Intelligent dough kneading mode selection method and system

Abstract

The present invention relates to the field of dough kneading control technology, and in particular to a method and system for selecting an intelligent dough kneading mode. The method comprises the following steps: obtaining a real-time dough monitoring image in a dough kneading machine; reconstructing the real-time dough monitoring image through edge sharpening, and performing sequential fitting of time frames to construct a dough monitoring time frame sequence; performing frame-by-frame dough contour recognition on the dough monitoring time frame sequence, and performing deep mining of the dough time state to construct a dough time state diagram; mining the dynamic changes of yeast particles frame by frame and predicting multi-moment activity trends on the dough monitoring time frame sequence, thereby extracting the optimal kneading time point; performing time state matching on the dough time state diagram based on the optimal kneading time point, and then performing dynamic kneading parameter decision-making to generate dynamic kneading parameters. The present invention realizes refined control of the dough kneading machine, automatically adapts to various production environments, and improves the intelligence level of the kneading process and the quality of the dough.

Description

Technical Field

[0001] The present invention relates to the technical field of dough kneading control, and in particular to a method and system for selecting an intelligent dough kneading mode. Background Art

[0002] With the continuous development of automation technology and the increasing popularity of artificial intelligence, the traditional dough kneading process is gradually moving towards intelligent and automated development. In food production industries such as bread and pastry, the dough kneading process is a key step affecting product quality. The kneading process requires thorough mixing of flour and water to form a dough with a certain degree of elasticity and ductility. However, due to factors such as flour, temperature, and humidity, the quality and efficiency of dough kneading are easily affected by changes in the external environment. Traditional manual operations or simple mechanical control methods often fail to meet the precision and consistency requirements of the production process.

[0003] During the long kneading process, the kneading effect is closely related to parameters such as kneading time, speed, and force, and is subject to significant variability and complexity. Traditional kneading control methods typically rely on manual observation and empirical judgment, manually adjusting the machine's operating mode or stop time based on the operator's experience. This method not only easily leads to inconsistent kneading results, but can also affect production efficiency and quality stability. In addition, due to the limitations of the kneading machine's mechanical structure and control system, traditional kneading control methods often lack real-time monitoring and analysis of the dough state, making it difficult to effectively adjust to changes in the dough state in the first place. Therefore, a more intelligent kneading mode control option is needed. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a method and system for selecting an intelligent dough kneading mode to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides a method for selecting an intelligent dough kneading mode, comprising the following steps:

[0006] Step S1: obtaining a real-time dough monitoring image in a dough kneading machine; performing edge sharpening and reconstruction on the real-time dough monitoring image, and performing time frame sequence fitting to construct a dough monitoring time frame sequence;

[0007] Step S2: performing dough contour recognition frame by frame on the dough monitoring time sequence frame sequence, and performing deep mining of the dough time sequence state to construct a dough time sequence state diagram;

[0008] Step S3: Mining the dynamic changes of yeast particles frame by frame and predicting the activity trend at multiple moments in the dough monitoring time series frame sequence to extract the optimal kneading time point;

[0009] Step S4: performing time state matching on the dough timing state diagram based on the optimal kneading time point, and then making dynamic kneading parameter decisions to generate dynamic kneading parameters;

[0010] Step S5: Evaluate the dough state based on the dynamic kneading parameters, perform real-time kneading parameter fine-tuning optimization, and construct a real-time kneading fine-tuning strategy;

[0011] Step S6: Perform comprehensive state evaluation based on the instant dough kneading fine-tuning strategy and conduct self-reinforcement dough kneading performance learning to generate an intelligent dough kneading control model.

[0012] The present invention provides a high-quality, real-time image data source for subsequent analysis through real-time visual monitoring of the dough state. Edge sharpening and reconstruction not only improves the accuracy of dough contour recognition, but also reduces the problem of image quality affected by ambient light or clutter on the dough surface, thereby enhancing the accuracy of subsequent processing. Through sequential fitting of time-series frames, dynamic tracking of dough changes during the kneading process can be achieved, providing a stable time series data basis for subsequent time-series state analysis, identifying dough contours frame by frame, ensuring that every detail of the dough during the kneading process can be accurately captured, and providing high-precision data support for analyzing dough morphology changes. Sequence analysis can dig out subtle trends in dough changes, such as expansion, stretching, and rupture during fermentation, help monitor dough status in real time, and build a dough time-series state diagram. This not only improves the understanding of dough state changes, but also provides an important basis for subsequent time dynamic prediction and parameter optimization. Dynamic change mining makes it possible to monitor the activity of yeast in dough in real time, help accurately judge the key changes in the fermentation process, predict the changing trend of yeast activity, and provide a basis for formulating the best kneading time, avoid excessive kneading or premature stopping, and ensure that the dough enters the next operation in the best state. By identifying the optimal kneading time point, This effectively avoids over- or under-kneading of dough due to improper timing, improving dough quality. Based on the precise matching of optimal kneading time with dough state, it enables refined control at different fermentation stages, enhancing kneading effectiveness and quality. This step provides flexible decision-making support for kneading parameters, enabling real-time adjustment of kneading intensity and time to ensure optimal dough conditions. Dynamic kneading parameter decisions better adapt to the actual state of the dough, avoiding the potential impact of traditional fixed parameter control on dough quality, and improving the consistency and taste of the final product. By monitoring dough conditions in real time, the system can quickly respond to changes during the kneading process, ensuring that the dough is always in optimal conditions. Instant fine-tuning can make appropriate adjustments based on the real-time state of the dough, avoiding over- or under-kneading and maximizing dough quality. Through self-reinforcement learning, the system optimizes control strategies based on the ever-changing dough state, gradually accumulating experience and improving the intelligence of kneading control. This enables personalized and customized kneading control, ensuring that each batch of dough receives the most optimal kneading parameters, significantly improving product consistency and quality. Over time, the system will increasingly accurately understand the relationship between kneading parameters and dough quality, further improving production efficiency and reducing energy consumption.

[0013] Preferably, step S1 includes the following steps:

[0014] Step S11: obtaining a real-time dough monitoring image in the dough kneading machine;

[0015] Step S12: performing edge sharpening and reconstruction on the real-time dough monitoring image to construct a detail-optimized and reconstructed dough image;

[0016] Step S13: extracting frame-by-frame image changes of the detail-optimized and reconstructed dough image to obtain a dough monitoring image for each frame;

[0017] Step S14: calculating the time stamp of sequential frame acquisition for the detail-optimized and reconstructed dough image to obtain the acquisition time stamp of each frame;

[0018] Step S15: performing time-series frame fitting on each frame of the dough monitoring image according to the acquisition timestamp of each frame to construct a dough monitoring time-series frame sequence.

[0019] The present invention can obtain sufficiently clear dough state images through high-resolution image acquisition, providing high-quality input data for subsequent detail processing and analysis, and is applicable to various dough kneading machine environments and different dough types, ensuring the wide applicability of the system. Edge sharpening technology can enhance subtle contours, cracks, bubbles and other details in the dough image, helping to make subsequent image processing and analysis more accurate. The clear image details enable subsequent algorithms (such as contour extraction, change detection, etc.) to more accurately identify the shape and state of the dough and reduce recognition errors. Since the shape of the dough may be different during the kneading process, edge sharpening processing can adapt to images of different dough types and maintain high recognition effect. Frame-by-frame image change extraction helps to identify subtle changes in each frame of the image, ensuring that the details of the dough state are fully captured and avoiding missing key changes. Through the change extraction of each frame of the image, the dynamic changes of the dough, such as expansion, stretching, rupture, etc., can be monitored in real time, providing detailed tracking of the dough state. Frame-by-frame extraction helps to focus on the dough. State changes, rather than static background, reduce the interference of redundant data and make subsequent analysis more efficient. Timestamp calculation ensures the precise synchronization of image data and time, so that each frame of image corresponds one-to-one to its corresponding time point, providing accurate time basis for timing analysis and necessary time information for subsequent timing frame sequence fitting, so that the system can perform time-based dynamic analysis and control. By accurately calculating the acquisition time, high-precision time synchronization can be achieved, which helps to avoid analysis deviations caused by time errors. Through timing frame sequence fitting, image data can be converted into time series data, so that each frame of image can be analyzed in the time dimension, enhancing the coherence of subsequent timing analysis. After arranging the images in chronological order, the change process of the dough from the initial state to the finished state can be analyzed, and the key time points and state changes can be identified. The timing frame sequence provides time series data support for subsequent intelligent analysis and decision-making, which can help the system make precise control decisions based on dough state changes.

[0020] Preferably, the specific steps of step S12 are:

[0021] Perform pixel-by-pixel recognition on the real-time dough monitoring image to extract all pixel points in the image;

[0022] Perform global pixel average calculation on all pixels in the image to generate the global pixel average of the image;

[0023] Image noise suppression is performed based on the global pixel average value of the image to obtain a denoised and optimized dough monitoring image;

[0024] Perform histogram equalization on the denoised and optimized dough monitoring image to generate an image grayscale histogram;

[0025] Perform grayscale distribution stretching on the image grayscale histogram and calculate the grayscale value of the stretched pixel;

[0026] performing contrast enhancement according to the grayscale values ​​of the stretched pixels to obtain a contrast-enhanced dough monitoring image;

[0027] Perform edge gradient information analysis on the texture detail enhanced dough monitoring image to obtain an edge map;

[0028] Performing slight texture change sharpening on the edge map to generate a micro-texture change sharpened edge map;

[0029] The contrast-enhanced dough monitoring image is edge-sharpened and reconstructed according to the micro-texture change sharp edge map to construct a detail-optimized reconstructed dough image.

[0030] The present invention ensures that every detail in the dough image is fully captured through pixel-by-pixel processing, providing a basis for subsequent image optimization, feature extraction and analysis. Accurate pixel data makes subsequent denoising, enhancement and other processing more accurate, and can effectively remove redundant data and extract subtle changes in the dough state. Through pixel-level processing, it can adapt to changes in different lighting conditions, dough types or environmental factors, ensuring that the system can maintain high-precision processing effects in various situations. The global pixel average can reflect the overall brightness level of the image, helping the system to identify whether the image needs to be adjusted or enhanced in brightness. Through global pixel averaging, unimportant details in the image can be effectively filtered out and image noise can be reduced. Impact on subsequent processing: Noise suppression can effectively remove stray signals in the image, improve image quality, and provide clearer images for subsequent detail processing and analysis. After removing noise, the actual shape and state of the dough are clearer, and details are easier to capture, thereby improving the image recognition. The denoising and optimized image can better adapt to subsequent analysis and feature extraction, reduce errors and deviations, and improve the accuracy of overall intelligent control. Histogram equalization enhances the contrast of the image, making the details of the dough more obvious, especially in low contrast or insufficient light conditions. The equalized image can enhance the details of important areas in the image without changing the original image content, helping subsequent feature extraction. The equalization of contrast makes the different layers of dough (such as surface texture, bubbles, cracks, etc.) easier to identify, improving the visual effect of the image. Through grayscale stretching, the contrast of tiny details in the image is enhanced, making the different states of the dough (such as bubbles, cracks, etc.) more prominent, which is convenient for analysis. The image after grayscale stretching helps to highlight the features in the image, making subsequent image recognition and analysis more accurate. The stretching process expands the grayscale range, effectively improves the grayscale contrast of the image, and enhances the expressiveness of the dough in the image. Contrast enhancement helps to highlight the key features of the dough, such as surface texture, bubbles, etc., which is convenient for subsequent recognition and analysis. After enhancing the contrast, the details of the dough are clearer in the image. The sharpening of the edge map can enhance the subtle changes in the dough, making the subtle structure, bubbles, cracks, etc. of the dough more obvious, which is conducive to accurate monitoring of the dough state. Sharpening the subtle texture changes improves the recognizability of image details and helps to more accurately analyze the dough state and defects. The sharpened edge map can provide more detailed texture information, which helps the system to judge the fermentation, stretching and forming state of the dough.

