Fermentation speed regulation and control method for keeping natural activity of fruits and vegetables and fermentation equipment

Abstract

The invention provides a fermentation speed regulation and control method capable of keeping natural activity of fruits and vegetables and fermentation equipment, and relates to the technical field of fruit and vegetable fermention.The method comprises the steps that multiple flora types corresponding to multiple liquid inlets of a fruit and vegetable fermentation tank are collected; performing oxygen adaptability analysis according to the plurality of flora types, and outputting a plurality of groups of hypoxia regulation and control parameters; controlling a plurality of low-oxygen regulation and control sub-modules to activate florae at a plurality of liquid inlets according to a plurality of groups of low-oxygen regulation and control parameters; constructing a mixed fermentation speed regulation and control model, inputting the flora type in the mixer into the mixed fermentation speed regulation and control model for activity retention analysis, and outputting mixed-hypoxia regulation and control parameters; and performing oxygen regulation on the mixer according to the mixing-hypoxia regulation and control parameters. The technical problems of low fermentation efficiency and nutrient loss caused by lack of fine regulation and control of oxygen requirements in the multi-flora fermentation process in the prior art are solved, and the technical effects of improving the fermentation efficiency and retaining natural components of fruits and vegetables are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of fruit and vegetable fermentation, and in particular to a fermentation speed control method and fermentation equipment for maintaining the natural activity of fruits and vegetables. Background Art

[0002] Fruit and vegetable fermentation is a processing technology that converts sugars, acids and other substances in fruits and vegetables into functional fermentation products through the action of microorganisms. It can not only extend the shelf life of fruits and vegetables, but also improve their nutritional value and flavor characteristics. Existing fruit and vegetable fermentation control methods are mostly focused on the cultivation of a single strain or a simple bacterial community, and the initial optimization of the fermentation process is achieved by adjusting the temperature, pH value or oxygen supply. These methods have certain effects in traditional fermentation environments, but with the application of multi-bacteria composite fermentation technology, traditional control methods can no longer meet complex control needs. First, the existing methods lack in-depth research and optimization of multi-species synergistic fermentation, resulting in the diversity and synergy of the bacterial community during the fermentation process. The effect is not fully utilized, which in turn affects the fermentation efficiency and product quality. Secondly, the existing fermentation control technology does not control key environmental factors such as oxygen precisely. Oxygen is an important factor affecting microbial metabolism and the generation of fermentation products, but traditional methods cannot be precisely regulated according to the specific needs of different bacterial communities. This not only limits the improvement of fermentation efficiency, but also easily leads to the loss of nutrients in fruits and vegetables under unfavorable fermentation conditions. Summary of the invention

[0003] The present application provides a fermentation speed control method and fermentation equipment for maintaining the natural activity of fruits and vegetables, which solves the technical problems of low fermentation efficiency and loss of nutrients due to the lack of fine control of oxygen demand in the fermentation process of multiple bacterial communities in the prior art, and achieves the technical effect of improving fermentation efficiency and retaining the natural ingredients of fruits and vegetables.

[0004] In view of the above problems, on the one hand, the present application provides a fermentation speed control method for maintaining the natural activity of fruits and vegetables, the method comprising: obtaining a fruit and vegetable fermentation tank, the fruit and vegetable fermentation tank comprising multiple liquid inlets and a mixer, wherein each liquid inlet corresponds to a hypoxia control submodule, and the mixer is provided with a hypoxia control main module; collecting the multiple liquid inlets for fermentation of the corresponding multiple bacterial community types; performing oxygen adaptability analysis according to the multiple bacterial community types, and outputting multiple groups of hypoxia control parameters, wherein each group of hypoxia control parameters includes oxygen concentration and delivery rate; controlling multiple hypoxia control submodules to activate the bacterial communities of the multiple liquid inlets according to the multiple groups of hypoxia control parameters, and the activated bacterial communities flow into the mixer through the multiple liquid inlets; constructing a mixed fermentation speed control model, inputting the bacterial community types in the mixer into the mixed fermentation speed control model for activity retention analysis, and outputting mixed-hypoxia control parameters; controlling the hypoxia control main module to perform oxygen regulation on the mixer according to the mixed-hypoxia control parameters.

[0005] On the other hand, the present application also provides a fermentation device for maintaining the natural activity of fruits and vegetables, the device comprising: a fermentation tank connection unit, used to obtain a fruit and vegetable fermentation tank, the fruit and vegetable fermentation tank comprising multiple liquid inlets and a mixer, wherein each liquid inlet corresponds to a hypoxia regulation submodule, and the mixer is provided with a hypoxia regulation main module; a flora type collection unit, used to collect multiple flora types corresponding to the multiple liquid inlets for fermentation; an oxygen adaptability analysis unit, used to perform oxygen adaptability analysis according to the multiple flora types, and output multiple groups of hypoxia regulation parameters, wherein each group of hypoxia regulation parameters includes Oxygen concentration and delivery rate; a hypoxia control unit, used to control multiple hypoxia control submodules to activate the bacterial flora of the multiple liquid inlets according to the multiple groups of hypoxia control parameters, and the activated bacterial flora flows into the mixer through the multiple liquid inlets; an activity retention analysis unit, used to construct a mixed fermentation speed control model, input the bacterial flora type in the mixer into the mixed fermentation speed control model for activity retention analysis, and output the mixed-hypoxia control parameters; an oxygen regulation unit, used to control the hypoxia control main module to regulate the oxygen of the mixer according to the mixed-hypoxia control parameters.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: By collecting multiple bacterial flora types corresponding to multiple liquid inlets for fermentation, the bacterial flora types involved in the fermentation process are clarified, and input conditions are provided for subsequent oxygen demand analysis and control parameter setting. Oxygen adaptability analysis is performed according to the multiple bacterial flora types, and multiple sets of hypoxia control parameters are output to ensure the optimal growth conditions of the bacterial flora in the activation stage. Control multiple hypoxia control submodules to activate the bacterial flora of the multiple liquid inlets according to the multiple sets of hypoxia control parameters, ensure that the bacterial flora enters the mixed fermentation stage with a high activity state, and improve the initial fermentation efficiency. Construct a mixed fermentation speed control model, input the bacterial flora type in the mixer into the mixed fermentation speed control model for activity retention analysis, achieve a balance between fermentation speed optimization and activity retention, and provide a decision-making basis for the mixed fermentation stage. Control the hypoxia control main module according to the mixed-hypoxia control parameters output by the mixed fermentation speed control model, and globally adjust the oxygen environment in the mixer to ensure that the fermentation process is uniform and efficient, and retain the natural activity of fruits and vegetables.

