Straw low-temperature high-efficiency rotting bacteria agent preparation control method and system

Abstract

This invention discloses a method and system for controlling the preparation of low-temperature, high-efficiency straw-promoting microbial agents, relating to the field of microbial agent preparation process control technology. It utilizes multiple types of sensors to collect a microbial agent preparation dataset RAW(t) at time t, calculates and constructs a metabolic feature vector Vm(t) at time t, achieving real-time quantification of the macroscopic metabolic activity of microorganisms. Furthermore, it calculates the respiratory entropy Q at time t. R (t) and calculate the enzyme synthesis potential E at time t. S (t) and construct the diagnostic state vector Ds(t) at time t, thereby transforming external measurable parameters into a deep insight into the internal production potential of the microbial cells, objectively and dynamically diagnosing the fermentation process into different physiological stages, and generating a stage identifier ID(t) at time t, automatically executing the matching fine control strategy AIR, ensuring that optimal regulation is automatically adopted at each stage, thereby significantly improving the quality, production efficiency and batch stability of the microbial agent product.

Description

Technical Field

[0001] This invention relates to the field of microbial agent preparation process control technology, specifically to a method and system for controlling the preparation of low-temperature, high-efficiency straw-promoting microbial agents. Background Technology

[0002] In the fields of modern agriculture and environmental protection, the efficient and environmentally friendly treatment and utilization of agricultural waste has become a key issue for achieving sustainable development. Among these, the resource utilization of crop straw, as a massive and renewable biomass resource, is a core issue in this field. An important technological approach is to utilize the decomposition of straw by microorganisms to transform it into organic matter that nourishes the soil. The core carrier in this process is the decomposition-promoting microbial agent. Especially in regions or seasons with low temperatures, the activity of conventional microbial agents decreases significantly. Therefore, low-temperature, high-efficiency decomposition-promoting microbial agents that can maintain high decomposition activity under such conditions have emerged. These agents provide important technical support for solving the problem of disposing of large amounts of straw after the autumn harvest in northern regions, reducing environmental pollution caused by burning, and developing ecological organic agriculture. However, the value of a highly efficient microbial agent lies not only in the quality of the microbial strain but also in the stability and controllability of its industrial preparation process. This leads to the need for automated control of the bio-fermentation process, particularly the preparation control methods and systems for these specialized microbial preparations.

[0003] Existing technologies for preparing low-temperature, high-efficiency composting agents have several shortcomings: First, poor batch-to-batch stability of product quality. Fermentation process control relies heavily on fixed process parameters or personnel experience, lacking analysis of the real-time physiological state of microorganisms. This leads to variations in viable cell count and composting efficacy between different batches, resulting in unstable product performance. Second, low production efficiency. Due to the lack of effective identification and process optimization of key metabolic stages in cell growth and target product synthesis, fermentation cycles are typically long, resulting in low yield per unit time. Third, lack of specificity in control strategies. Existing control methods are relatively general and lack specific, differentiated regulatory strategies designed for the unique physiological mechanisms of low-temperature strains in response to low-temperature stress and metabolic pathway transitions.

[0004] The aforementioned shortcomings arise because existing technologies typically simplify complex bio-fermentation processes into conventional chemical processes for control. These control methods are limited to maintaining stable process parameters such as temperature or preparation time points, neglecting dynamic metabolic information that reflects the intrinsic physiological state of microorganisms, such as the trends in respiration intensity and metabolic flux. This limitation can lead to the control system's inability to adapt when unexpected changes occur in the physiological state of the microorganisms, resulting in abnormal conditions such as reduced microbial activity and premature metabolic termination. Consequently, this can lead to low unit activity in the final microbial agent product, failing to meet straw decomposition efficiency requirements in practical applications, increasing production costs, and hindering the achievement of technical objectives. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for controlling the preparation of low-temperature, high-efficiency straw decomposition-promoting microbial agents, thus solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a method for controlling the preparation of a low-temperature, high-efficiency straw decomposition-promoting microbial agent, comprising the following steps:

[0007] S1. By deploying multiple types of sensors in the fermenter, the inoculant preparation dataset RAW(t) at time t in the fermenter is collected according to the preset acquisition time window △t.

[0008] S2. Based on the RAW(t) dataset of microbial agent preparation at time t, and combined with the material balance principle of biochemical reactors, calculate the oxygen uptake rate R of microorganisms in the fermenter at time t. O (t) and the carbon dioxide release rate R at time t C (t), and construct the metabolic feature vector Vm(t) at time t.

[0009] S3. Based on the metabolic feature vector Vm(t) at time t, calculate the respiratory entropy Q at time t. R (t), combined with the optimal respiratory entropy Q of the cells when entering the growth ramp-up phase obtained through experiments, and the enzyme synthesis potential E of the microorganisms in the fermenter at time t was calculated. S (t);

[0010] S4. Enzyme synthesis potential E of microorganisms in the fermenter at time t S (t), calculate the rate of change of enzyme synthesis potential dE within the preset collection time window Δt. S (t), and construct the diagnostic state vector Ds(t) at time t, and compare the diagnostic state vector Ds(t) at time t with the preset potential threshold θ and trend stability interval ϵ, and generate the stage identifier ID(t) at time t based on the comparison result.