[0031] Preferably, the specific steps of step S2 are:

[0032] Step S21: performing dough contour recognition frame by frame on the dough monitoring time sequence frame sequence, and extracting the dough contour line of each frame;

[0033] Step S22: Tracking the dynamic changes of the dough contour line of each frame to obtain a dynamic change trajectory of the dough contour;

[0034] Step S23: performing a contour change trend analysis on the dynamic change trajectory of the dough contour to generate a dough contour change trend feature;

[0035] Step S24: evaluating the dough surface roughness of the dough monitoring time series frame sequence to generate a dough surface roughness time series curve;

[0036] Step S25: performing frame-by-frame analysis of the dough crack generation situation on the dough monitoring time series frame sequence to obtain crack evolution characteristics in the dough;

[0037] Step S26: Conduct in-depth exploration of the dough temporal state based on the dough surface roughness temporal curve, dough contour change trend characteristics, and dough crack evolution characteristics to construct a dough temporal state diagram.

[0038] By extracting the dough's contour line frame by frame, the system can accurately obtain the dough's shape and size changes and capture the dough's morphological changes during the kneading process. Contour recognition can help the system better understand the dough's surface features, detect details such as cracks and bubbles, and provide data support for subsequent analysis. Contour recognition of each frame provides basic data for subsequent dynamic analysis, helping the system track the dough's changing trends in real time. Dynamic tracking of contour line changes can help the system observe the dough's shape changes in real time and detect key processes such as expansion and deformation. Contour change trend analysis can predict the dough's future morphological changes and identify the change pattern of the dough's state, thereby providing an important reference for subsequent control strategies. By analyzing the contour change trend, it can be timely determined whether the dough has reached the optimal kneading state, thereby adjusting the kneading process to achieve the best effect. The dough contour change trend provides an important basis for the system's intelligent decision-making and can achieve dynamic regulation based on the dough state. Roughness assessment can reflect the transition of the dough surface from smooth to rough during the kneading process. This change is very important for the fermentation and shaping of the dough. The roughness curve can be used as an important indicator for judging dough quality, helping the system understand whether the dough is in the optimal state. The system can monitor the surface state of the dough in real time by tracking the time series curve of the dough, so as to make more accurate kneading adjustments. The generation and evolution of cracks are crucial to the quality of the dough and the kneading process. By tracking the crack changes frame by frame, the system can promptly identify whether abnormal cracks appear in the dough to avoid over-kneading or stretching of the dough. The crack evolution characteristics help to realize dynamic monitoring of the dough state and avoid dough cracking problems caused by over-kneading. Crack detection can provide data support for parameter adjustment during the kneading process to ensure that the shape and structure of the dough remain optimal. By combining multiple features (such as roughness, contour changes and crack evolution), the system can accurately identify the cracks in the dough. The dough timing state diagram provides the system with a global view of the dough state, helping the system to comprehensively evaluate the overall state of the dough and make more accurate control decisions. By deeply exploring the dough's timing state, it can provide a scientific basis for the selection of intelligent kneading mode, ensuring that the kneading force and time adjustment at each stage can achieve the best effect. The deeply explored dough timing state diagram can help the system discover potential trends in dough state changes and make adjustments in advance, thereby optimizing the kneading process and avoiding over-kneading or under-kneading.

[0039] Preferably, the specific steps of step S3 are:

[0040] Step S31: mining the dynamic changes of yeast particles frame by frame in the dough monitoring time series frame sequence to extract the dynamic change features of yeast particles;

[0041] Step S32: performing yeast temporal activity analysis based on the dynamic change characteristics of yeast particles to generate a yeast temporal activity curve;

[0042] Step S33: Evolving the activity variation law of the yeast temporal activity curve to obtain the yeast activity variation law;

[0043] Step S34: performing multi-time activity trend prediction based on the yeast activity variation pattern, thereby generating yeast activity trend prediction data at multiple times;

[0044] Step S35: Calculate the optimal kneading time based on the yeast activity trend prediction data at multiple moments, thereby extracting the optimal kneading time point.

[0045] By mining the dynamic changes of yeast particles frame by frame, this invention can accurately track the distribution and abundance of yeast, capturing details of yeast activity fluctuations. By identifying the distribution and movement of yeast particles in dough, it is possible to understand the process of yeast reproduction, consumption, and activity changes, providing reliable data for subsequent fermentation analysis. Detailed particle dynamic mining can reveal subtle changes in yeast particle activity, facilitating timely adjustment of parameters during the dough kneading process to avoid premature or delayed activity imbalances. The time-series activity curve clearly displays fluctuations in yeast activity during the dough kneading process, helping operators or the system understand changes in yeast activity. This curve identifies the time period when yeast activity reaches its peak, allowing the optimal kneading timing to be determined, ensuring optimal dough fermentation. By plotting the yeast time-series activity curve in real time, the system can adjust the kneading intensity and duration during the dough kneading process based on activity fluctuations, achieving more precise fermentation control. Evolutionary analysis of the yeast activity curve can reveal periodic or trend-based changes in yeast activity, providing theoretical support for subsequent fermentation processes. By understanding the changing patterns of yeast activity, the system can predict critical moments in dough fermentation and dynamically adjust the kneading time to avoid over- or under-kneading. Analyzing the changing patterns of yeast activity allows for more precise prediction of key points in the dough fermentation process, providing a more refined control strategy. By predicting activity at multiple moments, the system can proactively identify changing trends in yeast activity, enabling preemptive control of the dough fermentation process and avoiding delays. The predicted yeast activity trends provide the basis for proactively adjusting the kneading strategy, ensuring that yeast activity fluctuates within an appropriate range to achieve optimal fermentation results. Determining the optimal kneading time ensures optimal yeast activity, avoiding under- or over-fermentation caused by kneading too early or too late. Accurately calculating the optimal kneading time maximizes dough fermentation, reduces time waste, and improves production efficiency. The optimal kneading time ensures that the dough reaches optimal fermentation during the kneading process, thereby improving final quality indicators such as taste and elasticity.

[0046] Preferably, the specific steps of step S35 are:

[0047] Perform multi-time activity trend peak calculation on yeast activity trend prediction data at multiple times, and extract the activity trend peak of each time period;

[0048] The activity trend peak of each time period is used to identify the inflection point of activity change and mark it as the highest peak point of yeast activity;

[0049] Define preset kneading time parameters;

[0050] Calculate the kneading buffer time at the highest peak point of yeast activity according to the preset kneading time parameters to obtain the kneading buffer time at the highest peak point;

[0051] The optimal kneading time is calculated based on the kneading buffer time at the highest peak point to obtain the optimal kneading time point.

[0052] The present invention can accurately identify the highest point of yeast activity in each time period through peak calculation, which helps to understand the period when yeast is most active, which is crucial for the most appropriate kneading time during the fermentation process. By extracting peak data, the system can provide operators or automated systems with clear decision-making basis, helping to make more accurate kneading time selection. The activity peak in each time period provides the system with the key point of periodic changes in yeast activity, which helps to optimize key operations in the dough fermentation process. Through inflection point identification, the peak of yeast activity change can be clearly identified, helping to determine the optimal kneading time for the dough. This moment is the moment when the dough has the greatest fermentation potential, and is therefore very critical. The identification of the inflection point can further accurately select the kneading time, avoiding missing the optimal kneading time, thereby avoiding insufficient or excessive yeast activity. By identifying the highest peak point of yeast activity, the fermentation process can be more accurately controlled to prevent yeast activity from reaching its peak too early, resulting in incomplete fermentation, or reaching its peak too late, resulting in delayed kneading time. The preset kneading time parameter provides a standardized control specification for the kneading process, which helps to ensure the consistency and repeatability of the kneading operation. It can be adjusted according to different dough types and fermentation requirements. The whole kneading time parameter enables the system to adapt to various production needs, thereby ensuring the optimal fermentation effect of the dough. The preset kneading time can provide a reference value for the system to avoid kneading time that is too short or too long due to improper operation, and ensure the best kneading conditions. By calculating the buffer time, it is possible to avoid kneading the dough immediately after the yeast activity reaches its peak, and prevent excessive consumption of yeast activity due to premature kneading. Reasonable calculation of the buffer time ensures that the yeast activity is fully utilized and provides appropriate time for subsequent kneading, so that the dough can obtain the best fermentation effect. The setting of the kneading buffer time can be The system or operator provides greater flexibility and can adjust the start time of kneading according to actual needs to avoid fluctuations in the dough state. By combining the buffer time and the highest peak point, the system can accurately calculate the optimal kneading time point to avoid uneven dough fermentation due to kneading operations too early or too late. The optimal kneading time point ensures that the yeast activity is in the most appropriate state, so that the dough can achieve the best fermentation effect during the kneading process and improve the quality of the final product. The precise kneading time point can not only improve the fermentation effect of the dough, but also improve the efficiency of the production line, reduce time waste, and increase production capacity.

[0053] Preferably, the specific steps of step S4 are:

[0054] Step S41: performing time state matching on the dough time sequence state diagram based on the optimal kneading time point, and extracting the dough time sequence state features at the optimal kneading time point;

[0055] Step S42: performing optimal dough state analysis on the dough time sequence state diagram to generate optimal dough state characteristics;

[0056] Step S43: performing an optimal state deviation analysis on the optimal dough state characteristics based on the dough temporal state characteristics at the optimal kneading time point to obtain the dough state deviation characteristics at the optimal kneading time point;

[0057] Step S44: making dynamic kneading parameter decisions based on the dough state deviation characteristics at the optimal kneading time point to generate dynamic kneading parameters.

[0058] Through temporal state matching, the present invention accurately captures the dough characteristics corresponding to the optimal kneading time point, providing basic data for subsequent analysis and adjustment. The temporal state characteristics of the optimal kneading time point provide the necessary data support for dynamically adjusting kneading parameters, ensuring that the kneading process is carried out under optimal conditions. Determining the dough state characteristics at the optimal time point avoids operational delays or premature decisions, ensuring that the dough is kneaded at the most suitable fermentation stage. Comprehensive analysis of the dough's temporal state diagram enables a scientific assessment of the overall dough quality, ensuring that the dough is kneaded under optimal fermentation conditions. The optimal dough state characteristics provide a standard for the ideal dough state, and the system can make timely adjustments to the kneading process based on these characteristics, thereby achieving better fermentation results and final quality. Data-driven optimal dough state analysis reduces errors in manual operations and ensures that each operation is performed based on accurate dough state characteristics. Deviation analysis promptly identifies discrepancies between the dough state and expectations, identifies potential fermentation or kneading issues, and prevents dough quality degradation caused by excessive deviations. The dough state deviation characteristics provide a basis for adjustment, allowing the system to automatically or manually adjust kneading parameters based on these deviations to ensure that the dough ultimately reaches the ideal state. By analyzing deviation characteristics, the system can dynamically adjust kneading strategies for different production environments or operating conditions, allowing for flexible response to various changes. Dynamic kneading parameter generation automatically adjusts various parameters during the kneading process based on the real-time dough state and deviation characteristics, achieving adaptive control. Dynamic adjustment of kneading parameters effectively optimizes the dough kneading process, improves kneading efficiency, and avoids problems such as over-kneading or under-fermentation. This step allows the kneading process to not only rely on fixed rules but also dynamically adjust based on real-time feedback and data, enhancing the intelligence of the production process and reducing manual intervention.

[0059] Preferably, the specific steps of step S5 are:

[0060] Step S51: performing real-time dough kneading control based on dynamic dough kneading parameters and acquiring real-time dough kneading monitoring images;

[0061] Step S52: performing dough shape analysis on the real-time dough kneading monitoring image to obtain the real-time dough kneading shape;

[0062] Step S53: performing dough state evaluation on the real-time dough shape during kneading to generate a real-time dough state evaluation value;

[0063] Step S54: performing instant kneading parameter fine-tuning optimization on the dynamic kneading parameters based on the real-time kneading state evaluation value, and constructing an instant kneading fine-tuning strategy.