[0007] In summary, this application provides a suitable growth environment for different bacterial communities by collecting multiple bacterial communities and conducting oxygen adaptability analysis, giving full play to the synergistic effect of multiple bacterial communities and promoting the generation and transformation of multiple beneficial components in fruits and vegetables. The constructed mixed fermentation speed control model and the precise regulation of the hypoxia control main module further optimize the fermentation conditions, improve the fermentation efficiency and the activity of the bacterial community, and effectively reduce the loss of nutrients in fruits and vegetables, maintaining the natural activity of fruits and vegetables.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic flow chart of a fermentation speed control method for maintaining the natural activity of fruits and vegetables provided in an embodiment of the present application.

[0010] Figure 2 A schematic diagram of a process for obtaining multiple sets of hypoxia control parameters in a fermentation speed control method for maintaining the natural activity of fruits and vegetables provided in an embodiment of the present application.

[0011] Figure 3 A schematic flow chart of constructing a mixed fermentation rate control model in a fermentation rate control method for maintaining the natural activity of fruits and vegetables provided in an embodiment of the present application.

[0012] Figure 4 A schematic diagram of the structure of a fermentation device for maintaining the natural activity of fruits and vegetables provided in an embodiment of the present application.

[0013] Explanation of the reference numerals: fermenter connection unit 10 , bacterial flora type collection unit 20 , oxygen adaptability analysis unit 30 , hypoxia control unit 40 , activity retention analysis unit 50 , oxygen regulation unit 60 . DETAILED DESCRIPTION

[0014] The embodiments of the present application provide a fermentation speed control method and fermentation equipment for maintaining the natural activity of fruits and vegetables, thereby solving the technical problems of low fermentation efficiency and loss of nutrients in the prior art due to the lack of fine control of oxygen demand in the multi-bacteria fermentation process, thereby achieving the technical effect of improving fermentation efficiency and retaining the natural ingredients of fruits and vegetables.

[0015] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a method for controlling the fermentation speed for maintaining the natural activity of fruits and vegetables, the method comprising: Step S1: obtaining a fruit and vegetable fermentation tank, wherein the fruit and vegetable fermentation tank comprises a plurality of liquid inlets and a mixer, wherein each liquid inlet corresponds to a hypoxia regulation submodule, and the mixer is provided with a hypoxia regulation main module.

[0016] Specifically, before fermentation, first determine a fruit and vegetable fermentation tank for fermentation. This fermentation tank has multiple liquid inlets and a mixer. Each liquid inlet is equipped with a low oxygen regulation submodule for independently controlling the oxygen environment of the bacterial community entering the inlet. A low oxygen regulation main module is provided inside the mixer to comprehensively adjust the oxygen environment in the mixer. During the fermentation process, different bacterial communities can enter the fermentation tank through multiple liquid inlets. The low oxygen regulation submodule equipped at each inlet can independently regulate the oxygen requirements of different bacterial communities to ensure that each bacterial community can grow and ferment in a suitable oxygen environment. As the main place for fermentation, the mixer is equipped with a low oxygen regulation main module responsible for comprehensive regulation of the oxygen concentration of the entire fermentation environment to ensure the stability and efficiency of the fermentation process.

[0017] The fruit and vegetable fermentation tank provides a fermentation system that can carry out multi-bacteria fermentation and has preliminary oxygen regulation function, providing the hardware foundation for the subsequent precise regulation of different bacteria communities.

[0018] Step S2: collecting the multiple liquid inlets for fermenting the corresponding multiple bacterial flora types.

[0019] Specifically, the flora type refers to different types of microbial communities used for fermentation, such as lactic acid bacteria, yeast, acetic acid bacteria, etc. Before fermentation, the flora type corresponding to each liquid inlet is determined according to the fermentation process design document. For example, according to the fermentation process design, lactic acid bacteria need to be introduced into the first liquid inlet, yeast into the second liquid inlet, and acetic acid bacteria into the third liquid inlet. These flora can be obtained from the bacterial strain library or laboratory cultured bacterial liquid by aseptic operation, and transported to the corresponding liquid inlet of the fermentation tank through pipelines.

[0020] By collecting multiple liquid inlets for multiple bacterial community types corresponding to the fermentation, the different bacterial communities involved in the fermentation were identified, providing a basis for subsequent oxygen regulation according to the characteristics of different bacterial communities.

[0021] Step S3: performing oxygen adaptability analysis according to the multiple bacterial colony types, and outputting multiple groups of hypoxia control parameters, wherein each group of hypoxia control parameters includes oxygen concentration and delivery rate.

[0022] Specifically, oxygen adaptability analysis refers to the analysis of the growth and metabolic characteristics of the bacterial community under different oxygen conditions to determine its demand and adaptability to oxygen. Hypoxia control parameters include parameters such as oxygen concentration and delivery rate, which are used to guide the regulation of the oxygen environment in which the bacterial community is located.

