Dynamic fermentation tank control system for the production of diabetic intestinal microecological regulation preparations

Abstract

The present invention relates to the field of biomedicine technology, and in particular to a dynamic fermentation tank control system for the production of diabetic intestinal microecological regulation preparations. The system includes a fermentation tank sensor feedback control network establishment module, a fermentation tank growth early stage nutrient supply control module, a fermentation tank growth late stage regulation control module, and a fermentation tank metabolic process environmental condition control module. An embedded sensor feedback control network can be generated by installing sensors and connecting to a central feedback control system, and synchronously monitoring the sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and oxygen and carbon dioxide gas ratio flow rate corresponding to the fermentation tank in real time to perform supply coordinated dynamic control and fermentation growth coordinated dynamic control, while performing metabolic environmental condition coordinated dynamic control to perform microbial growth environmental condition coordinated control work. The present invention can achieve efficient production of intestinal microecological regulation preparations by optimizing environmental control during the fermentation process.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine technology, and in particular to a dynamic fermentation tank control system for producing a diabetic intestinal microecological regulating preparation. Background Art

[0002] An imbalance in the intestinal flora is closely related to the occurrence and development of diabetes, insulin resistance, and fluctuations in blood sugar levels. The fermentation process of probiotics is crucial in the production of diabetic intestinal microecological regulatory preparations. Dynamic fermenters, as a common industrial fermentation equipment, play an important role in the large-scale production of probiotics. The fermenter control system is mainly used to monitor and regulate key parameters during the fermentation process, such as temperature, pH, dissolved oxygen, and stirring speed. Changes in these parameters directly affect the growth, metabolism, and activity of probiotics. Therefore, how to accurately control the operating status of the fermenter to ensure maximum probiotic production and activity is the key to improving product quality and production efficiency. However, although traditional dynamic fermenter control methods can monitor basic parameters during the fermentation process, they mainly rely on single parameters or fixed control modes and cannot flexibly respond to complex changes in the fermentation process, resulting in poor stability of the fermentation process, which in turn affects the quality of the final preparation. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a dynamic fermentation tank control system and system for the production of diabetic intestinal microecological regulating preparations to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a dynamic fermentation tank control system for the production of diabetic intestinal microecological regulation preparations includes the following modules:

[0005] A fermentation tank sensor feedback control network establishment module is used to install sugar source sensors, nitrogen source sensors, temperature sensors, pH sensors, concentration sensors, and flow sensors in the corresponding fermentation tanks used for the production of diabetic intestinal microecological regulation preparations, and connect them to the central feedback control system to generate an embedded sensor feedback control network;

[0006] The fermenter's early growth nutrient supply control module is used to synchronously monitor the fermenter's corresponding sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and oxygen and carbon dioxide gas flow ratio in real time using an embedded sensor feedback control network; based on the sugar source supply and nitrogen source supply, dynamic and coordinated supply control of the corresponding microorganisms in the fermenter's early growth stage is performed to implement coordinated control of the sugar-nitrogen nutrient source supply corresponding to the fermenter's early growth stage;

[0007] The fermentation tank late growth regulation control module is used to dynamically control the fermentation growth of the corresponding microorganisms in the fermentation tank in the late growth stage based on temperature and dissolved oxygen concentration, so as to perform the temperature-dissolved oxygen fermentation regulation coordinated control work corresponding to the fermentation tank late growth stage;

[0008] The fermentation tank metabolic process environmental condition control module is used to dynamically control the metabolic environmental conditions of the corresponding microbial metabolic process in the fermentation tank based on the pH value and the proportional flow rate of oxygen and carbon dioxide gases, so as to perform collaborative control of the microbial growth environmental conditions corresponding to the fermentation tank metabolic process.

[0009] Furthermore, the fermentation tank sensor feedback control network establishment module includes the following functions:

[0010] Obtain specific requirements for the production and fermentation of diabetic intestinal microecological regulation preparations;

[0011] Based on the specific production and fermentation requirements of diabetic intestinal microecological regulation preparations, functional requirements for sugar source sensors, nitrogen source sensors, temperature sensors, pH sensors, concentration sensors, and flow sensors are defined to obtain the corresponding working parameter range and accuracy selection requirements for each sensor;

[0012] Based on the corresponding working parameter range and accuracy selection requirements of each sensor, the corresponding sugar source sensor, nitrogen source sensor, temperature sensor, pH sensor, concentration sensor and flow sensor are installed in the corresponding fermenter used for the production of diabetic intestinal microecological regulation preparations, and the corresponding data format, signal transmission standard and real-time requirements of each sensor are obtained;

[0013] Based on the corresponding working parameter range and accuracy selection requirements of each sensor, the corresponding data format, signal transmission standard and real-time requirements are designed for data output compatible channels to generate corresponding data output compatible acquisition channels between each sensor;

[0014] According to the corresponding data output compatibility acquisition channels between various sensors and the central feedback control system, the control network connection is regulated to generate an embedded sensor feedback control network.

[0015] Furthermore, the working parameter ranges and accuracy selection requirements corresponding to each sensor are specifically as follows: a sugar source sensor with a working parameter range of 0-200g / L and an accuracy range of ±0.1g / L, a nitrogen source sensor with a working parameter range of 0-50g / L and an accuracy range of ±0.05g / L, a temperature sensor with a working parameter range of 20-45°C and an accuracy range of ±0.2°C, a pH sensor with a working parameter range of 3.5-7.5pH and an accuracy range of ±0.05pH, a concentration sensor with a working parameter range of 0-100g / L and an accuracy range of ±0.1g / L, and a flow sensor with a working parameter range of 0-500g / L and an accuracy range of ±0.5g / L.

[0016] Furthermore, the fermentation tank early growth nutrient supply control module includes the following functions:

[0017] The embedded sensor feedback control network is used to synchronously monitor the sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and oxygen and carbon dioxide gas ratio flow of the fermentation tank in real time;

[0018] Performing a forecast analysis on the early growth stage of the microorganisms in the fermentation tank to obtain the corresponding microbial growth demand in the early growth stage of the microorganisms in the fermentation tank;

[0019] Based on the sugar source supply and nitrogen source supply, the sugar source-nitrogen source microbial growth contribution calculation formula is used to calculate the microbial growth contribution of the microbial growth demand corresponding to the early stage of microbial growth in the fermentation tank, so as to obtain the contribution rate of sugar source supply to microbial growth and the contribution rate of nitrogen source supply to microbial growth;

[0020] The growth flux balance was calculated based on the contribution rate of sugar source supply to microbial growth and the contribution rate of nitrogen source supply to microbial growth, and the early sugar source-nitrogen source growth flux balance coefficient was obtained;

[0021] Based on the early stage of microbial growth in the fermentation tank, the supply delay response regulation analysis of the sugar source supply and nitrogen source supply was carried out to obtain the early sugar source-nitrogen source growth supply response regulation efficiency;

[0022] Based on the early sugar source-nitrogen source growth flux balance coefficient and the early sugar source-nitrogen source growth supply response regulation efficiency, the supply coordinated dynamic control of the corresponding microbial growth early stage in the fermentation tank is carried out to execute the sugar-nitrogen nutrient source supply coordinated control work corresponding to the early stage of fermentation tank growth.

[0023] Furthermore, the specific process of the synchronous real-time monitoring includes:

[0024] By starting the corresponding sugar source sensor, nitrogen source sensor, temperature sensor, pH sensor, concentration sensor and flow sensor in the embedded sensor feedback control network at the same time point, and using the sugar source sensor to monitor the sugar source supply corresponding to the fermentation process in the fermenter in real time, using the nitrogen source sensor to monitor the nitrogen source supply corresponding to the fermentation process in the fermenter in real time, using the temperature sensor to monitor the temperature corresponding to the fermentation process in the fermenter in real time, using the pH sensor to monitor the pH value corresponding to the fermentation process in the fermenter in real time, using the concentration sensor to monitor the dissolved oxygen concentration corresponding to the fermentation process in the fermenter in real time, and using the flow sensor to monitor the oxygen and carbon dioxide gas flows corresponding to the fermentation process in the fermenter in real time;

[0025] By calculating the gas ratio of oxygen and carbon dioxide gas flow corresponding to the fermentation process in the fermenter at each time point, the proportional flow of oxygen and carbon dioxide gas corresponding to the fermentation process in the fermenter is obtained;

[0026] The sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and oxygen to carbon dioxide gas ratio flow rate corresponding to the fermentation process in the fermenter are uploaded to the central feedback control system through the embedded sensor feedback control network for data cleaning to obtain the sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and oxygen to carbon dioxide gas ratio flow rate corresponding to the fermenter within the same time range.

[0027] Furthermore, the sugar source-nitrogen source microbial growth contribution calculation formula is specifically as follows:

[0028]

[0029] Where η C is the contribution rate of sugar source supply to microbial growth, η N is the contribution rate of nitrogen source supply to microbial growth, G s is the growth requirement of microorganisms, t is the time range parameter, τ is the integral time variable parameter, S C (τ) is the sugar source supply at time point τ, K C is the half-saturation constant corresponding to the sugar source, S N (τ) is the nitrogen source supply at time point τ, K N is the half-saturation constant corresponding to the nitrogen source, ξ C is the correction coefficient of sugar source supply contribution rate, ξ N It is the correction coefficient of nitrogen source supply contribution rate.

[0030] Furthermore, the supply delay response adjustment analysis of the sugar source supply and the nitrogen source supply based on the corresponding early stage of microbial growth in the fermenter includes the following steps:

[0031] Obtain the corresponding microbial growth cell density and microbial metabolite accumulation amount through the corresponding early stage of microbial growth in the fermentation tank;

[0032] The microbial fermentation reaction rate analysis is performed on the microbial growth cell density and the accumulation of microbial metabolites to generate the actual microbial fermentation reaction rate corresponding to the fermentation process of the microorganism in the early growth stage;

[0033] Based on the actual reaction rate of microbial fermentation corresponding to the fermentation process in the early stage of microbial growth and the principle of bioreaction kinetics, a sugar and nitrogen supply dynamic model is constructed for the sugar source supply and nitrogen source supply, and a dynamic mathematical model of sugar source and nitrogen source supply in the early stage of microbial growth is generated;

[0034] According to the dynamic mathematical model of sugar source-nitrogen source supply in the early stage of microbial growth, the delayed response time window identification analysis of sugar source supply and nitrogen source supply is carried out to obtain the delayed response time window between sugar source and nitrogen source supply to microbial growth; according to the delayed response time window between sugar source and nitrogen source supply to microbial growth, the specific effect of changes in sugar source and nitrogen source supply on microbial growth is determined to have a long delayed response time;

[0035] Based on the specific impact of changes in sugar and nitrogen source supply on microbial growth and the long delayed response time, the supply delayed response regulation of sugar and nitrogen source supply was quantified to obtain the early sugar source-nitrogen source growth supply response regulation efficiency.

[0036] Furthermore, the fermentation tank late growth regulation control module includes the following functions:

[0037] Performing growth dynamic curve fitting analysis on the corresponding late growth stage of the microorganisms in the fermentation tank to generate a dynamic fitting curve of the late growth stage of the microorganisms in the fermentation tank;

[0038] Based on temperature and dissolved oxygen concentration and combined with the growth and metabolism laws of microorganisms, a metabolic kinetic network analysis was performed on the dynamic fitting curve of the late growth of microorganisms in the fermentation tank to generate a growth and metabolism distribution kinetic network corresponding to microorganisms under different temperature and dissolved oxygen concentration conditions;

[0039] Based on the growth metabolic distribution dynamics network of microorganisms under different temperature and dissolved oxygen concentration conditions, the response function of the metabolic response relationship between temperature and dissolved oxygen concentration is derived to generate the metabolic response function between temperature and dissolved oxygen concentration in the late stage of microbial growth;

[0040] According to the metabolic response function between the temperature and dissolved oxygen concentration in the late stage of microbial growth, the fermentation growth of the corresponding microbial growth stage in the fermenter is dynamically controlled to perform the temperature-dissolved oxygen fermentation regulation coordinated control work corresponding to the late stage of fermenter growth.

