Dough kneading efficiency optimization method and system of dough kneading machine

Abstract

The invention relates to the technical field of data optimization, in particular to a dough kneading efficiency optimization method and system of a dough kneading machine. The method comprises the following steps: acquiring structure data of a dough kneading machine; carrying out photoelectric sensor array layout based on the dough kneading machine structure data, and generating photoelectric sensor array deployment data; performing photoelectric sensor signal acquisition on the photoelectric sensor array deployment data according to a preset data acquisition time interval to obtain standard reflected light intensity acquisition data; performing dough edge motion trail identification on the dough through the standard reflected light intensity acquisition data to generate dough motion trail identification data; obtaining operation parameters of the dough kneading machine; and performing dough periodic deformation feature analysis on the dough movement track identification data to obtain dough periodic deformation data. Through the intelligent photoelectric sensor array, dough development degree evaluation and operation parameter optimization, the dough kneading efficiency and accuracy of the dough kneading machine are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data optimization, and particularly to a method and system for optimizing the kneading efficiency of a dough kneader. Background Art

[0002] With the development of mechanization technology, dough kneaders came into being. The initial dough kneaders mainly achieved the stretching and kneading of dough through simple mechanical transmission systems. However, these early dough kneaders had problems such as uneven kneading, slow speed, and significant damage to the dough structure. With the development of electronic technology, dough kneaders began to introduce electronic control systems, achieving precise control of the kneading process. During this period, the design of dough kneaders began to focus on the uniformity of kneading and the quality of the dough, especially the control of internal bubbles and gluten in the dough. By introducing multiple kneading modes and automatic adjustment functions, the efficiency and quality of the dough kneader have been significantly improved. With the rapid development of intelligent technology, dough kneaders have gradually incorporated artificial intelligence and big data analysis. Modern dough kneaders not only have the functions of automatically adjusting the dough humidity, temperature, and kneading intensity, but also can optimize the kneading process through data analysis, reducing energy consumption and time costs. At the same time, the operation of the machine is more user-friendly, and it can automatically adjust the kneading strategy according to the characteristics of different flours, further improving the kneading efficiency and quality. However, currently, traditional dough kneaders often cannot accurately and real-time monitor the movement trajectory of the dough, resulting in uneven kneading or over-kneading. At the same time, the adjustment of operating parameters is relatively simple, usually relying on set fixed parameters and unable to dynamically adjust according to the actual development of the dough, thus resulting in low kneading efficiency and accuracy of the dough kneader. Summary of the Invention

[0003] Based on this, it is necessary to provide a method and system for optimizing the kneading efficiency of a dough kneader to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for optimizing the kneading efficiency of a dough kneader, the method includes the following steps:

[0005] Step S1: Obtain the structure data of the dough kneader; based on the structure data of the dough kneader, layout the optoelectronic sensor array to generate optoelectronic sensor array deployment data; collect optoelectronic sensor signals from the optoelectronic sensor array deployment data according to a preset data acquisition time interval to obtain standard reflected light intensity acquisition data; identify the movement trajectory of the dough edge through the standard reflected light intensity acquisition data to generate dough movement trajectory recognition data;

[0006] Step S2: Obtain the operating parameters of the dough mixer; analyze the periodic deformation characteristics of the dough from the dough movement trajectory recognition data to obtain the dough periodic deformation data; evaluate the dough elastic development degree based on the dough periodic deformation data to generate the dough elastic development degree evaluation data; establish a relationship model between the dough development degree and efficiency for the operating parameters of the dough mixer through the dough elastic development degree evaluation data to generate a dough development degree - efficiency relationship model;

[0007] Step S3: Predict the dough stage development relationship curve based on the dough development degree - efficiency relationship model for the dough stage development data to generate the dough stage development relationship curve; divide the curve inflection point segment of the dough stage development relationship curve to generate a pre - kneading time period and a post - kneading time period; optimize the kneading stratification parameters of the dough mixer operating parameters according to the pre - kneading time period and the post - kneading time period to generate an intelligent discrimination control instruction for the dough mixer;

[0008] Step S4: Adjust the working state of the dough mixer operating parameters according to the intelligent discrimination control instruction for the dough mixer to generate the dough mixer working state adjustment data; perform intelligent power adjustment on the dough mixer working state adjustment data to execute the dough kneading efficiency optimization operation.

[0009] The present invention realizes the precise monitoring of the dough movement trajectory by acquiring the kneader structure data and arranging the optoelectronic sensor array. Through the standard reflected light intensity data, the movement trajectory of the dough edge can be accurately identified, ensuring the uniformity and consistency of the dough during the kneading process, and avoiding the problem of uneven kneading in the traditional method. By analyzing the periodic deformation data of the dough, the development state of the dough can be quantified, and then the elastic development degree of the dough can be evaluated. By establishing a relationship model between the dough development degree and the kneading efficiency, the kneading process can be precisely adjusted to ensure that the dough reaches the optimal elastic development degree, improving the quality of the dough and the kneading efficiency, and avoiding over-kneading or under-kneading. Predict the dough development stage through the dough development degree-efficiency relationship model, and analyze the stage development relationship curve. By dividing the kneading process into pre-kneading and post-kneading time periods and optimizing the operation parameters of the kneader according to these data, precise kneading layer control is achieved, improving the kneading efficiency and reducing the ineffective kneading time. The working state of the kneader is precisely adjusted through the intelligent discrimination control instruction, and further through the intelligent power adjustment function, the kneader can dynamically adjust the working load according to the actual dough state, optimizing the energy consumption. Through the intelligent adjustment mechanism, the kneading efficiency is improved, the energy consumption is reduced, and the overall economy and sustainability of the kneader are improved. The four steps improve the efficiency, dough quality and energy efficiency of the kneader through precise monitoring, data analysis and intelligent adjustment, and solve the deficiencies of traditional kneaders in terms of efficiency, quality control and energy consumption management. Therefore, the present invention improves the kneading efficiency and accuracy of the kneader through the intelligent optoelectronic sensor array, dough development degree evaluation and operation parameter optimization.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Acquire the kneader structure data;

[0012] Step S12: Screen the boundary area of the kneading bar based on the kneader structure data to obtain the boundary area data of the kneading bar; Arrange the optoelectronic sensor array for the boundary area data of the kneading bar to generate the optoelectronic sensor array deployment data;

[0013] Step S13: Collect the optoelectronic sensor signals for the optoelectronic sensor array deployment data according to the preset data acquisition time interval to obtain the reflected light intensity acquisition data; Perform data preprocessing on the reflected light intensity acquisition data to generate the standard reflected light intensity acquisition data, where the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization;

[0014] Step S14: Identify the dough edge movement trajectory of the dough through the standard reflected light intensity acquisition data to generate the dough movement trajectory identification data.

[0015] Through the screening of the boundary area of the dough kneading bar, the present invention can accurately lock the key working area, reduce the redundancy of data collection, and improve the pertinence and efficiency of the layout of photoelectric sensors. Based on the data of the boundary area of the dough kneading bar, the layout of the photoelectric sensor array helps to reasonably distribute the positions of the sensors, ensure the coverage of the entire key dough kneading area, and at the same time reduce the hardware cost and resource waste. Through the standardized data collection and preprocessing in step S13, the influence of noise interference and missing values on the data quality can be effectively reduced, so as to generate high-quality and standardized reflected light intensity data, laying a solid foundation for subsequent analysis. Using the standardized data to identify the movement trajectory of the dough edge can accurately capture the movement state of the dough and reflect its dynamic changes in real time, providing a scientific basis for the subsequent optimization of the dough kneading process. By integrating sensor data and intelligent recognition technology, step S1 realizes the integrated process from hardware layout to data collection and analysis, improving the intelligent level and operation efficiency of the dough kneading machine. Through the identification and analysis of the dough movement trajectory, abnormal conditions (such as uneven movement) during the dough kneading process can be monitored in real time, thus providing an important basis for dough quality control.

[0016] Preferably, step S14 includes the following steps:

[0017] Step S141: Convert the collected data of the standard reflected light intensity into an edge contour to generate edge contour conversion data; confirm the time stamp of the contour change for the edge contour conversion data to obtain the time stamp of the dough edge contour change;

[0018] Step S142: Calculate the two-dimensional coordinates of the dough edge for the edge contour conversion data through the Cartesian coordinate system to obtain the two-dimensional coordinate data of the dough edge; use the time stamp of the dough edge contour change to analyze the dough position at each time point for the two-dimensional coordinate data of the dough edge to generate the real-time moving position information data of the dough;

[0019] Step S143: Construct the dough movement trajectory based on the real-time moving position information data of the dough to generate the real-time moving trajectory of the dough; perform displacement differential calculation on the real-time moving trajectory of the dough to generate the dough displacement speed data; perform displacement difference calculation on the dough displacement speed data to obtain the dough displacement acceleration data;

[0020] Step S144: Integrate the dough displacement speed data, the dough displacement acceleration data, and the real-time moving trajectory of the dough to generate the dough movement trajectory recognition data.

[0021] By converting the standard reflected light intensity data into an edge contour and timestamping the contour changes, the present invention can accurately record the dynamic changes of the dough edge and provide basic data for motion trajectory analysis. Based on the Cartesian coordinate system, the two-dimensional coordinates of the dough edge are calculated, and position information at each time point is generated in combination with the timestamp, thereby achieving high-precision two-dimensional reconstruction of the dough motion and improving the spatial resolution of data analysis. By constructing the real-time motion trajectory of the dough and performing differential and difference calculations, displacement velocity and acceleration data are accurately obtained, helping to comprehensively characterize the dynamic characteristics of the dough motion. These characteristic data can intuitively reflect the motion state and its change trend of the dough. Integrating the velocity, acceleration, and trajectory data to generate unified motion trajectory recognition data, this data fusion not only provides the overall picture of the motion trajectory but also simplifies the complexity of subsequent analysis. Step S14 realizes the real-time monitoring and analysis of the dough motion state, helps to detect abnormalities in a timely manner, such as uneven motion or speed fluctuations, so as to quickly adjust the operating parameters during the production process and improve the efficiency and stability of the dough mixer. Based on the velocity and acceleration data, the force and motion characteristics of the dough at different stages can be deeply analyzed, providing a physical basis for optimizing the dough kneading process parameters. For example, the force distribution during the dough kneading process can be optimized by analyzing the change in motion acceleration. The dough motion trajectory recognition data provides an important basis for the intelligent control of the dough kneading process, ensuring the standardization of the dough kneading process and ultimately improving the quality consistency of the dough.

[0022] Preferably, step S2 includes the following steps:

[0023] Step S21: Obtain the operating parameters of the dough mixer; perform periodic analysis of the dough displacement on the dough motion trajectory recognition data to generate dough displacement period data; perform analysis of the dough periodic deformation characteristics on the dough motion trajectory recognition data according to the dough displacement period data to obtain dough periodic deformation data;

[0024] Step S22: Perform analysis of the dough elastic characteristics on the dough displacement period data according to the dough periodic deformation data to generate dough elastic characteristic data; perform evaluation of the dough elastic development degree on the dough periodic deformation data based on the dough elastic characteristic data to generate dough elastic development degree evaluation data;

[0025] Step S23: Divide the dough into dough development stages through the dough elastic development degree evaluation data to generate dough development stage data;

[0026] Step S24: Based on the dough development stage data, establish a model of the relationship between the dough development degree and efficiency for the dough development stage data and the operating parameters of the dough mixer to generate a dough development degree-efficiency relationship model.

