Daqu fermentation regulation and control method and device based on fermentation stage and quality grade

Abstract

The invention relates to the technical field of wine making, discloses a yeast fermentation regulation and control method and device based on a fermentation stage and a quality grade, and aims to solve the problems of delay and poor consistency and accuracy of an existing regulation and control mode. The scheme mainly comprises the following steps: obtaining a metabolic compound content prediction result in a future time period based on a metabolic compound prediction model; obtaining a first microbial abundance prediction result of a future time period based on a microbial abundance prediction model; obtaining a first fermentation stage prediction result and a first quality grade prediction result in a future time period based on a Daqu prediction model; and according to a first difference between the first fermentation stage prediction result and the fermentation stage future time sequence data and a second difference between the first quality grade prediction result and the corresponding expected quality grade, regulating and controlling the environmental future time sequence data. The method avoids regulation and control delay, improves consistency and accuracy, and is suitable for monitoring the yeast fermentation process.

Description

Technical Field

[0001] The invention relates to the technical field of winemaking, and in particular to a method and device for regulating and controlling Daqu fermentation based on fermentation stages and quality grades. Background Art

[0002] Daqu is a type of koji that has been fermented for a long time. It is usually made from wheat, yellow rice, peas and other raw materials through a series of fermentation, koji culture and cooking processes. Daqu contains a variety of microorganisms, including molds, yeasts and bacteria. Daqu is one of the main fermentation agents used in the production of traditional Chinese liquor, and is widely used in the brewing process of Luzhou-flavor and Maotai-flavor liquors. Daqu fermentation refers to the process of brewing liquor using Daqu as a saccharification fermentation agent.

[0003] The Daqu fermentation process is affected by many factors, including changes in the fermentation environment and changes in the chemical composition and microbial community of the Daqu itself. In order to monitor, control and optimize the fermentation process of Daqu, the solution commonly used in the prior art is to conduct regular sampling and testing of Daqu, determine whether the Daqu fermentation is abnormal based on the sampling and testing results, and have the technical staff regulate the fermentation environment based on experience. This method is labor-intensive, and it is difficult to grasp the changes in the fermentation process in real time, and the regulation of the fermentation environment relies on manual experience, and the consistency and accuracy are poor. In addition, if this method detects that the Daqu fermentation is abnormal, a fermentation quality problem has already occurred, and the early prediction of the Daqu fermentation quality cannot be achieved, resulting in a delay in environmental regulation. Summary of the invention

[0004] The present invention aims to solve the problems of delay, poor consistency and accuracy in the existing Daqu fermentation environment control method, and proposes a Daqu fermentation control method and device based on fermentation stage and quality grade.

[0005] The technical solution adopted by the present invention to solve the above technical problems is:

[0006] In a first aspect, the present invention provides a method for regulating Daqu fermentation based on fermentation stage and quality grade, the method comprising:

[0007] Regularly obtain metabolic compound data and environmental data during the Daqu fermentation process, and record the actual fermentation stage data of the Daqu, generate metabolic compound historical time series data based on the metabolic compound data, generate environmental historical time series data based on the environmental data, and generate fermentation stage historical time series data based on the fermentation stage data;

[0008] Obtaining a prediction result of the content of the metabolic compound in a future period according to the metabolic compound historical time series data, the environmental historical time series data and the fermentation stage historical time series data and based on a pre-trained metabolic compound prediction model;

[0009] Determine the future time series data of the environment and the future time series data of the fermentation stage in the future time period, and obtain the first microbial abundance prediction result in the future time period according to the predicted result of the metabolic compound content in the future time period, the future time series data of the environment and the future time series data of the fermentation stage and based on the pre-trained microbial abundance prediction model;

[0010] Obtaining a first fermentation stage prediction result and a first quality grade prediction result for a future period according to the metabolic compound content prediction result and the first microbial abundance prediction result and based on a pre-trained Daqu prediction model;

[0011] Based on the first difference between the prediction result of the first fermentation stage and the future time series data of the fermentation stage, and the second difference between the prediction result of the first quality grade and the corresponding expected quality grade, it is judged whether the Daqu fermentation is normal. If the Daqu fermentation is abnormal, the future time series data of the environment in the future time period is regulated according to the first difference and the second difference to minimize the first difference and the second difference.

[0012] Furthermore, the training methods of the metabolic compound prediction model, the microbial abundance prediction model and the Daqu prediction model include:

[0013] Acquire fermentation data at each time point during the fermentation process of Daqu, wherein the fermentation data includes environmental data, fermentation stage data, metabolic compound data, microbial abundance data and quality grade data;

[0014] The metabolic compound data at historical time points and their corresponding environmental data and fermentation stage data are used as input features, and the corresponding metabolic compound data at future time points are used as output labels to train a time series neural network and obtain a metabolic compound prediction model;

[0015] The environmental data, fermentation stage data and metabolic compound data at each time point are used as input features, and the corresponding microbial abundance data are used as output labels to train the time series neural model and obtain the microbial abundance prediction model;

[0016] The metabolic compound data and microbial abundance data at each time point were used as input features, and the corresponding fermentation stage data and quality grade data were used as output labels. The composite neural network model was trained to obtain a Daqu prediction model for simultaneously predicting the fermentation stage and quality grade.

