Image Analysis-Based Malt Hydration Detection System and Method

Abstract

This invention discloses a malt hydration degree detection system and method based on image analysis, belonging to the field of malt hydration degree detection technology. The image analysis-based malt hydration degree detection system includes: a malt image quality assessment module, a malt hydration assessment module, and a hydration process parameter feedback module. This invention assesses the malt image quality by collecting image quality parameters from malt cross-section images, determining whether malt cross-section image quality optimization is necessary. Then, it assesses malt hydration based on malt feature parameters in the malt cross-section images, and determines whether hydration process parameter optimization is necessary based on the malt hydration assessment results. Finally, after outputting a detection report, it performs malt hydration degree detection, improving the detection accuracy of malt hydration degree and solving the problem of low detection accuracy due to feature extraction errors in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of malt hydration level detection technology, and in particular to a malt hydration level detection system and method based on image analysis. Background Technology

[0002] Malt hydration level is a crucial factor affecting malt quality, especially in the production of beverages such as beer and whiskey, where it directly impacts the brewing results. Malt hydration involves controlling its moisture content; excessively high or low moisture levels can affect subsequent processing. Therefore, timely and accurate detection of malt hydration level is essential for ensuring product quality. With advancements in computer vision and image processing technologies, image analysis-based hydration level detection is gradually becoming an important alternative to traditional methods, providing an efficient, accurate, and non-destructive solution for malt production and quality control.

[0003] Existing technology involves preprocessing and analyzing images of malt captured by a camera to establish a quantitative analysis model of the relationship between hydration level and image features. Based on the quantitative results, it determines whether to issue an alarm or make a suggestion, thus achieving rapid and non-destructive detection.

[0004] For example, the invention patent announcement CN112924337B discloses a method for evaluating malt solubility, which includes: malt produced by the soaking-germination step; and the subsequent soaking and water-cutting processes can be adjusted according to the soaking index before the end of the dry soaking step; if the evaluated malt solubility is not good or does not meet the requirements, the germination step after the soaking step can be improved in a timely manner by combining the soaking index before the end of each dry soaking step and the requirements for malt solubility.

[0005] For example, patent application CN118067766A discloses a method for predicting the filtration performance of beer fermentation liquid, including: (1) cutting malt to obtain a malt cross-section; (2) scanning the malt cross-section with a scanning electron microscope to obtain a backscattered electron image of the malt cross-section, and selecting the endosperm containing concentrated starch particles and the connected aleurone layer as the test area; (3) using a grid to segment the backscattered electron image of the test area, recording the total number of all grids in the endosperm and the transition part between the endosperm and the aleurone layer as the total number, and calculating the proportion of grids containing adhering small starch particles. The proportion is positively correlated with the filtration time of beer fermentation liquid and negatively correlated with the filtration performance of beer fermentation liquid.

[0006] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0007] In existing technologies, image quality can be unstable due to variations in lighting conditions (such as light source angle, intensity, and color temperature), equipment vibration, or light source aging during image acquisition. This leads to low accuracy in feature extraction based on the image, which in turn affects the detection precision of hydration level. Furthermore, existing methods lack collaborative analysis of multi-scale features, resulting in incomplete quantification of hydration level and low detection precision of malt hydration level due to feature extraction errors. Summary of the Invention

[0008] This application provides an image analysis-based malt hydration degree detection system and method, which solves the problem of low detection accuracy of malt hydration degree caused by feature extraction errors in the prior art, and improves the detection accuracy of malt hydration degree.

[0009] This application provides an image analysis-based malt hydration level detection system, including: a malt image quality assessment module, a malt hydration assessment module, and a hydration process parameter feedback module. The malt image quality assessment module evaluates the malt image quality based on the acquired image quality parameters of the malt cross-section image and determines whether malt cross-section image quality optimization is required. The malt hydration assessment module, if not optimizing the malt cross-section image quality, performs multi-dimensional feature extraction on the malt cross-section image to obtain malt feature parameters, evaluates malt hydration based on these malt feature parameters, and determines whether hydration process parameter optimization is required. If malt cross-section image quality optimization is required, the module performs multi-dimensional feature extraction on the malt cross-section image to obtain malt feature parameters, evaluates malt hydration based on these feature parameters, and determines whether hydration process parameter optimization is required. The optimization process involves extracting multi-dimensional features from the optimized malt cross-section image to obtain malt feature parameters. Based on these parameters, malt hydration is assessed, and the hydration process parameters are evaluated based on the assessment results and the soaking index. This process aims to improve the uniformity of water absorption during soaking. The hydration process parameter feedback module detects the degree of malt hydration based on the optimized parameters after outputting the test report if optimization is performed; otherwise, it directly detects the degree of malt hydration based on the current parameters after outputting the test report.

[0010] This application provides an image analysis-based method for detecting malt hydration levels, comprising the following steps: S1, evaluating the malt image quality based on the acquired image quality parameters of the malt cross-section image, and determining whether to optimize the malt cross-section image quality; S2, if not optimizing the malt cross-section image quality, extracting multi-dimensional features from the malt cross-section image to obtain malt feature parameters, evaluating malt hydration based on the malt feature parameters in the malt cross-section image, and determining whether to optimize the hydration process parameters; if malt cross-section image quality optimization is performed, then optimizing the malt cross-section image quality... Multi-dimensional feature extraction is performed on the malt cross-section image to obtain malt feature parameters. Malt hydration is evaluated based on the malt feature parameters in the malt cross-section image after quality optimization. The hydration process parameters are then used to determine whether to optimize the hydration process parameters based on the malt hydration evaluation results and the soaking index. Hydration process parameter optimization is used to improve the uniformity of water absorption during the soaking process of barley. S3, if hydration process parameter optimization is performed, the degree of malt hydration is detected based on the optimized hydration process parameters after the detection report is output. Otherwise, the degree of malt hydration is detected directly based on the current hydration process parameters after the detection report is output.

[0011] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0012] 1. Malt image quality is evaluated by acquiring image quality parameters to optimize malt cross-section image quality. Then, malt hydration is evaluated based on the optimized malt feature parameters. Finally, the hydration evaluation results and soaking index are used to determine whether to optimize hydration process parameters. This improves the quality of the acquired malt cross-section images and enhances the detection accuracy of malt hydration degree, effectively solving the problem of low detection accuracy of malt hydration degree caused by feature extraction errors in existing technologies.

[0013] 2. The image signal-to-noise ratio (SNR) of a single malt cross-section image is processed to obtain the image SNR contrast coefficient. Then, the image resolution is processed to obtain the image resolution contrast coefficient. Next, the light source brightness is processed to obtain the light source brightness contrast coefficient. Finally, a malt image quality assessment compensation value is introduced to process the illumination uniformity coefficient, image SNR contrast coefficient, image resolution contrast coefficient, and light source brightness contrast coefficient to obtain the malt image quality assessment index. This quantitatively assesses the quality of the malt cross-section image, thereby improving the quality of the malt cross-section image.

