Method for controlling fermentation process based on real-time monitoring of changes in dough physicochemical properties

Abstract

The present application relates to the technical field of operating parameter regulation, in particular to a fermentation process control method based on real-time monitoring of changes in dough physicochemical properties, comprising: respectively acquiring actual physicochemical index data and ideal physicochemical index data in real time and comparing the changes to obtain the regulation necessity of each physicochemical index; dividing a number of fermentation stages based on the change process of experimental physicochemical index data, and using the change of physicochemical index data in each fermentation stage to obtain a stage determination coefficient; using the correlation difference between different physicochemical index data and combining the regulation necessity of physicochemical index to obtain a physicochemical index chain reaction coefficient, so as to further adjust the regulation necessity of physicochemical index and obtain the real-time control response weight at the current moment, thereby controlling the fermentation process of the dough. The present application ensures the stability of the dough fermentation process under different conditions to improve the dough fermentation efficiency and product quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of operating parameter regulation, and in particular to a fermentation process control method based on real-time monitoring of changes in dough physicochemical properties. BACKGROUND

[0002] Dough fermentation is a highly dynamic and nonlinear process. Its physicochemical properties, such as volume, pH, concentration, and viscoelasticity, continuously change with yeast metabolic activity, directly affecting the dough's expansion performance, structure, and the final product's taste and quality. To ensure that the dough develops in an orderly manner under suitable environmental conditions, with its volume, structure, and flavor characteristics, and to achieve the desired fermentation results, real-time online monitoring of key physicochemical parameters (such as volume, pH, concentration, etc.) during the fermentation process is combined with predictive models and control strategies to dynamically adjust the fermentation environment, ensuring that the dough is always in an optimal fermentation state, thereby improving the consistency and stability of product quality. In the prior art, real-time monitoring of key physicochemical indicators of dough is performed to dynamically track the fermentation state of the dough, make fuzzy reasoning judgments, and output corresponding temperature adjustment instructions. However, in the fuzzy control of dough fermentation, multiple physicochemical indicators (such as concentration and pH) often exhibit coupling and potential conflicts, making it difficult to coordinate between control objectives. For example, in the fermentation state of the dough, both the concentration and the pH deviate, and need to be controlled and adjusted. Raising the temperature helps to promote yeast gas production and accelerate dough expansion, but it also speeds up the generation of organic acids, leading to a rapid drop in pH, which destroys the gluten structure and flavor balance. When a fuzzy controller faces such contradictory objectives as promoting gas production by raising the temperature and slowing down acidification by lowering the temperature, if there is a lack of effective priority judgment and coordination mechanism, it may output mutually contradictory control instructions, causing system regulation confusion or response lag, reducing the control precision of dough fermentation and product consistency. Therefore, the present application formulates a dynamic control strategy by analyzing the dominant role of physicochemical indicators in different stages of the dough fermentation process and combining the real-time state of the dough. SUMMARY

[0003] The present application provides a fermentation process control method based on real-time monitoring of changes in dough physicochemical properties to solve existing problems. The fermentation process control method based on real-time monitoring of changes in dough physicochemical properties of the present application adopts the following technical solution:

[0004] The fermentation process control method based on real-time monitoring of changes in dough physicochemical properties of the present application adopts the following technical solution:

[0005] One embodiment of the present application provides a fermentation process control method based on real-time monitoring of changes in dough physicochemical properties, which includes the following steps:

[0006] One embodiment of the present application provides a fermentation process control method based on real-time monitoring of changes in dough physicochemical properties, which includes the following steps:

[0007] ​​Respectively, real-time acquisition of several physical and chemical index data corresponding to physical and chemical indexes of the dough in the actual fermentation process and in the laboratory environment, and are respectively recorded as actual physical and chemical index data and ideal physical and chemical index data;

[0008] The change difference between the actual physical and chemical index data and the ideal physical and chemical index data is compared to obtain the control necessity of each physical and chemical index.

[0009] Based on the change process of each physical and chemical index data in the laboratory environment, the fermentation process of the dough is divided into several fermentation stages; the change of the physical and chemical index data in each fermentation stage under any physical and chemical index in the laboratory environment is used to obtain the stage determination coefficient of the physical and chemical index at the current time; the correlation difference between the physical and chemical index data corresponding to different physical and chemical indexes is obtained, and the control necessity of the physical and chemical index is obtained by combining the correlation difference.

[0010] The control necessity of the physical and chemical index is adjusted by combining the stage determination coefficient and the chain reaction coefficient of the physical and chemical index at the current time, to obtain the real-time control response weight of the physical and chemical index at the current time.

[0011] Based on the real-time control response weight of each physical and chemical index at the current time, the fermentation process of the dough is controlled.

