Assembleable intelligent adaptive illumination multi-spectral lens microscopic inspection system

Abstract

The present invention belongs to the field of multi-spectral imaging sample microscopic detection, and specifically provides an assemblable intelligent adaptive lighting multi-spectral lens microscopic detection system, wherein an adaptive intelligent lighting module is provided on the lens barrel of the multi-spectral camera shooting port through a first connecting thread connection, so that the adaptive intelligent lighting module is fastened to the lens barrel of the multi-spectral camera shooting port; the adaptive intelligent lighting module includes an intelligent visual optimization system for outputting the best adaptive intelligent lighting parameters; the microscope objective is connected to the adaptive intelligent lighting module through a connector; the intelligent terminal and the multi-spectral camera are connected by a network for communication, and an integrated detection system is built in the intelligent terminal, and the integrated detection system includes a multi-spectral microscopic space spectrum depth fusion module and a control intelligent lighting module. The present invention can dynamically adjust the lighting conditions for the multi-spectral camera in the sample detection process, ensure that clear image data can be obtained in different environments, and improve the detection accuracy.

Description

Technical Field

[0001] The invention belongs to the technical field of multi-spectral imaging sample microscopic detection, and in particular relates to an assemblable intelligent adaptive lighting multi-spectral lens microscopic detection system. Background Art

[0002] In agricultural production, facing the increasingly frequent high temperature weather, crop leaves are easily seriously affected, which may cause physiological damage and yield reduction. Traditional detection methods have limitations in accuracy and real-time performance. Therefore, it is urgent to develop new technologies that can efficiently and accurately monitor the physiological state of crop leaves under high temperature conditions to support agricultural producers in formulating scientific management strategies to ensure the healthy growth and stable yield of crops.

[0003] Multispectral imaging technology has a wide range of applications in modern science and engineering, especially in biomedicine, agricultural science, environmental monitoring and other fields. Compared with traditional RGB imaging, multispectral imaging technology can capture image information of multiple bands at the same time, thus providing richer and more detailed data information for analyzing the chemical composition, biological characteristics or other specific properties of objects. This technology can not only expand the perception capabilities of sensors, but also provide more accurate and comprehensive image information in complex environments.

[0004] In the field of microscopic inspection, multispectral microscopy has become a common tool in research and laboratory applications. As a key component in multispectral imaging systems, the quality and control of the light source are crucial to the image quality. However, due to the complexity of the sample and the changes in the optical environment, traditional light sources and lighting systems face challenges in multispectral microscopic imaging. For example, for samples or surfaces with high reflectivity, local overexposure or uneven illumination often occurs, which directly affects the quality of the image and the accuracy of the analysis.

[0005] At present, traditional light source adjustment usually relies on manually setting the intensity and angle of the light source. This method is not only inefficient, but also has limitations in real-time applications and automated systems, and cannot meet the requirements of complex samples and environmental changes for lighting uniformity and intensity. Summary of the invention

[0006] The purpose of the embodiments of the present invention is to provide an assemblable intelligent adaptive illumination multi-spectral lens microscopic detection system, aiming to solve the technical problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions.

[0008] An embodiment of the present invention provides an assemblable intelligent adaptive illumination multi-spectral lens microscopic detection system, comprising:

[0009] A multispectral camera, wherein the multispectral camera is mounted on the top of a base; a stage is provided on the base, and the stage is used to place samples to be detected; an adaptive intelligent lighting module is provided on the lens barrel of the shooting port of the multispectral camera through a first connecting thread, so that the adaptive intelligent lighting module is fastened to the lens barrel of the shooting port of the multispectral camera; the adaptive intelligent lighting module includes an intelligent visual optimization system, and the intelligent visual optimization system is used to output the best adaptive intelligent lighting parameters; wherein, in the intelligent visual optimization system, the data acquisition module collects image data under different lighting conditions; the region positioning module uses the rule library of the fuzzy inference system to extract color and texture features, and automatically locates the region of interest; the feature enhancement module uses the reinforcement learning agent to initialize and apply the Canny edge detection algorithm, and dynamically adjusts the edge detection parameters through the feedback of the PPO reinforcement learning algorithm; the difference analysis module performs grayscale histogram calculation and statistical feature extraction, uses the PPO reinforcement learning algorithm to dynamically adjust the parameters, and analyzes the differences in the grayscale value distribution in different images; the lighting parameter output module dynamically adjusts the threshold according to the analysis results, and outputs the best adaptive intelligent lighting parameters;