[0011] S5. Based on the stage identifier ID(t) at time t, automatically invoke and execute the matching fine-grained control strategy AIR.

[0012] Preferably, S1 includes:

[0013] By deploying multiple types of sensors in the fermenter, the inoculant preparation dataset RAW(t) at time t in the fermenter is collected according to the preset acquisition time window △t.

[0014] The RAW(t) dataset for inoculant preparation at time t includes the following:

[0015] The oxygen mole fraction O(t) and carbon dioxide mole fraction C(t) in the gas discharged from the fermenter at time t are collected by an online multi-component gas analyzer deployed on the main exhaust pipe at the top of the fermenter. The gas velocity F(t) entering the fermenter at time t is collected by a thermal mass flow controller deployed on the sterile air main pipe entering the fermenter. The pressure P(t) inside the fermenter at time t is collected by a pressure transmitter deployed in the gas phase space at the top of the fermenter. The temperature T(t) at time t is collected by a Pt100 platinum resistance temperature sensor deployed in a sanitary blind pipe on the side wall of the fermenter. The total weight W1(t) of the entire fermenter at time t is measured by a weighing sensor deployed under the support feet of the fermenter. The net weight W3(t) of the fermentation liquid at time t is obtained by subtracting the known empty equipment plus the unloaded weight of multiple sensors W2. The volume V(t) of the fermentation liquid at time t is calculated according to the liquid volume-weight calculation formula.

[0016] Generate the bacterial agent preparation dataset at time t: RAW(t) = [O(t), C(t), F(t), P(t), T(t).

[0017] V(t)].

[0018] Preferably, S2 includes S21:

[0019] S21. Based on the RAW(t) dataset of inoculum preparation at time t, and combined with the material balance principle of biochemical reactors, the gas flow rate F(t) entering the fermenter at time t and the oxygen mole fraction O(t) in the gas discharged at time t are measured, and a preset nitrogen mole fraction N that does not participate in the microbial fermentation reaction is introduced. in A formula for estimating oxygen uptake rate was constructed, and the oxygen uptake rate RO(t) of microorganisms in the fermenter at time t was calculated.

[0020] The formula for calculating the oxygen uptake rate is as follows:

[0021] ;

[0022] In the formula, R represents the preset ideal gas constant, with a value of 8.314, and O in This indicates the preset mole fraction of oxygen in the gas entering the fermenter, with a value of 0.2034 N. in This represents the preset nitrogen mole fraction in the gas entering the fermenter, with a value of 0.7902.

[0023] Preferably, S2 includes S22:

[0024] S22. Based on the RAW(t) dataset of inoculum preparation at time t, and combined with the material balance principle of biochemical reactors, the gas flow rate F(t) entering the fermenter at time t and the carbon dioxide mole fraction C(t) in the gas discharged at time t are measured, and a preset nitrogen mole fraction N that does not participate in the microbial fermentation reaction is introduced. in A formula for estimating carbon dioxide release rate was constructed, and the carbon dioxide release rate RC(t) of microorganisms in the fermenter at time t was calculated.

[0025] The formula for calculating the carbon dioxide release rate is as follows:

[0026] ;

[0027] The metabolic feature vector Vm(t) = [RO(t), RC(t)] at time t is constructed by combining the oxygen uptake rate RO(t) at time t and the carbon dioxide release rate RC(t) at time t.

[0028] Preferably, S3 includes S31:

[0029] S31. Based on the metabolic feature vector Vm(t) at time t, calculate the respiratory entropy Q of the microorganisms in the fermenter at time t according to the ratio of the carbon dioxide release rate RC(t) to the oxygen uptake rate RO(t) at time t. R (t) Through multiple experiments in the early stage, the fermentation of microorganisms in the fermenter was analyzed, and the optimal respiratory entropy Q of the cells when they entered the growth and climbing phase was obtained.

[0030] Preferably, S3 includes S32:

[0031] S32. Calculate the respiratory entropy Q of microorganisms in the fermenter at time t. R The difference between (t) and the optimal respiratory entropy Q at the start of the growth ramp-up phase is used, and a nonlinear mapping is performed using the hyperbolic tangent function tanh to calculate the enzyme synthesis potential E of the microorganisms in the fermenter at time t. S (t);

[0032] Among them, the enzyme synthesis potential E of microorganisms in the fermenter at time t S The expression for calculating (t) is as follows:

[0033] .