[0023] Use existing microbiological research data and experiments to analyze the adaptability of different bacterial communities to oxygen environments (such as oxygen concentration, oxygen supply rate, etc.) during fermentation. For example, through laboratory culture and measurement, it can be obtained that the aerobic threshold of lactic acid bacteria is 0.5% oxygen concentration, the hypoxic growth rate is 0.2 generations per hour, and the oxygen metabolism sensitivity is medium. According to the collected bacterial community type data, the oxygen concentration and delivery rate suitable for each bacterial community are determined through machine learning algorithms or mathematical models (such as linear regression models). For example, for lactic acid bacteria, it can be determined that the optimal oxygen concentration range is 0.3% to 0.7%, and the delivery rate is 0.1 liters of oxygen per minute.

[0024] Through oxygen adaptability analysis, we can accurately understand the oxygen requirements of different bacterial communities, providing a scientific basis for subsequent oxygen regulation, thereby creating a suitable growth environment for each bacterial community and improving fermentation efficiency and product quality.

[0025] Step S4: controlling the plurality of hypoxia control submodules to activate the bacterial colonies at the plurality of liquid inlets according to the plurality of groups of hypoxia control parameters, and the activated bacterial colonies flow into the mixer through the plurality of liquid inlets.

[0026] Specifically, activation refers to restoring the bacterial community from a dormant state or a low-activity state to an active growth and metabolic state by adjusting conditions such as the oxygen environment. Control multiple hypoxia control submodules, and adjust the oxygen concentration and delivery rate of each liquid inlet according to the multiple groups of hypoxia control parameters output by step S3, so that it meets the hypoxia control parameters of the corresponding bacterial community, thereby activating the bacterial community at the liquid inlet. For example, for the liquid inlet of lactic acid bacteria, the oxygen concentration can be adjusted to 0.5%, and the delivery rate can be adjusted to 0.1 liters of oxygen per minute, so that it can quickly recover in a suitable oxygen environment and enter an active growth and fermentation state. The activated bacterial community flows into the mixer via multiple liquid inlets, ready for mixed fermentation.

[0027] By activating the bacterial flora at multiple liquid inlets, the bacterial flora can be in a suitable fermentation starting state before entering the mixer, thereby improving the activity of the bacterial flora, preparing for subsequent fermentation in the mixer, and ensuring a smooth fermentation process.

[0028] Step S5: constructing a mixed fermentation speed control model, inputting the bacterial flora type in the mixer into the mixed fermentation speed control model for activity retention analysis, and outputting the mixed-hypoxia control parameters.

[0029] Specifically, the mixed fermentation speed control model is a mathematical model established based on the types and characteristics of the bacterial flora in the mixer, which is used to predict and optimize the oxygen regulation parameters in the mixed fermentation process. A mixed fermentation speed control model is constructed using a machine learning algorithm, and the bacterial flora type data in the mixer is input into this model. The model analyzes the activity retention of the bacterial flora according to a pre-set algorithm and outputs a mixed-hypoxia control parameter. This mixed-hypoxia control parameter is used to guide the regulation of the oxygen environment in the mixer.

[0030] The mixed fermentation speed control model can be used to accurately analyze the control parameters required for activity retention according to the type of bacterial flora in the mixer, which helps to retain the natural active ingredients of fruits and vegetables during the fermentation process.

[0031] Step S6: Control the hypoxia control main module to regulate oxygen in the mixer according to the mixing-hypoxia control parameters.

[0032] Specifically, according to the mixing-hypoxia control parameter outputted in step S5, the main module for controlling the hypoxia control is controlled to adjust the oxygen concentration and the delivery rate in the mixer to meet the mixing-hypoxia control parameter. For example, if the mixing-hypoxia control parameter requires the oxygen concentration to be 3%, then the main module for controlling the hypoxia control will adjust the oxygen supply device (such as an oxygen nozzle, etc.) in the mixer to adjust the oxygen concentration in the mixer to 3%, so as to optimize the fermentation conditions, improve the activity of the flora and the fermentation efficiency, and better retain the natural active ingredients of fruits and vegetables.

[0033] Further, such as Figure 2 As shown, step S3 includes: Step S31: Collect a bacterial sample library, and identify oxygen adaptability data of each bacterial sample in the bacterial sample library, wherein the oxygen adaptability data includes an aerobic threshold, a hypoxic growth rate, and oxygen metabolic sensitivity.

[0034] Step S32: grouping the oxygen adaptability data using a cluster analysis algorithm, and outputting a bacterial flora type classification result.

[0035] Step S33: generating a first fermentation regulation model according to the bacterial flora type classification result.

[0036] Step S34: performing oxygen adaptability analysis on the multiple bacterial community types respectively according to the first fermentation regulation model, calculating the optimal oxygen concentration range and delivery rate of each bacterial community, and outputting multiple groups of hypoxia regulation parameters.

[0037] Specifically, the microbial sample library is a collection that stores various microbial-related data. Oxygen adaptability data are data that describe the growth and metabolic characteristics of the microbial community under different oxygen conditions, including indicators such as aerobic threshold, hypoxic growth rate, and oxygen metabolic sensitivity. Among them, the aerobic threshold is the lowest or highest limit of the microbial community's demand for oxygen during growth and metabolism. The hypoxic growth rate is the growth rate of the microbial community under hypoxic conditions. Oxygen metabolic sensitivity is the sensitivity of the microbial community to changes in oxygen concentration, that is, the degree of influence of changes in oxygen concentration on the metabolic activities of the microbial community.

[0038] Obtain a variety of bacterial samples, such as lactic acid bacteria, yeast, acetic acid bacteria, etc., from laboratory culture or bacterial strain libraries and store them in a bacterial sample library. Then, the oxygen adaptability data of each bacterial sample is determined experimentally. For example, bacterial samples can be placed in culture environments with different oxygen concentrations, and their growth and metabolic activities can be observed and recorded. Oxygen sensors can be used to accurately measure the growth of bacterial communities under different oxygen concentrations to determine the aerobic threshold; the hypoxic growth rate can be calculated by regularly measuring the growth of the number of bacterial communities; metabolite analysis instruments (such as gas chromatography-mass spectrometry) are used to analyze the changes in the metabolites of the bacterial community under different oxygen concentrations to determine the oxygen metabolic sensitivity. The detailed oxygen adaptability data of these bacterial samples provide basic data support for the subsequent classification of bacterial communities and the calculation of oxygen regulation parameters.