[0010] It also includes a microscope objective and an intelligent terminal, the microscope objective is located above the stage; the microscope objective is connected to the adaptive intelligent lighting module through a connector; the intelligent terminal and the multispectral camera are connected to each other through a network, and an integrated detection system is built in the intelligent terminal, and the integrated detection system includes a multispectral microscopy spatial-spectral depth adaptation module and a control intelligent lighting module; wherein the control intelligent lighting module is used to adjust the parameters of the multispectral camera according to the adaptive intelligent lighting parameters output by the intelligent vision optimization system; the multispectral microscopy spatial-spectral depth adaptation module is used to locate the area of ​​interest of the collected multispectral microscopy image data by using threshold segmentation, and extract features from the spatial and spectral dimensions, calculate the fusion features by using the adaptive weights of the attention mechanism, build a deep learning model by using the fusion features, and output the detection results of the samples to be detected by using the optimized deep learning model.

[0011] Furthermore, the multispectral camera comprises:

[0012] a lens system, the lens system being used to focus the optical signal;

[0013] Multiple filter banks, multiple filter banks are used to selectively capture spectral information of specific bands;

[0014] A photosensitive element, wherein the photosensitive element is used to convert an optical signal into an electronic signal;

[0015] A data processing unit is used to process and digitize electronic signals.

[0016] Furthermore, the adaptive intelligent lighting module includes a multi-angle LED partition light source, which includes 12 LED lamp beads. Each LED lamp bead is installed on a stepped lamp holder on a ring base. The stepped lamp holder is composed of three concentric annular slopes of different angles and heights. Four LED lamp beads are distributed on each slope. The LED lamp beads on each slope have the same incident angle. The incident angle of the LED lamp beads is controlled by the annular slope to provide multi-angle lighting options, and can also improve the utilization efficiency and uniformity of light in complex lighting environments.

[0017] Furthermore, in the fuzzy inference system, the membership degree of each image region to the region of interest is calculated, and the region with a higher membership degree is determined as the recognition result of the region of interest;

[0018] Wherein, the fuzzy inference system comprises:

[0019] A fuzzification module converts the original image data into Convert to fuzzy set , use Gaussian membership function to blur the pixel gray value, expressed as:

[0020] ;

[0021] in, is the mean of the Gaussian function, is the standard deviation, Represents the membership of pixel value x in fuzzy set A;

[0022] A rule base module, the rule base module includes a first rule and a second rule; in the first rule, if the local contrast of the image is lower than a first threshold, the membership degree is expressed as: , then the enhancement of local contrast is increased; in the second rule, if the edge feature fuzziness of the target area is higher than the second threshold, the membership degree is expressed as: , then enhance the edge detection algorithm parameters;

[0023] The inference engine module uses the rules in the fuzzy rule base to fuzzify the fuzzy set Processing is performed to determine the required system parameter adjustments, and the Takagi-Sugeno model is used for reasoning;

[0024] A defuzzification module that converts the fuzzy inference results into a specific numerical form to generate optimized image data , all rules in the rule base are output as a fuzzy set and converted into specific values.

[0025] Furthermore, the edge detection stage in the feature enhancement module applies the Canny edge detection algorithm by initializing a reinforcement learning agent, and the reinforcement learning agent uses the PPO algorithm to learn and optimize the gradient threshold parameters of the Canny algorithm; the edge detection process of the Canny algorithm includes Gaussian smoothing, calculating gradients and edge connections.

[0026] Furthermore, the microscope objective lens comprises a lens housing, the top of the lens housing has a second connecting thread, and the second connecting thread is fixed to the connecting piece by a threaded connection structure; the microscope objective lens also comprises three liquid lenses, and the three liquid lenses are integrated inside the lens housing.

