Online Detection Method and System for Curving Uniformity

Abstract

The present invention relates to a method and system for online detection of material uniformity, which may include: data acquisition; data processing: uniformity evaluation of the acquired data information related to the material; and mixing scheme optimization. During data processing, a first uniformity evaluation index is calculated using data related to a first microscopic uniformity indicator extracted from a preprocessed image, or a second uniformity evaluation index is calculated using data related to a second microscopic uniformity indicator when a false alarm rate evaluation index related to the first microscopic uniformity indicator exceeds a preset threshold, thereby performing uniformity evaluation based on the first uniformity evaluation index and / or the second uniformity evaluation index, wherein the second microscopic uniformity indicator is a texture feature extracted from an area defined with the first microscopic uniformity indicator as the center. The system includes: mixing equipment, a monitoring module, and a central control module, and the central control module is capable of performing uniformity evaluation on images related to the material.

Description

Technical Field

[0001] The present invention relates to the technical field of automated liquor brewing, and in particular to a method and system for online detection of koji addition uniformity. Background Art

[0002] Daqu plays a crucial role in baijiu production. It contains not only a variety of microbial strains but also brewing enzymes such as amylase, protease, and esterase. These enzymes help break down starch, protein, and fat in the raw materials, thereby promoting the development of the wine's flavor. Adding koji, a key step in baijiu brewing, requires evenly adding koji powder to the mash after it has been spread out and cooled to ensure fermentation quality. However, traditional koji adding devices suffer from uneven addition and an inability to adaptively adjust the uniformity of addition, which directly affects the fermentation of the mash and the quality of the final wine.

[0003] CN117142063A discloses equipment for cooling and adding koji to white wine mash, comprising a conveyor, a feed hopper for adding koji to the mash on the conveyor belt, the front end of the feed hopper being the conveying entry direction of the mash; an adding component, the adding component being arranged in the feed hopper, the adding component adding koji to the mash on the conveyor belt at equal intervals; a measuring component, the measuring component being located at the front end of the feed hopper, the measuring component changing the amount of koji added by the adding component at the measuring point according to the thickness of the mash at the measuring point; by setting the adding component, when the conveyor belt transports the mash, the motor controls the transmission roller to add koji quantitatively to the mash on the surface of the conveyor belt, so that the koji is evenly added inside the mash; the measuring component measures the thickness of the mash on the surface of the conveyor belt, and controls the adding component to change the amount of koji added, so that the addition of koji is more even.

[0004] CN221117383U discloses a material mixing device for brewing wine, comprising a frame, a mixing and cooling cylinder and a koji adding mechanism. The mixing and cooling cylinder comprises an outer cylinder and an inner cylinder. The outer cylinder is arranged on the frame and can rotate radially on the frame. The inner cylinder is arranged in the outer cylinder and can rotate axially in the outer cylinder. A feeding port is provided at one end of the outer cylinder, and a feeding port is provided at the end of the inner cylinder near the feeding port. The koji adding mechanism is rotatably arranged on the frame and can be connected to or separated from the feeding port by rotation. A main fan is provided at the other end of the outer cylinder, and an air duct is formed between the outer cylinder and the inner cylinder. The air outlet of the main fan extends into the air duct. After the koji adding mechanism is connected to the feeding port, the gas transported by the main fan is discharged through the air duct and the koji adding mechanism in sequence.

[0005] Traditional koji addition processes often neglect real-time monitoring of uniformity, resulting in unevenness issues in the production process not being discovered and resolved promptly. This significantly prolongs mixing time and wastes resources. Furthermore, existing image acquisition equipment struggles to obtain high-quality images in complex industrial production environments, such as those subject to lighting changes and equipment vibration, impacting the accuracy of subsequent processing. This is particularly true when the mash in the mixing tank has high moisture content and a certain viscosity, posing a significant challenge to image acquisition equipment in obtaining high-quality images during the mixing process.

[0006] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the present invention provides a method and system for online detection of bending uniformity to solve at least part of the above technical problems.

[0008] The present invention discloses an online method for detecting the uniformity of added music, which may include the following steps:

[0009] Data collection: Use the collection unit to obtain data information related to materials;

[0010] Data processing: Conduct uniformity evaluation on the acquired material-related data information;

[0011] Mixing plan optimization: Optimize the mixing plan based on the uniformity evaluation results.

[0012] Preferably, when performing data processing, a first uniformity evaluation index is calculated using data related to the first microscopic uniformity indicator extracted from the preprocessed image, or a second uniformity evaluation index is calculated using data related to the second microscopic uniformity indicator when the false alarm rate evaluation index related to the first microscopic uniformity indicator exceeds a preset threshold, thereby performing uniformity evaluation based on the first uniformity evaluation index and / or the second uniformity evaluation index, wherein the second microscopic uniformity indicator is a texture feature extracted from an area defined with the first microscopic uniformity indicator as the center.

[0013] According to a preferred embodiment, in the grayscale image obtained after preprocessing the image acquired by the acquisition unit, the area of slight brightness change formed by the material structure and distribution characteristics based on the characteristics of the material surface reflected light in the mash accumulation area is extracted as the first microscopic uniformity indicator, wherein the data related to the first microscopic uniformity indicator includes: quantity, size, density, and distribution.

[0014] According to a preferred embodiment, a false alarm rate evaluation index calculated based on moisture content changes and / or composition differences is used to characterize the possibility of false alarm of the first uniformity evaluation index calculated using the first microscopic uniformity indicator, wherein when the false alarm rate evaluation index related to the first microscopic uniformity indicator does not reach a preset threshold, the first uniformity evaluation index can be directly used for uniformity evaluation.

[0015] According to a preferred embodiment, the preprocessed grayscale image is divided into multiple regions to extract the first microscopic uniformity indicator in each region, respectively. When the false alarm rate evaluation index related to the first microscopic uniformity indicator exceeds a preset threshold, a random number of first microscopic uniformity indicators are identified and marked in each region, and corresponding windows are divided with these first microscopic uniformity indicators as the center to extract the second microscopic uniformity indicator in the window.

[0016] According to a preferred embodiment, a local binary pattern is used to extract the second microscopic uniformity indicator, and the obtained LBP code is used to generate a corresponding histogram, and then the normalized histogram is used as the eigenvector of the second microscopic uniformity indicator to calculate the second uniformity evaluation index for judging the consistency of the eigenvalue, thereby realizing uniformity evaluation.