[0039] The oxygen adaptability data of the collected bacterial samples are input into a cluster analysis algorithm (such as K-means clustering algorithm, hierarchical clustering algorithm, etc.), and the algorithm will automatically divide the bacterial community into different categories according to the characteristic similarity of the data. Exemplarily, the K-means clustering algorithm can be used to first determine the number of groups to be divided into (such as 3 groups), and then the algorithm will divide the bacterial community samples into different groups according to characteristics such as aerobic threshold, hypoxic growth rate, and oxygen metabolic sensitivity. For example, lactic acid bacteria are anaerobic, yeasts are hypoxic tolerant, and acetic acid bacteria are aerobic. Through cluster analysis of oxygen adaptability data, bacterial community samples with similar oxygen adaptability characteristics are classified into one category, which can simplify the subsequent analysis and regulation process of the bacterial community and improve the accuracy and effectiveness of oxygen regulation.

[0040] The first fermentation regulation model is a model built based on the results of the classification of bacterial colony types, which is used to make preliminary regulation plans for the fermentation process. According to the results of the bacterial colony type classification, combined with the oxygen adaptability data of each bacterial colony, the first fermentation regulation model is established. The model can use mathematical modeling or machine learning methods, take the bacterial colony type and oxygen adaptability data as input, and output the corresponding oxygen regulation strategy. For example, a linear programming model can be used to set the objective function and constraints according to the oxygen demand characteristics of different bacterial colony categories (such as the growth advantage of a certain type of bacterial colony under a specific oxygen concentration, etc.), and use the oxygen concentration range and the delivery rate as independent variables, and the low oxygen growth rate as the dependent variable for regression fitting to establish the first fermentation regulation model. The first fermentation regulation model can also be constructed using a neural network: a neural network model is constructed using a deep learning framework such as TensorFlow, PyTorch, etc., and oxygen adaptability data of multiple bacterial colony types are collected as training data sets. The neural network model is iteratively trained using the training data, and the optimization algorithm such as the gradient descent method is used to continuously adjust the weight and bias of the model so that the model output is as close as possible to the actual value of the training data to improve the performance of the model. The trained first fermentation regulation model can accurately predict the growth and metabolic performance of different bacterial communities under different oxygen conditions, provide personalized oxygen regulation strategies for different bacterial communities, and guide oxygen regulation during the fermentation process to improve fermentation efficiency and product quality.

[0041] According to the first fermentation regulation model, oxygen adaptability analysis is performed on each bacterial type. The oxygen adaptability data and classification results of each bacterial type are input into the first fermentation regulation model. Based on these input information, the model will calculate the optimal oxygen concentration range and release rate for each bacterial type, so that the fermentation process is more in line with the growth and metabolic needs of the bacterial group, further improving the fermentation efficiency and product quality, while helping to better preserve the natural active ingredients of fruits and vegetables.

[0042] Further, such as Figure 3As shown, in step S5, a mixed fermentation speed control model is constructed, including: Step S51: obtaining mixed bacterial flora sample data, wherein the mixed bacterial flora sample data includes data under a mixed fermentation environment, including oxygen concentration changes, bacterial flora metabolic rate, and active ingredient retention rate.

[0043] Step S52: using the first fermentation regulation model as a pre-training model and outputting transfer learning parameters.

[0044] Step S53: determining an objective function, performing migration training based on the objective function with the migration learning parameters and the mixed flora sample data, and constructing a mixed fermentation speed control model.

[0045] Specifically, mixed flora sample data refers to flora sample data collected under a mixed fermentation environment, including indicators such as oxygen concentration changes, flora metabolic rate, and active ingredient retention rate. Among them, oxygen concentration changes refer to the changes in oxygen concentration over time during the fermentation process, reflecting the dynamic changes in oxygen supply. The flora metabolic rate is the rate at which the flora consumes nutrients and produces metabolites during the fermentation process, and is an important indicator for measuring flora activity. The active ingredient retention rate is the retention ratio of natural active ingredients (such as vitamins, antioxidants, etc.) in fruits and vegetables during the fermentation process, reflecting the impact of fermentation on the quality of fruits and vegetables. During the mixed fermentation process, data such as oxygen concentration changes, flora metabolic rate, and active ingredient retention rate are monitored and recorded in real time through sensors and detection equipment. For example, oxygen concentration can be monitored using an oxygen sensor, flora metabolic rate can be determined by biosensors or chemical analysis methods, and changes in the content of active ingredients can be detected using technologies such as high-performance liquid chromatography (HPLC), thereby obtaining mixed flora sample data.

[0046] The first fermentation regulation model is used as a pre-trained model, and the parameters and feature representations learned during the training process are extracted as transfer learning parameters. For example, if the first fermentation regulation model is a neural network, parameters such as the weight and bias of its hidden layer and the feature representation learned about the oxygen adaptability of the bacterial community can be extracted. In the actual operation process, the Python language can be combined with a deep learning framework (such as TensorFlow or PyTorch) to extract the parameters in the pre-trained model by calling the corresponding function. For example, in TensorFlow, the relevant model saving and loading functions can be used to load the first fermentation regulation model, and then the parameters useful for constructing the mixed fermentation speed regulation model can be obtained through a specific interface function. These parameters become transfer learning parameters after being sorted. By using the first fermentation regulation model as a pre-trained model, the training process of the mixed fermentation speed regulation model can be accelerated, the initial performance of the model can be improved, and the complex laws in the mixed fermentation process can be better captured under limited sample data.