[0041] Furthermore, the metabolic response function between the temperature and dissolved oxygen concentration in the late stage of microbial growth is specifically:

[0042]

[0043] Where, is the temperature T and dissolved oxygen concentration in the late stage of microbial growth The metabolic response function between max is the maximum growth rate of microbial fermentation under ideal conditions, k0 is the temperature-dependent prefactor, K s is the dissolved oxygen half-saturation constant, exp is the exponential function, E a is the temperature activation energy, R is the gas constant, and T0 is the reference temperature.

[0044] Furthermore, the fermentation tank metabolic process environmental condition control module includes the following functions:

[0045] Based on the ratio of oxygen and carbon dioxide gas flow rates, a metabolic feedback correlation analysis was conducted on the corresponding microbial metabolic process in the fermentation tank, and the metabolic feedback correlation relationship between the gas ratio flow rate and microbial metabolism during the fermentation process in the fermentation tank was obtained;

[0046] Based on the metabolic feedback relationship between the gas flow rate ratio and microbial metabolism during the fermentation process in the fermenter, the gas exchange ratio change of the oxygen and carbon dioxide gas flow rate ratio was analyzed to obtain the corresponding gas exchange ratio change between oxygen and carbon dioxide during the fermentation process in the fermenter;

[0047] The influence of the change in the gas exchange ratio between oxygen and carbon dioxide during the fermentation process in the fermenter on the pH value was evaluated and analyzed to obtain the degree of influence of the change in the gas exchange ratio between oxygen and carbon dioxide on the pH value.

[0048] Based on the influence of the change in the oxygen and carbon dioxide gas exchange ratio on the pH value, an intelligent coordinated regulation analysis is performed on the pH value and the oxygen and carbon dioxide gas ratio flow rate to obtain the coordinated regulation relationship between the gas ratio flow rate and the pH value;

[0049] According to the synergistic regulatory relationship between the gas proportional flow rate and the pH value, the metabolic environmental conditions of the corresponding microbial metabolic process in the fermentation tank are dynamically controlled to perform the synergistic control of the microbial growth environmental conditions corresponding to the fermentation tank metabolic process.

[0050] Beneficial effects of the present invention:

[0051] The dynamic fermentation tank control system for the production of diabetic intestinal microecological regulating preparations proposed in the present invention is composed of a fermentation tank sensor feedback control network establishment module, a fermentation tank early growth nutrient supply control module, a fermentation tank late growth regulation control module and a fermentation tank metabolic process environmental condition control module. Compared with the existing technology, the beneficial effect of the present application is that by installing sugar source sensors, nitrogen source sensors, temperature sensors, pH sensors, concentration sensors and flow sensors in the corresponding fermentation tanks for the production of diabetic intestinal microecological regulating preparations, they are respectively responsible for real-time monitoring and control of different production variables. By clarifying the working parameter range and accuracy requirements of each sensor, it is ensured that key parameters are accurately adjusted during the fermentation process, thereby ensuring the optimal growth environment of microorganisms and avoiding unnecessary production interruptions or product quality fluctuations caused by environmental fluctuations. This can ensure that they It can work stably and accurately throughout the entire production cycle, thereby providing reliable data support to subsequent control systems. At the same time, it is connected and regulated with the central feedback control system through the control network to form an embedded sensor feedback control network. The core advantage of this control network is that it can aggregate the data of multiple sensors in real time, and after processing, it can quickly feed back to the central control system to achieve dynamic regulation of key variables in the fermentation process. The central feedback control system will automatically adjust parameters such as temperature, pH, gas flow, sugar source and nitrogen source according to the real-time data collected to ensure that the fermentation process is maintained in the best state. This highly automated and intelligent control network can significantly improve production efficiency, reduce human intervention, improve the stability and consistency of the production process, and can flexibly respond to complex changes in the fermentation process, thereby providing strong technical support for the large-scale production of diabetic intestinal microecological regulation preparations. Secondly, through real-time monitoring of the embedded sensor network, changes in key parameters during the fermentation process can be accurately tracked. The fermentation process is affected by environmental factors such as temperature, pH, and dissolved oxygen. At the same time, the supply of sugar and nitrogen sources is crucial to microbial growth. By establishing a closed-loop feedback system, sensors collect data in real time, and the control system can adjust parameters in a timely manner to ensure that microorganisms can grow and metabolize under optimal conditions. By monitoring the ratio of gas flow, gas exchange in the fermentation tank can be optimized to avoid insufficient oxygen supply or carbon dioxide accumulation, thereby achieving a more efficient fermentation process and ensuring that the quality and yield of fermentation products can be improved.By dynamically controlling the supply of the corresponding microorganisms in the early stage of growth in the fermentation tank based on the sugar source supply and the nitrogen source supply, precise coordinated control of the sugar source and nitrogen source supply can be achieved to ensure the continuous and efficient operation of the fermentation process. By comprehensively considering the sugar source-nitrogen source growth flux balance and the supply response regulation efficiency, a dynamic adjustment strategy can be formulated to coordinate and optimize the supply of sugar source and nitrogen source in real time. This strategy can flexibly adjust the supply of sugar source and nitrogen source according to the actual needs of microbial growth to avoid growth stagnation or product quality degradation due to nutritional imbalance or insufficient supply. This coordinated control strategy can optimize the entire fermentation process and improve the efficiency and quality of preparation production on the basis of ensuring the healthy growth of microorganisms. Then, by dynamically controlling the fermentation growth of the corresponding microorganisms in the fermenter based on temperature and dissolved oxygen concentration, the core of this step is to adjust the temperature and dissolved oxygen concentration according to the real-time metabolic response so that the two adjustments cooperate with each other to maintain the optimal growth environment, thereby achieving the highest efficiency and the best product quality in the late stage of fermentation. This method avoids the limitations of single factor control in traditional fermentation processes and can more flexibly and finely adjust the fermentation conditions, thereby promoting the continuous optimization of bioengineering and microecological regulatory preparation production. Finally, by dynamically controlling the metabolic environmental conditions of the corresponding microbial metabolic process in the fermenter based on pH value and the ratio of oxygen to carbon dioxide gas flow, the core of this step is to accurately adjust the oxygen and carbon dioxide gas flow and pH value in the fermenter through dynamic control to ensure that the microbial metabolic process is always in the best state. This can automatically adjust the environmental conditions in the fermenter to ensure the stability and efficiency of the fermentation process. This dynamic control mechanism can automatically adjust the environmental parameters based on real-time monitoring of multiple key indicators such as gas flow, pH value, and microbial growth status, thereby optimizing the fermentation process, significantly improving the yield and quality of the microbial metabolic process, reducing manual intervention, and thus improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0053] Figure 1 This is a schematic diagram of a module of a dynamic fermentation tank control system for producing a diabetic intestinal microecological regulating preparation according to the present invention;

[0054] Figure 2 for Figure 1 Schematic diagram of the functional flow of the module for establishing the sensor feedback control network of the fermentation tank;

[0055] Figure 3 for Figure 1Schematic diagram of the functional flow of the nutrient supply control module in the early growth stage of the fermentation tank. DETAILED DESCRIPTION

[0056] The following is a clear and complete description of the technical system of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0057] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor systems and / or microcontroller systems.

[0058] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0059] To achieve this, please refer to Figures 1 to 3 The present invention provides a dynamic fermentation tank control system for the production of diabetic intestinal microecological regulating preparations, the system comprising the following modules:

[0060] A fermentation tank sensor feedback control network establishment module is used to install sugar source sensors, nitrogen source sensors, temperature sensors, pH sensors, concentration sensors, and flow sensors in the corresponding fermentation tanks used for the production of diabetic intestinal microecological regulation preparations, and connect them to the central feedback control system to generate an embedded sensor feedback control network;

[0061] The fermenter's early growth nutrient supply control module is used to synchronously monitor the fermenter's corresponding sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and oxygen and carbon dioxide gas flow ratio in real time using an embedded sensor feedback control network; based on the sugar source supply and nitrogen source supply, dynamic and coordinated supply control of the corresponding microorganisms in the fermenter's early growth stage is performed to implement coordinated control of the sugar-nitrogen nutrient source supply corresponding to the fermenter's early growth stage;

[0062] The fermentation tank late growth regulation control module is used to dynamically control the fermentation growth of the corresponding microorganisms in the fermentation tank in the late growth stage based on temperature and dissolved oxygen concentration, so as to perform the temperature-dissolved oxygen fermentation regulation coordinated control work corresponding to the fermentation tank late growth stage;

[0063] The fermentation tank metabolic process environmental condition control module is used to dynamically control the metabolic environmental conditions of the corresponding microbial metabolic process in the fermentation tank based on the pH value and the proportional flow rate of oxygen and carbon dioxide gases, so as to perform collaborative control of the microbial growth environmental conditions corresponding to the fermentation tank metabolic process.

[0064] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of a module of a dynamic fermentation tank control system for producing a diabetic intestinal microecological regulating preparation according to the present invention. In this example, the dynamic fermentation tank control system for producing a diabetic intestinal microecological regulating preparation includes the following modules:

[0065] S1: Fermentation tank sensor feedback control network establishment module, which is used to install sugar source sensors, nitrogen source sensors, temperature sensors, pH sensors, concentration sensors, and flow sensors in the corresponding fermentation tanks used for the production of diabetic intestinal microecological regulation preparations, and connect them to the central feedback control system to generate an embedded sensor feedback control network;

[0066] In an embodiment of the present invention, by clarifying the fermentation-specific requirements in the production process of diabetic intestinal microecological regulating preparations, this requirement should be deeply investigated and analyzed based on the production goals, the characteristics of the fermentation system and the requirements for product quality. For example, the production of diabetic intestinal microecological regulating preparations involves the cultivation of specific bacterial strains, and requires the control of the concentrations of sugar sources and nitrogen sources during the fermentation process, as well as parameters such as temperature, pH value, and oxygen content to ensure that the growth and metabolic process of the bacterial strains can proceed efficiently. In addition, based on the previously determined fermentation-specific requirements, the working parameters of the sugar source sensor, nitrogen source sensor, temperature sensor, pH sensor, concentration sensor and flow sensor need to be defined in detail. The selection of these sensors should fully consider the specific requirements of the production of diabetic intestinal microecological regulating preparations. For example, the sugar source sensor needs to be able to accurately measure The concentration range of sugars in the fermentation tank is 0-200 g / L, with an accuracy requirement of typically within 0.1 g / L, making it suitable for real-time regulation. Nitrogen source sensors need to be able to monitor changes in the concentration of amino acids or nitrogen compounds from 0-50 g / L, with an accuracy requirement of typically within 0.05 g / L. Temperature sensors need to be able to precisely control the fermentation temperature from 20-45°C, typically requiring an accuracy of within ±0.2°C. pH sensors need to operate efficiently within a pH range of 3.5 to 7.5, with an error of no more than ±0.05 pH units. Concentration sensors should be selected based on the concentration requirements of specific metabolites, ranging from 0-100 g / L, with an error control of no more than ±0.5 g / L. Flow sensors should be able to monitor the flow rate of gas or liquid during fermentation in real time, determining an operating range of 0-500 g / L and an error control of no more than ±0.5g / L, ensuring that the gas supply and liquid discharge meet the process requirements. At the same time, according to the previously determined sensor selection requirements, sugar source sensor, nitrogen source sensor, temperature sensor, pH sensor, concentration sensor and flow sensor are installed in the fermenter. The installation position of each sensor should be designed according to the specific fermenter structure to ensure that the changes in key parameters can be effectively detected and avoid data errors caused by improper position. The output signals of each sensor are integrated and designed for compatibility by combining the previously determined working parameter range and accuracy selection requirements of each sensor to ensure that the output data of all sensors can be transmitted through a unified acquisition channel, taking into account different types of sensors. Sensors output data in various formats (e.g., analog, digital, and frequency). A data acquisition channel is needed to transmit all sensor data to a central feedback control system to achieve closed-loop control of the dynamic fermentation tank. This central control system must have data processing and feedback capabilities, enabling real-time adjustments to the fermentation process based on collected data (e.g., sugar source concentration, nitrogen source concentration, temperature, pH, dissolved oxygen, and the oxygen-to-carbon dioxide ratio and flow rate). This control network maintains all fermentation tank parameters within optimal ranges, ensuring efficient and stable production of diabetic intestinal microecological modulators. Ultimately, these connections create an embedded sensor feedback control network.

