Fermentation process control method based on real-time monitoring of physicochemical property change of dough

Abstract

The invention relates to the technical field of operation parameter regulation and control, in particular to a fermentation process control method based on real-time monitoring of physicochemical property change of dough, which comprises the following steps: respectively acquiring actual physicochemical index data and ideal physicochemical index data in real time, and comparing change differences to obtain regulation and control necessity of each physicochemical index; dividing a plurality of fermentation stages based on the change process of the experimental physicochemical index data, and obtaining a stage determination coefficient by utilizing the change condition of the physicochemical index data in each fermentation stage; according to the method, a chain reaction coefficient of the physicochemical indexes is obtained by utilizing correlation differences among different physicochemical index data and combining regulation and control necessity of the physicochemical indexes, so that the regulation and control necessity of the physicochemical indexes is further regulated, a real-time control response weight at the current moment is obtained, and the fermentation process of the dough is controlled. The dough fermentation process is ensured to be stable under different conditions, so that the dough fermentation efficiency and the product quality are improved.

Description

Technical Field

[0001] This invention relates to the field of operating parameter control technology, specifically to a fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough. Background Technology

[0002] Dough fermentation is a highly dynamic and non-linear process, and its physicochemical properties, such as volume and pH value, are influenced by various factors. Concentration and viscoelasticity continuously change with yeast metabolic activity, directly affecting the dough's expansion properties, texture, and the final product's taste and quality. To ensure the dough develops its volume, structure, and flavor characteristics in an orderly manner under suitable environmental conditions and achieves ideal fermentation results, key physicochemical parameters of the dough during fermentation (such as volume, pH, etc.) are analyzed. Real-time online monitoring of concentration, etc., combined with predictive models and control strategies, dynamically adjusts the fermentation environment to ensure that the dough is always in the optimal fermentation state, thereby improving the consistency and stability of product quality.

[0003] In existing technologies, key physicochemical indicators of dough are monitored in real time, the fermentation state of the dough is dynamically tracked, fuzzy reasoning is used to make judgments, and corresponding temperature adjustment commands are output. However, in the fuzzy control of dough fermentation, multiple physicochemical indicators (such as...) are... Concentration and pH values ​​often exhibit coupling and potential conflicts, making it difficult to coordinate control objectives. For example, in the fermentation process of dough... Deviations in both concentration and pH value necessitate control and adjustment. Increasing temperature promotes yeast gas production and accelerates dough expansion, but it also accelerates the formation of organic acids, causing a rapid drop in pH value and disrupting gluten structure and flavor balance. When a fuzzy controller faces conflicting objectives—such as increasing temperature to promote gas production and decreasing temperature to slow acidification—a lack of effective priority judgment and coordination mechanisms may lead to contradictory control commands, resulting in system regulation chaos or response lag, reducing the accuracy of dough fermentation control and product consistency. Therefore, this invention analyzes the dominant role of physicochemical indicators in the fermentation state at different stages during dough fermentation and, combined with the real-time state of the dough, formulates a dynamic control strategy. Summary of the Invention

[0004] This invention provides a fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough, in order to solve existing problems.

[0005] The fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough of the present invention adopts the following technical solution: One embodiment of the present invention provides a fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough, the method comprising the following steps: The physicochemical index data corresponding to several physicochemical indicators of dough during the actual fermentation process and the fermentation process in the laboratory environment are acquired in real time and recorded as actual physicochemical index data and ideal physicochemical index data, respectively. By comparing the differences in changes between actual and ideal physicochemical index data, the necessity of regulating each physicochemical index can be determined. Based on the changes in the data of each physicochemical index during the fermentation process in a laboratory environment, the fermentation process of dough is divided into several fermentation stages. By utilizing the changes in the data of each physicochemical index under any physicochemical index in the fermentation stage under a laboratory environment, the stage determination coefficient of the physicochemical index at the current moment is obtained. By utilizing the correlation differences between the data of different physicochemical indices and the necessity of regulating the physicochemical index, the chain reaction coefficient of the physicochemical index is obtained. By combining the stage determination coefficient and chain reaction coefficient of the physicochemical index at the current moment, the necessity of regulating the physicochemical index is adjusted to obtain the real-time control response weight of the physicochemical index at the current moment. The fermentation process of the dough is controlled based on the real-time control response weights of each physicochemical index at the current moment.

[0006] Furthermore, the specific methods for comparing the differences in changes between actual and ideal physicochemical index data to determine the necessity of regulating each physicochemical index include: For any physicochemical index data, both actual and ideal, the differences between all corresponding data points in the actual and ideal data are obtained, resulting in a difference sequence. The slope of this difference sequence is obtained using the least squares method and denoted as the deviation trend parameter for the physicochemical index. Several preset time windows of fixed length are established and evenly distributed across the physicochemical index data, ensuring that the time windows for the same physicochemical index data are distributed at the same position. The mean of the absolute values ​​of the differences between all corresponding data points in the same time window for the same physicochemical index data is obtained and denoted as the mean deviation of the corresponding time window for the physicochemical index. Based on the deviation trend parameter and the difference between the mean deviations of adjacent time windows for the physicochemical index, the necessity for regulation of the physicochemical index is determined. The difference between the deviation trend parameter and the mean deviation of adjacent time windows is positively correlated with the necessity for regulation.

[0007] Furthermore, the fermentation process of dough is divided into several fermentation stages based on the changes in each physicochemical index data during fermentation under laboratory conditions. The specific methods include: For any physicochemical index, the inflection point detection method is used to obtain several inflection points in the physicochemical index data under the physicochemical index, and the physicochemical index data under the physicochemical index is divided into several fermentation stages by the inflection points.

