Fruit and vegetable quality nondestructive testing imaging control system

Abstract

The invention discloses a fruit and vegetable quality nondestructive testing imaging control system which comprises the following components: S1, a multispectral light source module which captures spectral information inside and on the surface of the multispectral light source module; s2, a precision imaging module generates multispectral image data; s3, extracting key image features related to fruit and vegetable quality by an image data preprocessing module; s4, the quality analysis module adjusts algorithm parameters in real time and generates an analysis data set corresponding to the fruit and vegetable quality indexes; s5, a spectrum enhancement and adaptation module, which optimizes the sensitivity and resolution of the multispectral signal; s6, a distributed detection control module is used for controlling system parameters to be adjusted in real time, and the detection efficiency and detection are optimized; and S7, a data management and output module which generates a fruit and vegetable quality evaluation report through a computer system. The method has the advantages of being high in detection precision, high in adaptability, good in nondestructive property and high in detection efficiency.

Description

Technical Field

[0001] The invention relates to the technical field of nondestructive testing, and in particular to an imaging control system for nondestructive testing of fruit and vegetable quality. Background Art

[0002] With the rapid development of modern agriculture and the increasing demand of consumers for fruit and vegetable quality, nondestructive testing technology has gradually become an important research direction in the field of food safety and agricultural production. In fruit and vegetable quality testing, visible light and near-infrared multispectral imaging technology has attracted widespread attention due to its nondestructiveness and high efficiency. However, the existing fruit and vegetable quality testing technology still faces many problems and challenges when facing the diverse types of fruits and vegetables and complex quality testing needs.

[0003] In current technology, traditional fruit and vegetable quality detection methods mainly rely on detection systems based on single spectrum or fixed algorithms. These methods show obvious limitations when dealing with complex fruit and vegetable quality index analysis. Specifically, existing technologies have shortcomings in the following aspects: 1. Limited spectral capture capability: Existing technologies mostly use static spectral imaging methods, which cannot flexibly adjust the spectral range and characteristics. As a result, when facing different types of fruits and vegetables and complex internal tissues, the accuracy and integrity of spectral signal capture are insufficient, limiting the applicability of detection.

[0004] 2. Poor algorithm adaptability: Traditional detection methods usually rely on fixed image processing algorithms or empirical models, and cannot be dynamically optimized according to the quality characteristics of different fruits and vegetables and the needs of multi-level correlation analysis, resulting in inaccurate or incomplete detection results.

[0005] 3. Low sensitivity of optical signals: The optical signal enhancement mechanism of existing systems is relatively simple and cannot achieve high-sensitivity and high-resolution capture of multi-spectral signals under low-energy conditions, which affects the reliability of detection.

[0006] 4. Fruits and vegetables are easily damaged during the inspection process: Traditional inspection technologies mostly use contact or low-precision non-contact imaging methods, which may cause damage to the surface of fruits and vegetables and cannot meet the quality inspection needs of high-value fruits and vegetables.

[0007] 5. Insufficient coordination of the detection process: Existing technologies often lack efficient coordination and dynamic parameter optimization mechanisms between multiple modules, and are unable to achieve full process optimization from light source control, imaging to quality analysis, resulting in low overall detection efficiency.

[0008] Therefore, how to provide a non-destructive detection imaging control system for fruit and vegetable quality is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0009] One object of the present invention is to propose an imaging control system for nondestructive detection of fruit and vegetable quality. The present invention fully combines visible light and near-infrared multispectral imaging technology, artificial intelligence adaptive learning algorithm and distributed detection control architecture, and describes in detail the full process method from multispectral light source regulation, precision imaging, image processing to quality analysis and data management. It has the advantages of high detection accuracy, strong adaptability, good nondestructiveness and high detection efficiency.

[0010] A fruit and vegetable quality nondestructive detection imaging control system according to an embodiment of the present invention includes: S1, multi-spectral light source module, used to generate multi-spectral light beams covering the visible light and near-infrared range. The multi-spectral light source module combines dynamic modulation technology and composite spectral filters to illuminate the target fruit and vegetable area with multi-dimensional spectral characteristics and capture the spectral information inside and on the surface; S2, a precision imaging module, including a flexible optical lens and a vibration suppression mechanism, for collecting images of fruits and vegetables in a target fruit and vegetable area that has been irradiated with multispectral light under non-contact conditions, and generating multispectral image data; S3, image data preprocessing module, used to perform layered processing on the multispectral image data output by the precision imaging module, using partition denoising, feature enhancement and region segmentation algorithms to extract key image features related to fruit and vegetable quality; S4, a quality analysis module, which performs multi-level correlation analysis on key image features output by the image data preprocessing module through an adaptive learning algorithm based on a deep neural network, adjusts algorithm parameters in real time, and generates a set of quality indicators corresponding to the quality indicators of fruits and vegetables; S5, spectral enhancement and adaptation module, receives the multi-spectral image data generated by the precision imaging module, and optimizes the input multi-spectral image data by using nano-level light wave coupling and optical signal enhancement mechanism to adapt it to the organizational structure and quality detection requirements of different types of fruits and vegetables; S6, distributed detection control module, based on a multi-node collaborative architecture, dynamically interacts with the multi-spectral light source module, precision imaging module and quality analysis module to adjust control system parameters in real time and optimize detection efficiency and detection; S7, data management and output module, integrates the test data and analysis results of each module, generates fruit and vegetable quality assessment reports through the computer system, and provides real-time visual analysis and dynamic data update functions to optimize the test process and result display.