[0017] Furthermore, the environmental data includes temperature data, humidity data and air oxygen content data; the fermentation stage data is the time node in the Daqu fermentation cycle, and the unit of the time node is day or hour.

[0018] Furthermore, the metabolic compound data includes content data of one or more compounds in esters, alcohols, aldehydes, acids, ketones, pyrazines, furans and aromatics, the esters include at least ethyl hexadecanoate and ethyl hexanoate, the alcohols include at least phenylethanol and 2,3-butanediol, the acids include at least acetic acid and hexanoic acid, and the pyrazines include at least 2,5-dimethylpyrazine, trimethylpyrazine and 2,6-dimethylpyrazine.

[0019] Furthermore, the microbial abundance data includes the types and quantities of microbial populations during the Daqu fermentation process, and the microbial populations include at least Bacillus, Aspergillus, Saccharomyces cerevisiae, Pediococcus acidilactici and Staphylococcus.

[0020] Furthermore, fermentation data at each time point during the Daqu fermentation process is obtained by sampling according to a preset sampling frequency, and the preset sampling frequency is half a day, one day, two days or three days.

[0021] Furthermore, the method further comprises:

[0022] Obtaining the current time series data of metabolic compounds, the current time series data of the environment, and the current time series data of the fermentation stage during the current period of the Daqu fermentation process;

[0023] Obtaining a second microbial abundance prediction result for the current time period according to the current time series data of the metabolic compound, the current time series data of the environment, and the current time series data of the fermentation stage and based on the microbial abundance prediction model;

[0024] According to the current time series data of the metabolic compound and the second microbial abundance prediction result and based on the Daqu prediction model, obtain the second fermentation stage prediction result and the second quality grade prediction result of the current period;

[0025] Whether the Daqu fermentation is normal is determined based on the third difference between the second fermentation stage prediction result and the current time series data of the fermentation stage, and the fourth difference between the second quality grade prediction result and the corresponding expected quality grade.

[0026] Furthermore, the method further comprises:

[0027] If it is determined that the Daqu fermentation is abnormal, an alarm will be issued for the abnormal Daqu fermentation.

[0028] Further, regulating the future time series data of the environment in the future period according to the first difference and the second difference includes:

[0029] Constructing a control model based on the microbial abundance prediction model and the Daqu prediction model and based on an evolutionary algorithm, wherein the evolutionary algorithm is a genetic algorithm, an ant colony algorithm or a simulated annealing algorithm;

[0030] When the fermentation of Daqu is abnormal, the historical time series data of metabolic compounds, the historical time series data of the environment, the historical time series data of the fermentation stage, the future time series data of the fermentation stage and the expected quality level are input into the control model for optimization, so as to obtain the target environmental data corresponding to the minimum of the first difference and the second difference, and the future time series data of the environment in the future time period are regulated according to the target environmental data.

[0031] In a second aspect, the present invention provides a Daqu fermentation control device based on fermentation stage and quality grade, the device comprising:

[0032] An acquisition unit is used to regularly acquire metabolic compound data and environmental data during the fermentation process of Daqu, and record the actual fermentation stage data of Daqu, generate metabolic compound historical time series data according to the metabolic compound data, generate environmental historical time series data according to the environmental data, and generate fermentation stage historical time series data according to the fermentation stage data;

[0033] A first prediction unit, configured to obtain a prediction result of the content of the metabolic compound in a future period according to the metabolic compound historical time series data, the environmental historical time series data and the fermentation stage historical time series data and based on a pre-trained metabolic compound prediction model;

[0034] A second prediction unit is used to determine the future time series data of the environment and the future time series data of the fermentation stage in the future time period, and obtain the first microbial abundance prediction result in the future time period according to the metabolic compound content prediction result in the future time period, the future time series data of the environment and the future time series data of the fermentation stage and based on a pre-trained microbial abundance prediction model;

[0035] A third prediction unit is used to obtain a first fermentation stage prediction result and a first quality grade prediction result in a future period according to the metabolic compound content prediction result and the first microbial abundance prediction result and based on a pre-trained Daqu prediction model;

[0036] The control unit is used to determine whether the Daqu fermentation is normal based on a first difference between the prediction result of the first fermentation stage and the future time series data of the fermentation stage, and a second difference between the prediction result of the first quality grade and the corresponding expected quality grade. If the Daqu fermentation is abnormal, the future time series data of the environment in the future time period is controlled based on the first difference and the second difference to minimize the first difference and the second difference.

[0037] The beneficial effects of the present invention are as follows: the method and device for regulating Daqu fermentation based on fermentation stage and quality grade provided by the present invention predicts the fermentation stage and quality grade of Daqu fermentation in the future period through pre-trained metabolic compound prediction model, microbial abundance prediction model and Daqu prediction model, and judges Daqu fermentation abnormality and regulates the environment according to the difference between the prediction result and the actual fermentation stage and the expected quality grade. The present invention realizes the early prediction of Daqu fermentation quality, avoids the delay of environmental regulation, and ensures the quality of Daqu fermentation. At the same time, the present invention does not rely on manual experience, and improves the consistency and accuracy of environmental regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of a process for regulating and controlling Daqu fermentation based on fermentation stage and quality grade provided in an embodiment;

[0039] Figure 2 A schematic diagram of the structure of a Daqu fermentation control device based on fermentation stage and quality grade provided in an embodiment. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution in this embodiment will be clearly and completely described below in conjunction with the drawings in this embodiment.