[0034] Preferably, S4 includes S41:

[0035] S41, Based on the enzyme synthesis potential E of microorganisms in the fermenter at time t S (t), calculate the rate of change dE of the enzyme synthesis potential of microorganisms in the fermenter within the preset collection time window Δt at time t. S (t);

[0036] Among them, the rate of change of enzyme synthesis potential dE at time t S The expression for calculating (t) is as follows:

[0037] ;

[0038] Based on the enzyme synthesis potential E of microorganisms in the fermenter at time t S (t) and the rate of change of enzyme synthesis potential dE at time t S (t), constructing the diagnostic state vector Ds(t) = [E at time t] S (t), dE S (t)].

[0039] Preferably, S4 includes S42:

[0040] S42. The diagnostic state vector Ds(t) at time t is compared with the preset potential threshold θ and trend stability interval ϵ. Based on the comparison result, a stage identifier ID(t) at time t is generated, where the preset trend stability interval ϵ includes the trend stability upper limit ϵ1 and the trend stability lower limit ϵ2.

[0041] If the enzyme synthesis potential E of the microorganisms in the fermenter at time t S (t) < the preset potential threshold θ, and the rate of change of enzyme synthesis potential dE of microorganisms in the fermenter at time t. S If (t) < trend stability upper limit ϵ1, then it is determined that the current stage is in the growth preparation period, and the stage identifier ID(t) at time t is generated and assigned a value of 0;

[0042] If the enzyme synthesis potential E of the microorganisms in the fermenter at time t S (t) < the preset potential threshold θ, and the rate of change of enzyme synthesis potential dE of microorganisms in the fermenter at time t. S If (t) ≥ trend stability upper limit ϵ1, then it is determined that the current stage is in the growth adaptation period, and a stage identifier ID(t) is generated at time t and assigned a value of 1;

[0043] If the enzyme synthesis potential E of the microorganisms in the fermenter at time t S(t) ≥ the preset potential threshold θ, and the rate of change of enzyme synthesis potential dE of microorganisms in the fermenter at time t. S If (t) ≥ trend stability upper limit ϵ1, then it is determined that the current stage is in the growth and climbing phase, and the stage identifier ID(t) at time t is generated and assigned the value 2;

[0044] If the enzyme synthesis potential E of the microorganisms in the fermenter at time t S (t) ≥ the preset potential threshold θ, and the lower limit of trend stability ϵ2 ≤ the rate of change of enzyme synthesis potential of microorganisms in the fermenter at time t, dE S If (t) < trend stability upper limit ϵ1, then it is determined that the current growth is in a stable period, and a stage identifier ID(t) is generated at time t and assigned a value of 3;

[0045] If the rate of change of enzyme synthesis potential of microorganisms in the fermenter at time t is dE S If (t) < trend stability lower limit ϵ2, and the stage identifier ID (t-△t) value of the previous monitoring is 3, then it is determined that the current period is in the growth decline period, and the stage identifier ID (t) at time t is generated and assigned the value 4.

[0046] Preferably, S5 includes:

[0047] Based on the stage identifier ID(t) at time t, the matching fine-grained control strategy AIR is automatically invoked and executed;

[0048] If the stage identifier ID(t) at time t is 0, execute the growth preparation period strategy and maintain the current growth environment;

[0049] If the stage identifier ID(t) is 1 at time t, the growth adaptation strategy is executed, the temperature T is adjusted to the activation temperature T1 preset by professionals in the field, and the carbon source feeding rate is increased to 110% of the original carbon source feeding rate;

[0050] If the stage identifier ID(t) at time t is 2, the growth ramp-up strategy is executed, including cold stress and nutrient restriction actions. The cold stress action is to reduce the temperature T from the activation temperature T1 to the optimal fermentation temperature T2 preset by a person skilled in the art within one hour. When the temperature T drops to the optimal fermentation temperature T2, the temperature T is reduced to the stress temperature T3 preset by a person skilled in the art within ten minutes. The nutrient restriction action is to reduce the carbon source feeding rate to 50% of the original carbon source feeding rate. After one hour, the cold stress and nutrient restriction actions are ended, and the temperature T is increased from the stress temperature T3 to the optimal fermentation temperature T2 within one hour, and the carbon source feeding rate is restored to the original carbon source feeding rate.

[0051] If the stage identifier ID(t) at time t is 3, execute the growth stabilization period strategy, maintain the temperature T at the optimal fermentation temperature T2, switch the carbon source feeding mode to open the carbon source feeding valve for five minutes every hour, supplement the carbon source at a feeding rate of 300% of the original carbon source feeding rate, and open the inducer valve to add inducer to the fermentation broth.

[0052] If the stage identifier ID(t) at time t is 4, the growth decline strategy is executed. Within four hours, the temperature T is reduced from the optimal fermentation temperature T2 to the refrigeration temperature T4 preset by professionals in the field. The carbon source feeding valve is closed and the protective agent valve is opened. A protective agent is added to the fermentation broth to promote the synthesis of extracellular polysaccharides by the cells to encapsulate themselves. The sterile air main pipeline is adjusted to reduce the gas flow rate F(t) entering the fermenter at time t by 50% until the operator confirms the material collection operation.