[0012] Further, the comparison of the change difference between the actual physical and chemical index data and the ideal physical and chemical index data to obtain the control necessity of each physical and chemical index includes the following specific method:

[0013] For the actual physical and chemical index data and the ideal physical and chemical index data under any physical and chemical index, the difference between all corresponding data points in the actual physical and chemical index data and the ideal physical and chemical index data is obtained, and the difference sequence data formed by the difference between all corresponding data points is obtained. The slope of the difference sequence data is obtained by using the least square method, and is recorded as the deviation trend parameter under the physical and chemical index; a plurality of preset fixed length time windows are established, and are equally spaced on any physical and chemical index data, and the distribution positions of the time windows on the actual physical and chemical index data and the ideal physical and chemical index data under the same physical and chemical index are consistent. The absolute value of the difference between all data points corresponding to the actual physical and chemical index data and the ideal physical and chemical index data in the same position time window under any same physical and chemical index is obtained, and is recorded as the deviation mean value of the corresponding time window under the physical and chemical index. According to the deviation trend parameter and the difference between the deviation mean values of adjacent time windows under the physical and chemical index, the control necessity of the physical and chemical index is obtained. The difference between the deviation trend parameter and the deviation mean value of the adjacent time window is positively correlated with the control necessity.

[0014] Further, the change process of each physicochemical index data in the fermentation process in the laboratory environment is used to divide the fermentation process of the dough into several fermentation stages, and the specific method includes:

[0015] 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.

[0016] Further, the change of the physicochemical index data in each fermentation stage under any physicochemical index in the laboratory environment is used to obtain the stage determination coefficient of the physicochemical index at the current time, and the specific method includes:

[0017] The change of the physicochemical index data in each fermentation stage under any physicochemical index in the laboratory environment is used to obtain the dynamic change rate of the physicochemical index in the fermentation stage, and according to 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 each physicochemical index corresponding to the fermentation stage of all physicochemical indexes at the current time in the laboratory environment, the stage determination coefficient of the physicochemical index at the current time is obtained.

[0018] Further, the change of the physicochemical index data in each fermentation stage under any physicochemical index in the laboratory environment is used to obtain the dynamic change rate of the physicochemical index in the fermentation stage, and the specific method includes:

[0019] For any physicochemical index data, the range of the data points in any fermentation stage is obtained, denoted as the change range parameter of the fermentation stage; any data point in the fermentation stage is denoted as a target data point, the absolute value of the difference between the target data point and the adjacent data point in the fermentation stage is obtained, denoted as the change amount parameter of the target data point, and the ratio of the change amount parameter of the target data point to the target data point is denoted as the change rate of the target data point. According to the average change rate of all data points in the fermentation stage and the change range parameter of the fermentation stage, the dynamic change rate of the physicochemical index in the fermentation stage is obtained.

[0020] Further, the change of the physicochemical index data in each fermentation stage under any physicochemical index in the laboratory environment is used to obtain the dynamic change rate of the physicochemical index in the fermentation stage, and the specific method includes:

[0021] For the current time, the fermentation stage to which the current time belongs is obtained, the average dynamic change rate of the fermentation stage to which the current time belongs in the ideal physicochemical index data under all physicochemical indexes is obtained, the difference between the dynamic change rate of the fermentation stage to which the current time belongs under any physicochemical index and the average dynamic change rate is obtained, and the difference is recorded as a first difference value. According to the first difference value and the average dynamic change rate, a stage determination coefficient of the current time under the physicochemical index is obtained.

[0022] Further, the linkage reaction coefficient of the physicochemical index is obtained by using the correlation difference between the physicochemical index data corresponding to different physicochemical indexes and combining the regulation necessity of the physicochemical index. The specific method comprises:

[0023] According to the change of the correlation between two physicochemical indexes, the correlation coupling parameter of each physicochemical index to other physicochemical indexes is obtained. According to the correlation coupling parameter of each physicochemical index to other physicochemical indexes, the linkage reaction coefficient of each physicochemical index to other physicochemical properties is obtained.

[0024] Further, the correlation coupling parameter of each physicochemical index to other physicochemical indexes is obtained according to the change of the correlation between two physicochemical indexes. The specific method comprises:

[0025] The Pearson correlation coefficient of the physicochemical index data of any two physicochemical indexes in the same time window is obtained, which is recorded as the correlation of the two physicochemical indexes in the time window. The correlation obtained from the actual physicochemical index data is recorded as the actual index correlation, and the correlation obtained from the ideal physicochemical index data is recorded as the ideal index correlation. The difference between the actual index correlation and the ideal index correlation of the two physicochemical indexes in the time window is obtained as the correlation difference parameter of the two physicochemical indexes in the time window. According to the correlation difference parameter in the adjacent time window and the regulation necessity of the physicochemical index, the correlation coupling parameter of the two physicochemical indexes is obtained.