[0014] 3. The malt morphology evaluation index was obtained by processing the average endosperm area ratio, average cell expansion degree, and average cell wall rupture coefficient. Then, the malt color evaluation index was obtained by processing the average malt brightness, average malt saturation, and malt contrast coefficient. Finally, the malt morphology evaluation index and the malt color evaluation index were processed to obtain the malt comprehensive hydration index. This comprehensively and quantitatively evaluated the hydration degree of malt, thereby improving the uniformity of water absorption by malt during the soaking process. Attached Figure Description

[0015] Figure 1 A schematic diagram of the image analysis-based malt hydration detection system provided in an embodiment of this application;

[0016] Figure 2 A flowchart of the image analysis-based malt hydration detection method provided in the embodiments of this application. Detailed Implementation

[0017] This application provides an image analysis-based malt hydration degree detection system and method, which solves the problem of low detection accuracy of malt hydration degree caused by feature extraction errors in the prior art. The system assesses the malt image quality by using image quality parameters from the acquired malt cross-section image to determine whether malt cross-section image quality optimization is necessary. Then, based on the malt feature parameters in the optimized malt cross-section image, malt hydration is assessed. Based on the malt hydration assessment results and the soaking index, the system determines whether hydration process parameters need optimization. Finally, after outputting the detection report, subsequent malt hydration degree detection is performed based on the optimized hydration process parameters, thus improving the detection accuracy of malt hydration degree.

[0018] The technical solution in this application embodiment is to solve the problem of low detection accuracy of malt hydration degree caused by feature extraction error. The overall idea is as follows:

[0019] Malt image quality is evaluated by collecting image quality parameters to optimize malt cross-section image quality. Then, malt hydration is evaluated based on the optimized malt characteristic parameters. Finally, the hydration evaluation results and soaking index are used to determine whether to optimize hydration process parameters, thereby improving the detection accuracy of malt hydration degree.

[0020] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0021] like Figure 1The diagram shown is a structural schematic of the malt hydration degree detection system based on image analysis provided in this application embodiment. The malt hydration degree detection system based on image analysis provided in this application embodiment includes: a malt image quality assessment module, a malt hydration assessment module, and a hydration process parameter feedback module.

[0022] The malt image quality assessment module is used to acquire malt cross-sectional images using a high-resolution industrial camera and a ring-shaped shadowless light source system. Based on the acquired image quality parameters of the malt cross-sectional images, the module assesses the malt image quality and determines whether to optimize it. The malt cross-sectional image is an image of the cross-sectional shape of malt. The cross-sectional shape of malt is obtained by acquiring soaked malt samples through an automated device and cutting them at a uniform speed along the longitudinal direction using a precision cutting device. The automated device is used to automatically acquire malt samples; for example, it can acquire samples through a specific robotic arm or other automated tools.

[0023] The malt hydration assessment module is used to extract multi-dimensional features from the malt cross-section image to obtain malt feature parameters if no malt cross-section image quality optimization is performed. Based on the malt feature parameters in the malt cross-section image, a malt hydration assessment is conducted, and it is determined whether to optimize the hydration process parameters. If malt cross-section image quality optimization is performed, multi-dimensional features are extracted from the optimized malt cross-section image to obtain malt feature parameters. Based on the malt feature parameters in the optimized malt cross-section image, a malt hydration assessment is conducted, and based on the malt hydration assessment results and the soaking index, it is determined whether to optimize the hydration process parameters. The optimization of the hydration process parameters is used to improve the uniformity of water absorption by malt during the soaking process.

[0024] The hydration process parameter feedback module is used to detect the degree of malt hydration based on the optimized hydration process parameters after outputting the test report if hydration process parameter optimization is performed; otherwise, it directly detects the degree of malt hydration based on the current hydration process parameters after outputting the test report. The test report visually displays the malt characteristic parameters and hydration level.

[0025] Before designing the malt hydration level detection system and method based on image analysis, a database is established to store various setting data. The database includes, but is not limited to, preset image signal-to-noise ratio, preset image resolution, malt image quality assessment compensation value, light source brightness mapping set, and malt morphology assessment compensation value. These values ​​are directly set by technical personnel. The preset image signal-to-noise ratio can be set based on preset personnel. For example, the preset image signal-to-noise ratio can be represented by the average value of historical image signal-to-noise ratios within a historical time period in the database. In addition, various values ​​in the database can be set and fine-tuned by technical personnel based on actual debugging. The historical time period is set based on preset personnel, for example, it can be set to one week.

[0026] In this embodiment, during image acquisition, changes in lighting conditions (such as light source angle, intensity, color temperature, etc.), equipment vibration, or light source aging may lead to unstable image quality, thus affecting the accuracy of hydration degree detection. Existing methods mainly rely on macroscopic features (such as color and texture) and lack quantitative analysis of malt microstructure (such as the degree of cell wall rupture and starch granule morphology), while microstructure is closely related to hydration degree; therefore, there is a problem of low detection accuracy of malt hydration degree due to feature extraction errors.

[0027] In this application, the ring-shaped shadowless light source system avoids shadows in the image through uniform illumination, thus ensuring that the image quality is not affected by uneven light source and ensuring the clarity of image details. A precision cutting device cuts the malt sample at a uniform speed along the longitudinal direction (longitudinal rib direction) to ensure the uniformity and accuracy of the cut surface, thereby obtaining a representative cross-sectional image. The malt hydration assessment module adaptively optimizes the hydration process parameters based on the malt hydration assessment results, ensuring the uniformity of the hydration process and thus improving production efficiency. Through the hydration process parameter feedback module, the process can be continuously optimized after each test, providing higher quality data support for subsequent tests. This ultimately improves the detection accuracy of the degree of malt hydration.

[0028] Furthermore, the malt image quality is evaluated based on the acquired image quality parameters of the malt cross-section images. The specific method is as follows:

[0029] C1, if the signal-to-noise ratio (SNR) of a single malt cross-section image is less than the preset SNR obtained from the preset database, then the SNR of the single malt cross-section image and the preset SNR obtained from the preset database are compared to obtain an image SNR comparison coefficient; otherwise, the SNR of the single malt cross-section image and the preset SNR obtained from the preset database are compared to obtain an image SNR comparison coefficient. The specific constraint expression for the image SNR comparison coefficient is as follows:

[0030]

[0031] In the formula, ZAO represents the image signal-to-noise ratio of the malt cross-section image, which is obtained by calculating the ratio of the average brightness of all pixel values ​​in the initial malt cross-section image to the standard deviation of brightness; ZAO0 represents the preset image signal-to-noise ratio, which is set according to preset personnel, for example, by the average value of the historical image signal-to-noise ratio within a historical time period in a preset database.

[0032] C2, if the image resolution of a single malt cross-section image is less than the preset image resolution obtained from the preset database, then the image resolution of the single malt cross-section image is compared with the preset image resolution obtained from the preset database to obtain an image resolution comparison coefficient; otherwise, the image resolution of the single malt cross-section image is compared with the preset image resolution obtained from the preset database to obtain an image resolution comparison coefficient; the specific constraint expression for the image resolution comparison coefficient is:

[0033]

[0034] In the formula, FEB represents the image resolution of the malt cross-section image. The gradient of the image in the horizontal and vertical directions is calculated by the Sobel operator. Then, the gradient of the horizontal and vertical gradients is squared and summed, and the square root is calculated to obtain the gradient magnitude of each pixel. Finally, the average gradient magnitude of all pixels in the image is used to represent the resolution. FEB0 represents the preset image resolution, which is set according to the preset personnel, such as the average resolution of historical images in the preset database within a historical time period.