[0053] A control system for preparing low-temperature, high-efficiency straw decomposition-promoting microbial agents includes a data acquisition module, a metabolic characteristic analysis module, an enzyme synthesis potential calculation module, an evaluation module, and an execution module;

[0054] The data acquisition module uses multiple types of sensors deployed in the fermenter to collect the inoculum preparation dataset RAW(t) at time t in the fermenter according to the preset acquisition time window △t.

[0055] The metabolic characteristic analysis module calculates the oxygen uptake rate Ro of microorganisms in the fermenter at time t using the RAW(t) dataset of microbial agent preparation at time t, combined with the material balance principle of biochemical reactors. O (t) and the carbon dioxide release rate R at time t C (t), and construct the metabolic feature vector Vm(t) at time t.

[0056] The enzyme synthesis potential calculation module calculates the respiratory entropy Q at time t using the metabolic feature vector Vm(t) based on time t. R (t), combined with the optimal respiratory entropy Q of the bacteria when entering the growth ramp-up phase obtained through experiments, an enzyme synthesis potential assessment model is constructed to calculate the enzyme synthesis potential E of microorganisms in the fermenter at time t. S (t);

[0057] The evaluation module assesses the enzyme synthesis potential E of microorganisms in the fermenter at time t. S (t), calculate the rate of change of enzyme synthesis potential dE within the preset collection time window Δt. S (t), and construct the diagnostic state vector Ds(t) at time t, and compare the diagnostic state vector Ds(t) at time t with the preset potential threshold θ and trend stability interval ϵ, and generate the stage identifier ID(t) at time t based on the comparison result.

[0058] The execution module automatically invokes and executes the fine-grained control strategy AIR based on the stage identifier ID(t) at time t.

[0059] This invention provides a method and system for controlling the preparation of low-temperature, high-efficiency straw decomposition-promoting microbial agents, which has the following beneficial effects:

[0060] (1) The metabolic feature vector Vm(t) at time t is accurately calculated using multi-source sensor data. This vector characterizes the overall activity of the microorganisms. Furthermore, this vector information is synthesized into the enzyme synthesis potential E of the microorganisms in the fermenter at time t. S (t), and based on the enzyme synthesis potential E of microorganisms in the fermenter at time t. S (t) and the rate of change of enzyme synthesis potential at time t, dE S (t) objectively diagnoses the fermentation process dynamically into multiple precise physiological stages. Finally, based on the generated stage identifier ID(t) at time t, the matching refined control strategy AIR is automatically executed. Through this set of interconnected technical means, the present invention achieves proactive and precise guidance of the microbial life cycle, fundamentally ensuring a comprehensive improvement in the performance, stability, and production efficiency of low-temperature, high-efficiency composting agent products.

[0061] (2) By deploying multiple types of sensors, this invention can acquire a comprehensive real-time dataset of microbial agent preparation, RAW(t), at time t, including key dynamic parameters such as the fermentation liquid volume V(t) at time t. Based on this high-fidelity data source, the system uses the material balance principle of biochemical reactors to transform the original physical parameters into oxygen uptake rate RO(t) and carbon dioxide release rate RC(t) of microorganisms in the fermenter at time t, which can directly characterize the overall life activity intensity of the microbial community. Based on this, a metabolic feature vector Vm(t) at time t is constructed. Furthermore, this invention does not stop at monitoring macroscopic rates, but calculates the respiratory entropy Q of microorganisms in the fermenter at time t. R (t) was used to perform nonlinear model calculations with the optimal respiratory entropy Q of the bacteria when entering the growth ramp-up phase, obtained through experiments. This innovatively constructed a model that can quantify the enzyme synthesis potential E of microorganisms in a fermenter at time t, capable of measuring the internal physiological state of the bacteria. S (t). This series of layer-by-layer refinement from data to information and then to abstract indicators has enabled the control system to have the ability to observe the intrinsic production potential of microorganisms in real time for the first time, providing an unprecedented, objective and quantitative basis for subsequent intelligent decision-making.

