A dough kneading efficiency optimization method and system for a dough kneader

Abstract

The present application relates to the technical field of data optimization, and more particularly to a dough mixer efficiency optimization method and system. The method comprises the following steps: obtaining dough mixer structure data; performing photoelectric sensor array layout based on the dough mixer structure data to generate photoelectric sensor array deployment data; performing 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; identifying the dough edge motion track of the dough through the standard reflected light intensity collection data to generate dough motion track identification data; obtaining dough mixer operation parameters; and performing periodic deformation feature analysis on the dough motion track identification data to obtain dough periodic deformation data. The present application improves the dough mixing efficiency and accuracy of the dough mixer through intelligent photoelectric sensor array, dough development degree evaluation and operation parameter optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data optimization, in particular to a dough kneading efficiency optimization method and system. BACKGROUND

[0002] With the development of mechanization technology, dough kneader emerges as the times require. The initial dough kneader mainly realizes the stretching and kneading of dough through a simple mechanical transmission system. However, these early dough kneaders have the problems of uneven kneading, slow speed and large damage to the structure of the dough. With the development of electronic technology, dough kneaders begin to introduce electronic control systems to realize accurate 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 the bubbles and the toughness inside the dough. By introducing various kneading modes and automatic adjustment functions, the efficiency and dough quality of dough kneaders have been significantly improved. With the rapid development of intelligent technology, dough kneaders gradually integrate artificial intelligence and big data analysis. Modern dough kneaders not only have the functions of automatically adjusting the moisture, temperature and kneading strength of the dough, but also can optimize the kneading process through data analysis to reduce energy consumption and time cost. At the same time, the operation of the machine is more humanized, which can automatically adjust the kneading strategy according to the characteristics of different flours, further improving the efficiency and quality of kneading. However, the traditional dough kneader cannot accurately monitor the motion trajectory of the dough in real time, which leads to uneven kneading or excessive kneading. At the same time, the running parameter adjustment is relatively simple, usually relying on fixed parameters set, and cannot be dynamically adjusted according to the actual development of the dough, which leads to low efficiency and accuracy of the dough kneader. SUMMARY

[0003] Therefore, it is necessary to provide a dough kneader kneading efficiency optimization method and system to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a dough kneader kneading efficiency optimization method, the method comprising the following steps:

[0005] Step S1: obtaining dough kneader structure data; arranging a photoelectric sensor array based on the dough kneader structure data to generate photoelectric sensor array deployment data; collecting photoelectric sensor signals according to a preset data collection time interval to obtain standard reflected light intensity collection data; identifying the motion trajectory of the dough edge of the dough through the standard reflected light intensity collection data to generate dough motion trajectory identification data;

[0006] Step S2: Obtain the dough mixer operation parameters; analyze the dough periodic deformation characteristics of the dough motion trajectory identification data to obtain dough periodic deformation data; evaluate the dough elasticity development degree of the dough periodic deformation data to generate dough elasticity development degree evaluation data; model the dough development degree and efficiency relationship of the dough elasticity development degree evaluation data on the dough mixer operation parameters to generate a dough development degree-efficiency relationship model;

[0007] Step S3: Based on the dough development degree-efficiency relationship model, predict the dough stage development relationship curve of the dough development stage data to generate a dough stage development relationship curve; divide the curve inflection point section of the dough stage development relationship curve to generate a pre-kneading time period and a post-kneading time period; optimize the kneading machine operation parameters according to the pre-kneading time period and the post-kneading time period to generate kneading machine intelligent discrimination control instructions;

[0008] Step S4: Adjust the working state of the dough mixer according to the dough mixer intelligent discrimination control instructions to generate dough mixer working state adjustment data; intelligently adjust the power of the dough mixer working state adjustment data to perform dough mixer kneading efficiency optimization operations.

[0009] The present application realizes accurate monitoring of the movement trajectory of the dough by acquiring the structure data of the kneading machine and laying out the photoelectric 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 uneven kneading problem in the traditional method. Through the analysis of the periodic deformation data of the dough, the development state of the dough can be quantified, and then the elasticity development degree of the dough is evaluated. By establishing the relationship model between the dough development degree and the kneading efficiency, the kneading process can be accurately adjusted to ensure that the dough reaches the best elasticity development degree, improve the quality of the dough and the kneading efficiency, and avoid excessive or insufficient kneading. Through the dough development degree-efficiency relationship model, the stage of dough development is predicted, and the stage development relationship curve is analyzed. By dividing the kneading process into pre-kneading and post-kneading time periods, and optimizing the kneading machine operation parameters according to these data, accurate kneading layer control is realized, the kneading efficiency is improved, and the invalid kneading time is reduced. Through intelligent discrimination control instructions, the working state of the kneading machine is accurately adjusted, and further through the intelligent power adjustment function, the kneading machine 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, and the energy consumption is reduced, improving the overall economy and sustainability of the kneading machine. The four steps of accurate monitoring, data analysis and intelligent adjustment improve the efficiency, dough quality and energy efficiency of the kneading machine, and solve the deficiencies of traditional kneading machines in efficiency, quality control and energy consumption management. Therefore, the present application improves the kneading efficiency and accuracy of the kneading machine through intelligent photoelectric sensor array, dough development degree evaluation and operation parameter optimization.

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

[0011] Step S11: acquiring kneading machine structure data;

[0012] Step S12: based on the kneading machine structure data, the kneading lever boundary area is screened to obtain kneading lever boundary area data; the photoelectric sensor array is laid out based on the kneading lever boundary area data to generate photoelectric sensor array deployment data;

[0013] Step S13: according to the preset data acquisition time interval, the photoelectric sensor signal acquisition is performed on the photoelectric sensor array deployment data to obtain reflected light intensity acquisition data; the data preprocessing is performed on the reflected light intensity acquisition data to generate standard reflected light intensity acquisition data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization;

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

[0015] The application can accurately lock the key working area by screening the kneading lever boundary area, reduce the redundancy of data collection, and improve the pertinence and efficiency of the photoelectric sensor arrangement. Based on the data of the kneading lever boundary area, the photoelectric sensor array layout helps to reasonably distribute the sensor position, ensures to cover the entire kneading key area, and reduces 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 data quality can be effectively reduced, so as to generate high-quality, standardized reflected light intensity data, and lay a solid foundation for subsequent analysis. Using the standardized data to identify the dough edge motion trajectory can accurately capture the dough motion state and reflect its dynamic changes in real time, and provide a scientific basis for subsequent dough kneading process optimization. Through the integration of sensor data and intelligent recognition technology, step S1 realizes the integrated process from hardware layout to data collection and analysis, improves the intelligent level and operation efficiency of the kneading machine. Through the identification and analysis of the dough motion trajectory, the abnormal conditions (such as uneven motion) occurring in the kneading process can be monitored in real time, thereby providing an important basis for dough quality control.

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

[0017] Step S141: converting the standard reflected light intensity collection data into edge contour conversion data; and confirming the contour change time stamp of the edge contour conversion data to obtain the dough edge contour change time stamp;

[0018] Step S142: calculating the dough edge two-dimensional coordinate data of the edge contour conversion data through the Cartesian coordinate system to obtain the dough edge two-dimensional coordinate data; and analyzing the dough position at each time point based on the dough edge contour change time stamp to generate the dough real-time moving position information data;

[0019] Step S143: constructing the dough moving trajectory based on the dough real-time moving position information data to generate the dough real-time moving trajectory; performing displacement differential calculation on the dough real-time moving trajectory to generate the dough displacement velocity data; and performing displacement difference calculation on the dough displacement velocity data to obtain the dough displacement acceleration data;

[0020] Step S144: integrating the dough displacement velocity data, the dough displacement acceleration data, and the dough real-time moving trajectory to generate the dough motion trajectory identification data.

[0021] This invention transforms standard reflected light intensity data into edge contours and timestamps the contour changes, enabling precise recording of the dynamic changes of the dough edge and providing fundamental data for motion trajectory analysis. Based on the Cartesian coordinate system, the two-dimensional coordinates of the dough edge are calculated, and the position information at each time point is generated by combining the timestamps, thereby achieving high-precision two-dimensional reconstruction of the dough's 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's motion. These characteristic data can intuitively reflect the dough's motion state and its changing trends. Integrating velocity, acceleration, and trajectory data generates unified motion trajectory recognition data. This data fusion not only provides a complete picture of the motion trajectory but also simplifies the complexity of subsequent analysis. Step S14 enables real-time monitoring and analysis of the dough's motion state, helping to promptly detect anomalies, such as uneven movement or velocity fluctuations, thereby allowing for rapid adjustment of operating parameters during production and improving the efficiency and stability of the dough mixer. Based on velocity and acceleration data, the force and motion characteristics of the dough at different stages can be analyzed in depth, providing a physical basis for optimizing kneading process parameters. For example, the force distribution during the kneading process can be optimized by analyzing changes in motion acceleration. Dough motion trajectory recognition data provides crucial information for the intelligent control of the kneading process, ensuring standardization and ultimately improving dough quality consistency.

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

[0023] Step S21: Obtain the operating parameters of the dough kneading machine; perform periodic analysis of dough displacement on the dough motion trajectory recognition data to generate dough displacement periodic data; perform periodic deformation feature analysis of dough on the dough motion trajectory recognition data based on the dough displacement periodic data to obtain periodic deformation data of dough.

[0024] Step S22: Analyze the elastic characteristics of dough based on the periodic deformation data of dough and the displacement periodic data of dough to generate dough elastic characteristic data; evaluate the elastic development degree of dough based on the periodic deformation data of dough and generate dough elastic development degree evaluation data.

[0025] Step S23: Divide the dough into development stages based on the dough elasticity development assessment data, and generate dough development stage data;

[0026] Step S24: Based on the dough development stage data, model the relationship between dough development degree and efficiency using the dough development stage data and the dough kneading machine operating parameters to generate a dough development degree-efficiency relationship model.