[0041] In some of the processes described in the specification of the present invention and the above-mentioned figures, multiple operations appearing in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The sequence numbers of the operations are only used to distinguish different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel.

[0042] At present, the technical solution commonly used for the detection and control of the Daqu fermentation process is to conduct regular sampling and testing of the Daqu, determine whether the Daqu fermentation is abnormal based on the sampling and testing results, and adjust the fermentation environment based on experience by technical personnel. The inventors have found through research that this method has the problems of delay, poor consistency and accuracy.

[0043] Based on this, the technical scheme of the present invention is proposed. In the present invention, metabolic compound data and environmental data in the fermentation process of Daqu are regularly obtained, and the actual fermentation stage data of Daqu is recorded; metabolic compound historical time series data are generated according to the metabolic compound data, environmental historical time series data are generated according to the environmental data, and fermentation stage historical time series data are generated according to the fermentation stage data; according to the metabolic compound historical time series data, the environmental historical time series data and the fermentation stage historical time series data and based on a pre-trained metabolic compound prediction model, a metabolic compound content prediction result for a future time period is obtained; the environmental future time series data and the fermentation stage future time series data for the future time period are determined, and according to the metabolic compound content prediction result for the future time period, the environmental future time series data and the fermentation stage future time series data are predicted According to the future time series data of the fermentation stage and based on the pre-trained microbial abundance prediction model, a first microbial abundance prediction result for the future time period is obtained; according to the metabolic compound content prediction result and the first microbial abundance prediction result and based on the pre-trained Daqu prediction model, a first fermentation stage prediction result and a first quality grade prediction result for the future time period are obtained; according to a first difference between the first fermentation stage prediction result and the future time series data of the fermentation stage, and a second difference between the first quality grade prediction result and the corresponding expected quality grade, it is judged whether the Daqu fermentation is normal; if the Daqu fermentation is abnormal, the environmental future time series data for the future time period is regulated according to the first difference and the second difference to minimize the first difference and the second difference.

[0044] Specifically, during the Daqu fermentation process, the present invention regularly obtains metabolic compound data, environmental data and fermentation stage data, and generates corresponding historical time series data accordingly, and then predicts the fermentation stage and quality grade of Daqu fermentation in the future period according to the pre-trained metabolic compound prediction model, microbial abundance prediction model and Daqu prediction model, and judges the abnormality of Daqu fermentation and regulates the environment according to the difference between the prediction result and the actual fermentation stage and the expected quality grade. The present invention realizes the early prediction of Daqu fermentation quality, avoids the delay of environmental regulation, and ensures the quality of Daqu fermentation. At the same time, the present invention does not rely on manual experience, and improves the consistency and accuracy of environmental regulation.

[0045] The technical solution in this embodiment will be clearly and completely described below in conjunction with the drawings in this embodiment. Obviously, the described embodiment is only a part of the embodiments of the present invention, rather than all the embodiments.

[0046] Figure 1 A schematic diagram of a method for regulating Daqu fermentation based on fermentation stage and quality grade is shown in FIG. Figure 1 , the method comprises the following steps:

[0047] Step 1: regularly obtain metabolic compound data and environmental data during the Daqu fermentation process, and record the actual fermentation stage data of the Daqu, generate metabolic compound historical time series data based on the metabolic compound data, generate environmental historical time series data based on the environmental data, and generate fermentation stage historical time series data based on the fermentation stage data.

[0048] In this embodiment, the environmental data includes temperature, humidity data and air oxygen content data. After obtaining the environmental data, the minimum and maximum normalization method can be used for standardization, and the maximum and minimum values ​​are obtained based on all the data in the data set. For example, the temperature and humidity data at a certain time point are [35,32]. Assuming that the maximum value of the temperature data is 60°C and the minimum value is 30°C, the maximum value of the humidity is 36 and the minimum value is 9, the temperature and humidity data [35,32] are normalized to [0.14,0.85].

[0049] In this embodiment, the fermentation stage data is a time node in the Daqu fermentation cycle, and the unit of the time node is day or hour. That is, the fermentation stage data is used to identify a specific moment in the current Daqu fermentation cycle, and can be preliminarily represented by days or hours. For example, if the fermentation time is the 1st day, it is preliminarily represented as 1, and if the fermentation time is the 16th day, it is preliminarily represented as 16. In this embodiment, the Daqu fermentation cycle is 30 days, so the preliminary data of the Daqu fermentation stage only includes data from 1 to 30 days; in this embodiment, the preliminary data of 30 days can be divided into 3 groups according to the time development as the Daqu fermentation stage data, with 0 representing the 1st stage, 1 representing the 2nd stage, and 2 representing the 3rd stage; therefore, there are 3 types of Daqu fermentation stages, namely the 1st stage, the 2nd stage, and the 3rd stage.