[0047] The objective function is a function used to evaluate model performance and optimization direction during model training, reflecting the target that the model needs to optimize. The objective function of the mixed fermentation speed control model is determined, usually including indicators such as active ingredient retention rate and degradation rate, to ensure that the model can optimize the oxygen regulation parameters in the fermentation process, improve the retention rate of active ingredients, and reduce the degradation rate. For example, the objective function can be expressed as maximizing the retention rate of active ingredients and minimizing the degradation rate. Based on the objective function, transfer learning parameters and mixed flora sample data are used for migration training. The transfer learning parameters are used as the initial parameters of the mixed fermentation speed control model, and the mixed flora sample data is input for training. The model parameters of the mixed fermentation speed control model are adjusted through the optimization algorithm to optimize the objective function value output by the model. For example, an optimization algorithm such as the gradient descent method can be used to gradually adjust the model parameters according to the gradient information of the objective function so that it better fits the sample data and achieves the purpose of optimizing the fermentation process.

[0048] By determining the objective function and conducting migration training, the constructed mixed fermentation speed control model can accurately predict and optimize the oxygen regulation parameters in the mixed fermentation process, improve fermentation efficiency and product quality, better retain the natural active ingredients of fruits and vegetables, and realize intelligent and precise control of the fermentation process.

[0049] Furthermore, the objective function in step S53 includes the active ingredient retention rate and the active ingredient degradation rate, wherein the expression of the objective function includes: ;in, is the retention rate of the kth type of active ingredient in fruits and vegetables at time t, represents the degradation rate of the active ingredient at time t, represents the weight of the kth type of active ingredient, m is the number of active ingredients in fruits and vegetables, is the weight coefficient of active ingredient degradation, which is used to balance the relationship between active ingredient retention rate and degradation.

[0050] Specifically, the objective function comprehensively considers the active ingredient retention rate and the active ingredient degradation rate. By introducing the weight coefficient, it highlights the retention contribution of important ingredients and balances the relationship between the active ingredient retention rate and the active ingredient degradation rate. It refers to the retention ratio of the kth type of active ingredient in fruits and vegetables at time t, reflecting the extent to which the ingredient has not been degraded or lost during the fermentation process. Refers to the rate at which the active ingredient is decomposed or lost at time t, usually expressed as a percentage or mass per unit time. The first term in the function represents the weighted sum of the retention rates of all active ingredients, with the retention rate of each ingredient By its weight The weighted components have a greater impact on the optimization results. Indicates the negative impact of the degradation rate of the active ingredient on the target, using the weight coefficient Control the impact. The value of can change the relative importance between active ingredient retention and degradation rate in the objective function. Indicates greater emphasis on reducing degradation rate; smaller Expressed a priority focus on improving retention rates.

[0051] In the optimization process, by solving the objective function , find the optimal oxygen regulation parameters so that the active ingredients are best retained during the fermentation process, while the degradation rate is effectively controlled, thereby improving the quality and nutritional value of the fermented products.

[0052] Furthermore, the expression for calculating the degradation rate of the active ingredient includes: ; Wherein, n is the total bacterial population of the mixer, is the metabolic sensitivity coefficient of bacterial colony i in the mixer, is the metabolic rate of bacterial colony i at time t, is the oxygen concentration in the mixer, is the optimal oxygen concentration for bacterial colony i.

[0053] Specifically, the expression of the degradation rate of the active ingredient can comprehensively consider the metabolic rate, metabolic sensitivity coefficient and oxygen concentration deviation of different bacterial communities in the mixer, and calculate the total degradation rate of the active ingredient, which provides an important reference for optimizing oxygen regulation. When calculating the degradation rate of the active ingredient, for each bacterial community i in the mixer, calculate its oxygen concentration deviation at time t, that is, .in, is the oxygen concentration in the mixer, is the optimal oxygen concentration of bacterial colony i, that is, the oxygen concentration corresponding to the highest metabolic efficiency of the bacterial colony. Then, the oxygen concentration deviation is squared to obtain , to eliminate the positive and negative effects of the deviation and amplify the contribution of the deviation. The squared deviation is compared with the metabolic rate of bacterial colony i. and metabolic sensitivity coefficient Multiply them together to get the degradation contribution of bacterial colony i at time t. Sum the degradation contributions of all bacterial colonies to get the total active ingredient degradation rate of the mixer at time t. By minimizing the degradation rate, the loss of active ingredients can be effectively controlled and the quality and nutritional value of the fermented products can be improved.

[0054] Furthermore, in step S5, the type of bacterial flora in the mixer is input into the mixed fermentation speed control model for activity retention analysis, including: Step S54: Identify the type of bacterial colonies in the mixer and extract key feature data of each bacterial colony.

[0055] Step S55: input the key feature data for feature vectorization processing, and output a feature vector set.

[0056] Step S56: calling the mixed fermentation speed control model to predict the active ingredient retention rate and active ingredient degradation rate of the mixer according to the feature vector set.

[0057] Step S57: outputting the mixing-hypoxia control parameters according to the relationship between the active ingredient retention rate and the active ingredient degradation rate.

[0058] Specifically, key characteristic data are data that can reflect the important characteristics of the bacterial community during the fermentation process, such as metabolic rate, oxygen sensitivity, etc. Use sensors and detection equipment, such as microbial detectors, metabolic analyzers, etc., to monitor and identify the bacterial community in the mixer in real time to determine the type of bacterial community present. Then, extract the key characteristic data of each bacterial community, such as metabolic rate, oxygen sensitivity, etc. For example, by analyzing the changes in the concentration of metabolites of the bacterial community, its metabolic rate can be calculated; by observing the growth of the bacterial community under different oxygen concentrations, its oxygen sensitivity can be evaluated. Identifying the type of bacterial community in the mixer and extracting key characteristic data provide accurate input information for subsequent activity retention analysis, enabling the model to accurately predict and regulate according to the actual state of the bacterial community.