[0027] The present application can accurately capture the periodic deformation of the dough during the kneading process through periodic analysis of the dough displacement data and deformation feature recognition, which provides more accurate physical parameter support for dynamic monitoring of dough quality. Based on the periodic deformation feature data, elasticity analysis and development degree evaluation can be performed to comprehensively characterize the elasticity properties of the dough and reflect the strength and uniformity of the internal network structure, which provides a scientific basis for judging the kneading effect and baking quality. Dividing the dough development process into multiple stages can more finely grasp the state of the dough during the kneading process, and this stage analysis helps to accurately control the kneading time and intensity, avoiding quality problems caused by excessive kneading or insufficient development. By integrating the dough development stage data and the operating parameters of the dough kneader to establish a development degree-efficiency relationship model, the operating parameter settings of the dough kneader can be optimized to achieve 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, and time) can be dynamically adjusted to significantly improve the kneading efficiency and product consistency. This feedback mechanism avoids the shortcomings of relying on experience in traditional processes. Based on the elasticity characteristics and development stage data of different types of dough (such as high-gluten flour and low-gluten flour), personalized kneading processes with strong targeting can be developed to provide differentiated solutions for industrial production. The periodic deformation data, elasticity feature data, and development stage data generated in step S2 provide complete data support for intelligent monitoring and control of the kneading process, promoting the development of dough kneaders towards intelligence and adaptability.

[0028] Preferably, the dough periodic deformation feature extraction from the dough motion trajectory recognition data based on the dough displacement periodic data comprises:

[0029] The dough motion trajectory recognition data is segmented into periodic trajectories using the dough displacement periodic data to generate complete kneading periodic data, wherein the complete kneading periodic data includes dough stretching stage data and dough retraction stage data; and the dough stretching stage data and the dough retraction stage data are dynamically time-warped to generate a segmented periodic trajectory data set;

[0030] The dough edge displacement value is calculated from the segmented periodic trajectory data set to obtain the dough edge displacement value; and the dough edge displacement value is discriminated 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;

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

[0032] D max =max(∣x(t)∣);

[0033]

[0034] In the formula, S r is the maximum stretching speed of the dough in a dough mixing cycle, D max is the maximum stretching distance, T cycle is the time for the dough to complete a complete dough mixing cycle, and x(t) is the modulus value of the movement position of the dough at time t.

[0035] The dough retraction rate is calculated by the displacement value of the dough retraction stage, and the dough retraction rate is obtained, wherein the formula for calculating the dough retraction rate is as follows:

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

[0037]

[0038] In the formula, D min is the maximum retraction distance, x(t) is the modulus value of the movement position of the dough at time t, R r is the maximum retraction speed of the dough in a dough mixing cycle, T cycle is the time for the dough to complete a complete dough mixing cycle.

[0039] The dough stretching rate and the dough retraction rate are integrated to generate the dough periodic deformation data.

[0040] The present application divides the dough movement trajectory into the stretching stage and the retraction stage according to periodicity, and dynamically time warps the two stages, so that the analysis is more accurate, which not only helps to capture the periodic deformation of the dough, but also provides clear stage data for subsequent deformation calculation. The positive and negative values of the edge displacement value of the dough are distinguished to clearly distinguish the behavior characteristics of the dough in different stages (stretching and retraction), which provides quantitative data for the physical properties such as elasticity and plasticity of the dough in different stages, and facilitates the analysis of the dough state. The maximum stretching speed (S r ) and the maximum retraction speed (R r), which can quantify the efficiency of dough stretching and retracting during the mixing process. This analysis can reveal the physical performance of the dough during the mixing process, helping to optimize the mixing process so that the dough achieves optimal elasticity and extensibility. By integrating the extension rate and retraction rate, comprehensive dough cyclic deformation data is generated, which provides a reliable basis for dough quality control and helps to identify changes in dough state for targeted adjustments. The calculation of dough extension rate and retraction rate makes the physical properties of the mixing process more controllable. During production, these indicators can be used to adjust mixing parameters (such as mixing time, mixing intensity, etc.) in real time, making the quality of each batch of dough more stable. Based on the cyclic deformation data, intelligent production equipment can be provided with accurate monitoring data to help real-time adjustment of the operating state of the production line, which is of great significance for improving production efficiency and reducing manual intervention. The extension rate and retraction rate of the dough can effectively quantify the elasticity and extensibility of the dough, ensuring that each batch of dough produced meets the set standards, thereby improving the quality consistency of the final product.

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

[0042] Step S221: Perform mixing arm stress data screening on the mixing machine operating parameters to obtain mixing machine mixing arm stress data; perform dynamic change analysis on the mixing machine mixing arm stress data to generate mixing machine mixing arm stress dynamic change data;

[0043] Step S222: Perform mixing machine-dough change time sequence matching on the mixing machine mixing arm stress dynamic change data and the dough cyclic deformation data to generate mixing process time sequence matching data; perform displacement curve fitting of the dough recovery process on the mixing process time sequence matching data according to the dough elasticity characteristic data to generate a dough recovery elasticity curve;

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

[0045] The present application can capture the direct relationship between the force exerted by the dough mixer on the dough and the dough deformation in real time by dynamically analyzing the operating parameters (force arm stress) of the dough mixer. This accurate stress data provides a basis for subsequent analysis of the elastic characteristics of the dough, helping to optimize the dough mixing process and making the matching between force and dough deformation more refined. By time-matching the dynamic stress data of the dough mixer with the periodic deformation data of the dough, the synchronous changes of stress and deformation of the dough during the mixing process can be comprehensively analyzed. This accurate matching provides a clear quantitative basis for the elastic recovery of the dough at each mixing stage, ensuring more accurate operation of the dough. By fitting the time-matching data of the dough mixing process with the elastic characteristic data of the dough, a dough recovery elasticity curve is generated. This curve can accurately show the elastic recovery characteristics of the dough during the mixing process and reveal the elastic development status of the dough at different mixing stages. Based on the dough recovery elasticity curve, an elastic development degree index is defined, and the elastic development degree of the dough is evaluated through this index. This process provides quantitative dough elasticity data to help determine whether the dough has reached the ideal elastic state, thereby providing operational decision-making basis for subsequent production. Accurate evaluation of 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 mixing intensity and mixing 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 elastic recovery and development degree evaluation 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 mixer can be automatically adjusted to reduce manual intervention, making the production process more efficient and stable. Through accurate evaluation of 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 excessive or insufficient development, which can significantly improve production efficiency and the consistency of the final product for large-scale production.

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

[0047] Step S241: data feature matching is performed on the dough development stage data and the operating parameters of the dough mixer to generate dough mixer mixing feature fusion data; multi-dimensional feature dimension reduction analysis is performed on the dough mixer mixing feature fusion data to generate dough mixer mixing feature dimension reduction data;

[0048] Step S242: a mathematical model of the development degree-efficiency relationship is established by a multiple regression algorithm on the dough mixer mixing feature dimension reduction data to generate a development degree-efficiency relationship pre-model; the dough mixer mixing feature dimension reduction data is divided into data sets to generate a model training set, a model test set, and a model validation set;

[0049] Step S243: model training is performed on the development degree-efficiency relationship pre-model using a model training set to generate a development degree-efficiency relationship training model; model optimization iteration is performed on the development degree-efficiency relationship training model according to a model test set, and model verification is performed on the development degree-efficiency relationship training model after optimization iteration using a model verification set to generate a dough development degree-efficiency relationship model.

[0050] The dough development stage data and the operation parameters of the dough mixer are matched by features, dough mixer kneading feature fusion data can be generated, the operation characteristics of the dough mixer and the development state of the dough are effectively fused, more rich and comprehensive data is provided for subsequent analysis. Then, through multi-dimensional feature dimension reduction analysis, redundant information is reduced and key features are retained, so that data processing is more efficient, and a more concise and accurate data set is provided for subsequent modeling. A mathematical relationship model between dough development degree and efficiency is formed by using a multiple regression algorithm to model the dough mixer kneading feature dimension reduction data, which reveals the internal relationship between dough development degree and kneading efficiency, and provides data support for process optimization. Through the model, each step in the production process can be adjusted according to the actual development degree, thereby improving the production efficiency and product quality. The development degree-efficiency relationship pre-model is trained using a model training set, and optimization iteration is performed through a test set to ensure the 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 model optimization iteration, the development degree-efficiency relationship training model is verified using a model verification set to ensure that the model has good generalization ability and practicality. The verification process ensures that the model can accurately predict the relationship between dough development degree and efficiency, avoids overfitting or failure in actual application, and enhances its operability in actual production. By establishing and verifying the development degree-efficiency relationship model, the kneading efficiency required for dough at different development stages can be accurately predicted, which provides a theoretical basis for real-time adjustment of the production process and helps operators accurately control the operation state of the dough mixer, thereby improving production efficiency and stability. The dough development degree-efficiency relationship model provides data support for the automatic control system, which can monitor the dough development degree in real time during the production process and automatically adjust the parameters of the dough mixer to achieve 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 comprises the following steps:

[0052] Step S31: based on the dough development degree-efficiency relationship model, a dough stage development relationship curve is predicted for the dough development stage data to generate a dough stage development relationship curve.

[0053] Step S32: The inflection point moment of the dough stage development relationship curve is extracted to obtain the optimal kneading time point of the dough stage; and the dough stage development relationship curve is divided into a pre-kneading time period and a post-kneading time period according to the optimal kneading time point of the dough stage;

[0054] Step S33: The efficiency priority parameter adjustment is performed on the kneading machine running parameters based on the pre-kneading time period to generate first efficiency optimization data of the kneading machine; and the kneading machine running parameters are stopped based on the pre-kneading time period to generate second efficiency optimization data of the kneading machine;

[0055] Step S34: The first efficiency optimization data and the second efficiency optimization data of the kneading machine are packaged into instructions to generate intelligent discrimination control instructions of the kneading machine.