[0011] Optionally, the S1 specifically includes: S11, light source array unit, consisting of a wavelength range of to It is composed of multiple narrow-band light sources, each of which can be independently controlled, and uses band selection technology to achieve dynamic spectrum output, which is used to generate specific spectrum combinations that are adapted to the internal organization and surface characteristics of fruits and vegetables; S12, the dynamic modulation and control unit is connected to the light source array unit, and the light intensity is controlled by using time dynamic modulation technology: ; in, Indicates wavelength Next time The instantaneous light intensity, The wavelength The initial light intensity, is the modulation depth coefficient, For modulation frequency, dynamically adjust and Used to optimize the spatiotemporal distribution of spectral output; S13, the beam shaping module is located at the output end of the light source array unit, and is composed of a lens group and a collimator, and is used to adjust the direction and uniformity of the output beam, and achieve uniform distribution of light intensity in the target fruit and vegetable area through beam focusing technology, while reducing spectral scattering and energy loss; S14, the spectral feedback and calibration unit is connected to the dynamic modulation and control unit, and the embedded spectral sensor is used to monitor the output spectral characteristics of the light source array unit in real time to obtain the wavelength deviation and light intensity deviation , and feed it back to the dynamic modulation and control unit to achieve real-time optimization and stability control of the spectral output through closed-loop regulation.

[0012] Optionally, the S2 specifically includes: S21, flexible optical lens module, composed of multiple groups of adjustable focus lenses, each group of lenses has an independent drive device and electronic control system, which can adjust the focal length in real time according to the optical properties of the surface and interior of fruits and vegetables, and use segmented focusing mode to optimize the optical path and capture multi-spectral signals; S22, vibration suppression and compensation unit, including an active vibration compensation module with integrated inertial sensor and a high elastic vibration reduction support structure, the active vibration compensation module generates a compensation torque based on the detected displacement error and vibration rate : ; in, To compensate the torque, is the stiffness coefficient, is the real-time displacement error, is the damping coefficient, is the vibration rate; S23, multi-spectral imaging sensor module, is set at the rear end of the flexible optical lens module, and uses a photoelectric sensor array to cover to The spectral range and spectral response characteristics match the multi-spectral light source module, and the rapid acquisition and digitization of multi-spectral image data are achieved through pixel-level parallel processing; S24, optical protection and cleaning module consists of a transparent coating protective cover and an automatic cleaning system. The transparent coating material has anti-reflection and anti-static functions. The automatic cleaning system removes attached particles through micro-vibration and air injection devices to keep the optical path stable for a long time; S25, the image correction and equalization unit is connected to the multispectral imaging sensor module, and is used to perform real-time correction and equalization processing on the original multispectral image data. The correction algorithm is based on pixel offset correction and light intensity uniformity adjustment: ; in, is the corrected pixel value, is the original pixel value, is the light intensity balance coefficient matrix, is the noise correction coefficient matrix; S26, a data output and interface module, is connected to a computer analysis system via an embedded interface, and transmits the corrected multispectral image data to each module in a standardized data format.

[0013] Optionally, the S3 specifically includes: S31, a spectral layering processing module receives the multispectral image data output by the precision imaging module, and divides the multispectral image data into multiple spectral layers according to the wavelength range, which are defined as ,in is a spectral layer data, indicating the wavelength is The corresponding image layer; S32, adaptive denoising unit, uses adaptive spatial filtering algorithm to denoise the spectral layer data Perform denoising based on neighborhood characteristics: ; in, represents the pixel value after denoising, is the pixel value corresponding to the original data, is the neighborhood pixel weight, Center pixel Neighborhood range; S33, multi-dimensional feature enhancement module for denoised spectral layer data Perform feature extraction and enhancement processing to improve edge and texture characteristics by calculating local gradient changes. The enhanced feature matrix is ​​defined as: ; in, is the enhanced eigenvalue, and are the gradient values ​​in the horizontal and vertical directions at the pixel position respectively; S34, the regional intelligent segmentation module, based on the feature matrix output by the multi-dimensional feature enhancement module, performs segmentation processing on the target fruit and vegetable region, and uses an adaptive segmentation algorithm based on regional growth and dynamic threshold adjustment to generate a segmentation mask to obtain a segmentation mask matrix: ; in, is the segmentation mask matrix, represents a threshold value that is dynamically adjusted based on local characteristics; S35, key quality feature extraction module, using the segmentation mask matrix generated by the regional intelligent segmentation module With spectral layer data , extract key image features related to fruit and vegetable quality, and form a set of key image feature vectors .

[0014] Optionally, the S4 specifically includes: S41, the multi-dimensional feature receiving module is connected to the image data preprocessing module to receive the key image feature vector set generated by it , classified into input feature matrices according to data type and feature dimension ,in represents the number of features, Represents feature dimension; S42, multi-layer correlation analysis unit, based on deep neural network to input feature matrix Conduct multi-level correlation modeling and construct quality prediction mapping function: ; in, is the prediction result vector, Contains the network weight matrix for the parameter set and the bias vector , is the network weight matrix, For the input feature matrix, the weight matrix is ​​optimized by recursive mapping Implement nonlinear modeling of high-dimensional data, It is a multi-layer correlation analysis model; S43, adaptive learning optimization module, combined with dynamic gradient descent algorithm to optimize the network weight matrix of deep neural network Perform real-time optimization, the optimization rules are: ; in, represents the network weight matrix at the next moment, is the time step The current network weight matrix, is the learning rate, is the loss function For the gradient of the network weight matrix, the weight is updated through real-time data to optimize the adaptability to different fruit and vegetable quality characteristics; S44, quality index extraction module, predicts the result vector of multi-layer correlation analysis unit Analyze and extract the core indicators related to fruit and vegetable quality to generate a set of quality indicators ,in For the Item quality characteristics; S45. Combine quality indicators The data are transmitted to the computer analysis system through a standardized interface and stored in the local database.

