Liquor brewing process simulation method and system

Abstract

The invention provides a Baijiu brewing process simulation method and system, and the method comprises the steps: constructing a metabolic network model of a flavor synthesis path in a Baijiu brewing process, initializing the boundary condition of the metabolic quantity in the metabolic network model, and carrying out the flux balance constraint of the metabolic path of a target flavor substance according to the boundary condition, obtaining the constraint flux of the metabolic pathway of the target flavor substance, and further calculating the metabolic flux distribution of the target flavor substance in the white spirit brewing process; determining a reaction rate constant of each process parameter combination according to the concentration gradient characteristic and the spatial distribution characteristic of the concentration change information of the target flavor substance; and performing metabolism simulation on the brewing reaction of the target flavor substance according to the metabolic flow distribution and each reaction rate constant, outputting the metabolic quantity of the target flavor substance under each process parameter combination, and further screening the brewing process parameters of the target flavor substance. Based on the scheme, multi-parameter metabolism simulation of a flavor substance synthesis path in a white spirit brewing process can be realized.

Description

Technical Field

[0001] The present application relates to the field of simulation technology, and more specifically, to a method and system for simulating the liquor brewing process. Background Art

[0002] At present, the liquor brewing process is in a stage of rapid development towards intelligence, greenness and efficiency. Intelligent brewing technology is constantly advancing, and liquor companies are transforming from traditional manual operations to mechanization, automation and intelligence, which has greatly improved production efficiency and product quality. At the same time, the concept of green development has been deeply rooted in the hearts of the people, focusing on energy conservation and emission reduction and resource recycling to promote sustainable development. In addition, modern technologies such as biotechnology and genetic engineering are also widely used in the front-end of brewing, further improving the stability and flavor of liquor quality.

[0003] During the liquor brewing process, differences in raw materials will lead to unstable initial metabolic substrate supply, affecting the succession and metabolic activity of microbial communities, and ultimately causing differences in the synthesis of key flavor substances such as esters and alcohols. At the same time, traditional solid-state fermentation relies on natural inoculation, and there are batch differences in the structure of the cellar mud microbial community. In particular, fluctuations in the proportion of functional bacteria will directly affect the synthesis efficiency of characteristic flavor components such as ethyl hexanoate, and parameter fluctuations in the distillation process will cause changes in the distillation ratio of flavor components with different boiling points, further amplifying the flavor differences of the final product. These factors together lead to deviations in the content of characteristic flavor substances between different production batches. Therefore, how to achieve multi-parameter metabolic simulation of the flavor substance synthesis pathway in the liquor brewing process, thereby improving the consistency of the target flavor substances in the liquor, has become a difficult problem faced by the industry. Summary of the Invention

[0004] The present application provides a liquor brewing process simulation method and system, which can realize multi-parameter metabolic simulation of the flavor substance synthesis pathway in the liquor brewing process.

[0005] In a first aspect, the present application provides a liquor brewing process simulation method, comprising: A metabolic network model of the flavor synthesis pathway in the liquor brewing process was constructed based on the known metabolic pathways of the core microorganisms of liquor fermentation; Initializing the boundary conditions of the metabolic amount in the metabolic network model by the process parameters of the liquor brewing process, performing flux balance constraints on the metabolic pathway of the target flavor substance in the liquor brewing process according to the boundary conditions of the metabolic amount, obtaining the constrained flux of the metabolic pathway of the target flavor substance, and then calculating the metabolic flux distribution of the target flavor substance in the liquor brewing process based on the constrained flux of the metabolic pathway; Using the metabolic network model to simulate concentration variation information of target flavor substances under different process parameter combinations, and determining the reaction rate constant of the target flavor substances under each process parameter combination based on the concentration gradient characteristics and spatial distribution characteristics of the flavor substances in the concentration variation information; The brewing reaction of the target flavor substance in the metabolic network model is metabolically simulated according to the metabolic flux distribution and each reaction rate constant, thereby screening out the brewing process parameters of the target flavor substance.

[0006] In some embodiments, the metabolic pathway of the target flavor substance in the liquor brewing process is subjected to flux balance constraints based on the boundary conditions of the metabolic amount, and the constrained flux of the metabolic pathway of the target flavor substance is obtained, specifically comprising: Obtaining a stoichiometric matrix of metabolite chemical reactions in liquor brewing from the metabolic network model; Using the boundary conditions of the metabolic amount in the metabolic network model as flux constraints in the stoichiometric matrix; The stoichiometric matrix with set flux constraints was used to perform flux balance on the metabolic pathway of the target flavor substance in the liquor brewing process, and the constrained flux of the metabolic pathway of the target flavor substance was obtained.