[0056] The dough development degree-efficiency relationship model is used to predict the dough development stage data, and the development relationship curve of the dough stage is generated, so that the production personnel can accurately grasp the development of the dough in 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 of dough development is accurately identified, so that the kneading can be stopped at the most appropriate time to avoid over-kneading or insufficient kneading, and ensure 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 division provides fine time management for the kneading process, so that the kneading work in different stages is more in line with the needs of dough development, improving the accuracy and flexibility of the production process. Based on the data of the pre-kneading time period, the running parameters of the kneading machine are optimized and adjusted for efficiency, thereby improving the running efficiency of the kneading machine. This optimization can reduce unnecessary time waste, improve production efficiency, and reduce equipment wear and tear. In the post-kneading time period, the optimization of stopping the operation of the kneading machine can avoid over-kneading, reduce energy consumption, and maintain the dough in the best development state. This operation not only saves energy, but also improves the quality of finished products such as bread. The optimized kneading machine running data is packaged into intelligent discrimination control instructions to realize real-time intelligent control of the kneading machine. Through the control system, the kneading machine can automatically adjust the running parameters according to the actual situation, optimize the production process, and improve the automation level of the production line. Through comprehensive analysis of the dough development degree, efficiency and kneading machine running parameters, the error in manual operation is reduced. Data-driven decision-making can more accurately control the production process, making each step more stable and consistent.

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

[0058] Step S41: Adjust the working state of the dough mixer according to the intelligent discrimination control instruction of the dough mixer, and generate dough mixer working state adjustment data.

[0059] Step S42: Real-time power output monitoring is performed on the dough mixer working state adjustment data to generate real-time power monitoring data; and intelligent power adjustment is performed on the dough mixer based on the real-time power monitoring data to perform dough mixing efficiency optimization operation.

[0060] The present application adjusts the operating parameters of the dough mixer in real time through the intelligent discrimination control instruction of the dough mixer. This intelligent adjustment process can ensure that the dough mixer always works in the most suitable conditions for the current dough development state, thereby improving the dough mixing effect and avoiding excessive or insufficient operation. Real-time power output monitoring of the working state adjustment of the dough mixer can obtain the power consumption of the dough mixer at any time, which provides valuable data support for further analysis and optimization of 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 mixer to maintain the best energy efficiency ratio. This adjustment not only ensures that the dough mixer always maintains a high-efficiency working state during operation, but also intelligently optimizes power distribution according to the actual dough development situation to maximize dough mixing efficiency. Through intelligent power adjustment, it can effectively avoid excessive or insufficient power output and reduce unnecessary energy consumption. By optimizing power output, the efficiency of the dough mixing process is ensured, and energy consumption is reduced, meeting the needs of sustainable production. Real-time power monitoring and intelligent adjustment help to identify and avoid the state of high-load operation of the equipment, reduce the risk of equipment wear and failure caused by long-term overload, thereby prolonging the service life of the dough mixer and reducing maintenance and replacement costs. By precisely adjusting the working state and power output of the dough mixer, the production process can be more stable and efficient, which means that the production line can maintain a higher work efficiency in long-term operation, thereby improving the overall production capacity and economic benefits.

[0061] In the present specification, a dough mixer dough mixing efficiency optimization system is provided for performing the above-mentioned dough mixer dough mixing efficiency optimization method, which comprises:

[0062] A dough movement analysis module is configured to obtain dough mixer structure data; perform photoelectric sensor array layout based on the dough mixer 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; and perform dough edge movement track identification on the dough based on the standard reflected light intensity collection data to generate dough movement track identification data.

[0063] The kneading stage division module is configured to acquire the kneading machine operation parameters; analyze dough periodic deformation characteristics based on dough movement track identification data to obtain dough periodic deformation data; evaluate dough elasticity development degree based on the dough periodic deformation data to generate dough elasticity development degree evaluation data; and model the relationship between dough development degree and efficiency based on the dough elasticity development degree evaluation data and the kneading machine operation parameters to generate a dough development degree-efficiency relationship model.

[0064] The kneading efficiency optimization module is configured to predict a dough stage development relationship curve based on the dough development degree-efficiency relationship model and the dough development stage data to generate the dough stage development relationship curve; divide the curve inflection point section of the dough stage development relationship curve to generate a pre-kneading time section and a post-kneading time section; optimize the kneading machine operation parameters based on the pre-kneading time section and the post-kneading time section to generate kneading machine intelligent discrimination control instructions;

[0065] The intelligent adjustment module is configured to adjust the working state of the kneading machine based on the kneading machine intelligent discrimination control instructions to generate kneading machine working state adjustment data; and intelligently adjust the power based on the kneading machine working state adjustment data to perform kneading efficiency optimization operations of the kneading machine.

[0066] The beneficial effects of this invention lie in ensuring comprehensive and accurate acquisition of dough motion data through reasonable sensor deployment. The accuracy of standard reflected light intensity data acquisition provides a reliable foundation for subsequent dough motion trajectory recognition. High-precision signal acquisition and processing enable accurate identification of dough edges, thereby extracting the dough's motion trajectory and aiding in the analysis of dough deformation characteristics during kneading. In-depth analysis of the dough's periodic deformation characteristics generates dough deformation data, providing data support for subsequent dough development assessment. Based on the dough's motion trajectory and deformation characteristics, a scientific assessment of the dough's elastic development helps to more precisely control the kneading process, ensuring the dough reaches its optimal development state. Modeling the relationship between dough development and kneading machine operating parameters provides a theoretical basis for optimizing the kneading machine's efficiency. Model prediction accurately predicts the dough's development at each stage, allowing for the development of reasonable kneading strategies to avoid over-kneading or under-kneading. Layered optimization of kneading machine operating parameters based on dough development stages ensures optimal kneading results at different stages. Intelligent judgment of control commands optimizes the kneading process and improves production efficiency. By intelligently judging and controlling commands to adjust the working state of the dough kneading machine and adjusting the power in real time, this not only ensures that the dough kneading machine operates in its optimal state but also optimizes energy use and improves work efficiency. Through real-time adjustment of working state and power optimization, the dough kneading machine can adapt to the state of the dough at different stages, maximizing kneading efficiency while reducing energy waste. Through the efficient operation of the intelligent adjustment module, the dough 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, thus reducing energy waste and improving the energy efficiency of the production process. Therefore, this invention improves the kneading efficiency and accuracy of the dough kneading machine through intelligent photoelectric sensor arrays, dough development assessment, and operating parameter optimization. Attached Figure Description

[0067] Figure 1 A schematic diagram illustrating the steps of a method for optimizing the kneading efficiency of a dough kneading machine;

[0068] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2.

[0069] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3.

[0070] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0071] The technical method of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

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

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

[0074] To achieve the above-mentioned purpose, please refer to Figures 1 to 3 A dough kneading efficiency optimization method, the method comprising the following steps:

[0075] Step S1: Obtain the dough kneader structure data; based on the dough kneader structure data, layout the photoelectric sensor array, generate the photoelectric sensor array deployment data; according to the preset data acquisition time interval, collect the photoelectric sensor signal of the photoelectric sensor array deployment data, obtain the standard reflected light intensity acquisition data; through the standard reflected light intensity acquisition data, identify the dough edge motion trajectory of the dough, generate the dough motion trajectory identification data;

[0076] Step S2: Obtain the dough kneader operation parameters; analyze the dough periodic deformation characteristics of the dough motion trajectory identification data, obtain the dough periodic deformation data; evaluate the dough elasticity development degree of the dough periodic deformation data, generate the dough elasticity development degree evaluation data; through the dough elasticity development degree evaluation data, model the relationship between the dough development degree and the efficiency of the dough kneader operation parameters, generate the dough development degree-efficiency relationship model;

[0077] Step S3: Based on the dough development degree-efficiency relationship model, the dough development stage data is predicted 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; and the kneading machine running parameters are optimized according to the pre-kneading time period and the post-kneading time period to generate kneading machine intelligent discrimination control instructions;

[0078] Step S4: The kneading machine running parameters are adjusted according to the kneading machine intelligent discrimination control instructions to generate kneading machine working state adjustment data; and the kneading machine working state adjustment data is intelligently power-adjusted to perform kneading machine kneading efficiency optimization operations.

[0079] The present application realizes accurate monitoring of the dough movement trajectory by acquiring kneading machine structure data and arranging an array of photoelectric sensors. The movement trajectory of the dough edge can be accurately identified through standard reflected light intensity data, ensuring the uniformity and consistency of the dough during the kneading process, and avoiding the uneven kneading problem in traditional methods. By analyzing the periodic deformation data of the dough, the development state of the dough can be quantified, and the elasticity 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 accurately adjusted to ensure that the dough reaches the optimal elasticity development degree, improve the quality of the dough and the kneading efficiency, and avoid excessive or insufficient kneading. The dough development stage is predicted by the dough development degree-efficiency relationship model, and the stage development relationship curve is analyzed. By dividing the kneading process into pre-kneading and post-kneading time periods, and optimizing the kneading machine running parameters according to these data, precise kneading layer control is achieved, the kneading efficiency is improved, and the invalid kneading time is reduced. The kneading machine is accurately adjusted in working state by the intelligent discrimination control instructions, and further adjusted by the intelligent power adjustment function, so that 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, and the energy consumption is reduced, improving the overall economy and sustainability of the kneading machine. The four steps of accurate monitoring, data analysis and intelligent adjustment improve the efficiency, dough quality and energy efficiency of the kneading machine, and solve the deficiencies of traditional kneading machines in efficiency, quality control and energy consumption management. Therefore, the present application improves the kneading efficiency and accuracy of the kneading machine through the intelligent array of photoelectric sensors, dough development degree evaluation and running parameter optimization.