[0015] Optionally, the S5 specifically includes: S51, nanoscale optical wave coupling module, including periodically distributed grating array and integrated waveguide structure, used to input spectral signal Perform coupling modulation to output enhanced spectral signals : ; in, is the coupling enhancement factor; S52, optical signal enhancement unit, receives the enhanced spectral signal output by the nanoscale light wave coupling module , enhanced by high-gain optical thin films and multi-layer dielectric structures, optimized spectral signal resolution for: ; in, and are the wavelength bandwidths of the input and output signals respectively; S53, spectrum adaptation module, receiving the spectrum signal resolution of the optical signal enhancement unit , the wavelength of the spectral signal is selected and adapted through the electronically controlled spectral filter and the dynamic wavelength adjustment unit, and the adapted spectral signal is output ; S54, signal homogenization processing module, for the spectral signal output by the spectral adaptation module To adjust the spatial distribution: ; in, is the spectral signal after homogenization, is the target detection area; S55, the spectrum output interface module receives the spectrum signal output by the signal homogenization processing module After that, it is transmitted to the precision imaging module through a standardized interface, and the spectral signal is dynamically adjusted to adapt to different fruit and vegetable detection needs.

[0016] Optionally, the S6 specifically includes: S61, multi-node detection coordination unit, multiple coordination nodes are set based on the distributed control architecture, each node includes a computing unit, a sensor interface and a communication module, which is used to interact with the multi-spectral light source module, the precision imaging module and the quality analysis module in real time to generate a node state matrix : ; in, Indicates the system Nodes at time The state set, Indicates Detection data, load and real-time feedback of each node; S62, dynamic parameter control module, receiving node status matrix , dynamically adjust the system parameter set based on the task optimization function : ; in, is the system task cost function, which comprehensively considers detection accuracy and resource allocation efficiency. To learn the step size, adjust the rules to ensure the parameter set at the next moment More adaptable to the current node status; S63, task allocation and coordination module, based on the optimization parameter set generated by the dynamic parameter control module and the node state matrix , generate the task allocation matrix : ; in, Indicates at time The specific detection tasks assigned to each node reflect the real-time distribution results of the node collaboration; S64, real-time communication and synchronization module, through the communication protocol and distributed time synchronization mechanism, the node state matrix and the task allocation matrix Synchronize and share between systems; S65. According to the task allocation matrix The execution results and detection feedback data of the node are updated in real time. , and optimize the parameter set through closed-loop control .

[0017] Optionally, the S7 specifically includes: S71, the data acquisition and integration module is connected with the multi-spectral light source module, the precision imaging module and the quality analysis module, receives the detection data and analysis results output by each module, and constructs a multi-dimensional data set ,in , Indicates time Moment The test parameters are The value of the feature dimension, To detect the number of parameters, is the number of feature dimensions; S72, data preprocessing module, for multidimensional data sets Perform data cleaning, noise reduction and standardization to generate a preprocessed data matrix ; S73, data analysis and report generation module, receiving the data matrix output by the data preprocessing module and the set of quality indicators generated by the quality analysis module , generate fruit and vegetable quality assessment results through comprehensive analysis model : ; in, To develop a comprehensive analysis model, multidimensional data is associated with quality indicators to generate evaluation results; S74, real-time visual analysis module, the fruit and vegetable quality evaluation results output by the data analysis and report generation module Perform dynamic display and generate real-time visual data through the time dimension Implement dynamic updates; S75, dynamic data update module, receiving new detection data and updating multidimensional data sets and quality index set , and adjust the data preprocessing module in real time to optimize the quality assessment results at the next moment.

[0018] The beneficial effects of the present invention are: (1) The present invention combines visible light and near-infrared multispectral imaging technology, dynamic spectral control technology, and nanoscale light wave coupling and optical signal enhancement mechanism to achieve high-sensitivity, multi-dimensional spectral characteristic capture of the internal organization and surface characteristics of fruits and vegetables, overcomes the shortcomings of traditional static spectral imaging methods in signal sensitivity and adaptability, and effectively adapts to the detection needs of different types of fruits and vegetables.

[0019] (2) The present invention introduces an artificial intelligence-based adaptive learning algorithm and a dynamic parameter optimization mechanism to construct a closed-loop optimization system from image data preprocessing to quality analysis. It is capable of performing multi-level correlation analysis on complex quality indicators and adjusting algorithm parameters in real time, thereby significantly improving detection accuracy and adaptability, and solving the problem of the traditional detection system's single algorithm and poor adaptability.

[0020] (3) The present invention realizes non-destructive and efficient fruit and vegetable quality detection through the collaborative work of a non-contact precision imaging module and a distributed detection control module, combined with a flexible optical lens and a real-time communication synchronization mechanism, avoiding the problem of damage to the fruit and vegetable surface in traditional detection technology, while improving the overall detection efficiency and stability of the system.

[0021] (4) The present invention provides a comprehensive perspective on fruit and vegetable quality assessment through multi-dimensional data integration, dynamic updating and real-time visual analysis of the data management and output module, making the test results more intuitive and efficient, providing strong technical support for optimizing the test process and result display, and solving the problem of insufficient data processing and result display capabilities in traditional test systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a general framework diagram of a nondestructive detection imaging control system for fruit and vegetable quality proposed by the present invention. DETAILED DESCRIPTION