[0035] C3, based on the relative deviation processing of the light source brightness and the preset image resolution obtained from the preset database, yields the light source brightness contrast coefficient, i.e. In the formula, GYL represents the light source brightness when acquiring the malt cross-section image, which is obtained by directly measuring the actual brightness of the light source using an illuminance meter; in the formula, GYL0 represents the preset light source brightness, which is set according to preset personnel, for example, by representing the average value of historical light source brightness within a historical time period in a preset database.

[0036] C4 introduces a malt image quality assessment compensation value to perform a coupling process on the illumination uniformity coefficient, image signal-to-noise ratio contrast coefficient, image resolution contrast coefficient, and light source brightness contrast coefficient, resulting in a malt image quality assessment index. The malt image quality assessment compensation value includes a first malt image quality assessment compensation value, a second malt image quality assessment compensation value, a third malt image quality assessment compensation value, and a fourth malt image quality assessment compensation value. The malt image quality assessment index is used to quantitatively assess the quality of the malt cross-section image. The illumination uniformity coefficient is obtained by converting the initial malt cross-section image into a grayscale image, performing block processing to obtain the average brightness value of each block, and finally calculating the ratio of the average value of all average brightness values ​​to the standard deviation of all average brightness values.

[0037] The malt image quality assessment compensation values ​​involved are obtained from a preset database. The first malt image quality assessment compensation value represents the influence of the illumination uniformity coefficient on the malt image quality assessment index; the second malt image quality assessment compensation value represents the influence of the light source brightness on the malt image quality index; the third malt image quality assessment compensation value represents the influence of the image signal-to-noise ratio on the malt image quality index; and the fourth malt image quality assessment compensation value represents the influence of the image resolution on the malt image quality index. The sum of these four values ​​is 1. For example, the illumination uniformity coefficient and the preset first malt image quality assessment compensation value form an illumination uniformity coefficient mapping set. Inputting the real-time illumination uniformity coefficient into the illumination uniformity coefficient mapping set yields the corresponding... The first malt image quality assessment compensation value; the light source brightness and the preset second malt image quality assessment compensation value form a light source brightness mapping set, and the real-time light source brightness is input into the light source brightness mapping set to obtain the corresponding second malt image quality assessment compensation value; the image signal-to-noise ratio and the preset third malt image quality assessment compensation value form an image signal-to-noise ratio mapping set, and the real-time image signal-to-noise ratio is input into the image signal-to-noise ratio mapping set to obtain the corresponding third malt image quality assessment compensation value; the image resolution and the preset fourth malt image quality assessment compensation value form an image resolution mapping set, and the real-time image resolution is input into the image resolution mapping set to obtain the corresponding fourth malt image quality assessment compensation value; the mapping relationship can be one-to-one or many-to-one.

[0038] The specific constraint expression for the malt image quality assessment index is as follows:

[0039]

[0040] In the formula, TU represents the malt image quality assessment index of the malt cross-section image, GJU represents the illumination uniformity coefficient of the malt cross-section image, ZA represents the image signal-to-noise ratio contrast coefficient of the malt cross-section image, FE represents the image resolution contrast coefficient of the malt cross-section image, T1 represents the first malt image quality assessment compensation value, T2 represents the second malt image quality assessment compensation value, T3 represents the third malt image quality assessment compensation value, and T4 represents the fourth malt image quality assessment compensation value.

[0041] The specific process for determining whether to perform malt cross-section image quality optimization is as follows:

[0042] A1. The average malt image quality assessment index is obtained by statistically analyzing the malt image quality assessment index of a single malt cross-section image. It is then determined whether the average malt image quality assessment index is less than a preset malt image quality threshold obtained from a preset database. If the average malt image quality assessment index is less than the preset malt image quality threshold obtained from the preset database, then light source optimization is performed; otherwise, no malt cross-section image quality optimization is performed. The average malt image quality assessment index represents the average quality of the malt cross-section image. Light source optimization is used to reduce the impact of changes in lighting conditions on the quality of the malt cross-section image. The preset malt image quality threshold is represented by the average value of the average malt image quality assessment index over a historical time period.

[0043] A2 determines whether the average malt image quality assessment index after light source optimization is less than the preset malt image quality threshold obtained from the preset database. If the average malt image quality assessment index after light source optimization is less than the preset malt image quality threshold obtained from the preset database, image processing is performed; otherwise, the malt cross-section image quality optimization ends. Image processing is used to improve the quality of the malt cross-section image. Image processing removes noise and enhances contrast by processing the malt cross-section image through median filtering and histogram equalization. The median filtering process is as follows: first, the malt cross-section image is converted into a grayscale image; then, an odd-sized window is selected, and the window is slid across the image pixel by pixel; next, the grayscale values ​​of all pixels within the window are sorted; then, the median value of the sorted grayscale values ​​is selected as the new grayscale value of the center pixel of the window; finally, all pixels in the image are processed... Repeat the above steps to complete median filtering. The window size should be chosen based on the image noise characteristics and detail preservation requirements. For malt slice images, if the noise is low and more details need to be preserved, a 3×3 window is preferable. The histogram equalization process is as follows: First, count the number of pixels at each gray level in the malt slice image to obtain a gray-level histogram. Then, calculate the cumulative distribution function based on the gray-level histogram. The cumulative distribution function reflects the cumulative proportion of pixels below each gray level. Next, map the gray values ​​of the original image through the cumulative distribution function to obtain new gray values. The specific formula is: New gray value = Total number of gray levels × Cumulative distribution function (original gray value) - 1. Finally, replace the gray values ​​of the original image with the mapped new gray values ​​to obtain the histogram equalized image. For 8-bit grayscale images, the total number of gray levels is usually 256.

[0044] A3 determines whether the average malt image quality assessment index after image processing is less than the preset malt image quality threshold obtained from the preset database. If the average malt image quality assessment index after image processing is less than the preset malt image quality threshold obtained from the preset database, feedback is provided; otherwise, the malt cross-section image quality optimization ends. Malt cross-section image quality optimization includes light source optimization and image processing. The feedback here indicates that the preset personnel's image quality does not meet the standards.

[0045] The specific methods for optimizing the light source are as follows: The average malt image quality assessment index and the light source brightness deviation are input into the light source brightness mapping set to obtain the optimized light source brightness. The light source brightness deviation is obtained by subtracting the light source brightness from the preset light source brightness. The light source brightness mapping set is a set obtained from a preset database that represents the mapping relationship between the average malt image quality assessment index, the light source brightness deviation, and the optimized light source brightness. After adjusting the light source brightness in the ring shadowless light source system to the optimized light source brightness, the malt cross-section image is re-acquired. The ring shadowless light source system is used to avoid the problem of poor stability of the acquired malt cross-section image caused by uneven illumination.

[0046] In this embodiment, the brightness of the light source directly affects the brightness of the malt cross-section image, which in turn affects the illumination uniformity coefficient. Excessively strong or weak light source brightness will lead to uneven illumination, which in turn affects the quality of the malt cross-section image. The less noise in a malt cross-section image with uniform illumination, the larger the illumination uniformity coefficient and the larger the image signal-to-noise ratio coefficient. A malt cross-section image with higher resolution can better offset the influence of noise, so the larger the image resolution, the larger the image signal-to-noise ratio coefficient. The image resolution is also related to the illumination uniformity coefficient and the brightness of the light source. A malt cross-section image with higher resolution can more accurately capture illumination changes and obtain more accurate illumination uniformity.