[0067] S2: A nutrient supply control module for the early growth stage of the fermenter, which is used to synchronously monitor the sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and oxygen and carbon dioxide gas ratio flow rate of the fermenter in real time using an embedded sensor feedback control network; based on the sugar source supply and nitrogen source supply, dynamic supply coordination control is performed on the corresponding microorganisms in the early growth stage of the fermenter to implement the coordinated control of sugar-nitrogen nutrient source supply corresponding to the early growth stage of the fermenter;

[0068] In an embodiment of the present invention, real-time monitoring of the fermentation tank is achieved by utilizing a previously established embedded sensor feedback control network. Specifically, the process includes installing a sugar source sensor, a nitrogen source sensor, a temperature sensor, a pH sensor, a dissolved oxygen sensor, a flow meter, and other sensing devices, and connecting them to the fermentation tank control system. The sugar source sensor monitors changes in sugar source supply within the fermentation tank in real time. The nitrogen source sensor also monitors changes in nitrogen source supply within the fermentation tank in real time. The temperature sensor monitors changes in tank temperature in real time. The pH sensor monitors the pH of the fermentation liquid. The dissolved oxygen sensor tracks the dissolved oxygen concentration within the tank. The flow meter measures gas flow. For sugar source and nitrogen source supply, specialized flow sensors are installed to monitor the volume and flow rate of the sugar and nitrogen source liquids flowing into the fermentation tank. Data fed back by these sensors is uploaded to a data processing center in real time via the control network, forming a real-time data stream during the fermentation process. All parameters are continuously compared and monitored to ensure that each indicator is within a predetermined range, thereby obtaining the corresponding sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and oxygen to carbon dioxide gas flow ratio. By predicting and analyzing the demand for microbial growth in the early stage of the fermentation tank, by analyzing the metabolic activity of microorganisms in the early stage of the fermentation process, combining historical fermentation data, microbial growth curves and physiological needs, a mathematical model is constructed to predict the demand for microbial growth in the early stage, so as to predict the microbial growth demand corresponding to the early stage of microbial growth in the fermentation tank, and combining the sugar source supply and nitrogen source supply to quantify the contribution of sugar source supply and nitrogen source supply to microbial growth. At the same time, based on the contribution rate of sugar source supply to microbial growth and the contribution rate of nitrogen source supply to microbial growth obtained from the previous quantitative calculation, a growth flux balance calculation is performed. According to the contribution rate of sugar source and nitrogen source, the balance coefficient of sugar source and nitrogen source is calculated by the following growth flux balance equation = contribution rate of sugar source supply to microbial growth / contribution rate of nitrogen source supply to microbial growth. In addition, by combining the supply of sugar source and nitrogen source in the early stage of microbial growth corresponding to the fermentation tank, The delayed response regulation efficiency is analyzed. Since the supply of sugar source and nitrogen source is often affected by the delayed response of the system during the fermentation process, too fast or too slow adjustment will have a negative impact on the growth of microorganisms. Therefore, by real-time monitoring of the supply status of sugar source and nitrogen source in the fermentation tank and combining the feedback control algorithm, the supply delayed response coefficient of sugar source and nitrogen source is calculated, and according to the sugar source-nitrogen source growth flux balance coefficient and supply response regulation efficiency calculated in the early stage, a dynamic control strategy is implemented to ensure the coordinated supply of sugar source and nitrogen source. In practice, real-time feedback data is used for dynamic adjustment to ensure that the supply of sugar source and nitrogen source is consistent with the actual needs of microorganisms, avoid waste of resources or insufficient supply, and ensure the best growth environment in the growth stage. The goal of this step is to achieve the coordinated supply of sugar source and nitrogen source, thereby maximizing the growth efficiency of microorganisms, and finally perform the coordinated control of sugar-nitrogen nutrient source supply corresponding to the early stage of fermentation tank growth.

[0069] S3: a fermenter late growth regulation control module, which is used to dynamically control the fermentation growth of the corresponding microorganisms in the fermenter at the late growth stage based on temperature and dissolved oxygen concentration, so as to perform temperature-dissolved oxygen fermentation regulation coordinated control work corresponding to the fermenter late growth stage;

[0070] In an embodiment of the present invention, by obtaining real-time data corresponding to the late growth of microorganisms in a fermenter, an online monitoring system is set up in the fermenter to measure the growth indicators of the microorganisms in real time, such as optical density (OD value), pH value, dissolved oxygen (DO) concentration, and temperature. After obtaining the growth data over a period of time, the growth curve of the microorganism is fitted and analyzed by statistical methods. Common fitting methods include Logistic model, Gompertz model or Baranyi model, which can reflect the changes in the exponential growth, plateau and decline stages of microorganisms in the late growth stage. Data analysis software such as Matlab or Python is used to fit and generate a dynamic fitting curve of the growth. By combining the previously generated growth dynamics curves, environmental variables such as temperature and dissolved oxygen concentration, and known microbial growth and metabolism laws, a metabolic kinetic network is constructed. In specific operations, metabolic flux analysis (MFA) and model-based systems biology methods can be used to combine the effects of temperature and dissolved oxygen concentration on microbial metabolic pathways to establish a multivariate metabolic network model. This model needs to consider the metabolic pathways in microbial cells under different temperatures and dissolved oxygen concentrations, including carbon source utilization, energy metabolism (such as ATP generation), and product generation (such as acid, alcohol, etc.). Based on the established microbial growth metabolic network, the functional relationship between temperature and dissolved oxygen concentration on microbial metabolic response is further derived through mathematical modeling methods. This process usually uses response surface methodology (RSM) or Lagrange multiplier method (RSM). Method) and other optimization methods. The specific operation is based on the previously generated metabolic network. Multiple simulation experiments are carried out under different temperature and dissolved oxygen concentration conditions to collect corresponding metabolic data. These data can be used to derive the response function of microbial metabolites and growth rate to environmental factors (such as temperature and dissolved oxygen concentration). Then, by combining the metabolic response function between the temperature and dissolved oxygen concentration in the late stage of microbial growth generated previously, the fermentation growth of the corresponding microbial growth stage in the fermenter is coordinated and controlled to achieve dynamic coordinated control of the temperature and dissolved oxygen concentration in the fermenter, optimize the growth and metabolic processes corresponding to the late stage of microorganisms, and accurately control the fermentation environment by adjusting equipment (such as temperature controller, gas supply system, etc.). At the same time, PID control algorithm or fuzzy control algorithm can be used to smoothly control the fluctuations of temperature and dissolved oxygen concentration to ensure the stability of environmental variables during the fermentation process, which is conducive to the production of diabetic intestinal microecological regulation preparations, and finally the temperature-dissolved oxygen fermentation regulation coordinated control corresponding to the late stage of fermentation tank growth is performed.

[0071] S4: Fermentation tank metabolic process environmental condition control module, used to dynamically control the metabolic environmental conditions of the corresponding microbial metabolic process in the fermentation tank based on the pH value and the proportional flow rate of oxygen and carbon dioxide gases, so as to perform collaborative control of the microbial growth environmental conditions corresponding to the fermentation tank metabolic process.

[0072] In an embodiment of the present invention, the proportional flow rate between oxygen and carbon dioxide gases during the fermentation process is detected in real time by a sensor, and the data is input into a data analysis platform to analyze the corresponding microbial metabolic process in the fermenter, so as to establish a metabolic model (for example, a model based on mass spectrometry analysis) to associate the gas proportional flow rate with the microbial metabolic activity, determine the influence of the proportional flow rate of oxygen and carbon dioxide on the microbial metabolic pathway, further reveal the feedback correlation between oxygen and carbon dioxide in the metabolic pathway, and further analyze the influence of the change in the ratio of oxygen and carbon dioxide gases during the fermentation process on the gas exchange process by combining the feedback correlation between the gas proportional flow rate and microbial metabolism obtained from the previous analysis. In combination with the real-time monitoring data, the gas exchange ratio in the fermenter is dynamically analyzed, and a gas analyzer (such as a gas chromatograph) is used to detect the changes in the oxygen and carbon dioxide concentrations in the fermenter in real time to determine the corresponding change in the gas exchange ratio. At the same time, by establishing a mathematical model of the influence of the change in the oxygen and carbon dioxide gas exchange ratio on the pH value, the change law of the pH value under different gas exchange ratios is obtained through experiments or literature data, and combined with the real-time monitored gas flow data and pH value data for analysis, a correlation model between the change in gas exchange ratio and pH value is established using regression analysis or machine learning algorithms (such as support vector machines, neural networks, etc.), and by evaluating according to the change in the gas exchange ratio, the specific influence of the change in the oxygen and carbon dioxide gas exchange ratio on the pH value can be quantified, and by using the influence of the change in the oxygen and carbon dioxide gas exchange ratio on the pH value obtained in the previous step, the corresponding pH value and the oxygen and carbon dioxide gas ratio flow rate are intelligently coordinated and adjusted, and the real-time data of oxygen and carbon dioxide gas flow and pH value are collected through a real-time data monitoring system (including gas flow sensors, pH sensors, etc.), and through an intelligent algorithm ( Such as PID control algorithm, fuzzy control algorithm or reinforcement learning, etc.), coordinately adjust and analyze the gas proportional flow rate and pH value. The specific adjustment process is to calculate the expected impact of the change in the oxygen and carbon dioxide gas exchange ratio on the pH value in the current state based on the real-time detected pH value and gas flow rate, and adjust the gas flow ratio through the algorithm (for example, increase the oxygen flow rate or reduce the carbon dioxide flow rate) to keep the pH value within a predetermined range. Then, combined with the real-time feedback data of microbial metabolism in the fermentation tank, the metabolic environment is dynamically controlled. Relying on the aforementioned intelligent algorithm, the gas exchange ratio, pH value and other environmental variables are adjusted according to the real-time oxygen and carbon dioxide gas flow rate data and pH value data to maintain the optimal metabolic conditions in the fermentation process, ensure the dynamic balance of the microbial metabolic environment, promote the stable production process of the diabetic intestinal microecological regulation preparation, and finally perform the coordinated control of the microbial growth environment conditions corresponding to the fermentation tank metabolic process.