[0008] Furthermore, the method for obtaining the stage determination coefficient of the physicochemical index at the current moment by utilizing the changes in physicochemical index data at each fermentation stage under arbitrary physicochemical indexes in a laboratory environment includes: By utilizing the changes in physicochemical index data at each fermentation stage under arbitrary physicochemical indices in a laboratory environment, the dynamic change rate of the physicochemical index at the fermentation stage is obtained. Based on the difference between the dynamic change rate of any physicochemical index at the fermentation stage and the overall level of the dynamic change rate of all physicochemical indices at the current moment corresponding to each fermentation stage in the laboratory environment, the stage determination coefficient of the physicochemical index at the current moment is obtained.

[0009] Furthermore, the method for obtaining the dynamic change rate of the physicochemical index in the fermentation stage by utilizing the changes in physicochemical index data under arbitrary physicochemical indicators in a laboratory environment at each fermentation stage includes the following specific methods: For ideal physicochemical index data under any physicochemical index, the range of data points in any fermentation stage of the ideal physicochemical index data is obtained and recorded as the variation range parameter of the fermentation stage; any data point in the fermentation stage is recorded as the target data point, and the absolute value of the difference between the target data point and its adjacent data points in the fermentation stage is obtained and recorded as the change amount parameter of the target data point; the ratio of the change amount parameter of the target data point to the target data point is recorded as the change rate of the target data point; based on the average change rate of all data points in the fermentation stage and the variation range parameter of the fermentation stage, the dynamic change rate of the physicochemical index in the fermentation stage is obtained.

[0010] Furthermore, the method for obtaining the stage determination coefficient of the physicochemical index at the current moment based on the difference between the dynamic change rate of any physicochemical index at the fermentation stage and the overall level of the dynamic change rate of each physicochemical index at the current moment corresponding to the fermentation stage under laboratory conditions includes: For the current moment, obtain the fermentation stage to which the current moment belongs, and obtain the average dynamic change rate of the fermentation stage to which the current moment belongs from the ideal physicochemical index data under all physicochemical indexes. Obtain the difference between the dynamic change rate of the fermentation stage at the current moment under any physicochemical index and the average dynamic change rate, and record it as the first difference. Based on the first difference and the average dynamic change rate, obtain the stage determination coefficient of the current moment under the physicochemical index.

[0011] Furthermore, the specific method for obtaining the chain reaction coefficient of the physicochemical index by utilizing the correlation differences between the physicochemical index data corresponding to different physicochemical indexes and combining the necessity of regulating the physicochemical indexes includes: Based on the changes in the correlation between the two physicochemical indicators, the correlation coupling parameters of each physicochemical indicator to other physicochemical indicators are obtained; based on the correlation coupling parameters of each physicochemical indicator to other physicochemical indicators, the chain reaction coefficient of each physicochemical indicator to other physicochemical properties is obtained.

[0012] Furthermore, the specific method for obtaining the correlation coupling parameters of each physicochemical indicator to other physicochemical indicators based on the changes in the correlation between the two physicochemical indicators includes: Obtain the Pearson correlation coefficient of any two physicochemical indicators within the same time window, denoted as the correlation between the two physicochemical indicators within that time window. The correlation obtained from actual physicochemical indicator data is denoted as the actual indicator correlation, and the correlation obtained from ideal physicochemical indicator data is denoted as the ideal indicator correlation. Obtain the difference between the actual and ideal indicator correlations of the two physicochemical indicators within that time window, as the correlation difference parameter of the two physicochemical indicators within that time window. Based on the correlation difference parameter in adjacent time windows and the necessity of regulating the physicochemical indicators, obtain the correlation coupling parameter of the two physicochemical indicators.

[0013] Furthermore, the specific method for obtaining the chain reaction coefficient of each physicochemical index on other physicochemical properties based on the correlation coupling parameters of each physicochemical index on other physicochemical indexes includes: For the current moment, any actual physical and chemical indicator is designated as the target indicator. The correlation between the actual physical and chemical indicator data of the target indicator and the actual indicators of other actual physical and chemical indicator data within the time window to which the current moment belongs is obtained. The correlation between the actual indicators is used to perform a weighted average of the correlation and coupling parameters of the two physical and chemical indicators to obtain the chain reaction coefficient of the target indicator.

[0014] Furthermore, the method for adjusting the necessity of regulating the physicochemical indicators by combining the stage determination coefficient and chain reaction coefficient of the physicochemical indicators at the current moment, and obtaining the real-time control response weight of the physicochemical indicators at the current moment, includes the following specific methods: Based on the ratio of the stage determination coefficient of any physicochemical index at the current moment to the average stage determination coefficient of all physicochemical indices at the current moment, and combined with the chain reaction coefficient, the real-time dominance of the physicochemical index at the current moment is obtained. Both the ratio and the chain reaction coefficient are positively correlated with the real-time dominance. For any physicochemical index, the real-time control response weight of the physicochemical index at the current moment is calculated by combining the real-time dominance and the necessity of regulation of the physicochemical index. Both the real-time dominance and the necessity of regulation of the physicochemical index are positively correlated with the real-time control response weight.