[0023] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0024] refer to Figure 1 , a fruit and vegetable quality non-destructive detection imaging control system, comprising: S1, multi-spectral light source module, used to generate multi-spectral light beams covering the visible light and near-infrared range. The multi-spectral light source module combines dynamic modulation technology and composite spectral filters to illuminate the target fruit and vegetable area with multi-dimensional spectral characteristics and capture the spectral information inside and on the surface; In this implementation, S1 specifically includes: S11, light source array unit, consisting of a wavelength range of to It is composed of multiple narrow-band light sources, each of which can be independently controlled, and uses band selection technology to achieve dynamic spectrum output, which is used to generate specific spectrum combinations that are adapted to the internal organization and surface characteristics of fruits and vegetables; S12, the dynamic modulation and control unit is connected to the light source array unit, and the light intensity is controlled by using time dynamic modulation technology: ; in, Indicates wavelength Next time The instantaneous light intensity, The wavelength The initial light intensity, is the modulation depth coefficient, For modulation frequency, dynamically adjust and Used to optimize the spatiotemporal distribution of spectral output; S13, the beam shaping module is located at the output end of the light source array unit, and is composed of a lens group and a collimator, and is used to adjust the direction and uniformity of the output beam, and achieve uniform distribution of light intensity in the target fruit and vegetable area through beam focusing technology, while reducing spectral scattering and energy loss; S14, the spectral feedback and calibration unit is connected to the dynamic modulation and control unit, and the embedded spectral sensor is used to monitor the output spectral characteristics of the light source array unit in real time to obtain the wavelength deviation and light intensity deviation , and feed it back to the dynamic modulation and control unit to achieve real-time optimization and stability control of the spectral output through closed-loop regulation.

[0025] This embodiment provides a highly flexible and high-precision spectrum generation and control solution by combining a light source array unit, a dynamic modulation and control unit, a beam shaping module, and a spectrum feedback and calibration unit. It can not only realize the dynamic output of a multi-spectral light source, but also optimize the spectral characteristics in real time to meet the detection needs of various internal tissue characteristics of fruits and vegetables, improve the adaptability and reliability of detection, and provide accurate light source support for non-destructive detection of fruit and vegetable quality.

[0026] S2, a precision imaging module, including a flexible optical lens and a vibration suppression mechanism, for collecting images of fruits and vegetables in a target fruit and vegetable area that has been irradiated with multispectral light under non-contact conditions, and generating multispectral image data; In this implementation, S2 specifically includes: S21, flexible optical lens module, composed of multiple groups of adjustable focus lenses, each group of lenses has an independent drive device and electronic control system, which can adjust the focal length in real time according to the optical properties of the surface and interior of fruits and vegetables, and use segmented focusing mode to optimize the optical path and capture multi-spectral signals; S22, vibration suppression and compensation unit, including an active vibration compensation module with integrated inertial sensor and a high elastic vibration reduction support structure, the active vibration compensation module generates a compensation torque based on the detected displacement error and vibration rate : ; in, To compensate the torque, is the stiffness coefficient, is the real-time displacement error, is the damping coefficient, is the vibration rate; S23, multi-spectral imaging sensor module, is set at the rear end of the flexible optical lens module, and uses a photoelectric sensor array to cover to The spectral range and spectral response characteristics match the multi-spectral light source module, and the rapid acquisition and digitization of multi-spectral image data are achieved through pixel-level parallel processing; S24, optical protection and cleaning module consists of a transparent coating protective cover and an automatic cleaning system. The transparent coating material has anti-reflection and anti-static functions. The automatic cleaning system removes attached particles through micro-vibration and air injection devices to keep the optical path stable for a long time; S25, the image correction and equalization unit is connected to the multispectral imaging sensor module, and is used to perform real-time correction and equalization processing on the original multispectral image data. The correction algorithm is based on pixel offset correction and light intensity uniformity adjustment: ; in, is the corrected pixel value, is the original pixel value, is the light intensity balance coefficient matrix, is the noise correction coefficient matrix; S26, a data output and interface module, is connected to a computer analysis system via an embedded interface, and transmits the corrected multispectral image data to each module in a standardized data format.

[0027] This embodiment realizes high-resolution multispectral image acquisition under non-contact conditions through the coordinated work of a flexible optical lens module, a vibration suppression and compensation unit, a multispectral imaging sensor module, etc., and combines the real-time correction function of the image correction unit to ensure the accuracy and stability of the detection data, providing accurate and reliable image data support for fruit and vegetable quality assessment.

[0028] S3, image data preprocessing module, used to perform layered processing on the multispectral image data output by the precision imaging module, using partition denoising, feature enhancement and region segmentation algorithms to extract key image features related to fruit and vegetable quality; In this implementation, S3 specifically includes: S31, a spectral layering processing module receives the multispectral image data output by the precision imaging module, and divides the multispectral image data into multiple spectral layers according to the wavelength range, which are defined as ,in is a spectral layer data, indicating the wavelength is The corresponding image layer; S32, adaptive denoising unit, uses adaptive spatial filtering algorithm to denoise the spectral layer data Perform denoising based on neighborhood characteristics: ; in, represents the pixel value after denoising, is the pixel value corresponding to the original data, is the neighborhood pixel weight, Center pixel Neighborhood range; S33, multi-dimensional feature enhancement module for denoised spectral layer data Perform feature extraction and enhancement processing to improve edge and texture characteristics by calculating local gradient changes. The enhanced feature matrix is ​​defined as: ; in, is the enhanced eigenvalue, and are the gradient values ​​in the horizontal and vertical directions at the pixel position respectively; S34, the regional intelligent segmentation module, based on the feature matrix output by the multi-dimensional feature enhancement module, performs segmentation processing on the target fruit and vegetable region, and uses an adaptive segmentation algorithm based on regional growth and dynamic threshold adjustment to generate a segmentation mask to obtain a segmentation mask matrix: ; in, is the segmentation mask matrix, represents a threshold value that is dynamically adjusted based on local characteristics; S35, key quality feature extraction module, using the segmentation mask matrix generated by the regional intelligent segmentation module With spectral layer data , extract key image features related to fruit and vegetable quality, and form a set of key image feature vectors .

[0029] This implementation method accurately extracts key features of fruit and vegetable quality through stratification, denoising, feature enhancement and intelligent segmentation of multi-dimensional spectral images, which not only improves the processing efficiency of complex image data, but also improves the accuracy and comprehensiveness of fruit and vegetable quality assessment through dynamic adaptive optimization analysis, providing reliable technical support for non-destructive testing.