[0073] Further, as an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 FIG. 1 is a functional flow diagram of a fermentation tank sensor feedback control network establishment module. In this embodiment, the fermentation tank sensor feedback control network establishment module includes the following functions:

[0074] S11: Obtain specific requirements for the production and fermentation of diabetic intestinal microecological regulatory preparations;

[0075] In an embodiment of the present invention, by clarifying the specific fermentation requirements in the production process of diabetic intestinal microecological regulation preparations, this requirement should be deeply investigated and analyzed based on the production goals, the characteristics of the fermentation system and the requirements for product quality. For example, the production of diabetic intestinal microecological regulation preparations involves the cultivation of specific bacterial strains, and requires the control of the concentrations of sugar sources and nitrogen sources, as well as temperature, pH value, oxygen content and other parameters during the fermentation process to ensure that the growth and metabolic process of the bacterial strains can proceed efficiently, thereby ensuring the activity and quality of the final product. At this time, it is necessary to discuss in combination with the production process requirements to determine the key parameters that need to be strictly monitored during the fermentation process, such as dissolved oxygen, fermentation time, and the ratio of the culture medium, so as to clarify the selection direction and functional requirements of various sensors, and finally obtain the specific fermentation requirements for the production of diabetic intestinal microecological regulation preparations.

[0076] S12: Based on the specific production and fermentation requirements of diabetic intestinal microecological regulation preparations, functional requirements for sugar source sensors, nitrogen source sensors, temperature sensors, pH sensors, concentration sensors, and flow sensors are defined to obtain the corresponding working parameter range and accuracy selection requirements for each sensor;

[0077] In an embodiment of the present invention, based on the previously determined specific fermentation requirements, it is necessary to define in detail the working parameters of the sugar source sensor, nitrogen source sensor, temperature sensor, pH sensor, concentration sensor and flow sensor. The selection of these sensors should fully consider the specific requirements of the production of diabetic intestinal microecological regulation preparations. For example, the sugar source sensor needs to be able to accurately measure the concentration range of sugar substances in the fermentation tank in the range of 0-200g / L, and the accuracy requirement is usually within 0.1g / L, which is suitable for real-time regulation; the nitrogen source sensor needs to be able to monitor the concentration changes of amino acids or nitrogen source compounds in the range of 0-50g / L, and the accuracy requirement is generally within 0.05g / L; the temperature sensor needs to be able to accurately control the fermentation temperature of 20-45°C, which is usually required. The accuracy must be within ±0.2°C; the pH sensor needs to work efficiently within the pH range of 3.5 to 7.5, with an error of no more than ±0.05 pH units; the concentration sensor should be selected based on the concentration requirement of specific metabolites (0-100g / L) and the error should be controlled to no more than ±0.5g / L; the flow sensor should be able to monitor the flow rate of gas or liquid during the fermentation process in real time to determine the working range of 0-500g / L and control the error to no more than ±0.5g / L to ensure that the gas supply and liquid discharge meet the process requirements. The accuracy, range and response speed of all these sensors must be strictly selected and configured based on matching the experimental conditions, and finally the corresponding working parameter range and accuracy selection requirements of each sensor are obtained.

[0078] S13: Based on the corresponding working parameter range and accuracy selection requirements of each sensor, corresponding sugar source sensors, nitrogen source sensors, temperature sensors, pH sensors, concentration sensors, and flow sensors are installed in the corresponding fermentation tank used for the production of the diabetic intestinal microecological regulation preparation, and the corresponding data format, signal transmission standard, and real-time requirements of each sensor are obtained;

[0079] In an embodiment of the present invention, by applying the previously determined sensor selection requirements, a sugar source sensor, a nitrogen source sensor, a temperature sensor, a pH sensor, a concentration sensor, and a flow sensor are installed in the fermenter. The installation position of each sensor should be designed according to the specific fermenter structure to ensure that changes in key parameters can be effectively detected and to avoid data errors caused by improper positioning. After installation, it is necessary to ensure that the data format, signal transmission standard, and real-time requirements of all sensors are met. For example, the temperature sensor and pH sensor use analog signal output, while the flow sensor and sugar source sensor use digital signal output, and the signals output by all sensors must comply with a unified data format standard to facilitate subsequent data processing and analysis. In addition, the signal transmission of each sensor needs to ensure real-time performance so that the system can be dynamically controlled according to real-time data. In order to ensure system stability and data accuracy, common industrial communication protocols such as MODBUS, PROFIBUS, and HART can be used for data transmission, and finally the data format, signal transmission standard, and real-time requirements corresponding to each sensor are obtained.

[0080] S14: Based on the corresponding operating parameter range and accuracy selection requirements of each sensor, the corresponding data format, signal transmission standard and real-time requirements are designed to generate corresponding data output compatible acquisition channels between each sensor;

[0081] In an embodiment of the present invention, the signals output by each sensor are integrated and designed for compatibility by combining the previously determined operating parameter ranges and accuracy selection requirements corresponding to each sensor, ensuring that the output data of all sensors can be transmitted through a unified acquisition channel. Considering that different types of sensors output data in different formats (such as analog signals, digital signals, frequency signals, etc.), it is necessary to design a data acquisition channel that can process various signals in real time and convert them into a standardized digital data format. For example, analog signals can be converted by an analog-to-digital converter (ADC), while digital signals can be transmitted directly. The acquisition channel should also meet real-time requirements to ensure that various data during the fermentation process can be fed back to the central control system in a short time for real-time monitoring and control. To improve the compatibility and scalability of the channel, multiple parallel data acquisition channels can be designed to facilitate the integration of new sensors when the system is expanded. In addition, the channel needs to meet the communication protocol requirements of different sensors to ensure that the data of each sensor can be seamlessly connected to the central feedback control system, ultimately generating a data output compatible acquisition channel corresponding to each sensor.

[0082] S15: Control network connection and regulation are performed based on the corresponding data output compatibility acquisition channels between the sensors and the central feedback control system to generate an embedded sensor feedback control network.

[0083] In an embodiment of the present invention, all sensor data are transmitted to the central feedback control system through the data output compatibility acquisition channel designed in the aforementioned steps to realize closed-loop control of the dynamic fermentation tank. The central control system needs to have data processing and feedback functions, and can adjust the fermentation process in real time according to the various types of data collected (such as sugar source concentration, nitrogen source concentration, temperature, pH, dissolved oxygen, and the ratio of oxygen and carbon dioxide gas flow, etc.). For example, when the temperature value fed back by the temperature sensor deviates from the set value, the central control system will automatically adjust the heating or cooling device to ensure that the fermentation temperature remains within a predetermined range. In order to achieve this process, the control system can adopt an embedded computing platform with a special control algorithm embedded in the platform, which can respond quickly according to the sensor data. The control network can be transmitted through industrial Ethernet or wireless network to ensure the stability and real-time performance of data transmission. With the support of this control network, the various parameters of the fermentation tank will be maintained within the optimal range, ensuring the efficient and stable production process of the diabetic intestinal microecological regulation preparation, and finally connected to generate an embedded sensor feedback control network.

[0084] Furthermore, the working parameter ranges and accuracy selection requirements corresponding to each sensor are specifically as follows: a sugar source sensor with a working parameter range of 0-200g / L and an accuracy range of ±0.1g / L, a nitrogen source sensor with a working parameter range of 0-50g / L and an accuracy range of ±0.05g / L, a temperature sensor with a working parameter range of 20-45°C and an accuracy range of ±0.2°C, a pH sensor with a working parameter range of 3.5-7.5pH and an accuracy range of ±0.05pH, a concentration sensor with a working parameter range of 0-100g / L and an accuracy range of ±0.1g / L, and a flow sensor with a working parameter range of 0-500g / L and an accuracy range of ±0.5g / L.

[0085] Further, as an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Schematic diagram of the functional flow of the nutrient supply control module in the early growth stage of the fermentation tank. In this embodiment, the nutrient supply control module in the early growth stage of the fermentation tank includes the following functions:

[0086] S21: Using an embedded sensor feedback control network to synchronously monitor the sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and oxygen to carbon dioxide gas ratio flow rate of the fermentation tank in real time;

[0087] In an embodiment of the present invention, real-time monitoring of the fermentation tank is achieved by utilizing a previously established embedded sensor feedback control network. Specifically, the process includes installing a sugar source sensor, a nitrogen source sensor, a temperature sensor, a pH sensor, a dissolved oxygen sensor, a flow meter, and other sensing devices, and connecting them to the fermentation tank control system. The sugar source sensor monitors changes in sugar source supply within the fermentation tank in real time. The nitrogen source sensor also monitors changes in nitrogen source supply within the fermentation tank in real time. The temperature sensor monitors changes in tank temperature in real time. The pH sensor monitors the pH of the fermentation liquid. The dissolved oxygen sensor tracks the dissolved oxygen concentration within the tank. The flow meter measures gas flow. For sugar source and nitrogen source supply, specialized flow sensors are installed to monitor the volume and flow rate of the sugar and nitrogen source liquids flowing into the fermentation tank. Data fed back by these sensors is uploaded to a data processing center in real time via the control network, forming a real-time data stream during the fermentation process. All parameters are continuously compared and monitored to ensure that each indicator is within a predetermined range. Ultimately, the corresponding sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and oxygen to carbon dioxide gas flow ratio are obtained.

[0088] S22: performing an early growth demand forecast analysis on the corresponding microorganisms in the fermentation tank in the early growth stage to obtain the microorganism growth demand corresponding to the early growth stage of the microorganisms in the fermentation tank;

[0089] In an embodiment of the present invention, a mathematical model is constructed to predict the demand for microbial growth in the early stage of fermentation by predicting and analyzing the metabolic activity of microorganisms in the early stage of the fermentation process, combining historical fermentation data, microbial growth curves and physiological needs. The specific method is to calculate the required sugar source and nitrogen source concentrations in the fermentation liquid based on the metabolic flux of the microorganisms and the consumption of sugar sources and nitrogen sources, and to use the microbial growth kinetics formula (such as the Monod equation), and then predict the supply of nutrients required in the early stage of fermentation. The prediction model can be adjusted by combining offline experimental data with online monitoring results to ensure that the prediction of microbial growth demand in this stage is accurate, and finally the microbial growth demand corresponding to the early stage of microbial growth in the fermentation tank is obtained.

[0090] S23: Based on the sugar source supply and the nitrogen source supply, a sugar source-nitrogen source microbial growth contribution calculation formula is used to calculate the microbial growth contribution of the microorganisms corresponding to the early stage of the fermentation tank microbial growth, so as to obtain the contribution rate of the sugar source supply to the microbial growth and the contribution rate of the nitrogen source supply to the microbial growth;

[0091] In an embodiment of the present invention, a suitable sugar source-nitrogen source microbial growth contribution calculation formula is constructed by using time variable parameters, sugar source supply, half-saturation constant corresponding to the sugar source, nitrogen source supply, half-saturation constant corresponding to the nitrogen source, microbial growth requirement and related parameters to perform growth contribution calculation, so as to quantitatively calculate the contribution of sugar source supply and nitrogen source supply to microbial growth, and finally obtain the contribution rate of sugar source supply to microbial growth and the contribution rate of nitrogen source supply to microbial growth.

[0092] S24: performing growth flux balance calculation based on the contribution rate of sugar source supply to microbial growth and the contribution rate of nitrogen source supply to microbial growth to obtain the early sugar source-nitrogen source growth flux balance coefficient;

[0093] In an embodiment of the present invention, a growth flux balance calculation is performed based on the contribution rate of sugar source supply to microbial growth and the contribution rate of nitrogen source supply to microbial growth obtained from the previous quantitative calculation. At this time, the supply of sugar source and nitrogen source in the fermentation tank and the growth requirement of the microorganism have been determined. According to the contribution rate of sugar source and nitrogen source, the balance coefficient of sugar source and nitrogen source is calculated by the following growth flux balance equation: contribution rate of sugar source supply to microbial growth / contribution rate of nitrogen source supply to microbial growth. This balance coefficient reflects the relative proportion between sugar source and nitrogen source supply. In practice, the ratio of sugar source and nitrogen source is dynamically adjusted according to the actual demand and supply situation of microbial growth to ensure that the supply meets the growth requirement of the microorganism while avoiding excess or deficiency of nutrition, and finally the early sugar source-nitrogen source growth flux balance coefficient is obtained.