[0015] The beneficial effects of the technical solution of this invention are as follows: By real-time monitoring and control of different physicochemical indicators, the physicochemical indicators in the actual fermentation process can be compared with those under ideal laboratory conditions, and the differences in their changes can be analyzed. This ensures that the subsequent control of the fermentation process is more in line with expectations, avoiding quality fluctuations caused by external factors or other unstable factors. Furthermore, when using laboratory data for comparison and analysis, based on the changes of each physicochemical indicator during the fermentation process under laboratory conditions, the dough fermentation process is divided into several stages. Further, the fermentation process can be controlled and optimized based on each stage, ensuring that the physicochemical environment at each stage is within the optimal range, thereby improving fermentation efficiency. By controlling different physicochemical indicators, the physicochemical environment at each fermentation stage is ensured to be stable, thereby improving the quality of the final product. Based on changes in the actual fermentation process, the control strategy is adjusted in real time to ensure that ideal fermentation results can still be obtained under different environments or conditions, thereby improving the overall production efficiency and product quality of the dough fermentation process. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough according to the present invention; Figure 2 This is a schematic diagram of carbon dioxide concentration curves under different conditions provided in one embodiment of the present invention; Figure 3 A flowchart illustrating the steps of a method for analyzing the chain reaction coefficient of physicochemical indicators according to an embodiment of the present invention; Figure 4 The flowchart shows the core features of the process for calculating the real-time control response weights provided in one embodiment of the present invention. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of the fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough provided by this invention.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough, according to an embodiment of the present invention. The method includes the following steps: Step S001: Acquire in real time the physicochemical index data corresponding to several physicochemical indicators of the dough during the actual fermentation process and the fermentation process under laboratory conditions, and record them as actual physicochemical index data and ideal physicochemical index data, respectively.

[0022] Specifically, in order to implement the fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough proposed in this embodiment, it is first necessary to collect physicochemical index data corresponding to several physicochemical indicators during the actual fermentation process and the fermentation process under laboratory conditions. The specific process is as follows: Step S101: Use multiple sensors to monitor the physicochemical index data of the dough in the actual fermentation process in the dough fermentation box in real time, and record them as actual physicochemical index data and ideal physicochemical index data respectively.

[0023] Step S102: Under strictly controlled constant temperature and humidity conditions, a standard dough is prepared using raw materials (including flour, water, yeast, sugar, and salt) in a uniform ratio to ensure the reproducibility of the experiment, and physicochemical index data are collected in the fermentation chamber under laboratory conditions.

[0024] It should be noted that in the laboratory environment of this invention embodiment, the preset temperature is 30℃ and the humidity is 80%. The specific temperature and humidity values ​​can be adjusted according to the specific situation, and this invention embodiment does not make specific limitations.

[0025] As an optional embodiment, the specific method for obtaining the physicochemical index data is as follows: First, various sensors are arranged in the fermentation tank, including an image sensor, a pH sensor, an infrared gas sensor, and a pressure probe.

[0026] Then, sensors are used to collect physicochemical data, including: an image sensor to acquire images of the dough and, combined with edge recognition and 3D reconstruction algorithms, to calculate the dough's expansion volume; a pH sensor, an embedded miniature pH electrode, to record the dough's acidity and alkalinity in real time; an infrared gas sensor to detect the carbon dioxide concentration in the fermentation chamber, indirectly reflecting yeast activity; and a pressure probe to obtain the dough's elasticity.

[0027] In step S103, the physicochemical index data of the actual fermentation process and the fermentation process under laboratory conditions are recorded as actual physicochemical index data and ideal physicochemical index data, respectively. The raw signal output by the sensor is converted from analog to digital and preprocessed by filtering, noise reduction, amplification and other signal processing to ensure data stability. In addition, during the data acquisition process using the sensor, a fixed sampling period is used to synchronously acquire data from all channels to maintain the consistency of data in time sequence.

[0028] It should be noted that, in the embodiments of the present invention, the fixed sampling period of the sensor is preset to once every 10 seconds based on experience. The specific sampling period can be preset according to the actual situation, and the embodiments of the present invention do not make specific limitations.

[0029] Thus, the above methods have yielded physicochemical index data corresponding to several physicochemical indicators in the actual fermentation process and in the laboratory environment.

[0030] Step S002: Compare the changes in actual physicochemical index data and ideal physicochemical index data to determine the necessity of regulation for each physicochemical index.

[0031] It should be noted that changes in the physicochemical properties of dough during fermentation (such as volume increase, pH decrease, and CO2 accumulation) directly determine the final fermentation state of the dough. Therefore, these key indicators need to be monitored in real time. When a certain physicochemical indicator deviates from the expected trajectory, the system should determine the strength of its impact on the fermentation state based on the current degree of deviation, and then assess the necessity of its regulation. This allows for dynamic identification and priority response to key fermentation parameters, enabling the control system to promptly correct abnormal fermentation trends and bring the fermentation process back to normal. Furthermore, during dough fermentation, due to factors such as ingredient ratios, yeast activity, and slight environmental variations, some differences in the trajectory of various physicochemical indicators between different batches are normal. However, if the deviation of a certain physicochemical indicator shows a continuous increase or a trend deviating from the expected curve, it indicates that the dough fermentation process has deviated from the normal trajectory. Figure 2 The diagram shows the carbon dioxide concentration curves under different conditions. The gray shaded area represents the deviation of the actual carbon dioxide concentration from the ideal carbon dioxide concentration. The dashed line represents the curve formed by the carbon dioxide concentration monitored during the actual fermentation of the dough, i.e., the actual carbon dioxide concentration curve, while the solid line represents the curve formed by the carbon dioxide concentration during the fermentation of the dough in a laboratory environment, i.e., the ideal carbon dioxide curve.