[0027] Through the periodic analysis of dough displacement data and the recognition of deformation characteristics, the periodic deformation that occurs during the dough kneading process can be accurately captured, which provides more precise physical parameter support for the dynamic monitoring of dough quality. Based on the periodic deformation characteristic data for elastic analysis and development degree evaluation, the elastic characteristics of the dough can be comprehensively characterized, reflecting the strength and uniformity of its internal network structure, which provides a scientific basis for judging the kneading effect and baking quality of the dough. Dividing the dough development process into multiple stages can more finely grasp the state reached by the dough during the kneading process. This phased analysis helps to precisely control the kneading time and strength, avoiding quality problems caused by over-kneading or insufficient development. By integrating the dough development stage data and the operating parameters of the kneading machine to establish a development degree-efficiency relationship model, the operating parameter settings of the kneading machine can be optimized to achieve a dynamic balance between kneading efficiency and dough development quality. By analyzing the relationship between the dough development state and the machine parameters, the operating parameters (such as speed, pressure, time) can be dynamically adjusted, significantly improving the kneading efficiency and the consistency of the finished product. This feedback mechanism avoids the deficiencies of relying on experience control in traditional processes. Based on the elastic characteristics and development stage data of different dough types (such as high-gluten flour, low-gluten flour), a highly targeted personalized kneading process can be developed, providing a differentiated solution for industrial production. The periodic deformation data, elastic characteristic data, and development stage data generated in step S2 provide complete data support for the intelligent monitoring and control of the kneading process, promoting the development of the kneading machine towards intelligence and adaptability.

[0028] Preferably, the extraction of dough periodic deformation characteristics from the dough movement trajectory recognition data according to the dough displacement cycle data includes:

[0029] Using the dough displacement cycle data to segment the dough movement trajectory recognition data to generate complete kneading cycle data, where the complete kneading cycle data includes dough stretching stage data and dough retraction stage data; performing dynamic time warping on the dough stretching stage data and the dough retraction stage data to generate a segmented cycle trajectory data set;

[0030] Calculating the dough edge displacement for the segmented cycle trajectory data set to obtain the dough edge displacement value; discriminating the positive and negative values of the dough edge displacement value. When the dough edge displacement value is positive, the corresponding dough edge displacement value is defined as the dough stretching stage displacement value;

[0031] When the dough edge displacement value is negative, the corresponding dough edge displacement value is defined as the dough retraction stage displacement value; calculating the dough stretching rate for the dough stretching stage displacement value to obtain the dough stretching rate, where the formula for calculating the dough stretching rate is as follows:

[0032] D max =max(∣x(t)∣);

[0033]

[0034] Wherein, S r is the maximum stretching speed of the dough within one kneading cycle, D max is the maximum stretching distance, T cycle is the time for the dough to complete one full kneading cycle, and x(t) is the modulus of the movement position of the dough at time t;

[0035] Calculate the dough shrinkage rate for the displacement value in the dough retraction stage to obtain the dough shrinkage rate. The formula for calculating the dough shrinkage rate is as follows:

[0036] D min = min(|x(t)|);

[0037]

[0038] Wherein, D min is the maximum retraction distance, x(t) is the modulus of the movement position of the dough at time t, R r is the maximum retraction speed of the dough within one kneading cycle, T cycle is the time for the dough to complete one full kneading cycle;

[0039] Integrate the dough stretching rate and the dough retraction rate to generate dough periodic deformation data.

[0040] In the present invention, the dough movement trajectory is divided into a stretching stage and a retraction stage according to periodicity, and dynamic time warping is performed on these two stages, making the analysis more accurate. This not only helps to capture the periodic deformation of the dough but also provides clear stage data for subsequent deformation calculation. By discriminating the positive and negative values of the edge displacement value of the dough, the behavior characteristics of the dough in different stages (stretching and retraction) are clearly distinguished, which provides quantitative data for the physical properties such as elasticity and plasticity of the dough in different stages, facilitating the analysis of the dough state. By formula calculation, the maximum stretching speed (S r ) and the maximum retraction speed (R r) It can quantify the efficiency of the dough's stretching and retraction during the kneading process. This kind of analysis can reveal the physical performance of the dough during kneading, help optimize the kneading process, and enable the dough to achieve the best elasticity and extensibility. By integrating the stretching rate and the retraction rate, comprehensive dough periodic deformation data is generated. This data provides a reliable basis for dough quality control, helps identify changes in the dough state, and then makes targeted adjustments. The calculation of the dough stretching rate and retraction rate makes the physical properties during the kneading process more controllable. During the production process, kneading parameters (such as kneading time, kneading force, etc.) can be adjusted in real time according to these indicators, making the quality of each batch of dough more stable. Based on the periodic deformation data, accurate monitoring data can be provided for intelligent production equipment, helping to adjust the operating state of the production line in real time, which is of great significance for improving production efficiency and reducing manual intervention. The stretching rate and retraction rate of the dough can effectively quantify the elasticity and extensibility of the dough, ensuring that the dough produced each time can meet the set standards, thereby improving the quality consistency of the final product.

[0041] Preferably, step S22 includes the following steps:

[0042] Step S221: Screen the kneading force arm stress data of the kneading machine's operating parameters to obtain the kneading force arm stress data of the kneading machine; conduct a dynamic change analysis on the kneading force arm stress data of the kneading machine to generate the dynamic change data of the kneading force arm stress of the kneading machine;

[0043] Step S222: Perform a time sequence matching of the kneading machine-dough change between the dynamic change data of the kneading force arm stress of the kneading machine and the dough periodic deformation data to generate the time sequence matching data of the kneading process; fit the displacement curve of the dough recovery process to the time sequence matching data of the kneading process according to the dough elastic characteristic data to generate the dough recovery elastic curve;

[0044] Step S223: Define the elastic development degree index based on the dough recovery elastic curve to obtain the elastic development degree index; evaluate the dough elastic development degree according to the elastic development degree index for the dough recovery elastic curve to generate the dough elastic development degree evaluation data.

[0045] By dynamically analyzing the operating parameters (arm stress) of the dough kneader, the present invention can capture in real time the direct relationship between the force exerted by the dough kneader on the dough and the deformation of the dough. This precise stress data provides a basis for subsequent analysis of the elastic characteristics of the dough, helps optimize the dough kneading process, and makes the matching between the force and the dough deformation more refined. By performing temporal matching on the stress dynamic change data of the dough kneader and the dough periodic deformation data, the synchronous changes of stress and deformation during the dough kneading process can be comprehensively analyzed. This precise matching provides a clear quantitative basis for the elastic recovery of the dough at each dough kneading stage, ensuring more precise operation of the dough. By fitting the displacement curve of the temporal matching data of the dough kneading process with the dough elastic characteristic data, the recovery elastic curve of the dough is generated. This curve can accurately show the elastic recovery characteristics of the dough during the dough kneading process and reveal the elastic development status of the dough at different dough kneading stages. Define the elastic development degree index based on the dough recovery elastic curve, and evaluate the elastic development degree of the dough through this index. This process provides quantitative dough elastic data, helps determine whether the dough has reached the ideal elastic state, and thus provides an operable decision-making basis for subsequent production. Accurately evaluating the elastic development degree of the dough can help production personnel understand the elastic development status of the dough in real time. Based on this evaluation, process parameters such as the dough kneading force and the dough kneading time can be adjusted to ensure that the dough enters the subsequent production stage in an ideal elastic state, thereby improving the quality and consistency of products such as bread. The evaluation of the elastic recovery and development degree of the dough can provide feedback data for intelligent production. By monitoring the elastic development status of the dough in real time, the operating parameters of the dough kneader can be automatically adjusted, reducing manual intervention and making the production process more efficient and stable. By accurately evaluating the elastic development degree of the dough, the consistency of each batch of dough during the development process can be ensured, avoiding quality fluctuations caused by overdevelopment or underdevelopment. For large-scale production, this can significantly improve production efficiency and the consistency of the final product.

[0046] Preferably, step S24 includes the following steps:

[0047] Step S241: Perform data feature matching on the dough development stage data and the operating parameters of the dough kneader to generate the dough kneading feature fusion data of the dough kneader; perform multi-dimensional feature dimensionality reduction analysis on the dough kneading feature fusion data of the dough kneader to generate the dough kneading feature dimensionality reduction data of the dough kneader;

[0048] Step S242: Establish a mathematical model of the development degree - efficiency relationship for the dough kneading feature dimensionality reduction data of the dough kneader through a multiple regression algorithm to generate a pre-model of the development degree - efficiency relationship; divide the dough kneading feature dimensionality reduction data of the dough kneader into data sets to generate a model training set, a model test set, and a model validation set;

[0049] Step S243: Use the model training set to train the pre-model of the development degree-efficiency relationship to generate a training model of the development degree-efficiency relationship; perform model optimization iteration on the training model of the development degree-efficiency relationship according to the model test set, and use the model validation set to verify the training model of the development degree-efficiency relationship after optimization iteration to generate a dough development degree-efficiency relationship model.

[0050] Through feature matching of the dough development stage data and the operating parameters of the dough mixer, the present invention can generate the feature fusion data of the dough mixing of the dough mixer. This process effectively integrates the operating characteristics of the dough mixer and the development state of the dough, providing richer and more comprehensive data for subsequent analysis. Then, through multi-dimensional feature dimensionality reduction analysis, redundant information is reduced and key features are retained, making data processing more efficient and providing a more concise and accurate data set for subsequent modeling. Using the multiple regression algorithm to model the dimensionality-reduced data of the dough mixing characteristics of the dough mixer can form a mathematical relationship model between the dough development degree and the efficiency. This model will reveal the internal relationship between the dough development degree and the dough mixing efficiency, providing data support for process optimization. Through this model, each step in the production process can be adjusted according to the actual development degree, thereby improving production efficiency and product quality. Use the model training set to train the pre-model of the development degree-efficiency relationship, and perform optimization iteration through the test set to ensure the high efficiency and accuracy of the model. Through multiple optimization iterations, the adaptability and prediction accuracy of the model to the actual production process can be effectively improved, thereby providing more reliable data support and avoiding human errors in the production process. After the model optimization iteration, use the model validation set to verify the training model of the development degree-efficiency relationship to ensure that the model has good generalization ability and practicality. The verification process ensures that the model can accurately predict the relationship between the dough development degree and the efficiency, avoiding overfitting or failure in actual applications, and enhancing its operability in actual production. By establishing and verifying the development degree-efficiency relationship model, the kneading efficiency required for the dough at different development stages can be accurately predicted, which provides a theoretical basis for the real-time adjustment of the production process, helps the operator accurately control the operating state of the dough mixer, and thus improves production efficiency and stability. The dough development degree-efficiency relationship model provides data support for the automatic control system, can monitor the dough development degree in real time during the production process, and automatically adjust the parameters of the dough mixer to achieve the optimal production efficiency. Through this intelligent process, manual intervention is reduced, and the automation and accuracy of the production process are improved.

[0051] Preferably, step S3 includes the following steps:

[0052] Step S31: Predict the dough stage development relationship curve based on the dough development degree-efficiency relationship model for the dough development stage data to generate the dough stage development relationship curve;

[0053] Step S32: Extract the inflection point moment of the dough stage development relationship curve to obtain the optimal kneading time point of the dough stage; divide the dough stage development relationship curve according to the optimal kneading time point of the dough stage to generate a pre-kneading time period and a post-kneading time period;

[0054] Step S33: Based on the pre-kneading time period, adjust the operating parameters of the kneader with efficiency priority to generate the first efficiency optimization data of the kneader; based on the pre-kneading time period, stop the kneading operation of the kneader to generate the second efficiency optimization data of the kneader;

[0055] Step S34: Package the first efficiency optimization data of the kneader and the second efficiency optimization data of the kneader into instructions to generate an intelligent discrimination control instruction for the kneader.