[0017] According to a preferred embodiment, when it is determined based on the uniformity evaluation result that there is at least one uneven area, the operating parameters of the mixing equipment are adjusted in real time until the mash and koji powder in the mixing tank are finally uniform. The optimal mixing path is calculated using the least squares method, so that the mixing equipment can be targetedly controlled in three dimensions, thereby obtaining the optimal mixing path.

[0018] According to a preferred embodiment, the acquisition unit is configured as an industrial camera to collect original images related to the material in the mixing tank, where the material refers to a mixture of mash and koji powder. The mixing tank adopts a flipping device in conjunction with stirring teeth provided in the tank to mix the mash and koji powder in the tank. Under the action of the flipping device, the mixing tank rotates in a forward and reverse direction in sequence, so that the acquisition unit can collect corresponding images each time the mixing tank flips back to the upright position.

[0019] The acquisition unit is configured as an industrial camera, specifically designed to capture raw images of the mixture of mash and koji powder in the mixing tank. The high-definition and high-frame-rate image data it provides can be used for subsequent image processing and uniformity assessment. Because the camera can adapt to various ambient light conditions, it can obtain high-quality images even in low or high light conditions. The mixing tank's flipping and stirring mechanism further ensures that the materials are fully mixed. The industrial camera captures images each time the mixing tank flips back to the upright position, providing accurate time points for uniformity assessment, thereby ensuring the accuracy and reliability of the assessment results.

[0020] According to a preferred embodiment, the collection unit can be arranged in an isolated area on the inner side of the top cover of the mixing tank, which is deviated from the center line of the mixing tank, so that when the mixing tank rotates, the collection unit located in the isolated area can collect the original image of the material in a way that the shooting area is not blocked by the material.

[0021] The acquisition unit is located in an isolated area off-center on the inside of the mixing tank's lid. This innovative layout cleverly leverages the tank's structural features to create a zone where material cannot enter. This ensures that the industrial camera's field of view is unobstructed by material, ensuring that the camera consistently captures clear and accurate raw images. This clarity is crucial for subsequent uniformity assessment and improves data processing accuracy. This design not only prevents material interference with image acquisition but also protects the camera from contamination and damage, improving system stability and ease of maintenance.

[0022] According to a preferred embodiment, the preprocessing operations include grayscale, filtering denoising and / or edge detection to improve image quality, wherein the grayscale processing adopts a weighted averaging method to comprehensively consider the contributions of different color channels; the filtering denoising processing uses a Gaussian filter to reduce the noise in the image; and the edge detection processing uses the Canny algorithm to identify the edges of objects in the image by finding areas with drastic brightness changes in the image.

[0023] Preprocessing operations include grayscale conversion, filtering and denoising, and edge detection. Together, they improve image quality, making subsequent feature extraction and uniformity assessment more accurate. Grayscale conversion optimizes image brightness information through weighted averaging, filtering and denoising reduces image noise, and edge detection accurately identifies the outlines of objects in the image. The combination of these steps provides clear and accurate image data for uniformity assessment.

[0024] According to a preferred embodiment, threshold segmentation, region growing or clustering algorithm is used to segment the image, thereby dividing the image related to the material into several meaningful regions so as to independently analyze the characteristics of each region, wherein the clustering algorithm includes K-means clustering algorithm.

[0025] Image segmentation is done using threshold segmentation, region growing, or clustering algorithms to effectively divide the image into multiple representative regions, providing structured image data for subsequent feature extraction and uniformity assessment. The K-means clustering algorithm, in particular, calculates the similarity between pixels and divides the image into several clusters. The pixels within each cluster have a high degree of similarity, making uniformity assessment more accurate.

[0026] According to a preferred embodiment, after the image is segmented into several regions, corresponding features are extracted for each region to evaluate the uniformity of the added curve, wherein the extracted features include one or more of the following features:

[0027] Color features: by calculating the color distribution of each area and expressing it in the form of a color histogram;

[0028] Texture features: extracted by using gray-level co-occurrence matrix or local binary pattern;

[0029] Shape features: obtained by calculating parameters such as the bounding rectangle and circularity of each area.

[0030] After the image is segmented into several regions, one or more features, such as color, texture, and shape, are extracted from each region, providing multi-dimensional feature information for uniformity assessment. Color features help identify the distribution and proportions of different components, texture features describe the spatial arrangement and grayscale variations of the material, and shape features describe the appearance and geometry of the object. The combined analysis of these features enables a more comprehensive and in-depth assessment of the uniformity of the mash and koji.

[0031] According to a preferred embodiment, a statistical method or a machine learning model is used to evaluate the uniformity of features, wherein, when using a statistical method, a corresponding standard deviation and / or variance is set to judge the uniformity of the feature value distribution by judging the size of the standard deviation or variance of a certain feature; when using a machine learning model, a support vector machine or a random forest model is used to predict the features of a new image by learning the features of known uniform and non-uniform samples, thereby evaluating its uniformity.

[0032] Using statistical methods or machine learning models to assess feature uniformity enables precise evaluation of material uniformity through quantitative analysis or pattern recognition. Statistical methods quantitatively describe the distribution of feature values by calculating standard deviation and variance, while machine learning models learn from the characteristics of known samples to predict the features of new images and assess their uniformity. This approach not only improves assessment accuracy but also enhances intelligence.

[0033] According to a preferred embodiment, when it is determined based on the uniformity evaluation result that there is at least one uneven area, the operating parameters of the mixing equipment are adjusted in real time until the mash and koji powder in the mixing tank are finally uniform. The optimal mixing path is calculated using the least squares method, so that the mixing equipment can be targetedly controlled in three dimensions, thereby obtaining the optimal mixing path.

[0034] Based on the uniformity assessment results, the operating parameters of the mixing equipment are adjusted in real time until uniformity is achieved, achieving real-time optimization and control of the mixing process. By calculating the optimal mixing path using the least squares method, targeted control is achieved in three dimensions to achieve the best mixing results. This approach not only improves production efficiency but also ensures consistent product quality.

[0035] The present invention also discloses an online detection system for the uniformity of koji addition, which is characterized in that it includes: a mixing device for accommodating and mixing materials; a monitoring module for acquiring data information related to the koji addition process; and a central control module for processing the data information acquired by the monitoring module and generating a control signal for regulating the mixing device.

[0036] Preferably, the mixing equipment includes a mixing tank, and the monitoring module includes a collection unit arranged on the top cover of the mixing tank to obtain images of the material in the mixing tank, so that the central control module can perform uniformity evaluation on the images related to the material obtained by the collection unit.