[0030] S4, a quality analysis module, which performs multi-level correlation analysis on key image features output by the image data preprocessing module through an adaptive learning algorithm based on a deep neural network, adjusts algorithm parameters in real time, and generates a set of quality indicators corresponding to the quality indicators of fruits and vegetables; In this implementation, S4 specifically includes: S41, the multi-dimensional feature receiving module is connected to the image data preprocessing module to receive the key image feature vector set generated by it , classified into input feature matrices according to data type and feature dimension ,in represents the number of features, Represents feature dimension; S42, multi-layer correlation analysis unit, based on deep neural network to input feature matrix Conduct multi-level correlation modeling and construct quality prediction mapping function: ; in, is the prediction result vector, Contains the network weight matrix for the parameter set and the bias vector , is the network weight matrix, For the input feature matrix, the weight matrix is ​​optimized by recursive mapping Implement nonlinear modeling of high-dimensional data, It is a multi-layer correlation analysis model; S43, adaptive learning optimization module, combined with dynamic gradient descent algorithm to optimize the network weight matrix of deep neural network Perform real-time optimization, the optimization rules are: ; in, represents the network weight matrix at the next moment, is the time step The current network weight matrix, is the learning rate, is the loss function For the gradient of the network weight matrix, the weight is updated through real-time data to optimize the adaptability to different fruit and vegetable quality characteristics; S44, quality index extraction module, predicts the result vector of multi-layer correlation analysis unit Analyze and extract the core indicators related to fruit and vegetable quality to generate a set of quality indicators ,in For the Item quality characteristics; S45. Combine quality indicators The data are transmitted to the computer analysis system through a standardized interface and stored in the local database.

[0031] This implementation method combines a multi-layer association analysis unit with an adaptive learning optimization module to achieve real-time dynamic analysis and parameter optimization of the multidimensional characteristics of fruit and vegetable quality. It not only improves the accuracy of complex feature associations, but also optimizes the model adaptability through a closed-loop feedback mechanism, significantly improving the accuracy and efficiency of fruit and vegetable quality assessment, and providing an efficient and reliable solution for quality analysis.

[0032] S5, spectral enhancement and adaptation module, receives the multi-spectral image data generated by the precision imaging module, and optimizes the input multi-spectral image data by using nano-level light wave coupling and optical signal enhancement mechanism to adapt it to the organizational structure and quality detection requirements of different types of fruits and vegetables; In this implementation, S5 specifically includes: S51, nanoscale optical wave coupling module, including periodically distributed grating array and integrated waveguide structure, used to input spectral signal Perform coupling modulation to output enhanced spectral signals : ; in, is the coupling enhancement factor; S52, optical signal enhancement unit, receives the enhanced spectral signal output by the nanoscale light wave coupling module , enhanced by high-gain optical thin films and multi-layer dielectric structures, optimized spectral signal resolution for: ; in, and are the wavelength bandwidths of the input and output signals respectively; S53, spectrum adaptation module, receiving the spectrum signal resolution of the optical signal enhancement unit , the wavelength of the spectral signal is selected and adapted through the electronically controlled spectral filter and the dynamic wavelength adjustment unit, and the adapted spectral signal is output ; S54, signal homogenization processing module, for the spectral signal output by the spectral adaptation module To adjust the spatial distribution: ; in, is the spectral signal after homogenization, is the target detection area; S55, the spectrum output interface module receives the spectrum signal output by the signal homogenization processing module After that, it is transmitted to the precision imaging module through a standardized interface, and the spectral signal is dynamically adjusted to adapt to different fruit and vegetable detection needs.

[0033] This implementation significantly improves the sensitivity and resolution of spectral signals through the collaborative work of nanoscale light wave coupling modules, optical signal enhancement units, and spectral adaptation modules, while achieving dynamic adaptation to different fruit and vegetable tissue structures. By combining signal homogenization processing with spectral output interface modules, it not only optimizes the uniformity of light intensity in the detection area, but also ensures efficient transmission and real-time adjustment of multi-spectral signals, providing accurate and scalable technical support for fruit and vegetable quality detection.

[0034] S6, distributed detection control module, based on a multi-node collaborative architecture, dynamically interacts with the multi-spectral light source module, precision imaging module and quality analysis module to adjust control system parameters in real time and optimize detection efficiency and detection; In this implementation, S6 specifically includes: S61, multi-node detection coordination unit, multiple coordination nodes are set based on the distributed control architecture, each node includes a computing unit, a sensor interface and a communication module, which is used to interact with the multi-spectral light source module, the precision imaging module and the quality analysis module in real time to generate a node state matrix : ; in, Indicates the system Nodes at time The state set, Indicates Detection data, load and real-time feedback of each node; S62, dynamic parameter control module, receiving node status matrix , dynamically adjust the system parameter set based on the task optimization function : ; in, is the system task cost function, which comprehensively considers detection accuracy and resource allocation efficiency. To learn the step size, adjust the rules to ensure the parameter set at the next moment More adaptable to the current node status; S63, task allocation and coordination module, based on the optimization parameter set generated by the dynamic parameter control module and the node state matrix , generate the task allocation matrix : ; in, Indicates at time The specific detection tasks assigned to each node reflect the real-time distribution results of the node collaboration; S64, real-time communication and synchronization module, through the communication protocol and distributed time synchronization mechanism, the node state matrix and the task allocation matrix Synchronize and share between systems; S65. According to the task allocation matrix The execution results and detection feedback data of the node are updated in real time. , and optimize the parameter set through closed-loop control .

[0035] This implementation method dynamically adjusts system parameters and task allocation through the collaborative work of the node state matrix, multi-dimensional optimization algorithm and task allocation matrix to achieve efficient coordination of distributed detection; combined with the closed-loop control mechanism, it improves the accuracy, real-time performance and resource utilization efficiency of fruit and vegetable quality detection, and provides a flexible and efficient solution for complex detection tasks.