[0056] The present invention predicts the dough stage development data through the dough development degree - efficiency relationship model to generate the development relationship curve of the dough stage, enabling production personnel to accurately grasp the development of the dough at different stages. This accurate prediction provides a scientific basis for dough processing and ensures that each kneading stage can reach the best state. By extracting the inflection point moment of the dough stage development relationship curve, the optimal time point for dough development is accurately identified. In this way, kneading can be stopped at the most appropriate time to avoid over-kneading or under-kneading, ensuring the best elasticity and quality of the dough. According to the optimal kneading time point, the dough stage development relationship curve is divided into a pre-kneading time period and a post-kneading time period. Such a division provides refined time management for the kneading process, making the kneading work in different stages more in line with the needs of dough development and improving the accuracy and flexibility of the production process. Based on the data of the pre-kneading time period, the operating parameters of the kneader are optimized and adjusted to improve the operating efficiency of the kneader. This optimization can reduce unnecessary time waste, improve production efficiency, and reduce equipment wear. In the post-kneading time period, by optimizing the stop operation of the kneader, over-kneading can be avoided, energy consumption can be reduced, and at the same time, the dough can be maintained in the best development state. This operation not only saves energy but also improves the quality of finished products such as bread. The optimized operating data of the kneader are packaged into intelligent discrimination control instructions to realize real-time intelligent control of the kneader. Through this control system, the kneader can automatically adjust the operating parameters according to the actual situation, optimize the production process, and improve the automation level of the production line. Through a comprehensive analysis of the dough development degree, efficiency, and the operating parameters of the kneader, the errors in manual operation are reduced. Data-driven decision-making can more accurately control the production process, making the execution of each step more stable and consistent.

[0057] Preferably, step S4 includes the following steps:

[0058] Step S41: Adjust the operating parameters of the dough kneader according to the intelligent discrimination control instruction of the dough kneader to generate dough kneader working state adjustment data;

[0059] Step S42: Monitor the real-time power output of the dough kneader working state adjustment data to generate real-time power monitoring data; based on the real-time power monitoring data, perform intelligent power adjustment on the dough kneader to execute the dough kneading efficiency optimization operation of the dough kneader.

[0060] Through the intelligent discrimination control instruction of the dough kneader, the operating parameters of the dough kneader are adjusted in real time. This intelligent adjustment process can ensure that the dough kneader always works under the conditions most suitable for the current dough development state, thereby improving the dough kneading effect and avoiding excessive or insufficient operations. Monitoring the real-time power output during the adjustment of the dough kneader's working state can obtain the power consumption of the dough kneader at any time, which provides valuable data support for further analyzing and optimizing the equipment operation, and avoids energy waste and equipment overload. Based on the real-time power monitoring data, the system can intelligently adjust the power output of the dough kneader to keep the dough kneader at the best energy efficiency ratio. This adjustment can not only ensure that the dough kneader always maintains a high-efficiency working state during operation, but also intelligently optimize the power distribution according to the actual dough development situation to ensure the maximization of the dough kneading efficiency. Through intelligent power adjustment, it is possible to effectively avoid too high or too low power output and reduce unnecessary energy consumption. By optimizing the power output, not only the high efficiency of the dough kneading process is ensured, but also the energy consumption is reduced, meeting the requirements of sustainable production. Real-time power monitoring and intelligent adjustment help identify and avoid the equipment being in a high-load operating state, reducing the equipment wear and failure risk caused by long-term overload, thereby extending the service life of the dough kneader and reducing the maintenance and replacement costs. By precisely adjusting the working state and power output of the dough kneader, the production process can be more stable and efficient, which means that the production line can maintain a higher operating efficiency during long-term operation, thereby improving the overall production capacity and economic benefits.

[0061] In this specification, a dough kneading efficiency optimization system for a dough kneader is provided, which is used to execute the above-mentioned dough kneading efficiency optimization method for the dough kneader. The dough kneading efficiency optimization system for the dough kneader includes:

[0062] The dough movement analysis module is used to obtain the dough kneader structure data; based on the dough kneader structure data, layout the photoelectric sensor array to generate photoelectric sensor array deployment data; collect photoelectric sensor signals according to the preset data acquisition time interval for the photoelectric sensor array deployment data to obtain standard reflected light intensity acquisition data; identify the dough edge movement trajectory of the dough through the standard reflected light intensity acquisition data to generate dough movement trajectory identification data;

[0063] The kneading stage division module is used to obtain the operating parameters of the kneading machine; analyze the periodic deformation characteristics of the dough based on the dough movement trajectory recognition data to obtain the periodic deformation data of the dough; evaluate the elastic development degree of the dough based on the periodic deformation data of the dough to generate the elastic development degree evaluation data of the dough; establish a relationship model between the dough development degree and efficiency for the operating parameters of the kneading machine through the elastic development degree evaluation data of the dough to generate a dough development degree - efficiency relationship model;

[0064] The kneading efficiency optimization module is used to predict the dough stage development relationship curve based on the dough development degree - efficiency relationship model for the dough development stage data to generate the dough stage development relationship curve; divide the curve inflection point segment of the dough stage development relationship curve to generate a pre - kneading time period and a post - kneading time period; optimize the kneading layer parameters of the kneading machine operating parameters according to the pre - kneading time period and the post - kneading time period to generate an intelligent discrimination control instruction for the kneading machine;

[0065] The intelligent adjustment module is used to adjust the working state of the kneading machine operating parameters according to the intelligent discrimination control instruction of the kneading machine to generate the working state adjustment data of the kneading machine; perform intelligent power adjustment on the working state adjustment data of the kneading machine to execute the kneading efficiency optimization operation of the kneading machine.

[0066] The beneficial effects of the present invention are as follows: Through reasonable sensor deployment, it is ensured that the motion data of the dough can be comprehensively and accurately obtained. The accuracy of the data collected on the standard reflected light intensity provides a reliable basis for the subsequent identification of the dough's motion trajectory. Through high-precision signal acquisition and processing, the accurate identification of the dough's edge can be achieved, thereby extracting the dough's motion trajectory and helping to analyze the deformation characteristics of the dough during the kneading process. By deeply analyzing the periodic deformation characteristics of the dough, the deformation data of the dough is generated, providing data support for the subsequent evaluation of the dough's development degree. Based on the dough's motion trajectory and deformation characteristics, a scientific evaluation of the dough's elastic development degree is carried out, which helps to more precisely control the kneading process and ensure that the dough reaches the optimal development state. By modeling the relationship between the dough's development degree and the operating parameters of the kneading machine, a theoretical basis can be provided for optimizing the working efficiency of the kneading machine. Through model prediction, the development of the dough at each development stage can be accurately predicted, thereby formulating a reasonable kneading strategy to avoid over-kneading or insufficient kneading. The operating parameters of the kneading machine are hierarchically optimized according to the dough's development stage to ensure that the kneading effect at different stages reaches the best. Through intelligent discrimination control instructions, the kneading process is optimized to improve production efficiency. By adjusting the working state of the kneading machine through intelligent discrimination control instructions and performing real-time power adjustment, not only is it ensured that the kneading machine works in the best state, but also the use of energy is optimized and the work efficiency is improved. Through real-time working state adjustment and power optimization, the kneading machine can adapt to the state of the dough at different stages, maximizing the kneading efficiency while reducing energy waste. Through the efficient operation of the intelligent adjustment module, the kneading machine can perform the most effective kneading operation at the most suitable time, thereby improving production efficiency and shortening the production cycle. Intelligent power adjustment not only ensures the kneading efficiency but also avoids ineffective energy consumption, thereby reducing energy waste and improving the energy use efficiency of the production process. Therefore, the present invention improves the kneading efficiency and accuracy of the kneading machine through an intelligent optoelectronic sensor array, dough development degree evaluation, and operating parameter optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic flow chart of the steps of a method for optimizing the kneading efficiency of a kneading machine;

[0068] Figure 2 is Figure 1 a detailed implementation step flow chart of step S2 in

[0069] Figure 3 is Figure 1 a detailed implementation step flow chart of step S3 in

[0070] The realization, functional characteristics, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work belong to the scope of protection of the present invention.

[0072] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0073] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0074] To achieve the above object, please refer to Figures 1 to 3 , a method for optimizing the kneading efficiency of a dough kneader, the method comprising the following steps:

[0075] Step S1: Obtain the structural data of the dough kneader; perform the layout of the optoelectronic sensor array based on the structural data of the dough kneader to generate the deployment data of the optoelectronic sensor array; collect the optoelectronic sensor signals from the deployment data of the optoelectronic sensor array according to the preset data acquisition time interval to obtain the standard reflected light intensity acquisition data; identify the movement trajectory of the dough edge through the standard reflected light intensity acquisition data to generate the dough movement trajectory identification data;

[0076] Step S2: Obtain the operating parameters of the dough kneader; analyze the periodic deformation characteristics of the dough from the dough movement trajectory identification data to obtain the dough periodic deformation data; evaluate the dough elastic development degree from the dough periodic deformation data to generate the dough elastic development degree evaluation data; establish a relationship model between the dough development degree and efficiency for the operating parameters of the dough kneader through the dough elastic development degree evaluation data to generate a dough development degree - efficiency relationship model;

[0077] Step S3: Based on the dough development degree - efficiency relationship model, predict the dough stage development relationship curve for the dough stage development data, and generate the dough stage development relationship curve; divide the curve inflection point section of the dough stage development relationship curve to generate the pre-kneading time period and the post-kneading time period; optimize the kneading stratification parameters of the kneading machine operating parameters according to the pre-kneading time period and the post-kneading time period, and generate the intelligent discrimination control instruction for the kneading machine.

[0078] Step S4: Adjust the working state of the kneading machine operating parameters according to the intelligent discrimination control instruction of the kneading machine, and generate the working state adjustment data of the kneading machine; perform intelligent power adjustment on the working state adjustment data of the kneading machine to execute the kneading efficiency optimization operation of the kneading machine.

[0079] The present invention realizes the precise monitoring of the dough movement trajectory by obtaining the kneading machine structure data and arranging the optoelectronic sensor array. Through the standard reflected light intensity data, the movement trajectory of the dough edge can be accurately identified, ensuring the uniformity and consistency of the dough during the kneading process, and avoiding the problem of uneven kneading in the traditional method. By analyzing the dough periodic deformation data, the development state of the dough can be quantified, and then the elastic development degree of the dough can be evaluated. By establishing the relationship model between the dough development degree and the kneading efficiency, the kneading process can be precisely adjusted to ensure that the dough reaches the optimal elastic development degree, improve the quality of the dough and the kneading efficiency, and avoid over-kneading or under-kneading. Predict the dough development stage through the dough development degree - efficiency relationship model, and analyze the stage development relationship curve. By dividing the kneading process into the pre-kneading and post-kneading time periods, and optimizing the kneading machine operating parameters according to these data, precise kneading stratification control is realized, the kneading efficiency is improved, and the ineffective kneading time is reduced. The working state of the kneading machine is precisely adjusted through the intelligent discrimination control instruction, and further through the intelligent power adjustment function, the kneading machine can dynamically adjust the working load according to the actual dough state and optimize the energy consumption. Through the intelligent adjustment mechanism, the kneading efficiency is improved, the energy consumption is reduced, and the overall economy and sustainability of the kneading machine are improved. The four steps improve the efficiency, dough quality and energy efficiency of the kneading machine through precise monitoring, data analysis and intelligent adjustment, and solve the deficiencies of traditional kneading machines in terms of efficiency, quality control and energy consumption management. Therefore, the present invention improves the kneading efficiency and accuracy of the kneading machine through the intelligent optoelectronic sensor array, dough development degree evaluation and operating parameter optimization.

