control submodule adjusts the adaptive gain through linear gain control and recursive least squares method based on the filtering processing results. According to the signal characteristics after filtering, the gain of the signal is adaptively adjusted through recursive least squares method to enhance the visualization effect of the signal or meet specific requirements, and generate gain adjustment results. This helps to optimize image brightness and contrast, and improve image visualization and information extraction capabilities.
动态范围扩展子模块基于增益调节结果，利用波形失真分析实施强光区域的动态范围扩展和抑制。通过分析增益调节后的信号波形，使用波形失真分析技术来判断强光区域，并对强光进行抑制和动态范围扩展处理。这有助于避免强光的过曝和失真问题，提高图像的动态范围，并生成强光抑制结果。The dynamic range extension submodule uses waveform distortion analysis to implement dynamic range expansion and suppression in the strong light area based on the gain adjustment results. By analyzing the signal waveform after gain adjustment, the waveform distortion analysis technology is used to determine the strong light area, and the strong light is suppressed and the dynamic range is expanded. This helps to avoid overexposure and distortion problems of strong light, improve the dynamic range of the image, and generate strong light suppression results.
请参阅图9，超分辨率算法子模块根据强光抑制结果，运用深度学习超分辨率算法进行高分辨率图像的重建，生成超分辨率重构结果；Please refer to FIG9 , the super-resolution algorithm submodule uses a deep learning super-resolution algorithm to reconstruct a high-resolution image based on the strong light suppression result to generate a super-resolution reconstruction result;
多帧图像融合子模块基于超分辨率重构结果，利用卷积神经网络与图像对齐技术合作完成多帧图像的融合，生成多帧融合结果；The multi-frame image fusion submodule uses convolutional neural network and image alignment technology to complete the fusion of multi-frame images based on the super-resolution reconstruction results and generate multi-frame fusion results;
图像细节捕捉子模块基于多帧融合结果，采用边缘检测和图像锐化算法细化捕捉图像的高频细节，生成高分辨率影像。The image detail capture submodule uses edge detection and image sharpening algorithms based on multi-frame fusion results to refine the high-frequency details of the captured image and generate high-resolution images.
首先，超分辨率算法子模块根据强光抑制结果，应用深度学习超分辨率算法进行高分辨率图像的重建。通过分析强光抑制结果所生成的图像，利用深度学习算法提高图像的分辨率并还原细节，生成超分辨率重构结果。这有助于提升图像的清晰度和细节表达能力，使得图像具有更高的视觉质量和可用性。First, the super-resolution algorithm submodule uses the deep learning super-resolution algorithm to reconstruct high-resolution images based on the strong light suppression results. By analyzing the images generated by the strong light suppression results, the deep learning algorithm is used to improve the image resolution and restore the details to generate super-resolution reconstruction results. This helps to improve the clarity and detail expression of the image, making the image have higher visual quality and usability.
其次，多帧图像融合子模块基于超分辨率重构结果，利用卷积神经网络与图像对齐技术合作完成多帧图像的融合。通过将多个经过超分辨率重建的图像进行融合，利用卷积神经网络和图像对齐技术实现图像的对齐和融合，生成多帧融合结果。这有助于提高图像的稳定性和一致性，减少图像中的噪声和伪影。Secondly, the multi-frame image fusion submodule uses convolutional neural networks and image alignment technology to complete the fusion of multi-frame images based on the super-resolution reconstruction results. By fusing multiple super-resolution reconstructed images, convolutional neural networks and image alignment technology are used to achieve image alignment and fusion, and generate multi-frame fusion results. This helps to improve the stability and consistency of the image and reduce noise and artifacts in the image.
图像细节捕捉子模块基于多帧融合结果，采用边缘检测和图像锐化算法细化捕捉图像的高频细节。通过边缘检测技术和图像锐化算法，强化图像的边缘和细节信息，进一步提升图像的清晰度和细节捕捉能力。这有助于捕捉图像中的细微结构和纹理，生成高分辨率影像。The image detail capture submodule uses edge detection and image sharpening algorithms based on the multi-frame fusion results to refine the high-frequency details of the captured image. Through edge detection technology and image sharpening algorithms, the edge and detail information of the image are enhanced, further improving the image clarity and detail capture capabilities. This helps capture the subtle structures and textures in the image and generate high-resolution images.
请参阅图10，波导结构制备子模块应用纳米刻蚀技术，以微纳加工技术创建波导结构结果；Please refer to FIG. 10 , the waveguide structure preparation submodule applies nano-etching technology to create the waveguide structure result by micro-nano processing technology;
光信号定向捕捉子模块结合波导结构结果和高分辨率影像，利用光场捕捉和光束形成技术进行光信号的定向捕捉和引导，生成定向捕捉结果；The optical signal directional capture submodule combines the waveguide structure results and high-resolution images, uses light field capture and beam forming technology to directional capture and guide optical signals, and generates directional capture results;
集成化处理子模块基于定向捕捉结果，运用光子集成电路技术和数字信号处理实现光信号传输，生成光信号传输结果。The integrated processing submodule realizes optical signal transmission based on the directional capture results, using photonic integrated circuit technology and digital signal processing to generate optical signal transmission results.