[0026] Further, the linkage reaction coefficient of each physicochemical index to other physicochemical properties is obtained according to the correlation coupling parameter of each physicochemical index to other physicochemical indexes. The specific method comprises:

[0027] For the current time, any actual physicochemical index is taken as a target index, the actual index correlation of the actual physicochemical index data of the target index and other any actual physicochemical index data in the time window to which the current time belongs is obtained, the correlation coupling parameter of the two physicochemical indexes is weighted and averaged by using the actual index correlation, and the linkage reaction coefficient of the target index is obtained.

[0028] Further, the combination of the physical and chemical indicators in the current time stage determines the coefficient and the linkage reaction coefficient, adjusts the regulation necessity of the physical and chemical indicators, obtains the real-time control response weight of the physical and chemical indicators in the current time, and the specific method comprises:

[0029] According to the ratio of the stage determining coefficient of any physical and chemical indicator in the current time to the average stage determining coefficient of all physical and chemical indicators in the current time, and combining the linkage reaction coefficient, the real-time dominance of the physical and chemical indicators in the current time is obtained, and the ratio and the linkage reaction coefficient are positively correlated with the real-time dominance; for any physical and chemical indicator, the real-time control response weight of the physical and chemical indicators in the current time is calculated by combining the real-time dominance and the regulation necessity of the physical and chemical indicators, and the real-time dominance and the regulation necessity of the physical and chemical indicators are positively correlated with the real-time control response weight.

[0030] The beneficial effects of the technical scheme of the present application are: by monitoring and regulating different physical and chemical indicators in real time, the differences between the actual fermentation process and the ideal physical and chemical indicators under laboratory conditions are analyzed, so that the subsequent control of the fermentation process can be more in line with the expectations, and the quality fluctuations caused by external factors or other unstable factors can be avoided, and when comparing and analyzing the laboratory data, based on the changes of each physical and chemical indicator in the fermentation process under laboratory environment, the fermentation process of the dough is divided into several stages, and the control and optimization of the fermentation process can be further carried out based on each stage of the dough fermentation, so that the physical and chemical environment of each stage is in the best range, thereby improving the fermentation efficiency, and through the control of different physical and chemical indicators, the physical and chemical environment of each fermentation stage is stable, thereby improving the quality of the final product, and according to the changes in the actual fermentation process, the control strategy is adjusted in real time, so 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. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical scheme in the embodiments of the present application or the prior art, the drawings needed in the following embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating labor.

[0032] Figure 1 The step flow chart of the fermentation process control method of the present application based on real-time monitoring of the changes of the physical and chemical properties of the dough;

[0033] Figure 2 The carbon dioxide concentration curve schematic diagram provided by one embodiment of the present application under different conditions;

[0034] Figure 3 A step flow chart of the linkage reaction coefficient analysis method of the physicochemical indexes provided by one embodiment of the present application is shown in

[0035] Figure 4 A core feature flow chart of the process of calculating the real-time control response weight provided by one embodiment of the present application is shown in DETAILED DESCRIPTION

[0036] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined purposes, the specific embodiments, structures, features and effects of the fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough according to the present application are described in detail as follows in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0037] 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 the present application belongs.

[0038] The specific scheme of the fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough provided by the present application is described in detail below in combination with the accompanying drawings.

[0039] Please refer to Figure 1 which shows a step flow chart of the fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough provided by one embodiment of the present application. The method includes the following steps:

[0040] Step S001: Real-time acquisition of physicochemical index data corresponding to several physicochemical indexes in the actual fermentation process and in the laboratory environment, respectively, and denoted as actual physicochemical index data and ideal physicochemical index data.

[0041] Specifically, in order to implement the fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough proposed by the present embodiment, it is necessary to first collect physicochemical index data corresponding to several physicochemical indexes in the actual fermentation process and in the laboratory environment. The specific process is as follows:

[0042] Step S101: Real-time monitoring of physicochemical index data of dough in the actual fermentation process in the dough fermentation box using multiple sensors, and denoted as actual physicochemical index data and ideal physicochemical index data.

[0043] Step S102, under strictly controlled constant temperature and humidity conditions, using a unified proportion of raw materials (including flour, water, yeast, sugar and salt) to prepare a standard dough, ensuring the repeatability of the experiment, and collecting the physicochemical index data in the laboratory environment in the fermentation box.

[0044] It should be noted that in the laboratory environment in the embodiment of the present application, the preset temperature is 30℃ and the humidity is 80%, and the specific temperature value and humidity value can be adjusted according to the specific circumstances, and the embodiment of the present application is not specifically limited.

[0045] As an optional embodiment, the specific acquisition method of the physicochemical index data is:

[0046] First, a plurality of sensors are arranged in the fermentation box, including image sensors, pH sensors, infrared gas sensors and pressure probes.