[0094] S25: performing supply delay response regulation analysis on the sugar source supply and the nitrogen source supply in the corresponding early stage of microbial growth in the fermenter to obtain the early stage sugar source-nitrogen source growth supply response regulation efficiency;

[0095] In an embodiment of the present invention, the supply delay response regulation efficiency of the sugar source and nitrogen source is analyzed in combination with the corresponding early stage of microbial growth in the fermenter. Since the supply amount of the sugar source and nitrogen source is often affected by the system delayed response during the fermentation process, too fast or too slow adjustment will have a negative impact on the growth of the microorganisms. Therefore, by real-time monitoring of the supply status of the sugar source and nitrogen source in the fermenter, combined with the feedback control algorithm, the supply delay response coefficient of the sugar source and nitrogen source is calculated. The specific implementation method is to adopt the model predictive control (MPC) technology in control theory, and determine the optimal supply adjustment strategy by simulating the system's response to different delay times. During the adjustment process, the calculated regulation efficiency is combined with the early growth demand prediction results to optimize the supply delay response of the sugar source and nitrogen source, ensure that the microorganisms can grow under suitable supply conditions, and finally obtain the early sugar source-nitrogen source growth supply response regulation efficiency.

[0096] S26: Based on the early sugar source-nitrogen source growth flux balance coefficient and the early sugar source-nitrogen source growth supply response regulation efficiency, the supply coordinated dynamic control of the corresponding microbial growth early stage in the fermentation tank is performed to execute the sugar-nitrogen nutrient source supply coordinated control work corresponding to the fermentation tank growth early stage.

[0097] In an embodiment of the present invention, a dynamic control strategy is implemented based on the sugar source-nitrogen source growth flux balance coefficient and the supply response regulation efficiency calculated in the early stage to ensure the coordinated supply of sugar source and nitrogen source. In practice, real-time feedback data is used for dynamic adjustment to ensure that the supply of sugar source and nitrogen source is consistent with the actual needs of microorganisms, thereby avoiding waste of resources or insufficient supply. The specific operation is to adjust the feed flow rate and concentration through advanced algorithm models, such as multivariable control systems, combined with the supply ratio of sugar source and nitrogen source, to maintain the optimal nutrient supply conditions required for microbial growth in the tank. During the control process, the supply of sugar source and nitrogen source is monitored and feedback is provided in real time, and the closed-loop control principle is used to fine-tune the supply amount to ensure the optimal growth environment in the growth stage. The goal of this step is to achieve the coordinated supply of sugar source and nitrogen source, thereby maximizing the growth efficiency of microorganisms, and finally performing the coordinated control of sugar-nitrogen nutrient source supply corresponding to the early stage of fermentation tank growth.

[0098] Furthermore, the specific process of the synchronous real-time monitoring includes:

[0099] By starting the corresponding sugar source sensor, nitrogen source sensor, temperature sensor, pH sensor, concentration sensor and flow sensor in the embedded sensor feedback control network at the same time point, and using the sugar source sensor to monitor the sugar source supply corresponding to the fermentation process in the fermenter in real time, using the nitrogen source sensor to monitor the nitrogen source supply corresponding to the fermentation process in the fermenter in real time, using the temperature sensor to monitor the temperature corresponding to the fermentation process in the fermenter in real time, using the pH sensor to monitor the pH value corresponding to the fermentation process in the fermenter in real time, using the concentration sensor to monitor the dissolved oxygen concentration corresponding to the fermentation process in the fermenter in real time, and using the flow sensor to monitor the oxygen and carbon dioxide gas flows corresponding to the fermentation process in the fermenter in real time;

[0100] In an embodiment of the present invention, in the operation of the fermentation tank control system, first, by starting each sensor of the embedded sensor feedback control network at the same time point, the key parameters of the fermentation process are monitored in real time. At the start, the sugar source sensor, nitrogen source sensor, temperature sensor, pH sensor, concentration sensor and flow sensor are started synchronously to monitor the sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and oxygen and carbon dioxide gas flow in the fermentation tank respectively. The sugar source sensor obtains the supply of the sugar source in real time by measuring the dissolved sugar concentration in the fermentation liquid to ensure that the sugar source can be input into the fermentation system at an appropriate rate; the nitrogen source sensor monitors the nitrogen source concentration in the fermentation liquid in real time to ensure that the nitrogen source is fully supplied. The temperature sensor continuously monitors the temperature changes of the fermentation tank to ensure that the fermentation process is carried out in an ideal temperature-controlled environment; the pH sensor monitors the pH value of the fermentation liquid in real time to avoid excessive fluctuations in the pH value during the fermentation process, which affects the metabolic activity of the microorganisms; the concentration sensor monitors the concentration of dissolved oxygen to ensure that the dissolved oxygen is maintained at an appropriate level during the fermentation process; and the flow sensor monitors the gas flow of oxygen and carbon dioxide in the fermentation tank to ensure normal gas exchange during the fermentation process, provide the required oxygen for the microorganisms, and discharge carbon dioxide in time. All sensors work at the same time and ultimately obtain the corresponding sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and gas flow of oxygen and carbon dioxide.

[0101] Preferably, the oxygen and carbon dioxide gas ratio flow rates corresponding to the fermentation process in the fermenter are calculated by calculating the gas ratio of the oxygen and carbon dioxide gas flow rates corresponding to the fermentation process in the fermenter at each time point to obtain the oxygen and carbon dioxide gas ratio flow rates corresponding to the fermentation process in the fermenter;

[0102] In an embodiment of the present invention, the oxygen flow and carbon dioxide flow data collected by the flow sensor are used to calculate the gas flow ratio at each time point. The calculation usually adopts a flow ratio algorithm to perform a ratio operation on the oxygen flow and the carbon dioxide flow to obtain a real-time flow ratio of the two. The gas ratio calculation method is based on the collected oxygen and carbon dioxide flow data, and is processed by an algorithm in an embedded control system to obtain a real-time oxygen and carbon dioxide gas flow ratio. This ratio is determined according to the oxygen demand and carbon dioxide production in the fermentation process, and is one of the important indicators for measuring the fermentation status. By monitoring the flow ratio, the ventilation volume in the fermentation process can be adjusted in real time to ensure an appropriate gas exchange ratio, thereby optimizing the fermentation efficiency and microbial growth conditions, and finally obtaining the oxygen and carbon dioxide gas flow ratio corresponding to the fermentation process in the fermenter.

[0103] Preferably, the sugar source supply amount, nitrogen source supply amount, temperature, pH value, dissolved oxygen concentration, and oxygen to carbon dioxide gas ratio flow rate corresponding to the fermentation process in the fermenter are uploaded to the central feedback control system through the embedded sensor feedback control network for data cleaning to obtain the sugar source supply amount, nitrogen source supply amount, temperature, pH value, dissolved oxygen concentration, and oxygen to carbon dioxide gas ratio flow rate corresponding to the fermenter within the same time range.

[0104] In an embodiment of the present invention, in the process of uploading the real-time data collected by each sensor to the central feedback control system, first, the sugar source sensor, nitrogen source sensor, temperature sensor, pH sensor, concentration sensor and flow sensor transmit their monitored real-time data to the central feedback control system through the embedded sensor feedback control network. The data collected by each sensor is annotated according to a specific timestamp to form a time series data set. The system transmits the data through the embedded network and performs data cleaning processing. During the data cleaning step, any outliers or noise data will be removed to ensure the accuracy of the input data. The cleaned data includes the sugar source supply amount, nitrogen source supply amount, fermentation liquid temperature, pH value, dissolved oxygen concentration, and oxygen and carbon dioxide gas flow ratio. These data will be summarized and formed into a standardized format for subsequent fermentation process analysis and optimization. The central feedback control system performs real-time analysis on these data, and can adjust the corresponding supply amount or operating parameters at different time points in the fermentation process to ensure the stability and efficiency of the fermentation process. Ultimately, the corresponding sugar source supply amount, nitrogen source supply amount, temperature, pH value, dissolved oxygen concentration and oxygen and carbon dioxide gas flow ratio of the fermentation tank within the same time range are obtained.

[0105] Furthermore, the sugar source-nitrogen source microbial growth contribution calculation formula is specifically as follows:

[0106]

[0107] Where η C is the contribution rate of sugar source supply to microbial growth, η N is the contribution rate of nitrogen source supply to microbial growth, G s is the growth requirement of microorganisms, t is the time range parameter, τ is the integral time variable parameter, S C (τ) is the sugar source supply at time point τ, K C is the half-saturation constant corresponding to the sugar source, S N (τ) is the nitrogen source supply at time point τ, K N is the half-saturation constant corresponding to the nitrogen source, ξ C is the correction coefficient of sugar source supply contribution rate, ξ N It is the correction coefficient of nitrogen source supply contribution rate.

[0108] The present invention obtains a sugar source-nitrogen source microbial growth contribution calculation formula by using a specific mathematical model and verification, which is used to calculate the growth contribution of the microbial growth demand corresponding to the early stage of microbial growth in the fermentation tank. The sugar source-nitrogen source microbial growth contribution calculation formula can accurately quantify the contribution of sugar sources and nitrogen sources to microbial growth by mathematically modeling the relationship between the supply of sugar sources and nitrogen sources and the microbial growth demand. Specifically, the two parts in the formula: the sugar source contribution rate represents the promoting effect of sugar source supply on microbial growth, and the nitrogen source contribution rate represents the promoting effect of nitrogen source supply on microbial growth. This quantification allows the resource supply in the fermentation process to be accurately regulated, thereby improving the growth efficiency of the microorganism and the production efficiency of metabolites. The time integral in the formula takes into account the changes in supply at different time points, especially the dynamic characteristics of the concentration of sugar sources and nitrogen sources changing with time during the fermentation process. In this way, the contribution of sugar sources and nitrogen sources to microbial growth at different periods can be more accurately reflected, thereby achieving more precise process control. The parameter half-saturation constant can describe the absorption rate of sugar and nitrogen sources in the organism, and thus help determine the effect of supply. Different types of microorganisms have different requirements for sugar and nitrogen sources, which reflects the "efficiency" of the supply. In addition, the correction coefficient in this formula is used to adjust the actual growth contribution rate of sugar and nitrogen sources. These correction coefficients can be adjusted according to specific conditions in the actual fermentation process (such as environmental conditions, material utilization efficiency, etc.) to make the calculation results more in line with the actual situation. The introduction of correction coefficients can effectively deal with non-ideal conditions that may occur during the fermentation process, such as unstable supply of sugar or nitrogen sources, metabolic changes of microorganisms, etc. By calculating the specific contribution of sugar and nitrogen sources to microbial growth, a scientific basis can be provided for subsequent growth flux balance calculations. By adjusting and analyzing the balance and response of sugar and nitrogen source supply, the supply amount can be optimized to avoid excess or shortage of a certain resource, thereby improving resource utilization efficiency, reducing waste, and providing the best nutritional supply conditions for microbial growth. In summary, this formula fully considers the contribution rate η of sugar source supply to microbial growth. C , the contribution rate of nitrogen source supply to microbial growth η N , microbial growth requirement G s , time range parameter t, integral time variable parameter τ, sugar source supply S at time point τ C (τ), half-saturation constant K corresponding to the sugar source C , the nitrogen source supply S at time point τ N (τ), half-saturation constant K corresponding to the nitrogen source N , correction coefficient of sugar source supply contribution rate ξ C , correction coefficient ξ of nitrogen source supply contribution rate N , where by combining the microbial growth requirement Gs , time range parameter t, integral time variable parameter τ, sugar source supply S at time point τ C (τ) and the half-saturation constant K corresponding to the sugar source C It constitutes a contribution rate of sugar source supply to microbial growth η C Functional relationship By combining the microbial growth requirement G s , time range parameter t, integral time variable parameter τ, nitrogen source supply S at time point τ N (τ) and the half-saturation constant K corresponding to the nitrogen source N It constitutes a contribution rate of nitrogen source supply to microbial growth η N Functional relationship This formula can realize the calculation process of the growth contribution of microbial growth demand corresponding to the early stage of microbial growth in the fermentation tank. At the same time, the correction coefficient ξ of the sugar source supply contribution rate is used. C and the correction coefficient ξ of the nitrogen source supply contribution rate N The introduction of can be adjusted according to the errors that occur during the calculation process, thereby improving the accuracy and applicability of the calculation formula for the contribution of sugar source-nitrogen source microbial growth.