[0080] In the embodiment of the present application, as shown in the reference Figure 1 The kneading machine kneading efficiency optimization method includes the following steps:

[0081] Step S1: obtaining dough kneader structure data; performing photoelectric sensor array layout based on the dough kneader structure data to generate photoelectric sensor array deployment data; performing 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; identifying a dough edge motion track of the dough through the standard reflected light intensity collection data to generate dough motion track identification data;

[0082] In the embodiments of the present application, by obtaining the structural drawing or model file (such as CAD file) of the dough kneader, the file contains the detailed size and position of each component of the dough kneader, including the dough contact area. Determine the movement mode of the dough kneader (such as rotation, up and down swing, etc.) and the movement path of the dough. According to different dough kneader designs, determine the contact surface and movement range of the dough. If necessary, the actual measurement of the dough kneader can be carried out by using a laser scanner or a 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-mentioned structural data, select appropriate photoelectric sensors (for example: infrared photoelectric sensor, laser sensor or ultrasonic sensor, etc.). When selecting, the detection range, accuracy and response speed of the sensor should be considered. Layout the sensor array on the contact area and movement path of the dough kneader to ensure that the edge and movement trajectory of the dough can be covered. Generally, the sensor array should be arranged above, below or beside the dough to ensure that the movement of the dough can be fully perceived. Determine the arrangement angle and distance of the sensor. For example, place a sensor at each key position of the dough movement trajectory, and through reasonable design of angle and position, ensure that enough data can be captured. Convert the deployment data of the arranged sensors (such as sensor coordinates, installation angle, distance, type, etc.) into specific deployment diagram to generate an executable deployment scheme. The data can be simulated and optimized by using digital design software (such as AutoCAD or SolidWorks). Set up the data acquisition system to ensure that each photoelectric sensor starts collecting signals at a specified time interval. Generally, according to actual needs, 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 (such as working frequency, signal gain, sensitivity, etc.) of the sensor to ensure that effective data of reflected light intensity can be obtained. In the sensor array, each sensor will record the light reflection intensity (i.e. the light signal intensity reflected by the surface of the dough). In the preset time interval, start the sensor system and collect the data of each sensor synchronously 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 basis data for subsequent dough movement trajectory identification. Preprocess the collected standard reflected light intensity data. According to the change of different reflected light intensity, use image processing algorithms (such as edge detection, contour extraction, etc.) to identify the edge area of the dough. Combined with the spatial layout of the sensor and the reflected intensity change of the dough edge, 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 algorithm or interpolation algorithm based on time series) to generate the position of the dough at different time points.The dynamic characteristics such as acceleration and speed of the dough in the process of kneading are analyzed. The digital data of the dough motion trajectory are finally output, including the position information, speed, acceleration and other dynamic parameters of the dough at different times. The data can be used for subsequent analysis, optimization of the working state of the kneader or for other automatic control systems.

[0083] Step S2: obtaining the kneader operation parameters; performing dough periodic deformation characteristic analysis on the dough motion trajectory identification data to obtain dough periodic deformation data; performing dough elasticity development degree evaluation on the dough periodic deformation data to generate dough elasticity development degree evaluation data; and modeling the relationship between the dough development degree and the efficiency of the kneader operation parameters through the dough elasticity development degree evaluation data to generate a dough development degree-efficiency relationship model;

[0084] In the embodiments of the present application, the running parameters of the dough mixer are obtained from the control system of the dough mixer. The running parameters include but are not limited to: speed, pressure, temperature, humidity, mixing time, vibration frequency, etc. These parameters are monitored and recorded in real time by sensors on the dough mixer, such as speed sensors, temperature sensors, humidity sensors, pressure sensors, etc. The obtained running parameters are stored in the data acquisition system and time-synchronized with the dough trajectory identification data for subsequent analysis and modeling. If necessary, adjust the running parameters of the dough mixer to ensure that they are within the predetermined range, ensuring that the dough's movement and development process can be accurately evaluated. Based on the dough trajectory identification data obtained in the first step, the periodic deformation characteristics of the dough during the mixing 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 trajectory are analyzed, and the frequency, amplitude, period, and other key parameters of the periodic deformation are extracted. Through these parameters, the periodic deformation characteristics of the dough during the mixing process can be quantified. Combined with 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 the dough periodic deformation data. The dough elasticity development degree generally represents the elasticity, ductility, and tensile strength of the dough. The elasticity development degree is closely related to the deformation behavior of the dough and can be evaluated by the periodic deformation characteristics of the dough. According to the periodic deformation data of the dough, the mechanical model (such as Hooke's law, stress-strain relationship, etc.) is used to calculate the elastic coefficient and plastic coefficient of the dough, thereby evaluating the elasticity development degree of the dough. The dough elasticity development degree evaluation can be achieved by the following way: according to the amplitude, frequency, etc. of the dough deformation, the dough resilience, viscosity, and ductility are calculated. Using these data, the development index of the dough (for example, by the formula: E = σ / ε, where E is the elasticity development degree, σ is the stress, and ε is the strain) is calculated, and the dough elasticity development degree evaluation data, including development index, elasticity, ductility, and other key indicators, are generated, which provide the basis for further analysis of the development status of the dough. The dough elasticity development degree evaluation data is combined with the running parameters of the dough mixer. Under each running cycle, there will be a certain relationship between the dough elasticity development degree and the speed, pressure, time, etc. parameters of the dough mixer. By integrating the data, the relationship between the dough development degree and the efficiency is constructed. Regression analysis (such as linear regression, polynomial regression, support vector machine, etc.) or machine learning algorithms (such as decision tree, neural network, etc.) are used to establish a mathematical model between the dough development degree and the efficiency of the dough mixer. This model can be used to predict the optimal relationship between the dough development degree and the efficiency of the dough mixer. Historical data (including different running parameters and dough development degree data) are used to train the model. Through cross-validation, error analysis, and other methods, the model is optimized to ensure its accuracy and robustness. Finally, a mathematical model that can predict the relationship between the dough development degree and the efficiency of the dough mixer is obtained.The model can be used to adjust the operation parameters of the dough mixer to improve the efficiency of the dough mixing while ensuring the quality of the dough development.

[0085] Step S3: based on the dough development degree-efficiency relationship model, the dough development stage data is subjected to dough stage development relationship curve prediction to generate a dough stage development relationship curve; the dough stage development relationship curve is subjected to curve inflection point segment division to generate a pre-dough mixing time period and a post-dough mixing time period; and the dough mixing machine operation parameters are subjected to dough mixing layered parameter optimization according to the pre-dough mixing time period and the post-dough mixing time period to generate a dough mixing machine intelligent discrimination control instruction;

[0086] In the embodiments of the present application, by collecting data of the dough at different development stages, including the development degree of the dough, the operating parameters of the kneader (such as speed, pressure, time, etc.), and the state of the dough at each development stage (such as elasticity, extensibility, etc.). The established "dough development degree-efficiency relationship model" is used to input the development degree data of the dough, and the corresponding efficiency is predicted, which can be obtained by inputting different development degrees of the dough into the model to get the development efficiency value at each stage. According to the prediction result, a curve fitting algorithm (such as spline interpolation, least squares method, etc.) is used to fit the development relationship curve of the dough. The curve represents the trend of the change between the development degree of the dough and the efficiency, which can help understand the influence of different development stages on the efficiency. Finally, a dough stage development relationship curve is obtained, reflecting the efficiency change in the dough development process and the development state corresponding to different stages. By analyzing the dough stage development relationship curve, the inflection points of the curve are identified, which usually represent important change nodes in the dough development process, such as the moments when the softness, extensibility, and elasticity of the dough change significantly. According to the shape of the dough development relationship curve, the inflection points of the curve are determined. The rising, plateau, and falling sections 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 completion degree of the dough development. According to the initial stage (such as the rising section) of the curve, the pre-kneading time period is determined. This stage usually corresponds to the process when the dough just starts to develop, and the flexibility and extensibility gradually improve. According to the end stage (such as the falling section or plateau section) of the curve, the post-kneading time period is determined. This stage usually corresponds to the stage when the dough development is completed, and the dough development reaches the optimal state, and further kneading leads to over-development of the dough. According to the position of the inflection point, the specific time interval of the pre-kneading time period and the post-kneading time period is generated, providing a basis for subsequent operations. In the pre-kneading time period, the operating parameters of the kneader are adjusted so that the dough can reach the best development effect in this stage. For example, a lower speed and appropriate pressure are set to gradually develop the dough to a soft and extensible state. In the post-kneading time period, the operating parameters of the kneader are adjusted according to the development degree of the dough to avoid over-kneading. Generally, the speed of the kneader can be increased, the pressure can be increased, or the kneading time can be shortened to ensure that the dough completes the kneading in the optimal state. An optimization algorithm (such as genetic algorithm, particle swarm optimization algorithm, etc.) is used to optimize the parameters of the pre-kneading and post-kneading stages in layers to ensure that the kneading effect of each stage matches the development stage of the dough, thereby improving the kneading efficiency and the quality of the dough. Based on the optimized operating parameters, intelligent control instructions for the kneader are generated. The intelligent control instructions include but are not limited to adjustment instructions for adjusting the speed, pressure, time, etc. parameters to ensure that the kneading process can be accurately executed in each stage. The generated control instructions are input into the kneader 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 working state of the dough mixer according to the intelligent discrimination control instruction, generate dough mixer working state adjustment data; intelligently adjust the power of the dough mixer to perform dough mixing efficiency optimization operation.