[0050] In the present embodiment, the metabolite compound data represents compound data related to Daqu fermentation, and the compound data may be compound content data. After obtaining the metabolite compound data, the metabolite compound data may be standardized using a minimum-maximum normalization method. For example, the metabolite compound data include one or more compound content data in esters, alcohols, aldehydes, acids, pyrazines, furans, and aromatics, wherein the esters include at least ethyl hexadecanoate and ethyl hexanoate, the alcohols include at least 2,3-butanediol, and the pyrazines include at least 2,5-dimethylpyrazine, trimethylpyrazine, and 2,6-dimethylpyrazine. The unit of compound content data is ppm, and for example, the metabolite compound data at a certain moment is shown as: [1.017500007, 0.360192202, 0.28742875, 0.282392042, 0, 0.071789635, ...]. If the maximum value of the metabolite compound data is 4ppm and the minimum value is 0ppm, the metabolite compound data after normalization processing is shown as: [0.254375002, 0.090048051, 0.071857188, 0.070598011, 0, 0.017947409, …].

[0051] In practical applications, the metabolic compound data during the fermentation process of Daqu can be collected regularly according to a preset frequency to generate corresponding metabolic compound historical time series data. Environmental data can be monitored regularly according to a preset frequency to generate corresponding environmental historical time series data. The actual fermentation stage data of Daqu can be recorded to generate corresponding fermentation stage historical time series data. The preset frequency can be half a day, one day, two days, three days or other sampling frequencies determined by experts in the field.

[0052] Step 2: Obtain a prediction result of the content of the metabolic compound in the future time period according to the metabolic compound historical time series data, the environmental historical time series data and the fermentation stage historical time series data and based on a pre-trained metabolic compound prediction model.

[0053] In this embodiment, the training method of the metabolic compound prediction model includes:

[0054] Obtain fermentation data at each time point during the Daqu fermentation process, the fermentation data including environmental data, fermentation stage data and metabolic compound data; use the metabolic compound data at historical time points and their corresponding environmental data and fermentation stage data as input features, use the corresponding metabolic compound data at future time points as output labels, train a time series neural network, and obtain a metabolic compound prediction model;

[0055] In practical applications, first, the metabolic compound data at historical time points and their corresponding environmental data and fermentation stage data are used as input features, and the corresponding metabolic compound data at future time points are used as output labels of the time series neural network model to construct a training set and a validation set. Then, based on the training set and using a supervised learning method to train the time series neural network model, the output features of the time series neural network model and the corresponding output labels are compared, the corresponding loss function is calculated, and the model is optimized by back propagation. Finally, the time series neural network model is verified based on the validation set and the cross-validated test set. When the loss function of the time series neural network model is the smallest, and the prediction error of the validation set or the cross-validated test set is less than the error threshold, the corresponding time series neural network model is used as the metabolic compound prediction model.

[0056] After obtaining the historical time series data of metabolic compounds, the historical time series data of the environment and the historical time series data of the fermentation stage, the historical time series data of metabolic compounds, the historical time series data of the environment and the historical time series data of the fermentation stage are input into the trained metabolic compound prediction model to obtain the metabolic compound content of Daqu in the future period.

[0057] The future time period refers to a preset time period after the current time point, for example, one, two, or three days after the current time point. The specific time length can be set according to actual needs.

[0058] Step 3: determine the future time series data of the environment and the future time series data of the fermentation stage for the future time period, and obtain the first microbial abundance prediction result for the future time period based on the metabolic compound content prediction result of the future time period, the environmental future time series data and the future time series data of the fermentation stage and based on the pre-trained microbial abundance prediction model.

[0059] It can be understood that the environmental future time series data is the environmental time series data of a preset future period, and the fermentation stage future time series data is the fermentation stage time series data of an actual future period.

[0060] In this embodiment, the training method of the microbial abundance prediction model includes:

[0061] Fermentation data at each time point in the Daqu fermentation process are obtained, and the fermentation data include environmental data, fermentation stage data, metabolic compound data and microbial abundance data; the environmental data, fermentation stage data and metabolic compound data at each time point are used as input features, and the corresponding microbial abundance data are used as output labels to train a time series neural model and obtain a microbial abundance prediction model.

[0062] In practical applications, first, the environmental data, fermentation stage data, and metabolic compound data at each time point are used as input features, and the corresponding microbial abundance data are used as the output label of the time series neural network model to construct a training set and a validation set. Then, based on the training set, the time series neural network model is trained using a supervised learning method, the output features of the time series neural network model are compared with the corresponding output labels, the corresponding loss function is calculated, and the model is optimized by back propagation. Finally, the time series neural network model is verified based on the validation set. When the loss function of the time series neural network model is minimized and the prediction error is less than the error threshold, the corresponding time series neural network model is used as the microbial abundance prediction model.

[0063] After obtaining the predicted results of the metabolic compound content in the future time period, the future time series data of the environment, and the future time series data of the fermentation stage, the predicted results of the metabolic compound content in the future time period, the future time series data of the environment, and the future time series data of the fermentation stage are input into the trained microbial abundance prediction model to obtain the first microbial abundance prediction result in the future time period.

[0064] Step 4: According to the predicted result of the content of the metabolic compound and the predicted result of the first microorganism abundance and based on the pre-trained Daqu prediction model, the predicted result of the first fermentation stage and the predicted result of the first quality grade in the future period are obtained.