[0032] Specifically, as a preferred embodiment, the method for obtaining the necessity of regulation includes: for actual and ideal physicochemical index data under any physicochemical index, obtaining the difference between all corresponding data points in the actual and ideal physicochemical index data, obtaining a difference sequence data formed by the differences between all corresponding data points, obtaining the slope of the difference sequence data using the least squares method, and recording it as the deviation trend parameter under the physicochemical index; establishing several preset time windows of fixed length, equally spaced on the arbitrary physicochemical index data, and ensuring that the corresponding data under the same physicochemical index are... The time window distribution positions of the actual physicochemical index data and the ideal physicochemical index data are consistent. Under any identical physicochemical index, the mean of the absolute values ​​of the differences between all data points corresponding to the actual physicochemical index data and the ideal physicochemical index data in the same time window is obtained, and denoted as the mean deviation of the corresponding time window under the physicochemical index. Based on the deviation trend parameter and the difference between the mean deviations of adjacent time windows under the physicochemical index, the necessity of regulation of the physicochemical index is obtained. The difference between the deviation trend parameter and the mean deviation of adjacent time windows is positively correlated with the necessity of regulation.

[0033] It should be noted that, in this embodiment of the invention, each time window is pre-set to contain 5 data points based on experience, that is, the length of the time window is 5. This can be adjusted according to the actual situation, and this embodiment of the invention does not make a specific limitation. In addition, in this embodiment of the invention, the number of time windows in the physicochemical index data is pre-set to be 5 based on experience. This can be adjusted according to the actual situation, and this embodiment of the invention does not make a specific adjustment.

[0034] As an optional embodiment, the specific calculation method for the necessity of regulation is as follows: ; in, Indicates the first The necessity of regulating individual physicochemical indicators; Indicates the first Deviation trend parameters of individual physical and chemical indicators; Indicates the number of time windows; Indicates the first The first of the physicochemical indicators The mean deviation of each time window; Indicates the first The first of the physicochemical indicators The mean deviation of each time window; This represents the linear normalization function.

[0035] It should be noted that, Indicates the first Within adjacent time windows, comparing the deviation levels of actual and ideal physicochemical index data, the greater the value of the necessity for regulation, the greater the difference between the actual and ideal physicochemical index data within the time window, and the greater the deviation trend. Therefore, the greater the necessity for regulating this physicochemical index during subsequent fermentation process control.

[0036] Thus, the necessity of regulating physicochemical indicators has been demonstrated through the above methods.

[0037] Step S003: Based on the changes in the data of each physicochemical index during the fermentation process under laboratory conditions, the fermentation process of the dough is divided into several fermentation stages; using the changes in the data of each physicochemical index under any physicochemical index under laboratory conditions, the stage determination coefficient of the physicochemical index at the current moment is obtained; using the correlation differences between the physicochemical index data corresponding to different physicochemical indexes, and in combination with the necessity of regulating the physicochemical index, the chain reaction coefficient of the physicochemical index is obtained.

[0038] It should be noted that the changes in physicochemical properties during dough fermentation exhibit significant time-dependent, synergistic, and nonlinear characteristics, collectively reflecting the dynamic process of microbial metabolic activity and dough structural evolution. Physicochemical indicators such as volume, pH, gas content, and viscoelasticity show phased changes over time: in the early stages, microorganisms mainly engage in adaptive metabolism; in the middle stages, they primarily... The process is characterized by accelerated growth and rapid volume expansion, along with increased viscoelasticity; the structure gradually stabilizes in the later stages, and the pH continuously decreases. When multiple physicochemical indicators deviate from their expected trajectories simultaneously, traditional fuzzy control methods struggle to determine the priority order of regulation for each indicator, easily leading to control conflicts. Therefore, it is necessary to analyze the dominance of each indicator at different stages based on ideal laboratory fermentation data, and identify the chain reaction of its changes on other indicators. This allows for the construction of a dynamic control strategy centered on dominance, ensuring the system effectively addresses dough fermentation anomalies in multi-objective regulation and safeguarding product quality. Figure 3 The diagram shows the steps of the chain reaction coefficient analysis method for physicochemical indicators.

[0039] Specifically, in step S301, based on the changes in the data of each physicochemical index during the fermentation process under laboratory conditions, the fermentation process of the dough is divided into several fermentation stages.

[0040] It should be noted that the stronger the trend of change in physicochemical indicators during a certain fermentation stage, the more representative they are of the core biochemical activities and structural evolution characteristics of that stage. For example, in the middle stage of fermentation, the rapid expansion of dough volume directly reflects the enhanced gas production activity of yeast and the effective expansion of the gluten network, which is the core process of dough structure development. Volume changes during this fermentation stage are the most dominant indicator reflecting fermentation progress. Therefore, by analyzing the dynamic changes of each physicochemical indicator in the corresponding fermentation stages, the dominant physicochemical properties in the real-time dough fermentation process can be determined.

[0041] As an optional embodiment, the method for obtaining the fermentation stage includes: for any physicochemical index, using an inflection point detection method to obtain several inflection points in the physicochemical index data under the physicochemical index, and dividing the physicochemical index data under the physicochemical index into several fermentation stages through the inflection points.

[0042] It should be noted that in the process of dividing the fermentation stages for each physicochemical indicator based on its respective data change characteristics, the division of fermentation stages varies for different physicochemical indicators. The changes in the physicochemical properties of dough during fermentation are non-linear. By collecting physicochemical indicator data from the fermentation process of standard dough under laboratory conditions, the fermentation process is divided into different stages. For example, in the dough fermentation process, the stages of rapid and slow changes in carbon dioxide concentration are distinguished.

[0043] Step S302: Using the changes in physicochemical index data at each fermentation stage under arbitrary physicochemical indexes in a laboratory environment, obtain the stage determination coefficient of the physicochemical index at the current moment.