[0047] By comparing the image signal-to-noise ratio, resolution, light source brightness, and corresponding preset values, defects in the malt cross-section image (such as noise, insufficient resolution, low light intensity, etc.) can be identified. Introducing compensation values ​​makes the evaluation process more flexible, allowing for fine-tuning of the malt cross-section image quality based on the actual acquisition environment, ensuring accurate evaluation results. For example, if the ambient light source is unstable, the compensation values ​​for the first and second malt image quality assessments may be higher. Through these steps, the quality of the malt cross-section image is quantitatively evaluated, allowing for timely identification of quality issues and providing a foundation for subsequent malt cross-section image quality optimization. This ensures the accuracy of subsequent malt cross-section image analysis and avoids misjudgments caused by poor malt cross-section image quality.

[0048] By reducing the impact of varying lighting conditions on the quality of malt cross-section images, the image quality can be maintained relatively stably under different lighting environments. For example, it avoids overexposure or underexposure due to excessively strong or weak lighting, and reduces problems such as shadows and reflections caused by uneven lighting, thereby improving image sharpness and detail, providing a better foundation for subsequent image processing and image quality assessment. Median filtering can preserve edge and detail information in malt cross-section images while removing noise interference, making the malt cross-section images smoother and clearer. Adjusting the grayscale distribution of malt cross-section images makes the grayscale levels more evenly distributed across the entire grayscale range, thereby improving the visibility and information content of the malt cross-section images, which helps to more accurately identify and analyze the features of malt cross-sections.

[0049] By dynamically adjusting the brightness of the light source, the brightness difference between different tissues in the malt cross-section image can be increased, the contrast can be improved, and the details of the malt cross-section can be made more clearly visible, thereby improving the reliability of malt hydration detection.

[0050] Furthermore, malt hydration is assessed based on malt characteristic parameters in the malt cross-section image, using the following specific method:

[0051] D1 introduces malt morphology assessment compensation values ​​to perform assignment and coupling processing on the average endosperm region ratio, average cell swelling degree, and average cell wall rupture coefficient in a single malt cross-section image, resulting in a malt morphology feature assessment index. The malt morphology assessment compensation values ​​include a first malt morphology assessment compensation value, a second malt morphology assessment compensation value, and a third malt morphology assessment compensation value. The malt morphology feature assessment index is used to quantitatively assess the hydration degree of malt in terms of morphological characteristics. The specific constraint expression of the malt morphology feature assessment index is as follows:

[0052] XT=X1×PRQ+X2×PZD+X3×XPL;

[0053] In the formula, XT represents the malt morphology feature evaluation index of the malt cross-section image, PRQ represents the average endosperm region proportion of the malt in the malt cross-section image. The cross-sectional image of a single malt in the malt cross-section image is segmented using image processing software (such as ImageJ, MATLAB) to distinguish the endosperm region from other tissues (such as seed coat, plumule), the pixel area of ​​the endosperm region is measured, and the ratio of the pixel area of ​​the endosperm region to the total cross-sectional area of ​​the malt is calculated to obtain the endosperm region proportion of a single malt in the malt cross-section image; the average endosperm region proportion is obtained by averaging the endosperm region proportions of all malts in the malt cross-section image; PZD represents the average cell swelling degree of the malt in the malt cross-section image, which is the area of ​​a single cell measured using image processing software (such as ImageJ, MATLAB), and then immersed in water... The cell swelling degree of a single malt in the malt cross-section image is obtained by calculating the ratio of the area of ​​the malt after soaking to the area of ​​the malt before soaking. The average cell swelling degree of all malts in the malt cross-section image is obtained by averaging the cell swelling degrees of all malts in the malt cross-section image. XPL represents the average cell wall rupture coefficient of malt in the malt cross-section image. The cell wall region is segmented and the area of ​​the ruptured cell wall is obtained by using image processing software (such as ImageJ, MATLAB). Then, the cell wall rupture coefficient of a single malt in the malt cross-section image is obtained by calculating the ratio of the area of ​​the ruptured cell wall to the total cell wall area. The average cell wall rupture coefficient of all malts in the malt cross-section image is obtained by averaging the cell wall rupture coefficients of all malts in the malt cross-section image. X1 represents the first malt morphology evaluation compensation value, X2 represents the second malt morphology evaluation compensation value, and X3 represents the third malt morphology evaluation compensation value.

[0054] The malt morphology assessment compensation values ​​are obtained from a preset database. The first malt morphology assessment compensation value represents the influence of the average endosperm region proportion on the malt morphology characteristic assessment index; the second malt morphology assessment compensation value represents the influence of the average cell swelling degree on the malt morphology characteristic assessment index; and the third malt morphology assessment compensation value represents the influence of the average cell wall rupture coefficient on the malt morphology characteristic assessment index. The sum of the three is 1. For example, the average endosperm region proportion and the preset first malt morphology assessment compensation value form an average endosperm region proportion mapping set. The real-time average endosperm region proportion is input into the average endosperm... The region proportion mapping set yields the corresponding first malt morphology assessment compensation value; the average cell swelling degree and the preset second malt morphology assessment compensation value form the average cell swelling degree mapping set, and the real-time average cell swelling degree is input into the average cell swelling degree mapping set to obtain the corresponding second malt morphology assessment compensation value; the average cell wall rupture coefficient and the preset third malt morphology assessment compensation value form the average cell wall rupture coefficient mapping set, and the real-time average cell wall rupture coefficient is input into the average cell wall rupture coefficient mapping set to obtain the corresponding third malt morphology assessment compensation value; the mapping relationship can be one-to-one or many-to-one.

[0055] D2, the malt contrast coefficient is obtained by inversely scaling the de-unitized average malt contrast in a single malt cross-section image. In the formula, MDB represents the average malt contrast of malt in the malt cross-section image. The average malt contrast can be obtained by quantizing the grayscale image into a preset number of gray levels and calculating the gray-level co-occurrence matrix, using the greycoprops function in the scikit-image library. The preset number is set according to the preset number of users, for example, 8 gray levels.

[0056] D3 introduces malt color evaluation compensation values ​​to perform assignment and coupling processing on the deunited average malt brightness, deunited average malt saturation, and malt contrast coefficient in a single malt cross-section image, resulting in a malt color feature evaluation index. The malt color evaluation compensation values ​​include a first malt color evaluation compensation value, a second malt color evaluation compensation value, and a third malt color evaluation compensation value. The malt color feature evaluation index is used to quantify the degree of hydration of malt in terms of color features. The specific constraint expression for the malt color feature evaluation index is as follows:

[0057]

[0058] In the formula, YT represents the malt color feature evaluation index of the malt cross-section image, MLD represents the average malt brightness of the malt in the malt cross-section image, the BGR image is converted to an HSV image using the cvtColor function of OpenCV, then the S channel (saturation) and V channel (brightness) are separated from the HSV image, and finally the average value of the S channel and V channel is calculated using NumPy to obtain the average malt brightness and average malt saturation; MBD represents the average malt saturation of the malt in the malt cross-section image, Y1 represents the first malt color evaluation compensation value, Y2 represents the second malt color evaluation compensation value, and Y3 represents the third malt color evaluation compensation value.