[0007] In some embodiments, calculating the metabolic flux distribution of the target flavor substance in the liquor brewing process based on the constrained flux of the metabolic pathway specifically includes: Obtain multiple key output nodes of the synthesis pathway of the target flavor substance in the liquor brewing process, and then determine the feasible range of reaction flux in each key output node; Determining the metabolic flux value of each reaction flux in the synthesis pathway of the target flavor substance by the constraint flux of the metabolic pathway and the feasible range of each reaction flux; All metabolic flux values ​​were visualized as the metabolic flux distribution of target flavor substances in the liquor brewing process.

[0008] In some embodiments, determining the reaction rate constant of the target flavor substance under various process parameter combinations based on the concentration gradient characteristics and spatial distribution characteristics of the flavor substance in the concentration change information specifically includes: For each process parameter combination, extracting the maximum concentration gradient and the average concentration gradient under the process parameter combination from the concentration change information as the concentration gradient characteristics of the flavor substance; Establishing a kinetic model of the target flavor substance under the process parameter combination based on the spatial distribution characteristics of the flavor substance in the concentration change information; The maximum concentration gradient and the average concentration gradient are subjected to reaction constraints by the kinetic model to obtain the reaction rate constant of the target flavor substance under the process parameter combination, and further obtain the reaction rate constant of the target flavor substance under each process parameter combination.

[0009] In some embodiments, metabolic simulation of the brewing reaction of the target flavor substance in the metabolic network model is performed based on the metabolic flux distribution and each reaction rate constant, and the brewing process parameters of the target flavor substance are screened out, specifically including: For each process parameter combination, obtaining the reaction rate constant of the target flavor substance under each process parameter combination; Performing a kinetic simulation of the brewing reaction of the target flavor substance using the reaction rate constant and the metabolic flux distribution to obtain the metabolic amount of the output target flavor substance under the process parameter combination, and further obtaining the metabolic amount of the output target flavor substance under each process parameter combination; The brewing process parameters of the target flavor substances are screened out through the metabolic amounts of each brewing process.

[0010] In some embodiments, the core microorganisms are caproic acid bacteria, ester-producing yeast and pit mud functional bacteria.

[0011] In some embodiments, the process parameters include temperature, pH, water activity, and brewing raw material ratio.

[0012] In a second aspect, the present application provides a liquor brewing process simulation system, comprising: Building modules for constructing metabolic network models of flavor synthesis pathways in the liquor brewing process based on known metabolic pathways of core liquor fermentation microorganisms; a processing module, configured to initialize the boundary conditions of the metabolic amount in the metabolic network model using the process parameters of the liquor brewing process, perform flux balance constraints on the metabolic pathway of the target flavor substance in the liquor brewing process based on the boundary conditions of the metabolic amount, obtain the constrained flux of the metabolic pathway of the target flavor substance, and further calculate the metabolic flux distribution of the target flavor substance in the liquor brewing process based on the constrained flux of the metabolic pathway; The processing module is further configured to use the metabolic network model to simulate concentration variation information of the target flavor substance under different process parameter combinations, and determine the reaction rate constant of the target flavor substance under each process parameter combination based on the concentration gradient characteristics and spatial distribution characteristics of the flavor substance in the concentration variation information; An execution module is used to perform metabolic simulation on the brewing reaction of the target flavor substance in the metabolic network model according to the metabolic flux distribution and each reaction rate constant, and then screen out the brewing process parameters of the target flavor substance.

[0013] In a third aspect, the present application provides a computer device, which includes a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned liquor brewing process simulation method.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned liquor brewing process simulation method when executing the computer.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The present application provides a method and system for simulating the liquor brewing process, which constructs a metabolic network model of the flavor synthesis pathway in the liquor brewing process based on the known metabolic pathway of the core microorganisms of liquor fermentation; initializes the boundary conditions of the metabolic amount in the metabolic network model through the process parameters of the liquor brewing process, and performs flux balance constraints on the metabolic pathway of the target flavor substance in the liquor brewing process according to the boundary conditions of the metabolic amount to obtain the constrained flux of the metabolic pathway of the target flavor substance, and then calculates the metabolic flux distribution of the target flavor substance in the liquor brewing process based on the constrained flux of the metabolic pathway; uses the metabolic network model to simulate the concentration change information of the target flavor substance under different process parameter combinations, and determines the reaction rate constant of the target flavor substance under each process parameter combination through the concentration gradient characteristics and spatial distribution characteristics of the flavor substance in the concentration change information; metabolically simulates the brewing reaction of the target flavor substance in the metabolic network model according to the metabolic flux distribution and each reaction rate constant, and then screens out the brewing process parameters of the target flavor substance.