[0047] Then, the physicochemical index data is collected by using the sensors, and the specific content includes: the image sensor is used to collect the dough image, and combined with edge recognition and three-dimensional reconstruction algorithm, the swelling volume of the dough is calculated; the pH sensor is an embedded micro pH electrode, which is used to record the pH value of the dough in real time; the infrared gas sensor is used to detect the concentration of carbon dioxide in the fermentation box, which indirectly reflects the activity of the yeast; the pressure probe is used to obtain the elasticity of the dough.

[0048] Step S103, respectively, the physicochemical index data in the actual fermentation process and the laboratory environment in the fermentation process are recorded as actual physicochemical index data and ideal physicochemical index data; the original signal output by the sensor is subjected to analog-digital conversion, and signal pretreatment such as filtering, denoising and amplification is carried out to ensure the stability of the data, in addition, in the process of data collection by using the sensor, a fixed sampling period is adopted to synchronously collect all channels to maintain the consistency of the data in time sequence.

[0049] It should be noted that in the embodiment of the present application, the fixed sampling period of the sensor is empirically preset to be collected once every 10 seconds, and the specific sampling period can be preset according to the actual situation, and the embodiment of the present application is not specifically limited.

[0050] At this point, the physicochemical index data corresponding to a plurality of physicochemical indexes in the actual fermentation process and in the laboratory environment in the fermentation process is obtained by the above method.

[0051] Step S002: comparing the change difference between the actual physicochemical index data and the ideal physicochemical index data, obtaining the necessity of regulating each physicochemical index.

[0052] It should be noted that during the fermentation process of the dough, the changes in physical and chemical properties (such as volume growth, pH decrease, CO2 concentration accumulation) directly determine the final fermentation state of the dough, so these key indicators need to be monitored in real time. When a certain physical and chemical indicator deviates from the expected change trajectory, the system should judge the influence of the physical and chemical indicator on the fermentation state according to its current deviation degree, and then evaluate the necessity of its regulation, so as to realize the dynamic identification and priority response of the key fermentation parameters, so that the control system can timely correct the abnormal fermentation trend and promote the fermentation process to return to the normal track. In addition, due to the influence of factors such as raw material ratio, yeast activity, and environmental micro-difference, the change trajectory of each physical and chemical indicator varies between different batches, which is a normal phenomenon. However, if the deviation of a certain physical and chemical indicator shows a state of continuous expansion or trend deviation from the expected curve, it means that the dough fermentation process has deviated from the normal track. For example Figure 2 As shown in FIG. 8, the carbon dioxide concentration curve under different conditions is shown, and the gray shaded area represents the deviation of the actual carbon dioxide concentration from the ideal carbon dioxide concentration. The dashed line represents the carbon dioxide concentration curve monitored in the actual fermentation process of the dough, that is, the actual carbon dioxide concentration curve, and the solid line represents the carbon dioxide concentration curve formed in the laboratory environment during the fermentation process of the dough, that is, the ideal carbon dioxide concentration curve.

[0053] Specifically, as a preferred embodiment, the method for obtaining the regulation necessity comprises: for the actual physical and chemical indicator data and the ideal physical and chemical indicator data under any physical and chemical indicator, obtaining the difference between all corresponding data points in the actual physical and chemical indicator data and the ideal physical and chemical indicator data, obtaining the difference sequence data formed by the difference between all corresponding data points, and obtaining the slope of the difference sequence data by using the least square method, which is recorded as the deviation trend parameter under the physical and chemical indicator; a plurality of preset fixed-length time windows are established, which are distributed at equal intervals on any physical and chemical indicator data, and the distribution positions of the time windows on the actual physical and chemical indicator data and the ideal physical and chemical indicator data under the same physical and chemical indicator are consistent, the absolute value of the difference between all data points corresponding to the actual physical and chemical indicator data and the ideal physical and chemical indicator data in the same position time window under any same physical and chemical indicator is obtained, which is recorded as the deviation mean value of the corresponding time window under the physical and chemical indicator, and the regulation necessity of the physical and chemical indicator is obtained according to the deviation trend parameter and the difference between the deviation mean values of adjacent time windows under the physical and chemical indicator, and the deviation trend parameter and the difference between the deviation mean values of adjacent time windows are positively correlated with the regulation necessity.

[0054] It should be noted that in the embodiment of the present application, 5 data points are preset in each time window according to experience, i.e., the length of the time window is 5, which can be adjusted according to actual conditions, and the embodiment of the present application is not specifically limited; in addition, in the embodiment of the present application, the number of time windows in the physicochemical index data is 5, which can be adjusted according to actual conditions, and the embodiment of the present application is not specifically adjusted.