[0027] Furthermore, the liquid lens adopts an electrowetting liquid lens.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] First, the multispectral camera of the present invention is used to capture the spectral information of the sample to be detected in different bands; according to the image comparison and analysis differences of the sample under different lighting conditions, the system adaptively and intelligently adjusts the lighting parameters to achieve efficient intelligent lighting;

[0030] Second, in the intelligent visual optimization system of the embodiment of the present invention, by blurring the image, applying fuzzy rules to adjust parameters, using the Takagi-Sugeno model for reasoning, and the final defuzzification process, the intelligent visual optimization system can dynamically adjust parameters according to actual needs in different image processing tasks, thereby improving the image quality, enhancing the feature extraction and analysis capabilities, and optimizing the overall visual effect of the image;

[0031] Third, in the edge detection stage, the intelligent vision optimization system of the present invention applies a reinforcement learning agent and initializes the Canny edge detection algorithm, and dynamically adjusts the edge detection parameters through the PPO reinforcement learning algorithm to improve the accuracy and clarity of edge feature extraction; the intelligent vision optimization system performs grayscale histogram calculation and statistical feature extraction, analyzes the differences in grayscale value distribution in different images, and dynamically adjusts parameters using the PPO algorithm to ensure the accuracy and adaptability of grayscale difference analysis; the intelligent vision optimization system dynamically adjusts the threshold according to the analysis results, and outputs the optimal adaptive intelligent lighting parameters;

[0032] Fourth, the multispectral camera, lens barrel, adaptive intelligent lighting module, and lens microscope objective of the present invention are all detachable assembled connection structures, which can adapt to various usage requirements and environmental conditions. Suitable multispectral cameras or other modules can be selected according to specific detection tasks, and each component and module can be easily replaced, repaired and upgraded; this assemblability not only improves the adaptability and scalability of the system, but also effectively controls costs; users can freely choose and configure according to actual needs, which is suitable for diverse detection and analysis needs, thereby ensuring efficient, accurate and reliable detection performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0034] Figure 1 It is a structural schematic diagram of the assemblable intelligent adaptive illumination multi-spectral lens microscopic detection system of the present invention;

[0035] Figure 2 A schematic diagram of the structure of the adaptive lighting device provided by the present invention;

[0036] Figure 3 A flow chart of the intelligent visual optimization system provided by the present invention;

[0037] Figure 4 A schematic diagram of the internal structure of the microscope objective lens provided by the present invention;

[0038] Figure 5 A flow chart of a multi-spectral microscopy spatial spectrum depth adaptation module in an integrated detection system provided by the present invention;

[0039] Figure 6 A structural block diagram of the integrated detection system provided by the present invention;

[0040] Figure 7 This is a structural block diagram of the intelligent visual optimization system provided by the present invention.

[0041] The reference numerals are as follows:

[0042] 1. Intelligent terminal; 11. Integrated detection system; 111. Multi-spectral microscopy spatial spectrum depth adaptation module; 112. Control intelligent lighting module; 2. Connectors; 3. Multi-spectral camera; 4. Lens barrel; 41. First connecting thread; 5. Adaptive intelligent lighting module; 51. Intelligent visual optimization system; 511. Data acquisition module; 512. Regional positioning module; 513. Feature enhancement module; 514. Difference analysis module; 515. Lighting parameter output module; 6. Microscope objective; 61. Second connecting thread; 62. Lens housing; 63. Liquid lens; 7. Base; 8. Stage. DETAILED DESCRIPTION

[0043] 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.

[0044] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0045] like Figure 1 As shown, an assemblable intelligent adaptive illumination multi-spectral lens microscopic detection system is provided in one embodiment of the present disclosure, including a multi-spectral camera 3, which is used to capture the spectral information of the sample to be detected in different bands; the multi-spectral camera 3 is installed on the top of the base 7; this embodiment uses a multi-spectral camera 3 that can support band information of 300nm-700nm, 800nm-1000nm and 1100nm-1700nm;

[0046] In addition, a stage 8 is provided on the base 7, and the stage 8 is used to place the sample to be tested;

[0047] Furthermore, the multispectral camera 3 includes a lens system, a plurality of filter groups, a photosensitive element and a data processing unit; the focusing function of the lens system ensures the accurate capture and transmission of the optical signal, so that the camera can maintain high-quality imaging capabilities at different distances and focal lengths; the presence of multiple filters allows the selective capture of spectral information in specific bands, which is crucial for analyzing and identifying specific chemicals or physical properties, enabling the camera to cope with complex spectral analysis tasks; the photosensitive element converts the captured optical signal into an electronic signal, and this conversion process is the basis of digital processing, providing the necessary raw data for subsequent data processing and analysis; the data processing unit is responsible for processing and digitizing the electronic signal, and extracting key features in the spectral information through various algorithms and techniques.