[0037] The online koji uniformity monitoring system integrates the mixing equipment, monitoring module, and central control module, providing a new approach to comprehensively monitor and control the koji addition process. The mixing equipment provides the physical foundation for effective material mixing, while the monitoring module, located on the top cover of the mixing tank, captures real-time image data of the material, ensuring the timeliness and accuracy of the monitoring data. This data acquisition method eliminates the need for manual intervention in the monitoring process, reducing the potential for human error and providing a reliable foundation for subsequent data analysis. The central control module processes and analyzes the captured image data, not only enabling real-time assessment of material uniformity but also generating corresponding control signals to directly control the mixing equipment. This closed-loop control mechanism enables the system to quickly implement adjustments when non-uniformity is detected, ensuring stable and consistent production processes and effectively avoiding delays and resource waste caused by uneven mixing. This dynamic control capability, often difficult to achieve in traditional production systems, highlights the advanced nature of this system. The system's integrated design enhances overall monitoring capabilities of the production process, allowing operators to monitor production dynamics in real time and respond quickly to emergencies, thereby improving production efficiency and safety. By combining material image processing with control signal generation, the system also provides a rich foundation for the accumulation and subsequent analysis of production data, enabling the optimization and improvement of production processes. This data-driven decision-making mechanism not only enhances the level of intelligent production but also provides new directions for future production management and technological innovation.

[0038] According to a preferred embodiment, the collection unit can be arranged in an isolation area on the inner side of the top cover of the mixing tank, which is deviated from the center line of the mixing tank, wherein a transparent protective cover is provided outside the collection unit for covering the isolation area, so as to block the collection unit from the space inside the mixing tank while ensuring light transmittance.

[0039] The acquisition unit is located inside the mixing tank's top cover, off-center, in an isolated area and equipped with a transparent protective cover. This cover not only ensures image quality but also significantly protects the acquisition unit (e.g., an industrial camera) from material contamination and damage. Industrial cameras are inevitably exposed to dust, liquids, and other substances during the normal mixing process. Placing them in an isolated area effectively extends the equipment's lifespan and reduces maintenance frequency and costs. This protective mechanism ensures long-term stable operation of the system while also making maintenance more convenient for operators. It eliminates the need for complex protective devices or cleaning mechanisms for the acquisition unit, reducing system complexity. Compared to traditional methods, this simplified design helps reduce overall system cost and improve reliability. The enhanced stability of the acquisition unit enables continuous, real-time monitoring, enabling more frequent and reliable data collection during the mixing process. Enhanced real-time monitoring allows for earlier identification and resolution of unevenness issues during production, thereby improving overall production efficiency and product quality. This closed-loop feedback mechanism not only enhances the intelligence of the production process but also promotes optimized production management. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flowchart of the steps of a method for online detection of added-curve uniformity according to a preferred embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of the installation of a mixing tank according to a preferred embodiment of the present invention;

[0042] Figure 3 This is a schematic diagram of the installation of a collection unit according to a preferred embodiment of the present invention;

[0043] Figure 4 is an original image captured by a capture unit in a preferred embodiment of the present invention;

[0044] Figure 5 is an image after data processing according to a preferred embodiment of the present invention;

[0045] Figure 6 This is a processing diagram showing an uneven area according to a preferred embodiment of the present invention;

[0046] Figure 7 is another preferred embodiment provided by the present invention, showing a processing diagram with an uneven area;

[0047] Figure 8 This is a hardware connection diagram of an online detection and curvature uniformity system according to a preferred embodiment of the present invention.

[0048] Reference Signs List

[0049] 100: Mixing equipment; 110: Mixing tank; 111: Top cover; 112: Isolation area; 113: Protective cover; 120: Turning device; 200: Monitoring module; 210: Collection unit; 300: Central control module. DETAILED DESCRIPTION

[0050] The following is a detailed description with reference to the accompanying drawings.

[0051] Example 1

[0052] like Figure 1 As shown, the present invention discloses an online method for detecting the uniformity of added curves, which includes one or more of the following steps:

[0053] S1. Data collection: using the collection unit 210 to obtain data information related to the material;

[0054] S2. Data processing: uniformity evaluation of the acquired data information related to the material;

[0055] S3. Mixing scheme optimization: Optimize the mixing scheme based on the uniformity evaluation results.

[0056] Preferably, in step S1, the acquisition unit 210 may be configured as an industrial camera to collect original images related to the material in the mixing tank 110, wherein the industrial camera generally has high resolution, high frame rate and good anti-interference ability. Preferably, in the present invention, the material may refer to a mixture of fermented grains and koji powder. Preferably, when configuring the industrial camera, selection may be made according to the production environment and requirements. For example, a camera with more than 2 million pixels may be selected to ensure image clarity; if it is necessary to collect images during the material mixing process, a suitable shutter speed may be selected according to the material mixing speed to avoid motion blur; and LED lights may be configured for lighting to ensure that suitable image quality can be obtained under various ambient light conditions.

[0057] Preferably, if Figure 2 As shown, the fermented mash and koji powder can be stirred and mixed within the mixing tank 110. The mixing tank 110 can utilize a tilting device 120 in conjunction with stirring teeth (not shown) provided within the tank to thoroughly mix the fermented mash and koji powder within the tank. Furthermore, the tilting device 120 can be equipped with a tilting motor to provide corresponding tilting power according to the matching gear position. This power is then transmitted to the tilting mechanism via a speed reducer, thereby driving the mixing tank 110 to rotate. Preferably, as the mixing tank 110 rotates, the fermented mash and koji powder within the tank are further mixed due to the loosening action of the stirring teeth.