[0055] As an optional embodiment, the specific calculation method of the regulation necessity is:

[0056] ;

[0057] wherein, represents the regulation necessity of the i-th physicochemical index; represents the deviation trend parameter of the i-th physicochemical index; represents the number of time windows; represents the deviation mean of the i-th time window of the i-th physicochemical index; represents the deviation mean of the i-th time window of the i-th physicochemical index; represents the linear normalization function. It should be noted that, represents the deviation level between the actual physicochemical index data and the ideal physicochemical index data in the adjacent time window of the i-th physicochemical index, and the greater the value of the regulation necessity is, the greater the difference between the data points in the actual physicochemical index data and the ideal physicochemical index data in the time window is, and the greater the deviation trend is, so that the necessity of regulating the physicochemical index is greater in the subsequent fermentation process regulation. At this point, the regulation necessity of the physicochemical index is obtained by the above method. Step S003: based on the change process of each physicochemical index data in the fermentation process in the laboratory environment, the fermentation process of the dough is divided into a plurality of fermentation stages; the change of the physicochemical index data in each fermentation stage under any physicochemical index in the laboratory environment is used to obtain the stage determination coefficient of the physicochemical index at the current time; the correlation difference between the physicochemical index data corresponding to different physicochemical indexes is obtained, and the regulation necessity of the physicochemical index is combined to obtain the chain reaction coefficient of the physicochemical index.

[0058]

[0059] At this point, the regulation necessity of the physicochemical index is obtained by the above method.

[0060] Step S003: based on the change process of each physicochemical index data in the fermentation process in the laboratory environment, the fermentation process of the dough is divided into a plurality of fermentation stages; the change of the physicochemical index data in each fermentation stage under any physicochemical index in the laboratory environment is used to obtain the stage determination coefficient of the physicochemical index at the current time; the correlation difference between the physicochemical index data corresponding to different physicochemical indexes is obtained, and the regulation necessity of the physicochemical index is combined to obtain the chain reaction coefficient of the physicochemical index.

[0061] ​​​​​It should be noted that the changes in the physicochemical properties of the dough during fermentation have significant time dependence, synergy and non-linear characteristics, which collectively reflect the dynamic process of microbial metabolic activity and dough structure evolution. Physicochemical indicators such as volume, pH, gas content and viscoelasticity will show stage changes over time: in the early stage, microorganisms mainly perform adaptive metabolism; in the middle stage, the dough mainly performs active metabolism; in the late stage, the dough mainly performs stable metabolism. The generation of acceleration and rapid expansion of volume is dominant, and the viscoelasticity is enhanced; in the later stage, the structure is gradually stabilized, and the pH continues to decrease. When multiple physicochemical indicators deviate from the expected trajectory, it is difficult for traditional fuzzy control methods to determine the priority control order of each indicator, and control conflicts are easily generated. Therefore, it is necessary to analyze the dominance of each indicator in different stages based on the ideal fermentation data in the laboratory, and to identify the chain reaction of the changes of each indicator on other indicators, so as to build a dynamic control strategy with dominance as the core, to ensure that the system can effectively respond to dough fermentation abnormalities in multi-objective regulation and protect product quality; such as Figure 3 As shown in the step flow chart of the chain reaction coefficient analysis method of physicochemical indicators.

[0062] Specifically, in step S301, based on the change process of each physicochemical indicator data in the fermentation process in the laboratory environment, the fermentation process of the dough is divided into several fermentation stages.

[0063] It should be noted that the stronger the change trend of the physicochemical indicator in a certain fermentation stage, the more it represents the core biochemical activity and structural evolution characteristics of that stage. For example, in the middle stage of fermentation, the rapid expansion of the volume of the dough directly reflects the enhancement of the gas production activity of the yeast and the effective expansion of the gluten network, which is the core process of dough structure development, and the volume change in this fermentation stage is the dominant indicator that best reflects the progress of fermentation. Therefore, by analyzing the dynamic changes of each physicochemical indicator in the corresponding divided fermentation stage, the physicochemical properties that play a dominant role in the real-time dough fermentation process are determined.

[0064] As an optional embodiment, the method for obtaining the fermentation stage includes: for any physicochemical indicator, using the inflection point detection method to obtain several inflection points in the physicochemical indicator data of the physicochemical indicator, and dividing the physicochemical indicator data of the physicochemical indicator into several fermentation stages by the inflection points.

[0065] It should be noted that in the process of dividing each physicochemical indicator into its corresponding fermentation stage according to its own data change characteristics, the division of the fermentation stage of different physicochemical indicators is different, and the change of the physicochemical properties of the dough fermentation process is nonlinear. By collecting the physicochemical indicator data in the standard dough fermentation process under laboratory conditions, the fermentation process is divided into different stages. For example, the stages of rapid change and slow change of carbon dioxide concentration in the dough fermentation process are divided.