[0065] In this embodiment, the Daqu prediction model includes a metabolic compound data sub-model, a microbial abundance data sub-model, a fusion layer, a Daqu fermentation stage prediction layer, and a Daqu quality grade prediction layer. The training method of the Daqu prediction model includes:

[0066] First, the fermentation data at each time point in the Daqu fermentation process is obtained, and the fermentation data includes fermentation stage data, metabolite compound data, microbial abundance data and quality grade data; then, the metabolite compound data sub-model is trained based on the metabolite compound data in the Daqu fermentation data set and its corresponding metabolite compound features, so that the metabolite compound data sub-model can extract metabolite compound features related to the fermentation stage and quality grade from the input metabolite compound data. Then, the microbial abundance data sub-model is trained based on the microbial abundance data in the Daqu fermentation data set and its corresponding microbial abundance features, so that the microbial abundance data sub-model can extract microbial abundance features related to the fermentation stage and quality grade from the input microbial abundance data. Then, the metabolic compound features corresponding to the metabolic compound data in the Daqu fermentation dataset and the microbial abundance features corresponding to the microbial abundance data are feature fused to obtain the metabolic-microbial fusion features of each group of data in the Daqu fermentation dataset. Finally, the Daqu fermentation stage prediction layer is trained based on the metabolic-microbial fusion features and their corresponding fermentation stage data, and the Daqu quality grade prediction layer is trained based on the metabolic-microbial fusion features and their corresponding quality grade data, so that the Daqu fermentation stage prediction layer can predict the fermentation stage of Daqu according to the input metabolic-microbial fusion features, and the Daqu quality grade prediction layer can predict the quality grade of Daqu according to the input metabolic-microbial fusion features.

[0067] After obtaining the metabolic compound content prediction results and the first microbial abundance prediction results, the metabolic compound content prediction results and the first microbial abundance prediction results are input into the trained Daqu prediction model to obtain the first fermentation stage prediction results and the first quality grade prediction results for the future period.

[0068] In this embodiment, the microbial abundance data includes the types and quantities of microbial populations during the fermentation of Daqu, and the microbial populations include at least Bacillus, Aspergillus, Saccharomyces cerevisiae, Pediococcus acidilactici and Staphylococcus. For example, the microbial abundance data at a certain moment is shown as: [0.01207594, 0.017445474, 0.022036947, ...]; the microbial abundance data itself is normalized, so normalization is not required, and in each set of microbial abundance data, the sum of the abundances of various microorganisms is 1.

[0069] In this embodiment, the quality grade data indicates the quality grade of Daqu, which may include first-class, second-class and third-class qu, that is, the quality grade data includes three types, with 0 representing first-class qu, 1 representing second-class qu, and 2 representing third-class qu. The quality grade data may be divided according to actual production needs, or according to the grading standards used by experts in the field.

[0070] In this embodiment, the fermentation data at each time point used for model training is obtained by sampling according to a preset sampling frequency, and the preset sampling frequency is half a day, one day, two days or three days.

[0071] In this embodiment, the temporal neural network model may be, but is not limited to, a long short-term memory network (LSTM), a gated recurrent unit (GRU), or a temporal neural network model based on an attention mechanism.

[0072] Step 5: Determine whether the Daqu fermentation is normal based on the first difference between the prediction result of the first fermentation stage and the future time series data of the fermentation stage, and the second difference between the prediction result of the first quality grade and the corresponding expected quality grade. If the Daqu fermentation is abnormal, adjust the future time series data of the environment in the future time period based on the first difference and the second difference to minimize the first difference and the second difference.

[0073] After the prediction results of the first fermentation stage and the first quality grade are obtained, when the first difference between the prediction results of the first fermentation stage in the future time period and the future time series data of the actual fermentation stage of Daqu is greater than the preset fermentation stage difference threshold, or the second difference between the first quality grade prediction results in the future time period and the corresponding expected Daqu quality grade is greater than the preset fermentation stage difference threshold, it indicates that the Daqu fermentation is abnormal. At this time, an abnormal alarm is issued for the fermentation situation of this batch of Daqu, and environmental control is performed.

[0074] In this embodiment, the preset fermentation stage difference threshold can be set according to factors such as the Daqu fermentation cycle and quality requirements in the actual production environment, or it can be set by experts in the field through statistical analysis of historical data.

[0075] In this embodiment, a warning threshold that is one level different from the expected level can be set. For example, if the future time period should actually belong to the second stage of the Daqu fermentation stage, but the predicted result is the first stage, then a warning will be issued for the Daqu fermentation situation; or, if the expected Daqu level is the first level, but the predicted Daqu level is the second level, then a warning will be issued for the Daqu fermentation situation.

[0076] In this embodiment, regulating the future time series data of the environment in the future period according to the first difference and the second difference includes:

[0077] A control model is constructed based on the microbial abundance prediction model and the Daqu prediction model and based on an evolutionary algorithm, wherein the evolutionary algorithm is a genetic algorithm, an ant colony algorithm or a simulated annealing algorithm; when the Daqu fermentation is abnormal, the historical time series data of metabolic compounds, the historical time series data of the environment, the historical time series data of the fermentation stage, the future time series data of the fermentation stage and the expected quality grade are input into the control model for optimization, so as to obtain the target environmental data corresponding to the minimum of the first difference and the second difference, and the future time series data of the environment in the future time period are regulated according to the target environmental data.