[0058] Preferably, the acquisition unit 210 configured as an industrial camera can be installed in a key location within the mixing tank 110 so that its camera area fully covers the material, thereby capturing the status of each area. However, unlike conventional installation locations, the present invention also incorporates a special design for the industrial camera. Specifically, the industrial camera can be installed inside the top cover 111 of the mixing tank 110, close to but slightly offset from the centerline of the mixing tank 110. Furthermore, the direction of this offset is related to the structure of the mixing tank 110 and its internal stirring teeth. Due to the different structures of the mixing tank 110 and its internal stirring teeth, the top cover 111 of the mixing tank 110 has an isolated area 112 that prevents the fermented grains and koji powder from entering during mixing. Placing the industrial camera within this isolated area 112 minimizes obstruction of the camera's camera area by the fermented grains and koji powder. Furthermore, due to the camera's top-down viewing angle, it fully covers the material within the tank, enabling more accurate data analysis and processing. Furthermore, the mixing tank 110 can rotate in a forward and reverse direction. During the rotation of the mixing tank 110, since the stirring teeth provided therein have a certain rotation direction, the isolation area 112 can appear near the center line of the mixing tank 110 but slightly to the left or right. Figure 3 The figure shows an industrial camera installed in an isolated area 112 on the left side of the top cover 111 of the mixing tank 110. Preferably, the industrial camera can capture corresponding images each time the mixing tank 110 is flipped back to the upright position. Flipping the mixing tank 110 back to the upright position refers to flipping the mixing tank 110 to a position where its top cover 111 is located on top of the tank body.

[0059] Preferably, isolation area 112 can be determined based on field experiments and / or model simulations. Preferably, a transparent crystal protective cover 113 can be added to the industrial camera located within isolation area 112. The refractive index properties of this protective cover 113 can affect light propagation, thereby improving the quality and clarity of images captured by the industrial camera. This transparent protective cover 113 not only provides physical protection from damage and contamination, but also optimizes optical performance, ensuring more accurate image capture.

[0060] Preferably, if Figure 1 As shown, in step S2, one or more of the following sub-steps may be included:

[0061] S2.1. Preprocessing: The acquired images are preprocessed to improve image quality and highlight specific features;

[0062] S2.2, Region Segmentation: The image is divided into multiple regions, each region represents a part of the material, and real-time online detection calculation is performed with any white point in a segmented region as the center;

[0063] S2.3, Feature extraction: Extract features from each region;

[0064] S2.4. Uniformity assessment: Evaluate the uniformity of the mash and koji by comparing the characteristics of different areas.

[0065] Preferably, in step S2.1, preprocessing is performed on the acquired image to improve image quality for subsequent analysis, wherein the preprocessing operation may include grayscale conversion, filtering and denoising, and / or edge detection.

[0066] Preferably, grayscale processing refers to converting a color image into a grayscale image, allowing most image processing algorithms to perform better when processing single-channel images. This operation can reduce computational complexity while preserving important information in the image. Preferably, when processing images related to materials, grayscale processing helps to highlight brightness changes, facilitating subsequent edge detection and region segmentation. For example, functions in image processing libraries such as OpenCV can be used to implement grayscale processing. Preferably, the grayscale processing can use the following function:

[0067] I gray (x,y)=0.299·I R (x,y)+0.587·I G (x,y)+0.114·I B (x,y)

[0068] Among them, I gary (x, y) is the pixel value of the grayscale image at position (x, y). The grayscale image is a single-channel image. The value of each pixel represents its brightness, usually ranging from 0 (black) to 255 (white); I R (x,y),I G (x,y),I B (x, y) represents the pixel values of the red, green and blue channels at position (x, y), respectively. Preferably, 0.299, 0.587 and 0.114 in the above formula are the weights of the red channel, green channel and blue channel, respectively, representing the human eye's perception of red, green and blue. These coefficients are used to adjust the contribution of each color channel to the final grayscale value. Since the human eye has different sensitivities to different colors, these coefficients are selected to better reflect the visual perception of the human eye. Among them, the human eye has the strongest perception of green and relatively weak perception of blue. By taking a weighted average, the contributions of different color channels are comprehensively considered, so that the generated grayscale image can better reflect the actual perception of the human eye. For example, due to the strong perception of green light, the weight of green is set to the highest.

[0069] Preferably, the filtering and denoising process can use Gaussian filtering to remove noise in the image, wherein Gaussian filtering is a linear smoothing filter that reduces the noise in the image by calculating the weighted average of the pixel neighborhood. The Gaussian function has a bell-shaped curve shape in the spatial domain, which can effectively remove high-frequency noise while maintaining the edge features of the image. Preferably, when processing images related to materials, the noise may come from environmental factors (such as light changes) or limitations of the image sensor, and the filtering and denoising process can improve the accuracy of subsequent analysis, wherein the grayscale image is filtered to generate a smooth image for use in subsequent steps. Preferably, the present invention can use a two-dimensional Gaussian filter for smoothing and denoising, wherein the formula used is as follows:

[0070]

[0071] Among them, I filtered (x, y) is the pixel value of the output image at position (x, y), the result of Gaussian filtering; I(x+m, y+n) is the pixel value of the input image at position (x+m, y+n), and the variables m and n represent the offset of the filter in the image; σ 2 is the variance of the Gaussian function, which controls the width of the Gaussian distribution.

[0072] Preferably, the edge detection process can use the Canny algorithm to identify the edges of objects in the image by finding areas with drastic brightness changes in the image. Preferably, the Sobel operator or other gradient operators (such as Prewitt or Laplacian) can be used to calculate the gradient magnitude G and direction θ of the image, where the calculation formula is as follows:

[0073]

[0074] Among them, G x and G y are the gradients of the image in the horizontal and vertical directions, respectively.

[0075] Preferably, the specific configuration of the Sobel operator can select a 3x3 convolution kernel. Preferably, by setting two thresholds (a high threshold and a low threshold), pixels are divided into three categories: strong edges with a gradient amplitude greater than the high threshold; weak edges with a gradient amplitude between the low threshold and the high threshold; and non-edges with a gradient amplitude less than the low threshold. Furthermore, if a pixel is marked as a strong edge, it is retained unconditionally; if it is a weak edge, it is retained only if there is a strong edge in its adjacent pixels. In the edge connection stage, strong edge pixels are used to connect weak edges to form a complete edge. A connection algorithm (such as an eight-connected domain or a four-connected domain) is used to detect weak edges and share the adjacency relationship of strong edges.

[0076] Preferably, in step S2.2, threshold segmentation, region growing or clustering algorithm (such as K-means) can be used to segment the image, thereby dividing the image into several meaningful regions so as to independently analyze the characteristics of each region.

[0077] Preferably, the threshold segmentation method divides the image into different regions by setting one or more thresholds, wherein the portion with pixel values above the threshold is divided into one region, and the portion below the threshold is divided into another region, thereby quickly identifying the region of interest. For each pixel I(x,y), if its grayscale value is greater than a set threshold T, the pixel is marked as foreground (or target area), otherwise it is marked as background, resulting in the following formula:

[0078]

[0079] Where R(x,y) is the segmented image, 1 represents the foreground, and 0 represents the background. Furthermore, adaptive thresholding or global thresholding can be used to select an appropriate threshold.