[0048] Please continue to refer to Figure 1 and Figure 2The present invention further includes an adaptive intelligent lighting module 5, and the lens barrel 4 of the shooting port of the multi-spectral camera 3 is connected with the adaptive intelligent lighting module 5 through a first connecting thread 41, so that the adaptive intelligent lighting module 5 is fastened to the lens barrel 4 of the shooting port of the multi-spectral camera 3;

[0049] The adaptive intelligent lighting module 5 includes an intelligent visual optimization system 51, and the intelligent visual optimization system 51 is used to output the best adaptive intelligent lighting parameters to achieve flexible lighting control;

[0050] Among them, Figure 7 As shown, the intelligent visual optimization system 51 includes:

[0051] A data acquisition module 511 is used to acquire image data under different lighting conditions;

[0052] A region positioning module 512 is used to extract color and texture features using a rule base of a fuzzy inference system and automatically locate the region of interest;

[0053] A feature enhancement module 513 is used to initialize and apply the Canny edge detection algorithm using a reinforcement learning agent, and dynamically adjust the parameters of edge detection through feedback from the PPO reinforcement learning algorithm;

[0054] The difference analysis module 514 is used to calculate the grayscale histogram and extract statistical features, dynamically adjust parameters using the PPO reinforcement learning algorithm, and analyze the differences in grayscale value distribution in different images;

[0055] The lighting parameter output module 515 is used to dynamically adjust the threshold according to the analysis results and output the best adaptive intelligent lighting parameters.

[0056] The adaptive intelligent lighting module 5 also includes a multi-angle LED partition light source; by initializing and setting lighting parameters and adjusting the multi-angle LED partition light source, the adaptive intelligent lighting module 5 can stably collect image data under various lighting conditions.

[0057] Furthermore, the multi-angle LED partition light source provided by the embodiment of the present invention includes 12 LED lamp beads, each of which is installed on a stepped lamp holder of a ring base, and the stepped lamp holder is composed of three concentric annular slopes of different angles and heights, and 4 LED lamp beads are distributed on each slope. The LED lamp beads on each slope have the same incident angle, and the incident angle of the LED lamp beads is controlled by the annular slope to provide multi-angle lighting options, and can also improve the utilization efficiency and uniformity of light in complex lighting environments.

[0058] Further, such as Figure 3As shown, the intelligent visual optimization system initializes lighting parameters and collects image data under different lighting conditions, and improves the quality and clarity of the image through a data preprocessing stage; then, the intelligent visual optimization system uses the rule base of the fuzzy inference system to extract color and texture features, and automatically locates the region of interest ROI. In the fuzzy inference system, the membership degree of each image region belonging to the region of interest is calculated, so as to determine the region with a higher membership degree as the recognition result of the region of interest;

[0059] Specifically, the fuzzy inference system includes:

[0060] A fuzzification module converts the original image data into Convert to fuzzy set , use Gaussian membership function to blur the pixel gray value, which is expressed as:

[0061] ;

[0062] in, is the mean of the Gaussian function, is the standard deviation, Represents the membership of pixel value x in fuzzy set A;

[0063] A rule base module, the rule base module includes a first rule and a second rule; in the first rule, if the local contrast of the image is lower than a first threshold, the membership degree is expressed as: , then the enhancement degree of local contrast is increased; in the second rule, if the edge feature of the target area is blurred, that is, the fuzziness of the edge feature of the target area is higher than the second threshold, the membership degree is expressed as: , then enhance the edge detection algorithm parameters.

[0064] In the embodiment of the present invention, the first threshold and the second threshold are both set values. The first threshold is used to represent a degree threshold of contrast; the second threshold is used to represent a threshold of the degree of blurring of edge features.