[0064] By acquiring real-time dough kneading monitoring images, the present invention enables the system to timely capture changes in the shape, texture and state of the dough during the kneading process, thereby providing accurate visual data for subsequent analysis. By performing real-time dough kneading control based on dynamic dough kneading parameters, the system can automatically perform adjustments to ensure that the kneading process is carried out according to the optimal strategy without relying on manual intervention, thereby improving the level of automation and accuracy. Real-time monitoring images provide an effective means for continuous tracking of the dough state, helping the system to quickly determine whether it deviates from the target state and take timely adjustment measures. Through real-time analysis of the dough shape, the system can determine whether the dough has irregular deformation or cracking during the kneading process, and then analyze the dough processing effect, which is the basis for adjusting the kneading parameters. If the shape of the dough changes abnormally (such as unevenness, overstretching or collapse), the system can promptly discover the problem and take corresponding adjustment measures to avoid deviations in the kneading process affecting the final dough quality. The shape of the dough is the key to the quality of the dough. An important sign, the shape analysis can intuitively reflect the dough state, thus providing an effective basis for subsequent control and optimization. By converting the shape changes of the dough into quantitative state evaluation values, the system can accurately evaluate the dough in a data-driven manner, avoiding the uncertainty in traditional empirical operations. The real-time evaluation value can quickly feedback the state of the dough, providing a scientific basis for dynamically adjusting the kneading parameters to ensure that the dough is always in the best state. Through instant fine-tuning of the kneading parameters, the system can optimize the dough state according to real-time feedback, avoid over-kneading or uneven fermentation of the dough, and ensure the best quality of the final dough. The instant fine-tuning strategy enables the system to flexibly adapt to different dough types, environmental changes and other factors according to the dynamic changes of the dough state, thereby realizing intelligent kneading control. Real-time adjustment of kneading parameters can maximize the kneading effect and production efficiency, avoid affecting product quality due to too long or too short kneading time, thereby saving production time and improving production capacity.

[0065] Preferably, the specific steps of step S6 are:

[0066] Step S61: extracting a final dough kneading image based on the real-time dough kneading monitoring image;

[0067] Step S62: performing a comprehensive state evaluation on the final dough kneading image according to the optimal dough state characteristics to obtain a final dough state evaluation value;

[0068] Step S63: Based on the final dough state evaluation value, the instant dough kneading fine-tuning strategy is subjected to self-reinforcement dough kneading performance learning, thereby generating an intelligent dough kneading control model.

[0069] By extracting the final kneading image, the present invention enables the system to obtain complete visual data, thereby accurately judging the changes in the dough during the entire kneading process and ensuring that the final dough meets the quality requirements. The final kneading image provides clear and intuitive image data for the final state of the dough, which is convenient for analyzing and judging whether it meets the expected fermentation standards. These final image data not only contribute to the final evaluation of the dough shape and quality, but also serve as key inputs for subsequent learning and optimization. By combining the optimal dough state characteristics with image analysis, the system can quantitatively evaluate the final state of the dough. This quantitative evaluation is more accurate than traditional manual sensory evaluation and avoids human errors. The evaluation not only involves the shape of the dough, but also takes into account the intrinsic quality of the dough (such as elasticity, moisture, etc.), providing a comprehensive quality judgment. The final evaluation value provides data for further optimizing the kneading strategy. It is supported that if the evaluation value deviates from the target, the system can use this data to further adjust the parameters and improve the subsequent kneading process. The system adjusts its operating strategy based on the feedback after each kneading (the final dough state evaluation value) through a self-reinforcement learning mechanism. Each adjustment helps the system to continuously optimize and improve the kneading effect next time. The intelligent model can automatically adjust the kneading parameters, such as kneading time, kneading intensity and speed, eliminating the uncertainty in manual operation and further improving the accuracy and consistency of the kneading process. As the feedback data after each kneading is added to the learning model, the system can continuously accumulate experience, thereby optimizing the control strategy during long-term use, improving kneading quality and production efficiency. The system can adjust its kneading strategy according to different dough types and environmental conditions, so that the consistency of dough quality can be maintained in various production environments.

[0070] In this specification, a system for selecting an intelligent dough kneading mode is provided, which is used to execute the method for selecting an intelligent dough kneading mode as described above, including:

[0071] An image reconstruction module is used to obtain a real-time dough monitoring image in a dough kneading machine; perform edge sharpening and reconstruction on the real-time dough monitoring image, and perform time series frame sequence fitting to construct a dough monitoring time series frame sequence;

[0072] The timing state diagram module is used to identify the dough contour frame by frame in the dough monitoring timing frame sequence, conduct in-depth mining of the dough timing state, and construct a dough timing state diagram;

[0073] The activity trend prediction module is used to mine the dynamic changes of yeast particles frame by frame in the dough monitoring time series frame sequence and predict the activity trend at multiple moments, thereby extracting the optimal kneading time point. The dynamic kneading module is used to match the time state of the dough time series state diagram based on the optimal kneading time point, and then make dynamic kneading parameter decisions to generate dynamic kneading parameters.

[0074] Parameter fine-tuning module, used to evaluate dough state based on dynamic kneading parameters, perform real-time kneading parameter fine-tuning optimization, and build real-time kneading fine-tuning strategy;

[0075] Intelligent dough kneading control is used to perform comprehensive state evaluation based on the real-time dough kneading fine-tuning strategy and conduct self-reinforcement kneading performance learning to generate an intelligent dough kneading control model.

[0076] The present invention uses sensors such as cameras to obtain dough images in a dough kneading machine in real time, providing raw data for subsequent analysis, performing edge sharpening processing on the obtained real-time images to highlight the contours and details of the dough, ensuring that subsequent image analysis is more accurate, and fitting multiple frames of images in a time-series frame sequence to construct a time-series frame sequence for dough monitoring, helping the system to identify the dynamic changes of the dough during the kneading process, tracking the contour changes of the dough in real time, and ensuring that the system can detect slight changes in the dough morphology. After constructing the dough time-series state diagram, it is possible to understand the changing trends of the dough at different time points, providing accurate data for further analysis and decision-making. Deep mining of the time-series state helps to identify potential kneading problems (such as over-kneading or over-relaxation), and improve the system's ability to identify and predict the dough state. By performing multi-time prediction of yeast activity, the system can dynamically adjust parameters during the kneading process to ensure the optimal fermentation state of the yeast, accurately calculate the optimal kneading time point, and avoid kneading the dough when the yeast activity is not appropriate, thereby improving the fermentation effect and dough quality. The activity trend prediction module improves the control accuracy during the dough fermentation process. , ensuring that the dough can reach the ideal fermentation state. By matching the optimal kneading time point, the accuracy of the kneading time is ensured to avoid over-kneading or under-kneading. According to the real-time feedback of the dough state, the kneading parameters are dynamically adjusted to make the kneading process more personalized and adapt to the needs of different doughs. Accurate kneading control can improve kneading efficiency and ensure that each stage of the dough is in the best state. By fine-tuning the kneading parameters, the system can respond to changes in the dough state in a timely manner to ensure that the dough is always in the best state. No matter what changes occur in the dough during the kneading process, the system can flexibly adjust the kneading parameters to reduce the impact caused by environmental changes or other factors. The fine-tuning strategy can help maintain the consistency of the quality of each kneading and avoid quality fluctuations caused by operational errors. Through self-reinforcement learning, the system can continuously accumulate experience and data, thereby continuously optimizing the kneading control strategy. The generated intelligent kneading control model can automatically adapt to various production environments, improve the intelligence of the kneading process, and reduce manual intervention. As more data accumulates, the intelligent model will become more and more accurate, continuously improving kneading efficiency and product quality consistency. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a schematic flow chart of the steps of a method for selecting an intelligent dough kneading mode according to the present invention;

[0078] Figure 2 Detailed implementation flow chart of step S1;

[0079] Figure 3 Detailed implementation flow chart of step S2;

[0080] Figure 4 Schematic diagram of the detailed implementation steps of step S3. DETAILED DESCRIPTION

[0081] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0082] This application provides a method and system for selecting an intelligent dough kneading mode. The execution entities of the method and system include, but are not limited to, the following: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered as general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of an audio and image management system, an information management system, and a cloud data management system.

[0083] See also Figures 1 to 4 The present invention provides a method for selecting an intelligent dough kneading mode, and the method for selecting an intelligent dough kneading mode comprises the following steps:

[0084] Step S1: obtaining a real-time dough monitoring image in a dough kneading machine; performing edge sharpening and reconstruction on the real-time dough monitoring image, and performing time frame sequence fitting to construct a dough monitoring time frame sequence;

[0085] Step S2: performing dough contour recognition frame by frame on the dough monitoring time sequence frame sequence, and performing deep mining of the dough time sequence state to construct a dough time sequence state diagram;

[0086] Step S3: Mining the dynamic changes of yeast particles frame by frame and predicting the activity trend at multiple moments in the dough monitoring time series frame sequence to extract the optimal kneading time point;

[0087] Step S4: performing time state matching on the dough timing state diagram based on the optimal kneading time point, and then making dynamic kneading parameter decisions to generate dynamic kneading parameters;

[0088] Step S5: Evaluate the dough state based on the dynamic kneading parameters, perform real-time kneading parameter fine-tuning optimization, and construct a real-time kneading fine-tuning strategy;

[0089] Step S6: Perform comprehensive state evaluation based on the instant dough kneading fine-tuning strategy and conduct self-reinforcement dough kneading performance learning to generate an intelligent dough kneading control model.

[0090] The present invention provides a high-quality, real-time image data source for subsequent analysis through real-time visual monitoring of the dough state. Edge sharpening and reconstruction not only improves the accuracy of dough contour recognition, but also reduces the problem of image quality affected by ambient light or clutter on the dough surface, thereby enhancing the accuracy of subsequent processing. Through sequential fitting of time-series frames, dynamic tracking of dough changes during the kneading process can be achieved, providing a stable time series data basis for subsequent time-series state analysis, identifying dough contours frame by frame, ensuring that every detail of the dough during the kneading process can be accurately captured, and providing high-precision data support for analyzing dough morphology changes. Sequence analysis can dig out subtle trends in dough changes, such as expansion, stretching, and rupture during fermentation, help monitor dough status in real time, and build a dough time-series state diagram. This not only improves the understanding of dough state changes, but also provides an important basis for subsequent time dynamic prediction and parameter optimization. Dynamic change mining makes it possible to monitor the activity of yeast in dough in real time, help accurately judge the key changes in the fermentation process, predict the changing trend of yeast activity, and provide a basis for formulating the best kneading time, avoid excessive kneading or premature stopping, and ensure that the dough enters the next operation in the best state. By identifying the optimal kneading time point, This effectively avoids over- or under-kneading of dough due to improper timing, improving dough quality. Based on the precise matching of optimal kneading time with dough state, it enables refined control at different fermentation stages, enhancing kneading effectiveness and quality. This step provides flexible decision-making support for kneading parameters, enabling real-time adjustment of kneading intensity and time to ensure optimal dough conditions. Dynamic kneading parameter decisions better adapt to the actual state of the dough, avoiding the potential impact of traditional fixed parameter control on dough quality, and improving the consistency and taste of the final product. By monitoring dough conditions in real time, the system can quickly respond to changes during the kneading process, ensuring that the dough is always in optimal conditions. Instant fine-tuning can make appropriate adjustments based on the real-time state of the dough, avoiding over- or under-kneading and maximizing dough quality. Through self-reinforcement learning, the system optimizes control strategies based on the ever-changing dough state, gradually accumulating experience and improving the intelligence of kneading control. This enables personalized and customized kneading control, ensuring that each batch of dough receives the most optimal kneading parameters, significantly improving product consistency and quality. Over time, the system will increasingly accurately understand the relationship between kneading parameters and dough quality, further improving production efficiency and reducing energy consumption.