[0059] The malt color evaluation compensation values ​​involved are obtained from a preset database. The first malt color evaluation compensation value represents the influence of average malt brightness on the malt color feature evaluation index; the second malt color evaluation compensation value represents the influence of average malt saturation on the malt color feature evaluation index; and the third malt color evaluation compensation value represents the influence of average malt contrast on the malt color feature evaluation index. The sum of the three is 1. For example, the average malt brightness and the preset first malt color evaluation compensation value form a brightness mapping set, and the real-time average malt brightness is input into the brightness mapping set to obtain the corresponding first malt color evaluation compensation value; the average malt saturation and the preset second malt color evaluation compensation value form a saturation mapping set, and the real-time average malt saturation is input into the saturation mapping set to obtain the corresponding second malt color evaluation compensation value; the average malt contrast and the preset third malt color evaluation compensation value form a contrast mapping set, and the real-time average malt contrast is input into the contrast mapping set to obtain the corresponding third malt color evaluation compensation value. The mapping relationship can be one-to-one or many-to-one.

[0060] D4. The comprehensive hydration compensation value of malt is introduced to perform a value assignment and coupling process on the malt morphological feature evaluation index and the malt color feature evaluation index in a single malt cross-section image to obtain the comprehensive hydration index of malt. The comprehensive hydration index of malt is used to comprehensively and quantitatively evaluate the hydration degree of malt.

[0061] The specific limiting expression for the malt composite hydration index is:

[0062] SH = α × XT + (1 - α) × YT;

[0063] In the formula, SH represents the malt comprehensive hydration index of the malt cross-section image, and α represents the malt comprehensive hydration compensation value.

[0064] The malt comprehensive hydration compensation value involved is obtained from a preset database. The malt comprehensive hydration compensation value represents the degree of influence of the malt morphological feature evaluation index on the malt comprehensive hydration index. Its value ranges from 0 to 1. For example, the malt morphological feature evaluation index and the preset first malt color evaluation compensation value form a morphological mapping set. The real-time malt morphological feature evaluation index is input into the morphological mapping set to obtain the corresponding first malt color evaluation compensation value. The mapping relationship can be one-to-one or many-to-one.

[0065] In this embodiment, the morphological changes of malt, such as the size of the endosperm region, the degree of cell expansion, and the degree of cell wall rupture, are reflected by the average endosperm region proportion, average cell expansion, and average cell wall rupture coefficient. The hydration degree of malt in terms of morphological characteristics is quantitatively evaluated. If the endosperm region proportion of malt increases, the cell expansion increases, and the degree of cell wall rupture is greater, it indicates that the hydration degree of malt is higher.

[0066] In the malt morphology characteristic evaluation index algorithm, as cell swelling increases, the hydration of the endosperm region also accelerates, and the water permeability increases. After cell swelling, the effective hydration area of ​​the endosperm region increases. Therefore, the greater the cell swelling, the larger the proportion of the endosperm region. When cell swelling increases, cell wall rupture may intensify, which means that the process of water penetrating into the cell will accelerate, thereby increasing the degree of hydration. Therefore, the greater the cell swelling, the greater the cell wall rupture coefficient.

[0067] Contrast reflects the color difference or brightness difference of different parts in the malt cross-section image; malt brightness and saturation reflect the color characteristics of malt in the malt cross-section image, and quantitatively evaluate the degree of hydration of malt in terms of color characteristics. If the malt brightness and saturation are higher and the malt contrast is lower, it indicates that the degree of hydration of malt is higher.

[0068] In the malt color feature evaluation index algorithm, the higher the degree of hydration, the higher the malt brightness and malt saturation; the higher the malt saturation, the lower the clarity of the malt surface texture and the weaker the malt contrast; as the malt brightness increases, the smooth and moist characteristics of the surface may lead to a decrease in contrast.

[0069] By comprehensively and quantitatively evaluating the hydration level of malt from two dimensions (morphology and color), a foundation is provided for optimizing subsequent hydration process parameters, improving the uniformity of water absorption by malt during soaking. Changes in morphological characteristics (such as cell swelling and increased endosperm area) are often accompanied by changes in color characteristics. For example, an increase in the cell wall rupture coefficient, cell swelling degree, and endosperm area proportion indicates better hydration, resulting in higher malt brightness, malt saturation, and malt contrast.

[0070] Furthermore, before determining whether to optimize hydration process parameters based on the results of malt hydration assessment and soaking index, the process also includes obtaining hydration level classification thresholds. The specific procedure is as follows:

[0071] B1. The initial hydration level of the wheat soaking index is classified according to the initial hydration level classification threshold. The initial hydration level classification threshold includes the maximum and minimum acceptable values ​​of the initial hydration level of the wheat soaking index. The initial hydration level classification threshold is set according to the preset personnel. The wheat soaking index represents the water absorption rate of the wheat grains within the preset soaking time, in grams per hour. The preset soaking time is set according to the preset personnel.

[0072] B2. Based on the average malt comprehensive hydration index corresponding to the maximum qualified initial malt immersion index in the historical time period within the preset database, it is determined whether to adjust the maximum qualified initial malt immersion index. The average malt comprehensive hydration index represents the average hydration level of the current batch of malt, and the maximum qualified initial malt immersion index represents the maximum qualified malt immersion index in the historical time period within the preset database.

[0073] If the average malt hydration index corresponding to the initial maximum acceptable malt hydration index is not within the preset acceptable malt hydration range obtained from the preset database, then a first-level classification adjustment factor is introduced to correct the initial maximum acceptable malt hydration index to obtain the maximum acceptable malt hydration index. The first-level classification adjustment factor is obtained by subtracting the average malt hydration index corresponding to the initial maximum acceptable malt hydration index from the preset database. Otherwise, the initial maximum acceptable malt hydration index is directly recorded as the maximum acceptable malt hydration index. The preset acceptable malt hydration range is between the preset minimum acceptable malt hydration value and the preset maximum acceptable malt hydration value. The preset minimum acceptable malt hydration value is represented by the minimum average malt hydration index within the historical time period, and the preset maximum acceptable malt hydration value is represented by the maximum average malt hydration index within the historical time period. The maximum acceptable malt hydration index is obtained by multiplying the first-level classification adjustment factor and the initial maximum acceptable malt hydration index.

[0074] B3. Based on the average comprehensive malt hydration index corresponding to the minimum qualified initial simmering index in the historical time period in the preset database, determine whether to adjust the minimum qualified initial simmering index. The minimum qualified initial simmering index represents the minimum qualified simmering index in the historical time period in the preset database.

[0075] If the average malt hydration index corresponding to the minimum acceptable initial malt hydration index is not within the preset acceptable malt hydration range obtained from the preset database, a second-level classification adjustment factor is introduced to correct the minimum acceptable initial malt hydration index to obtain the minimum acceptable malt hydration index. The second-level classification adjustment factor is obtained by subtracting the average malt hydration index corresponding to the minimum acceptable initial malt hydration index from the preset database. Otherwise, the minimum acceptable initial malt hydration index is directly recorded as the minimum acceptable malt hydration index. The minimum acceptable malt hydration index is obtained by multiplying the second-level classification adjustment factor and the minimum acceptable initial malt hydration index.