[0080] In the embodiment of the present invention, with reference to Figure 1 as shown, it is a schematic flow chart of the steps of a method for optimizing the kneading efficiency of a kneading machine according to the present invention. In this example, the method for optimizing the kneading efficiency of a kneading machine includes the following steps:

[0081] Step S1: Obtain the structural data of the dough kneader; perform the layout of the photoelectric sensor array based on the structural data of the dough kneader to generate the deployment data of the photoelectric sensor array; collect the photoelectric sensor signals for the deployment data of the photoelectric sensor array according to the preset data acquisition time interval to obtain the standard reflected light intensity acquisition data; identify the edge movement trajectory of the dough through the standard reflected light intensity acquisition data to generate the dough movement trajectory identification data;

[0082] In the embodiments of the present invention, by obtaining the structural drawings or model files (such as CAD files) of the dough kneader, which contain the detailed dimensions and positions of the various components of the dough kneader, including the dough contact area. Determine the movement mode of the dough kneader (such as rotation, up and down swinging, etc.) and the movement path of the dough. According to different dough kneader designs, determine the contact surface and movement range of the dough. When necessary, the dough kneader can be actually measured using a laser scanner or sensor (such as a 3D scanning device) to obtain more accurate structural data. This step needs to be completed in the actual working environment. According to the above-obtained structural data, select an appropriate optoelectronic sensor (for example: infrared optoelectronic sensor, laser sensor, ultrasonic sensor, etc.). When selecting, the detection range, accuracy, and response speed of the sensor need to be considered. Layout a sensor array on the contact area and movement path of the dough kneader to ensure that it can cover the edge and movement trajectory of the dough. Usually, the sensor array should be arranged above, below, or on the side of the dough to ensure that the movement of the dough can be comprehensively sensed. Determine the arrangement angle and distance of the sensors. For example, place sensors at each key position of the dough movement trajectory, and through reasonable design of the angle and position, ensure that sufficient data can be captured. Convert the deployed sensor deployment data (such as sensor coordinates, installation angle, distance, type, etc.) into a specific deployment diagram to generate an executable deployment plan. This data can be simulated and optimized using digital design software (such as AutoCAD or SolidWorks). Set up a data acquisition system to ensure that each optoelectronic sensor starts collecting signals at a specified time interval. Usually, according to actual requirements, the time interval can be set to 1 second, 5 seconds, or shorter (the specific time is preset by the system control software). Configure the working parameters of the sensors (such as working frequency, signal gain, sensitivity, etc.) to ensure that effective data of the reflected light intensity can be obtained. In the sensor array, each sensor will record the light reflection intensity (i.e., the intensity of the light signal reflected from the dough surface). At the preset time interval, start the sensor system, and synchronously collect the data of each sensor through system control. The collected reflected light intensity data should be recorded according to the time stamp for subsequent processing. Perform preliminary filtering and calibration on the collected raw data to eliminate interference caused by environmental factors (such as light changes, sensor noise, etc.). The calibrated data will be used as the basic data for subsequent dough movement trajectory recognition. Preprocess the collected standard reflected light intensity data. According to the changes in different reflected light intensities, use image processing algorithms (such as edge detection, contour extraction, etc.) to identify the edge area of the dough. Combine the spatial layout of the sensors and the change in the reflected intensity of the dough edge, and use tracking algorithms (such as Kalman filtering or optical flow method) to continuously track the position of the dough edge. Through these data, the movement path of the dough can be accurately calculated. Based on the time series data of the dough edge, use motion analysis algorithms (such as dynamic programming algorithms or interpolation algorithms based on time series) to generate the positions of the dough at different time points.Analyze the dynamic characteristics of the dough during the kneading process, such as acceleration and speed. Finally, output the digital data of the dough's movement trajectory, including the position information, speed, acceleration and other dynamic parameters of the dough at different times. The data can be used for subsequent analysis, optimizing the working state of the kneading machine or for other automated control systems.

[0083] Step S2: Obtain the operating parameters of the kneading machine; analyze the periodic deformation characteristics of the dough for the dough movement trajectory recognition data to obtain the dough periodic deformation data; evaluate the dough elastic development degree for the dough periodic deformation data to generate the dough elastic development degree evaluation data; model the relationship between the dough development degree and efficiency for the kneading machine operating parameters through the dough elastic development degree evaluation data to generate the dough development degree - efficiency relationship model.

[0084] In the embodiments of the present invention, the operating parameters of the dough kneader control system are obtained. The operating parameters include, but are not limited to: rotational speed, pressure, temperature, humidity, kneading time, vibration frequency, etc. These parameters are monitored and recorded in real time by sensors on the dough kneader (such as rotational speed sensors, temperature sensors, humidity sensors, pressure sensors, etc.). The obtained operating parameters are stored in the data acquisition system and synchronized with the dough movement trajectory recognition data for subsequent analysis and modeling. When necessary, the operating parameters of the dough kneader are adjusted to ensure that they are within a predetermined range to ensure accurate evaluation of the movement and development process of the dough. Based on the dough movement trajectory recognition data obtained in the first step, the periodic deformation characteristics of the dough during the kneading process are extracted. By analyzing the changes in the dough edge in the time series, the periodic stretching, compression, and deformation patterns of the dough can be identified. Using signal processing techniques, such as Fourier transform (FFT) or wavelet transform, the frequency domain characteristics of the movement trajectory are analyzed, and key parameters such as the frequency, amplitude, and period of the periodic deformation are extracted. Through these parameters, the periodic deformation characteristics of the dough during the kneading process can be quantified. Combining the physical properties of the dough, a mathematical model of dough deformation (such as elastic deformation model, viscoelastic model, etc.) is established to further analyze the deformation behavior of the dough and obtain dough periodic deformation data. The elastic development degree of the dough usually represents the properties such as elasticity, ductility, and tensile strength of the dough. The elastic development degree is closely related to the deformation behavior of the dough and can be evaluated through the periodic deformation characteristics of the dough. According to the dough periodic deformation data, mechanical models (such as Hooke's law, stress-strain relationship, etc.) are used to calculate the elastic coefficient and plastic coefficient of the dough, thereby evaluating the elastic development degree of the dough. The evaluation of the dough elastic development degree can be achieved in the following way: According to the amplitude, frequency, etc. of the dough deformation, the resilience, viscosity, and ductility of the dough are calculated. Using these data, the development degree index of the dough is calculated (for example, through the formula: E = σ / ε, where E is the elastic development degree, σ is the stress, and ε is the strain), and the dough elastic development degree evaluation data, including key indicators such as the development degree index, elasticity, and ductility, are generated. These data provide a basis for further analyzing the development status of the dough. The dough elastic development degree evaluation data is combined with the operating parameters of the dough kneader. In each operating cycle, there will be a certain relationship between the elastic development degree of the dough and parameters such as the rotational speed, pressure, and time of the dough kneader. By integrating the data, the relationship between the dough development degree and efficiency is constructed. Regression analysis (such as linear regression, polynomial regression, support vector machine, etc.) or machine learning algorithms (such as decision trees, neural networks, etc.) are used to establish a mathematical model between the dough development degree and the dough kneader efficiency. This model can be used to predict the optimal relationship between the dough development degree and the dough kneader efficiency. Historical data (including different operating parameters and dough development degree data) is used to train the model. The model is optimized through methods such as cross-validation and error analysis to ensure its accuracy and robustness. Finally, a mathematical model that can predict the relationship between the dough development degree and the dough kneader efficiency is obtained.This model can be used to adjust the operating parameters of the dough kneader to improve the kneading efficiency while ensuring the dough development quality.

[0085] Step S3: Based on the dough development degree - efficiency relationship model, predict the dough stage development relationship curve for the dough development stage data to generate the dough stage development relationship curve; divide the curve inflection point segment of the dough stage development relationship curve to generate the pre-kneading time period and the post-kneading time period; optimize the kneading layer parameters of the dough kneader operating parameters according to the pre-kneading time period and the post-kneading time period to generate the intelligent discrimination control instruction for the dough kneader;

[0086] In the embodiments of the present invention, by collecting data at different development stages of the dough, these data include the degree of dough development, the operating parameters of the kneading machine (such as rotation speed, pressure, time, etc.), and the dough state (such as elasticity, ductility, etc.) at each development stage. Using the established "dough development degree - efficiency relationship model" to input the dough development degree data and predict the corresponding efficiency, which can be achieved by inputting different dough development degrees into the model to obtain the development efficiency values at each stage. According to the prediction results, using curve fitting algorithms (such as spline interpolation, least squares method, etc.) to fit the dough development relationship curve. This curve represents the change trend between the dough development degree and efficiency, which can help understand the impact of different development stages on efficiency. Finally, a dough stage development relationship curve is obtained, reflecting the efficiency change during the dough development process and the corresponding development states at different stages. By analyzing the dough stage development relationship curve, identify the inflection points of the curve. These inflection points usually represent important change nodes during the dough development process, such as the moments when the softness, ductility, elasticity, etc. of the dough change significantly. Determine the inflection points of the curve according to the shape of the dough development relationship curve. The ascending section, plateau section, and descending section of the curve correspond to different development stages, and these inflection points can be regarded as key nodes in the dough development process, marking the degree of dough development completion. Determine the pre-kneading time period according to the initial stage of the curve (such as the ascending section). This stage usually corresponds to the process when the dough just starts to develop, and the flexibility and ductility gradually increase. Determine the post-kneading time period according to the end stage of the curve (such as the descending section or plateau section). This stage usually corresponds to the stage when the dough development is completed, and the dough development reaches the optimal state. Continuing to knead the dough will cause overdevelopment of the dough. According to the inflection point positions, generate specific time intervals for the pre-kneading time period and the post-kneading time period, providing a basis for subsequent operations. During the pre-kneading time period, adjust the operating parameters of the kneading machine so that the dough can achieve the best development effect at this stage. For example, set a lower rotation speed and moderate pressure to gradually develop the dough to a soft and well-ductile state. During the post-kneading time period, adjust the operating parameters of the kneading machine according to the dough development degree to avoid over-kneading. Usually, the rotation speed of the kneading machine can be increased, the pressure can be increased, or the kneading time can be shortened to complete the kneading of the dough in the best state. Use optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) to hierarchically optimize the parameters of the pre-kneading and post-kneading stages to ensure that the kneading effect at each stage matches the dough development stage, thereby improving the kneading efficiency and dough quality. Based on the optimized operating parameters, generate intelligent control instructions for the kneading machine. The intelligent discrimination control instructions include, but are not limited to: adjustment instructions for parameters such as rotation speed, pressure, time, etc., to ensure that the kneading process can be accurately executed at each stage. Input the generated control instructions into the kneading machine control system to execute the intelligent control instructions. At the same time, the system should have a feedback mechanism to further optimize the parameter settings through real-time sensor data feedback to ensure that the kneading effect meets the expectations.

[0087] Step S4: Adjust the operating parameters of the dough kneader according to the intelligent discrimination control instruction of the dough kneader to generate the working state adjustment data of the dough kneader; perform intelligent power adjustment on the working state adjustment data of the dough kneader to execute the operation of optimizing the dough kneading efficiency of the dough kneader.