[0065] The inference engine module uses the rules in the fuzzy rule base to fuzzify the fuzzy set Processing is performed to determine the required system parameter adjustments, reasoning using the Takagi-Sugeno model;

[0066] A defuzzification module that converts the fuzzy inference results into a specific numerical form to generate optimized image data , all rules in the rule base are output as a fuzzy set and converted into specific values.

[0067] Therefore, in the intelligent visual optimization system of the embodiment of the present invention, by blurring the image, applying fuzzy rules to adjust parameters, using the Takagi-Sugeno model for reasoning and the final defuzzification process, the intelligent visual optimization system can dynamically adjust parameters according to actual needs in different image processing tasks, thereby improving the image quality, enhancing the feature extraction and analysis capabilities, and optimizing the overall visual effect of the image.

[0068] The intelligent visual optimization system of the present invention can effectively cope with complex image processing requirements and improve the intelligence level and processing efficiency of the system through the application of fuzzy reasoning technology in locating areas of interest.

[0069] For further information, please refer to Figure 3 In the edge detection stage, the intelligent vision optimization system of the present invention applies a reinforcement learning agent and initializes the Canny edge detection algorithm, and dynamically adjusts the edge detection parameters through the PPO reinforcement learning algorithm to improve the accuracy and clarity of edge feature extraction; the intelligent vision optimization system performs grayscale histogram calculation and statistical feature extraction, analyzes the differences in grayscale value distribution in different images, and dynamically adjusts parameters using the PPO algorithm to ensure the accuracy and adaptability of grayscale difference analysis; the intelligent vision optimization system dynamically adjusts the threshold according to the analysis results, and outputs the optimal adaptive intelligent lighting parameters.

[0070] Specifically, the PPO algorithm of reinforcement learning in the intelligent visual optimization system of the present invention uses the current blurred image data as the environment; the system interacts with the environment by performing different operations (i.e., adjusting system parameters); the policy network outputs the operation, evaluates its effect and updates the policy to take better operations under similar states; at the same time, the value network evaluates the value of each state, i.e., the expected improvement in image quality after adjustment, guiding policy improvement and parameter adjustment; further, based on the feedback of reinforcement learning, the system adjusts the fuzzification module, the fuzzy rules in the rule base, and the working mode of the inference engine to achieve more effective image enhancement and feature extraction.

[0071] Furthermore, in an embodiment of the present invention, the intelligent vision optimization system applies the Canny edge detection algorithm by initializing a reinforcement learning agent in the edge detection stage. The agent uses the PPO algorithm to learn and optimize the gradient threshold parameters of the Canny algorithm to ensure the accuracy and clarity of image edge extraction under different lighting conditions. The edge detection process of the Canny algorithm includes Gaussian smoothing, gradient calculation, and edge connection. These steps are dynamically adjusted through the PPO algorithm, so that the system can effectively adapt to real-time changes in ambient lighting and produce high-quality edge detection results.

[0072] In the grayscale value difference analysis stage, the system performs grayscale histogram calculation and statistical feature extraction. Through the feedback of the PPO algorithm, the system dynamically adjusts the analysis parameters, including statistical features such as grayscale mean and variance, to compare and analyze the grayscale value distribution differences of images under different lighting conditions. These adjustments ensure that the system can accurately capture and process changes in grayscale values ​​in complex visual environments, and obtain adaptive lighting parameters based on the analysis results.

[0073] Therefore, these parameters of the intelligent visual optimization system 51 of the present invention ensure the best visual effect under different lighting conditions, while accurately identifying the area of ​​interest, so that the system can effectively respond to complex visual needs and environmental changes in practical applications; the role of this intelligent visual optimization system is to improve the degree of automation and efficiency of image processing, enhance the accuracy of image analysis, and thus provide high-quality visual solutions for various application scenarios.