[0080] Preferably, the region growing method is a segmentation method based on pixel similarity, which mainly selects seed points and gradually expands the region based on similarity. Starting from one or more seed points, the pixels in its neighborhood are checked to see if they meet certain similarity conditions (such as color, grayscale, etc.). If they do, they are included in the current region.

[0081] Preferably, the K-means clustering algorithm is an unsupervised learning algorithm that groups data points into K clusters with high similarity within the clusters. The algorithm iteratively calculates the distance between each point and the cluster center and updates the cluster center until convergence. The K-means clustering algorithm treats image pixels as points in a high-dimensional space and iteratively optimizes the cluster center to reduce the distance from each point to the center of the cluster to which it belongs. In material image segmentation, the K-means clustering algorithm can divide the image into multiple regions, ensuring that each region represents a certain part of the material. Preferably, the objective function of the K-means clustering algorithm is:

[0082]

[0083] Among them, S i is the i-th cluster, μ i is the center of the cluster, ∥x-μ i ∥ is the distance from pixel x to cluster center μ iEuclidean distance. Preferably, when using the K-means clustering algorithm for region segmentation, K initial cluster centers can be randomly selected first. These centers can be randomly selected pixel values in the image, representing different color or brightness features; then each pixel is assigned to the cluster center closest to it to form a preliminary region division; after completing the pixel assignment, the algorithm will recalculate the center of each cluster by calculating the average value of all pixels in each cluster and using this new average value as the new cluster center; finally, the above pixel assignment and cluster center update steps are repeated until the cluster center no longer changes significantly or the preset number of iterations is reached to ensure the stability of each cluster, thereby forming several meaningful clusters that can represent different areas of the material.

[0084] Preferably, in step S2.3, features are extracted from each region for use in evaluating the uniformity of the added curve, for example, color features: by calculating the color distribution of each region and representing it in the form of a color histogram; texture features: extracted using a gray-level co-occurrence matrix (GLCM) or a local binary pattern (LBP); shape features: calculating the bounding rectangle, circularity, etc. of each region.

[0085] Preferably, a color histogram can convert the color information in an image into digital features by calculating the frequency of each color appearing in the image. In the analysis of fermented mash and koji, a color histogram can help identify the distribution and proportions of different components. Furthermore, the calculation of color distribution may include: for each area, traversing each pixel therein to obtain its color value; mapping the color value to the corresponding bin (interval) of the histogram. For example, in the RGB color space, the value of each color channel (red, green, and blue) can be divided into several intervals, and then the number of pixels in each interval is calculated.

[0086] Preferably, texture features are used to describe the spatial arrangement and grayscale changes of pixels in an image, and are usually used to identify materials, patterns or structures in an image. In the analysis of mash and koji, texture features can identify the uniformity and separation of materials and help determine the mixing effect.

[0087] Preferably, shape features are used to describe the appearance and geometry of an object, which can help identify the shape features of a material and determine its uniformity and distribution.

[0088] Preferably, in step 2.4, a statistical method or a machine learning model may be used to evaluate the uniformity of the features.

[0089] Preferably, the following standard deviation can be set in the statistical method, which is a statistic used to describe the degree to which the data deviates from the mean and reflects the degree of dispersion of the data:

[0090]

[0091] Where fi is the i-th eigenvalue, μ is the mean of the eigenvalues, and N is the number of eigenvalues.

[0092] Preferably, a variance Var(f) may also be provided in the statistical method, which is the square of the standard deviation and is used to measure the degree of dispersion of the data.

[0093] Preferably, if the standard deviation or variance of a certain feature is small, it indicates that the feature value distribution in the region is relatively uniform; conversely, a large standard deviation or variance indicates that there is a large change in the feature value distribution, indicating that the region is uneven.

[0094] Preferably, a machine learning model can be used to evaluate uniformity in a more complex way, by learning the features of known uniform and non-uniform samples to make predictions, wherein the following models can be used: Support Vector Machine (SVM): suitable for classification of high-dimensional spatial data; Random Forest: classification by constructing multiple decision trees, suitable for processing complex features. Preferably, labeled training data (known uniform and non-uniform samples) can be used to train the model so that the uniformity of the new image can be evaluated after the features of the new image are input into the trained model. Preferably, a machine learning framework such as Scikit-learn can be used to build and train the model, and parameters can be adjusted according to the characteristics of the data set and the requirements of the model, such as: the kernel function type of SVM, the number of trees of random forest, etc.

[0095] In one embodiment, the original image captured by the industrial camera is as follows: Figure 4 As shown, the method of step S2 is performed on the original image obtained by the industrial camera to evaluate its uniformity. Figure 5 As shown in the figure, the original image can first undergo preprocessing operations such as grayscale conversion, filtering and denoising, and edge detection. Grayscale conversion can convert a color image into a grayscale image to reduce computational complexity while retaining important structural information. Filtering and denoising can remove fog and random noise points in the original image, thereby improving the overall quality of the image. Edge detection can effectively identify important features and structures in the image, such as the outline, boundary, and corner points of an object. The image is then divided into 64 matrices, and the size of each matrix is reasonably divided according to the overall size of the image to facilitate local uniformity analysis, so that each area can be evaluated independently. Then, in each matrix, random white points are identified and marked as the center of uniformity calculation, and uniformity calculation is performed on the areas centered on these white points to evaluate the uniformity of the area. The calculation results include uniformity values, which will reflect the consistency of the area in terms of texture, brightness, color distribution, etc. Finally, the calculated uniformity values will be returned to the corresponding processed image for visual comparison.

[0096] Preferably, the white dots in the grayscale image used as markers for determining uniformity can be defined as "first microscopic uniformity indicators" in the present invention. The first microscopic uniformity indicator refers to a region of slight brightness variation within the mash accumulation area, resulting from the material structure and distribution characteristics based on the properties of light reflected from the material surface. Due to the different refractive indices and scattering properties of different components in the material, the formation of this region indicates a change in local optical properties, reflecting the uniformity of material distribution at the microscopic level and revealing the interaction forces between particles and material migration characteristics. Furthermore, in addition to visual physical uniformity, the first microscopic uniformity indicator also represents the distribution of different components in the material, particularly different components in the mash (such as sugars, yeast, and acidic substances), characterizing local chemically reactive regions and thus reflecting the chemical uniformity of the material at the microscopic level. The presence and distribution of the first microscopic uniformity indicator can affect the reaction rate during fermentation and the taste level of the final product. For example, Table 1 shows experimental data related to the first microscopic uniformity indicator measured in a preferred embodiment.