[0076] The process involves determining whether to optimize hydration process parameters based on the results of malt hydration assessment and malting index. The specific procedure is as follows:

[0077] Hydration levels are classified based on the soaking index and hydration level classification thresholds, which include the maximum and minimum acceptable soaking index values.

[0078] When the saturation index is greater than the maximum acceptable value, the corresponding saturation index is classified as the first hydration level, and the first hydration process parameters are optimized. When the saturation index is not greater than the maximum acceptable value, but greater than the minimum acceptable value, the corresponding saturation index is classified as the second hydration level, and no hydration process parameter optimization is performed. When the saturation index is not greater than the minimum acceptable value, the corresponding saturation index is classified as the third hydration level, and the second hydration process parameters are optimized.

[0079] The specific methods for optimizing the first hydration process parameters are as follows:

[0080] The average malt comprehensive hydration index, soaking index, and soaking time deviation are input into the soaking time mapping set to obtain the optimized maximum soaking time. The soaking time deviation is obtained by subtracting the current soaking time from the maximum soaking time in the second hydration level. The maximum soaking time in the second hydration level is represented by the maximum soaking time in the second hydration level within the historical time period.

[0081] The average malt hydration index, soaking index, and soaking temperature deviation are input into the soaking temperature mapping set to obtain the optimized maximum soaking temperature. The soaking temperature deviation is obtained by subtracting the current soaking temperature from the maximum soaking temperature in the second hydration level. The maximum soaking temperature in the second hydration level is represented by the maximum soaking temperature in the second hydration level during the historical time period.

[0082] The optimal soaking time is obtained by first pre-adjusting according to the preset ratio of the maximum optimal soaking time, and the optimal soaking temperature is obtained by first pre-adjusting according to the preset ratio of the maximum optimal soaking temperature, until the termination condition is met; the preset ratio is set according to the preset personnel, for example, 10%; the first pre-adjustment means gradually reducing.

[0083] The specific method for optimizing the second hydration process parameters is as follows:

[0084] The average malt hydration index, soaking index, and soaking time deviation are input into the soaking time mapping set to obtain the optimized maximum soaking time. The soaking time deviation is obtained by subtracting the current soaking time from the maximum soaking time in the second hydration level. The average malt hydration index represents the average hydration level of the current batch of malt.

[0085] The average malt hydration index, soaking index, and soaking temperature deviation are input into the soaking temperature mapping set to obtain the optimized maximum soaking temperature. The soaking temperature deviation is obtained by subtracting the current soaking temperature from the maximum soaking temperature in the second hydration level.

[0086] The optimized soaking time is obtained by a second pre-adjustment based on a preset ratio of the optimized maximum soaking time, and the optimized soaking temperature is obtained by a second pre-adjustment based on a preset ratio of the optimized maximum soaking temperature, until the termination condition is met; the second pre-adjustment means gradually increasing.

[0087] The termination conditions involved are as follows:

[0088] When the optimized soaking time is less than the optimized maximum soaking time and the optimized soaking temperature is less than the optimized maximum soaking temperature, determine whether the hydration level of the corresponding batch of malt is at the second hydration level. If the hydration level of the corresponding batch of malt is at the second hydration level, terminate the optimization of the first hydration process parameters; otherwise, continue the optimization of the first hydration process parameters. When the optimized soaking time is less than the optimized maximum soaking time and the optimized soaking temperature is not less than the optimized maximum soaking temperature, determine whether the hydration level of the corresponding batch of malt is at the second hydration level. If the hydration level of the corresponding batch of malt is not at the second hydration level, terminate the optimization of the soaking temperature and maintain the optimization of the soaking time; otherwise, terminate the optimization. The first hydration process parameter optimization involves: when the optimized soaking time is not less than the optimized maximum soaking time and the optimized soaking temperature is less than the optimized maximum soaking temperature, determining whether the hydration level of the corresponding batch of malt is at the second hydration level. If the hydration level of the corresponding batch of malt is not at the second hydration level, feedback is provided; otherwise, the first hydration process parameter optimization is terminated.

[0089] In this embodiment, the hydration level of malt is initially classified by the immersion index, providing an initial standard for subsequent optimization of hydration process parameters. Based on the immersion index and the corresponding average comprehensive hydration index of malt, it is checked whether the maximum and minimum acceptable values ​​of the initial immersion index need to be adjusted, which improves the accuracy of hydration level classification, ensures that the hydration process parameters can adapt to the actual conditions of different batches of malt, effectively avoids errors caused by static threshold settings, and ensures the rationality of hydration process parameters. By introducing an adjustment factor, overly strict or overly lenient evaluation standards can be avoided, reducing errors caused by inaccurate hydration level classification.

[0090] Malt is classified into different hydration levels based on its soaking index, thereby determining whether further hydration process optimization is needed; the hydration process is optimized by adjusting soaking time and temperature to ensure that the degree of malt hydration is within the appropriate range; and each batch of malt is processed under ideal hydration conditions to improve production efficiency.

[0091] The optimization process is stopped when the required hydration level is reached based on the optimized soaking time and temperature, ensuring that the optimization process has a termination condition and avoids endless adjustments, thus preventing the waste of time and resources. By gradually adjusting the optimization parameters and providing feedback, the process optimization becomes more precise, ensuring that the process is not over-adjusted and improving the accuracy and controllability of malt hydration level detection.

[0092] like Figure 2 The flowchart shown is a process for detecting the degree of malt hydration based on image analysis provided in this application embodiment. It includes the following steps: S1, acquiring malt cross-sectional images using a high-resolution industrial camera and a ring-shaped shadowless light source system; evaluating the malt image quality based on the acquired image quality parameters; determining whether to optimize the malt cross-sectional image quality; the malt cross-sectional image is an image of the malt's cross-sectional shape, obtained by acquiring soaked malt samples using an automated device and cutting them uniformly along the longitudinal direction using a precision cutting device; S2, if no malt cross-sectional image quality optimization is performed, multi-dimensional feature extraction is performed on the malt cross-sectional image to obtain malt feature parameters; and the malt hydration level is determined based on these malt feature parameters. The evaluation process assesses whether hydration parameters need optimization. If malt cross-section image quality optimization is performed, multi-dimensional feature extraction is performed on the optimized malt cross-section image to obtain malt feature parameters. Malt hydration is evaluated based on the malt feature parameters in the optimized malt cross-section image, and the hydration process parameter optimization is determined based on the malt hydration evaluation results and the soaking index. Hydration process parameter optimization is used to improve the uniformity of water absorption by barley during the soaking process. S3, if hydration process parameter optimization is performed, malt hydration degree is detected based on the optimized hydration process parameters after the test report is output; otherwise, malt hydration degree is detected directly based on the current hydration process parameters after the test report is output.

[0093] In this embodiment, image quality assessment ensures that the acquired malt cross-section images have sufficient resolution and clarity to effectively display the internal structure of the malt, providing an accurate data source for subsequent feature extraction and hydration assessment. If the quality of the malt cross-section image is poor, optimization is performed to avoid misjudgments caused by poor image quality in subsequent analysis. Through multi-dimensional feature extraction, the hydration status of the malt can be analyzed in depth to understand key information such as water absorption uniformity and endosperm structure. By optimizing the quality of the malt cross-section image, the extracted features are ensured to be more accurate, and the results of the malt hydration assessment are more reliable. By optimizing the hydration process, the degree of hydration of the malt during soaking is ensured to be moderate, avoiding excessive or insufficient water absorption, thereby improving production quality. This ultimately improves the accuracy of malt hydration detection.