[0062] (3) By analyzing the enzyme synthesis potential E of microorganisms in the fermenter at time t SWith the precise quantification of (t), this invention further demonstrates its significant advantages in intelligent diagnosis and differentiated regulation. This invention analyzes the enzyme synthesis potential E of microorganisms in a fermenter at time t. S (t) and the rate of change of enzyme synthesis potential dE at time t S A two-dimensional diagnostic state vector Ds(t) at time t is constructed, and based on this, the entire complex fermentation process is objectively and dynamically divided into several precise physiological stages, including the growth preparation stage, growth adaptation stage, growth ramp-up stage, growth stabilization stage, and growth decline stage. This dynamic diagnosis based on intrinsic physiological state completely breaks away from the rigid mode of traditional technologies that rely on fixed time or single parameter thresholds. On the basis of this precise diagnosis, the system can automatically execute a finely matched control strategy AIR for each stage's specific biological objectives. For example, a hot-start strategy is implemented during the growth adaptation stage to shorten lag; a unique combination of cold stress and nutrient restriction is applied during the growth ramp-up stage to maximize the induced expression of the target enzyme system; pulsed feeding is used during the growth stabilization stage to effectively prevent cell dormancy while maintaining cell growth and enzyme production activity; and a slow-descent survival strategy is used during the growth decline stage to improve the storage and application efficiency of the final product. This complete closed loop, from precise diagnosis to differentiated execution, ensures that the microbial cells are always guided to operate on the optimal physiological trajectory at every critical stage of fermentation, thereby fundamentally guaranteeing the high-efficiency preparation and high-quality formation of the final microbial agent product. Attached Figure Description

[0063] Figure 1 This is a schematic diagram of the steps in the preparation and control method of a low-temperature, high-efficiency straw decomposition-promoting microbial agent according to the present invention;

[0064] Figure 2 This is a schematic diagram of a control system for the preparation of a low-temperature, high-efficiency straw decomposition-promoting microbial agent according to the present invention.

[0065] Figure 3 The enzyme synthesis potential E at time t S (t) and the respiratory entropy Q at time t R The relevant curve image of (t). Detailed Implementation

[0066] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0067] Example 1

[0068] This invention provides a method for controlling the preparation of a low-temperature, high-efficiency straw decomposition-promoting microbial agent. Please refer to [link / reference]. Figure 1 This includes the following steps:

[0069] S1. By deploying multiple types of sensors in the fermenter, the inoculant preparation dataset RAW(t) at time t in the fermenter is collected according to the preset acquisition time window △t.

[0070] S2. Based on the RAW(t) dataset of microbial agent preparation at time t, and combined with the material balance principle of biochemical reactors, calculate the oxygen uptake rate R of microorganisms in the fermenter at time t. O (t) and the carbon dioxide release rate R at time t C (t), and construct the metabolic feature vector Vm(t) at time t.

[0071] S3. Based on the metabolic feature vector Vm(t) at time t, calculate the respiratory entropy Q at time t. R (t), combined with the optimal respiratory entropy Q of the cells when entering the growth ramp-up phase obtained through experiments, and the enzyme synthesis potential E of the microorganisms in the fermenter at time t was calculated. S (t);

[0072] S4. Enzyme synthesis potential E of microorganisms in the fermenter at time t S (t), calculate the rate of change of enzyme synthesis potential dE within the preset collection time window Δt. S (t), and construct the diagnostic state vector Ds(t) at time t, and compare the diagnostic state vector Ds(t) at time t with the preset potential threshold θ and trend stability interval ϵ, and generate the stage identifier ID(t) at time t based on the comparison result.

[0073] S5. Based on the stage identifier ID(t) at time t, automatically invoke and execute the matching fine-grained control strategy AIR.

[0074] In this embodiment, by deploying multiple types of sensors to collect the inoculum preparation dataset RAW(t) at time t, and based on this, the metabolic feature vector Vm(t) at time t is calculated. This method achieves accurate quantification and real-time monitoring of the macroscopic metabolic activity of microorganisms. Next, this vector is further synthesized into the enzyme synthesis potential E of the microorganisms in the fermenter at time t. S (t) This led to the construction of a novel assessment system capable of providing insights into the internal physiological state of microorganisms. Subsequently, the enzyme synthesis potential E of microorganisms in the fermenter at time t was analyzed. S (t) and its rate of change of enzyme synthesis potential at time t, dE SThe system can dynamically diagnose the stage identifier ID(t) at time t, thereby accurately grasping the actual biological stage of the fermentation process. Finally, based on this stage identifier ID(t) at time t, the system automatically executes the matching refined control strategy AIR, ensuring optimal regulatory actions are applied at different physiological stages. This overall approach not only strengthens the dynamic regulation of the microbial fermentation process but also enables effective adaptive control based on changes in the physiological state of the microorganisms. It effectively addresses the shortcomings of traditional preparation methods, such as blind control and unstable product quality due to the inability to obtain real-time physiological states of the microorganisms, particularly the insufficient identification and utilization of key metabolic stages. Traditional methods often treat bio-fermentation as a fixed chemical process for programmed control, while this method uses the enzyme synthesis potential index E of the microorganisms in the fermenter at time t. S The construction and dynamic diagnosis of (t) provide a more intelligent and precise control scheme for the preparation process of microbial agents, avoiding the limitations of relying on experience or fixed time in traditional schemes, and ensuring the high efficiency and high stability of the final microbial agent product.

[0075] Example 2

[0076] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: S1 includes:

[0077] By deploying multiple types of sensors in the fermenter, the inoculant preparation dataset RAW(t) at time t in the fermenter is collected according to the preset acquisition time window △t.