[0088] In the embodiment of the present invention, through the intelligent discrimination control instruction of the dough kneader generated in the previous step, these instructions will include adjustment instructions for operating parameters (such as rotation speed, pressure, kneading time, etc.) in different stages. Adjust the operating parameters of the dough kneader according to the control instructions. For example: according to the development stage of the dough, gradually adjust the rotation speed of the dough kneader. In the pre-kneading stage of the dough, a relatively low rotation speed is usually required to avoid excessive stress on the dough; in the post-kneading stage, the rotation speed can be increased to improve the kneading efficiency. According to the elasticity and development degree of the dough, adjust the pressure of the dough kneader in a timely manner to avoid excessive compression or insufficient development of the dough. According to the development cycle of the dough, adjust the kneading time to ensure the best development within an appropriate time. According to these parameter adjustments, generate the working state adjustment data of the dough kneader, record parameters such as rotation speed, pressure, and time in each stage, and this data is used for subsequent analysis and further optimization. Analyze the required power according to the operating state of the dough kneader (including rotation speed, pressure, time, etc.). Usually, the power requirement of the dough kneader fluctuates with the change of operating parameters. For example, an increase in rotation speed and pressure usually means an increase in power requirement. Use intelligent algorithms (such as fuzzy logic control, neural network, prediction model, etc.) to predict the power required by the dough kneader under different working states. For example: when the dough kneader is in a low rotation speed and low pressure state, the power requirement is relatively low; while in a high rotation speed and high pressure state, the power requirement will increase significantly. According to the real-time monitored working state adjustment data, the intelligent algorithm can accurately predict the optimal power required for each period and adjust the power supply of the dough kneader. Based on the intelligent power adjustment algorithm, automatically adjust the power output of the dough kneader to ensure that the machine can operate effectively under different working states and avoid the situation of excessive or insufficient power. At this time, the power adjustment system will perform real-time adjustment on the motor power, drive system, etc. to adapt to the current operating load. Record the process of power adjustment and adjustment data to ensure that the power can be adjusted according to the real-time demand in each operation step. In actual operation, according to the intelligent power adjustment and working state adjustment data, adjust the operating parameters and power output of the dough kneader in real time to make it reach the best working state in each stage, so as to maximize the dough kneading efficiency.

[0089] Preferably, step S1 includes the following steps:

[0090] Step S11: Obtain the structural data of the dough kneader;

[0091] Step S12: Screen the boundary area of the kneading bar based on the structural data of the dough kneader to obtain the boundary area data of the kneading bar; perform the layout of the photoelectric sensor array on the boundary area data of the kneading bar to generate the deployment data of the photoelectric sensor array;

[0092] Step S13: Collect the photoelectric sensor signals for the deployment data of the photoelectric sensor array according to the preset data acquisition time interval to obtain the reflected light intensity acquisition data; perform data preprocessing on the reflected light intensity acquisition data to generate the standard reflected light intensity acquisition data, where the data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization;

[0093] Step S14: Identify the movement trajectory of the dough edge through the standard reflected light intensity acquisition data to generate the dough movement trajectory identification data.

[0094] In the embodiments of the present invention, by obtaining the design drawings and structural data of the dough kneader, including the physical dimensions, structural features, working principles, etc. of the dough kneader. Through the analysis of the structure of the dough kneader, key areas related to dough processing are extracted, such as the kneading bar, transmission system, etc. Combining the actual application requirements, virtual modeling of the dough kneader is carried out using 3D modeling software (such as AutoCAD, SolidWorks, etc.) to obtain accurate structural data. According to the structural data of the dough kneader, especially the design parameters and working area of the kneading bar, the boundary areas of the kneading bar are identified, and these areas are the key parts where the dough contacts the kneading bar. Using image processing or geometric analysis techniques, the virtual model of the dough kneader is processed to clarify the boundary of the kneading bar, ensuring that the area to be monitored can be accurately delimited, and obtaining the boundary area data of the kneading bar, including the spatial coordinates, geometric shape, and the contact mode between the dough and the kneading bar of the area. Based on the boundary area data of the kneading bar, a suitable installation position is selected in the dough kneader structure for the layout of the photoelectric sensors. This layout should consider the coverage range, sensitivity of the sensors, and the distance from the dough contact area. Determine the type of photoelectric sensors (such as infrared sensors, laser sensors, etc.) and the layout method (such as linear array, two-dimensional array, etc.). The sensor array should be able to continuously obtain the reflected light signals at different stages during the entire dough kneading process. According to these parameters, design the deployment scheme of the sensor array, determine the number, arrangement method, installation position, etc. of the sensors, and generate the deployment data of the photoelectric sensor array. According to the working cycle of the dough kneader and the preset time interval, configure the acquisition frequency of the photoelectric sensors to ensure that the reflected light signals can be collected in a timely manner during the movement of the dough. At every set time interval, the photoelectric sensor array will collect signals at each sensor position and record the light intensity reflected from the dough edge. The collected data includes the reflected light intensity value and the time stamp, which can reflect the dynamic changes of the contact between the dough edge and the kneading bar. Remove any abnormal or invalid data, such as signal interruption, sensor failure data, etc. Use signal filtering methods (such as low-pass filtering, median filtering, etc.) to remove the noise in the reflected light intensity data and retain the valid signals. In the case where the sensors fail to collect data for some reason, use interpolation algorithms (such as linear interpolation, spline interpolation, etc.) to fill in the missing light intensity data. Standardize the collected reflected light intensity data and convert it into a unified scale (such as between 0 and 1) for subsequent analysis and modeling. Through the above preprocessing steps, standard reflected light intensity acquisition data is generated, and these data can be used as the basis for subsequent analysis. Use image processing or signal processing algorithms to analyze the standard reflected light intensity data to identify the dynamic changes of the dough edge. Specific methods include: applying edge detection algorithms (such as Canny edge detection, Sobel operator, etc.) to extract the dough edge information in the reflected light intensity image. Based on the edge detection results, analyze the movement trajectory of the dough edge. Combining the time stamp information, obtain the movement trajectory of the dough edge changing with time.Fit and analyze the movement trajectory of the dough edge to generate dough movement trajectory recognition data, including parameters such as movement speed, direction, and amplitude. Finally, generate complete dough movement trajectory recognition data, which can be used to further analyze the deformation characteristics, development status, and kneading process efficiency of the dough.

[0095] Preferably, step S14 includes the following steps:

[0096] Step S141: Convert the edge contour of the standard reflected light intensity acquisition data to generate edge contour conversion data; confirm the contour change timestamp of the edge contour conversion data to obtain the dough edge contour change timestamp;

[0097] Step S142: Calculate the two-dimensional coordinates of the dough edge from the edge contour conversion data through the Cartesian coordinate system to obtain the two-dimensional coordinate data of the dough edge; analyze the dough position at each time point using the dough edge contour change timestamp for the two-dimensional coordinate data of the dough edge to generate dough real-time movement position information data;

[0098] Step S143: Construct the dough movement trajectory based on the dough real-time movement position information data to generate the dough real-time movement trajectory; perform displacement differential calculation on the dough real-time movement trajectory to generate dough displacement speed data; perform displacement difference calculation on the dough displacement speed data to obtain dough displacement acceleration data;

[0099] Step S144: Integrate the dough displacement speed data, dough displacement acceleration data, and dough real-time movement trajectory to generate dough movement trajectory recognition data.

[0100] In the embodiments of the present invention, edge detection is performed on the collected data of the standard reflected light intensity. Common edge detection algorithms, such as the Canny edge detection algorithm or the Sobel operator, can be used to extract the edge contour of the dough from the reflected light intensity image. The detected edge information is extracted from the original data to form edge contour conversion data, which includes the change information of the dough's edge contour at different time points and can reflect the morphological changes of the dough. According to the time interval of data collection and in combination with the change situation of the image's edge contour, the time point at which each change in the edge contour occurs is confirmed. Each change point generates a timestamp to mark the position of the dough edge at that time point. By confirming the temporal change of the edge contour, the change timestamp of the dough edge contour is obtained. The position of the edge points in the edge contour conversion data is mapped to a two-dimensional coordinate system using a Cartesian coordinate system. According to the detected edge contour points, their accurate coordinates in the two-dimensional space are calculated, and these coordinate data will show the exact position of the dough edge in the image and can be further used to analyze the position change of the dough. According to the change timestamp of the dough edge contour, the two-dimensional coordinate data of the dough edge corresponding to each time point is analyzed to identify the position change of the dough at different time points. These position information are arranged in the order of timestamps to generate the real-time moving position information data of the dough, which represents the dynamic position of the dough throughout the process. Combining the real-time moving position information data of the dough, the moving trajectory of the dough is constructed by connecting the position information at consecutive time points. This trajectory represents the path of the dough from the starting point to the ending point during the entire kneading process. According to these trajectory data, the movement trajectory and its change trend of the dough at each stage can be clearly displayed. Displacement differential calculation is performed on the real-time moving trajectory of the dough. The differential calculation can be carried out by taking the derivative of the difference between adjacent positions, thereby calculating the displacement speed of the dough at each time point. The displacement speed data can reveal the dynamic changes of the dough during the kneading process and reflect its movement speed in each time period. Further, differential calculation is performed on the displacement speed data to obtain the displacement acceleration data of the dough. The acceleration calculation helps to reveal the movement trend of the dough, such as acceleration, deceleration, and whether there is a stop or reverse movement. Through the differential operation, the change in the movement intensity of the dough in different time periods can be clearly identified. The dough displacement speed data, the dough displacement acceleration data, and the real-time moving trajectory data of the dough are integrated to form the comprehensive movement trajectory information of the dough. This integrated displacement information can comprehensively describe the movement state of the dough during the kneading process, including its speed, acceleration, and complete movement trajectory. Combining the movement trajectory, speed, and acceleration data of the dough, complete dough movement trajectory recognition data are generated. These data provide detailed information about the dough movement and can be used for subsequent analysis and control decisions, such as the evaluation of the dough's development degree and the optimization of the kneading efficiency.

[0101] As an example of the present invention, refer toFigure 2 As shown, in this example, step S2 includes:

[0102] Step S21: Obtain the operating parameters of the dough kneader; perform a periodic analysis of the dough displacement on the dough movement trajectory recognition data to generate dough displacement period data; perform an analysis of the dough periodic deformation characteristics on the dough movement trajectory recognition data according to the dough displacement period data to obtain dough periodic deformation data;

[0103] Step S22: Perform an analysis of the dough elastic characteristics on the dough displacement period data according to the dough periodic deformation data to generate dough elastic characteristic data; perform an evaluation of the dough elastic development degree on the dough periodic deformation data based on the dough elastic characteristic data to generate dough elastic development degree evaluation data;

[0104] Step S23: Divide the dough development stage through the dough elastic development degree evaluation data to generate dough development stage data;

[0105] Step S24: Based on the dough development stage data, establish a relationship model between the dough development degree and efficiency for the dough development stage data and the operating parameters of the dough kneader to generate a dough development degree - efficiency relationship model.