[0091] In the embodiment of the present invention, see Figure 1 , is a schematic flow chart of the steps of a method for selecting an intelligent dough kneading mode according to the present invention. In this example, the steps of the method include:

[0092] Step S1: obtaining a real-time dough monitoring image in a dough kneading machine; performing edge sharpening and reconstruction on the real-time dough monitoring image, and performing time frame sequence fitting to construct a dough monitoring time frame sequence;

[0093] In this embodiment, a suitable image capture device is selected, such as a high-definition camera or an industrial camera, to ensure that it can provide sufficiently clear images. The device needs to be able to operate stably in the working environment of the dough kneading machine. The camera is installed in an appropriate position to ensure that the image of the entire kneading area can be captured. During installation, the lighting conditions and the distance between the camera and the dough need to be considered. The VideoCapture class of the OpenCV library is used to start the camera and obtain real-time images. The cap.read() function can be used to periodically capture the current frame image to ensure the continuity and temporal consistency of image capture.

[0094] import cv2

[0095] cap = cv2.VideoCapture(0) # 0 indicates the default camera

[0096] while True:

[0097] ret, frame = cap.read()

[0098] if not ret:

[0099] break

[0100] # Process the image (subsequent steps) Preprocess the captured original image, including converting it to grayscale and removing noise. Grayscale conversion can be achieved using the cv2.cvtColor() function, and denoising can be achieved using Gaussian blur. Apply the Canny edge detection algorithm to extract the edges in the image. Use the cv2.Canny() function to set an appropriate threshold to accurately capture the edge contour of the dough. To enhance the edge effect, the edge image can be processed using the Laplacian operator or a custom sharpening filter. This will make the edges of the dough more obvious, facilitating subsequent analysis. gray =cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

[0101] blurred = cv2.GaussianBlur(gray, (5, 5), 0)

[0102] edges = cv2.Canny(blurred, 100, 200) Stores each frame of edge-sharpened image in a list for subsequent construction of a time-series frame sequence. You can define an empty list and append it each time a new frame is captured. Make sure that when storing time-series frames, they are stored in the order in which they were captured. You can use a timestamp or a simple counter to mark the order of each frame. Once a sufficient number of frames are captured, they can be organized into a time-series data structure to form a time-series frame sequence for subsequent analysis. frames = []

[0103] while True:

[0104] ret, frame = cap.read()

[0105] if not ret:

[0106] break

[0107] edges = process_frame(frame) # Edge processing function

[0108] frames.append(edges) # Store the processed frames. After completing real-time monitoring, ensure that camera resources are released to avoid memory leaks. Use cap.release() to release the camera. If desired, save the time-series frame sequence to a file for subsequent data analysis and processing. Use NumPy's np.save() function to save the array as a .npy file.

[0109] cap.release()

[0110] np.save('dough_frames.npy', frames) # Save frame sequence.

[0111] Step S2: performing dough contour recognition frame by frame on the dough monitoring time sequence frame sequence, and performing deep mining of the dough time sequence state to construct a dough time sequence state diagram;

[0112] In this embodiment, necessary pre-processing steps are performed on each frame, including grayscale conversion, denoising, and edge detection. Grayscale conversion uses cv2.cvtColor(), denoising generally uses Gaussian blur, and edge detection can use the Canny algorithm.

[0113] import cv2

[0114] contours = []

[0115] for frame in frames:

[0116] gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

[0117] blurred = cv2.GaussianBlur(gray, (5, 5), 0)

[0118] edges = cv2.Canny(blurred, 100, 200)

[0119] Use cv2.findContours() function to extract the contours in each frame. By setting the appropriate mode and method, the outer contour of the dough is extracted.

[0120] cnts, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

[0121] contours.append(cnts) # Store the contour of each frame

[0122] Relevant features such as area, perimeter, shape, and center point are calculated from the extracted contours. These features can reflect the state changes of the dough at different times.

[0123] features = []

[0124] for cnt in contours:

[0125] area = cv2.contourArea(cnt)

[0126] perimeter = cv2.arcLength(cnt, True)

[0127] M = cv2.moments(cnt)

[0128] centroid = (int(M['m10'] / M['m00']), int(M['m01'] / M['m00']))if M['m00'] != 0 else (0, 0)

[0129] features.append({'area': area, 'perimeter': perimeter, 'centroid':centroid})

[0130] Organize the extracted features in chronological order to form a time series feature dataset. Use a Pandas DataFrame to store this data for easy analysis and visualization. By analyzing the time series features, identify trends in the dough's state. For example, calculate the rate of change, maximum value, and minimum value of each feature to understand the dough's fermentation and state changes. Use an appropriate visualization tool (such as Matplotlib) to plot the dough's state. Set the chart's title, labels, and colors to clearly present the data.

[0131] import matplotlib.pyplot as plt

[0132] plt.figure(figsize=(12, 6))

[0133] plt.plot(feature_df['area'], label='Area', color='blue')

[0134] plt.plot(feature_df['perimeter'], label='Perimeter', color='orange')

[0135] plt.title('Dough State Over Time')

[0136] plt.xlabel('Time Frame')

[0137] plt.ylabel('Feature Value')

[0138] plt.legend()

[0139] Step S3: Mining the dynamic changes of yeast particles frame by frame and predicting the activity trend at multiple moments in the dough monitoring time series frame sequence to extract the optimal kneading time point;

[0140] In this embodiment, image processing technology is used to analyze the dough monitoring time series frame sequence frame by frame to extract the yeast particles in each frame. A threshold segmentation method can be used to binarize the image to facilitate the identification of yeast particles.

[0141] Use the cv2.threshold function for binarization, setting an appropriate threshold to ensure that the yeast particles are clearly isolated. For each binarized image frame, use the cv2.findContours function to extract the outlines of the yeast particles and calculate their number, area, and shape characteristics. Record the characteristics of the yeast particles in each frame and use a data structure (such as a dictionary or DataFrame) to store relevant information for each frame, including the number of particles, average area, and shape changes.

[0142] By comparing yeast particle features between adjacent frames, dynamic change indicators, such as the rate of change in yeast particle number and area between frames, are calculated. These change indicators can help identify dynamic trends in yeast activity. Using this extracted dynamic change data, activity trend prediction models can be constructed. Yeast activity can be modeled using linear regression, time series analysis, or machine learning methods such as random forests or support vector machines.

[0143] The extracted features (such as time, number of yeast particles, and area) are used as input to build a prediction model and train the model using historical data. For example, the scikit-learn library can be used for model training: from sklearn.linear_model import LinearRegression

[0144] model = LinearRegression()

[0145] model.fit(X_train, y_train) # Training model.

[0146] Predict future time points and generate an activity trend graph to reflect the changes in yeast activity at different time points.

[0147] Based on the activity trend prediction results, identify the time point when yeast activity is highest. You can set a threshold to determine the peak location of the activity trend and extract the corresponding timestamp.

[0148] Find the local maximum in the activity trend graph and mark the optimal kneading time point. Peak detection can be achieved through the scipy.signal.find_peaks function: from scipy.signal import find_peaks

[0149] peaks, _ = find_peaks(activity_trend, height=threshold)

[0150] The obtained optimal kneading time point is associated with the actual kneading process record for subsequent operation and analysis.

[0151] The extracted optimal kneading time points and their corresponding yeast activity data are stored in the database for subsequent reference and analysis.

[0152] You can draw activity trend charts, mark the optimal kneading time points, and use Matplotlib for visualization to help the production team intuitively understand the data.

[0153] Step S4: performing time state matching on the dough timing state diagram based on the optimal kneading time point, and then making dynamic kneading parameter decisions to generate dynamic kneading parameters;

[0154] In this embodiment, data of the dough time series state diagram are extracted from the database. These data should include time series and state characteristics (such as temperature, humidity, physical properties of the dough, etc.).

[0155] Ensure the determination of the optimal kneading time point and record its corresponding state characteristics for matching and analysis.

[0156] Time state matching:

[0157] Determine the optimal kneading time point and format it into a matching data format (such as a timestamp).

[0158] Using conditional filtering, extract the state feature data corresponding to the optimal kneading time from the dough time series state diagram. Use the loc method in the Pandas library for exact matching: matched_state = state_data.loc[state_data['time'] == optimal_time] . Record the matching results, including the state features at the optimal time and their corresponding time information. Based on the extracted state feature data, establish a dynamic kneading parameter decision model. Parameter decisions can be made using rule-based decision logic or machine learning models (such as decision trees or random forests).

[0159] Set up decision rules, such as:

[0160] If the temperature is above a certain threshold, the kneading time is increased.

[0161] If the dough moisture is too low, increase the amount of water. Input the matched state feature data into the decision model to calculate the dynamic kneading parameters that need to be adjusted. These parameters should include kneading time, kneading intensity, speed, etc. Adjust the parameters according to the model output: if matched_state['temperature'] > threshold:

[0162] dynamic_parameters['kneading_time'] += additional_time

[0163] The generated dynamic kneading parameters are saved in the database, and the corresponding optimal kneading time points and state characteristics are recorded for subsequent analysis and monitoring. During the actual kneading process, the state changes of the dough are monitored in real time, and the dynamic parameters are adjusted according to the feedback to ensure the optimal state of the dough.

[0164] Step S5: Evaluate the dough state based on the dynamic kneading parameters, perform real-time kneading parameter fine-tuning optimization, and construct a real-time kneading fine-tuning strategy;

[0165] In this embodiment, in actual operation, the dynamic kneading parameters generated in the previous step are applied to the kneading machine to ensure that the equipment can be adjusted according to these parameters, covering key parameters such as kneading time, speed, and intensity. During the implementation process, the operating status of the equipment is monitored in real time to ensure that it works normally according to the set parameters. The industrial control system or PLC (programmable logic controller) can be used to set the parameters. After the dynamic parameters are applied, the state changes of the dough are continuously monitored, focusing on indicators such as the temperature, humidity, texture and appearance of the dough. These data can be captured by sensors (such as temperature and humidity sensors, texture meters), and the data acquisition system is used to record the dough state at regular intervals for subsequent analysis. The data can be stored in a database to ensure the integrity and traceability of the data. A dough state evaluation model is constructed, and the dough state data collected previously is used to define evaluation criteria. The evaluation criteria may include the uniformity, viscosity, elasticity, etc. of the dough. A machine learning algorithm (such as a support vector machine or a decision tree) can be used to train the model to perform state evaluation based on real-time data. Based on the model evaluation results, the dough is judged. The system can determine whether the current dough state meets the expected goal. If the dough state is found to be unsatisfactory (such as too dry or too wet), immediate parameter fine-tuning is required. For example, if the dough is too sticky, the kneading time can be reduced or the kneading intensity can be reduced. If the dough is too dry, the amount of water added can be increased. A feedback control mechanism is used to dynamically adjust the kneading parameters based on the evaluation results. The fine-tuned parameters are recorded in the database to ensure that all adjustments are traceable. The records should include the reasons for the fine-tuning, the adjusted parameter values, and their corresponding timestamps. The immediate fine-tuning strategy is evaluated and the effect of the adjustment is analyzed. If the effect is significant, it can be incorporated into the standard operating procedure. If the effect is not good, the strategy needs to be further adjusted. Historical data should be reviewed regularly and the fine-tuning strategy should be optimized to improve decision-making efficiency. Throughout the process, the changes in dough state and kneading parameters are continuously monitored, feedback data is collected, and this data is used for model retraining and optimization. Through machine learning methods, the evaluation model and fine-tuning strategy are continuously updated to adapt to the characteristics and changes of different batches of dough to ensure that each kneading process achieves the best effect.