[0016] It can be seen that in the present application, metabolic simulation of the brewing reaction of the target flavor substance in the metabolic network model is carried out according to the metabolic flux distribution and each reaction rate constant, and then the brewing process parameters of the target flavor substance are screened out; first, the metabolic flux distribution can be determined to obtain the flux distribution of each reaction node on the synthesis path of the target flavor substance in the liquor brewing process. Based on the metabolic flux distribution map, the process parameters such as temperature and pH can be adjusted in a targeted manner to optimize the flux of the key enzyme reaction, and the resource allocation of the competing pathway can be balanced by regulating the raw material ratio. The precise control method based on the metabolic flux distribution can solve the blindness problem of the traditional empirical adjustment, so that different batches of production can stably maintain the optimal metabolic flux distribution pattern, thereby significantly improving the batch consistency of the target flavor substance; then, the reaction rate constant can be determined to obtain the reaction rate constant reflecting the specific Quantitative parameters of the intrinsic characteristics of enzymatic reactions under process conditions are obtained, thereby establishing a quantitative relationship between process parameters and microbial metabolic activity. This quantitative relationship can convert abstract process conditions into calculable metabolic activity parameters, thereby being able to predict the change in the synthesis rate of the target substance when a certain process parameter is adjusted, and thus intelligently recommend the optimal parameter combination. At the same time, by real-time monitoring of changes in metabolite concentrations during the fermentation process, the process parameters can be dynamically adjusted to maintain the ideal reaction rate, overcome the interference caused by raw material fluctuations and environmental changes, and ensure the consistency of microbial metabolic activity in different batches of production, thereby ensuring the stability of the target flavor substance content in the final product. In summary, based on the above scheme, multi-parameter metabolic simulation of the flavor substance synthesis pathway in the liquor brewing process can be realized, thereby improving the consistency of the target flavor substances in the liquor. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 is an exemplary flow chart of a liquor brewing process simulation method according to some embodiments of the present application; Figure 2 is a schematic flow chart of the winemaking process according to some embodiments of the present application; Figure 3 is a schematic diagram of a process for implementing metabolic simulation according to some embodiments of the present application; Figure 4 is a schematic structural diagram of a liquor brewing process simulation system according to some embodiments of the present application; Figure 5It is a structural diagram of a computer device for implementing a liquor brewing process simulation method according to some embodiments of the present application. DETAILED DESCRIPTION

[0019] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0020] refer to Figure 1 , which is an exemplary flow chart of a liquor brewing process simulation method according to some embodiments of the present application, and the liquor brewing process simulation method mainly includes the following steps: In step 101, a metabolic network model of the flavor synthesis pathway in the liquor brewing process is constructed based on the known metabolic pathways of the core microorganisms of liquor fermentation.

[0021] It should be noted that in this application, the metabolic network model is a chemical reaction system expressed in the form of a mathematical matrix. The nodes of the metabolic network model are metabolites and the edges are enzyme-catalyzed reactions. The metabolic network model can not only completely cover the entire process of chemical reactions from sorghum starch degradation to final product distillation, but also quantify the deviation between the theoretical yield of key flavor substances and the actual fermentation; the core microorganisms are caproic acid bacteria, ester-producing yeast and cellar mud functional bacteria.

[0022] In the specific implementation, first, the complete gene sequence of the core microorganisms is obtained through microbial genome sequencing technology, and its functional genes are annotated using bioinformatics tools, with a focus on identifying key enzyme genes related to flavor metabolism such as glycolysis and ester synthesis; secondly, the enzymatic reaction data in the authoritative database are integrated to establish a complete reaction chain from the decomposition of raw materials to the generation of flavor substances, and the synthesis pathways of characteristic products such as ethyl hexanoate and ethyl lactate are specially marked; finally, based on literature mining and experimental verification data, the special metabolic pathways of the cellar mud microbial community are supplemented, and finally a multi-dimensional metabolic network including carbon and nitrogen flow, energy balance and flavor substance secretion is formed as a metabolic network model of the flavor synthesis pathway in the liquor brewing process.

[0023] In some embodiments, reference Figure 2 As described, the figure is a flow chart of the winemaking process shown in some embodiments of the present application. First, the raw materials are crushed and then mixed with water and steamed rice husks to prepare the ingredients for the wine cellar; then, the ingredients undergo a saccharification and fermentation process to produce mash; the mash then enters the distillation stage to separate the wine and lees; after the wine is graded and stored, part of the wine returns to the cellar for further fermentation, while the lees are discarded; in another path, the block koji is crushed and added to the cellar process, mixed with the mash for adding koji and stirring, and then discharged and cooled. The entire process reflects the winemaking process from raw material processing to the production of finished wine, as well as the treatment of by-products.

[0024] In step 102, the boundary conditions of the metabolic amount in the metabolic network model are initialized by the process parameters of the liquor brewing process, and the flux balance constraints are performed on the metabolic pathways of the target flavor substances in the liquor brewing process based on the boundary conditions of the metabolic amount to obtain the constrained flux of the metabolic pathways of the target flavor substances, and then the metabolic flux distribution of the target flavor substances in the liquor brewing process is calculated based on the constrained flux of the metabolic pathways.