[0036] S7, data management and output module, integrates the test data and analysis results of each module, generates fruit and vegetable quality assessment reports through the computer system, and provides real-time visual analysis and dynamic data update functions to optimize the test process and result display.

[0037] In this implementation, S7 specifically includes: S71, the data acquisition and integration module is connected with the multi-spectral light source module, the precision imaging module and the quality analysis module, receives the detection data and analysis results output by each module, and constructs a multi-dimensional data set ,in , Indicates time Moment The test parameters are The value of the feature dimension, To detect the number of parameters, is the number of feature dimensions; S72, data preprocessing module, for multidimensional data sets Perform data cleaning, noise reduction and standardization to generate a preprocessed data matrix ; S73, data analysis and report generation module, receiving the data matrix output by the data preprocessing module and the set of quality indicators generated by the quality analysis module , generate fruit and vegetable quality assessment results through comprehensive analysis model : ; in, To develop a comprehensive analysis model, multidimensional data is associated with quality indicators to generate evaluation results; S74, real-time visual analysis module, the fruit and vegetable quality evaluation results output by the data analysis and report generation module Perform dynamic display and generate real-time visual data through the time dimension Implement dynamic updates; S75, dynamic data update module, receiving new detection data and updating multidimensional data sets and quality index set , and adjust the data preprocessing module in real time to optimize the quality assessment results at the next moment.

[0038] This implementation method generates fruit and vegetable quality assessment results by constructing a multidimensional data set and a set of quality indicators, combining them with a comprehensive analysis model, and dynamically displays them through a real-time visualization module. It also supports dynamic updating and standardized processing of new data, thereby improving the integration, analysis and display capabilities of test data and optimizing the accuracy of quality assessment and the adaptability of the system.

[0039] Embodiment 1: In order to verify the feasibility of the present invention, the present invention was applied to the quality inspection production line of a large fruit and vegetable export company. The company needs to conduct quality inspection on about 50,000 pieces of fruits and vegetables every day, and requires that the inspection process does not cause damage to the fruits and vegetables, while ensuring the accuracy and efficiency of the inspection results. However, due to the variety of fruits and vegetables and the complexity of quality standards, the traditional static spectral detection method is difficult to meet production needs.

[0040] In this scenario, the enterprise deployed the imaging control system for nondestructive detection of fruit and vegetable quality proposed in the present invention. The system solves the problems of low spectral capture accuracy, insufficient signal sensitivity, delayed detection results and damaged fruits and vegetables in traditional detection technology through the collaborative work of multi-spectral light source module, precision imaging module, image data preprocessing module, quality analysis module, spectral enhancement and adaptation module, distributed detection control module and data management and output module.

[0041] In actual applications, the system first generates a multispectral beam in the visible to near-infrared range through a multispectral light source module, and uses dynamic modulation and spectral adaptation functions to adjust the light source parameters according to the internal and surface characteristics of different fruits and vegetables to ensure high-sensitivity capture of spectral signals. Subsequently, the precision imaging module uses a non-contact flexible optical lens and vibration suppression system to image the fruits and vegetables after being irradiated by the multispectral beam and generate multispectral image data.

[0042] The image data preprocessing module performs denoising, feature enhancement and layering on multispectral image data, extracts key quality features of fruits and vegetables and generates standardized data sets. The quality analysis module combines artificial intelligence adaptive learning algorithms to perform quality analysis on the extracted feature data and generate fruit and vegetable quality assessment results. The data management and output module visualizes the test results and optimizes the test process and data output through a real-time update mechanism.

[0043] After the system was deployed, the company conducted a two-month continuous operation test on the quality inspection of different types of fruits and vegetables (such as apples, oranges, grapes, etc.). The following is the detailed data of the test results: Table 1 Performance test report of fruit and vegetable quality nondestructive testing system

[0044] As can be seen from Table 1 above, in the embodiment, before the deployment of the system, the company tested an average of about 20,000 pieces of fruits and vegetables per day, and the detection accuracy was only 85.2%. Due to the use of contact detection methods, the damage rate of fruits and vegetables was as high as 4.7%. After the deployment of the system of the present invention, the detection efficiency was increased to 50,000 pieces per day, the detection accuracy was increased to 97.8%, and the damage rate of fruits and vegetables was reduced to 0.2%. In addition, the system significantly shortened the detection time of a single piece of fruit and vegetable from 18 seconds to 5 seconds through a dynamic optimization mechanism, realizing an efficient detection process. In terms of spectral signal sensitivity, the system has achieved a 5.4-fold improvement through spectral enhancement and adaptation modules, effectively adapting to the detection needs of 50 different types of fruits and vegetables, far exceeding the 15 types of adaptability of traditional technologies.

[0045] During the test period, the company needed to complete the quality inspection of 100,000 apples within 12 hours in a sudden inspection task. The system achieved efficient light source control and quality analysis through the multi-node collaborative architecture of the distributed inspection control module, and completed all inspections in just 10 hours. The inspection results were accurate and reliable, and no damage to fruits and vegetables was found.

[0046] The fruit and vegetable quality nondestructive detection imaging control system proposed in this invention solves the problems of low efficiency, low accuracy, poor adaptability and fruit and vegetable damage in traditional detection technology through multi-module collaboration, and provides a dynamic, efficient and nondestructive solution for fruit and vegetable quality detection. In practical applications, it significantly improves detection efficiency and reliability, and provides strong technical support for quality control of fruit and vegetable export companies.