[0074] Please continue to refer to Figure 1 and Figure 4 The present invention further includes a microscope objective lens 6, and a stage 8 is located directly below the microscope objective lens 6; the microscope objective lens 6 is connected to the adaptive intelligent lighting module 5 through a connector 2, wherein the microscope objective lens 6 includes a lens housing 62, and the top of the lens housing 62 has a second connecting thread 61, and the second connecting thread 61 is fixed to the connector 2 by a threaded connection structure;

[0075] The microscope objective lens 6 further includes three liquid lenses 63, which are integrated inside the lens housing 62;

[0076] Preferably, the liquid lens 63 is an electrowetting liquid lens;

[0077] In addition, the microscope objective lens 6 of the embodiment of the present disclosure further includes two fixed lenses, the solid lens is responsible for part of the optical focal length, and the liquid lens 63 inside the lens housing 62 acts as a zoom component to achieve continuous optical zoom without mechanical movement;

[0078] The microscope objective lens 6 of the present invention can adjust the optical focal length by adjusting the electric field strength or voltage of the three liquid lenses 63, without moving the lens or changing the position like a traditional mechanical zoom system; the microscope objective lens can achieve fast and accurate focus adjustment according to different needs and sample characteristics, making the entire microscopic detection system more flexible and extensible.

[0079] For further information, please refer to Figure 1The assemblable intelligent adaptive illumination multi-spectral lens microscopic detection system of the disclosed embodiment also includes an intelligent terminal 1, and the intelligent terminal 1 and the multi-spectral camera 3 are connected to each other through a network to realize data transmission; the network is a medium for providing a communication link between the intelligent terminal 1 and the multi-spectral camera 3, including various connection types, such as wired, wireless communication links or dedicated data lines, etc., which can not only realize real-time image data acquisition, but also ensure the integrity and accuracy of data during data transmission, thereby providing a reliable data basis for subsequent data processing and analysis.

[0080] Preferably, the smart terminal 1 can be hardware or software. When the smart terminal 1 is hardware, it can be various electronic devices with communication functions, including but not limited to smart phones, tablet computers, laptop computers and desktop computers, etc.; when the smart terminal 1 is software, it can be installed in the electronic devices listed above, and it can be implemented as multiple software or software modules, or as a single software or software module, which is not specifically limited here.

[0081] Among them, Figure 1 and Figure 6 As shown, the intelligent terminal 1 of the embodiment of the present invention has a built-in integrated detection system 11. The control intelligent lighting module 112 of the integrated detection system 11 adjusts the parameters of the multispectral camera 3 according to the optimal adaptive intelligent lighting parameters output by the lighting parameter output module 515 to optimize data acquisition and imaging quality, and flexibly adjusts according to detection requirements and environmental conditions; the multispectral microscopy spatial spectrum depth fusion module 111 is used to locate the region of interest using threshold segmentation of the collected multispectral microscopy image data, and extract features from the spatial and spectral dimensions, calculate the fusion features using the adaptive weights of the attention mechanism, construct a deep learning model using the fusion features, and output the detection results of the samples to be detected using the optimized deep learning model.

[0082] For details, please continue to refer to Figure 5 The multi-spectral microscopy spatial spectrum depth fusion module 111 included in the integrated detection system 11 collects multi-spectral microscopy image data and accurately locates the position of the region of interest (ROI) using the threshold segmentation technology; in the data preprocessing stage, the module uses the pre-trained deep model to extract the spatial features of the ROI and perform spatial dimension reduction processing; at the same time, the deep model extracts the spectral features of the data; in order to comprehensively utilize the spatial and spectral features, the module introduces an adaptive weight calculation method based on the attention mechanism to ensure the optimal feature fusion effect;

[0083] Furthermore, the fused features are input into the deep learning model for construction, and undergo systematic optimization and parameter adjustment to ensure model performance; after the model is optimized, it is evaluated using a validation set to select the most appropriate model configuration, and the accuracy and generalization ability of the model are verified using a test set; in actual applications, the multispectral microscopy spatial spectrum deep fusion module 111 continuously monitors the performance of the model, and performs necessary updates and optimizations based on real-time feedback data to ensure continuous optimization of model performance and applicability; through intuitive visualization of detection results, the system presents accurate detection and detailed classification results of the area of ​​interest.