[0044] As a preferred embodiment, the method for obtaining the stage determination coefficient includes: using the changes in physicochemical index data under any physicochemical index in each fermentation stage under laboratory conditions to obtain the dynamic change rate of the physicochemical index in the fermentation stage; and based on the difference between the dynamic change rate of any physicochemical index in the fermentation stage and the overall level of the dynamic change rate of all physicochemical indexes in the laboratory environment at the current moment corresponding to each physicochemical index in the fermentation stage, obtaining the stage determination coefficient of the physicochemical index at the current moment.

[0045] As a preferred embodiment, the method for obtaining the dynamic change rate includes: for ideal physicochemical index data under any physicochemical index, obtaining the range of data points in any fermentation stage of the ideal physicochemical index data, and recording it as the change range parameter of the fermentation stage; recording any data point in the fermentation stage as a target data point, obtaining the absolute value of the difference between the target data point and adjacent data points in the fermentation stage, and recording it as the change amount parameter of the target data point; recording the ratio of the change amount parameter of the target data point to the target data point as the change rate of the target data point; and obtaining the dynamic change rate of the physicochemical index in the fermentation stage based on the average change rate of all data points in the fermentation stage and the change range parameter of the fermentation stage.

[0046] As a preferred embodiment, the method for obtaining the stage determination coefficient of the physicochemical index at the current moment based on the difference between the dynamic change rate of any physicochemical index at the fermentation stage and the overall level of the dynamic change rate of all physicochemical indexes at the current moment corresponding to the fermentation stage under the laboratory environment is as follows: for the current moment, obtain the fermentation stage to which the current moment belongs; obtain the average dynamic change rate of the fermentation stage to which the current moment belongs from the ideal physicochemical index data under all physicochemical indexes; obtain the difference between the dynamic change rate of the fermentation stage at the current moment under any physicochemical index and the average dynamic change rate, and record it as the first difference; and obtain the stage determination coefficient of the physicochemical index at the current moment based on the first difference and the average dynamic change rate.

[0047] As an optional embodiment, the specific method for calculating the dynamic rate of change is as follows: ; in, Indicates the first Among the ideal physicochemical index data under each physicochemical index, the first... The dynamic rate of change of each fermentation stage; Indicates the first Among the ideal physicochemical index data under each physicochemical index, the first... The number of data points within each fermentation stage; Indicates the first Among the ideal physicochemical index data under each physicochemical index, the first... Within the first fermentation stage One data point; Indicates the first Among the ideal physicochemical index data under each physicochemical index, the first... Within the first fermentation stage One data point; Indicates the first Among the ideal physicochemical index data under each physicochemical index, the first... The variation range parameters for each fermentation stage; This represents the absolute value function.

[0048] It should be noted that, Indicates the first The physicochemical index is the first one corresponding to this index. The average rate of change of ideal index values ​​in each fermentation stage reflects the degree of change of physicochemical indicators in the fermentation reaction at that stage.

[0049] It should be noted that in the control of dough fermentation process, the stronger the dynamic change rate of the physicochemical index at the current moment, the more active its biochemical activity is at that stage. Therefore, it is more responsive and discriminative, can reveal subtle changes in the fermentation state, plays a decisive role in the changes in the current fermentation state, and has higher regulatory value in control decision-making.

[0050] By comparing the dynamic change rates of different physicochemical indicators during the fermentation process at the current moment, the determining role of each physicochemical indicator in the corresponding fermentation stage at the current moment can be obtained.

[0051] As an optional embodiment, the specific calculation method for the stage determination coefficient is as follows: ; in, Indicates the first The coefficient of determination of a physicochemical indicator for the current stage; Indicating the first in laboratory environment The dynamic rate of change of each physicochemical indicator at the current moment corresponding to the fermentation stage; This represents the average dynamic change rate of all physicochemical indicators in the laboratory environment at the current moment corresponding to each fermentation stage.

[0052] It should be noted that during the fermentation process, the more frequently the physicochemical index data corresponding to the physicochemical index changes, the stronger the corresponding dynamic change rate. In this embodiment of the invention, based on the fermentation process of dough under laboratory conditions, the change process of physicochemical index data corresponding to different physicochemical indexes under the ideal fermentation state of the dough is tested, so as to analyze the changes in each fermentation stage. Indicates the laboratory fermentation test number The ratio of the difference in the dynamic change rate of each physicochemical index compared to all other physicochemical indexes at the current fermentation stage reflects the decisive role of that physicochemical index in the fermentation process. The larger the ratio, the more influential that physicochemical index is on the final fermentation state of the dough at the current fermentation stage, and the stronger its dominant role.

[0053] Step S303: Utilize the correlation differences between physicochemical index data corresponding to different physicochemical indicators, and combine this with the necessity of regulating the physicochemical indicators, to obtain the chain reaction coefficient of the physicochemical indicators.

[0054] As a preferred embodiment, the method for obtaining the chain reaction coefficient includes: obtaining the correlation coupling parameter of each physicochemical indicator to other physicochemical indicators based on the change in the correlation between the two physicochemical indicators; and obtaining the chain reaction coefficient of each physicochemical indicator to other physicochemical properties based on the correlation coupling parameter of each physicochemical indicator to other physicochemical indicators.

[0055] As a preferred embodiment, the method for obtaining the correlation coupling parameter includes: obtaining the Pearson correlation coefficient of any two physicochemical indicators within the same time window, denoted as the correlation between the two physicochemical indicators within that time window; denoting the correlation obtained from actual physicochemical indicator data as the actual indicator correlation; and denoting the correlation obtained from ideal physicochemical indicator data as the ideal indicator correlation; obtaining the difference between the actual indicator correlation and the ideal indicator correlation of the two physicochemical indicators within that time window, as the correlation difference parameter of the two physicochemical indicators within that time window; and obtaining the correlation coupling parameter of the two physicochemical indicators based on the correlation difference parameter in adjacent time windows and the necessity of regulating the physicochemical indicators.