[0059] The extracted key feature data are processed by feature vectorization. For example, data such as metabolic rate and oxygen sensitivity are converted into numerical form and normalized so that they are within the same numerical range, which is convenient for model calculation and comparison. The processed feature data are organized into a feature vector set, each of which contains the key feature information of a bacterial community type. For example, in the process of fermenting tomato juice, lactic acid bacteria, yeast and acetic acid bacteria may exist in the mixer. After the key feature data such as metabolic rate and oxygen sensitivity are processed by feature vectorization, the corresponding feature vector set is obtained, such as {[0.3, 0.5], [0.2, 0.8], [0.4, 0.3]}.

[0060] The obtained feature vector set is used as input data to load the constructed mixed fermentation speed control model. The model calculates the input feature vector set according to its internal algorithm structure (such as the weights and activation functions of each layer in the neural network structure, etc.), and predicts the active ingredient retention rate and active ingredient degradation rate in the mixer based on the patterns and rules learned in the previous construction and training process. By calling the mixed fermentation speed control model, the active ingredient retention rate and active ingredient degradation rate in the mixed fermentation process can be quickly and accurately predicted using the existing data and model structure, providing an important basis for monitoring and controlling the fermentation process.

[0061] Based on the predicted values ​​of active ingredient retention rate and active ingredient degradation rate, mathematical calculation and logical judgment are used to determine the mixed-hypoxia control parameters. For example, if the active ingredient retention rate is low and the degradation rate is high, it may be necessary to reduce the oxygen supply to reduce the metabolic rate of the bacterial community, thereby increasing the retention rate of the active ingredient. A simple algorithm can be set up, such as when the active ingredient retention rate is lower than a certain threshold (such as 30%) and the degradation rate is higher than a certain value (such as 0.1 unit / hour), the oxygen concentration is reduced by a certain proportion (such as 10%), thereby determining the corresponding mixed-hypoxia control parameters, including oxygen concentration and delivery rate.

[0062] The mixed-hypoxia control parameters determined through the above steps can provide specific control directions and numerical basis for the mixed fermentation process, which helps to achieve maximum retention of active ingredients during the fermentation process and improve the quality and nutritional value of the fermented products.

[0063] Furthermore, after step S6, the following steps are further included: Step S71: obtaining a feedback data set of the mixer after adjustment, wherein the feedback data set includes oxygen concentration feedback data and activity detection feedback data.

[0064] Step S72: performing feedback optimization on the mixed fermentation speed control model according to the feedback data set, and outputting the optimized mixed fermentation speed control model.

[0065] Specifically, the feedback data set is the data obtained from the mixer after adjusting the oxygen environment, which is used to evaluate the adjustment effect and model performance. It includes oxygen concentration feedback data and activity detection feedback data. Among them, the oxygen concentration feedback data is the actual oxygen concentration data in the mixer after adjustment, reflecting the actual effect of oxygen adjustment. The activity detection feedback data is the activity data of the flora in the mixer after adjustment and the retention of active ingredients, which is used to evaluate the quality and efficiency of the fermentation process.

[0066] After adjusting the oxygen environment in the mixer, use sensors and detection equipment to monitor and record oxygen concentration feedback data and activity detection feedback data in real time. For example, the percentage concentration of oxygen in the mixer is measured in real time by an oxygen sensor. The activity of the bacterial community and the retention of active ingredients are detected by biosensors or chemical analysis methods.

[0067] The obtained feedback data set is input into the mixed fermentation speed control model to evaluate the prediction error and performance indicators of the model, such as mean square error (MSE) or determination coefficient (R²). According to the evaluation results, the model optimization algorithm in machine learning is used to optimize and adjust the model, including adjusting model parameters, improving model structure or updating model algorithm. Exemplarily, for the mixed fermentation speed control model built based on neural network, the back propagation algorithm can be used for optimization. First, the feedback data set is divided into a training set and a test set. With the training set data as input, the error between the predicted output of the model and the actual feedback data (oxygen concentration feedback data and activity detection feedback data) is calculated. Then, the weights and parameters of the model are adjusted according to the error back propagation. For example, if there is a large deviation between the oxygen concentration predicted by the model and the actual oxygen concentration feedback data, the weights of the neural network layer related to the oxygen concentration prediction in the model will be adjusted through the back propagation algorithm to reduce this deviation. In the process of adjusting parameters, an optimizer, such as the Adam optimizer, can be used to adaptively adjust the learning rate and accelerate the convergence speed of the model. After completing the parameter adjustment, the optimized model is verified using the test set data to ensure that the performance of the model is improved rather than overfitting. Finally, the optimized mixed fermentation rate control model is output.

[0068] Through feedback optimization, the mixed fermentation speed control model can better adapt to the actual mixed fermentation process, improve the accuracy of predicting oxygen concentration, active ingredient retention rate, active ingredient degradation rate, etc., so as to more effectively guide the control operation in the mixed fermentation process and improve fermentation efficiency and product quality.

[0069] In summary, the fermentation speed control method for maintaining the natural activity of fruits and vegetables provided in the embodiments of the present application has the following technical effects: By obtaining a fruit and vegetable fermentation tank with multiple liquid inlets and mixers, an independent oxygen control environment is provided for different bacterial communities, ensuring that each bacterial community can grow and ferment under suitable oxygen conditions. Then, a variety of bacterial community types are collected, and oxygen adaptability analysis is performed to output multiple sets of hypoxia control parameters, so that the fermentation process can be personalized according to the specific needs of the bacterial community and the fermentation conditions are optimized. Then, a mixed fermentation speed control model is constructed, and the first fermentation control model is generated through oxygen adaptability data and cluster analysis algorithm, which further improves the accuracy and scientificity of oxygen control. In addition, by real-time monitoring of the bacterial community type and key feature data in the mixer and performing feature vector quantization processing, the model can accurately predict the retention rate and degradation rate of the active ingredient, thereby outputting the optimal mixed-hypoxia control parameters and realizing dynamic optimization of the fermentation process. Finally, by obtaining the adjusted feedback data set and performing feedback optimization on the model, the continuous improvement and stable operation of the fermentation process are ensured.