[0088] In the embodiment of the application, the intelligent discrimination control instruction generated in the previous step will contain adjustment instructions for different stages of operation parameters (such as speed, pressure, and dough mixing time). Adjust the operation parameters of the dough mixer according to the control instruction. For example: according to the development stage of the dough, gradually adjust the speed of the dough mixer. In the pre-dough mixing stage of the dough, a lower speed is usually required to avoid excessive stress on the dough; in the post-dough mixing stage, the speed can be increased to increase the efficiency of dough mixing. According to the elasticity and development degree of the dough, adjust the pressure of the dough mixer in time to avoid excessive compression or insufficient development of the dough. According to the development cycle of the dough, adjust the dough mixing time to ensure that the best development is completed within the appropriate time. According to the adjustment of these parameters, the working state adjustment data of the dough mixer is generated, recording the speed, pressure, time and other parameters of each stage. This data is used for subsequent analysis and further optimization. According to the running state of the dough mixer (including speed, pressure, time, etc.), analyze the required power. Generally, the power demand of the dough mixer fluctuates with the change of the operation parameters. For example, the increase of speed and pressure usually means that the demand for power will increase. Use intelligent algorithms (such as fuzzy logic control, neural network, prediction model, etc.) to predict the power required by the dough mixer under different working conditions. For example: when the dough mixer is in a low speed and low pressure state, the power demand is relatively low; while in a high speed and high pressure state, the power demand will increase significantly. According to the real-time monitoring of the working state adjustment data, the intelligent algorithm can accurately predict the optimal power required at each time period and adjust the power supply of the dough mixer. Based on the intelligent power regulation algorithm, automatically adjust the power output of the dough mixer to ensure that the machine can effectively operate under different working conditions, avoiding the situation of power excess or deficiency. At this time, the power regulation system will adjust the motor power, drive system, etc. in real time to adapt to the current operation load. Record the process and adjustment data of power regulation 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 regulation and working state adjustment data, the operation parameters and power output of the dough mixer are adjusted in real time to make it reach the best working state in each stage, thereby realizing the maximization of dough mixing efficiency.

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

[0090] Step S11: Obtain the structure data of the dough mixer;

[0091] Step S12: Based on the dough kneader structure data, the dough kneader edge boundary area is screened to obtain dough kneader edge boundary area data; the dough kneader edge boundary area data is arranged with a photoelectric sensor array to generate photoelectric sensor array deployment data;

[0092] Step S13: According to the preset data acquisition time interval, the photoelectric sensor array deployment data is collected to obtain reflected light intensity acquisition data; the reflected light intensity acquisition data is preprocessed to generate standard reflected light intensity acquisition data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization;

[0093] Step S14: The dough edge motion trajectory is identified by the standard reflected light intensity acquisition data to generate dough motion trajectory identification data.

[0094] In the embodiments of the present application, by obtaining the design drawings and structural data of the dough kneader, including physical dimensions, structural features, working principles, etc. By analyzing the structure of the dough kneader, the key areas related to dough processing are extracted, such as the kneading lever, the transmission system, etc. Combined with the actual application requirements, use three-dimensional modeling software (such as AutoCAD, SolidWorks, etc.) to perform virtual modeling of the dough kneader, and obtain accurate structural data. According to the structural data of the dough kneader, especially the design parameters and working area of the kneading lever, the boundary area of the kneading lever is identified, which is the key part of the contact between the dough and the kneading lever. Using image processing or geometric analysis techniques, the virtual model of the dough kneader is processed to clearly define the boundary of the kneading lever, ensuring that the area to be monitored can be accurately delineated, and the boundary area data of the kneading lever is obtained, including the spatial coordinates of the area, the geometric shape, and the contact mode between the dough and the kneading lever. Based on the boundary area data of the kneading lever, select appropriate installation positions in the structure of the dough kneader for the layout of the photoelectric sensor. The layout should consider the coverage range, sensitivity of the sensor, and the distance from the dough contact area. Determine the type of photoelectric sensor (such as infrared sensor, laser sensor, etc.) and the layout method (such as linear array, two-dimensional array, etc.). The sensor array should be able to continuously acquire reflected light signals during different stages of the entire dough kneading process. According to these parameters, design the deployment scheme of the sensor array, determine the number, arrangement, and installation position of the sensors, and generate the photoelectric sensor array deployment data. According to the working cycle of the dough kneader and the preset time interval, configure the acquisition frequency of the photoelectric sensor to ensure that the reflected light signals can be collected in time during the movement of the dough. At every set time interval, the photoelectric sensor array will collect signals at each sensor position, recording the light intensity reflected back 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 dough edge contacting the kneading lever. 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 noise in the reflected light intensity data and retain valid signals. In the case that the sensor fails to collect data due to some reasons, 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 to 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, which can be used as the basis for subsequent analysis. Use image processing or signal processing algorithms to analyze the standard reflected light intensity data and identify the dynamic changes of the dough edge. The 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 motion trajectory of the dough edge. Combined with the time stamp information, obtain the motion trajectory of the dough edge over time.The motion trajectory of the dough edge is fitted and analyzed to generate dough motion trajectory identification data, including motion speed, direction, amplitude and other parameters. Finally, complete dough motion trajectory identification data is generated, which can be used for further analysis of the deformation characteristics, development state of the dough and the efficiency of the kneading process.

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

[0096] Step S141: edge contour transformation is performed on the standard reflected light intensity acquisition data to generate edge contour transformation data; contour change time stamp confirmation is performed on the edge contour transformation data to obtain dough edge contour change time stamp;

[0097] Step S142: two-dimensional coordinate calculation of the dough edge is performed on the edge contour transformation data by using the Cartesian coordinate system to obtain two-dimensional coordinate data of the dough edge; dough position analysis at each time point is performed on the two-dimensional coordinate data of the dough edge by using the dough edge contour change time stamp to generate dough real-time moving position information data;

[0098] Step S143: dough moving trajectory construction is performed based on the dough real-time moving position information data to generate dough real-time moving trajectory; displacement differential calculation is performed on the dough real-time moving trajectory to generate dough displacement speed data; displacement difference calculation is performed on the dough displacement speed data to obtain dough displacement acceleration data;

[0099] Step S144: dough displacement information integration is performed on the dough displacement speed data, the dough displacement acceleration data and the dough real-time moving trajectory to generate dough motion trajectory identification data.

[0100] In the embodiments of the present application, edge detection is performed on the collected data of 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 profile of the dough from the reflected light intensity image. The detected edge information is extracted from the original data to form edge profile conversion data, which includes the change information of the edge profile of the dough at different time points and can reflect the morphological changes of the dough. According to the time interval of data collection and the edge profile change of the image, the time point at which each edge profile change occurs is confirmed. Each change point generates a timestamp, which is used to mark the edge position of the dough at that time point. By confirming the time sequence change of the edge profile, the dough edge profile change timestamp is obtained. The edge point position in the edge profile conversion data is mapped into a two-dimensional coordinate system using the Cartesian coordinate system. According to the detected edge profile points, the accurate coordinates of the points in the two-dimensional space are calculated. These coordinate data will show the precise position of the dough edge in the image and can be further used to analyze the position change of the dough. According to the dough edge profile change timestamp, 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 is arranged in the order of the timestamp to generate dough real-time moving position information data, which represents the dynamic position of the dough in the entire process. Combined with the dough real-time moving position information data, the position information at consecutive time points is connected to construct the moving trajectory of the dough. This trajectory represents the path of the dough from the starting point to the ending point in the entire kneading process. According to these trajectory data, the movement trajectory of the dough at each stage and its change trend can be clearly shown. The real-time moving trajectory of the dough is subjected to displacement differential calculation. Differential calculation can calculate the displacement speed of the dough at each time point by taking the derivative of the difference between adjacent positions. The displacement speed data can reveal the dynamic change of the dough during the kneading process and reflect its movement speed in each time period. Further differential calculation of the displacement speed data gives the displacement acceleration data of the dough. 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 differential operation, the change in movement intensity of the dough in different time periods can be clearly identified. The dough displacement speed data, dough displacement acceleration data, and dough real-time moving trajectory data are integrated to form 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. The movement trajectory, speed, and acceleration data of the dough are integrated to generate complete dough movement trajectory identification data, which provides detailed information about the movement of the dough and can be used for subsequent analysis and control decisions, such as dough development degree evaluation and kneading efficiency optimization.

[0101] As an example of the present application, reference is made toFigure 2 As shown, in this example, the step S2 includes:

[0102] Step S21: Obtain the dough mixer operation parameters; perform dough displacement periodicity analysis on the dough motion trajectory identification data to generate dough displacement periodicity data; perform dough periodicity deformation feature analysis on the dough motion trajectory identification data based on the dough displacement periodicity data to obtain dough periodicity deformation data;

[0103] Step S22: Perform dough elasticity feature analysis on the dough displacement periodicity data based on the dough periodicity deformation data to generate dough elasticity feature data; perform dough elasticity development degree evaluation on the dough periodicity deformation data based on the dough elasticity feature data to generate dough elasticity development degree evaluation data;

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

[0105] Step S24: Model the relationship between dough development degree and efficiency based on the dough development stage data and the dough development stage data and the dough mixer operation parameters to generate a dough development degree-efficiency relationship model.