[0078] Among them, the optimization process of the regulation model is specifically as follows: all possible metabolic compound contents and microbial abundances are clearly input into the Daqu prediction model, and the fermentation stage data and quality grade are predicted, and then the predicted fermentation stage data and quality grade are compared with the corresponding future time series data of the fermentation stage and the expected quality grade, respectively, and the fitness is calculated according to the fitness function, and the optimal metabolic compound content and the optimal microbial abundance are determined according to the fitness, and the optimal metabolic compound content, future time series data of the fermentation stage and all possible environmental data are input into the microbial abundance prediction model to predict the microbial abundance, and then the predicted microbial abundance is compared with the optimal microbial abundance, and the fitness is calculated according to the fitness function, and the optimal environmental data is determined according to the fitness, which is used as the target environmental data.

[0079] In this embodiment, a genetic algorithm is used to implement the optimization process of the above control model, and the corresponding Python code is shown as follows:

[0080] # Initialization phase

[0081] # Input the current metabolite data, environmental data and actual fermentation stage data, and define the length of the future time period data_metabolite:np.ndarray#Current metabolite data

[0082] data_environment:np.ndarray#Current environment data

[0083] data_stage:np.ndarray#Current actual fermentation stage data

[0084] time_future: int#Future time length

[0085] data_stage_future_real:np.ndarray#actual fermentation stage of Daqu in the future

[0086] data_grade_future_target:np.ndarray#Expected quality level of the big song in the future

[0087] #Define model

[0088] MetabolitePredictionModel:Callable#Metabolite compound prediction model

[0089] MicrobePredictionModel:Callable#Microbial abundance prediction model

[0090] StageGradePredictionModel:Callable#Fermentation stage and quality grade prediction model

[0091] #Genetic algorithm parameters

[0092] population_size=50#population size

[0093] generations = 100 # Maximum number of iterations

[0094] # Initialize the population

[0095] population = initialize the environment variable population (population_size, time_future)

[0096] #Fitness function

[0097] def fitness_function(candidate_environment):

[0098] data_metabolite_future=MetabolitePredictionModel(data_metabolite,data_environment,data_stage)

[0099] data_microbes_future=MicrobePredictionModel(data_metabolite_future, candidate_environment, data_stage_future_real)

[0100] data_stage_future_pred,data_grade_future_pred=StageGradePredictionModel(data_metabolite_future,data_microbes_future)

[0101] stage_error = calculate the future stage prediction error (data_stage_future_pred, data_stage_future_real)

[0102] grade_error = calculate quality grade prediction error (data_grade_future_pred, data_grade_future_target)

[0103] return comprehensive fitness (stage_error, grade_error)

[0104] #Genetic algorithm main loop

[0105] for generation in range(generations):

[0106] #Evaluate the current population fitness

[0107] fitness_scores=[fitness_function(candidate)for candidate inpopulation]

[0108] #Selection operation (e.g. tournament selection)

[0109] selected_population=select(population,fitness_scores)

[0110] #Crossover operation (such as single point crossover or uniform crossover)

[0111] offspring=cross(selected_population)

[0112] #Mutation operation (such as random perturbation)

[0113] offspring=variation (offspring)

[0114] # Update population

[0115] population = update population (offspring, selected_population)

[0116] # Output current generation information

[0117] Output the current generation's optimal fitness information (population, fitness_scores)

[0118] # Output the optimal solution

[0119] best_environment = Get the best environment index (population, fitness_scores)

[0120] In this embodiment, the method further includes:

[0121] Obtain current time series data of metabolic compounds, current time series data of the environment, and current time series data of the fermentation stage during the current time period of the Daqu fermentation process; obtain a second microbial abundance prediction result for the current time period based on the current time series data of metabolic compounds, current time series data of the environment, and current time series data of the fermentation stage and based on the microbial abundance prediction model; obtain a second fermentation stage prediction result and a second quality grade prediction result for the current time period based on the current time series data of metabolic compounds and the second microbial abundance prediction result and based on the Daqu prediction model; determine whether the Daqu fermentation is normal based on a third difference between the second fermentation stage prediction result and the current time series data of the fermentation stage, and a fourth difference between the second quality grade prediction result and the corresponding expected quality grade.

[0122] It can be understood that the present embodiment also determines the second fermentation stage prediction result and the second quality grade prediction result of the current time period. Specifically, first, after obtaining the current time series data of metabolic compounds, the current time series data of the environment, and the current time series data of the fermentation stage of the current time period during the Daqu fermentation process, the current time series data of the metabolic compounds, the current time series data of the environment, and the current time series data of the fermentation stage are input into the microbial abundance prediction model to obtain the second microbial abundance prediction result of the current time period. Then, the current time series data of the metabolic compounds and the second microbial abundance prediction result are input into the Daqu prediction model to obtain the second fermentation stage prediction result and the second quality grade prediction result of the current time period.