[0056] As a preferred embodiment, the method for obtaining the chain reaction coefficient includes: for the current moment, taking any actual physical and chemical indicator as the target indicator, obtaining the correlation between the actual physical and chemical indicator data of the target indicator and the actual indicator data of other actual physical and chemical indicators in the time window to which the current moment belongs, and using the correlation between the actual indicators to perform a weighted average of the correlation coupling parameters of the two physical and chemical indicators to obtain the chain reaction coefficient of the target indicator.

[0057] It should be noted that during dough fermentation, there are complex coupling relationships between different physicochemical indicators, and indicators with strong dynamic change rates can exert a pulling or triggering effect on other physicochemical properties. For example, An increased formation rate not only directly affects dough expansion but may also influence viscoelasticity by altering internal pressure or decrease pH through interaction with acidic byproducts. Therefore, dynamically changing indicators can be driving factors that trigger cascading reactions in other indicators. When prioritizing control targets, physicochemical indicators that generate cascading reactions with other physicochemical indicators are more indicative.

[0058] It should be noted that during dough fermentation, there are mutual influences among the physicochemical properties. If a certain physicochemical index deviates from the ideal fermentation trajectory due to factors such as the fermentation environment, the correlation between this index and other physicochemical indexes is disrupted. Due to the mutual influence, the difference between the correlation and the ideal state will gradually decrease with fermentation time, indicating that other physicochemical indexes are affected by this index and change.

[0059] As an optional embodiment, the specific calculation method for the correlation coupling parameter is as follows: ; in, Indicates the first Individual physicochemical indicators for the first Correlation and coupling parameters of individual physicochemical indicators; This indicates the number of time windows in the neighborhood of the physicochemical index; Indicates the first Individual physicochemical indicators and the first The physicochemical index at the first The difference between the Pearson correlation coefficient of each time window and the Pearson correlation coefficient of the corresponding fermentation stage in the laboratory; Indicates the first Individual physicochemical indicators and the first The physicochemical index at the first The difference between the Pearson correlation coefficient of each time window and the Pearson correlation coefficient of the corresponding time window in the laboratory; Indicates the first The necessity of regulating individual physicochemical indicators; This represents the linear normalization function.

[0060] It should be noted that, Indicates the first Individual physicochemical indicators and the first The changing trends of individual physicochemical indicators with fermentation time, when the... The deviation of some physicochemical indicators disrupts the correlation with the first... The corresponding changes in the physicochemical indicators lead to the first As each physicochemical indicator undergoes a corresponding change, the corresponding relationship is restored, and the differences in the Pearson correlation coefficient become smaller and smaller.

[0061] As an optional embodiment, the specific method for calculating the chain reaction coefficient is as follows: ; in ' Indicates the first Chain reaction coefficients of individual physicochemical indicators; This indicates the quantity of physicochemical indicators during dough fermentation; Indicates the first Individual physicochemical indicators for the first The correlation and coupling effect of individual physicochemical indicators; Indicates the first Individual physicochemical indicators and the first Pearson correlation coefficients of individual physicochemical indicators within the current time window.

[0062] It should be further noted that, in order to more accurately assess the impact of a certain indicator on the overall fermentation state, this embodiment of the invention introduces correlation as a weight. This allows for a more objective reflection of the driving effect and transmission effect of each indicator on other indicators in the current fermentation state, thereby comprehensively considering the actual degree of influence between indicators and avoiding overreaction to weakly coupled indicators or neglect of strongly coupled indicators during the control process. Therefore, this embodiment of the invention selects to use... Indicates according to the first Individual physicochemical indicators and the first The correlation of the individual physicochemical indicators will be the first Individual physicochemical indicators for the first The weighted average is calculated based on the correlation and coupling effects of individual physicochemical indicators.

[0063] Thus, the chain reaction coefficients of the physicochemical indicators are obtained through the above method.

[0064] Step S004: Combine the stage determination coefficient and chain reaction coefficient of the physicochemical index at the current moment to adjust the necessity of regulating the physicochemical index, and obtain the real-time control response weight of the physicochemical index at the current moment.

[0065] It should be noted that during dough fermentation, the importance and control priority of various physicochemical indicators change depending on the dominant mechanism at different stages. To enhance the control system's adaptability and anomaly response capability, focused control is needed when key indicators exhibit abnormalities. Indicators with strong dominance and significant deviations have the greatest impact on overall fermentation quality and should be prioritized for control. Dynamically allocating control resources and adjustment strategies ensures that adjustments to control variables are focused on key objectives, avoiding imbalances in dough fermentation regulation caused by conflicting objectives in fuzzy control.

[0066] Specifically, as a preferred embodiment, the method for obtaining the real-time control response weight of the physicochemical index includes: obtaining the real-time dominance of the physicochemical index at the current moment based on the stage determination coefficient of the physicochemical index at the current moment relative to the average stage determination coefficient of all physicochemical indexes, and combining the chain reaction coefficient of the physicochemical index; and adjusting the control necessity of the physicochemical index using the real-time dominance of the physicochemical index at the current moment to obtain the real-time control response weight of the physicochemical index at the current moment.