[0166] Step S6: Perform comprehensive state evaluation based on the instant dough kneading fine-tuning strategy and conduct self-reinforcement dough kneading performance learning to generate an intelligent dough kneading control model.

[0167] In this example, a comprehensive state assessment model is constructed based on real-time kneading fine-tuning. This model should take into account multiple factors, including the physical properties of the dough (such as temperature, humidity, and elasticity), yeast activity, and historical kneading parameters.

[0168] Select appropriate evaluation indicators, such as dough viscosity, uniformity, and fermentation degree, and use methods such as multiple linear regression or decision tree to build models. You can use the scikit-learn library to create and train the model: fromsklearn.linear_model import LinearRegression

[0169] model = LinearRegression()

[0170] model.fit(X_train, y_train) # training model

[0171] In actual operation, dough state data is continuously collected, including real-time monitoring of temperature, humidity, kneading parameters, and their corresponding evaluation results. Sensors and data logging systems are used to automate data collection. Feature data is extracted, including the impact of immediate parameter adjustments on dough state and historical state evaluation results. This data is stored in a database for subsequent analysis and model training. A self-reinforcement learning framework is designed, selecting an appropriate algorithm, such as Q-learning or a deep Q-network (DQN). This framework should be able to automatically adjust kneading parameters based on dough state evaluation results to optimize the kneading process. The state space (e.g., current dough state characteristics), action space (adjustable kneading parameters), and reward mechanism (e.g., dough quality score) should be determined. The reward mechanism should be designed appropriately to encourage the learning algorithm to optimize the kneading process. For example, a scoring system based on final dough quality can be established. Model training is performed using collected data and a self-reinforcement learning algorithm. Through continuous trial and error and feedback, the algorithm learns the optimal relationship between kneading parameters and dough state. During training, real-time feedback is used to adjust model parameters to ensure that the model can adapt to changes in dough state. Use the experience replay mechanism to store historical states to improve learning efficiency and effectiveness.

[0172] In this embodiment, refer to Figure 2 , is a flowchart of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0173] Step S11: obtaining a real-time dough monitoring image in the dough kneading machine;

[0174] Step S12: performing edge sharpening and reconstruction on the real-time dough monitoring image to construct a detail-optimized and reconstructed dough image;

[0175] Step S13: extracting frame-by-frame image changes of the detail-optimized and reconstructed dough image to obtain a dough monitoring image for each frame;

[0176] Step S14: calculating the time stamp of sequential frame acquisition for the detail-optimized and reconstructed dough image to obtain the acquisition time stamp of each frame;

[0177] Step S15: performing time-series frame fitting on each frame of the dough monitoring image according to the acquisition timestamp of each frame to construct a dough monitoring time-series frame sequence.

[0178] In this embodiment, a suitable camera (such as an industrial camera or a high-resolution USB camera) is selected to ensure that it can provide clear images. The camera is installed in an appropriate position on the dough kneading machine to ensure that it can cover the entire dough kneading area. Use image capture software (such as OpenCV or a custom image acquisition program) to set the camera parameters (such as resolution, frame rate, exposure, etc.). Ensure that the camera connection is stable and can transmit image data in real time. Start the camera and start capturing dough images in real time. Save the captured image data stream to the memory or directly transfer it to the processing module for subsequent processing. Perform color space conversion (such as from BGR to grayscale) on the acquired real-time dough monitoring image to simplify processing. Select a suitable edge detection algorithm, such as Canny edge detection or Sobel operator. Apply the selected edge detection algorithm to extract edge information from the image. Use a sharpening filter (such as a Laplacian filter) to enhance the edge features of the image. Synthesize the processed edge information with the original image to generate a detail-optimized and reconstructed dough image. Save the processed image for subsequent frame-by-frame extraction. Load the detail-optimized and reconstructed dough image sequence into the memory. Compare each image frame with the previous one and use a difference detection algorithm (such as the absolute difference method) to extract changes. A threshold can be set to filter out small or unimportant changes. Save the extracted changes as a new image frame, forming a new dough monitoring image sequence. Ensure that each frame's change image reflects the dynamic changes in the dough state. Record the initial time at the beginning of image capture (for example, using Python's time module). For each image frame, calculate the difference between the initial time and the initial time to generate a timestamp. Record the acquisition timestamp in milliseconds or seconds in a list. Load each frame's acquisition timestamp and the corresponding image frame from storage. Sort the image frames by timestamp to ensure that the image sequence is in chronological order. This can be achieved using the sorted() function in Python or another sorting algorithm. The sorted image frames and timestamps form a time-series frame sequence for subsequent analysis. Save the time-series frame sequence as a video file or image sequence for visualization and analysis.

[0179] In this embodiment, the specific steps of step S12 are:

[0180] Perform pixel-by-pixel recognition on the real-time dough monitoring image to extract all pixel points in the image;

[0181] Perform global pixel average calculation on all pixels in the image to generate the global pixel average of the image;

[0182] Image noise suppression is performed based on the global pixel average value of the image to obtain a denoised and optimized dough monitoring image;

[0183] Perform histogram equalization on the denoised and optimized dough monitoring image to generate an image grayscale histogram;

[0184] Perform grayscale distribution stretching on the image grayscale histogram and calculate the grayscale value of the stretched pixel;

[0185] performing contrast enhancement according to the grayscale values ​​of the stretched pixels to obtain a contrast-enhanced dough monitoring image;

[0186] Perform edge gradient information analysis on the texture detail enhanced dough monitoring image to obtain an edge map;

[0187] Performing slight texture change sharpening on the edge map to generate a micro-texture change sharpened edge map;

[0188] The contrast-enhanced dough monitoring image is edge-sharpened and reconstructed according to the micro-texture change sharp edge map to construct a detail-optimized reconstructed dough image.

[0189] In this example, an image processing library (such as OpenCV, PIL, or scikit-image) is used to load a real-time dough monitoring image. Ensure the image format is correct, typically RGB or grayscale. Convert the image to an array for pixel-by-pixel access. In Python, use the numpy library to convert the image data into an array with a shape of (height, width, number of channels). Iterate over each pixel and extract the RGB value (or grayscale value) for each pixel using a double loop (an outer loop over rows and an inner loop over columns). Create a list or array to store the extracted pixel values ​​for subsequent processing. Each pixel value can be stored as a tuple or dictionary containing its coordinates (x, y) and corresponding color value. Store all pixel data in memory or write it to a temporary file for subsequent processing. Record the total number of extracted pixels and basic image information (such as width and height) to provide context for subsequent analysis. Create variables to store the total sum of each color channel (e.g., total_red, total_green, total_blue) and a pixel count variable, initialized to zero. Traverse the extracted pixel list and accumulate the RGB value of each pixel into the corresponding sum variable. Each time a pixel is traversed, the pixel count variable is increased by one. After the traversal is completed, the accumulated sum is divided by the total number of pixels to calculate the average value of each channel:

[0190] average_red = total_red / pixel_count,

[0191] average_green = total_green / pixel_count,

[0192] average_blue = total_blue / pixel_count. This function stores the calculated global average in a dictionary or tuple formatted as (average_red, average_green, average_blue). The average type (e.g., RGB or grayscale) is recorded to provide necessary information for subsequent image processing. The noise type is determined; common examples include Gaussian noise and salt and pepper noise. Based on the noise type, an appropriate denoising algorithm is selected, such as mean filtering, median filtering, or Gaussian filtering. The selected filter is applied to the image. For example, using Gaussian filtering, the image is smoothed using the cv2.GaussianBlur function with an appropriate convolution kernel size (e.g., 5x5 or 7x7). Alternatively, a custom denoising algorithm can be implemented, using a convolution operation to traverse the image and calculate a weighted average for each pixel. The denoised image is generated, preserving the same dimensions and data type as the original image to ensure compatibility with subsequent processing. The denoised image is stored in memory or written to a file for subsequent histogram equalization and other processing steps. If the current image is RGB, it must be converted to grayscale for histogram equalization. Use the cv2.cvtColor function to convert the image from RGB to grayscale. Use the cv2.calcHist function to calculate the histogram of the grayscale image, returning the number of pixels at each grayscale level. The histogram typically ranges from 0 to 255, corresponding to all possible values ​​in an 8-bit grayscale image. Apply the cv2.equalizeHist function to equalize the grayscale image, which adjusts the image's grayscale distribution to achieve a more uniform contrast. Store the equalized image in memory and save the histogram data for subsequent analysis and visualization. Optionally, plot and display the histogram to easily observe the effects of the equalization. Find the maximum and minimum grayscale values ​​in the equalized image by looping through all pixels in the image. Stretch the grayscale value of each pixel using the following formula: new_value = (pixel_value − min_value) / (max_value − min_value) × 255, linearly stretching the grayscale value to the range of 0 to 255. Apply the calculated new grayscale value to the image to generate the updated image.

[0193] Choose a contrast enhancement method based on your needs. Common methods include linear contrast enhancement, logarithmic transformation, or gamma correction. For linear enhancement, apply the following: Apply a linear transformation to each pixel: enhanced_value = α × pixel_value + β, where α is the contrast gain factor and β is the brightness offset. Ensure that the enhanced pixel values ​​are between 0 and 255. If necessary, apply clipping to set out-of-range values ​​to 0 or 255. Select an appropriate edge detection algorithm, such as the Sobel operator, Canny edge detection, or Laplace operator. Canny edge detection generally performs well, effectively processing noise and extracting fine details. If you choose the Canny algorithm, use the cv2.Canny function, pass the image, and set appropriate thresholds (such as low and high thresholds). If you use the Sobel operator, first smooth the image, then calculate the x- and y-gradients, and finally synthesize an edge map. Generate an edge map, which displays the primary edge information extracted from the image. This is typically a binary image, with white representing edges and black representing non-edges. Store the edge map in memory for subsequent sharpening of subtle texture changes. Select an appropriate sharpening filter, such as a Laplacian filter or a custom sharpening convolution kernel. Use the cv2.filter2D function to apply the selected sharpening convolution kernel to the edge map to enhance edges and details.

[0194] Ensure that the sharpening operation does not introduce too much noise, and combine it with a smoothing operation if necessary. Generate a micro-texture change sharpened edge map to show enhanced details and clarity. Store the sharpened micro-texture change edge map in memory for subsequent detail optimization and reconstruction processing. Combine the micro-texture change sharpened edge map with the contrast-enhanced image using the weighted superposition method: optimized_image=enhanced_image+λ×sharpened_edges. Among them, λ is the weight coefficient that controls the degree of edge enhancement. Pixel value normalization: Ensure that the pixel values ​​of the reconstructed image are between 0 and 255, and perform necessary cropping operations. Generate a detail-optimized reconstructed dough image to facilitate observation of the details and state of the dough. Store the final detail-optimized reconstructed dough image in a database or file system and prepare it for display and analysis.

[0195] In this embodiment, refer to Figure 3 , is a flowchart of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0196] Step S21: performing dough contour recognition frame by frame on the dough monitoring time sequence frame sequence, and extracting the dough contour line of each frame;

[0197] Step S22: Tracking the dynamic changes of the dough contour line of each frame to obtain a dynamic change trajectory of the dough contour;

[0198] Step S23: performing a contour change trend analysis on the dynamic change trajectory of the dough contour to generate a dough contour change trend feature;

[0199] Step S24: evaluating the dough surface roughness of the dough monitoring time series frame sequence to generate a dough surface roughness time series curve;

[0200] Step S25: performing frame-by-frame analysis of the dough crack generation situation on the dough monitoring time series frame sequence to obtain crack evolution characteristics in the dough;

[0201] Step S26: Conduct in-depth exploration of the dough temporal state based on the dough surface roughness temporal curve, dough contour change trend characteristics, and dough crack evolution characteristics to construct a dough temporal state diagram.