[0047] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A fruit and vegetable quality nondestructive detection imaging control system, characterized in that: include: S1, multi-spectral light source module, used to generate multi-spectral light beams covering the visible light and near-infrared range. The multi-spectral light source module combines dynamic modulation technology and composite spectral filters to illuminate the target fruit and vegetable area with multi-dimensional spectral characteristics and capture the spectral information inside and on the surface; S2, a precision imaging module, including a flexible optical lens and a vibration suppression mechanism, for collecting images of fruits and vegetables in a target fruit and vegetable area that has been irradiated with multispectral light under non-contact conditions, and generating multispectral image data; S3, image data preprocessing module, used to perform layered processing on the multispectral image data output by the precision imaging module, using partition denoising, feature enhancement and region segmentation algorithms to extract key image features related to fruit and vegetable quality; S4, a quality analysis module, which performs multi-level correlation analysis on key image features output by the image data preprocessing module through an adaptive learning algorithm based on a deep neural network, adjusts algorithm parameters in real time, and generates a set of quality indicators corresponding to the fruit and vegetable quality indicators; S5, spectral enhancement and adaptation module, receives the multispectral image data generated by the precision imaging module, and optimizes the input multispectral image data by using nano-level light wave coupling and optical signal enhancement mechanism to adapt it to the organizational structure and quality detection requirements of different types of fruits and vegetables; S6, distributed detection control module, based on a multi-node collaborative architecture, dynamically interacts with the multi-spectral light source module, precision imaging module and quality analysis module to adjust control system parameters in real time and optimize detection efficiency and detection; S7, data management and output module, integrates the test data and analysis results of each module, generates fruit and vegetable quality assessment reports through the computer system, and provides real-time visual analysis and dynamic data update functions to optimize the test process and result display.

2. The imaging control system for nondestructive detection of fruit and vegetable quality according to claim 1, characterized in that: The S1 specifically includes: S11, light source array unit, consisting of a wavelength range of to It is composed of multiple narrow-band light sources, each of which can be independently controlled, and uses band selection technology to achieve dynamic spectrum output, which is used to generate specific spectrum combinations that are adapted to the internal organization and surface characteristics of fruits and vegetables; S12, the dynamic modulation and control unit is connected to the light source array unit, and the light intensity is controlled by using time dynamic modulation technology: ； in, Indicates wavelength Next time The instantaneous light intensity, The wavelength The initial light intensity, is the modulation depth coefficient, For modulation frequency, dynamically adjust and Used to optimize the spatiotemporal distribution of spectral output; S13, the beam shaping module is located at the output end of the light source array unit, and is composed of a lens group and a collimator, and is used to adjust the direction and uniformity of the output beam, and achieve uniform distribution of light intensity in the target fruit and vegetable area through beam focusing technology, while reducing spectral scattering and energy loss; S14, the spectral feedback and calibration unit is connected to the dynamic modulation and control unit, and the embedded spectral sensor is used to monitor the output spectral characteristics of the light source array unit in real time to obtain the wavelength deviation and light intensity deviation , and feed it back to the dynamic modulation and control unit to achieve real-time optimization and stability control of the spectral output through closed-loop regulation.

3. The imaging control system for nondestructive detection of fruit and vegetable quality according to claim 1, characterized in that: The S2 specifically includes: S21, flexible optical lens module, composed of multiple groups of adjustable focus lenses, each group of lenses has an independent drive device and electronic control system, which can adjust the focal length in real time according to the optical properties of the surface and interior of fruits and vegetables, and use segmented focusing mode to optimize the optical path and capture multi-spectral signals; S22, vibration suppression and compensation unit, including an active vibration compensation module with integrated inertial sensor and a high elastic vibration reduction support structure, the active vibration compensation module generates a compensation torque based on the detected displacement error and vibration rate : ； in, To compensate the torque, is the stiffness coefficient, is the real-time displacement error, is the damping coefficient, is the vibration rate; S23, multi-spectral imaging sensor module, is set at the rear end of the flexible optical lens module, and uses a photoelectric sensor array to cover to The spectral range and spectral response characteristics match the multi-spectral light source module, and the rapid acquisition and digitization of multi-spectral image data are achieved through pixel-level parallel processing; S24, optical protection and cleaning module consists of a transparent coating protective cover and an automatic cleaning system. The transparent coating material has anti-reflection and anti-static functions. The automatic cleaning system removes attached particles through micro-vibration and air injection devices to keep the optical path stable for a long time; S25, the image correction and equalization unit is connected to the multispectral imaging sensor module, and is used to perform real-time correction and equalization processing on the original multispectral image data. The correction algorithm is based on pixel offset correction and light intensity uniformity adjustment: ； in, is the corrected pixel value, is the original pixel value, is the light intensity balance coefficient matrix, is the noise correction coefficient matrix; S26, a data output and interface module, is connected to a computer analysis system via an embedded interface, and transmits the corrected multispectral image data to each module in a standardized data format.

4. The imaging control system for nondestructive detection of fruit and vegetable quality according to claim 1, characterized in that: The S3 specifically includes: S31, a spectral layering processing module receives the multispectral image data output by the precision imaging module, and divides the multispectral image data into multiple spectral layers according to the wavelength range, which are defined as ,in, is a spectral layer data, indicating the wavelength is The corresponding image layer; S32, adaptive denoising unit, uses adaptive spatial filtering algorithm to denoise the spectral layer data Perform denoising based on neighborhood characteristics: ； in, represents the pixel value after denoising, is the pixel value corresponding to the original data, is the neighborhood pixel weight, Center pixel Neighborhood range; S33, multi-dimensional feature enhancement module for denoised spectral layer data Perform feature extraction and enhancement processing to improve edge and texture characteristics by calculating local gradient changes. The enhanced feature matrix is ​​defined as: ； in, is the enhanced eigenvalue, and are the gradient values ​​in the horizontal and vertical directions at the pixel position respectively; S34, the regional intelligent segmentation module, based on the feature matrix output by the multi-dimensional feature enhancement module, performs segmentation processing on the target fruit and vegetable region, and uses an adaptive segmentation algorithm based on regional growth and dynamic threshold adjustment to generate a segmentation mask to obtain a segmentation mask matrix: ； in, is the segmentation mask matrix, represents a threshold value that is dynamically adjusted based on local characteristics; S35, key quality feature extraction module, using the segmentation mask matrix generated by the regional intelligent segmentation module With spectral layer data , extract key image features related to fruit and vegetable quality, and form a set of key image feature vectors .