[0094] In summary, the embodiments of this application improve the quality of malt cross-section images by acquiring image quality parameters to evaluate malt image quality, then evaluate malt hydration based on the optimized malt feature parameters, and finally determine whether to optimize hydration process parameters based on the malt hydration evaluation results and malting index. This improves the quality of the acquired malt cross-section images and enhances the detection accuracy of malt hydration, effectively solving the problem of low detection accuracy of malt hydration caused by feature extraction errors in the prior art.

[0095] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0097] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0099] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0100] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A malt hydration level detection system based on image analysis, characterized in that, include: Malt image quality assessment module, malt hydration assessment module, and hydration process parameter feedback module; The malt image quality assessment module is used to assess the malt image quality based on the acquired image quality parameters of the malt cross-section image and determine whether to optimize the malt cross-section image quality. The malt hydration assessment module is used to perform multi-dimensional feature extraction on the malt cross-section image to obtain malt feature parameters if no malt cross-section image quality optimization is performed, to perform malt hydration assessment based on the malt feature parameters in the malt cross-section image, and to determine whether to optimize the hydration process parameters. If malt cross-section image quality optimization is performed, multi-dimensional feature extraction is performed on the optimized malt cross-section image to obtain malt feature parameters, to perform malt hydration assessment based on the malt feature parameters in the optimized malt cross-section image, and to determine whether to optimize the hydration process parameters based on the malt hydration assessment results and the malt soaking index. The optimization of the hydration process parameters is used to improve the uniformity of water absorption by malt during the soaking process. The hydration process parameter feedback module is used to perform malt hydration degree detection based on the optimized hydration process parameters after outputting the test report if hydration process parameters are optimized; otherwise, it will directly perform malt hydration degree detection based on the current hydration process parameters after outputting the test report. The specific process for determining whether to optimize hydration process parameters based on the results of malt hydration assessment and malt soaking index is as follows: Hydration levels are classified based on the soaking index and hydration level classification thresholds, wherein the hydration level classification thresholds include the maximum and minimum acceptable values ​​of the soaking index. When the soaking index is greater than the maximum acceptable value, the corresponding soaking index is classified as the first hydration level, and the first hydration process parameters are optimized. When the soaking index is not greater than the maximum acceptable value of the soaking index, but greater than the minimum acceptable value of the soaking index, the corresponding soaking index is classified as the second hydration level, and no hydration process parameter optimization is performed. When the soaking index is not greater than the minimum acceptable value of the soaking index, the corresponding soaking index is classified as the third hydration level, and the second hydration process parameters are optimized. The specific method for optimizing the first hydration process parameters is as follows: The average malt comprehensive hydration index, malt soaking index, and malt soaking time deviation are input into the malt soaking time mapping set to obtain the optimized maximum malt soaking time. The malt soaking time deviation is obtained by deviating between the current malt soaking time and the maximum malt soaking time in the second hydration level. The average malt comprehensive hydration index, malt soaking index, and malt soaking temperature deviation are input into the malt soaking temperature mapping set to obtain the optimized maximum malt soaking temperature. The malt soaking temperature deviation is obtained by performing a deviation processing between the current malt soaking temperature and the maximum malt soaking temperature in the second hydration level. The optimal soaking time is obtained by first pre-adjusting according to the preset ratio of the maximum optimal soaking time, and the optimal soaking temperature is obtained by first pre-adjusting according to the preset ratio of the maximum optimal soaking temperature, until the termination condition is met. Specific methods for optimizing the second hydration process parameters; The average malt comprehensive hydration index, soaking index, and soaking time deviation are input into the soaking time mapping set to obtain the optimized maximum soaking time. The soaking time deviation is obtained by deviating between the current soaking time and the maximum soaking time in the second hydration level. The average malt comprehensive hydration index represents the average hydration level of the current batch of malt. The average malt comprehensive hydration index, malt soaking index, and malt soaking temperature deviation are input into the malt soaking temperature mapping set to obtain the optimized maximum malt soaking temperature. The malt soaking temperature deviation is obtained by performing a deviation processing between the current malt soaking temperature and the maximum malt soaking temperature in the second hydration level. The optimal soaking time is obtained by a second pre-adjustment based on a preset ratio of the maximum optimal soaking time, and the optimal soaking temperature is obtained by a second pre-adjustment based on a preset ratio of the maximum optimal soaking temperature, until the termination condition is met.

2. The malt hydration level detection system based on image analysis as described in claim 1, characterized in that, The method for evaluating the malt image quality based on the acquired image quality parameters of the malt cross-section image is as follows: If the signal-to-noise ratio of a single malt cross-section image is less than the preset image signal-to-noise ratio obtained from the preset database, the image signal-to-noise ratio of the single malt cross-section image and the preset image signal-to-noise ratio obtained from the preset database are compared to obtain the image signal-to-noise ratio comparison coefficient; otherwise, the image signal-to-noise ratio comparison coefficient is directly recorded as 0. If the image resolution of a single malt cross-section image is less than the preset image resolution obtained from the preset database, the image resolution of the single malt cross-section image is compared with the preset image resolution obtained from the preset database to obtain the image resolution comparison coefficient; otherwise, the image resolution comparison coefficient is directly recorded as 0. The light source brightness contrast coefficient is obtained by performing relative deviation processing based on the light source brightness and the preset image resolution obtained from the preset database. A malt image quality assessment compensation value is introduced to perform a coupling process on the illumination uniformity coefficient, image signal-to-noise ratio contrast coefficient, image resolution contrast coefficient, and light source brightness contrast coefficient to obtain a malt image quality assessment index. The malt image quality assessment compensation value includes a first malt image quality assessment compensation value, a second malt image quality assessment compensation value, a third malt image quality assessment compensation value, and a fourth malt image quality assessment compensation value. The malt image quality assessment index is used to quantitatively assess the quality of malt cross-section images.

3. The malt hydration level detection system based on image analysis as described in claim 2, characterized in that, The specific process for determining whether to perform malt cross-section image quality optimization is as follows: A1. The average malt image quality assessment index is obtained by statistically analyzing the malt image quality assessment index of a single malt cross-section image. If the average malt image quality assessment index is less than the preset malt image quality threshold obtained from the preset database, then light source optimization is performed; otherwise, no malt cross-section image quality optimization is performed. The average malt image quality assessment index represents the average quality of the malt cross-section image. The light source optimization is used to reduce the impact of changes in lighting conditions on the quality of the malt cross-section image. A2. If the average malt image quality evaluation index after light source optimization is less than the preset malt image quality threshold obtained from the preset database, then image processing is performed; otherwise, the malt cross-section image quality optimization is terminated. The image processing is used to improve the malt cross-section image quality. The image processing involves median filtering and histogram equalization to process the malt cross-section image to remove noise and enhance contrast. A3. If the average malt image quality evaluation index after image processing is less than the preset malt image quality threshold obtained from the preset database, feedback will be provided; otherwise, the malt cross-section image quality optimization will end. The optimization of malt cross-section image quality includes light source optimization and image processing.