[0202] In this embodiment, an image processing library (such as OpenCV) is used to load an image sequence of each frame, ensuring that the image format is consistent, such as RGB or grayscale images. The images are preprocessed, including denoising, smoothing, and grayscale conversion. Common denoising methods include Gaussian filtering to reduce the influence of noise in the image. The image is smoothed using the cv2.GaussianBlur function and converted to a grayscale image using cv2.cvtColor. An edge detection algorithm, such as Canny edge detection, is applied to identify edges in the image. The cv2.Canny function is used to set an appropriate threshold to effectively extract the edges. To extract the dough's contour from the edge map, use the cv2.findContours function. This function returns a list of contour coordinate points. The outermost contour can be selected to represent the dough's contour. The extracted contours are stored in a list for subsequent processing. The contour information for each frame is saved to a file or database. The contour point data and its corresponding timestamp are recorded for each frame to provide basic data for tracking dynamic changes. For continuous contours in time-series frames, shape matching algorithms (such as Hu moments or shape context) are used for matching. Dynamic changes in contours are determined by calculating the similarity between contours. Using cv2.The matchShapes function obtains the similarity score between contours, compares contours frame by frame, records the changes between each frame and the previous frame, calculates the position, area, and shape changes of the contours, and stores the change information (such as the movement distance and shape change of the contours) in an array to form a dynamic change trajectory. Use visualization tools (such as Matplotlib) to draw a dynamic change trajectory graph to show how the dough contour changes over time. Different colors or markers can be used to represent different degrees of change to help analyze dynamic trends. The dynamic change trajectory is stored in a database and the change parameters at each time point are recorded for subsequent analysis and feature extraction. Trend modeling is performed on the dynamic change trajectory data. Linear regression or moving average method can be used to analyze the change trend of the dough contour. Linear regression modeling is performed using Python's scikit-learn library to obtain a function expression of the change trend. Important features are extracted from the model, such as the rate of change, maximum change amplitude, etc. These features can help understand the changes in the dough state. The standard deviation and mean of the changes are calculated to analyze its stability and fluctuation. Use visualization tools Draw a trend graph to show the changing characteristics of the dough profile for easy analysis and interpretation. You can choose different time periods for comparison and analyze the differences in characteristics. Store the extracted trend characteristics in a database and record the characteristic values ​​and their corresponding times to facilitate subsequent analysis and report generation. Select an appropriate roughness assessment method, such as average roughness (Ra) or root mean square roughness (Rq). These indicators can be extracted from the grayscale value changes of the image. Calculate the grayscale value of each frame and the roughness. Use the numpy library to operate on arrays to quickly calculate the roughness index. Record the roughness value of each frame and generate a time series curve. Use Matplotlib to draw a time series curve graph to show the trend of roughness change over time. Save the roughness time series curve and its values ​​in the database. Record the roughness value at each time point to facilitate subsequent analysis and reporting. Select an appropriate crack detection algorithm, such as one based on edge or texture features. Canny edge detection or Hough transform can be used to detect cracks. Apply the crack detection algorithm to each frame to identify and extract crack information. Use cv2.The findContours function extracts crack contours, compares crack changes frame by frame, and records crack count, length, and width, among other characteristics. Crack evolution can be analyzed by calculating the total length and number of cracks in each frame. Crack evolution characteristics are stored in a database, and crack characteristics at each time point are recorded for subsequent analysis and report generation. Time-series data on dough surface roughness, contour change trends, and crack evolution are extracted from the database and integrated into a comprehensive dataset. An appropriate state analysis model, such as multivariate regression or cluster analysis, is then selected to deeply explore the dough's temporal state. This integrated data is then deeply mined, analyzing the relationships between different characteristics and identifying key factors influencing dough quality. Based on the analysis results, a dough temporal state diagram is constructed to showcase the characteristic changes of dough under different states. Visualization tools are used to create the temporal state diagram, demonstrating the state change trends for intuitive understanding and analysis. The temporal state diagram is stored in the database, and a report is generated to document the analysis results and recommendations to support decision-making and subsequent research.

[0203] In this embodiment, refer to Figure 4 , is a flowchart of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0204] Step S31: mining the dynamic changes of yeast particles frame by frame in the dough monitoring time series frame sequence to extract the dynamic change features of yeast particles;

[0205] Step S32: performing yeast temporal activity analysis based on the dynamic change characteristics of yeast particles to generate a yeast temporal activity curve;

[0206] Step S33: Evolving the activity variation law of the yeast temporal activity curve to obtain the yeast activity variation law;

[0207] Step S34: performing multi-time activity trend prediction based on the yeast activity variation pattern, thereby generating yeast activity trend prediction data at multiple times;

[0208] Step S35: Calculate the optimal kneading time based on the yeast activity trend prediction data at multiple moments, thereby extracting the optimal kneading time point.

[0209] In this embodiment, an image processing library (such as OpenCV) is used to load the time-series image frames of dough monitoring, ensuring that the loading format of each frame is consistent and can be easily processed. Each frame of the image is preprocessed, including noise removal and contrast enhancement. Gaussian filtering (cv2.GaussianBlur) can be used to reduce image noise, and histogram equalization is used to improve contrast. The image is converted to a grayscale image for subsequent feature extraction. Morphological operations (such as opening and closing operations) are applied to enhance the visualization of yeast particles and segmentation is performed. Morphological processing is performed using the cv2.morphologyEx function, and threshold segmentation (such as the Otsu method) is used to extract yeast particles. The mother particles are extracted and the particle contours in each frame are extracted using the cv2.findContours function. The number, average area, shape characteristics (such as aspect ratio, roundness, etc.) of yeast particles in each frame and the movement trajectory of the particles are calculated. These features can be calculated by traversing the extracted contours and storing the extracted features in a data structure for subsequent analysis. Indicators for evaluating yeast activity are determined, such as the number of particles, average area or particle movement speed. These indicators can well reflect the changes in yeast activity. For each frame of the image, the activity value is calculated based on the selected activity indicator. For example, the number of particles is used as the activity value, or the average area of ​​the particles is normalized to the activity score, and the calculated activity is converted into the activity score. The values ​​are stored in a time series data structure. Visualization tools such as Matplotlib are used to draw the yeast time series activity curve, with time on the x-axis and activity value on the y-axis. To ensure that the curve is clear and easy to read, key time points (such as when yeast activity changes significantly) can be marked in the graph for easy analysis. The generated yeast time series activity curve and its values ​​are saved to the database, and the activity value at each time point is recorded for subsequent analysis and reporting. In order to analyze the change pattern, the yeast time series activity curve is first smoothed. The moving average method or Savitzky-Golay filter can be used to reduce the impact of noise on the pattern analysis. The rolling function of the pandas library is used to implement the moving average. Set an appropriate window size, observe the smoothed activity curve, and analyze the activity change trend using linear regression or polynomial regression. Use the regression model in scikit-learn to build a model and extract key features, such as slope and inflection point location, to characterize the activity change trend. Based on the analysis results, summarize the patterns of yeast activity changes, including the time periods of increase and decrease in activity and the magnitude of change. These patterns help understand yeast behavior during fermentation and select an appropriate prediction model, such as an ARIMA model, time series regression, or LSTM (long short-term memory network). Select the optimal model based on the data characteristics. If the data volume is small, consider using a linear regression model.If the data volume is large and has complex nonlinear relationships, you can consider the LSTM model, divide the historical activity data into a training set and a test set, use the training set to train the prediction model, use the scikit-learn or statsmodels library to train the model, adjust the model parameters to ensure that the model can accurately capture the activity change pattern, use the trained model to predict future yeast activity, set the prediction time point, generate activity trend prediction data at multiple moments, visualize the prediction results, draw the future activity trend curve, and compare it with the historical data to observe the rationality of the prediction and determine the standard for the optimal kneading time, such as when the yeast activity value reaches the maximum When the yeast activity value reaches a maximum, or during periods of time when the activity value remains at a stable high level, analyze the yeast activity trend forecast data to identify the peak location and duration of the activity value. Use the argmax function in the numpy library to find the index of the maximum activity value and calculate the time point when the activity value reaches a preset threshold. This is used as the potential kneading time. Based on the activity variation pattern and historical data, the optimal time point is selected to ensure that the dough is kneaded when the yeast activity is highest and the fermentation effect is optimal. Consider setting multiple time points for flexible selection in actual operation. The extracted optimal kneading time points are stored in the database, along with the corresponding activity value and timestamp, to provide a reference for actual production.

[0210] In this embodiment, the specific steps of step S35 are:

[0211] Perform multi-time activity trend peak calculation on yeast activity trend prediction data at multiple times, and extract the activity trend peak of each time period;

[0212] The activity trend peak of each time period is used to identify the inflection point of activity change and mark it as the highest peak point of yeast activity;

[0213] Define preset kneading time parameters;

[0214] Calculate the kneading buffer time at the highest peak point of yeast activity according to the preset kneading time parameters to obtain the kneading buffer time at the highest peak point;

[0215] The optimal kneading time is calculated based on the kneading buffer time at the highest peak point to obtain the optimal kneading time point.

[0216] In this example, yeast activity trend prediction data, which should include time series and corresponding activity values, were extracted from a database. The data was organized into a format suitable for analysis, such as by converting it into a DataFrame using the Pandas library. An appropriate peak detection algorithm was selected, such as the find_peaks function in the SciPy library, which can effectively identify local peaks in the signal. Peak detection parameters, including minimum peak height and minimum distance, were set to ensure that only significant activity peaks were detected. The scipy.signal.The find_peaks function analyzes the activity trend data, identifies all peaks and their corresponding time points and activity values, stores the detected peaks in a list or DataFrame, records the time, activity value and position of each peak, sorts the extracted peaks, and presents them visually to facilitate observation of the activity trend peaks in each time period. In the visualization graph, you can use Matplotlib to draw the activity curve and mark the peaks, save the peak results to a file or database, and provide basic data for subsequent identification of activity change inflection points. Select a suitable inflection point detection algorithm, such as using the first-order derivative or rate of change. You can identify rising and falling inflection points by calculating the derivative of the activity value, set a threshold, and determine the obvious inflection point location. For example, by setting a threshold for the minimum rate of change, calculate the first-order derivative of the activity value sequence, and identify the point where the positive changes to negative as the inflection point. In Python, you can use the diff function of the numpy library to calculate the derivative. For each peak, check the changes before and after it to identify Identify the highest peak of the activity value, mark the identified highest peak point as the "highest peak point of yeast activity", and record its timestamp and activity value. Store all inflection points and their corresponding peak information in the database for subsequent use. Select appropriate kneading time parameters based on actual operating experience or experimental data. This time should take into account the fermentation characteristics of yeast and the physical properties of dough. For example, the kneading time can be set to 30 minutes, 45 minutes, or 60 minutes, etc. The specific time period can be adjusted according to process requirements. Store the selected kneading time parameters in the configuration file or database for call in subsequent steps. It can be defined as a global variable for reference in subsequent calculations. Confirm whether the preset kneading time parameters meet the production standards and record their selection basis for subsequent evaluation and adjustment. Define the calculation formula for the buffer time. For example, the buffer time can be set as the "time of the highest peak point of yeast" plus the "preset kneading time". For example, if the highest peak point occurs at time t and the preset kneading time is 30 minutes, then the buffer time is t + 30 minutes, extract the time from the marked highest peak point, and combine it with the preset kneading time to perform calculations to ensure that the calculated buffer time is logical. The buffer time result is then combined with the current time to determine the optimal kneading time within a reasonable time range. For example, if the buffer time is t, the optimal kneading time is the current time plus t. Python date and time processing libraries (such as datetime) can be used for time operations to verify that the calculated optimal kneading time is within a reasonable time range and meets production scheduling requirements. This ensures that this time point maximizes yeast activity and improves dough fermentation. The optimal kneading time is recorded in a database, along with relevant information (such as the corresponding yeast activity value and timestamp) for subsequent query and report generation. The optimal kneading time and its recommended output are provided for the production team's reference.