[0106] In the embodiments of the present invention, through sensors and control systems, various operating parameters of the dough mixer are obtained, such as rotational speed, time, kneading intensity, temperature, etc. These operating parameters will serve as the basis for subsequent analysis and affect the movement and deformation characteristics of the dough. Based on the dough movement trajectory recognition data, a periodic analysis of the displacement of the dough within one cycle is performed. The periodic analysis can be carried out by methods such as Fourier transform or waveform analysis to identify the displacement period of the dough during the kneading process. Through these analyses, dough displacement period data is generated, which can reflect the repetitive and regular movement characteristics of the dough. Using the dough displacement period data, the deformation characteristics of the dough within each cycle are further analyzed. By calculating the deformation characteristics such as stretching and compression of the dough in each cycle, the periodic deformation process of the dough is analyzed. These deformation characteristics can be obtained through mechanical models or image analysis methods. For example, the maximum and minimum displacements of the dough surface during periodic movement are calculated, or an optical sensor is used to detect the deformation of the dough surface. Finally, dough periodic deformation data is obtained, which reflects information such as the elasticity, stretchability, and flexibility of the dough. Using the dough periodic deformation data, a detailed analysis of the elastic characteristics of the dough is carried out. The analysis can include elastic property indicators such as elastic modulus, rebound time, and strain rate. These analyses can be obtained by fitting the deformation curve of the dough or by performing numerical calculations using a stress-strain model to identify the elastic response of the dough under different stretching or compression states. The generated dough elastic characteristic data can include elastic recovery ability, viscoelastic properties of the dough, etc., which reflect the structural elasticity of the dough. Based on the dough elastic characteristic data, an assessment of the dough elastic development degree is performed. By evaluating the elastic properties of the dough, it is judged whether the dough has reached an ideal development state during the kneading process. The elastic development degree can be evaluated by comparing with a standard value or according to a set development degree threshold. For example, the development degree is calculated through the elastic modulus and strain recovery force of the dough. The generated dough elastic development degree assessment data reflects the maturity and development level of the dough, providing a basis for the subsequent division of development stages. Based on the dough elastic development degree assessment data, according to the set development degree threshold or standard, the development stages of the dough are divided. Generally, the development process of the dough is divided into multiple stages, such as the initial development stage, the development peak stage, and the mature stage, etc. The division of the development stage can be used to judge the development process of the dough by setting a threshold. For example, the elasticity of the dough is relatively low in the initial stage, gradually increases significantly when transitioning to the development peak stage, and finally enters the mature stage, where the elasticity tends to be stable. According to the dough development stage data and the operating parameters of the dough mixer, a relationship model between the dough development degree and the kneading efficiency is established. This model can be constructed through regression analysis, neural network models, or machine learning methods.During the modeling process, the dough development degree is used as the dependent variable, and the operating parameters of the kneading machine (such as rotational speed, kneading time, temperature, etc.) are used as the independent variables. By analyzing large data samples, the most suitable relationship between development degree and efficiency is obtained. This modeling method quantifies the relationship between the dough development stage and the kneading efficiency, thereby realizing the optimization and intelligent control of the kneading process.

[0107] Preferably, extracting the dough periodic deformation characteristics from the dough motion trajectory recognition data according to the dough displacement period data includes:

[0108] Using the dough displacement period data to segment the dough motion trajectory recognition data to generate complete kneading cycle data, where the complete kneading cycle data includes dough stretching stage data and dough retraction stage data; performing dynamic time warping on the dough stretching stage data and the dough retraction stage data to generate a segmented periodic trajectory data set;

[0109] Calculating the dough edge displacement for the segmented periodic trajectory data set to obtain the dough edge displacement value; discriminating the positive and negative values of the dough edge displacement value. When the dough edge displacement value is positive, the corresponding dough edge displacement value is defined as the dough stretching stage displacement value;

[0110] When the dough edge displacement value is negative, the corresponding dough edge displacement value is defined as the dough retraction stage displacement value; calculating the dough stretching rate for the dough stretching stage displacement value to obtain the dough stretching rate, where the formula for calculating the dough stretching rate is as follows:

[0111] D max = max(|x(t)|);

[0112]

[0113] In the formula, S r is the maximum stretching speed of the dough in one kneading cycle, D max is the maximum stretching distance, T cycle is the time for the dough to complete one full kneading cycle, and x(t) is the modulus of the dough's motion position at time t;

[0114] Calculating the dough retraction rate for the dough retraction stage displacement value to obtain the dough retraction rate, where the formula for calculating the dough retraction rate is as follows:

[0115] D min = min(|x(t)|);

[0116]

[0117] In the formula, D minis the maximum retraction distance, x(t) is the modulus of the movement position of the dough at time t, and R r is the maximum retraction speed of the dough within one kneading cycle, and T cycle is the time for the dough to complete one full kneading cycle;

[0118] Integrate the dough elongation rate and the dough retraction rate to obtain dough periodic deformation data, generating dough periodic deformation data.

[0119] In the embodiments of the present invention, by analyzing the displacement cycle data of the dough, the movement trajectory of the dough during the kneading cycle is divided into an elongation stage and a retraction stage. According to the positive and negative changes of the displacement data, it is judged whether the dough is in the elongation state or the retraction state. During this process, based on the cycle data, the complete kneading cycle of the dough is divided into two stages: the dough elongation stage (positive displacement) and the dough retraction stage (negative displacement). The generated complete kneading cycle data includes: when the displacement value is positive, it indicates that the dough is in the elongation stage. When the displacement value is negative, it indicates that the dough is in the retraction stage. Perform dynamic time warping (DTW) on the segmented cycle trajectory data sets (elongation stage and retraction stage data). By this method, the time alignment between different time steps is ensured, improving the synchronization and consistency of the data. Dynamic time warping can eliminate the timing error caused by the slight fluctuations in the operation of the kneading machine, ensuring effective extraction of dough deformation characteristics throughout the cycle. According to the data set after dynamic time warping, calculate the edge displacement of the dough. Calculate the displacement value of the dough at each time point, that is, the modulus of the movement position of the dough at time t (i.e., ∣x(t)∣), generating an edge displacement data set of the dough, recording the displacement amount of the dough at each moment. Determine the positive and negative values of the calculated edge displacement value of the dough: when the edge displacement value of the dough is positive (i.e., the displacement is outward), this displacement value is defined as the displacement value in the dough elongation stage. When the edge displacement value of the dough is negative (i.e., the displacement is inward), this displacement value is defined as the displacement value in the dough retraction stage. Calculate the maximum elongation speed of the dough during the kneading cycle. Through the formula: where S r is the maximum elongation speed of the dough within one kneading cycle, D max is the maximum elongation distance, T cycle is the time for the dough to complete one full kneading cycle, and x(t) is the modulus of the movement position of the dough at time t; Calculate the maximum retraction speed of the dough during the retraction stage. Through the formula: D min = min(∣x(t)∣);

[0120] where D min is the maximum retraction distance, x(t) is the modulus of the movement position of the dough at time t, and R ris the maximum retraction speed of the dough within one kneading cycle, T cycle is the time for the dough to complete one full kneading cycle; the dough extensibility S r and the dough retraction rate R r are integrated to form dough periodic deformation data. This data reflects the elastic and deformation characteristics of the dough during the kneading process. By comparing and analyzing these two characteristics, the development of the dough can be evaluated. For example, a high extensibility indicates better elasticity of the dough, which is suitable for further processing, while a high retraction rate indicates that the dough is still in an earlier development stage.

[0121] Preferably, step S22 includes the following steps:

[0122] Step S221: Screen the kneading force arm stress data of the kneading machine operating parameters to obtain the kneading force arm stress data of the kneading machine; perform dynamic change analysis on the kneading force arm stress data of the kneading machine to generate the dynamic change data of the kneading force arm stress of the kneading machine;

[0123] Step S222: Perform time sequence matching of the kneading machine-dough change on the dynamic change data of the kneading force arm stress of the kneading machine and the dough periodic deformation data to generate the time sequence matching data of the kneading process; perform displacement curve fitting of the dough recovery process on the time sequence matching data of the kneading process according to the dough elastic characteristic data to generate the dough recovery elastic curve;

[0124] Step S223: Define the elastic development degree index based on the dough recovery elastic curve to obtain the elastic development degree index; evaluate the dough elastic development degree on the dough recovery elastic curve according to the elastic development degree index to generate the dough elastic development degree evaluation data.

[0125] In the embodiments of the present invention, by screening the arm stress data generated during the operation of the dough kneader according to the operation parameters of the dough kneader, such as rotation speed, pressure, time, etc., these data reflect the forces exerted on the dough by the dough kneader at various stages of dough kneading. The screened data includes the forces exerted by the dough kneader and their distribution at different time points. Perform time series analysis on the arm stress data of the dough kneader during dough kneading to identify its dynamic change trend during the dough kneading process. During the analysis process, mainly focus on the dynamic processes such as the fluctuation, increase, and decrease of the arm stress. Through dynamic change analysis, obtain the time series data of the arm stress of the dough kneader during dough kneading, which describes the periodic changes and fluctuations of the arm stress. Match the dynamic change data of the arm stress of the dough kneader with the periodic deformation data of the dough (such as the stretching and retracting stages). This process is based on a time alignment algorithm to compare the change trends of the two on the time axis to ensure that the actions of the dough kneader at each time point correspond to the deformation of the dough. The generated time series matching data of the dough kneading process can accurately reflect the mutual relationship between the force exerted by the dough kneader and the dough deformation at each dough kneading stage. Utilize the elastic characteristic data of the dough to fit the dough recovery process during dough kneading to generate a dough recovery elasticity curve. The key to this step is to describe the elastic recovery process of the dough after being affected by the kneading force by fitting the recovery curve of the dough. The fitting process is based on the displacement information of the dough at different time points and simulates the speed and degree of the dough's elastic recovery through appropriate mathematical models (such as exponential regression models, stretching-retracting curves, etc.). Based on the dough recovery elasticity curve, define an elastic development degree index. This index comprehensively considers the stretching, retracting, and recovery abilities of the dough and quantifies the elastic level of the dough. The elastic development degree index can be calculated by the following method: Wherein, E f is the elastic development degree index, S r is the dough stretching rate, R r is the dough retracting rate. This calculation method integrates the elastic recovery ability of the dough and reflects its overall development level. According to the elastic development degree index, evaluate the elastic development degree of the dough to generate dough elastic development degree evaluation data. This data can quantitatively describe the elastic level of the dough during dough kneading, thereby providing a basis for optimizing the subsequent dough kneading efficiency. The dough elastic development degree evaluation data will be used to judge the development stage of the dough, whether it reaches the ideal fermentation state, or whether further dough kneading is required.

[0126] Preferably, step S24 includes the following steps:

[0127] Step S241: Match the data characteristics of the dough development stage data and the operation parameters of the dough kneader to generate dough kneading feature fusion data of the dough kneader; perform multi-dimensional feature dimensionality reduction analysis on the dough kneading feature fusion data of the dough kneader to generate dough kneading feature dimensionality reduction data;

[0128] Step S242: Establish a mathematical model of the development degree - efficiency relationship for the dimensionality - reduced data of the kneading characteristics of the dough kneader through a multiple regression algorithm to generate a pre - model of the development degree - efficiency relationship; divide the dimensionality - reduced data of the kneading characteristics of the dough kneader into data sets to generate a model training set, a model test set, and a model validation set;

[0129] Step S243: Use the model training set to train the pre - model of the development degree - efficiency relationship to generate a trained model of the development degree - efficiency relationship; optimize and iterate the trained model of the development degree - efficiency relationship according to the model test set, and use the model validation set to validate the optimized and iterated trained model of the development degree - efficiency relationship to generate a dough development degree - efficiency relationship model.