[0106] In the embodiments of the present application, through the sensor and control system, various operating parameters of the dough mixer are obtained, such as speed, time, kneading intensity, temperature, etc. These operating parameters will serve as the basis for subsequent analysis, affecting the movement and deformation characteristics of the dough. Based on the dough trajectory recognition data, periodic analysis is performed on the displacement of the dough within a cycle. Periodic analysis can be performed through Fourier transform or waveform analysis, etc. 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, further analysis of the dough deformation characteristics in each cycle is performed. By calculating the stretching, compression and other deformation characteristics 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, such as calculating the maximum and minimum displacement of the dough surface in the periodic motion, or using optical sensors to detect the deformation of the dough surface. Finally, the dough periodic deformation data is obtained, reflecting the information of the dough's elasticity, stretchability and flexibility, etc. Using the dough periodic deformation data, a detailed analysis of the dough's elastic characteristics is performed. The analysis can include elastic modulus, rebound time, strain rate and other elastic property indicators. These analyses can be obtained by fitting the dough deformation curve, or using a stress-strain model for numerical calculation to identify the elastic response of the dough under different stretching or compression conditions. The generated dough elastic characteristic data can include elastic recovery ability, dough viscoelasticity characteristics, etc., reflecting the structural elasticity of the dough. Based on the dough elastic characteristic data, the dough elasticity development degree is evaluated. By evaluating the elastic properties of the dough, it is determined whether the dough has reached the ideal development state during the kneading process. The elasticity development degree can be evaluated by comparing the standard value or according to the set development degree threshold, such as calculating the development degree of the dough through its elastic modulus and strain recovery force. The generated dough elasticity development degree evaluation data reflects the maturity and development level of the dough, providing a basis for subsequent development stage division. Based on the dough elasticity development degree evaluation data, the development stages of the dough are divided according to the set development degree threshold or standard. 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 development stages can be used to judge the development process of the dough by setting a threshold value. For example, the elasticity of the dough is low in the initial stage, gradually increases in the development peak stage, and finally tends to be stable in the mature stage. According to the dough development stage data and the operating parameters of the dough mixer, a model of the relationship between the dough development degree and the kneading efficiency is established. This model can be constructed through regression analysis, neural network model or machine learning method.In the modeling process, the dough development degree is taken as the dependent variable, and the operation parameters of the dough mixer (such as the rotating speed, the dough mixing time, the temperature, etc.) are taken as the independent variables. Through the analysis of a large number of data samples, the most suitable development degree-efficiency relationship is obtained. This modeling method quantifies the relationship between the dough development stage and the mixing efficiency, so as to realize the optimization and intelligent control of the dough mixing process.

[0107] Preferably, the dough periodic deformation feature extraction of the dough motion trajectory recognition data according to the dough displacement periodic data comprises:

[0108] The dough motion trajectory recognition data is segmented by using the dough displacement periodic data to generate complete dough mixing periodic data, wherein the complete dough mixing periodic data comprises dough stretching stage data and dough retraction stage data; and the dough stretching stage data and the dough retraction stage data are subjected to dynamic time warping to generate a segmented periodic trajectory data set;

[0109] The dough edge displacement value is obtained by calculating the dough edge displacement of the segmented periodic trajectory data set; and the dough edge displacement value is subjected to positive and negative value discrimination, and 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; and the dough stretching rate is calculated by using the dough stretching stage displacement value to obtain the dough stretching rate, wherein 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 dough mixing period, D max is the maximum stretching distance, T cycle is the time for the dough to complete one complete dough mixing period, and x(t) is the modulus value of the motion position of the dough at time t.

[0114] The dough retraction rate is calculated by using the dough retraction stage displacement value to obtain the dough retraction rate, wherein 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 extension distance, x(t) is the modulus value of the movement position of the dough at time t, R r is the maximum retraction speed of the dough in a dough mixing cycle, T cycle is the time for the dough to complete a complete dough mixing cycle

[0118] The dough extension rate and the dough retraction rate are integrated to generate the dough periodic deformation data.

[0119] In the embodiment of the present application, by analyzing the displacement periodic data of the dough, the movement trajectory of the dough in the dough mixing cycle is divided into an extension stage and a retraction stage. According to the positive and negative changes of the displacement data, it is judged whether the dough is in an extension state or a retraction state. In this process, based on the periodic data, the complete dough mixing cycle of the dough is divided into two stages: the dough extension stage (positive displacement) and the dough retraction stage (negative displacement), and the generated complete dough mixing cycle data includes: when the displacement value is positive, it indicates that the dough is in the extension stage. When the displacement value is negative, it indicates that the dough is in the retraction stage. The segmented periodic trajectory data set (extension stage and retraction stage data) is subjected to dynamic time warping (DTW). Through this method, the time alignment between different time steps is ensured, and the synchronization and consistency of the data are improved. Dynamic time warping can eliminate the timing errors caused by slight fluctuations in the operation of the dough mixer, and ensure effective dough deformation feature extraction in the entire cycle. According to the data set subjected to dynamic time warping, the edge displacement of the dough is calculated. The displacement value of the dough at each time point is calculated, that is, the modulus value of the movement position of the dough at time t (i.e. |x(t)|), and the edge displacement data set of the dough is generated, which records the displacement amount of the dough at each time. The calculated edge displacement value of the dough is discriminated by positive and negative values: when the edge displacement value of the dough is positive (i.e. displacement outward), the displacement value is defined as the extension stage displacement value of the dough. When the edge displacement value of the dough is negative (i.e. displacement inward), the displacement value is defined as the retraction stage displacement value of the dough. The maximum extension speed of the dough in the dough mixing cycle is calculated. Through the formula: In the formula, S r is the maximum extension speed of the dough in a dough mixing cycle, D max is the maximum extension distance, T cycle is the time for the dough to complete a complete dough mixing cycle, x(t) is the modulus value of the movement position of the dough at time t; the maximum retraction speed of the dough in the retraction stage is calculated. Through the formula: D min = min(|x(t)|);

[0120] In the formula, D min is the maximum retraction distance, x(t) is the modulus value of the movement position of the dough at time t, R rT is the maximum retraction speed of the dough during a kneading cycle cycle T is the time for the dough to complete a full kneading cycle; S is the dough extensibility r R is the dough retraction r The dough periodic deformation data is integrated to form a dough periodic deformation data. This data reflects the elasticity 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, high extensibility indicates that the dough has good elasticity and is suitable for further processing, while high retraction indicates that the dough is still in an early development stage.

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

[0122] Step S221: Perform a kneading arm stress data screening on the kneader operating parameters to obtain kneader kneading arm stress data; Perform a dynamic change analysis on the kneader kneading arm stress data to generate kneader kneading arm stress dynamic change data;

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

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

[0125] In the embodiment of the present application, the force arm stress data generated by the dough kneader during the working process is screened according to the operating parameters of the dough kneader, such as the speed, pressure, time, etc. These data reflect the force applied to the dough by the dough kneader in each stage of the kneading process. The screened data includes the force applied by the dough kneader and its distribution at different time points. Time series analysis is performed on the dough kneader's kneading force arm stress data to identify its dynamic change trend during the kneading process. During the analysis process, the main concern is the dynamic process of the fluctuation, growth, and reduction of the force arm stress. Through dynamic change analysis, the time series data of the dough kneader's kneading force arm stress is obtained, which describes the periodic changes and fluctuations of the force arm stress. The dynamic change data of the dough kneader's kneading force arm stress is time-matched with the periodic deformation data of the dough (such as the stretching and shrinking 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 dough kneader's action at each time point corresponds to the dough deformation, and the generated time-matched data of the kneading process can accurately reflect the relationship between the force applied by the dough kneader and the dough deformation in each kneading stage. The elastic characteristic data of the dough is used to fit the dough recovery process during the kneading process 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 through appropriate mathematical models (such as exponential regression model, stretching-shrinking curve, etc.), the speed and degree of dough recovery elasticity are simulated. Based on the dough recovery elasticity curve, the elasticity development degree index is defined, which considers the stretching, shrinking, and recovery ability of the dough to quantify the elasticity level of the dough. The elasticity development degree index can be calculated by the following method: wherein, E f is the elasticity development degree index, S r is the dough stretching rate, and R r is the dough shrinking rate. This calculation method integrates the elastic recovery ability of the dough and reflects its overall development level. According to the elasticity development degree index, the elasticity development degree of the dough is evaluated to generate dough elasticity development degree evaluation data. This data can quantitatively describe the elasticity level of the dough during the kneading process, thereby providing a basis for subsequent optimization of the kneading efficiency. The dough elasticity development degree evaluation data will be used to determine the development stage of the dough, whether it has reached the ideal fermentation state, or whether further kneading is needed.

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

[0127] Step S241: data feature matching is performed on the dough development stage data and the operating parameters of the dough kneader to generate dough kneader kneading feature fusion data; and multi-dimensional feature dimension reduction analysis is performed on the dough kneader kneading feature fusion data to generate dough kneader kneading feature dimension reduction data;

[0128] Step S242: A mathematical model of the development degree-efficiency relationship is established for the dough kneading characteristic dimension reduction data of the dough kneader by a multiple regression algorithm to generate a development degree-efficiency relationship pre-model; the dough kneading characteristic dimension reduction data is divided into data sets to generate a model training set, a model test set, and a model validation set;

[0129] Step S243: The development degree-efficiency relationship pre-model is trained by the model training set to generate a development degree-efficiency relationship training model; the development degree-efficiency relationship training model is iteratively optimized according to the model test set, and the development degree-efficiency relationship training model after the optimization iteration is verified by the model validation set to generate a dough development degree-efficiency relationship model.

[0130] In the embodiments of the present application, by matching the dough development stage data (such as the elasticity development degree of the dough, periodic deformation, etc.) with the operation parameters of the dough mixer (such as the dough mixing force, speed, time, etc.), this process is synchronized through time series technology, so that the dough development data and the operation data of the dough mixer are aligned on the same time axis, and the generated dough mixer mixing feature fusion data includes various features of the dough mixer operation and the dough development state, which is the basic data set for establishing the subsequent model. The dough mixer mixing feature fusion data is subjected to multi-dimensional feature dimension reduction analysis, the purpose being to remove redundant features and extract the most representative features. Common dimension reduction methods such as principal component analysis (PCA) or linear discriminant analysis (LDA) project high-dimensional data into a lower-dimensional space, retain most of the useful information in the data, and generate dough mixer mixing feature dimension reduction data that retains important information while significantly reducing the complexity of the data, facilitating subsequent model training and analysis. A development degree-efficiency relationship pre-model is established using a multiple regression algorithm. Through multiple regression analysis, the relationship between the dough mixer mixing feature dimension reduction data and the dough development degree and the dough mixing efficiency is modeled. The multiple regression model considers multiple influencing factors such as the dough mixing force, dough mixing time, and dough development state, combines the effects of these factors together, generates a mathematical model, and is used to predict the relationship between the dough development degree and the dough mixing efficiency. The dough mixer mixing feature dimension reduction data is divided into a model training set, a model test set, and a model validation set. The purpose of data set division is to train, test, and validate the model, ensuring the generalization ability and accuracy of the model. The 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. The development degree-efficiency relationship pre-model is trained using the model training set. During the training process, the model learns how to predict the dough mixing efficiency based on the operation parameters of the dough mixer and the development stage of the dough. Through continuous iteration, the model adjusts its parameters to minimize the prediction error and gradually improves the accuracy of the prediction. After the model training is completed, the development degree-efficiency relationship training model is optimized and iterated using the model test set. The test set is used to evaluate the accuracy of the model, adjust the parameters of the model, and avoid overfitting. The optimization process ensures that the model maintains good prediction performance on different data sets through multiple training, validation, and adjustment. The optimized development degree-efficiency relationship training model is finally verified using the model validation set. The validation set is used to test the prediction ability of the model on unseen data and ensure the robustness and generalization ability of the model. After verification, the final dough development degree-efficiency relationship model is generated, which can accurately predict the influence of the dough development state on the dough mixing efficiency and provide a basis for the operation optimization of the dough mixer.