[0109] Furthermore, the supply delay response adjustment analysis of the sugar source supply and the nitrogen source supply based on the corresponding early stage of microbial growth in the fermenter includes the following steps:

[0110] Obtain the corresponding microbial growth cell density and microbial metabolite accumulation amount through the corresponding early stage of microbial growth in the fermentation tank;

[0111] In an embodiment of the present invention, the growth status of the microorganisms in the early growth stage needs to be monitored and recorded in the fermenter. To achieve this, a densitometer or flow cytometer or other equipment can be used to detect the cell concentration in the fermentation liquid in real time to determine the growth cell density of the microorganisms. At this time, the temperature, pH, dissolved oxygen and other parameters in the fermenter need to be kept stable to ensure that the microorganisms are in a suitable growth environment. By comparing the cell concentrations at different time points, a cell growth curve can be obtained. At the same time, by regularly sampling the fermenter liquid sample, a gas chromatograph (GC) or a high-performance liquid chromatograph (HPLC) can be used to analyze the accumulation of metabolites (such as short-chain fatty acids, amino acids, etc.) in the fermentation liquid, and finally the microbial growth cell density and the accumulation of microbial metabolites can be obtained.

[0112] Preferably, the microbial fermentation reaction rate analysis is performed on the microbial growth cell density and the accumulation of microbial metabolites to generate the actual microbial fermentation reaction rate corresponding to the fermentation process of the microorganism in the early growth stage;

[0113] In an embodiment of the present invention, after obtaining data on microbial growth cell density and metabolite accumulation, the next step is to perform a microbial fermentation reaction rate analysis to analyze the relationship between cell density and metabolite accumulation by using a mathematical model, and calculate the microbial growth and metabolic rate based on the Laplace growth model, Monod model or other commonly used fermentation kinetic models. Specifically, the actual fermentation reaction rate can be derived by calculating the growth rate of the microorganism in the early growth stage (such as the maximum specific growth rate, μ_max) and combining it with the metabolite accumulation rate of the microorganism. During the analysis process, the system should consider the consumption rate of the sugar source, nitrogen source and oxygen in the reaction, as well as the generation rate of the metabolites, and comprehensively derive the fermentation reaction rate of the microorganism, and finally generate the actual reaction rate of the microbial fermentation corresponding to the fermentation process of the microorganism in the early growth stage.

[0114] Preferably, a sugar and nitrogen supply dynamic model is constructed based on the actual reaction rate of microbial fermentation corresponding to the fermentation process of the microorganism in the early growth stage and the principle of bioreaction kinetics for the sugar source supply and the nitrogen source supply, thereby generating a sugar source-nitrogen source supply dynamic mathematical model in the early growth stage of the microorganism;

[0115] In an embodiment of the present invention, based on the analysis results of the above-mentioned microbial fermentation reaction rate, a dynamic relationship model between the supply of sugar source and nitrogen source is established using the principle of biological reaction kinetics. To this end, it is necessary to combine the principle of material balance and consider the consumption rates of sugar source and nitrogen source, the growth rate of microorganisms and metabolic pathways at the same time. According to the demand for sugar source by microorganisms in the early growth stage, combined with the reaction rate and the change in sugar source concentration in the fermentation tank, a sugar source supply model is derived. Similarly, by analyzing the consumption rate of nitrogen source by microorganisms during the fermentation process and combining the reaction rate data, a dynamic supply model of nitrogen source is established. The sugar source and nitrogen source supply models are combined to generate a dynamic mathematical model of sugar source-nitrogen source supply in the early stage of microbial growth. The model should include the factors affecting the growth of microorganisms and nitrogen sources, especially the specific effects on the growth rate and the production rate of metabolites under different supply conditions, and finally generate a dynamic mathematical model of sugar source-nitrogen source supply in the early stage of microbial growth.

[0116] Preferably, a delayed response time window identification analysis is performed on the sugar source supply and the nitrogen source supply according to a dynamic mathematical model of sugar source-nitrogen source supply in the early stage of microbial growth, so as to obtain a delayed response time window between the sugar source and nitrogen source supply and the microbial growth; and the specific effect of changes in sugar source and nitrogen source supply on microbial growth is determined based on the delayed response time window between the sugar source and nitrogen source supply and the microbial growth, and the delayed response time is long;

[0117] In an embodiment of the present invention, by utilizing the established sugar source-nitrogen source supply dynamic mathematical model, the effect of changes in sugar source and nitrogen source supply on microbial growth, especially the delayed response effect between them, can be further analyzed by analyzing the reaction time of sugar source and nitrogen source supply changes and microbial growth in the model, identifying the response delay time after the sugar source and nitrogen source supply changes, and the analysis can quantify the effect of sugar source and nitrogen source supply changes on microbial growth by a systematic cross-correlation analysis method. By drawing a comparison chart of sugar source, nitrogen source supply changes and microbial growth curves through experimental data, and finding the most suitable delay time window, the delayed response time window between sugar source and nitrogen source supply on microbial growth is obtained. At the same time, by comparing the microbial growth curves under different experimental conditions according to the delayed response time window between sugar source and nitrogen source supply on microbial growth, the response delay time of changes in sugar source and nitrogen source supply can be determined. This process can further optimize the determination of the response time window using a regression analysis method, and ultimately obtain the specific effect of changes in sugar source and nitrogen source supply on microbial growth.

[0118] Preferably, based on the long delayed response time of the specific impact of changes in sugar source and nitrogen source supply on microbial growth, the supply delayed response regulation of the sugar source supply and the nitrogen source supply is quantified to obtain the early sugar source-nitrogen source growth supply response regulation efficiency.

[0119] In an embodiment of the present invention, the supply of sugar source and nitrogen source is further adjusted based on the delayed response time of the change in sugar source and nitrogen source supply to the growth of microorganisms. To achieve this, it is necessary to adjust the supply of sugar source and nitrogen source according to the previously obtained delayed response time window. In actual operation, the control system of the fermenter can be automatically adjusted to ensure that the microbial growth can be optimized to the greatest extent within the delay time after the change in sugar source or nitrogen source supply. Specifically, the sugar supply system and nitrogen source addition system of the fermenter can be adjusted in real time, and the supply rate can be adjusted according to the feedback signal to quantify the efficiency of the adjustment by comparing the microbial growth effect, metabolite yield and reaction rate before and after the adjustment. This process requires the use of a dynamic optimization algorithm (such as a PID control algorithm) to accurately adjust the supply of sugar source and nitrogen source to maximize the growth rate and metabolite yield of the microorganism, and ultimately obtain the early sugar source-nitrogen source growth supply response adjustment efficiency.

[0120] Furthermore, the fermentation tank late growth regulation control module includes the following functions:

[0121] Performing growth dynamic curve fitting analysis on the corresponding late growth stage of the microorganisms in the fermentation tank to generate a dynamic fitting curve of the late growth stage of the microorganisms in the fermentation tank;

[0122] In an embodiment of the present invention, real-time data corresponding to the late growth of microorganisms in a fermenter is obtained, and an online monitoring system is set up in the fermenter to measure the growth indicators of the microorganisms in real time, such as optical density (OD value), pH value, dissolved oxygen (DO) concentration, and temperature. After obtaining the growth data over a period of time, the growth curve of the microorganism is fitted and analyzed by statistical methods. Common fitting methods include logistic model, Gompertz model or Baranyi model, which can reflect the changes in the exponential growth, plateau and decline stages of microorganisms in the late growth stage. Using data analysis software such as Matlab or Python, the collected microbial growth data is curve fitted by a fitting algorithm to extract key parameters of the late growth stage of microorganisms, including the maximum specific growth rate and the rate of metabolite production. The generated growth dynamic curve can be further used to analyze and predict the trend of microbial growth, and finally a dynamic fitting curve of the late growth stage of microorganisms in the fermenter is fitted.

[0123] Preferably, a metabolic kinetic network analysis is performed on the dynamic fitting curve of the late growth period of the microorganisms in the fermentation tank based on the temperature and dissolved oxygen concentration and combined with the growth and metabolism law of the microorganisms to generate a growth and metabolism distribution kinetic network corresponding to the microorganisms under different temperature and dissolved oxygen concentration conditions;

[0124] In an embodiment of the present invention, a metabolic kinetic network is constructed based on the previously generated growth dynamics curve, as well as environmental variables such as temperature and dissolved oxygen concentration, combined with known microbial growth and metabolic laws. In specific operations, metabolic flux analysis (MFA) and model-based systems biology methods can be used to combine the effects of temperature and dissolved oxygen concentration on microbial metabolic pathways to establish a multivariate metabolic network model. This model needs to consider the metabolic pathways in microbial cells under different temperature and dissolved oxygen concentration conditions, including carbon source utilization, energy metabolism (such as ATP production), and product generation (such as acid, alcohol, etc.). By combining the steady-state metabolic model with dynamic simulation, quantitative analysis can be performed on different fermentation conditions to generate the metabolic flux distribution of microorganisms under different operating conditions, and then a metabolic dynamic network under changes in temperature and dissolved oxygen concentration can be drawn. This metabolic network will provide a theoretical basis for microbial growth and metabolic regulation, and can predict the distribution of microbial metabolic products under different environmental conditions, and ultimately generate a growth metabolic distribution kinetic network corresponding to microorganisms under different temperature and dissolved oxygen concentration conditions.

[0125] Preferably, a response function is derived based on the metabolic response relationship between temperature and dissolved oxygen concentration based on the growth metabolic distribution dynamics network of the microorganism under different temperature and dissolved oxygen concentration conditions to generate a metabolic response function between the temperature and dissolved oxygen concentration in the late stage of microbial growth;

[0126] In an embodiment of the present invention, based on the established microbial growth metabolic distribution kinetic network, a mathematical modeling method is further used to derive the functional relationship between temperature and dissolved oxygen concentration on the microbial metabolic response. This process generally uses an optimization method such as the response surface method (RSM) or the Lagrange multiplier method. The specific operation is based on the previously generated metabolic kinetic network, and multiple simulation experiments are performed under different temperature and dissolved oxygen concentration conditions to collect corresponding metabolic data. These data can be used to derive the response function of microbial metabolites and growth rates to environmental factors (such as temperature and dissolved oxygen concentration). In the derivation of the response function, the nonlinear effects of temperature and dissolved oxygen concentration on metabolic flux need to be considered. A metabolic response function between temperature and dissolved oxygen concentration can be generated to describe the effects of these two factors on microbial growth generation, and finally a metabolic response function between temperature and dissolved oxygen concentration in the later stage of microbial growth is derived.

[0127] Preferably, the fermentation growth of the corresponding microorganisms in the fermentation tank is dynamically controlled according to the metabolic response function between the temperature and the dissolved oxygen concentration in the late stage of microbial growth, so as to perform the temperature-dissolved oxygen fermentation regulation coordinated control work corresponding to the late stage of fermentation tank growth.