[0130] In the embodiments of the present invention, by performing data feature matching on the dough development stage data (such as the elasticity development degree of the dough, periodic deformation, etc.) and the operating parameters of the dough kneader (such as kneading force, rotation speed, time, etc.), this process uses time series synchronization technology to align the dough development data and the operating data of the dough kneader on the same time axis. The generated kneading machine kneading feature fusion data includes various features of the kneading machine operation and the dough development state, and is the basic data set for establishing the subsequent model. Perform multi-dimensional feature dimensionality reduction analysis on the kneading machine kneading feature fusion data, aiming to remove redundant features and extract the most representative features. Common dimensionality reduction methods such as principal component analysis (PCA) or linear discriminant analysis (LDA) project high-dimensional data into a lower-dimensional space, retaining most of the useful information in the data. The generated kneading machine kneading feature dimensionality reduction data significantly reduces the data complexity while retaining important information, facilitating subsequent model training and analysis. Use a multiple regression algorithm to establish a pre-model of the development degree-efficiency relationship. Through multiple regression analysis, model the relationship between the kneading machine kneading feature dimensionality reduction data and the dough development degree and kneading efficiency. The multiple regression model considers multiple influencing factors, such as kneading force, kneading time, dough development state, etc., combines the influences of these factors, and generates a mathematical model for predicting the relationship between the dough development degree and kneading efficiency. Divide the kneading machine kneading feature dimensionality reduction data into a model training set, a model test set, and a model validation set. The purpose of data set division is to perform model training, testing, and validation to ensure the generalization ability and accuracy of the model. A common division ratio is: 70% of the data is used for training, 15% of the data is used for testing, and 15% of the data is used for validation. Use the model training set to train the pre-model of the development degree-efficiency relationship. During the training process, the model will learn how to predict the kneading efficiency based on the operating parameters of the dough kneader and the dough development stage. Through continuous iteration, the model adjusts its parameters to minimize the prediction error and gradually improve the prediction accuracy. After the model training is completed, use the model test set to optimize and iterate the trained development degree-efficiency relationship training model. The role of the test set is to evaluate the accuracy of the model, adjust the parameters of the model, and avoid overfitting. The optimization process ensures that the model can maintain good prediction performance on different data sets through multiple trainings, validations, and adjustments. Use the model validation set to finally validate the optimized development degree-efficiency relationship training model. The validation set is used to test the prediction ability of the model on unseen data to ensure the robustness and generalization ability of the model. After validation, generate the final dough development degree-efficiency relationship model, which can accurately predict the impact of the dough development state on the kneading efficiency and provide a basis for optimizing the operation of the dough kneader.

[0131] As an example of the present invention, refer to Figure 3 shown, in this example, step S3 includes:

[0132] Step S31: Predict the dough stage development relationship curve for the dough stage data based on the dough development degree - efficiency relationship model, and generate the dough stage development relationship curve;

[0133] Step S32: Extract the inflection point moment of the dough stage development relationship curve to obtain the optimal kneading time point for the dough stage; divide the dough stage development relationship curve according to the optimal kneading time point for the dough stage to generate the pre - kneading time period and the post - kneading time period;

[0134] Step S33: Adjust the operating parameters of the kneading machine with efficiency priority based on the pre - kneading time period to generate the first efficiency optimization data of the kneading machine; stop the kneading operation of the kneading machine based on the pre - kneading time period to generate the second efficiency optimization data of the kneading machine;

[0135] Step S34: Package the first efficiency optimization data and the second efficiency optimization data of the kneading machine to generate the intelligent discrimination control instruction of the kneading machine.

[0136] In the embodiments of the present invention, based on the established dough development degree - efficiency relationship model, the input dough development stage data is predicted. Specifically, the model is used to generate a dough stage development relationship curve, which shows the change relationship between the development degree and efficiency at different time points during the dough development process. This process predicts the development change trend of the dough in different time periods by considering the initial state of the dough, the operating parameters of the kneading machine, and the current development of the dough. The generated dough stage development relationship curve can be used to understand the development dynamics of the dough during the kneading process, and further help determine the optimal kneading time. Analyze the dough stage development relationship curve and extract the inflection point moment of the curve, that is, the moment when the efficiency change is obvious during the dough development process. The inflection point moment represents the optimal development stage of the dough, usually meaning that the dough has reached the ideal kneading state. After extracting these inflection point moments, the optimal kneading time points for the dough stage are obtained, and this moment provides a key reference for subsequent operations. According to the extracted optimal kneading time point, the dough stage development relationship curve is divided into two time periods: the pre-kneading time period and the post-kneading time period. The pre-kneading time period represents the initial stage of dough development, while the post-kneading time period represents the adjustment stage after the dough development is completed. Through this division, it can be clear which stage should be processed first during the kneading process and which operation steps can be postponed. Based on the data of the pre-kneading time period, analyze the efficiency change trend during the dough development process and propose an efficiency-priority parameter adjustment. The specific operations include adjusting parameters such as the rotation speed, kneading force, and kneading time of the kneading machine to maximize the efficiency of the pre-kneading stage. The first efficiency optimization data of the kneading machine is the new operating parameters obtained through these adjustments, and these parameters can improve the development efficiency of the dough in the pre-stage and shorten the total kneading time. According to the development of the dough in the post-kneading time period, judge whether it is necessary to stop the kneading operation. By analyzing the elastic development degree and development stage of the dough, determine the optimal stopping time to avoid over-kneading. The second efficiency optimization data of the kneading machine is the stop operation time point determined according to the development relationship curve at this stage, which helps to save energy and improve work efficiency. Package the first efficiency optimization data of the kneading machine (i.e., the optimized parameters in the pre-stage) and the second efficiency optimization data of the kneading machine (i.e., the stop instruction in the post-stage) obtained through the foregoing steps to generate intelligent discrimination control instructions for the kneading machine. These control instructions contain detailed operation steps and parameter adjustments, guiding the kneading machine how to adjust the operation according to the actual development state of the dough, so as to achieve the best kneading effect. Finally, the kneading machine can intelligently judge the development stage of the dough according to this instruction during actual operation, automatically adjust the kneading intensity and kneading time, and even stop the operation at the appropriate time to ensure the optimization of the dough development process.

[0137] Preferably, step S4 includes the following steps:

[0138] Step S41: Adjust the operating parameters of the dough mixer according to the intelligent discrimination control instruction of the dough mixer to generate the dough mixer operating state adjustment data;

[0139] Step S42: Monitor the real-time power output of the dough mixer operating state adjustment data to generate real-time power monitoring data; based on the real-time power monitoring data, perform intelligent power adjustment on the dough mixer to execute the dough kneading efficiency optimization operation of the dough mixer.

[0140] In the embodiment of the present invention, according to the intelligent discrimination control instruction of the dough mixer generated in step S34, the operating parameters of the dough mixer are adjusted. These operating parameters include but are not limited to rotational speed, kneading force, kneading time, and operating mode, etc. The goal of the adjustment is to ensure that the dough mixer can operate with optimal parameters in different dough kneading stages (pre-set time period or post-set time period) to meet the needs of dough development. By executing the intelligent control instruction, the operating state of the dough mixer is adjusted in real time, and these adjustment results are recorded to generate the dough mixer operating state adjustment data. This data includes the current operating mode of the dough mixer, power output, operating time period, and adjusted operating parameters, etc. Use the built-in power monitoring module of the dough mixer to collect the power output data of the dough mixer under different operating parameters in real time to generate real-time power monitoring data. The real-time power monitoring data includes the current instantaneous power, average power output of the dough mixer, and the power change trend within each time period. These data reflect the load condition and operating efficiency of the dough mixer. According to the real-time power monitoring data, use the power optimization algorithm to perform intelligent adjustment on the power output of the dough mixer. The goal of power adjustment is: during the dough development stage, reduce unnecessary power output to save energy. During the key dough kneading stage (such as the dough stretching stage), provide sufficient power support to optimize the dough kneading effect. After completing the power adjustment, the dough mixer will execute the dough kneading operation with the optimized power output. By precisely adjusting the power distribution, ensure that the dough kneading process reaches the best balance between efficiency and quality. At the same time, monitor the power data in real time to ensure the stability of the power output and the accuracy of the adjustment, and avoid the occurrence of power shortage or overload phenomena.

[0141] In this specification, a dough kneading efficiency optimization system for a dough mixer is provided to execute the above-mentioned dough kneading efficiency optimization method for a dough mixer. The dough kneading efficiency optimization system for a dough mixer includes:

[0142] A dough movement analysis module, configured to obtain the dough mixer structure data; based on the dough mixer structure data, perform the layout of the optoelectronic sensor array to generate the optoelectronic sensor array deployment data; according to the preset data acquisition time interval, collect the optoelectronic sensor signals of the optoelectronic sensor array deployment data to obtain the standard reflected light intensity acquisition data; identify the dough edge movement trajectory of the dough through the standard reflected light intensity acquisition data to generate the dough movement trajectory identification data;

[0143] The kneading stage division module is used to obtain the operating parameters of the kneading machine; analyze the periodic deformation characteristics of the dough based on the dough movement trajectory recognition data to obtain the dough periodic deformation data; evaluate the dough elastic development degree based on the dough periodic deformation data to generate the dough elastic development degree evaluation data; establish a relationship model between the dough development degree and efficiency for the operating parameters of the kneading machine through the dough elastic development degree evaluation data to generate a dough development degree - efficiency relationship model;

[0144] The kneading efficiency optimization module is used to predict the dough stage development relationship curve based on the dough development degree - efficiency relationship model for the dough development stage data to generate the dough stage development relationship curve; divide the curve inflection point segment of the dough stage development relationship curve to generate a pre - kneading time period and a post - kneading time period; optimize the kneading layer parameters of the kneading machine operating parameters according to the pre - kneading time period and the post - kneading time period to generate an intelligent discrimination control instruction for the kneading machine;

[0145] The intelligent adjustment module is used to adjust the working state of the kneading machine operating parameters according to the intelligent discrimination control instruction of the kneading machine to generate the kneading machine working state adjustment data; perform intelligent power adjustment on the kneading machine working state adjustment data to execute the kneading efficiency optimization operation of the kneading machine.

[0146] The beneficial effects of the present invention are as follows. Through reasonable sensor deployment, it is ensured that the motion data of the dough can be comprehensively and accurately obtained. The accuracy of the standard reflected light intensity acquisition data provides a reliable basis for the subsequent identification of the dough motion trajectory. Through high-precision signal acquisition and processing, the accurate identification of the dough edge can be achieved, thereby extracting the motion trajectory of the dough and helping to analyze the deformation characteristics of the dough during the kneading process. By deeply analyzing the periodic deformation characteristics of the dough, the deformation data of the dough is generated, providing data support for the subsequent evaluation of the dough development degree. Based on the motion trajectory and deformation characteristics of the dough, the elastic development degree of the dough is scientifically evaluated, which helps to more precisely control the kneading process and ensure that the dough reaches the optimal development state. By modeling the relationship between the development degree of the dough and the operating parameters of the kneading machine, a theoretical basis can be provided for optimizing the working efficiency of the kneading machine. Through model prediction, the development of the dough at each development stage can be accurately predicted, thereby formulating a reasonable kneading strategy to avoid over-kneading or insufficient kneading. The operating parameters of the kneading machine are hierarchically optimized according to the development stage of the dough to ensure that the kneading effect at different stages reaches the best. Through intelligent discrimination control instructions, the kneading process is optimized to improve production efficiency. By adjusting the working state of the kneading machine through intelligent discrimination control instructions and performing power adjustment in real time, this not only ensures that the kneading machine works in the best state, but also optimizes the use of energy and improves work efficiency. Through real-time working state adjustment and power optimization, the kneading machine can adapt to the state of the dough at different stages, maximizing the kneading efficiency while reducing energy waste. Through the efficient operation of the intelligent adjustment module, the kneading machine can perform the most effective kneading operation at the most suitable time, thereby improving production efficiency and shortening the production cycle. Intelligent power adjustment not only ensures kneading efficiency but also avoids ineffective energy consumption, thereby reducing energy waste and improving the energy use efficiency of the production process. Therefore, the present invention improves the kneading efficiency and accuracy of the kneading machine through intelligent optoelectronic sensor arrays, dough development degree evaluation, and operating parameter optimization.