[0097] Table 1 Experimental data

[0098]

[0099] Preferably, the appearance of the first microscopic uniformity indicator indicates that the distribution of the material is uneven or there is some feature that is different from the surrounding situation, which reflects the uniformity of the material mixing in the image, wherein the data related to the first microscopic uniformity indicator can be used to realize the evaluation of the uniformity of the material mixing. Preferably, the data related to the first microscopic uniformity indicator may include: quantity, size (i.e., area or diameter), density, and distribution, wherein the more the number of first microscopic uniformity indicators, the more uneven the material distribution; the larger the size of the first microscopic uniformity indicator means the aggregation of the material, and the smaller the size means that the material is in a relatively uniform dispersion state. If the size difference is large, it may also indicate that the material distribution is more uneven; the more the number of first microscopic uniformity indicators in a unit area and the higher the density, the more uneven the distribution of the material in the local area. Preferably, the first uniformity evaluation index U1 based on the first microscopic uniformity indicator can be calculated using the following formula:

[0100]

[0101] Where N w is the number of the first microscopic uniformity indicator, that is, the total number of white spots detected, N t is the total number of pixels, S avg is the average size (area) of the first microscopic uniformity indicator, S maxis the size of the largest first microscopic uniformity indicator (can be set according to image characteristics), D w is the density of the first microscopic uniformity indicator, C d is the distribution coefficient of the first microscopic uniformity indicator, w1, w2, w3, and w4 are the weight coefficients of each item, reflecting the importance of each parameter in the final evaluation.

[0102] Preferably, in the calculation formula of the first uniformity evaluation index U1, the first term is the ratio of the number of first microscopic uniformity indicators to the total number of pixels, which indicates the overall level of unevenness in the image, wherein the higher the ratio, the more uneven the mixing; the second term is the ratio of the average size to the maximum size of the first microscopic uniformity indicator, which reflects the influence of the size of the first microscopic uniformity indicator on the uniformity, wherein the closer the value is to 1, the smaller the white spots are and the more uniform the mixing is; the third term is the density of the first microscopic uniformity indicator, which is the concentration of white spots in the local area, and the higher the density, the greater the unevenness; the fourth term is the white spot distribution coefficient, and the closer it is to 1, the higher the uniformity.

[0103] Further preferably, the distribution coefficient of the first microscopic uniformity indicator can be calculated using the following formula:

[0104]

[0105] Where D is the non-uniformity measure of the distribution of the first microscopic uniformity indicator, such as the standard deviation, which reflects the distribution difference of the first microscopic uniformity indicator in different regions. max is the maximum possible value of the uneven distribution.

[0106] Preferably, if the first uniformity evaluation index U1 calculated by the first microscopic uniformity indicator is used to evaluate the uniformity of material mixing, there is a possibility of false alarms in some cases. These situations may include: (1) Moisture content change: The moisture content of the wet mash may cause changes in the grayscale value. Some areas show brighter white spots due to uneven moisture distribution, which are mistakenly judged as unevenness; (2) Component differences: Different components in the mash (such as fiber, starch, protein) have different light reflection characteristics, which may produce obvious brightness differences in the image. Therefore, the present invention can characterize the possibility of false alarms by the false alarm rate evaluation index B, wherein the false alarm rate evaluation index B can be calculated using the following formula:

[0107]

[0108] Where M max is the maximum value of moisture content, M min is the minimum value of moisture content, M avg is the average moisture content, C dis the component difference coefficient, which calculates the difference between the light reflection coefficients of different components (such as fiber, starch, protein), C max is the maximum possible value of the composition difference, which can be obtained based on experimental data or literature values, and w5 and w6 are weight coefficients.

[0109] Preferably, when the calculated false alarm rate evaluation index B does not reach the preset threshold, the first uniformity evaluation index U1 calculated based on the first microscopic uniformity indicator can be used to quickly evaluate the uniformity of the material mixture, so as to save computing power and improve evaluation efficiency; when the calculated false alarm rate evaluation index B exceeds the preset threshold, it can be further evaluated based on other microscopic uniformity indicators to ensure the accuracy of the evaluation results. Preferably, when computing power is sufficient or the accuracy of the evaluation results is required to be high, multiple microscopic uniformity indicators can also be used for comprehensive evaluation. When multiple microscopic uniformity indicators are used for comprehensive evaluation, the importance of the evaluation indicators calculated by each microscopic uniformity indicator can be flexibly adjusted according to the relationship between the false alarm factors of different microscopic uniformity indicators and existing conditions.

[0110] Preferably, when the calculated false alarm rate evaluation indicator B exceeds a preset threshold, the first microscopic uniformity indicator can be used to further obtain a second microscopic uniformity indicator, where the second microscopic uniformity indicator is texture features extracted from a region centered around the first microscopic uniformity indicator. Grains contain multiple components (such as residual starch, protein, cellulose, etc.), which exhibit different texture features in images. The heterogeneity of grains makes texture-based uniformity analysis more effective. The size, shape, and distribution of particles in the grains directly affect their texture features. The presence of particles results in different local structures in the image, reflecting the uniformity or heterogeneity of the material. The moisture content of the grains also affects their surface texture. Uneven moisture distribution can result in different brightness and texture features in the image. Texture features are used as the second microscopic uniformity indicator because they can effectively reflect the local structure and distribution of different components in the grains, thereby enabling more clear identification of component uniformity. In addition, texture features can extract a lot of information, such as contrast, directionality, roughness, etc., which can comprehensively evaluate the uniformity of the mash, while other features cannot provide such rich information. Especially compared with color features (converted into brightness information in grayscale images) and shape features (which have high requirements on the shape of particles), texture features are more robust to noise and small local changes, and can provide a more stable uniformity assessment.