[0131] As an example of the present application, reference is made to Figure 3 In this example, the step S3 comprises:

[0132] Step S31: based on the dough development degree-efficiency relationship model, the dough stage development relationship curve is predicted for the dough development stage data, and a dough stage development relationship curve is generated;

[0133] Step S32: the curve inflection point time of the dough stage development relationship curve is extracted to obtain the optimal kneading time point of the dough stage; the curve segment of the dough stage development relationship curve is divided according to the optimal kneading time point of the dough stage, and a pre-kneading time segment and a post-kneading time segment are generated;

[0134] Step S33: based on the pre-kneading time segment, the efficiency priority parameter adjustment is performed on the kneading machine running parameters to generate the first efficiency optimization data of the kneading machine; based on the pre-kneading time segment, the kneading machine running parameters are stopped to generate the second efficiency optimization data of the kneading machine;

[0135] Step S34: the first efficiency optimization data of the kneading machine and the second efficiency optimization data of the kneading machine are packaged into instructions to generate intelligent discrimination control instructions of the kneading machine.

[0136] In the embodiments of the present application, the input dough development stage data is predicted based on an established dough development degree-efficiency relationship model. Specifically, a dough stage development relationship curve is generated using the model, 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 trend of the dough in different time periods by considering the initial state of the dough, the operating parameters of the kneader, and the current development situation 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 to determine the optimal kneading time. By analyzing the dough stage development relationship curve, the inflection point time of the curve is extracted, which is the time when the efficiency changes significantly during the dough development process. The inflection point time represents the optimal development stage of the dough, which usually means that the dough has reached the ideal kneading state. After extracting these inflection point times, the optimal kneading time point of the dough stage is obtained, which 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 prioritized and which operation steps can be delayed during the kneading process. Based on the data of the pre-kneading time period, the efficiency change trend of the dough development process is analyzed, and the efficiency priority parameter adjustment is proposed. The specific operation includes adjusting the speed, kneading force and kneading time of the kneader and other parameters to maximize the efficiency of the pre-kneading stage. The first efficiency optimization data of the kneader is obtained through these adjustments, which can improve the development efficiency of the dough in the pre-kneading stage and shorten the total kneading time. According to the development situation of the dough in the post-kneading time period, it is determined whether to stop the kneading operation. Through the analysis of the elasticity development degree and development stage of the dough, it is determined when the best stopping time is to avoid over-kneading. The second efficiency optimization data of the kneader is the stopping operation time point determined according to the development relationship curve in this stage, which helps to save energy and improve work efficiency. The first efficiency optimization data of the kneader (i.e. the optimization parameters of the pre-kneading period) and the second efficiency optimization data of the kneader (i.e. the stopping instruction of the post-kneading period) obtained through the foregoing steps are packaged to generate intelligent discrimination control instructions for the kneader. These control instructions contain detailed operation steps and parameter adjustments, which guide the kneader how to adjust the operation according to the actual development state of the dough, so as to achieve the best kneading effect. Finally, the kneader can intelligently judge the development stage of the dough according to the instructions in actual operation, automatically adjust the kneading intensity and time, and even stop the operation at the right time to ensure the optimization of the dough development process.

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

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

[0139] Step S42: monitoring the real-time power output of the dough mixer working state adjustment data to generate real-time power monitoring data; and intelligently adjusting the power of the dough mixer based on the real-time power monitoring data to perform dough mixing efficiency optimization operation.

[0140] In the embodiment of the present application, the running parameters of the dough mixer are adjusted according to the intelligent discrimination control instruction generated in step S34, including but not limited to speed, mixing force, mixing time and operation mode. The goal of adjustment is to ensure that the dough mixer can run with optimal parameters in different mixing stages (preliminary time period or post time period) to adapt to the needs of dough development. Through the execution of intelligent control instruction, the running state of the dough mixer is adjusted in real time, and the adjustment results are recorded to generate dough mixer working state adjustment data. The data includes the current running mode, power output, operation time period and adjusted running parameters of the dough mixer. The built-in power monitoring module of the dough mixer is used to collect the power output data of the dough mixer under different running 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 and power change trend in each time period, which reflects the load condition and running efficiency of the dough mixer. According to the real-time power monitoring data, the power output of the dough mixer is intelligently adjusted using power optimization algorithm. The goal of power adjustment is to reduce unnecessary power output during the development stage of the dough, thereby saving energy. In the key mixing stage (such as the stretching stage of the dough), sufficient power support is provided to optimize the mixing effect. After completing the power adjustment, the dough mixer will perform the mixing operation with the optimized power output. By accurately adjusting the power distribution, the best balance between efficiency and quality is ensured in the mixing process. At the same time, the power data is monitored in real time to ensure the stability and accuracy of the power output, avoiding the occurrence of power shortage or overload.

[0141] In the present specification, a dough mixer mixing efficiency optimization system is provided for performing the above-mentioned dough mixer mixing efficiency optimization method, which comprises:

[0142] A dough movement analysis module is configured to obtain dough mixer structure data, arrange an array of photoelectric sensors based on the dough mixer structure data to generate array deployment data of the photoelectric sensors, collect photoelectric sensor signals from the array deployment data of the photoelectric sensors according to a preset data collection time interval to obtain standard reflected light intensity collection data, and identify the movement trajectory of the edge of the dough based on the standard reflected light intensity collection data to generate dough movement trajectory identification data.

[0143] The kneading stage division module is configured to acquire the kneading machine operation parameters; analyze dough periodic deformation characteristics of dough movement trajectory identification data to obtain dough periodic deformation data; evaluate dough elasticity development degree of the dough periodic deformation data to generate dough elasticity development degree evaluation data; model the dough development degree and efficiency relationship of the kneading machine operation parameters through the dough elasticity development degree evaluation data to generate a dough development degree-efficiency relationship model;

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

[0145] The intelligent adjustment module is configured to adjust the working state of the kneading machine according to the kneading machine intelligent discrimination control instruction to generate kneading machine working state adjustment data; and intelligently adjust the power of the kneading machine according to the kneading machine working state adjustment data to perform the kneading efficiency optimization operation of the kneading machine.

[0146] The dough motion data can be comprehensively and accurately acquired through reasonable sensor deployment. The accuracy of standard reflected light intensity acquisition data provides a reliable basis for subsequent dough motion trajectory identification. Through high-precision signal acquisition and processing, the dough edge can be accurately identified, 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 subsequent dough development degree evaluation. 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 accurately control the kneading process and ensure that the dough reaches the best development state. By modeling the relationship between the development degree of the dough and the operating parameters of the dough kneader, a theoretical basis can be provided for optimizing the working efficiency of the dough kneader. Through model prediction, the development of the dough at each development stage can be accurately predicted, so as to develop a reasonable kneading strategy and avoid over-kneading or insufficient kneading. According to the development stage of the dough, the operating parameters of the dough kneader are optimized in layers to ensure that the kneading effect at different stages reaches the best. Through intelligent discrimination control instructions, the kneading process is optimized, and the production efficiency is improved. Through intelligent discrimination control instructions to adjust the working state of the dough kneader, real-time power adjustment is performed, which not only ensures that the dough kneader works in the best state, but also optimizes the use of energy and improves the working efficiency. Through real-time working state adjustment and power optimization, the dough kneader can adapt to the state of the dough at different stages, maximize the efficiency of kneading, and reduce energy waste. Through the efficient operation of the intelligent adjustment module, the dough kneader can perform the most effective kneading operation at the most suitable time, thereby improving the production efficiency and shortening the production cycle. Intelligent power regulation not only ensures the kneading efficiency, but also avoids invalid energy consumption, thereby reducing energy waste and improving the energy use efficiency of the production process. Therefore, the present application improves the kneading efficiency and accuracy of the dough kneader through intelligent photoelectric sensor array, dough development degree evaluation and operating parameter optimization.