[0147] Therefore, in any regard, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to encompass all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0148] The above description is only the specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the kneading efficiency of a dough kneading machine, characterized in that: The following steps are involved: Step S1: Acquire the structure data of the dough kneading machine; perform photoelectric sensor array layout based on the structure data of the dough kneading machine to generate photoelectric sensor array deployment data; perform photoelectric sensor signal collection on the photoelectric sensor array deployment data according to a preset data collection time interval to obtain standard reflected light intensity collection data; perform dough edge motion trajectory recognition on the dough using the standard reflected light intensity collection data to generate dough motion trajectory recognition data; Step S2: obtaining the operation parameters of the dough kneading machine; performing dough periodic deformation characteristic analysis on the dough motion trajectory recognition data to obtain dough periodic deformation data; The dough elasticity development degree is evaluated on the dough periodic deformation data to generate dough elasticity development degree evaluation data; the relationship between dough development degree and efficiency is modeled on the dough kneading machine operation parameters through the dough elasticity development degree evaluation data to generate a dough development degree-efficiency relationship model; Step S3: predicting a dough stage development relationship curve for dough development stage data based on a dough development degree-efficiency relationship model to generate a dough stage development relationship curve; The dough stage development relationship curve is divided into curve inflection point segments to generate a pre-kneading time period and a post-kneading time period; the kneading layering parameters of the kneading machine operation parameters are optimized according to the pre-kneading time period and the post-kneading time period to generate intelligent identification control instructions for the kneading machine; Step S4: adjusting the working state of the kneading machine operating parameters according to the kneading machine intelligent identification control instruction to generate the kneading machine working state adjustment data; performing intelligent power adjustment on the kneading machine working state adjustment data to perform the kneading efficiency optimization operation of the kneading machine.

2. The method for optimizing the kneading efficiency of a dough kneading machine according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire the dough kneading machine structure data; Step S12: Screening the boundary area of ​​the kneading bar based on the kneading machine structure data to obtain the boundary area data of the kneading bar; performing photoelectric sensor array layout on the boundary area data of the kneading bar to generate photoelectric sensor array deployment data; Step S13: collecting photoelectric sensor signals on the photoelectric sensor array deployment data according to a preset data collection time interval to obtain reflected light intensity collection data; performing data preprocessing on the reflected light intensity collection data to generate standard reflected light intensity collection data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization; Step S14: identifying the movement trajectory of the dough edge of the dough by collecting data based on the standard reflected light intensity, and generating dough movement trajectory identification data.

3. The method for optimizing the kneading efficiency of a dough kneading machine according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: performing edge contour conversion on the standard reflected light intensity acquisition data to generate edge contour conversion data; performing contour change timestamp confirmation on the edge contour conversion data to obtain a dough edge contour change timestamp; Step S142: Calculate the two-dimensional coordinates of the dough edge using the Cartesian coordinate system for the edge contour transformation data to obtain the two-dimensional coordinate data of the dough edge; perform dough position analysis on the two-dimensional coordinate data of the dough edge at each time point using the dough edge contour change timestamp to generate real-time movement position information data of the dough; Step S143: constructing a dough movement trajectory based on the real-time movement position information data of the dough to generate a real-time movement trajectory of the dough; performing displacement differential calculation on the real-time movement trajectory of the dough to generate dough displacement velocity data; performing displacement differential calculation on the dough displacement velocity data to obtain dough displacement acceleration data; Step S144: Integrate dough displacement information using dough displacement velocity data, dough displacement acceleration data, and dough real-time movement trajectory, thereby generating dough movement trajectory recognition data.

4. The method for optimizing the kneading efficiency of a dough kneading machine according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: obtaining the operation parameters of the dough kneading machine; performing dough displacement periodicity analysis on the dough motion trajectory recognition data to generate dough displacement periodicity data; performing dough periodic deformation characteristic analysis on the dough motion trajectory recognition data according to the dough displacement periodicity data to obtain dough periodic deformation data; Step S22: performing dough elasticity characteristic analysis on the dough displacement periodic data according to the dough periodic deformation data to generate dough elasticity characteristic data; performing dough elasticity development evaluation on the dough periodic deformation data based on the dough elasticity characteristic data to generate dough elasticity development evaluation data; Step S23: dividing the dough into dough development stages according to the dough elasticity development evaluation data to generate dough development stage data; Step S24: Modeling the relationship between dough development degree and efficiency based on the dough development stage data and the dough kneading machine operating parameters based on the dough development stage data to generate a dough development degree-efficiency relationship model.

5. The method for optimizing the kneading efficiency of a dough kneading machine according to claim 4, characterized in that: Extracting dough periodic deformation features from dough motion trajectory recognition data based on dough displacement periodic data includes: The dough movement trajectory recognition data is segmented into periodic trajectory segments using the dough displacement cycle data to generate complete dough kneading cycle data, wherein the complete dough kneading cycle data includes dough stretching phase data and dough retraction phase data; the dough stretching phase data and the dough retraction phase data are dynamically time-warped to generate a segmented periodic trajectory data set; The dough edge displacement is calculated for the segmented periodic trajectory data set to obtain the dough edge displacement value; the dough edge displacement value is judged as positive or negative, and when the dough edge displacement value is positive, the corresponding dough edge displacement value is defined as the dough stretching stage displacement value; When the dough edge displacement value is negative, the corresponding dough edge displacement value is defined as the dough retraction stage displacement value; the dough stretching rate is calculated for the dough stretching stage displacement value to obtain the dough stretching rate, wherein the dough stretching rate calculation formula is as follows: D max =max(∣x(t)∣); In the formula, S r D is the maximum stretching speed of the dough in a kneading cycle. max is the maximum stretch distance, T cycle is the time it takes for the dough to complete a complete kneading cycle, and x(t) is the modulus of the movement position of the dough at time t; The dough shrinkage rate is calculated for the displacement value in the dough shrinkage stage to obtain the dough shrinkage rate, wherein the dough shrinkage rate calculation formula is as follows: D min =min(∣x(t)∣); Where D min is the maximum retraction distance, x(t) is the modulus of the dough’s movement position at time t, and R r is the maximum retraction speed of the dough in a kneading cycle, T cycle The time it takes for the dough to complete a full kneading cycle; The dough extension rate and dough shrinkage rate are integrated with the dough periodic deformation data to generate the dough periodic deformation data.

6. The method for optimizing the kneading efficiency of a dough kneading machine according to claim 4, characterized in that: Step S22 includes the following steps: Step S221: screening the kneading arm stress data of the dough kneading machine operating parameters to obtain the kneading arm stress data of the dough kneading machine; performing dynamic change analysis on the kneading arm stress data of the dough kneading machine to generate the dynamic change data of the kneading arm stress of the dough kneading machine; Step S222: performing kneading machine-dough change time series matching on the kneading machine kneading arm stress dynamic change data and the dough periodic deformation data to generate kneading process time series matching data; performing displacement curve fitting on the kneading process time series matching data according to the dough elasticity characteristic data to generate a dough recovery elasticity curve; Step S223: defining an elasticity development index based on the dough recovery elasticity curve to obtain the elasticity development index; evaluating the dough elasticity development of the dough recovery elasticity curve according to the elasticity development index to generate dough elasticity development evaluation data.

7. The method for optimizing the kneading efficiency of a dough kneading machine according to claim 4, characterized in that: Step S24 includes the following steps: Step S241: performing data feature matching on the dough development stage data and the kneading machine operation parameters to generate kneading machine kneading feature fusion data; performing multi-dimensional feature dimensionality reduction analysis on the kneading machine kneading feature fusion data to generate kneading machine kneading feature dimensionality reduction data; Step S242: Establishing a mathematical model of the relationship between development degree and efficiency for the reduced-dimensionality data of dough kneading characteristics of the dough kneading machine by a multiple regression algorithm, and generating a preliminary model of the relationship between development degree and efficiency; dividing the reduced-dimensionality data of dough kneading characteristics of the dough kneading machine into data sets, and generating a model training set, a model test set, and a model verification set; Step S243: Use the model training set to perform model training on the development degree-efficiency relationship pre-model to generate a development degree-efficiency relationship training model; perform model optimization iteration on the development degree-efficiency relationship training model according to the model test set, and use the model verification set to perform model verification on the development degree-efficiency relationship training model after the optimization iteration to generate a dough development degree-efficiency relationship model.

8. The method for optimizing the kneading efficiency of a dough kneading machine according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: predicting a dough stage development relationship curve for dough development stage data based on a dough development degree-efficiency relationship model to generate a dough stage development relationship curve; Step S32: extracting the inflection point of the dough stage development relationship curve to obtain the optimal dough kneading time point of the dough stage; dividing the dough stage development relationship curve into curve segments according to the optimal dough kneading time point of the dough stage to generate a pre-kneading time period and a post-kneading time period; Step S33: adjusting the kneading machine operating parameters with efficiency priority based on the pre-kneading time period to generate first efficiency optimization data for the kneading machine; stopping the kneading operation of the kneading machine operating parameters based on the pre-kneading time period to generate second efficiency optimization data for the kneading machine; Step S34: performing instruction encapsulation on the first efficiency optimization data of the dough kneading machine and the second efficiency optimization data of the dough kneading machine to generate intelligent identification control instructions for the dough kneading machine.

9. The method for optimizing dough kneading efficiency of a dough kneading machine according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: adjusting the working state of the dough kneading machine operating parameters according to the dough kneading machine intelligent identification control instruction to generate dough kneading machine working state adjustment data; Step S42: Perform real-time power output monitoring on the working state adjustment data of the dough kneading machine to generate real-time power monitoring data; perform intelligent power adjustment on the dough kneading machine based on the real-time power monitoring data to perform the dough kneading efficiency optimization operation of the dough kneading machine.

10. A system for optimizing kneading area and kneading efficiency, characterized in that: Used to execute the kneading area and kneading efficiency optimization method as claimed in claim 1, the kneading area and kneading efficiency optimization system comprises: The dough motion analysis module is used to obtain the dough kneading machine structure data; perform photoelectric sensor array layout based on the dough kneading machine structure data to generate photoelectric sensor array deployment data; perform photoelectric sensor signal collection on the photoelectric sensor array deployment data according to a preset data collection time interval to obtain standard reflected light intensity collection data; perform dough edge motion trajectory recognition on the dough through the standard reflected light intensity collection data to generate dough motion trajectory recognition data; The dough kneading stage division module is used to obtain the operation parameters of the dough kneading machine; the dough motion trajectory recognition data is analyzed for the dough periodic deformation characteristics to obtain the dough periodic deformation data; the dough periodic deformation data is evaluated for the dough elasticity development to generate the dough elasticity development evaluation data; the dough kneading machine operation parameters are modeled for the relationship between the dough development and efficiency through the dough elasticity development evaluation data to generate the dough development-efficiency relationship model; The dough kneading efficiency optimization module is used to predict the dough stage development relationship curve of the dough development stage data based on the dough development degree-efficiency relationship model to generate the dough stage development relationship curve; divide the dough stage development relationship curve into curve inflection point segments to generate the pre-kneading time period and the post-kneading time period; optimize the kneading layer parameters of the dough kneading machine operating parameters according to the pre-kneading time period and the post-kneading time period to generate the intelligent identification control instructions of the dough kneading machine; The intelligent adjustment module is used to adjust the working state of the kneading machine operating parameters according to the intelligent identification control instructions of the kneading machine, generate the kneading machine working state adjustment data; and perform intelligent power adjustment on the kneading machine working state adjustment data to perform the kneading efficiency optimization operation of the kneading machine.

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