[0123] After predicting and obtaining the second fermentation stage prediction result and the second quality grade prediction result of the current time period, if the third difference between the second fermentation stage prediction result of the current time period and the current time series data of the actual fermentation stage of Daqu is greater than the preset fermentation stage difference threshold, or the fourth difference between the second quality grade prediction result of the current time period and the corresponding expected Daqu quality grade is greater than the preset fermentation stage difference threshold, it indicates that the Daqu fermentation is abnormal, and an abnormal alarm is issued for the fermentation of this batch of Daqu.

[0124] In summary, the Daqu fermentation control method based on fermentation stage and quality grade provided in this embodiment predicts the fermentation stage and quality grade of Daqu fermentation in the future period through pre-trained metabolic compound prediction model, microbial abundance prediction model and Daqu prediction model, and judges Daqu fermentation abnormality and regulates the environment according to the difference between the prediction result and the actual fermentation stage and the expected quality grade. This embodiment realizes the early prediction of Daqu fermentation quality, avoids the delay of environmental regulation, and ensures the quality of Daqu fermentation. At the same time, this embodiment does not rely on manual experience, and improves the consistency and accuracy of environmental regulation.

[0125] Figure 2 A schematic diagram of a Daqu fermentation control device based on fermentation stage and quality grade is shown, see Figure 2 , the device comprises:

[0126] An acquisition unit is used to regularly acquire metabolic compound data and environmental data during the fermentation process of Daqu, and record the actual fermentation stage data of Daqu, generate metabolic compound historical time series data according to the metabolic compound data, generate environmental historical time series data according to the environmental data, and generate fermentation stage historical time series data according to the fermentation stage data;

[0127] A first prediction unit, configured to obtain a prediction result of the content of the metabolic compound in a future period according to the metabolic compound historical time series data, the environmental historical time series data and the fermentation stage historical time series data and based on a pre-trained metabolic compound prediction model;

[0128] A second prediction unit is used to determine the future time series data of the environment and the future time series data of the fermentation stage in the future time period, and obtain the first microbial abundance prediction result in the future time period according to the metabolic compound content prediction result in the future time period, the future time series data of the environment and the future time series data of the fermentation stage and based on a pre-trained microbial abundance prediction model;

[0129] A third prediction unit is used to obtain a first fermentation stage prediction result and a first quality grade prediction result in a future period according to the metabolic compound content prediction result and the first microbial abundance prediction result and based on a pre-trained Daqu prediction model;

[0130] The control unit is used to determine whether the Daqu fermentation is normal based on a first difference between the prediction result of the first fermentation stage and the future time series data of the fermentation stage, and a second difference between the prediction result of the first quality grade and the corresponding expected quality grade. If the Daqu fermentation is abnormal, the future time series data of the environment in the future time period is controlled based on the first difference and the second difference to minimize the first difference and the second difference.

[0131] It can be understood that since the Daqu fermentation regulation device based on fermentation stage and quality grade described in this embodiment is a device for implementing the Daqu fermentation regulation method based on fermentation stage and quality grade described in the embodiment, for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant matters, please refer to the partial description of the method, which will not be repeated here.

Claims

1. A method for regulating and controlling Daqu fermentation based on fermentation stage and quality grade, characterized in that: The method comprises: Regularly obtain metabolic compound data and environmental data during the fermentation process of Daqu, and record the actual fermentation stage data of Daqu, generate metabolic compound historical time series data based on the metabolic compound data, generate environmental historical time series data based on the environmental data, and generate fermentation stage historical time series data based on the fermentation stage data; Obtaining a prediction result of the content of the metabolic compound in a future period according to the metabolic compound historical time series data, the environmental historical time series data and the fermentation stage historical time series data and based on a pre-trained metabolic compound prediction model; Determine the future time series data of the environment and the future time series data of the fermentation stage in the future time period, and obtain the first microbial abundance prediction result in the future time period according to the metabolic compound content prediction result in the future time period, the future time series data of the environment and the future time series data of the fermentation stage and based on the pre-trained microbial abundance prediction model; Obtaining a first fermentation stage prediction result and a first quality grade prediction result for a future period according to the metabolic compound content prediction result and the first microbial abundance prediction result and based on a pre-trained Daqu prediction model; Based on the first difference between the prediction result of the first fermentation stage and the future time series data of the fermentation stage, and the second difference between the prediction result of the first quality grade and the corresponding expected quality grade, it is judged whether the Daqu fermentation is normal. If the Daqu fermentation is abnormal, the future time series data of the environment in the future time period is regulated according to the first difference and the second difference to minimize the first difference and the second difference.

2. The Daqu fermentation control method based on fermentation stage and quality grade according to claim 1, characterized in that: The training methods of the metabolic compound prediction model, the microbial abundance prediction model and the Daqu prediction model include: Obtaining fermentation data at each time point during the fermentation process of Daqu, wherein the fermentation data includes environmental data, fermentation stage data, metabolic compound data, microbial abundance data, and quality grade data; The metabolic compound data at historical time points and their corresponding environmental data and fermentation stage data are used as input features, and the corresponding metabolic compound data at future time points are used as output labels to train a time series neural network and obtain a metabolic compound prediction model; The environmental data, fermentation stage data and metabolic compound data at each time point are used as input features, and the corresponding microbial abundance data are used as output labels to train the time series neural model and obtain the microbial abundance prediction model; The metabolic compound data and microbial abundance data at each time point were used as input features, and the corresponding fermentation stage data and quality grade data were used as output labels. The composite neural network model was trained to obtain a Daqu prediction model for simultaneously predicting the fermentation stage and quality grade.