[0084] Exemplarily, the embodiment of the present invention selects soybean leaves that have been treated with high temperature as the sample to be detected. Specifically, the implementation process of the assemblable intelligent adaptive lighting multi-spectral lens microscopic detection system of the embodiment of the present disclosure includes the following specific steps:

[0085] Sample preparation: Place the soybean leaf sample that has been treated with high temperature on the stage 8 of the system to ensure its stable position and suitable observation angle;

[0086] Start the system: start the multispectral camera 3 and the adaptive intelligent lighting module 5. The adaptive intelligent lighting module 5 dynamically adjusts the lighting conditions according to the preset algorithm and environmental conditions to ensure that the system can obtain clear and detailed image data in different environments;

[0087] Image acquisition: Multispectral camera 3 starts to capture multispectral microscopic image data of soybean high-temperature leaves, and these image data are transmitted to the intelligent visual optimization system through a data line for subsequent processing and analysis;

[0088] Adaptive lighting parameter optimization: The intelligent visual optimization system analyzes image data under different lighting conditions and determines the best adaptive lighting parameters through algorithms and models; these parameters are dynamically adjusted by the adaptive intelligent lighting module 5 controlled by the integrated detection system to ensure that the collected image data has the best visual effect and clarity;

[0089] Second image acquisition: After adjustment, the system takes image data of soybean high-temperature leaves again; this step ensures that the optimized image can provide an accurate data basis to support subsequent analysis and detection work;

[0090] Multispectral microscopy spatial-spectral deep fusion processing: The optimized image data enters the multispectral microscopy spatial-spectral deep fusion module for processing; this module uses deep learning technology and attention mechanism to accurately locate and extract features of the region of interest, and constructs a deep learning model with fused features;

[0091] Result visualization: The system visualizes the processed test results so that users can intuitively understand the analysis results and the accuracy of the test.

[0092] It should be noted that the multispectral camera 3, lens barrel 4, adaptive intelligent lighting module 5, and lens microscope objective 6 of the present invention are all detachable connection structures and are assembled designs. Such a design can adapt to various usage requirements and environmental conditions; suitable multispectral cameras or other modules can be selected according to specific detection tasks, and each component and module can be easily replaced, repaired and upgraded; this assemblability not only improves the adaptability and scalability of the system, but also effectively controls costs; users can freely choose and configure according to actual needs; this design not only simplifies the assembly and adjustment process of the system, but also improves the customization capability and ease of operation of the system, and is suitable for diverse detection and analysis needs, thereby ensuring efficient, accurate and reliable detection performance.

[0093] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and the embodiments. It can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily realized. Therefore, without departing from the general concept defined by the claims and equivalent scope, the present invention is not limited to the specific details and the illustrations shown and described here.

Claims

1. Assembleable intelligent adaptive illumination multi-spectral lens microscopic inspection system, including a multi-spectral camera (3); The multi-spectral camera (3) is mounted on the top of a base (7); a stage (8) is provided on the base (7); an adaptive intelligent lighting module (5) is connected to the lens barrel (4) of the shooting port of the multi-spectral camera (3) via a first connecting thread (41), so that the adaptive intelligent lighting module (5) is fastened to the lens barrel (4) of the shooting port of the multi-spectral camera (3); the characteristics are: The adaptive intelligent lighting module (5) comprises an intelligent visual optimization system (51), wherein the intelligent visual optimization system (51) is used to output optimal adaptive intelligent lighting parameters; wherein, in the intelligent visual optimization system (51), the data acquisition module (511) acquires image data under different lighting conditions; the region positioning module (512) extracts color and texture features using a rule base of a fuzzy inference system, and automatically locates a region of interest; the feature enhancement module (513) uses a reinforcement learning agent to initialize and apply a Canny edge detection algorithm, and dynamically adjusts edge detection parameters through feedback from a PPO reinforcement learning algorithm; the difference analysis module (514) performs grayscale histogram calculation and statistical feature extraction, and dynamically adjusts parameters using a PPO reinforcement learning algorithm to analyze differences in grayscale value distributions in different images; and the lighting parameter output module (515) dynamically adjusts thresholds according to analysis results, and outputs optimal adaptive intelligent lighting parameters; The invention also comprises a microscope objective lens (6) and an intelligent terminal (1), wherein the microscope objective lens (6) is located above the stage (8); the microscope objective lens (6) is connected to the adaptive intelligent lighting module (5) via a connecting piece (2); the intelligent terminal (1) and the multi-spectral camera (3) are connected to each other through a network, and the intelligent terminal (1) has a built-in integrated detection system (11), wherein the integrated detection system (11) comprises a multi-spectral microscopic spatial spectrum depth fusion module (111) and a control intelligent lighting module (112); wherein the control intelligent lighting module (112) is used to adjust the parameters of the multi-spectral camera (3) according to the adaptive intelligent lighting parameters output by the intelligent visual optimization system (51); the multi-spectral microscopic spatial spectrum depth fusion module (111) is used to locate the region of interest by using threshold segmentation of the collected multi-spectral microscopic image data, extract features in spatial and spectral dimensions, calculate fusion features by using the adaptive weight of the attention mechanism, construct a deep learning model by using the fusion features, and output the detection result of the sample to be detected by using the optimized deep learning model.