[0070] In general, the embodiments of the present application achieve dynamic optimization of fermentation conditions throughout the entire process from bacterial activation to mixed fermentation by performing staged, fine-tuned low oxygen regulation based on the oxygen demand characteristics of multiple bacterial communities. This not only ensures the independent activity of each bacterial community, but also achieves efficient and unified oxygen environment regulation in the mixed fermentation stage, thereby significantly increasing the fermentation speed, improving the quality of the fermented products, and successfully preserving the natural activity of fruits and vegetables.

[0071] Embodiment 2, as Figure 4 As shown, based on the same inventive concept as in Example 1, this embodiment of the present application provides a fermentation device for maintaining the natural activity of fruits and vegetables, the device comprising: The fermentation tank connection unit 10 is used to obtain a fruit and vegetable fermentation tank, which includes a plurality of liquid inlets and a mixer, wherein each liquid inlet corresponds to a hypoxia regulation submodule, and the mixer is provided with a hypoxia regulation main module.

[0072] The bacterial flora type collection unit 20 is used to collect the multiple bacterial flora types corresponding to the multiple liquid inlets for fermentation.

[0073] The oxygen adaptability analysis unit 30 is used to perform oxygen adaptability analysis according to the multiple bacterial colony types and output multiple groups of hypoxia control parameters, wherein each group of hypoxia control parameters includes oxygen concentration and delivery rate.

[0074] The hypoxia control unit 40 is used to control the multiple hypoxia control submodules to activate the bacterial colonies at the multiple liquid inlets according to the multiple groups of hypoxia control parameters, and the activated bacterial colonies flow into the mixer through the multiple liquid inlets.

[0075] The activity retention analysis unit 50 is used to construct a mixed fermentation speed control model, input the bacterial flora type in the mixer into the mixed fermentation speed control model to perform activity retention analysis, and output the mixed-hypoxia control parameters.

[0076] The oxygen regulating unit 60 is used to control the hypoxia regulating main module to regulate the oxygen in the mixer according to the mixing-hypoxia regulating parameters.

[0077] Furthermore, the oxygen adaptability analysis unit 30 of the embodiment of the present application is used to perform the following steps: Collect a flora sample library, identify oxygen adaptability data of each flora sample in the flora sample library, wherein the oxygen adaptability data includes an aerobic threshold, a hypoxic growth rate, and oxygen metabolic sensitivity; group the oxygen adaptability data by a cluster analysis algorithm, and output a flora type classification result; generate a first fermentation regulation model according to the flora type classification result; perform oxygen adaptability analysis on the multiple flora types according to the first fermentation regulation model, calculate the optimal oxygen concentration range and delivery rate of each flora, and output multiple groups of hypoxia regulation parameters.

[0078] Furthermore, the activity retention analysis unit 50 of the embodiment of the present application is used to perform the following steps: Acquire mixed flora sample data, wherein the mixed flora sample data includes data under a mixed fermentation environment, including changes in oxygen concentration, flora metabolic rate, and active ingredient retention rate; use the first fermentation regulation model as a pre-training model and output transfer learning parameters; determine an objective function, and perform migration training based on the objective function using the transfer learning parameters and the mixed flora sample data to construct a mixed fermentation speed regulation model.

[0079] Furthermore, the objective function includes active ingredient retention rate and active ingredient degradation rate, wherein the expression of the objective function includes: ;in, is the retention rate of the kth type of active ingredient in fruits and vegetables at time t, represents the degradation rate of the active ingredient at time t, represents the weight of the kth type of active ingredient, m is the number of active ingredients in fruits and vegetables, is the weight coefficient of active ingredient degradation, which is used to balance the relationship between active ingredient retention rate and degradation.

[0080] Furthermore, the expression for calculating the degradation rate of the active ingredient includes: ; Wherein, n is the total bacterial population of the mixer, is the metabolic sensitivity coefficient of bacterial colony i in the mixer, is the metabolic rate of bacterial colony i at time t, is the oxygen concentration in the mixer, is the optimal oxygen concentration for bacterial colony i.

[0081] Furthermore, the activity retention analysis unit 50 of the embodiment of the present application is also used to perform the following steps: Identify the type of bacterial flora in the mixer and extract key feature data of each bacterial flora; input the key feature data for feature vectorization processing and output a feature vector set; call the mixed fermentation speed control model to predict the active ingredient retention rate and active ingredient degradation rate of the mixer according to the feature vector set; output the mixing-hypoxia control parameters according to the relationship between the active ingredient retention rate and the active ingredient degradation rate.

[0082] Furthermore, the device described in the embodiment of the present application also includes a feedback optimization unit, and the feedback optimization unit is used to perform the following steps: Acquire a feedback data set of the adjusted mixer, wherein the feedback data set includes oxygen concentration feedback data and activity detection feedback data; perform feedback optimization on the mixed fermentation speed control model according to the feedback data set, and output the optimized mixed fermentation speed control model.

[0083] Through the above detailed description of a method for controlling the fermentation speed for maintaining the natural activity of fruits and vegetables, those skilled in the art can clearly understand the fermentation equipment for maintaining the natural activity of fruits and vegetables in this embodiment. For the equipment disclosed in Example 2, since it corresponds to the method disclosed in Example 1 and has corresponding functional units and beneficial effects, the relevant parts can be referred to the description of the method part.