3. The Daqu fermentation control method based on fermentation stage and quality grade according to claim 2, characterized in that: The environmental data include temperature data, humidity data and air oxygen content data; the fermentation stage data are time nodes in the Daqu fermentation cycle, and the unit of the time node is day or hour.

4. The Daqu fermentation control method based on fermentation stage and quality grade according to claim 2, characterized in that: The metabolic compound data include content data of one or more compounds in esters, alcohols, aldehydes, acids, ketones, pyrazines, furans and aromatics, wherein the esters include at least ethyl hexadecanoate and ethyl hexanoate, the alcohols include at least phenylethanol and 2,3-butanediol, the acids include at least acetic acid and hexanoic acid, and the pyrazines include at least 2,5-dimethylpyrazine, trimethylpyrazine and 2,6-dimethylpyrazine.

5. The Daqu fermentation control method based on fermentation stage and quality grade according to claim 2, characterized in that: The microbial abundance data include the types and quantities of microbial populations during the Daqu fermentation process, and the microbial populations include at least Bacillus, Aspergillus, Saccharomyces cerevisiae, Pediococcus acidilactici and Staphylococcus.

6. The Daqu fermentation control method based on fermentation stage and quality grade according to claim 2, characterized in that: The fermentation data at each time point during the Daqu fermentation process is obtained by sampling according to a preset sampling frequency, and the preset sampling frequency is half a day, one day, two days or three days.

7. The Daqu fermentation control method based on fermentation stage and quality grade according to claim 1, characterized in that: The method further comprises: Obtaining the current time series data of metabolic compounds, the current time series data of the environment, and the current time series data of the fermentation stage during the current period of the Daqu fermentation process; Obtaining a second microbial abundance prediction result for the current time period according to the current time series data of the metabolic compound, the current time series data of the environment, and the current time series data of the fermentation stage and based on the microbial abundance prediction model; According to the current time series data of the metabolic compound and the second microbial abundance prediction result and based on the Daqu prediction model, obtain the second fermentation stage prediction result and the second quality grade prediction result of the current period; Whether the Daqu fermentation is normal is determined based on the third difference between the second fermentation stage prediction result and the current time series data of the fermentation stage, and the fourth difference between the second quality grade prediction result and the corresponding expected quality grade.

8. The Daqu fermentation control method based on fermentation stage and quality grade according to claim 1 or 7, characterized in that: The method further comprises: If it is determined that the Daqu fermentation is abnormal, an alarm will be issued for the abnormal Daqu fermentation.

9. The Daqu fermentation control method based on fermentation stage and quality grade according to claim 1, characterized in that: Adjusting the future time series data of the environment in the future period according to the first difference and the second difference includes: Constructing a control model based on the microbial abundance prediction model and the Daqu prediction model and based on an evolutionary algorithm, wherein the evolutionary algorithm is a genetic algorithm, an ant colony algorithm or a simulated annealing algorithm; When the fermentation of Daqu is abnormal, the historical time series data of metabolic compounds, the historical time series data of the environment, the historical time series data of the fermentation stage, the future time series data of the fermentation stage and the expected quality level are input into the control model for optimization, so as to obtain the target environmental data corresponding to the minimum of the first difference and the second difference, and the future time series data of the environment in the future time period are regulated according to the target environmental data.

10. A Daqu fermentation control device based on fermentation stage and quality grade, characterized in that: The device comprises: An acquisition unit is used to regularly acquire metabolic compound data and environmental data during the fermentation process of Daqu, and record the actual fermentation stage data of Daqu, generate metabolic compound historical time series data according to the metabolic compound data, generate environmental historical time series data according to the environmental data, and generate fermentation stage historical time series data according to the fermentation stage data; A first prediction unit, configured to obtain a prediction result of the content of the metabolic compound in a future period according to the metabolic compound historical time series data, the environmental historical time series data and the fermentation stage historical time series data and based on a pre-trained metabolic compound prediction model; A second prediction unit is used to determine the future time series data of the environment and the future time series data of the fermentation stage in the future time period, and obtain the first microbial abundance prediction result in the future time period according to the metabolic compound content prediction result in the future time period, the future time series data of the environment and the future time series data of the fermentation stage and based on a pre-trained microbial abundance prediction model; A third prediction unit is used to obtain a first fermentation stage prediction result and a first quality grade prediction result in a future period according to the metabolic compound content prediction result and the first microbial abundance prediction result and based on a pre-trained Daqu prediction model; The control unit is used to determine whether the Daqu fermentation is normal based on a first difference between the prediction result of the first fermentation stage and the future time series data of the fermentation stage, and a second difference between the prediction result of the first quality grade and the corresponding expected quality grade. If the Daqu fermentation is abnormal, the future time series data of the environment in the future time period is controlled based on the first difference and the second difference to minimize the first difference and the second difference.

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