[0025] In some embodiments, initializing the boundary conditions of the metabolic amount in the metabolic network model through the process parameters of the liquor brewing process can be achieved in the following manner, namely: collecting the process parameters in the liquor brewing process, the process parameters include temperature, pH, water activity and brewing raw material ratio, converting all process parameters into boundary conditions that can be recognized by the metabolic network, for example: converting the temperature parameter into the metabolic enzyme activity coefficient within the optimal growth temperature range of each bacterial species, converting the raw material ratio into the input flux upper limit of the carbon source and nitrogen source, combining the physiological characteristics of the microorganisms (for example: the ethanol tolerance threshold of yeast) and the characteristics of the fermentation container (for example: the oxygen permeability of the cellar), and dynamically adjusting the boundary conditions to ensure that they are consistent with the actual production process and can accurately reflect the physical and chemical limitations of microbial metabolism, thereby completing the initialization of the boundary conditions in the metabolic network model.

[0026] In some embodiments, the metabolic pathway of the target flavor substance in the liquor brewing process is subjected to flux balance constraints based on the boundary conditions of the metabolic amount, and the constrained flux of the metabolic pathway of the target flavor substance is obtained by the following steps: Obtaining a stoichiometric matrix of metabolite chemical reactions in liquor brewing from the metabolic network model; Using the boundary conditions of the metabolic amount in the metabolic network model as flux constraints in the stoichiometric matrix; The stoichiometric matrix with set flux constraints was used to perform flux balance on the metabolic pathway of the target flavor substance in the liquor brewing process, and the constrained flux of the metabolic pathway of the target flavor substance was obtained.

[0027] It should be noted that in this application, the constrained flux is the actual flux value of the metabolic reaction of the target flavor substance under given boundary conditions. The constrained flux reflects the activity intensity and regulatory characteristics of each link in the synthesis pathway of the target substance; the stoichiometric matrix is ​​a mathematical matrix used to quantitatively describe the equilibrium relationship of all chemical reaction substances in the metabolic network. The element values ​​in the stoichiometric matrix represent the participation coefficients of each metabolite in the reaction; the flux constraint is used to define the limiting range of the stoichiometric matrix for solving the metabolic reaction rate.

[0028] In specific implementation, first, the stoichiometric matrix of the chemical reaction of metabolites in liquor brewing is obtained from the metabolic network model, which can be achieved in the following way, namely: based on the constructed metabolic network model of liquor brewing, all the metabolites involved and the biochemical reactions they participate in are sorted out, and according to the principle that the reactant coefficient is negative and the product coefficient is positive, a complete stoichiometric matrix is ​​established. The rows of the matrix correspond to various metabolites, and the columns correspond to specific enzyme-catalyzed reactions, which fully show the quantitative relationship of material transformation. The stoichiometric matrix of the quantitative relationship is used as the stoichiometric matrix of the chemical reaction of metabolites in liquor brewing; then, the boundary of the metabolic amount in the metabolic network model is used. The conditions are used as flux constraints in the stoichiometric matrix; finally, the stoichiometric matrix with set flux constraints is used to perform flux balance on the metabolic pathway of the target flavor substance in the liquor brewing process, and the constrained flux of the metabolic pathway of the target flavor substance can be obtained in the following way, namely: the metabolic pathway of the target flavor substance in the liquor brewing process is obtained from the console of the liquor brewing platform, and a linear programming method is used to maximize the target metabolite yield and minimize the by-product under the constraints to perform linear balance on the theoretical flux values ​​of each reaction node in the metabolic pathway, and the result of the linear balance is used as the constrained flux of the metabolic pathway of the target flavor substance.

[0029] It should be noted that flux balance is based on the stoichiometric relationship and mass conservation law of the metabolic network. The metabolic flux distribution is predicted by establishing a linear programming model. This method converts each reaction in the metabolic pathway into a column vector of the stoichiometric matrix, and takes the maximization of the yield of the target flavor substance and the minimization of the generation of by-products as the optimization objective function. At the same time, the flux constraints of the process parameter conversion are used as linear constraints. By solving the linear programming problem, the optimal flux distribution scheme for each reaction node under given process conditions can be obtained, thereby determining the constrained flux distribution of the target metabolic pathway, which can effectively predict the steady-state behavior of the metabolic network without involving complex kinetic parameters.

[0030] In some embodiments, the calculation of the metabolic flux distribution of the target flavor substance in the liquor brewing process based on the constrained flux of the metabolic pathway can be achieved by the following steps: Obtain multiple key output nodes of the synthesis pathway of the target flavor substance in the liquor brewing process, and then determine the feasible range of reaction flux in each key output node; Determining the metabolic flux value of each reaction flux in the synthesis pathway of the target flavor substance by the constraint flux of the metabolic pathway and the feasible range of each reaction flux; All metabolic flux values ​​were visualized as the metabolic flux distribution of target flavor substances in the liquor brewing process.