[0066] In step S302, the stage determining coefficient of the physicochemical index at the current time is obtained according to the change of the physicochemical index data in each fermentation stage under any physicochemical index in the laboratory environment.

[0067] As a preferred embodiment, the stage determining coefficient is obtained by: obtaining the dynamic change rate of the physicochemical index in the fermentation stage according to the change of the physicochemical index data in each fermentation stage under any physicochemical index in the laboratory environment, and obtaining the stage determining coefficient of the physicochemical index at the current time according to 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 corresponding fermentation stage of each physicochemical index at the current time in the laboratory environment.

[0068] As a preferred embodiment, the dynamic change rate is obtained by: obtaining the range parameter of the ideal physicochemical index data in any fermentation stage as the range parameter of the change of the ideal physicochemical index data in the fermentation stage, obtaining the change amount parameter of the target data point as the absolute value of the difference between the target data point and the adjacent data point in the fermentation stage, obtaining the change rate of the target data point as the ratio of the change amount parameter of the target data point to the target data point, and obtaining the dynamic change rate of the physicochemical index in the fermentation stage according to the average change rate of all data points in the fermentation stage and the range parameter of the change of the fermentation stage.

[0069] As a preferred embodiment, the stage determining coefficient of the physicochemical index at the current time is obtained according to 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 corresponding fermentation stage of each physicochemical index at the current time in the laboratory environment, including: obtaining the fermentation stage to which the current time belongs, obtaining the average dynamic change rate of the fermentation stage to which the current time belongs in the ideal physicochemical index data of all physicochemical indexes, obtaining the first difference value between the dynamic change rate of the fermentation stage at the current time under any physicochemical index and the average dynamic change rate, and obtaining the stage determining coefficient of the physicochemical index at the current time according to the first difference value and the average dynamic change rate.

[0070] As an optional embodiment, the specific calculation method of the dynamic change rate is as follows:

[0071] ;

[0072] wherein, represents the ideal physicochemical index data under the i th physicochemical index, the i th data point in the ideal physicochemical index data under the i th physicochemical index. a dynamic change rate of the fermentation stage; represents the ideal physicochemical index data under the first physicochemical index, and the number of data points in the first fermentation stage; represents the ideal physicochemical index data under the first physicochemical index, and the number of data points in the first fermentation stage; represents the ideal physicochemical index data under the first physicochemical index, and the number of data points in the first fermentation stage; represents the ideal physicochemical index data under the first physicochemical index, and the number of data points in the first fermentation stage; represents an absolute value function.

[0073] It should be noted that, represents the average of the change rate of the ideal index value of the first physicochemical index in the first fermentation stage corresponding to the index, reflecting the degree of change of the physicochemical index in the fermentation reaction in the stage.

[0074] It should be noted that in the process control of dough fermentation, the physicochemical index with a stronger dynamic change rate at the current time usually reflects that its biochemical activity is most active in the stage, and thus it is more responsive and discriminative, can reveal the subtle changes of the fermentation state, plays a decisive role in the change of the current fermentation state, and has higher regulation value in control decision.

[0075] By comparing the dynamic change rates of different physicochemical indexes in the current fermentation process, the decision role of each physicochemical index on the corresponding fermentation stage at the current time is obtained.

[0076] As an optional embodiment, the specific calculation method of the stage decision coefficient is:

[0077] ;

[0078] wherein, represents the stage decision coefficient of the first physicochemical index at the current time; represents the dynamic change rate of the first physicochemical index in the corresponding fermentation stage at the current time in the laboratory environment; represents the average of the dynamic change rates of all physicochemical indexes in the corresponding fermentation stage of each physicochemical index at the current time in the laboratory environment. ​​​

[0079] It should be noted that, in the fermentation process, when the physicochemical index corresponding to the physicochemical index data shows more frequent change characteristics, the corresponding dynamic change rate is stronger. In the embodiments of the present application, the change process of the physicochemical index data corresponding to different physicochemical indexes in the ideal fermentation state of the dough is tested according to the fermentation process of the dough in the laboratory environment, so as to analyze the change in each fermentation stage. The formula represents the difference between the dynamic change rate of the laboratory fermentation test of the i-th physicochemical index and the dynamic change rate of all physicochemical indexes in the current fermentation stage. The formula reflects the decisive role of the physicochemical index on the fermentation process. The larger the formula, the stronger the dominant effect of the physicochemical index on the final fermentation state of the dough in the current fermentation stage.

[0080] In step S303, the correlation difference between the physicochemical index data corresponding to different physicochemical indexes is utilized, and the regulation necessity of the physicochemical index is combined to obtain the chain reaction coefficient of the physicochemical index.