[0111] Preferably, the area defined by the first microscopic uniformity indicator as the center can be set according to specific needs and can be set as a fixed-size window. For example, a 3×3, 5×5, or 7×7 square window can be selected. The boundary of the set window can be expanded around the first microscopic uniformity indicator to ensure that all calculations are within the window. Preferably, in the above-set window, the local binary pattern (LBP) can be used to extract the second microscopic uniformity indicator. The reasons are: LBP focuses more on capturing local structures and patterns in the image, can effectively reflect the local characteristics of the texture, and performs well in processing areas with large brightness changes; LBP has good robustness to illumination changes, and its calculation is based on relative changes (i.e., comparison of adjacent pixels), which can better capture the local characteristics of the object without being affected by illumination; LBP calculation is relatively simple and fast, suitable for real-time processing and large-scale image analysis, and the feature vector it generates is relatively small and easy to store and process. Therefore, when processing images of materials such as fermented mash, especially when the texture of the fermented mash in the image shows strong local structural changes (such as granularity, block structure, etc.), LBP will be a more appropriate choice because it can effectively capture these local features. For example, for a 3×3 neighborhood window, the grayscale value of the center pixel of the window is compared with the grayscale value of the surrounding 8 pixels. If the grayscale value of the surrounding pixels is greater than that of the center pixel, the corresponding position is marked as 1, otherwise it is marked as 0. The resulting binary number is then converted to a decimal number to form an LBP code, and then a corresponding histogram is generated for the formed LBP code, indicating the frequency of different LBP values.

[0112] Preferably, the normalized histogram is regarded as a feature vector of the second microscopic uniformity indicator, and the feature vector has the same size as the histogram, wherein the feature vector can be expressed as:

[0113] F=[f1,f2,…,f n ]

[0114] Where, f i is a second microscopic uniformity indicator for each extraction.

[0115] Furthermore, the eigenvector of the second microscopic uniformity indicator can be used to calculate a second uniformity evaluation index U2 for judging the consistency of the eigenvalue, thereby achieving uniformity evaluation. The calculation formula of the second uniformity evaluation index U2 is as follows:

[0116]

[0117] Wherein, μ is the average value of the characteristic value. Preferably, the closer the value of the second uniformity evaluation index U2 is to 1, the higher the uniformity.

[0118] Preferably, the calculated second uniformity evaluation index U2 is mapped into the image as a uniformity value for visual comparison, wherein a heat map or other visualization techniques may be used to more intuitively observe the uniformity differences in different regions.

[0119] For example, Figure 6 As shown, for areas with low uniformity of features (for example, the brightness of the middle area of the material is lower than that of the evenly mixed material), they can be identified as uneven areas and displayed on the processing diagram, so that users can intuitively check whether the uniformity is qualified, and the uneven results will be recorded to facilitate subsequent optimization of the mixing plan. Preferably, in the present invention, when collecting original images, the industrial camera can collect images at two consecutive adjacent moments to facilitate correction of the evaluation results, such as Figure 6 and Figure 7 Two processing diagrams are shown.

[0120] Preferably, the uneven area can be determined based on the uniformity evaluation result obtained in step S2, wherein when there is at least one uneven area, step S3 can be started to optimize the mixing scheme according to the distribution of the uneven area until there is no uneven area.

[0121] Preferably, during the mixing process, the uniformity evaluation results can be monitored in real time, and the mixing scheme can be adjusted in real time according to the evaluation value, such as adjusting the mixing time, speed or other parameters to ensure the uniformity of the final result. In step S3, the main goal of the mixing scheme optimization is to eliminate the unevenness in the material, and by adjusting the operating parameters of the mixing equipment 100, the mash and koji powder in the mixing tank 110 are finally uniform. Among them, the optimization goals may include: improving the overall uniformity of the material: ensuring that all areas meet the set uniformity standards; reducing the mixing time: optimizing the mixing process to improve production efficiency while ensuring uniformity; reducing energy consumption: reducing the energy consumption of equipment operation through intelligent control to achieve economic benefits. Preferably, when adjusting the operating parameters of the mixing equipment 100, the underlying control logic may include three dimensions, namely horizontal control, longitudinal control and vertical direction control. These three dimensions are independent of each other and closely integrated to ensure the comprehensiveness and effectiveness of the mixing. Preferably, in the optimization scheme, the least squares method can be used to calculate the optimal mixing path, wherein the optimal operation path is found by minimizing the objective function (i.e., the sum of squared errors between the actual mixing effect and the desired effect). Furthermore, by constructing a mathematical model representing the relationship between various parameters and uniformity during the mixing process, an error function is defined to calculate the error between the current mixing scheme and the target uniformity. The path of the mixing device 100 is then adjusted through iterative calculation to minimize the error function, thereby obtaining the optimal mixing path.

[0122] Mixing scheme optimization can quickly respond to and handle material inhomogeneities through real-time monitoring and feedback, while also providing intelligent support for decision-making through the combination of big data analysis and machine learning. This process significantly improves the uniformity of the final product, ensures production quality, and effectively reduces production costs, eliminating unnecessary repeated mixing and energy consumption. In addition, the method of the present invention exhibits good stability in complex production environments, reduces human intervention, and achieves automated production. At the same time, as the production environment and raw material characteristics change, the system can quickly adapt and adjust the mixing scheme to maintain high production efficiency, thereby providing companies with a more efficient and energy-saving solution.

[0123] Preferably, by establishing a data feedback mechanism, the result of each mixing (qualified or unqualified) can be used as training data and continuously input into the algorithm, so that after each operation, the model is updated according to the current mixing result to form a closed-loop learning. Preferably, according to historical data and the current mixing situation, the K value and the selection of cluster centers are dynamically adjusted to adapt to the characteristics of different batches of mash, so as to optimize the clustering process through practical feedback, thereby improving the overall segmentation accuracy. As data is continuously acquired, historical success stories can be used to extract empirical formulas, which can be used to predict future mixing effects, reduce trial and error costs, and improve efficiency. Combined with machine learning, an intelligent system can be designed to provide recommendations for optimizing mixing solutions based on real-time data and historical experience, thereby further improving the uniformity and quality of mixing.

[0124] Example 2

[0125] This embodiment is a further improvement of embodiment 1, and repeated contents will not be repeated here.

[0126] like Figure 8 As shown, the present invention discloses an online system for detecting the uniformity of koji addition, which can execute the online method for detecting the uniformity of koji addition as described in Example 1. Preferably, the system of the present invention may include: a mixing device 100 for receiving and mixing materials; a monitoring module 200 for acquiring data and information related to the koji addition process; and a central control module 300 for processing the data and information acquired by the monitoring module 200 and generating control signals for regulating the mixing device 100.