[0031] In the specific implementation, first, multiple output key nodes of the target flavor substance synthesis path in the liquor brewing process are obtained, and then the feasible range of the reaction flux in each output key node is determined. This can be achieved in the following way, namely: multiple output key nodes of the target flavor substance synthesis path in the liquor brewing process are obtained from the console of the liquor brewing platform, and for each output key node, all reaction fluxes of the output key node in the historical time period (the default is the most recent month) are obtained, thereby screening out the minimum and maximum values ​​from all reaction fluxes, and taking the value interval between the minimum and maximum values ​​as the feasible range of the reaction flux. The feasible range of the reaction flux in each output key node can be obtained by the above method; then, the metabolic flux value of each reaction flux in the target flavor substance synthesis path is determined by the constraint flux of the metabolic pathway and the feasible range of each reaction flux. This is achieved in the following way: for each reaction flux in the synthesis pathway of the target flavor substance, an energy balance model based on linear programming is initialized, the constraint flux of the metabolic pathway is used as the constraint condition in the energy balance model, the feasible range of the reaction flux is used as the variable limit in the energy balance model, the reaction flux is evaluated by flux combination using the energy balance model, and the result of the flux combination evaluation is used as the metabolic flux value of the reaction flux. The metabolic flux value of each reaction flux in the synthesis pathway of the target flavor substance can be obtained by the above method; finally, all metabolic flux values ​​are visualized as the metabolic flux distribution of the target flavor substance in the liquor brewing process. This can be achieved in the following way: each reaction flux value is visualized through a heat map, and the flux size is represented by the depth of color or the thickness of the arrow, so that the metabolic flux distribution of the target flavor substance in the liquor brewing process can be obtained.

[0032] It should be noted that in this application, the metabolic flux distribution represents the overall pattern of the magnitude and direction of each reaction flux in a metabolic network presented in a graphical manner; the feasible range refers to the minimum and maximum value range that each reaction flux in a metabolic pathway can reach while satisfying all constraints, reflecting the activity regulation potential of the reaction; the metabolic flux value represents the actual substance conversion rate of a specific metabolic reaction under optimized conditions, and the metabolic flux value can quantify metabolic activity; the energy balance model is a metabolic network analysis tool based on the linear programming method. By establishing a set of mathematical equations reflecting the balance between metabolite production and consumption, the optimal flux distribution is solved under given constraints. The energy balance model uses the constrained flux of the metabolic pathway as an equality constraint to ensure conservation of matter; the feasible range of the reaction flux is used as the upper and lower bounds of the variable to limit the scope of the solution space; by setting an objective function (such as maximizing the target product flux) for linear optimization, the reasonable combination of each reaction flux is evaluated. The energy balance model transforms the complex biological metabolic network into a computable mathematical problem, explains the regulation characteristics of key reaction nodes by quantifying the metabolic flux distribution, and provides a theoretical basis for process optimization, thereby integrating stoichiometric constraints with physiological constraints to achieve a system balance analysis of metabolic flux.

[0033] In step 103, the metabolic network model is used to simulate the concentration variation information of the target flavor substance under different process parameter combinations, and the reaction rate constant of the target flavor substance under each process parameter combination is determined based on the concentration gradient characteristics and spatial distribution characteristics of the flavor substance in the concentration variation information.

[0034] In some embodiments, using the metabolic network model to simulate the concentration change information of the target flavor substance under different process parameter combinations can be achieved in the following manner, namely: setting multiple process parameter combinations of the target flavor substance through historical experience of a historical process parameter set, for each process parameter combination, inputting the process parameters into the metabolic network model, continuously solving the metabolic network model within a set time step (for example, one calculation cycle every 6 hours), tracking the concentration change trajectory of the target flavor substance and its precursor metabolites, and recording the accumulation of by-products (for example, higher alcohols) as the concentration change record of the target flavor substance under the process parameter combination. In this way, the concentration change record of the target flavor substance under each process parameter combination can be obtained, and the collection of all concentration change records can be used as the concentration change information of the target flavor substance.

[0035] It should be noted that, in this application, concentration change information is quantitative data reflecting the changes of target flavor substances and their related metabolites over time during the fermentation process; process parameter combination refers to the coordination of different values ​​of multiple process variables in the simulation system during the simulation process, and this process parameter combination can be used to explore the synergistic or antagonistic effects between different process parameters.

[0036] In some embodiments, determining the reaction rate constant of the target flavor substance under various process parameter combinations based on the concentration gradient characteristics and spatial distribution characteristics of the flavor substance in the concentration change information can be achieved by the following steps: For each process parameter combination, extracting the maximum concentration gradient and the average concentration gradient under the process parameter combination from the concentration change information as the concentration gradient characteristics of the flavor substance; Establishing a kinetic model of the target flavor substance under the process parameter combination based on the spatial distribution characteristics of the flavor substance in the concentration change information; The maximum concentration gradient and the average concentration gradient are subjected to reaction constraints by the kinetic model to obtain the reaction rate constant of the target flavor substance under the process parameter combination, and further obtain the reaction rate constant of the target flavor substance under each process parameter combination.