首先，波导结构制备子模块应用纳米刻蚀技术和微纳加工技术创建波导结构。通过纳米刻蚀技术将波导结构刻写在适当的材料上，利用微纳加工技术进行精细的控制和制备。这有助于实现高度准确和可控的波导结构，为光信号的定向传输和引导提供了基础。First, the waveguide structure preparation submodule uses nano-etching technology and micro-nano processing technology to create a waveguide structure. The waveguide structure is engraved on the appropriate material through nano-etching technology, and finely controlled and prepared using micro-nano processing technology. This helps to achieve highly accurate and controllable waveguide structures, providing a basis for the directional transmission and guidance of optical signals.
其次，光信号定向捕捉子模块结合波导结构结果和高分辨率影像，应用光场捕捉和光束形成技术进行光信号的定向捕捉和引导。通过分析波导结构结果和高分辨率影像，利用光场捕捉和光束形成技术实现对光信号的精确定位和引导，使其沿着波导结构传输。这有助于保持光信号的稳定性和定向性，生成定向捕捉结果。Secondly, the optical signal directional capture submodule combines the waveguide structure results and high-resolution images, and applies light field capture and beam forming technology to directional capture and guide the optical signal. By analyzing the waveguide structure results and high-resolution images, the optical signal is precisely positioned and guided using light field capture and beam forming technology, so that it is transmitted along the waveguide structure. This helps maintain the stability and directionality of the optical signal and generate directional capture results.
集成化处理子模块基于定向捕捉结果，运用光子集成电路技术和数字信号处理实现光信号的传输和处理。通过光子集成电路技术将光信号进行集成和控制，结合数字信号处理算法对光信号进行分析和处理。这有助于实现光信号的高效传输和处理，生成光信号传输结果。Based on the directional capture results, the integrated processing submodule uses photonic integrated circuit technology and digital signal processing to achieve the transmission and processing of optical signals. The optical signals are integrated and controlled through photonic integrated circuit technology, and analyzed and processed in combination with digital signal processing algorithms. This helps to achieve efficient transmission and processing of optical signals and generate optical signal transmission results.
工作原理：working principle:
高速信号处理模块使用FFT（快速傅里叶变换）和小波变换算法对高速采样的动态光信号进行实时处理和特征提取，生成高速特征分析结果。The high-speed signal processing module uses FFT (Fast Fourier Transform) and wavelet transform algorithms to perform real-time processing and feature extraction on high-speed sampled dynamic optical signals to generate high-speed feature analysis results.
多通道融合模块基于高速特征分析结果，采用卡尔曼滤波和神经网络算法进行多通道数据的融合处理，生成多通道融合结果。The multi-channel fusion module uses Kalman filtering and neural network algorithms to perform fusion processing of multi-channel data based on high-speed feature analysis results to generate multi-channel fusion results.
光学校准模块利用光学标定和校准算法，对基于多通道融合结果的光电传感器进行精度校准，生成精度校准结果。The optical calibration module uses optical calibration and calibration algorithms to perform precision calibration on the photoelectric sensor based on multi-channel fusion results and generate precision calibration results.
智能算法优化模块通过深度学习和卷积神经网络技术，对精度校准结果进行模型训练和特征优化，生成优化特征结果。The intelligent algorithm optimization module uses deep learning and convolutional neural network technology to perform model training and feature optimization on the precision calibration results to generate optimized feature results.
自适应光学系统模块根据优化特征结果，利用自适应光学元件完成对光信号强度、波长、方向的自动调节和优化，生成光信号优化结果。The adaptive optical system module uses adaptive optical elements to automatically adjust and optimize the intensity, wavelength, and direction of the optical signal based on the optimization feature results, and generates optical signal optimization results.
强光抑制模块根据光信号优化结果，通过滤波和自适应增益控制，对强光区域的干扰进行抑制，生成强光抑制结果。The strong light suppression module suppresses the interference in the strong light area through filtering and adaptive gain control according to the optical signal optimization result, and generates a strong light suppression result.
高分辨率成像模块基于强光抑制结果，采用超分辨率算法和多帧图像融合方法进行高分辨率成像，生成高分辨率影像。The high-resolution imaging module uses super-resolution algorithm and multi-frame image fusion method to perform high-resolution imaging based on the strong light suppression results to generate high-resolution images.
光学波导耦合模块利用微纳加工技术，将光敏元件与基于高分辨率影像的光学系统耦合，实现高效信号传输，生成光信号传输结果。The optical waveguide coupling module uses micro-nano processing technology to couple photosensitive elements with optical systems based on high-resolution imaging to achieve efficient signal transmission and generate optical signal transmission results.
以上，仅是本发明的较佳实施例而已，并非对本发明作其他形式的限制，任何熟悉本专业的技术人员可能利用上述揭示的技术内容加以变更或改型为等同变化的等效实施例应用于其他领域，但是凡是未脱离本发明技术方案内容，依据本发明的技术实质对以上实施例所做的任何简单修改、等同变化与改型，仍属于本发明技术方案的保护范围。The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.
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