[0078] The RAW(t) dataset for inoculant preparation at time t includes the following:

[0079] The oxygen mole fraction O(t) and carbon dioxide mole fraction C(t) in the gas discharged from the fermenter at time t are collected by an online multi-component gas analyzer deployed on the main exhaust pipe at the top of the fermenter. The gas velocity F(t) entering the fermenter at time t is collected by a thermal mass flow controller deployed on the sterile air main pipe entering the fermenter. The pressure P(t) inside the fermenter at time t is collected by a pressure transmitter deployed in the gas phase space at the top of the fermenter. The temperature T(t) at time t is collected by a Pt100 platinum resistance temperature sensor deployed in a sanitary blind pipe on the side wall of the fermenter. The total weight W1(t) of the entire fermenter at time t is measured by a weighing sensor deployed under the support feet of the fermenter. The net weight W3(t) of the fermentation liquid at time t is obtained by subtracting the known empty equipment plus the unloaded weight of multiple sensors W2. The volume V(t) of the fermentation liquid at time t is calculated according to the liquid volume-weight calculation formula.

[0080] The formula for calculating liquid volume versus weight is as follows:

[0081] ;

[0082] In the formula, ρ represents the average density of the fermentation broth obtained through multiple experiments;

[0083] Generate the bacterial agent preparation dataset at time t: RAW(t) = {O(t), C(t), F(t), P(t), T(t)}.

[0084] V(t)};

[0085] S2 includes S21:

[0086] S21. Based on the RAW(t) dataset of inoculum preparation at time t, and combined with the material balance principle of biochemical reactors, the gas flow rate F(t) entering the fermenter at time t and the oxygen mole fraction O(t) in the gas discharged at time t are measured, and a preset nitrogen mole fraction N that does not participate in the microbial fermentation reaction is introduced. in A formula for estimating oxygen uptake rate was constructed, and the oxygen uptake rate RO(t) of microorganisms in the fermenter at time t was calculated.

[0087] The formula for calculating the oxygen uptake rate is as follows:

[0088] ;

[0089] In the formula, R represents the preset ideal gas constant, with a value of 8.314, and O in This indicates the preset mole fraction of oxygen in the gas entering the fermenter, with a value of 0.2034 N. in This represents the preset mole fraction of nitrogen in the gas entering the fermenter, with a value of 0.7902.

[0090] S2 includes S22:

[0091] S22. Based on the RAW(t) dataset of inoculum preparation at time t, and combined with the material balance principle of biochemical reactors, the gas flow rate F(t) entering the fermenter at time t and the carbon dioxide mole fraction C(t) in the gas discharged at time t are measured, and a preset nitrogen mole fraction N that does not participate in the microbial fermentation reaction is introduced. in A formula for estimating carbon dioxide release rate was constructed, and the carbon dioxide release rate RC(t) of microorganisms in the fermenter at time t was calculated.

[0092] The formula for calculating the carbon dioxide release rate is as follows:

[0093] ;

[0094] The metabolic feature vector Vm(t) = [RO(t), RC(t)] at time t is constructed by combining the oxygen uptake rate RO(t) at time t and the carbon dioxide release rate RC(t) at time t.

[0095] In this embodiment, an integrated sensor array is used to collect the inoculum preparation dataset RAW(t) at time t according to a preset acquisition time window Δt. The specific sensor deployment is as follows: an online multi-component gas analyzer is deployed on the main exhaust pipe at the top of the fermenter to obtain the oxygen mole fraction O(t) and carbon dioxide mole fraction C(t) in the gas discharged from the fermenter at time t; a thermal mass flow controller is deployed on the main intake pipe to accurately control and measure the gas flow rate F(t) entering the fermenter at time t; and the pressure P(t) and temperature T(t) inside the fermenter at time t are simultaneously acquired. To address the uncertainty caused by liquid volume changes during fed-batch fermentation, a weighing sensor is deployed below the load-bearing support feet of the fermenter to directly measure and calculate the fermentation liquid volume V(t) at time t using a weighing method. This measure aims to eliminate the cumulative error caused by indirect estimation methods such as feed integrals, thereby improving the accuracy of the raw data. The inoculum preparation dataset RAW(t) collected at time t is strictly controlled according to the law of conservation of mass, and the inert gas tracer method from biochemical reaction engineering is applied. The principle is that nitrogen in the air is neither consumed nor produced during microbial fermentation, and can serve as an effective endogenous tracer. By utilizing the conservation of nitrogen molar amounts in the inlet and outlet airflows, the actual change in the total outlet gas flow rate due to oxygen consumption and carbon dioxide production can be accurately calculated, thus correcting the calculation results and improving accuracy. First, the oxygen uptake rate calculation formula is called, and relevant parameters from the inoculum preparation dataset RAW(t) at time t are substituted to calculate the oxygen uptake rate RO(t) of the microorganisms in the fermenter at time t. This rate reflects the intensity of aerobic respiration in the microbial community. Subsequently, the carbon dioxide release rate calculation formula is called in parallel to calculate the carbon dioxide release rate RC(t) of the microorganisms in the fermenter at time t. This rate reflects the intensity of carbon metabolism. Ultimately, these two core metabolic rates are combined to construct a standardized metabolic feature vector at time t, Vm(t) = [RO(t), RC(t)]. This vector converts physical measurements into biological indicators. It not only objectively quantifies the intensity of microbial life activities, but more importantly, it refines complex raw data into standardized input signals, providing a stable, reliable, and biologically meaningful analytical foundation for subsequent algorithm steps.