[0037] In specific implementation, first, for each process parameter combination, the maximum concentration gradient and the average concentration gradient under the process parameter combination are extracted from the concentration change information as the concentration gradient characteristics of the flavor substance. This can be achieved in the following manner, namely: for each process parameter combination, the concentration change record of the target flavor substance is obtained from the concentration change information, and the difference in concentration values ​​between adjacent time steps in the concentration change record is calculated as the concentration gradient, so that all concentration gradients of the target flavor substance can be obtained, thereby taking the maximum value of all concentration gradients as the maximum concentration gradient under the process parameter combination, and taking the average value of all concentration gradients as the average concentration gradient under the process parameter combination; then, based on the spatial distribution characteristics of the flavor substance in the concentration change information, a kinetic model of the target flavor substance under the process parameter combination is established. This can be achieved in the following manner, namely: based on the basic principle of enzymatic reaction, an extended Michaelis-Menten kinetic model including substrate concentration, product inhibition effect and environmental parameter correction factor is constructed. A mechanical equation is constructed, and the parameter values ​​of each process parameter are used as model input variables to establish a kinetic model framework that can reflect the influence of multi-parameter coupling as a kinetic model of the target flavor substance under the process parameter combination; finally, the maximum concentration gradient and the average concentration gradient are subjected to reaction constraints by the kinetic model to obtain the reaction rate constant of the target flavor substance under the process parameter combination, and then the reaction rate constant of the target flavor substance under each process parameter combination can be obtained in the following manner, namely: the maximum concentration gradient is used as the dynamic response coefficient in the kinetic model, the average concentration gradient is used as the steady-state equilibrium factor in the kinetic model, the reaction rate of the target flavor substance under the process parameter combination is optimized and solved using the kinetic model, and the result of the optimization solution is used as the reaction rate constant of the target flavor substance under the process parameter combination. The reaction rate constant of the target flavor substance under each process parameter combination can be obtained in the above manner.

[0038] It should be noted that, in the present application, the reaction rate constant is a quantitative parameter reflecting the intrinsic characteristics of the enzymatic reaction under specific process conditions; the maximum concentration gradient represents the instantaneous maximum rate of change of the concentration of the target flavor substance over time, and the maximum concentration gradient can reflect the peak metabolic activity of the synthesis or consumption of the substance; the average concentration gradient represents the average rate of change of the concentration of the target flavor substance over time, and the average concentration gradient can reflect the overall metabolic efficiency of the synthesis of the substance; the kinetic model is a mathematical model that describes the quantitative relationship between the synthesis rate of the target substance and the substrate concentration and environmental parameters. By integrating the substrate binding characteristics of the classical Michaelis-Menten equation, the product feedback inhibition mechanism, and the correction functions of environmental parameters such as temperature / pH, a multivariable coupled differential equation system is established. The kinetic model uses process parameters as input variables, captures the instantaneous metabolic peak characteristics through dynamic response coefficients, and uses steady-state equilibrium factors to reflect the continuous reaction state. Finally, a rate constant with clear physiological significance is obtained by solving the model through a nonlinear optimization algorithm. It not only retains the theoretical basis of enzyme kinetics, but also accurately reflects the complex characteristics of the synergistic effect of multiple parameters in the actual fermentation system.

[0039] In step 104, metabolic simulation is performed on the brewing reaction of the target flavor substance in the metabolic network model according to the metabolic flux distribution and each reaction rate constant, thereby screening out brewing process parameters of the target flavor substance.

[0040] In some embodiments, the brewing reaction of the target flavor substance in the metabolic network model is metabolically simulated according to the metabolic flux distribution and each reaction rate constant, and then the brewing process parameters of the target flavor substance are screened out. Figure 3 The figure is a schematic diagram of the process of implementing metabolic simulation in some embodiments of the present application. In this embodiment, metabolic simulation can be implemented by the following steps: In step 1041, for each process parameter combination, the reaction rate constant of the target flavor substance under each process parameter combination is obtained; In step 1042, a kinetic simulation is performed on the brewing reaction of the target flavor substance using the reaction rate constant and the metabolic flux distribution to obtain the metabolic amount of the output target flavor substance under the process parameter combination, and further obtain the metabolic amount of the output target flavor substance under each process parameter combination; In step 1043, brewing process parameters of the target flavor substance are screened out through the metabolic amounts of each brewing process.