5. The imaging control system for nondestructive detection of fruit and vegetable quality according to claim 4, characterized in that: The S4 specifically includes: S41, the multi-dimensional feature receiving module is connected to the image data preprocessing module to receive the key image feature vector set generated by it , classified into input feature matrices according to data type and feature dimension ,in represents the number of features, Represents feature dimension; S42, multi-layer correlation analysis unit, based on deep neural network to input feature matrix Conduct multi-level correlation modeling and construct quality prediction mapping function: ； in, is the prediction result vector, Contains the network weight matrix for the parameter set and the bias vector , is the network weight matrix, For the input feature matrix, the weight matrix is ​​optimized by recursive mapping Implement nonlinear modeling of high-dimensional data, It is a multi-layer correlation analysis model; S43, adaptive learning optimization module, combined with dynamic gradient descent algorithm to optimize the network weight matrix of deep neural network Perform real-time optimization, the optimization rules are: ； in, represents the network weight matrix at the next moment, is the time step The current network weight matrix, is the learning rate, is the loss function For the gradient of the network weight matrix, the weight is updated through real-time data to optimize the adaptability to different fruit and vegetable quality characteristics; S44, quality index extraction module, predicts the result vector of multi-layer correlation analysis unit Analyze and extract the core indicators related to fruit and vegetable quality to generate a set of quality indicators ,in For the Item quality characteristics; S45. Combine quality indicators The data are transmitted to the computer analysis system through a standardized interface and stored in the local database.

6. The imaging control system for nondestructive detection of fruit and vegetable quality according to claim 1, characterized in that: The S5 specifically includes: S51, nanoscale optical wave coupling module, including periodically distributed grating array and integrated waveguide structure, used to input spectral signal Perform coupling modulation to output enhanced spectral signals : ； in, is the coupling enhancement factor; S52, optical signal enhancement unit, receives the enhanced spectral signal output by the nanoscale light wave coupling module , enhanced by high-gain optical thin films and multi-layer dielectric structures, optimized spectral signal resolution for: ； in, and are the wavelength bandwidths of the input and output signals respectively; S53, spectrum adaptation module, receiving the spectrum signal resolution of the optical signal enhancement unit , the wavelength of the spectral signal is selected and adapted through the electronically controlled spectral filter and the dynamic wavelength adjustment unit, and the adapted spectral signal is output ; S54, signal homogenization processing module, for the spectral signal output by the spectral adaptation module To adjust the spatial distribution: ； in, is the spectral signal after homogenization, is the target detection area; S55, the spectrum output interface module receives the spectrum signal output by the signal homogenization processing module After that, it is transmitted to the precision imaging module through a standardized interface, and the spectral signal is dynamically adjusted to adapt to different fruit and vegetable detection needs.

7. The imaging control system for nondestructive detection of fruit and vegetable quality according to claim 1, characterized in that: The S6 specifically includes: S61, multi-node detection coordination unit, multiple coordination nodes are set based on the distributed control architecture, each node includes a computing unit, a sensor interface and a communication module, which is used to interact with the multi-spectral light source module, the precision imaging module and the quality analysis module in real time to generate a node state matrix : ； in, Indicates the system Nodes at time The state set, Indicates Detection data, load and real-time feedback of each node; S62, dynamic parameter control module, receiving node status matrix , dynamically adjust the system parameter set based on the task optimization function : ； in, is the system task cost function, which comprehensively considers detection accuracy and resource allocation efficiency. To learn the step size, adjust the rules to ensure the parameter set at the next moment More adaptable to the current node status; S63, task allocation and coordination module, based on the optimization parameter set generated by the dynamic parameter control module and the node state matrix , generate the task allocation matrix : ； in, Indicates at time The specific detection tasks assigned to each node reflect the real-time distribution results of the node collaboration; S64, real-time communication and synchronization module, through the communication protocol and distributed time synchronization mechanism, the node state matrix and the task allocation matrix Synchronize and share between systems; S65. According to the task allocation matrix The execution results and detection feedback data of the node are updated in real time. , and optimize the parameter set through closed-loop control .

8. The imaging control system for nondestructive detection of fruit and vegetable quality according to claim 5, characterized in that: The S7 specifically includes: S71, the data acquisition and integration module is connected with the multi-spectral light source module, the precision imaging module and the quality analysis module, receives the detection data and analysis results output by each module, and constructs a multi-dimensional data set ,in , Indicates time Moment The test parameters are The value of the feature dimension, To detect the number of parameters, is the number of feature dimensions; S72, data preprocessing module, for multidimensional data sets Perform data cleaning, noise reduction and standardization to generate a preprocessed data matrix ; S73, data analysis and report generation module, receiving the data matrix output by the data preprocessing module and the set of quality indicators generated by the quality analysis module , generate fruit and vegetable quality assessment results through comprehensive analysis model : ； in, To develop a comprehensive analysis model, multidimensional data is associated with quality indicators to generate evaluation results; S74, real-time visual analysis module, the fruit and vegetable quality evaluation results output by the data analysis and report generation module Perform dynamic display and generate real-time visual data through the time dimension Implement dynamic updates; S75, dynamic data update module, receiving new detection data and updating multidimensional data sets and quality index set , and adjust the data preprocessing module in real time to optimize the quality assessment results at the next moment.

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