[0217] In this embodiment, step S4 includes the following steps:

[0218] Step S41: performing time state matching on the dough time sequence state diagram based on the optimal kneading time point, and extracting the dough time sequence state features at the optimal kneading time point;

[0219] Step S42: performing optimal dough state analysis on the dough time sequence state diagram to generate optimal dough state characteristics;

[0220] Step S43: performing an optimal state deviation analysis on the optimal dough state characteristics based on the dough temporal state characteristics at the optimal kneading time point to obtain the dough state deviation characteristics at the optimal kneading time point;

[0221] Step S44: making dynamic kneading parameter decisions based on the dough state deviation characteristics at the optimal kneading time point to generate dynamic kneading parameters.

[0222] In this example, data from a dough time series state diagram is extracted from a database, including time series and state features (such as roughness, contour changes, and cracking). Ensure that the data format is suitable for processing, such as using a Pandas DataFrame, to facilitate subsequent operations. The optimal kneading time point is determined and converted to a suitable format, such as a timestamp. Conditional filtering is used to extract the state features corresponding to the optimal kneading time point from the time series state data. The Pandas loc method can be used to achieve exact matching. Relevant dough state features are extracted from the matching results, including but not limited to surface roughness, dynamic change trajectory, and cracking. These features can be accessed through simple indexing. The extracted state features are stored in a new data structure for subsequent analysis. The extracted dough time series state features at the optimal kneading time point are saved to a database or file, and the corresponding timestamps and feature values ​​are recorded to provide basic data for subsequent analysis. Determine the analysis method. Descriptive statistics, cluster analysis, or principal component analysis (PCA) can be used to extract the overall state features of the dough. Select a suitable Python library, such as scikit-learn for cluster analysis or statsmodels for statistical analysis. Preprocess the dough time-series state graph data to ensure data integrity and consistency. Address missing values ​​and outliers to ensure the reliability of the analysis results. Use the Pandas dropna method to remove missing values ​​and perform data cleaning. Based on the selected analysis method, extract the optimal dough state features. For example, if using PCA, project the data into a low-dimensional space to extract the principal components. Record the contribution rate of each feature to understand which features play a significant role in the overall state. Determine a method for calculating deviation. Metrics such as absolute error, relative error, or standard deviation can be used to quantify the deviation of state features. Select an appropriate Python library for calculations, such as numpy for numerical computation and scipy for statistical analysis. Extract the dough time-series state features and the optimal dough state features at the optimal kneading time from the database, ensuring consistent data formats. Ensure that the features of the two data sets match and set the same feature dimensions for comparison. For each feature, calculate the deviation between the optimal kneading time feature and the optimal dough state feature. For example, use the following formula to calculate absolute deviation: Deviation = Actual state feature − Optimal state feature. Decision-making methods for determining dynamic kneading parameters can use rule engines, linear regression, or fuzzy logic. Select an appropriate Python library, such as sklearn for linear regression or fuzzywuzzy for fuzzy logic decision making. Build a decision model based on the dough state deviation characteristics. For example, build a linear regression model to predict the relationship between the optimal kneading time and state deviation. Train the model using the deviation characteristics as input variables and dynamic parameters (such as kneading time and kneading intensity) as output variables.The established model predicts the current dough state deviation and generates corresponding dynamic kneading parameters. For example, input the current state deviation and output a recommended kneading time and intensity. This ensures that the generated dynamic parameters are reasonable, executable, and meet actual operational requirements.

[0223] In this embodiment, step S5 includes the following steps:

[0224] Step S51: performing real-time dough kneading control based on dynamic dough kneading parameters and acquiring real-time dough kneading monitoring images;

[0225] Step S52: performing dough shape analysis on the real-time dough kneading monitoring image to obtain the real-time dough kneading shape;

[0226] Step S53: performing dough state evaluation on the real-time dough shape during kneading to generate a real-time dough state evaluation value;

[0227] Step S54: performing instant kneading parameter fine-tuning optimization on the dynamic kneading parameters based on the real-time kneading state evaluation value, and constructing an instant kneading fine-tuning strategy.

[0228] In this embodiment, real-time dynamic dough kneading parameters are extracted from the database. These parameters should include dough kneading time, dough kneading speed, dough kneading intensity, etc. These parameters should be adjusted according to the analysis results of the previous stage, and the initial state of the control system is set to ensure that all control devices (such as dough kneading machines) can receive and execute these dynamic parameters. A real-time control system is constructed, and a PLC (programmable logic controller) or an industrial computer is used to control the operation of the dough kneading machine. Real-time control of the dough kneading machine is achieved through programming to ensure that it can be adjusted according to the dynamic parameters. A suitable control algorithm (such as PID control) is used to ensure accurate control of dough kneading, and the dough kneading intensity and time are adjusted in time. A high-resolution camera is installed on the dough kneading machine to obtain real-time monitoring images of the dough, and the position of the camera is ensured to clearly capture the shape and state of the dough. Image acquisition software is configured to regularly take real-time images and store them locally or upload them to a cloud server for subsequent analysis. In the real-time control system, the dynamic parameters and corresponding monitoring images at each time point are recorded. These data are saved in a database or file system for subsequent analysis and verification. The monitoring system should have real-time feedback capabilities to ensure timely adjustment of operations when parameters change. Image processing libraries (such as OpenCV) are used to preprocess the real-time monitoring images, including noise removal and contrast enhancement. Gaussian filtering and histogram equalization can be used to improve image quality. The images are converted to grayscale images for subsequent processing and analysis. The Canny edge detection algorithm is applied to identify edges in the image. The edge map is obtained using the cv2.Canny function. The cv2.findContours function is used to extract the contour of the dough and obtain the boundary information of the dough. The shape characteristics of the dough, including the area, perimeter, and aspect ratio of the dough, are calculated. These features will be used for subsequent status evaluation using cv2.contourArea and cv2.The arcLength function obtains the area and perimeter of the contour, and then calculates shape features such as the aspect ratio. The extracted dough shape features are saved in the database, and the corresponding timestamps and image information are recorded for subsequent analysis and decision support. The criteria for dough state evaluation are determined. For example, the shape features (such as area, aspect ratio, uniformity, etc.) are used to determine whether the dough state is qualified. Thresholds can be set to define the criteria for good and bad states for subsequent evaluation. According to the defined evaluation criteria, a dough state evaluation model is constructed. A simple rule engine can be used to generate state evaluation values ​​through logical judgment. For example, if the aspect ratio of the dough is within a certain range, the dough state is considered to be good, otherwise it is considered to be bad. The dough shape features of each frame are evaluated and the state evaluation value is calculated. The extracted shape features are input into the model to obtain real-time evaluation results. The evaluation values ​​are recorded and associated with the corresponding timestamps and image information. According to the real-time state evaluation values, a dough state evaluation model is set. Fine-tuning strategies can be designed. For example, if the evaluation value is below a preset threshold, the kneading time or intensity can be increased; if the evaluation value is above the threshold, the kneading time may need to be reduced. A clear set of rules can be established to guide the adjustment of dynamic parameters. State evaluation values ​​can be monitored in real time, and kneading parameters can be dynamically adjusted according to the fine-tuning strategy. Control parameters can be directly modified through the control system interface or API interface. A feedback control algorithm can be used to iteratively adjust parameters based on the current state and preset targets. After adjusting the parameters, the state of the dough is continuously monitored to evaluate the effectiveness of the new parameters. The effectiveness of the fine-tuning strategy can be verified by comparing the state evaluation values ​​before and after the adjustment. Short-term feedback cycles can be set, such as re-evaluating the state every few minutes to achieve timely adjustments. The adjusted dynamic kneading parameters and their corresponding state evaluations are recorded in a database for subsequent analysis and optimization. Historical data can be reviewed regularly to evaluate the effectiveness of the fine-tuning strategy and continuously optimize the adjustment plan based on actual conditions.

[0229] In this embodiment, step S6 includes the following steps:

[0230] Step S61: extracting a final dough kneading image based on the real-time dough kneading monitoring image;

[0231] Step S62: performing a comprehensive state evaluation on the final dough kneading image according to the optimal dough state characteristics to obtain a final dough state evaluation value;

[0232] Step S63: Based on the final dough state evaluation value, the instant dough kneading fine-tuning strategy is subjected to self-reinforcement dough kneading performance learning, thereby generating an intelligent dough kneading control model.

[0233] In this embodiment, to ensure that the real-time image capture system is working properly, the camera should be correctly installed and able to capture high-quality images at different kneading stages. The image acquisition frequency is configured to ensure that clear images can be obtained at critical moments. It can usually be set to capture an image every few seconds. The captured real-time image is processed using an image processing library (such as OpenCV), such as denoising, contrast enhancement, and grayscale conversion. During the processing, the clarity and details of the image are preserved to facilitate subsequent status evaluation. A trigger condition is set, such as when the kneading time reaches a predetermined value or the dough state meets the standard, the current image is extracted as the final kneading image. The trigger mechanism is implemented in the code using conditional judgment statements (such as if statements). Ensure that the final image is automatically saved when the conditions are met. Save the extracted final kneaded dough image to local storage or the cloud for subsequent access and analysis. Use the save method of the PIL library to save the image in JPEG or PNG format. Record the image timestamp and related parameters for subsequent tracking and analysis. Extract the optimal dough state features from the database. These features should include indicators such as shape, texture, and uniformity. These will serve as the benchmark for evaluation. Ensure that the feature data is complete and accurate to ensure the validity of the evaluation results. Perform feature extraction on the final kneaded dough image. Use the same image processing methods as before to extract key features such as shape, area, and contour. Use OpenCV's findContours and cv2.Functions such as contourArea are used to obtain the shape features of the image. The state evaluation value of the final image is calculated according to the defined evaluation criteria (such as threshold-based judgment or machine learning model). The evaluation values ​​of each feature can be combined using a simple weighted average method to form a comprehensive state evaluation value. The calculated final dough state evaluation value is stored in the database, and its corresponding timestamp, image and related parameters are recorded for subsequent analysis and reference. Historical kneading parameters, state evaluation values ​​and final images are extracted from the database for performance analysis and model training to ensure the integrity of the data set, including different states and corresponding evaluation values ​​in multiple kneading processes. Suitable self-reinforcement learning algorithms are selected, such as Q-learning and deep Q network (DQN). These algorithms, such as policy gradient methods, continuously optimize the decision-making process through feedback, determining the state space, action space, and reward mechanism to guide the learning process. For example, the state space can be dough characteristics, and the action space can be adjustments to kneading parameters. The extracted data is used to train an intelligent kneading control model. Through continuous iteration and adjustment, the model learns the relationship between the optimal kneading parameters and dough state evaluation values. During training, model performance is monitored in real time, and the learning rate and other hyperparameters are adjusted to ensure effective model convergence. After model training is complete, validation testing is conducted to evaluate the model's performance in real-time kneading control to ensure it effectively improves dough quality. The resulting intelligent kneading control model is then deployed in production, with real-time feedback used to adjust parameters to further optimize the kneading process.