[0127] Preferably, if Figure 2As shown, the mixing apparatus 100 may include a mixing tank 110. The mixing tank 110 utilizes a tilting device 120 in conjunction with stirring teeth (not shown) provided within the tank to thoroughly mix the fermented grains and koji powder within the tank. Furthermore, the tilting device 120 may be equipped with a tilting motor to provide corresponding tilting power based on the matching gear position. This power is then transmitted to the tilting mechanism via a speed reducer, thereby driving the mixing tank 110 to rotate. Preferably, as the mixing tank 110 rotates, the fermented grains and koji powder within the tank are further mixed due to the loosening action of the stirring teeth.

[0128] Preferably, if Figure 3 As shown, the monitoring module 200 may include a capture unit 210 for capturing images of the material. The capture unit 210 may be located within an isolated area 112 on the top cover 111 of the mixing tank 110. Preferably, the industrial camera located within the isolated area 112 may be covered with a protective cover 113 made of a transparent crystal material. The refractive index of this protective cover 113 influences light propagation, thereby improving the quality and clarity of images captured by the industrial camera. This transparent protective cover 113 not only provides physical protection from damage and contamination but also optimizes optical performance, ensuring more accurate image capture. Preferably, the protective cover 113 of the present invention is made of a material with high light transmittance to minimize light absorption and scattering, thereby maintaining image clarity. Furthermore, the protective cover 113 may be made of a material with a moderate refractive index to reduce light refraction distortion. A moderate refractive index, for example, may be between 1.4 and 1.6.

[0129] Preferably, the monitoring module 200 may also include other types of sensors for acquiring physical and chemical parameters of the material, such as a temperature sensor, a humidity sensor, etc.

[0130] Preferably, the central control module 300 may perform uniformity evaluation on the material-related images acquired by the acquisition unit 210 , wherein the central control module 300 may execute the uniformity evaluation steps as described in Example 1.

[0131] Preferably, the central control module 300 can determine the uneven area based on the obtained uniformity evaluation results. When there is at least one uneven area, the central control module 300 can adjust the mixing scheme of the mixing device in real time according to the evaluation value, such as adjusting the mixing time, speed or other parameters to ensure the uniformity of the final result.

[0132] It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also belong to the disclosure scope of the present invention and fall within the protection scope of the present invention. Those skilled in the art should understand that the present invention specification and its drawings are illustrative and do not constitute a limitation on the claims. The scope of protection of the present invention is defined by the claims and their equivalents. The present invention specification contains multiple inventive concepts, such as "preferably" or "according to a preferred embodiment", which means that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept. Throughout the text, the features guided by "preferably" are only an optional method and should not be understood as having to be set. Therefore, the applicant reserves the right to abandon or delete the relevant preferred features at any time.

Claims

1. A method for online detection of bending uniformity, characterized in that: It includes: Data collection: Use the collection unit to obtain data information related to materials; Data processing: Conduct uniformity evaluation on the acquired material-related data information; Mixing scheme optimization: Optimize the mixing scheme based on the uniformity evaluation results. In which, during data processing, a first uniformity evaluation index is calculated using data related to a first microscopic uniformity indicator extracted from a plurality of regions into which a preprocessed grayscale image is segmented, from regions of minute brightness variations formed by the material structure and distribution characteristics based on the characteristics of light reflected from the material surface. Alternatively, when a false alarm rate evaluation index related to the first microscopic uniformity indicator exceeds a preset threshold, a plurality of random first microscopic uniformity indicators are identified and marked in each region, and corresponding windows are divided with these first microscopic uniformity indicators as the center. A second microscopic uniformity indicator in the window is extracted using a local binary pattern, and a corresponding histogram is generated from the obtained LBP code. The normalized histogram is then used as a feature vector for calculating the second microscopic uniformity indicator. A second uniformity evaluation index for determining the consistency of feature values is calculated using the data related to the second microscopic uniformity indicator, thereby performing uniformity evaluation based on the first uniformity evaluation index and / or the second uniformity evaluation index. When at least one uneven area is determined based on the uniformity assessment results, the operating parameters of the mixing equipment are adjusted in real time until the mash and koji powder in the mixing tank are finally uniform. The optimal mixing path is calculated using the least squares method, so that the mixing equipment can be controlled in three dimensions to obtain the optimal mixing path. The data related to the first microscopic uniformity indicator include: quantity, size, density, and distribution; the second microscopic uniformity indicator is a texture feature extracted from an area defined with the first microscopic uniformity indicator as the center.

2. The method according to claim 1, characterized in that The false alarm rate evaluation index calculated based on the moisture content change and / or composition difference is used to characterize the possibility of false alarm of the first uniformity evaluation index calculated using the first microscopic uniformity indicator, wherein when the false alarm rate evaluation index related to the first microscopic uniformity indicator does not reach the preset threshold, the first uniformity evaluation index can be directly used for uniformity evaluation.

3. The method according to claim 1, characterized in that The acquisition unit is used to collect original images related to the material in the mixing tank, where the material refers to a mixture of mash and koji powder. The mixing tank uses a flipping device in conjunction with stirring teeth provided in the tank to mix the mash and koji powder in the tank. Under the action of the flipping device, the mixing tank rotates in a forward and reverse direction in sequence, so that the acquisition unit can collect corresponding images each time the mixing tank flips back to the upright position.

4. The method according to claim 3, characterized in that The acquisition unit can be arranged in an isolated area on the inner side of the top cover of the mixing tank, which is deviated from the center line of the mixing tank, so that when the mixing tank rotates, the acquisition unit located in the isolated area can capture the original image of the material in a way that the shooting area is not blocked by the material.

5. An online detection system for adding a curved line uniformity, capable of executing the online detection method for adding a curved line uniformity according to any one of claims 1 to 4, characterized in that: It includes: Mixing equipment, used to contain and mix materials; Monitoring module, used to obtain data information related to the addition process; The central control module is used to process the data information obtained by the monitoring module and generate control signals for regulating the mixing equipment. Wherein, the mixing equipment includes a mixing tank, and the monitoring module includes a collection unit arranged on the top cover of the mixing tank to obtain images of the material in the mixing tank, so that the central control module can perform uniformity evaluation on the images related to the material obtained by the collection unit.

6. The system according to claim 5, characterized in that The collection unit can be arranged in an isolation area on the inner side of the top cover of the mixing tank, which is deviated from the center line of the mixing tank, wherein a transparent protective cover is provided outside the collection unit for covering the isolation area, so as to block the collection unit from the space inside the mixing tank while ensuring light transmittance.

Images