[0096] Example 3

[0097] This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 and Figure 3Specifically: S3 includes S31:

[0098] S31. Based on the metabolic feature vector Vm(t) at time t, calculate the respiratory entropy Q of the microorganisms in the fermenter at time t according to the ratio of the carbon dioxide release rate RC(t) to the oxygen uptake rate RO(t) at time t. R (t), through multiple experiments in the early stage, the biomass of the bacteria was sampled and measured at regular intervals, and the growth curve was plotted. The middle time t1 of the logarithmic phase in the growth curve was selected, which is the stage with the fastest growth rate. The average respiratory entropy that is stable and repeatable within the middle time t1 of the logarithmic phase in the growth curve was determined as the optimal respiratory entropy Q of the bacteria when it enters the growth ramp-up phase. Thus, the optimal respiratory entropy Q of the bacteria when it enters the growth ramp-up phase was obtained.

[0099] Among them, the respiratory entropy Q of microorganisms in the fermenter at time t R The formula for calculating (t) is as follows:

[0100] ;

[0101] S3 includes S32:

[0102] S32. Calculate the respiratory entropy Q of microorganisms in the fermenter at time t. R The difference between (t) and the optimal respiratory entropy Q at the start of the growth ramp-up phase is used, and a nonlinear mapping is performed using the hyperbolic tangent function tanh to calculate the enzyme synthesis potential E of the microorganisms in the fermenter at time t. S (t);

[0103] Among them, the enzyme synthesis potential E of microorganisms in the fermenter at time t S The expression for calculating (t) is as follows:

[0104] ;

[0105] S4 includes S41:

[0106] S41, Based on the enzyme synthesis potential E of microorganisms in the fermenter at time t S (t), calculate the rate of change dE of the enzyme synthesis potential of microorganisms in the fermenter within the preset collection time window Δt at time t. S (t);

[0107] Among them, the rate of change of enzyme synthesis potential dE at time t S The expression for calculating (t) is as follows:

[0108] ;

[0109] Based on the enzyme synthesis potential ES(t) of microorganisms in the fermenter at time t and the rate of change of enzyme synthesis potential dES(t) at time t, construct the diagnostic state vector Ds(t) = [ES(t), dES(t)] at time t;

[0110] S4 includes S42:

[0111] S42. The diagnostic state vector Ds(t) at time t is compared with the preset potential threshold θ and trend stability interval ϵ. Based on the comparison result, a stage identifier ID(t) at time t is generated, where the preset trend stability interval ϵ includes the trend stability upper limit ϵ1 and the trend stability lower limit ϵ2.

[0112] If the enzyme synthesis potential E of the microorganisms in the fermenter at time t S (t) < the preset potential threshold θ, and the rate of change of enzyme synthesis potential dE of microorganisms in the fermenter at time t. S If (t) < trend stability upper limit ϵ1, then it is determined that the current stage is in the growth preparation period, and the stage identifier ID(t) at time t is generated and assigned a value of 0;

[0113] If the enzyme synthesis potential E of the microorganisms in the fermenter at time t S (t) < the preset potential threshold θ, and the rate of change of enzyme synthesis potential dE of microorganisms in the fermenter at time t. S If (t) ≥ trend stability upper limit ϵ1, then it is determined that the current stage is in the growth adaptation period, and a stage identifier ID(t) is generated at time t and assigned a value of 1;

[0114] If the enzyme synthesis potential E of the microorganisms in the fermenter at time t S (t) ≥ the preset potential threshold θ, and the rate of change of enzyme synthesis potential dE of microorganisms in the fermenter at time t. S If (t) ≥ trend stability upper limit ϵ1, then it is determined that the current stage is in the growth and climbing phase, and the stage identifier ID(t) at time t is generated and assigned the value 2;

[0115] If the enzyme synthesis potential E of the microorganisms in the fermenter at time t S (t) ≥ the preset potential threshold θ, and the lower limit of trend stability ϵ2 ≤ the rate of change of enzyme synthesis potential of microorganisms in the fermenter at time t, dE S If (t) < trend stability upper limit ϵ1, then it is determined that the current growth is in a stable period, and a stage identifier ID(t) is generated at time t and assigned a value of 3;

[0116] If the rate of change of enzyme synthesis potential of microorganisms in the fermenter at time t is dE SIf (t) < trend stability lower limit ϵ2, and the stage identifier ID (t-△t) value of the previous monitoring is 3, then it is determined that the current stage is in the growth decline period, and the stage identifier ID (t) at time t is generated and assigned a value of 4;

[0117] Enzyme synthesis potential E of microorganisms in the fermenter at time t S (t) A specific calculation example is as follows:

[0118] The oxygen mole fraction O(t) in the gas discharged from the fermenter at time t is 0.1850.