[0081] As a preferred embodiment, the method for obtaining the chain reaction coefficient comprises: obtaining the correlation coupling parameter of each physicochemical index to other physicochemical indexes according to the change of the correlation between two physicochemical indexes; and obtaining the chain reaction coefficient of each physicochemical index to other physicochemical properties according to the correlation coupling parameter of each physicochemical index to other physicochemical indexes.

[0082] As a preferred embodiment, the method for obtaining the correlation coupling parameter comprises: obtaining the Pearson correlation coefficient of the physicochemical index data of any two physicochemical indexes in the same time window, denoted as the correlation between the two physicochemical indexes in the time window; obtaining the correlation between the actual physicochemical index data, denoted as the actual index correlation; obtaining the correlation between the ideal physicochemical index data, denoted as the ideal index correlation; obtaining the difference between the actual index correlation and the ideal index correlation of the two physicochemical indexes in the time window as the correlation difference parameter of the two physicochemical indexes in the time window; and obtaining the correlation coupling parameter of the two physicochemical indexes according to the correlation difference parameter in the adjacent time window and the regulation necessity of the physicochemical index.

[0083] As a preferred embodiment, the method for obtaining the chain reaction coefficient comprises: for the current time, taking any actual physicochemical index as a target index, obtaining the actual index correlation of the actual physicochemical index data of the target index and other any actual physicochemical index data in the time window of the current time; and utilizing the actual index correlation to weight and average the correlation coupling parameter of the two physicochemical indexes to obtain the chain reaction coefficient of the target index.

[0084] ​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.

[0085] 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.

[0086] As an optional embodiment, the specific calculation method for the correlation coupling parameter is as follows:

[0087] ;

[0088] in, Indicates the first Individual physicochemical indicators for the first Correlation and coupling parameters of individual physical and chemical 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.

[0089] 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 The corresponding changes of the physicochemical indexes are generated, and the corresponding change relationship is restored, and the difference of the Pearson correlation coefficient is smaller and smaller.

[0090] As an optional embodiment, the specific calculation method of the linkage reaction coefficient is as follows:

[0091] ;

[0092] Wherein ' The linkage reaction coefficient of the first physicochemical index is represented by the formula (1). The linkage reaction coefficient of the first physicochemical index is represented by the formula (1). The number of physicochemical indexes in the dough fermentation process is represented by the formula (2). The coupling effect of the first physicochemical index on the second physicochemical index is represented by the formula (3). The coupling effect of the first physicochemical index on the second physicochemical index is represented by the formula (3). The Pearson correlation coefficient of the first physicochemical index and the second physicochemical index in the current time window is represented by the formula (4). The Pearson correlation coefficient of the first physicochemical index and the second physicochemical index in the current time window is represented by the formula (4). The Pearson correlation coefficient of the first physicochemical index and the second physicochemical index in the current time window is represented by the formula (4). The Pearson correlation coefficient of the first physicochemical index and the second physicochemical index in the current time window is represented by the formula (4). The Pearson correlation coefficient of the first physicochemical index and the second physicochemical index in the current time window is represented by the formula (4).

[0093] It should be noted that further need to be explained is that in order to more accurately evaluate the influence of a certain index on the overall fermentation state, the correlation is introduced as a weight, which can more objectively reflect the driving action and transmission effect of each index on other indexes in the current fermentation state, so as to comprehensively consider the actual influence degree between the indexes, and avoid overreaction to weakly coupled indexes or neglect of strongly coupled indexes in the control process; therefore, in the embodiment of the present application, the coupling effect of the first physicochemical index on the second physicochemical index is weighted and averaged according to the correlation between the first physicochemical index and the second physicochemical index. The coupling effect of the first physicochemical index on the second physicochemical index is weighted and averaged according to the correlation between the first physicochemical index and the second physicochemical index. The coupling effect of the first physicochemical index on the second physicochemical index is weighted and averaged according to the correlation between the first physicochemical index and the second physicochemical index. The coupling effect of the first physicochemical index on the second physicochemical index is weighted and averaged according to the correlation between the first physicochemical index and the second physicochemical index. The coupling effect of the first physicochemical index on the second physicochemical index is weighted and averaged according to the correlation between the first physicochemical index and the second physicochemical index. The coupling effect of the first physicochemical index on the second physicochemical index is weighted and averaged according to the correlation between the first physicochemical index and the second physicochemical index.

[0094] Thus, the linkage reaction coefficient of the physicochemical index is obtained by the above method.

[0095] Step S004: The regulation necessity of the physicochemical index is adjusted by combining the stage determination coefficient and the linkage reaction coefficient of the physicochemical index at the current moment, and the real-time control response weight of the physicochemical index at the current moment is obtained.