[0041] It should be noted that, in the present application, brewing process parameters refer to the brewing conditions set in the process of liquor brewing in order to control the target flavor substances; in specific implementation, first, for each process parameter combination, the reaction rate constant of the target flavor substance under each process parameter combination is obtained; then, the brewing reaction of the target flavor substance is kinetically simulated by the reaction rate constant and the metabolic flux distribution, and the metabolic amount of the output target flavor substance under the process parameter combination is obtained, and then the metabolic amount of the output target flavor substance under each process parameter combination is obtained. This can be achieved in the following way, namely: constructing a simulation environment for the brewing reaction of the target flavor substance, taking the reaction rate constant as the kinetic parameter of the target flavor substance in the simulation environment, and taking the metabolic flux distribution as the target flavor substance in the simulation environment. The initial metabolic conditions of the substance are determined, and the simulation environment is used to simulate the metabolic amount of the target flavor substance in the brewing reaction, so that the simulation result is used as the output of the metabolic amount of the target flavor substance under the process parameter combination. The metabolic amount of the target flavor substance under each process parameter combination can be obtained in the above manner. This metabolic amount is a set of core indicators for quantifying the synthesis efficiency of the target flavor substance; finally, the brewing process parameters of the target flavor substance are screened out through the metabolic amount of each brewing process, which can be achieved in the following way, namely: obtaining the standard metabolic amount of the target flavor substance from the standard library of liquor brewing, and then calculating the absolute value of the difference between the metabolic amount of each brewing process and the standard metabolic amount as the metabolic difference value, and taking the process parameters corresponding to the brewing process with the smallest metabolic difference value as the brewing process parameters of the target flavor substance.

[0042] In addition, in another aspect of the present application, in some embodiments, the present application provides a liquor brewing process simulation system, referring to Figure 4 , which is a schematic diagram of the structure of a liquor brewing process simulation system according to some embodiments of the present application. The liquor brewing process simulation system includes: a construction module 201, a processing module 202 and an execution module 203, which are described as follows: Construction module 201, in this application, construction module 201 is mainly used to construct a metabolic network model of the flavor synthesis pathway in the liquor brewing process based on the known metabolic pathways of the core microorganisms of liquor fermentation; Processing module 202, in the present application, is used to initialize the boundary conditions of the metabolic amount in the metabolic network model using the process parameters of the liquor brewing process, perform flux balance constraints on the metabolic pathway of the target flavor substance in the liquor brewing process based on the boundary conditions of the metabolic amount, obtain the constrained flux of the metabolic pathway of the target flavor substance, and then calculate the metabolic flux distribution of the target flavor substance in the liquor brewing process based on the constrained flux of the metabolic pathway; It should be noted that the processing module 202 is further configured to use the metabolic network model to simulate concentration variation information of the target flavor substance under different process parameter combinations, and determine the reaction rate constant of the target flavor substance under each process parameter combination based on the concentration gradient characteristics and spatial distribution characteristics of the flavor substance in the concentration variation information; Execution module 203, in this application, execution module 203 is mainly used to perform metabolic simulation on the brewing reaction of the target flavor substance in the metabolic network model according to the metabolic flux distribution and each reaction rate constant, and then screen out the brewing process parameters of the target flavor substance.

[0043] The above describes in detail the examples of the liquor brewing process simulation method and system provided by the embodiments of the present application. It can be understood that, in order to realize the above functions, the corresponding device includes hardware structures and / or software modules corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0044] In some embodiments, the present application also provides a computer device, which includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned white wine brewing process simulation method.

[0045] In some embodiments, reference Figure 5 The dotted line in the figure indicates that the unit or module is optional. The figure is a structural diagram of a computer device for implementing a liquor brewing process simulation method according to an embodiment of the present application. The liquor brewing process simulation method described in the above embodiment can be Figure 5 The computer device shown in the figure is implemented, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device can be a terminal device, a server or a chip.

[0046] The processor 301 may be a general-purpose processor or a dedicated processor. For example, the processor 301 may be a central processing unit (CPU), which may be used to control the computer device, execute software programs, and process data from the software programs. The computer device may also include a communication unit 305 for inputting (receiving) and outputting (transmitting) signals.

[0047] For example, the computer device may be a chip, the communication unit 305 may be an input and / or output circuit of the chip, or the communication unit 305 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other device.

[0048] For another example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0049] The computer device may include one or more memories 302, on which a program 304 is stored. The program 304 can be executed by the processor 301 to generate instructions 303, so that the processor 301 executes the method described in the above method embodiment according to the instructions 303. Optionally, data (such as a target audit model) can also be stored in the memory 302. Optionally, the processor 301 can also read data stored in the memory 302. The data can be stored at the same storage address as the program 304, or at a different storage address from the program 304.

[0050] The processor 301 and the memory 302 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of a terminal device.

[0051] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0052] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] For example, in some embodiments, the present application also provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned liquor brewing process simulation method when executing.