[0067] As a preferred embodiment, the method for obtaining the real-time control response weight of the physicochemical index includes: obtaining the real-time dominance of the physicochemical index at the current moment based on the ratio of the stage determination coefficient of any physicochemical index at the current moment to the average stage determination coefficient of all physicochemical indexes at the current moment, and in combination with the chain reaction coefficient, wherein the ratio and the chain reaction coefficient are both positively correlated with the real-time dominance; for any physicochemical index, calculating the real-time control response weight of the physicochemical index at the current moment by combining the real-time dominance and the necessity of regulation of the physicochemical index, wherein the real-time dominance and the necessity of regulation of the physicochemical index are both positively correlated with the real-time control response weight.

[0068] As an optional embodiment, the specific calculation method for the real-time dominance of the physicochemical indicators is as follows: ; in, Indicates the first The real-time dominance of individual physical and chemical indicators at the current moment; Indicates the first The coefficient of determination of each physicochemical indicator for the fermentation stage at the current moment; This represents the average stage determination coefficient of all physicochemical indicators at the current moment; Indicates the first The chain reaction coefficient of individual physicochemical indicators.

[0069] It should be noted that the roles of different physicochemical indicators in dough fermentation are dynamically changing at different stages. To achieve precise control, it is necessary to assess the dominance of each physicochemical indicator in real time. Therefore, this embodiment of the invention selects to comprehensively evaluate the real-time dominance of physicochemical indicators based on their determining role in the current fermentation stage, i.e., whether their changing trends can directly reflect the abnormal fermentation state of the dough, and their ability to have a chain reaction effect on other physicochemical indicators; such as Figure 4 The diagram shown is a flowchart illustrating the core features of the process for calculating the weights of the real-time control response.

[0070] As an optional embodiment, the specific calculation method for the real-time control response weight is as follows: ; in, Indicates the first Real-time control response weights for individual physical and chemical indicators; Indicates the first The real-time dominance of individual physicochemical indicators; Indicates the first The necessity of regulating individual physicochemical indicators.

[0071] Thus, the real-time control response weights of the physicochemical indicators are obtained through the above method.

[0072] Step S005: Control the fermentation process of the dough based on the real-time control response weights of each physicochemical index at the current moment.

[0073] Specifically, firstly, the actual physicochemical index data of the dough during the actual fermentation process are used as state variables. A Model Predictive Control (MPC) framework is introduced, with the state variables as inputs. The system prediction model predicts the state evolution for several future steps. The real-time control response weights of the actual physicochemical index data are used as the weights of each state variable in the objective function of the system prediction model. A weighted multi-objective optimization objective function is constructed, aiming to minimize the weighted state deviation and control input changes. The optimal sequence of control variables is dynamically solved. The control variables are the heating power and humidification rate of the dough fermentation chamber, thereby achieving multi-variable coordinated control of the physicochemical properties of the dough.

[0074] Then, a rolling optimization approach is used to recalculate the control response at each time step, and the adjustment results are fed back in real time, enabling the control strategy to adapt quickly and respond precisely to changes in the fermentation state. This allows the system to automatically focus at different fermentation stages. By controlling key physicochemical indicators such as dough volume and pH, continuous, flexible, and intelligent control of the fermentation process can be achieved, thereby ensuring the stability and consistency of dough structure and flavor.

[0075] In addition, in other embodiments of the present invention, a data table showing the correlation between different physicochemical indicators and time is provided, as shown in Table 1. The table shows that different physicochemical indicators of dough have a positive correlation over time during the fermentation process.

[0076] Table 1. Correlation data of different physicochemical properties over time.

[0077] This concludes the embodiment.

[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough, characterized in that, The method includes the following steps: The physicochemical index data corresponding to several physicochemical indicators of dough during the actual fermentation process and the fermentation process in the laboratory environment are acquired in real time and recorded as actual physicochemical index data and ideal physicochemical index data, respectively. By comparing the differences in changes between actual and ideal physicochemical index data, the necessity of regulating each physicochemical index can be determined. Based on the changes in the data of each physicochemical index during the fermentation process in a laboratory environment, the fermentation process of dough is divided into several fermentation stages. By utilizing the changes in the data of each physicochemical index under any physicochemical index in the fermentation stage under a laboratory environment, the stage determination coefficient of the physicochemical index at the current moment is obtained. By utilizing the correlation differences between the data of different physicochemical indices and the necessity of regulating the physicochemical index, the chain reaction coefficient of the physicochemical index is obtained. By combining the stage determination coefficient and chain reaction coefficient of the physicochemical index at the current moment, the necessity of regulating the physicochemical index is adjusted to obtain the real-time control response weight of the physicochemical index at the current moment. The fermentation process of the dough is controlled based on the real-time control response weights of each physicochemical index at the current moment.

2. The fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough according to claim 1, characterized in that, The method for comparing the differences between actual and ideal physicochemical index data to determine the necessity of regulating each physicochemical index includes the following specific methods: For any physicochemical index data, both actual and ideal, the differences between all corresponding data points in the actual and ideal data are obtained, resulting in a difference sequence. The slope of this difference sequence is obtained using the least squares method and denoted as the deviation trend parameter for the physicochemical index. Several preset time windows of fixed length are established and evenly distributed across the physicochemical index data, ensuring that the time windows for the same physicochemical index data are distributed at the same position. The mean of the absolute values ​​of the differences between all corresponding data points in the same time window for the same physicochemical index data is obtained and denoted as the mean deviation of the corresponding time window for the physicochemical index. Based on the deviation trend parameter and the difference between the mean deviations of adjacent time windows for the physicochemical index, the necessity for regulation of the physicochemical index is determined. The difference between the deviation trend parameter and the mean deviation of adjacent time windows is positively correlated with the necessity for regulation.