[0147] Therefore, the embodiments should be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the description given above, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

[0148] The above description is merely that of a specific implementation of the present application, which enables those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application shall not be limited to these embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing kneading efficiency of a dough kneader, characterized by, The method comprises the following steps: Step S1: obtaining dough mixer structure data; performing photoelectric sensor array layout based on the dough mixer structure data to generate photoelectric sensor array deployment data; performing 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; performing dough edge motion track identification on the dough based on the standard reflected light intensity collection data to generate dough motion track identification data; Step S2: obtaining dough mixer operation parameters; performing dough periodic deformation feature analysis on the dough motion track identification data to obtain dough periodic deformation data; performing dough elasticity development degree evaluation on the dough periodic deformation data to generate dough elasticity development degree evaluation data; performing dough development degree and efficiency relationship modeling on the dough mixer operation parameters based on the dough elasticity development degree evaluation data to generate a dough development degree-efficiency relationship model; wherein, step S2 comprises the following steps: Step S21: obtaining dough mixer operation parameters; performing dough displacement periodicity analysis on the dough motion track identification data to generate dough displacement periodicity data; performing dough periodic deformation feature analysis on the dough motion track identification data based on the dough displacement periodicity data to obtain dough periodic deformation data; Step S22: performing dough elasticity feature analysis on the dough displacement periodicity data based on the dough periodic deformation data to generate dough elasticity feature data; performing dough elasticity development degree evaluation on the dough periodic deformation data based on the dough elasticity feature data to generate dough elasticity development degree evaluation data; Step S23: performing dough development stage division on the dough based on the dough elasticity development degree evaluation data to generate dough development stage data; Step S24: performing dough development degree and efficiency relationship modeling on the dough development stage data and the dough mixer operation parameters to generate a dough development degree-efficiency relationship model; Step S3: performing dough stage development relationship curve prediction on the dough development stage data based on the dough development degree-efficiency relationship model to generate a dough stage development relationship curve; performing curve inflection point segment division on the dough stage development relationship curve to generate a pre-kneading time period and a post-kneading time period; performing dough layering parameter optimization on the dough mixer operation parameters according to the pre-kneading time period and the post-kneading time period to generate dough mixer intelligent discrimination control instructions; Step S4: adjusting the working state of the dough mixer according to the dough mixer intelligent discrimination control instructions to generate dough mixer working state adjustment data; performing intelligent power adjustment on the dough mixer working state adjustment data to perform dough kneading efficiency optimization operation.

2. The dough-mixing-efficiency-optimization method of claim 1, wherein Step S1 comprises the following steps: Step S11: obtaining dough mixer structure data; Step S12: performing dough kneading lever boundary region screening based on the dough mixer structure data to obtain dough kneading lever boundary region data; performing photoelectric sensor array layout on the dough kneading lever boundary region data to generate photoelectric sensor array deployment data; Step S13: According to the preset data acquisition time interval, the photoelectric sensor array deployment data is collected to obtain reflected light intensity acquisition data; the reflected light intensity acquisition data is preprocessed to generate standard reflected light intensity acquisition data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization; Step S14: The dough edge motion trajectory is identified by the standard reflected light intensity acquisition data to generate dough motion trajectory identification data.

3. The dough-mixing-efficiency-optimization method of claim 2, wherein, Step S14 includes the following steps: Step S141: The edge contour transformation is performed on the standard reflected light intensity acquisition data to generate edge contour transformation data; the contour change timestamp is confirmed by the edge contour transformation data to obtain the dough edge contour change timestamp; Step S142: The dough edge two-dimensional coordinate data is calculated by the edge contour transformation data through the Cartesian coordinate system to obtain the dough edge two-dimensional coordinate data; the dough position at each time point is analyzed by the dough edge two-dimensional coordinate data using the dough edge contour change timestamp to generate the dough real-time moving position information data; Step S143: The dough moving trajectory is constructed based on the dough real-time moving position information data to generate the dough real-time moving trajectory; the displacement differential calculation is performed on the dough real-time moving trajectory to generate the dough displacement velocity data; the displacement differential calculation is performed on the dough displacement velocity data to obtain the dough displacement acceleration data; Step S144: The dough displacement information is integrated by the dough displacement velocity data, the dough displacement acceleration data and the dough real-time moving trajectory to generate the dough motion trajectory identification data.

4. The dough-mixing-efficiency-optimization method of claim 1, wherein According to the dough displacement periodic data, the dough motion trajectory identification data is subjected to dough periodic deformation feature extraction, including: The complete kneading cycle data is generated by segmenting the dough motion trajectory identification data using the dough displacement periodic data, wherein the complete kneading cycle data includes dough stretching stage data and dough retracting stage data; the dynamic time warping is performed on the dough stretching stage data and the dough retracting stage data to generate a segmented periodic trajectory data set; The dough edge displacement value is calculated by the segmented periodic trajectory data set to obtain the dough edge displacement value; the positive and negative values of the dough edge displacement value are distinguished, 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 retracting stage displacement value; the dough stretching rate is calculated by the dough stretching stage displacement value to obtain the dough stretching rate, and the formula of the dough stretching rate calculation is as follows: ； ； wherein is the maximum extension speed of the dough over one kneading cycle, is the maximum extension distance, is the time for the dough to complete one full kneading cycle, is the maximum extension speed of the dough over one kneading cycle, is the modulus of the motion position of the dough at time The dough retracting rate is calculated by the dough retracting stage displacement value to obtain the dough retracting rate, and the formula of the dough retracting rate calculation is as follows: ； ； wherein is the maximum retraction distance, is the maximum retraction velocity of the dough during a kneading cycle, is the modulus of the movement position of the dough at the moment t, is the maximum retraction velocity of the dough during a kneading cycle, is the time for the dough to complete a complete kneading cycle; The dough periodic deformation data is integrated by the dough stretching rate and the dough retracting rate to generate the dough periodic deformation data.

5. The dough-mixing-efficiency-optimization method of claim 1, wherein Step S22 includes the following steps: Step S221: Perform dough kneading arm stress data screening on the dough kneader operation parameters to obtain dough kneader kneading arm stress data; perform dynamic change analysis on the dough kneader kneading arm stress data to generate dough kneader kneading arm stress dynamic change data; Step S222: Perform dough kneader-dough change time sequence matching on the dough kneader kneading arm stress dynamic change data and the dough periodic deformation data to generate dough kneading process time sequence matching data; perform dough recovery process displacement curve fitting on the dough kneading process time sequence matching data according to the dough elasticity characteristic data to generate a dough recovery elasticity curve; Step S223: Define an elasticity development degree index based on the dough recovery elasticity curve to obtain the elasticity development degree index; perform dough elasticity development degree evaluation on the dough recovery elasticity curve according to the elasticity development degree index to generate dough elasticity development degree evaluation data.

6. The dough-mixing-efficiency-optimization method of claim 1, wherein Step S24 includes the following steps: Step S241: Perform data feature matching on the dough development stage data and the dough kneader operation parameters to generate dough kneader kneading feature fusion data; perform multi-dimensional feature dimension reduction analysis on the dough kneader kneading feature fusion data to generate dough kneader kneading feature dimension reduction data; Step S242: Perform development degree-efficiency relationship mathematical model establishment on the dough kneader kneading feature dimension reduction data by a multiple regression algorithm to generate a development degree-efficiency relationship pre-model; divide the dough kneader kneading feature dimension reduction data into a model training set, a model test set, and a model validation set; Step S243: Perform model training on the development degree-efficiency relationship pre-model using the model training set 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 perform model verification on the optimized and iterated development degree-efficiency relationship training model using the model validation set to generate a dough development degree-efficiency relationship model.

7. The dough-mixing-efficiency-optimization method of claim 1, wherein, Step S3 includes the following steps: Step S31: Perform dough stage development relationship curve prediction on the dough development stage data based on the dough development degree-efficiency relationship model to generate a dough stage development relationship curve; Step S32: Extract a curve inflection point time from the dough stage development relationship curve to obtain a dough stage optimal kneading time point; divide the dough stage development relationship curve into a pre-kneading time period and a post-kneading time period according to the dough stage optimal kneading time point; Step S33: Perform efficiency priority parameter adjustment on the dough kneader operation parameters based on the pre-kneading time period to generate dough kneader first efficiency optimization data; perform dough kneader operation stop on the dough kneader operation parameters based on the pre-kneading time period to generate dough kneader second efficiency optimization data; Step S34: Perform instruction packaging on the dough kneader first efficiency optimization data and the dough kneader second efficiency optimization data to generate a dough kneader intelligent discrimination control instruction.

8. The dough-mixing-efficiency-optimization method of claim 1, wherein, Step S4 includes the following steps: Step S41: Perform work state adjustment on the dough kneader operation parameters according to the dough kneader intelligent discrimination control instruction to generate dough kneader work state adjustment data; Step S42: Real-time power output monitoring is performed on the dough mixer working state adjustment data to generate real-time power monitoring data; intelligent power adjustment is performed on the dough mixer based on the real-time power monitoring data to execute the dough mixer dough mixing efficiency optimization operation.

9. A kneading area kneading efficiency optimization system, characterized by, The dough mixer dough mixing efficiency optimization system is used to execute the dough mixer dough mixing efficiency optimization method as claimed in claim 1, and comprises: A dough movement analysis module is configured to acquire dough mixer structure data; perform photoelectric sensor array layout based on the dough mixer 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 movement track identification on the dough based on the standard reflected light intensity collection data to generate dough movement track identification data; A dough mixing phase division module is configured to acquire dough mixer operation parameters; perform dough periodic deformation feature analysis on the dough movement track identification data to obtain dough periodic deformation data; perform dough elasticity development degree evaluation on the dough periodic deformation data to generate dough elasticity development degree evaluation data; perform dough development degree and efficiency relationship modeling on the dough mixer operation parameters based on the dough elasticity development degree evaluation data to generate a dough development degree-efficiency relationship model; A dough mixing efficiency optimization module is configured to perform dough stage development relationship curve prediction on the dough development stage data based on the dough development degree-efficiency relationship model to generate a dough stage development relationship curve; perform curve inflection point segment division on the dough stage development relationship curve to generate a pre-dough mixing time period and a post-dough mixing time period; perform dough mixing layered parameter optimization on the dough mixer operation parameters according to the pre-dough mixing time period and the post-dough mixing time period to generate dough mixer intelligent discrimination control instructions; An intelligent adjustment module is configured to perform working state adjustment on the dough mixer operation parameters according to the dough mixer intelligent discrimination control instructions to generate dough mixer working state adjustment data; perform intelligent power adjustment on the dough mixer working state adjustment data to execute the dough mixer dough mixing efficiency optimization operation. An intelligent adjustment module is configured to perform working state adjustment on the dough mixer operation parameters according to the dough mixer intelligent discrimination control instructions to generate dough mixer working state adjustment data; perform intelligent power adjustment on the dough mixer working state adjustment data to execute the dough mixer dough mixing efficiency optimization operation.

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