[0054] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0055] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A liquor brewing process simulation method, characterized in that: The steps include: A metabolic network model of the flavor synthesis pathway in the liquor brewing process was constructed based on the known metabolic pathways of the core microorganisms of liquor fermentation; Initializing the boundary conditions of the metabolic amount in the metabolic network model by the process parameters of the liquor brewing process, performing flux balance constraints on the metabolic pathway of the target flavor substance in the liquor brewing process according to the boundary conditions of the metabolic amount, obtaining the constrained flux of the metabolic pathway of the target flavor substance, and then calculating the metabolic flux distribution of the target flavor substance in the liquor brewing process based on the constrained flux of the metabolic pathway; Using the metabolic network model to simulate concentration variation information of target flavor substances under different process parameter combinations, and determining the reaction rate constant of the target flavor substances under each process parameter combination based on the concentration gradient characteristics and spatial distribution characteristics of the flavor substances in the concentration variation information; The brewing reaction of the target flavor substance in the metabolic network model is metabolically simulated according to the metabolic flux distribution and each reaction rate constant, thereby screening out the brewing process parameters of the target flavor substance.

2. The method according to claim 1, wherein According to the boundary conditions of the metabolic amount, the metabolic pathway of the target flavor substance in the liquor brewing process is subjected to flux balance constraints, and the constrained flux of the metabolic pathway of the target flavor substance is obtained, which specifically includes: Obtaining a stoichiometric matrix of metabolite chemical reactions in liquor brewing from the metabolic network model; Using the boundary conditions of the metabolic amount in the metabolic network model as flux constraints in the stoichiometric matrix; The stoichiometric matrix with set flux constraints was used to perform flux balance on the metabolic pathway of the target flavor substance in the liquor brewing process, and the constrained flux of the metabolic pathway of the target flavor substance was obtained.

3. The method according to claim 1, wherein The calculation of the metabolic flux distribution of the target flavor substance in the liquor brewing process based on the constrained flux of the metabolic pathway specifically includes: Obtain multiple key output nodes of the synthesis pathway of the target flavor substance in the liquor brewing process, and then determine the feasible range of reaction flux in each key output node; Determining the metabolic flux value of each reaction flux in the synthesis pathway of the target flavor substance by the constraint flux of the metabolic pathway and the feasible range of each reaction flux; All metabolic flux values ​​were visualized as the metabolic flux distribution of target flavor substances in the liquor brewing process.

4. The method according to claim 1, wherein Determining the reaction rate constant of the target flavor substance under various process parameter combinations based on the concentration gradient characteristics and spatial distribution characteristics of the flavor substance in the concentration change information specifically includes: For each process parameter combination, extracting the maximum concentration gradient and the average concentration gradient under the process parameter combination from the concentration change information as the concentration gradient characteristics of the flavor substance; Establishing a kinetic model of the target flavor substance under the process parameter combination based on the spatial distribution characteristics of the flavor substance in the concentration change information; The maximum concentration gradient and the average concentration gradient are subjected to reaction constraints by the kinetic model to obtain the reaction rate constant of the target flavor substance under the process parameter combination, and further obtain the reaction rate constant of the target flavor substance under each process parameter combination.

5. The method according to claim 1, wherein The brewing process parameters of the target flavor substance are screened out by performing metabolic simulation on the brewing reaction of the target flavor substance in the metabolic network model according to the metabolic flux distribution and each reaction rate constant, specifically including: For each process parameter combination, obtaining the reaction rate constant of the target flavor substance under each process parameter combination; Performing a kinetic simulation of the brewing reaction of the target flavor substance using the reaction rate constant and the metabolic flux distribution to obtain the metabolic amount of the output target flavor substance under the process parameter combination, and further obtaining the metabolic amount of the output target flavor substance under each process parameter combination; The brewing process parameters of the target flavor substances are screened out through the metabolic amounts of each brewing process.

6. The method according to claim 1, wherein The core microorganisms are caproic acid bacteria, ester-producing yeast and pit mud functional bacteria.

7. The method according to claim 1, wherein The process parameters include temperature, pH, water activity and brewing raw material ratio.

8. A liquor brewing process simulation system, characterized in that: include: Building modules for constructing metabolic network models of flavor synthesis pathways in the liquor brewing process based on known metabolic pathways of core liquor fermentation microorganisms; a processing module, configured to initialize the boundary conditions of the metabolic amount in the metabolic network model using the process parameters of the liquor brewing process, perform flux balance constraints on the metabolic pathway of the target flavor substance in the liquor brewing process based on the boundary conditions of the metabolic amount, obtain the constrained flux of the metabolic pathway of the target flavor substance, and further calculate the metabolic flux distribution of the target flavor substance in the liquor brewing process based on the constrained flux of the metabolic pathway; The processing module is further configured to use the metabolic network model to simulate concentration variation information of the target flavor substance under different process parameter combinations, and determine the reaction rate constant of the target flavor substance under each process parameter combination based on the concentration gradient characteristics and spatial distribution characteristics of the flavor substance in the concentration variation information; An execution module is used to perform metabolic simulation on the brewing reaction of the target flavor substance in the metabolic network model according to the metabolic flux distribution and each reaction rate constant, and then screen out the brewing process parameters of the target flavor substance.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the liquor brewing process simulation method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions or codes, and when the instructions or codes are executed on a computer, the computer implements the liquor brewing process simulation method as described in any one of claims 1 to 7.

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