[0234] In this embodiment, a system for selecting an intelligent dough kneading mode is provided, which is used to execute the intelligent dough kneading mode selection method described above, including:

[0235] An image reconstruction module is used to obtain a real-time dough monitoring image in a dough kneading machine; perform edge sharpening and reconstruction on the real-time dough monitoring image, and perform time series frame sequence fitting to construct a dough monitoring time series frame sequence;

[0236] The timing state diagram module is used to identify the dough contour frame by frame in the dough monitoring timing frame sequence, conduct in-depth mining of the dough timing state, and construct a dough timing state diagram;

[0237] The activity trend prediction module is used to mine the dynamic changes of yeast particles frame by frame in the dough monitoring time series and predict the activity trend at multiple moments, thereby extracting the optimal kneading time point;

[0238] The dynamic dough kneading module is used to match the time state of the dough timing state diagram based on the optimal kneading time point, and then make dynamic kneading parameter decisions to generate dynamic kneading parameters;

[0239] Parameter fine-tuning module, used to evaluate dough state based on dynamic kneading parameters, perform real-time kneading parameter fine-tuning optimization, and build real-time kneading fine-tuning strategy;

[0240] Intelligent dough kneading control is used to perform comprehensive state evaluation based on the real-time dough kneading fine-tuning strategy and conduct self-reinforcement kneading performance learning to generate an intelligent dough kneading control model.

[0241] The present invention uses sensors such as cameras to obtain dough images in a dough kneading machine in real time, providing raw data for subsequent analysis, performing edge sharpening processing on the obtained real-time images to highlight the contours and details of the dough, ensuring that subsequent image analysis is more accurate, and fitting multiple frames of images in a time-series frame sequence to construct a time-series frame sequence for dough monitoring, helping the system to identify the dynamic changes of the dough during the kneading process, tracking the contour changes of the dough in real time, and ensuring that the system can detect slight changes in the dough morphology. After constructing the dough time-series state diagram, it is possible to understand the changing trends of the dough at different time points, providing accurate data for further analysis and decision-making. Deep mining of the time-series state helps to identify potential kneading problems (such as over-kneading or over-relaxation), and improve the system's ability to identify and predict the dough state. By performing multi-time prediction of yeast activity, the system can dynamically adjust parameters during the kneading process to ensure the optimal fermentation state of the yeast, accurately calculate the optimal kneading time point, and avoid kneading the dough when the yeast activity is not appropriate, thereby improving the fermentation effect and dough quality. The activity trend prediction module improves the control accuracy during the dough fermentation process. , ensuring that the dough can reach the ideal fermentation state. By matching the optimal kneading time point, the accuracy of the kneading time is ensured to avoid over-kneading or under-kneading. According to the real-time feedback of the dough state, the kneading parameters are dynamically adjusted to make the kneading process more personalized and adapt to the needs of different doughs. Accurate kneading control can improve kneading efficiency and ensure that each stage of the dough is in the best state. By fine-tuning the kneading parameters, the system can respond to changes in the dough state in a timely manner to ensure that the dough is always in the best state. No matter what changes occur in the dough during the kneading process, the system can flexibly adjust the kneading parameters to reduce the impact caused by environmental changes or other factors. The fine-tuning strategy can help maintain the consistency of the quality of each kneading and avoid quality fluctuations caused by operational errors. Through self-reinforcement learning, the system can continuously accumulate experience and data, thereby continuously optimizing the kneading control strategy. The generated intelligent kneading control model can automatically adapt to various production environments, improve the intelligence of the kneading process, and reduce manual intervention. As more data accumulates, the intelligent model will become more and more accurate, continuously improving kneading efficiency and product quality consistency.

[0242] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0243] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for selecting an intelligent dough kneading mode, characterized in that: The following steps are involved: Step S1: obtaining a real-time dough monitoring image in a dough kneading machine; performing edge sharpening and reconstruction on the real-time dough monitoring image, and performing time frame sequence fitting to construct a dough monitoring time frame sequence; Step S2: performing dough contour recognition frame by frame on the dough monitoring time sequence frame sequence, and performing deep mining of the dough time sequence state to construct a dough time sequence state diagram; Step S3: Mining the dynamic changes of yeast particles frame by frame and predicting the activity trend at multiple moments in the dough monitoring time series frame sequence to extract the optimal kneading time point; Step S4: performing time state matching on the dough timing state diagram based on the optimal kneading time point, and then making dynamic kneading parameter decisions to generate dynamic kneading parameters; Step S5: Evaluate the dough state based on the dynamic kneading parameters, perform real-time kneading parameter fine-tuning optimization, and construct a real-time kneading fine-tuning strategy; Step S6: Performing a comprehensive state evaluation based on the instant dough kneading fine-tuning strategy and performing self-reinforcement dough kneading performance learning to generate an intelligent dough kneading control model; The specific steps of step S4 are: Step S41: performing time state matching on the dough time sequence state diagram based on the optimal kneading time point, and extracting the dough time sequence state features at the optimal kneading time point; Step S42: performing optimal dough state analysis on the dough time sequence state diagram to generate optimal dough state characteristics; Step S43: performing an optimal state deviation analysis on the optimal dough state characteristics based on the dough temporal state characteristics at the optimal kneading time point to obtain the dough state deviation characteristics at the optimal kneading time point; Step S44: making dynamic kneading parameter decisions based on the dough state deviation characteristics at the optimal kneading time point to generate dynamic kneading parameters.

2. The method for selecting an intelligent dough kneading mode according to claim 1, wherein: The specific steps of step S1 are: Step S11: obtaining a real-time dough monitoring image in the dough kneading machine; Step S12: performing edge sharpening and reconstruction on the real-time dough monitoring image to construct a detail-optimized and reconstructed dough image; Step S13: extracting frame-by-frame image changes of the detail-optimized and reconstructed dough image to obtain a dough monitoring image for each frame; Step S14: calculating the time stamp of sequential frame acquisition for the detail-optimized and reconstructed dough image to obtain the acquisition time stamp of each frame; Step S15: performing time-series frame fitting on each frame of the dough monitoring image according to the acquisition timestamp of each frame to construct a dough monitoring time-series frame sequence.

3. The method for selecting an intelligent dough kneading mode according to claim 2, wherein: The specific steps of step S12 are: Perform pixel-by-pixel recognition on the real-time dough monitoring image to extract all pixel points in the image; Perform global pixel average calculation on all pixels in the image to generate the global pixel average of the image; Image noise suppression is performed based on the global pixel average value of the image to obtain a denoised and optimized dough monitoring image; Perform histogram equalization on the denoised and optimized dough monitoring image to generate an image grayscale histogram; Perform grayscale distribution stretching on the image grayscale histogram and calculate the grayscale value of the stretched pixel; performing contrast enhancement according to the grayscale values ​​of the stretched pixels to obtain a contrast-enhanced dough monitoring image; Perform edge gradient information analysis on the texture detail enhanced dough monitoring image to obtain an edge map; Performing slight texture change sharpening on the edge map to generate a micro-texture change sharpened edge map; The contrast-enhanced dough monitoring image is edge-sharpened and reconstructed according to the micro-texture change sharp edge map to construct a detail-optimized reconstructed dough image.

4. The method for selecting an intelligent dough kneading mode according to claim 1, wherein: The specific steps of step S2 are: Step S21: performing dough contour recognition frame by frame on the dough monitoring time sequence frame sequence, and extracting the dough contour line of each frame; Step S22: Tracking the dynamic changes of the dough contour line of each frame to obtain a dynamic change trajectory of the dough contour; Step S23: performing a contour change trend analysis on the dynamic change trajectory of the dough contour to generate a dough contour change trend feature; Step S24: evaluating the dough surface roughness of the dough monitoring time series frame sequence to generate a dough surface roughness time series curve; Step S25: performing frame-by-frame analysis of the dough crack generation situation on the dough monitoring time series frame sequence to obtain crack evolution characteristics in the dough; Step S26: Conduct in-depth exploration of the dough temporal state based on the dough surface roughness temporal curve, dough contour change trend characteristics, and dough crack evolution characteristics to construct a dough temporal state diagram.

5. The method for selecting an intelligent dough kneading mode according to claim 1, wherein: The specific steps of step S3 are: Step S31: mining the dynamic changes of yeast particles frame by frame in the dough monitoring time series frame sequence to extract the dynamic change features of yeast particles; Step S32: performing yeast temporal activity analysis based on the dynamic change characteristics of yeast particles to generate a yeast temporal activity curve; Step S33: Evolving the activity variation law of the yeast temporal activity curve to obtain the yeast activity variation law; Step S34: performing multi-time activity trend prediction based on the yeast activity variation pattern, thereby generating yeast activity trend prediction data at multiple times; Step S35: Calculate the optimal kneading time based on the yeast activity trend prediction data at multiple moments, thereby extracting the optimal kneading time point.

6. The method for selecting an intelligent dough kneading mode according to claim 5, characterized in that: The specific steps of step S35 are: Perform multi-time activity trend peak calculation on yeast activity trend prediction data at multiple times, and extract the activity trend peak of each time period; The activity trend peak of each time period is used to identify the inflection point of activity change and mark it as the highest peak point of yeast activity; Define preset kneading time parameters; Calculate the kneading buffer time at the highest peak point of yeast activity according to the preset kneading time parameters to obtain the kneading buffer time at the highest peak point; The optimal kneading time is calculated based on the kneading buffer time at the highest peak point to obtain the optimal kneading time point.

7. The method for selecting an intelligent dough kneading mode according to claim 1, wherein: The specific steps of step S5 are: Step S51: performing real-time dough kneading control based on dynamic dough kneading parameters and acquiring real-time dough kneading monitoring images; Step S52: performing dough shape analysis on the real-time dough kneading monitoring image to obtain the real-time dough kneading shape; Step S53: performing dough state evaluation on the real-time dough shape during kneading to generate a real-time dough state evaluation value; Step S54: performing instant kneading parameter fine-tuning optimization on the dynamic kneading parameters based on the real-time kneading state evaluation value, and constructing an instant kneading fine-tuning strategy.

8. The method for selecting an intelligent dough kneading mode according to claim 1, wherein: The specific steps of step S6 are: Step S61: extracting a final dough kneading image based on the real-time dough kneading monitoring image; Step S62: performing a comprehensive state evaluation on the final dough kneading image according to the optimal dough state characteristics to obtain a final dough state evaluation value; Step S63: Based on the final dough state evaluation value, the instant dough kneading fine-tuning strategy is subjected to self-reinforcement dough kneading performance learning, thereby generating an intelligent dough kneading control model.

9. An intelligent dough kneading mode selection system, characterized in that: The method for selecting the intelligent dough kneading mode according to claim 1 comprises: An image reconstruction module is used to obtain a real-time dough monitoring image in a dough kneading machine; perform edge sharpening and reconstruction on the real-time dough monitoring image, and perform time series frame sequence fitting to construct a dough monitoring time series frame sequence; The timing state diagram module is used to identify the dough contour frame by frame in the dough monitoring timing frame sequence, conduct in-depth mining of the dough timing state, and construct a dough timing state diagram; The activity trend prediction module is used to mine the dynamic changes of yeast particles frame by frame in the dough monitoring time series and predict the activity trend at multiple moments, thereby extracting the optimal kneading time point; The dynamic dough kneading module is used to perform time-state matching on the dough timing state diagram based on the optimal kneading time point, and then make dynamic kneading parameter decisions to generate dynamic kneading parameters. Specifically, it is used to: perform time-state matching on the dough timing state diagram based on the optimal kneading time point to extract the dough timing state characteristics at the optimal kneading time point; perform optimal dough state analysis on the dough timing state diagram to generate optimal dough state characteristics; perform optimal state deviation analysis on the optimal dough state characteristics based on the dough timing state characteristics at the optimal kneading time point to obtain the dough state deviation characteristics at the optimal kneading time point; and make dynamic kneading parameter decisions based on the dough state deviation characteristics at the optimal kneading time point to generate dynamic kneading parameters. Parameter fine-tuning module, used to evaluate dough state based on dynamic kneading parameters, perform real-time kneading parameter fine-tuning optimization, and build real-time kneading fine-tuning strategy; Intelligent dough kneading control is used to perform comprehensive state evaluation based on the real-time dough kneading fine-tuning strategy and conduct self-reinforcement kneading performance learning to generate an intelligent dough kneading control model.

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