3. The fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough according to claim 1, characterized in that, The process of fermentation based on the changes in each physicochemical index data under laboratory conditions divides the dough fermentation process into several fermentation stages, including the following specific methods: For any physicochemical index, an inflection point detection method is used to obtain several inflection points in the physicochemical index data under the stated physicochemical index, and the physicochemical index data under the stated physicochemical index is divided into several fermentation stages by using the inflection points.

4. The fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough according to claim 1, characterized in that, The method for obtaining the stage determination coefficient of the physicochemical index at the current moment by utilizing the changes in physicochemical index data at each fermentation stage under arbitrary physicochemical indexes in a laboratory environment includes the following specific methods: By utilizing the changes in physicochemical index data at each fermentation stage under arbitrary physicochemical indices in a laboratory environment, the dynamic change rate of the physicochemical index at the fermentation stage is obtained. Based on the difference between the dynamic change rate of any physicochemical index at the fermentation stage and the overall level of the dynamic change rate of all physicochemical indices at the current moment corresponding to each fermentation stage in the laboratory environment, the stage determination coefficient of the physicochemical index at the current moment is obtained.

5. The fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough according to claim 4, characterized in that, The method for obtaining the dynamic change rate of a physicochemical index during each fermentation stage by utilizing the changes in physicochemical index data under arbitrary physicochemical indexes in a laboratory environment includes the following specific methods: For ideal physicochemical index data under any physicochemical index, the range of data points in any fermentation stage of the ideal physicochemical index data is obtained and recorded as the variation range parameter of the fermentation stage; any data point in the fermentation stage is recorded as the target data point, and the absolute value of the difference between the target data point and its adjacent data points in the fermentation stage is obtained and recorded as the change amount parameter of the target data point; the ratio of the change amount parameter of the target data point to the target data point is recorded as the change rate of the target data point; based on the average change rate of all data points in the fermentation stage and the variation range parameter of the fermentation stage, the dynamic change rate of the physicochemical index in the fermentation stage is obtained.

6. The fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough according to claim 4, characterized in that, The method for obtaining the stage determination coefficient of a physicochemical index at the current moment, based on the difference between the dynamic change rate of any physicochemical index at the fermentation stage and the overall level of the dynamic change rate of each physicochemical index at the current moment corresponding to the fermentation stage in a laboratory environment, includes the following specific methods: For the current moment, obtain the fermentation stage to which the current moment belongs, and obtain the average dynamic change rate of the fermentation stage to which the current moment belongs from the ideal physicochemical index data under all physicochemical indexes. Obtain the difference between the dynamic change rate of the fermentation stage at the current moment under any physicochemical index and the average dynamic change rate, and record it as the first difference. Based on the first difference and the average dynamic change rate, obtain the stage determination coefficient of the current moment under the physicochemical index.

7. The fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough according to claim 1, characterized in that, The method for obtaining the chain reaction coefficient of a physicochemical indicator by utilizing the correlation differences between physicochemical indicator data corresponding to different physicochemical indicators and combining the necessity of regulating the physicochemical indicator includes the following specific methods: Based on the changes in the correlation between the two physicochemical indicators, the correlation coupling parameters of each physicochemical indicator to other physicochemical indicators are obtained; based on the correlation coupling parameters of each physicochemical indicator to other physicochemical indicators, the chain reaction coefficient of each physicochemical indicator to other physicochemical properties is obtained.

8. The fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough according to claim 7, characterized in that, The method for obtaining the correlation coupling parameters of each physicochemical indicator to other physicochemical indicators based on the changes in the correlation between the two physicochemical indicators includes the following specific methods: Obtain the Pearson correlation coefficient of any two physicochemical indicators within the same time window, denoted as the correlation between the two physicochemical indicators within that time window. The correlation obtained from actual physicochemical indicator data is denoted as the actual indicator correlation, and the correlation obtained from ideal physicochemical indicator data is denoted as the ideal indicator correlation. Obtain the difference between the actual and ideal indicator correlations of the two physicochemical indicators within that time window, as the correlation difference parameter of the two physicochemical indicators within that time window. Based on the correlation difference parameter in adjacent time windows and the necessity of regulating the physicochemical indicators, obtain the correlation coupling parameter of the two physicochemical indicators.

9. The fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough according to claim 8, characterized in that, The method for obtaining the chain reaction coefficient of each physicochemical index on other physicochemical properties based on the correlation coupling parameters of each physicochemical index on other physicochemical indexes includes the following specific methods: For the current moment, any actual physical and chemical indicator is designated as the target indicator. The correlation between the actual physical and chemical indicator data of the target indicator and the actual indicators of other actual physical and chemical indicator data within the time window to which the current moment belongs is obtained. The correlation between the actual indicators is used to perform a weighted average of the correlation and coupling parameters of the two physical and chemical indicators to obtain the chain reaction coefficient of the target indicator.

10. The fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough according to claim 1, characterized in that, The method of adjusting the necessity of regulating the physicochemical indicators by combining the stage determination coefficient and chain reaction coefficient of the physicochemical indicators at the current moment to obtain the real-time control response weight of the physicochemical indicators at the current moment includes the following specific methods: The real-time dominance of the physicochemical index at the current moment is obtained by the ratio of the stage determination coefficient of any physicochemical index at the current moment to the average stage determination coefficient of all physicochemical indexes at the current moment, and by combining the chain reaction coefficient. The ratio and the chain reaction coefficient are both positively correlated with the real-time dominance. For any physicochemical index, the real-time control response weight of the physicochemical index at the current moment is calculated by combining the real-time dominance and control necessity of the physicochemical index. The real-time dominance and control necessity of the physicochemical index are both positively correlated with the real-time control response weight.

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