[0084] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling the fermentation rate to maintain the natural activity of fruits and vegetables, characterized in that: The method comprises: Obtain a fruit and vegetable fermentation tank, the fruit and vegetable fermentation tank comprising a plurality of liquid inlets and a mixer, wherein each liquid inlet corresponds to a hypoxia regulation submodule, and the mixer is provided with a hypoxia regulation main module; Collect the multiple liquid inlets for fermenting the corresponding multiple bacterial colony types; Performing oxygen adaptability analysis according to the multiple bacterial colony types, and outputting multiple groups of hypoxia control parameters, wherein each group of hypoxia control parameters includes oxygen concentration and delivery rate; Controlling a plurality of hypoxia control submodules to activate the bacterial colonies at the plurality of liquid inlets according to the plurality of groups of hypoxia control parameters, so that the activated bacterial colonies flow into the mixer through the plurality of liquid inlets; Constructing a mixed fermentation speed control model, inputting the bacterial flora type in the mixer into the mixed fermentation speed control model for activity retention analysis, and outputting a mixed-hypoxia control parameter; The hypoxia control main module is controlled to perform oxygen regulation on the mixer according to the mixing-hypoxia control parameters.

2. A method for controlling the fermentation rate for maintaining the natural activity of fruits and vegetables as claimed in claim 1, characterized in that: Performing oxygen adaptability analysis according to the multiple bacterial colony types and outputting multiple groups of hypoxia control parameters, the method includes: Collecting a bacterial sample library, identifying oxygen adaptability data of each bacterial sample in the bacterial sample library, wherein the oxygen adaptability data includes aerobic threshold, hypoxic growth rate, and oxygen metabolic sensitivity; Grouping the oxygen adaptability data by a cluster analysis algorithm, and outputting a bacterial flora type classification result; generating a first fermentation regulation model according to the bacterial flora type classification result; According to the first fermentation regulation model, oxygen adaptability analysis is performed on the multiple bacterial community types respectively, the optimal oxygen concentration range and delivery rate of each bacterial community are calculated, and multiple groups of hypoxia regulation parameters are output.

3. A method for controlling the fermentation speed for maintaining the natural activity of fruits and vegetables as claimed in claim 2, characterized in that: Constructing a mixed fermentation speed control model, the method includes: Acquiring mixed bacterial flora sample data, wherein the mixed bacterial flora sample data includes data under a mixed fermentation environment, including oxygen concentration changes, bacterial flora metabolic rate, and active ingredient retention rate; Using the first fermentation regulation model as a pre-training model, outputting transfer learning parameters; An objective function is determined, and migration training is performed based on the objective function using the migration learning parameters and the mixed flora sample data to construct a mixed fermentation speed control model.

4. A method for controlling the fermentation rate for maintaining the natural activity of fruits and vegetables as claimed in claim 3, characterized in that: The objective function includes the active ingredient retention rate and the active ingredient degradation rate, wherein the expression of the objective function includes: ; in, is the retention rate of the kth type of active ingredient in fruits and vegetables at time t, represents the degradation rate of the active ingredient at time t, represents the weight of the kth type of active ingredient, is the amount of active ingredients in fruits and vegetables, is the weight coefficient of active ingredient degradation, which is used to balance the relationship between active ingredient retention rate and degradation.

5. A method for controlling the fermentation rate for maintaining the natural activity of fruits and vegetables as claimed in claim 4, characterized in that: The expression for calculating the degradation rate of the active ingredient includes: ； Wherein, n is the total bacterial population of the mixer, is the metabolic sensitivity coefficient of bacterial colony i in the mixer, is the metabolic rate of bacterial colony i at time t, is the oxygen concentration in the mixer, is the optimal oxygen concentration for bacterial colony i.

6. A method for controlling the fermentation rate for maintaining the natural activity of fruits and vegetables as claimed in claim 1, characterized in that: Inputting the bacterial flora type in the mixer into the mixed fermentation speed control model to perform activity retention analysis, the method comprising: Identify the type of bacterial colonies in the mixer and extract key characteristic data of each bacterial colony; Input the key feature data to perform feature vectorization processing, and output a feature vector set; Calling the mixed fermentation speed control model to predict the active ingredient retention rate and active ingredient degradation rate of the mixer according to the feature vector set; According to the relationship between the active ingredient retention rate and the active ingredient degradation rate, the mixed-hypoxia control parameter is output.

7. A method for controlling the fermentation rate for maintaining the natural activity of fruits and vegetables as claimed in claim 1, characterized in that: After controlling the hypoxia control main module to adjust the oxygen in the mixer according to the mixing-hypoxia control parameter, the method further includes: Acquire a feedback data set of the mixer after adjustment, wherein the feedback data set includes oxygen concentration feedback data and activity detection feedback data; Feedback optimization is performed on the mixed fermentation speed control model according to the feedback data set, and an optimized mixed fermentation speed control model is output.

8. A fermentation device for maintaining the natural activity of fruits and vegetables, characterized in that: The device is used to implement a fermentation speed control method for maintaining the natural activity of fruits and vegetables as described in any one of claims 1 to 7, comprising: A fermentation tank connection unit, used to obtain a fruit and vegetable fermentation tank, wherein the fruit and vegetable fermentation tank comprises a plurality of liquid inlets and a mixer, wherein each liquid inlet corresponds to a hypoxia regulation submodule, and the mixer is provided with a hypoxia regulation main module; A bacterial flora type collection unit, used for collecting the plurality of bacterial flora types corresponding to the plurality of liquid inlets for fermentation; An oxygen adaptability analysis unit, configured to perform oxygen adaptability analysis according to the plurality of bacterial colony types, and output a plurality of groups of hypoxia control parameters, wherein each group of hypoxia control parameters includes oxygen concentration and delivery rate; A hypoxia control unit, used for controlling the plurality of hypoxia control submodules to activate the bacterial colonies at the plurality of liquid inlets according to the plurality of groups of hypoxia control parameters, so that the activated bacterial colonies flow into the mixer through the plurality of liquid inlets; An activity retention analysis unit is used to construct a mixed fermentation speed control model, input the bacterial flora type in the mixer into the mixed fermentation speed control model to perform activity retention analysis, and output a mixed-hypoxia control parameter; The oxygen regulating unit is used to control the hypoxia regulating main module to regulate the oxygen in the mixer according to the mixing-hypoxia regulating parameters.

Images