4. The malt hydration level detection system based on image analysis as described in claim 3, characterized in that, The specific method for optimizing the light source is as follows: The average malt image quality assessment index and the light source brightness deviation are input into the light source brightness mapping set to obtain the optimized light source brightness. The light source brightness deviation is obtained by deviation processing between the light source brightness and the preset light source brightness. The light source brightness mapping set is a set obtained from the preset database that represents the mapping relationship between the average malt image quality assessment index, the light source brightness deviation and the optimized light source brightness. After adjusting the brightness of the light source in the ring-shaped shadowless light source system to the optimized brightness, the malt cross-section image was re-acquired. The ring-shaped shadowless light source system is used to avoid the problem of poor stability of the acquired malt cross-section image caused by uneven illumination.

5. The malt hydration level detection system based on image analysis as described in claim 1, characterized in that, The method for assessing malt hydration based on malt feature parameters in malt cross-section images is as follows: A malt morphology evaluation compensation value is introduced to perform a value coupling process on the average endosperm region ratio, average cell swelling degree, and average cell wall rupture coefficient in a single malt cross-section image, resulting in a malt morphology feature evaluation index. The malt morphology evaluation compensation value includes a first malt morphology evaluation compensation value, a second malt morphology evaluation compensation value, and a third malt morphology evaluation compensation value. The malt morphology feature evaluation index is used to quantitatively evaluate the degree of hydration of malt in terms of morphological features. The malt contrast coefficient is obtained by performing an inverse proportional operation on the de-unitized average malt contrast in a single malt cross-section image. A malt color evaluation compensation value is introduced to perform a coupling process on the deunited average malt brightness, deunited average malt saturation, and malt contrast coefficient in a single malt cross-section image, resulting in a malt color feature evaluation index. The malt color evaluation compensation value includes a first malt color evaluation compensation value, a second malt color evaluation compensation value, and a third malt color evaluation compensation value. The malt color feature evaluation index is used to quantify and evaluate the degree of hydration of malt in terms of color features. A comprehensive hydration compensation value for malt is introduced to perform a value assignment coupling process on the malt morphological feature evaluation index and the malt color feature evaluation index in a single malt cross-section image, resulting in a comprehensive hydration index for malt. This comprehensive hydration index is used to comprehensively and quantitatively evaluate the degree of malt hydration.

6. The malt hydration level detection system based on image analysis as described in claim 5, characterized in that, The step of determining whether to optimize hydration process parameters based on the results of malt hydration assessment and malt soaking index also includes obtaining hydration level classification thresholds. The specific process for obtaining the hydration level classification threshold is as follows: B1. The initial hydration level of the wheat soaking index is classified according to the initial hydration level classification threshold, wherein the initial hydration level classification threshold includes the maximum qualified value and the minimum qualified value of the initial wheat soaking index. B2, based on the average comprehensive malt hydration index corresponding to the maximum qualified value of the initial soaking index in the historical time period in the preset database, determine whether to adjust the maximum qualified value of the initial soaking index. If the average malt comprehensive hydration index corresponding to the initial maximum malt hydration index is not within the preset malt hydration qualification range obtained from the preset database, then a first-level classification adjustment factor is introduced to correct the initial maximum malt hydration index to obtain the maximum malt hydration index. The first-level classification adjustment factor is obtained by processing the deviation between the average malt comprehensive hydration index corresponding to the initial maximum malt hydration index and the preset maximum malt hydration qualification range obtained from the preset database. Otherwise, the initial maximum malt hydration index is directly recorded as the maximum malt hydration index. The preset malt hydration qualification range is between the preset minimum malt hydration qualification value and the preset maximum malt hydration qualification value. B3. Based on the average comprehensive hydration index of malt corresponding to the minimum qualified value of the initial soaking index in the historical time period in the preset database, it is determined whether to adjust the minimum qualified value of the initial soaking index. If the average malt hydration index corresponding to the minimum acceptable initial malt hydration index is not within the preset acceptable malt hydration range obtained from the preset database, a second-level classification adjustment factor is introduced to correct the minimum acceptable initial malt hydration index to obtain the minimum acceptable malt hydration index. The second-level classification adjustment factor is obtained by processing the deviation between the average malt hydration index corresponding to the minimum acceptable initial malt hydration index and the preset minimum acceptable malt hydration index obtained from the preset database. Otherwise, the minimum acceptable initial malt hydration index is directly recorded as the minimum acceptable malt hydration index.

7. The malt hydration level detection system based on image analysis as described in claim 1, characterized in that, The process involves first pre-adjusting the wheat soaking time according to a preset ratio of the maximum optimal soaking time, and first pre-adjusting the wheat soaking temperature according to a preset ratio of the maximum optimal soaking temperature, until the termination condition is met, as follows: When the optimized soaking time is less than the optimized maximum soaking time and the optimized soaking temperature is less than the optimized maximum soaking temperature, if the hydration level of the corresponding batch of malt is at the second hydration level, the optimization of the first hydration process parameters shall be terminated; otherwise, the optimization of the first hydration process parameters shall continue. When the optimized soaking time is less than the optimized maximum soaking time and the optimized soaking temperature is not less than the optimized maximum soaking temperature, if the hydration level of the corresponding batch of malt is not at the second hydration level, the optimization of the soaking temperature will be terminated and the optimization of the soaking time will be maintained; otherwise, the optimization of the first hydration process parameters will be terminated. When the optimized soaking time is not less than the optimized maximum soaking time and the optimized soaking temperature is less than the optimized maximum soaking temperature, if the hydration level of the corresponding batch of malt is not at the second hydration level, feedback will be provided; otherwise, the optimization of the first hydration process parameters will be terminated. When the optimized soaking time is not less than the optimized maximum soaking time and the optimized soaking temperature is not less than the optimized maximum soaking temperature, if the hydration level of the corresponding batch of malt is not at the second hydration level, feedback will be provided; otherwise, the optimization of the first hydration process parameters will be terminated.

8. A method for detecting malt hydration level based on image analysis, used in the malt hydration level detection system based on image analysis as described in any one of claims 1-7, characterized in that, Includes the following steps: S1. Evaluate the quality of the malt image based on the acquired image quality parameters of the malt cross-section image, and determine whether to optimize the quality of the malt cross-section image. S2, if no malt cross-section image quality optimization is performed, multi-dimensional feature extraction is performed on the malt cross-section image to obtain malt feature parameters. Malt hydration is evaluated based on the malt feature parameters in the malt cross-section image, and it is determined whether to optimize the hydration process parameters. If malt cross-section image quality optimization is performed, multi-dimensional feature extraction is performed on the malt cross-section image after quality optimization to obtain malt feature parameters. Malt hydration is evaluated based on the malt feature parameters in the malt cross-section image after quality optimization, and it is determined whether to optimize the hydration process parameters based on the malt hydration evaluation results and the soaking index. The optimization of the hydration process parameters is used to improve the uniformity of water absorption by malt during the soaking process. S3. If the hydration process parameters are optimized, the degree of malt hydration will be tested based on the optimized hydration process parameters after the test report is output. Otherwise, the degree of malt hydration will be tested directly based on the current hydration process parameters after the test report is output.

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