[0128] In an embodiment of the present invention, by combining the metabolic response function between the temperature and dissolved oxygen concentration in the late stage of microbial growth generated previously, the fermentation growth of the corresponding microbial growth stage in the fermenter is coordinated and controlled to achieve dynamic coordinated control of the temperature and dissolved oxygen concentration in the fermenter, and optimize the growth and metabolic processes corresponding to the late stage of microorganisms. Specifically, the response function is first input into the automatic control system of the fermenter, and combined with the online monitored temperature and dissolved oxygen concentration data, the temperature and dissolved oxygen in the fermenter are adjusted in real time through a feedback loop. The control system needs to calculate the optimal temperature and dissolved oxygen concentration settings in real time, and accurately control the fermentation environment through regulating equipment (such as thermostats, gas supply systems, etc.). At the same time, PID control algorithms or fuzzy control algorithms can be used to smoothly control the fluctuations of temperature and dissolved oxygen concentration to ensure the stability of environmental variables during the fermentation process, which is conducive to the production of diabetic intestinal microecological regulation preparations, and finally the temperature-dissolved oxygen fermentation regulation coordinated control work corresponding to the late stage of fermentation tank growth is performed.

[0129] Furthermore, the metabolic response function between the temperature and dissolved oxygen concentration in the late stage of microbial growth is specifically:

[0130]

[0131] Where, is the temperature T and dissolved oxygen concentration in the late stage of microbial growth The metabolic response function between max is the maximum growth rate of microbial fermentation under ideal conditions, k0 is the temperature-dependent prefactor, K s is the dissolved oxygen half-saturation constant, exp is the exponential function, E a is the temperature activation energy, R is the gas constant, and T0 is the reference temperature.

[0132] The present invention obtains a metabolic response function by using a specific mathematical model and verifying it, which is used to derive a response function for the metabolic response relationship between temperature and dissolved oxygen concentration. The metabolic response function plays a key role. It provides a quantitative relationship between temperature and dissolved oxygen concentration for the microbial growth process, and combines these factors into the growth control and optimization of the fermentation process. Through this function, the changes in the growth rate of microorganisms under different temperature and dissolved oxygen conditions can be quantitatively evaluated, thereby facilitating precise dynamic control of the fermentation process. Through the metabolic response function, the optimal growth environment of microorganisms under different conditions can be clearly defined. For example, under high temperature or low oxygen conditions, the growth rate of microorganisms decreases. At this time, the fermentation conditions can be optimized by adjusting the temperature or oxygen supply, thereby improving the yield and efficiency. This process provides a strong basis for the environmental control of the fermentation tank, making the production process more accurate and efficient. The metabolic response function clearly defines the relationship between temperature and dissolved oxygen concentration, revealing the complex interaction of these two factors on microbial growth. For example, the effect of the corresponding temperature on the growth rate is expressed as an exponential relationship, while the dissolved oxygen concentration follows the typical Michaelis-Menten dynamics. The interactive effect between the two is the core of microbial metabolic dynamics. Understanding this helps to accurately adjust the temperature and oxygen supply during the fermentation process and achieve better metabolic control. In addition, the metabolic response function is derived based on the metabolic relationship between temperature and dissolved oxygen concentration, which can provide theoretical support for the coordinated dynamic control of the fermentation tank. In the later stages of the fermentation process, by real-time monitoring of temperature and oxygen concentration, the metabolic response function can be used to predict the state of microbial growth and dynamically adjust parameters such as temperature and oxygen to ensure that microorganisms carry out metabolic activities under optimal conditions and improve the yield and efficiency of fermentation. The optimization of the microbial fermentation process is not only to improve efficiency, but also to improve the quality of the product. By precisely controlling the temperature and dissolved oxygen concentration, some situations such as incomplete metabolism or excessive by-product production caused by environmental maladaptation can be avoided, thereby ensuring the purity and yield of the target product. In summary, this function fully takes into account the temperature T and dissolved oxygen concentration in the later stage of microbial growth. Metabolic response function Under ideal conditions, the maximum growth rate μ corresponding to microbial fermentation max , temperature-dependent prefactor k0, dissolved oxygen half-saturation constant K s , exponential function exp, temperature activation energy E a , gas constant R, reference temperature T0, according to the late growth temperature T of microorganisms and dissolved oxygen concentration Metabolic response function The mutual relationship between the following parameters forms a functional relationship:

[0133]

[0134] The function can realize the response function derivation process of the metabolic response relationship between temperature and dissolved oxygen concentration, thereby improving the accuracy and applicability of the metabolic response function.

[0135] Furthermore, the fermentation tank metabolic process environmental condition control module includes the following functions:

[0136] Based on the ratio of oxygen and carbon dioxide gas flow rates, a metabolic feedback correlation analysis was conducted on the corresponding microbial metabolic process in the fermentation tank, and the metabolic feedback correlation relationship between the gas ratio flow rate and microbial metabolism during the fermentation process in the fermentation tank was obtained;

[0137] In an embodiment of the present invention, the proportional flow rate between oxygen (O2) and carbon dioxide (CO2) gases during the fermentation process is detected in real time by a sensor, and the data is input into a data analysis platform to analyze the corresponding microbial metabolic process in the fermentation tank, so as to associate the gas proportional flow rate with the microbial metabolic activity by establishing a metabolic model (for example, a model based on mass spectrometry analysis). Specifically, the gas proportional flow rate data is used to infer the oxygen consumption rate and carbon dioxide production rate of the microorganisms during the fermentation process, and the relationship between them and the microbial metabolites (such as lactic acid, alcohol, esters, etc.) is quantitatively analyzed. Through the metabolic flux analysis method, the influence of the proportional flow rate of oxygen and carbon dioxide on the microbial metabolic pathway is determined, and the feedback effect of oxygen and carbon dioxide in the metabolic pathway is further revealed, thereby obtaining the feedback correlation relationship between the proportional flow rate of oxygen and carbon dioxide gases and microbial metabolism, and finally obtaining the metabolic feedback correlation relationship between the proportional flow rate of gas and microbial metabolism during the fermentation process in the fermentation tank.

[0138] Preferably, based on the metabolic feedback correlation between the gas ratio flow rate and microbial metabolism during the fermentation process in the fermenter, the gas exchange ratio change analysis of the oxygen and carbon dioxide gas ratio flow rate is performed to obtain the corresponding gas exchange ratio change between oxygen and carbon dioxide during the fermentation process in the fermenter;

[0139] In an embodiment of the present invention, by combining the metabolic feedback correlation between the gas ratio flow rate and microbial metabolism obtained from the previous analysis, the impact of changes in the oxygen and carbon dioxide gas ratio during the fermentation process on the gas exchange process is further analyzed. Combined with real-time monitoring data, the gas exchange ratio in the fermenter is dynamically analyzed, and a gas analyzer (such as a gas chromatograph) is used to detect changes in oxygen and carbon dioxide concentrations in the fermenter in real time, and the data is compared with related changes in microbial metabolism. Based on these data, a mathematical model can be established to analyze the changes in gas exchange during the fermentation process under different oxygen and carbon dioxide gas ratio flow rates, revealing the intrinsic relationship between the gas exchange process and microbial metabolism. For example, when the oxygen flow rate increases, the change in carbon dioxide concentration and its impact on microbial metabolism, and vice versa, are analyzed. Through the dynamic analysis of the change in the gas exchange ratio, the change in the oxygen and carbon dioxide gas exchange ratio is obtained, and finally the corresponding change in the gas exchange ratio between oxygen and carbon dioxide during the fermentation process in the fermenter is obtained.

[0140] Preferably, the impact of the change in the gas exchange ratio between oxygen and carbon dioxide during the fermentation process in the fermenter on the pH value is evaluated and analyzed to obtain the degree of influence of the change in the gas exchange ratio between oxygen and carbon dioxide on the pH value;

[0141] In an embodiment of the present invention, a mathematical model of the effect of changes in the oxygen and carbon dioxide gas exchange ratio on the pH value is established, so as to obtain the change pattern of the pH value under different gas exchange ratios through experiments or literature data, and analyze it in combination with real-time monitored gas flow data and pH value data. Specifically, the change in the gas ratio in the fermentation tank (especially the change in the oxygen and carbon dioxide ratio) will affect the solubility of carbon dioxide in the fermentation liquid, thereby changing the acidity and alkalinity (pH value) of the solution. By combining the online pH sensor data, the change in pH value at different time points and different gas flow rates is recorded, and a correlation model between the change in gas exchange ratio and pH value is established using regression analysis or machine learning algorithms (such as support vector machines, neural networks, etc.). By evaluating according to the change in the gas exchange ratio, the specific degree of influence of the change in the oxygen and carbon dioxide gas exchange ratio on the pH value can be quantified, and finally the degree of influence of the change in the oxygen and carbon dioxide gas exchange ratio on the pH value is obtained.

[0142] Preferably, based on the degree of influence of the change in the oxygen and carbon dioxide gas exchange ratio on the pH value, an intelligent coordinated regulation analysis is performed on the pH value and the oxygen and carbon dioxide gas ratio flow rate to obtain a coordinated regulation relationship between the gas ratio flow rate and the pH value;

[0143] In an embodiment of the present invention, the corresponding pH value and the oxygen and carbon dioxide gas ratio flow rate are intelligently coordinated by utilizing the influence of the change in the oxygen and carbon dioxide gas exchange ratio on the pH value obtained by the analysis in the previous step. The real-time data monitoring system (including a gas flow sensor, a pH sensor, etc.) collects real-time data of oxygen and carbon dioxide gas flow rates and pH values, and the gas ratio flow rate and pH value are coordinated and analyzed by an intelligent algorithm (such as a PID control algorithm, a fuzzy control algorithm, or reinforcement learning, etc.). The specific adjustment process is to calculate the expected impact of the change in the oxygen and carbon dioxide gas exchange ratio on the pH value in the current state based on the real-time detected pH value and gas flow rate, and adjust the gas flow ratio (for example, increasing the oxygen flow rate or reducing the carbon dioxide flow rate) through the algorithm to keep the pH value within a predetermined range. The process is adjusted by real-time feedback of the control system to ensure the optimal ratio of the gas ratio flow rate and the pH value during the fermentation process, thereby maintaining the optimal metabolic environment of the microorganisms, and ultimately obtaining a synergistic regulatory relationship between the gas ratio flow rate and the pH value.

[0144] Preferably, the metabolic environmental conditions of the corresponding microbial metabolic process in the fermentation tank are dynamically controlled according to the synergistic regulatory relationship between the gas proportional flow rate and the pH value, so as to perform synergistic control of the microbial growth environmental conditions corresponding to the metabolic process in the fermentation tank.

[0145] In an embodiment of the present invention, by utilizing the synergistic regulatory relationship between the gas ratio flow rate and the pH value obtained in the previous analysis, combined with the real-time feedback data of microbial metabolism in the fermentation tank, the metabolic environment is dynamically controlled. Relying on the aforementioned intelligent algorithm, the optimal metabolic conditions in the fermentation process are maintained by adjusting the gas exchange ratio, pH value and other environmental variables according to the real-time oxygen and carbon dioxide gas flow data and pH value data. In the specific implementation process, the metabolic change trend of the microorganisms is predicted in real time based on the system model (such as the fermentation kinetics model), and the operating parameters are dynamically adjusted according to the current environmental conditions. For example, at a certain stage, if the gas exchange ratio causes the pH value to deviate from the set range, the gas flow ratio will be automatically adjusted or the addition of acid-base regulators will be increased to maintain the stability of the pH value. At the same time, the entire control process needs to be automatically executed in the control system of the fermentation tank to ensure the dynamic balance of the microbial metabolic environment, so as to promote the stable production process of the diabetic intestinal microecological regulation preparation, and finally perform the coordinated control of the microbial growth environment conditions corresponding to the metabolic process of the fermentation tank.