[0096] It should be noted that during the fermentation process of the dough, due to the different dominant mechanisms at different stages, the importance and control priority of each physicochemical index also changes. In order to make the control system have stronger stage adaptability and abnormal response ability, focused control needs to be realized when the key indicators appear abnormally. The indicators with strong dominance and serious deviation have the greatest impact on the overall fermentation quality and should be controlled first. Dynamically allocate control resources and adjustment strategies to make the adjustment of control variables focus on key targets and avoid imbalance in dough fermentation adjustment due to target conflicts in fuzzy control.

[0097] Specifically, as a preferred embodiment, the method for obtaining the real-time control response weight of the physicochemical index comprises: obtaining the real-time dominance of the physicochemical index at the current moment according to the phase decision coefficient of the physicochemical index at the current moment relative to the average phase decision coefficient of all physicochemical indexes, and combining the chain reaction coefficient of the physicochemical index; and adjusting the control necessity of the physicochemical index by 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.

[0098] As a preferred embodiment, the method for obtaining the real-time control response weight of the physicochemical index comprises: obtaining the real-time dominance of the physicochemical index at the current moment according to the ratio of the phase decision coefficient of any physicochemical index at the current moment to the average phase decision coefficient of all physicochemical indexes at the current moment, and combining the chain reaction coefficient, the ratio and the chain reaction coefficient are positively correlated with the real-time dominance; for any physicochemical index, combining the real-time dominance and the control necessity of the physicochemical index to calculate the real-time control response weight of the physicochemical index at the current moment, the real-time dominance and the control necessity of the physicochemical index are positively correlated with the real-time control response weight.

[0099] As an optional embodiment, the specific calculation method of the real-time dominance of the physicochemical index is:

[0100] ;

[0101] Wherein, represents the real-time dominance of the i-th physicochemical index at the current moment; represents the phase decision coefficient of the i-th physicochemical index for the fermentation stage corresponding to the current moment; represents the average phase decision coefficient of all physicochemical indexes at the current moment; represents the chain reaction coefficient of the i-th physicochemical index.

[0102] ​​​It should be noted that the role of different physicochemical indicators in different fermentation stages is dynamically changing during the fermentation process of the dough, in order to realize accurate control, it is necessary to evaluate the dominance of each physicochemical indicator in real time, therefore, the embodiment of the present application selects to evaluate the real-time dominance of physicochemical indicators based on the decisive role of the indicators on the current fermentation stage state, that is, whether the change trend can directly reflect the abnormal state of the fermentation of the dough and the linkage influence ability on other physicochemical indicators; for example Figure 4 As shown in the core feature flowchart of the process of calculating the real-time control response weight.

[0103] As an optional embodiment, the specific calculation method of the real-time control response weight is as follows:

[0104] ;

[0105] Among them, indicates the real-time control response weight of the i-th physicochemical indicator; indicates the real-time dominance of the i-th physicochemical indicator; indicates the regulation necessity of the i-th physicochemical indicator.

[0106] Up to now, the real-time control response weight of the physicochemical indicator is obtained by the above method.

[0107] Step S005: Control the fermentation process of the dough based on the real-time control response weight of each physicochemical indicator at the current time.

[0108] Specifically, first, the actual physicochemical indicator data of the dough in the actual fermentation process is taken as a state variable, introduced into the model predictive control (MPC, Model Predictive Control) framework, the state variable is taken as an input, the state evolution of the future several steps is predicted through the system prediction model, the real-time control response weight of the actual physicochemical indicator data is taken as the weight of each state variable in the objective function of the system prediction model, a multi-objective optimization objective function with weight is constructed, and the optimal control variable sequence is dynamically solved to minimize the weighted state deviation and control input change, the control variable is the heating power and the humidification rate of the dough fermentation box, so as to realize the multivariable coordinated control of the physicochemical properties of the dough.

[0109] Then, the rolling optimization method is used to recalculate the control response at each time step, and the real-time feedback adjustment result is realized, so as to realize the rapid adaptation and fine response of the control strategy to the change of the fermentation state. The system can automatically focus on , the core physicochemical indicators such as the volume of the dough and the pH, realize the continuous, flexible and intelligent control of the fermentation process, and thus guarantee the stability and consistency of the structure and flavor of the dough.​​​

[0110] In addition, in other embodiments of the present application, a correlation data table of different physicochemical indexes changing with time is also provided, as shown in Table 1, in which a positive correlation between different physicochemical indexes of the dough in the fermentation process over time is embodied.

[0111] Table 1 Correlation data table of different physicochemical indexes changing with time

[0112]

[0113] Thus, the present embodiment is completed.

[0114] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the principles of the present application shall be included in the protection scope of the present application.

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. 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.

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 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.

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 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.

4. The fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough according to claim 3, 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.

5. The fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough according to claim 3, 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.

6. 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.

7. The fermentation process control method based on real-time monitoring of changes in the physicochemical properties of dough according to claim 6, 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.

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 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.

9. 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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