2. The assemblable intelligent adaptive illumination multi-spectral lens microscopic detection system according to claim 1 is characterized in that: The multispectral camera (3) comprises: a lens system, the lens system being used to focus the optical signal; Multiple filter banks, multiple filter banks are used to selectively capture spectral information of specific bands; A photosensitive element, wherein the photosensitive element is used to convert an optical signal into an electronic signal; A data processing unit is used to process and digitize electronic signals.

3. The assemblable intelligent adaptive illumination multi-spectral lens microscopic detection system according to claim 2 is characterized in that: The adaptive intelligent lighting module (5) comprises a multi-angle LED partition light source, which comprises 12 LED lamp beads, each of which is mounted on a stepped lamp holder of a ring base, the stepped lamp holder being composed of three concentric annular inclined surfaces of different angles and heights, each of which is distributed with four LED lamp beads, and each of which has the same incident angle.

4. The assemblable intelligent adaptive illumination multi-spectral lens microscopic detection system according to claim 3 is characterized in that: In the fuzzy inference system, the membership degree of each image region to the region of interest is calculated, and the region with a higher membership degree is determined as the recognition result of the region of interest; Wherein, the fuzzy inference system comprises: A fuzzification module converts the original image data into Convert to fuzzy set , use Gaussian membership function to blur the pixel gray value, expressed as: ; in, is the mean of the Gaussian function, is the standard deviation, Represents the membership of pixel value x in fuzzy set A; A rule base module, the rule base module includes a first rule and a second rule; in the first rule, if the local contrast of the image is lower than a first threshold, the membership degree is expressed as: , then the enhancement of local contrast is increased; in the second rule, if the edge feature fuzziness of the target area is higher than the second threshold, the membership degree is expressed as: , then enhance the edge detection algorithm parameters; The inference engine module uses the rules in the fuzzy rule base to fuzzify the fuzzy set Processing is performed to determine the required system parameter adjustments, and the Takagi-Sugeno model is used for reasoning; A defuzzification module that converts the fuzzy inference results into a specific numerical form to generate optimized image data , all rules in the rule base are output as a fuzzy set and converted into specific values.

5. The assemblable intelligent adaptive illumination multi-spectral lens microscopic detection system according to claim 4 is characterized in that: The edge detection stage in the feature enhancement module (513) applies the Canny edge detection algorithm by initializing a reinforcement learning agent, and the reinforcement learning agent uses the PPO algorithm to learn and optimize the gradient threshold parameters of the Canny algorithm; the edge detection process of the Canny algorithm includes Gaussian smoothing, calculating gradients and edge connections.

6. The assemblable intelligent adaptive illumination multi-spectral lens microscopic detection system according to claim 5, characterized in that: The microscope objective lens (6) comprises a lens housing (62), the top of the lens housing (62) having a second connecting thread (61), the second connecting thread (61) and the connecting member (2) being fixed by a threaded connection structure; the microscope objective lens (6) further comprises three liquid lenses (63), the three liquid lenses (63) being integrated and arranged inside the lens housing (62).

7. The assemblable intelligent adaptive illumination multi-spectral lens microscopic detection system according to claim 6, characterized in that: The liquid lens (63) is an electrowetting liquid lens.

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