[0146] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily 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 invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A dynamic fermentation tank control system for the production of diabetic intestinal microecological regulating preparations, characterized in that: Includes the following modules: A fermentation tank sensor feedback control network establishment module is used to install sugar source sensors, nitrogen source sensors, temperature sensors, pH sensors, concentration sensors, and flow sensors in the corresponding fermentation tanks used for the production of diabetic intestinal microecological regulation preparations, and connect them to the central feedback control system to generate an embedded sensor feedback control network; The fermentation tank early growth nutrient supply control module is used to use the embedded sensor feedback control network to synchronously monitor the sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration and oxygen and carbon dioxide gas ratio flow rate of the fermentation tank in real time; based on the sugar source supply and nitrogen source supply, the corresponding microbial growth early stage in the fermentation tank is dynamically controlled to perform the sugar-nitrogen nutrient source supply coordinated control work corresponding to the fermentation tank early growth stage; Features include: The embedded sensor feedback control network is used to synchronously monitor the sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and oxygen and carbon dioxide gas ratio flow of the fermentation tank in real time; These include: By starting the corresponding sugar source sensor, nitrogen source sensor, temperature sensor, pH sensor, concentration sensor and flow sensor in the embedded sensor feedback control network at the same time point, and using the sugar source sensor to monitor the sugar source supply corresponding to the fermentation process in the fermenter in real time, using the nitrogen source sensor to monitor the nitrogen source supply corresponding to the fermentation process in the fermenter in real time, using the temperature sensor to monitor the temperature corresponding to the fermentation process in the fermenter in real time, using the pH sensor to monitor the pH value corresponding to the fermentation process in the fermenter in real time, using the concentration sensor to monitor the dissolved oxygen concentration corresponding to the fermentation process in the fermenter in real time, and using the flow sensor to monitor the oxygen and carbon dioxide gas flows corresponding to the fermentation process in the fermenter in real time; By calculating the gas ratio of oxygen and carbon dioxide gas flow corresponding to the fermentation process in the fermenter at each time point, the proportional flow of oxygen and carbon dioxide gas corresponding to the fermentation process in the fermenter is obtained; The sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and oxygen to carbon dioxide gas ratio flow rate corresponding to the fermentation process in the fermenter are uploaded to the central feedback control system through the embedded sensor feedback control network for data cleaning to obtain the sugar source supply, nitrogen source supply, temperature, pH value, dissolved oxygen concentration, and oxygen to carbon dioxide gas ratio flow rate corresponding to the fermenter in the same time range; Performing a forecast analysis on the early growth stage of the microorganisms in the fermentation tank to obtain the corresponding microbial growth demand in the early growth stage of the microorganisms in the fermentation tank; Based on the sugar source supply and the nitrogen source supply, the growth contribution calculation formula of the sugar source-nitrogen source microbial growth contribution is used to calculate the microbial growth demand corresponding to the early stage of microbial growth in the fermentation tank to obtain the contribution rate of the sugar source supply to the microbial growth and the contribution rate of the nitrogen source supply to the microbial growth; wherein the sugar source-nitrogen source microbial growth contribution calculation formula is specifically: ； ； Where, is the contribution rate of sugar source supply to microbial growth, is the contribution rate of nitrogen source supply to microbial growth, is the amount required for microbial growth, is the time range parameter, is the integral time variable parameter, For the time point The sugar supply at is the half-saturation constant corresponding to the sugar source, For the time point The nitrogen supply at is the half-saturation constant corresponding to the nitrogen source, is the correction coefficient of the sugar source supply contribution rate, is the correction coefficient of nitrogen source supply contribution rate; The growth flux balance was calculated based on the contribution rate of sugar source supply to microbial growth and the contribution rate of nitrogen source supply to microbial growth, and the early sugar source-nitrogen source growth flux balance coefficient was obtained; Based on the early stage of microbial growth in the fermenter, the supply delay response regulation analysis of the sugar source supply and nitrogen source supply was performed to obtain the early sugar source-nitrogen source growth supply response regulation efficiency; this includes: Obtain the corresponding microbial growth cell density and microbial metabolite accumulation amount through the corresponding early stage of microbial growth in the fermentation tank; The microbial fermentation reaction rate analysis is performed on the microbial growth cell density and the accumulation of microbial metabolites to generate the actual microbial fermentation reaction rate corresponding to the fermentation process of the microorganism in the early growth stage; Based on the actual reaction rate of microbial fermentation corresponding to the fermentation process in the early stage of microbial growth and the principle of bioreaction kinetics, a sugar and nitrogen supply dynamic model is constructed for the sugar source supply and nitrogen source supply, and a dynamic mathematical model of sugar source and nitrogen source supply in the early stage of microbial growth is generated; According to the dynamic mathematical model of sugar source-nitrogen source supply in the early stage of microbial growth, the delayed response time window identification analysis of sugar source supply and nitrogen source supply is carried out to obtain the delayed response time window between sugar source and nitrogen source supply to microbial growth; according to the delayed response time window between sugar source and nitrogen source supply to microbial growth, the specific effect of changes in sugar source and nitrogen source supply on microbial growth is determined to have a long delayed response time; Based on the specific effects of changes in sugar and nitrogen source supply on microbial growth, the delayed response time of sugar and nitrogen source supply was quantified to obtain the early sugar and nitrogen source growth supply response regulation efficiency. Based on the early sugar source-nitrogen source growth flux balance coefficient and the early sugar source-nitrogen source growth supply response regulation efficiency, the supply coordinated dynamic control of the corresponding microbial growth early stage in the fermentation tank is performed to perform the sugar-nitrogen nutrient source supply coordinated control work corresponding to the early growth stage of the fermentation tank; The fermentation tank late growth regulation control module is used to dynamically control the fermentation growth of the corresponding microorganisms in the fermentation tank in the late growth stage based on temperature and dissolved oxygen concentration, so as to perform the temperature-dissolved oxygen fermentation regulation coordinated control work corresponding to the fermentation tank late growth stage; The fermentation tank metabolic process environmental condition control module is used to dynamically control the metabolic environmental conditions of the corresponding microbial metabolic process in the fermentation tank based on the pH value and the ratio of oxygen and carbon dioxide gas flow, so as to perform the coordinated control of the microbial growth environmental conditions corresponding to the fermentation tank metabolic process. It includes the following functions: Based on the ratio of oxygen and carbon dioxide gas flow rates, a metabolic feedback correlation analysis was conducted on the corresponding microbial metabolic process in the fermentation tank, and the metabolic feedback correlation relationship between the gas ratio flow rate and microbial metabolism during the fermentation process in the fermentation tank was obtained; Based on the metabolic feedback relationship between the gas flow rate ratio and microbial metabolism during the fermentation process in the fermenter, the gas exchange ratio change of the oxygen and carbon dioxide gas flow rate ratio was analyzed to obtain the corresponding gas exchange ratio change between oxygen and carbon dioxide during the fermentation process in the fermenter; The influence of the change in the gas exchange ratio between oxygen and carbon dioxide during the fermentation process in the fermenter on the pH value was evaluated and analyzed to obtain the degree of influence of the change in the gas exchange ratio between oxygen and carbon dioxide on the pH value. Based on the influence of the change in the oxygen and carbon dioxide gas exchange ratio on the pH value, an intelligent coordinated regulation analysis is performed on the pH value and the oxygen and carbon dioxide gas ratio flow rate to obtain the coordinated regulation relationship between the gas ratio flow rate and the pH value; According to the synergistic regulatory relationship between the gas proportional flow rate and the pH value, the metabolic environmental conditions of the corresponding microbial metabolic process in the fermentation tank are dynamically controlled to perform the synergistic control of the microbial growth environmental conditions corresponding to the fermentation tank metabolic process.

2. The dynamic fermentation tank control system for the production of diabetic intestinal microecological regulating preparations according to claim 1, characterized in that: The fermentation tank sensor feedback control network establishment module includes the following functions: Obtain specific requirements for the production and fermentation of diabetic intestinal microecological regulation preparations; Based on the specific production and fermentation requirements of diabetic intestinal microecological regulation preparations, functional requirements for sugar source sensors, nitrogen source sensors, temperature sensors, pH sensors, concentration sensors, and flow sensors are defined to obtain the corresponding working parameter range and accuracy selection requirements for each sensor; Based on the corresponding working parameter range and accuracy selection requirements of each sensor, the corresponding sugar source sensor, nitrogen source sensor, temperature sensor, pH sensor, concentration sensor and flow sensor are installed in the corresponding fermenter used for the production of diabetic intestinal microecological regulation preparations, and the corresponding data format, signal transmission standard and real-time requirements of each sensor are obtained; Based on the corresponding working parameter range and accuracy selection requirements of each sensor, the corresponding data format, signal transmission standard and real-time requirements are designed for data output compatible channels to generate corresponding data output compatible acquisition channels between each sensor; According to the corresponding data output compatibility acquisition channels between various sensors and the central feedback control system, the control network connection is regulated to generate an embedded sensor feedback control network.

3. The dynamic fermentation tank control system for the production of diabetic intestinal microecological regulating preparations according to claim 2, characterized in that: The working parameter ranges and accuracy selection requirements corresponding to each sensor are specifically as follows: a sugar source sensor with a working parameter range of 0-200g / L and an accuracy range of ±0.1g / L, a nitrogen source sensor with a working parameter range of 0-50g / L and an accuracy range of ±0.05g / L, a temperature sensor with a working parameter range of 20-45°C and an accuracy range of ±0.2°C, a pH sensor with a working parameter range of 3.5-7.5pH and an accuracy range of ±0.05pH, a concentration sensor with a working parameter range of 0-100g / L and an accuracy range of ±0.1g / L, and a flow sensor with a working parameter range of 0-500g / L and an accuracy range of ±0.5g / L.

4. The dynamic fermentation tank control system for the production of diabetic intestinal microecological regulating preparations according to claim 1, characterized in that: The fermentation tank late growth regulation control module includes the following functions: Performing growth dynamic curve fitting analysis on the corresponding late growth stage of the microorganisms in the fermentation tank to generate a dynamic fitting curve of the late growth stage of the microorganisms in the fermentation tank; Based on temperature and dissolved oxygen concentration and combined with the growth and metabolism laws of microorganisms, a metabolic kinetic network analysis was performed on the dynamic fitting curve of the late growth of microorganisms in the fermentation tank to generate a growth and metabolism distribution kinetic network corresponding to microorganisms under different temperature and dissolved oxygen concentration conditions; Based on the growth metabolic distribution dynamics network of microorganisms under different temperature and dissolved oxygen concentration conditions, the response function of the metabolic response relationship between temperature and dissolved oxygen concentration is derived to generate the metabolic response function between temperature and dissolved oxygen concentration in the late stage of microbial growth; According to the metabolic response function between the temperature and dissolved oxygen concentration in the late stage of microbial growth, the fermentation growth of the corresponding microbial growth stage in the fermenter is dynamically controlled to perform the temperature-dissolved oxygen fermentation regulation coordinated control work corresponding to the late stage of fermenter growth.

5. The dynamic fermentation tank control system for the production of diabetic intestinal microecological regulating preparations according to claim 4, characterized in that: The metabolic response function between the temperature and dissolved oxygen concentration in the late stage of microbial growth is specifically: ； Where, Temperature in the late growth stage of microorganisms and dissolved oxygen concentration The metabolic response function between is the maximum growth rate corresponding to microbial fermentation under ideal conditions, is the temperature-dependent prefactor, is the dissolved oxygen half-saturation constant, is an exponential function, is the temperature activation energy, is the gas constant, is the reference temperature.

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