[0119] The mole fraction of carbon dioxide C(t) in the gas discharged from the fermenter at time t is 0.0150.

[0120] The gas flow rate F(t) entering the fermenter at time t is 1500 (L / h).

[0121] The pressure P(t) inside the fermenter at time t is 121590 (Pa).

[0122] The temperature at time t is T(t): 20 (°C) = 293.15 (K);

[0123] The fermentation liquid volume V(t) at time t is 410 (L).

[0124] The optimal respiratory entropy Q of the bacteria when entering the growth ramp-up phase is 1.05;

[0125] The oxygen uptake rate RO(t) at time t is calculated as follows:

[0126] ;

[0127] The carbon dioxide release rate RC(t) at time t is calculated as follows:

[0128] ;

[0129] The metabolic feature vector at time t is Vm(t) = [0.1511, 0.1603].

[0130] The respiratory entropy Q at time t R (t) The specific calculation is as follows:

[0131] ;

[0132] Enzyme synthesis potential E at time t S (t) The specific calculation is as follows:

[0133] .

[0134] In this embodiment, based on the two components of the metabolic feature vector Vm(t) at time t, namely the oxygen uptake rate RO(t) and the carbon dioxide release rate RC(t) of the microorganisms in the fermenter at time t, the respiratory entropy Q of the microorganisms in the fermenter at time t is calculated by ratio calculation. R (t). The respiratory entropy Q at time t is selected. R (t) is used as a core indicator because this dimensionless parameter effectively reflects the metabolic pathways utilized by microorganisms, and its changes are strongly correlated with the cell's transition from primary metabolism, primarily focused on growth, to secondary metabolism, primarily focused on the synthesis of target products. Subsequently, the respiratory entropy Q at time t is calculated. R (t) is compared with a baseline parameter obtained through previous experiments—the optimal respiratory entropy Q of the bacteria when entering the growth ramp-up phase—and the difference is substituted into a nonlinear evaluation formula based on the hyperbolic tangent function. This formula is designed to transform the deviation of the respiratory entropy into a bounded and sensitive output signal. Finally, the system calculates the output of this step—the enzyme synthesis potential E of the microorganisms in the fermenter at time t. S (t) transforms the intrinsic physiological state of microorganisms, which cannot be directly measured, into an objective and continuous quantitative value. This is to clearly distinguish between microorganisms with similar E values. S For physiological stages with different development trends, the system first calculates the rate of change of enzyme synthesis potential dES(t) at time t by numerically differentiating the enzyme synthesis potential ES(t) data of microorganisms in a continuous fermenter at time t. Then, the system combines these two values ​​to construct a two-dimensional diagnostic state vector Ds(t) at time t. Finally, the two components of this vector are logically compared with a preset potential threshold θ and a trend stability interval ε, where the trend stability interval ε includes an upper limit ε1 and a lower limit ε2. This logical judgment rule divides the two-dimensional state space into five non-overlapping regions, each region uniquely corresponding to a physiological stage, namely the growth preparation stage, growth adaptation stage, growth ramp-up stage, growth stabilization stage, or growth decline stage. Through this method, a unique stage identifier ID(t) at time t is finally output. This identifier objectively and in real time reflects the real biological stage of the fermentation process, providing a reliable decision-making basis for implementing precise and differentiated control strategies.

[0135] Example 4

[0136] This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 Specifically: S5 includes:

[0137] Based on the stage identifier ID(t) at time t, the matching fine-grained control strategy AIR is automatically invoked and executed;

[0138] If the stage identifier ID(t) at time t is 0, execute the growth preparation period strategy and maintain the current growth environment;

[0139] If the stage identifier ID(t) is 1 at time t, the growth adaptation strategy is executed, the temperature T is adjusted to the activation temperature T1 preset by professionals in the field, and the carbon source feeding rate is increased to 110% of the original carbon source feeding rate;

[0140] The activation temperature T1 was determined through gradient temperature culture experiments in a small fermenter. The principle is to significantly improve the initial growth rate without causing heat stress damage to the bacteria. For the strain in this embodiment, the preferred range of activation temperature T1 is 20°C to 25°C. In this embodiment, the activation temperature T1 is 23°C.

[0141] If the stage identifier ID(t) at time t is 2, the growth ramp-up strategy is executed, including cold stress and nutrient restriction actions. The cold stress action is to reduce the temperature T from the activation temperature T1 to the optimal fermentation temperature T2 preset by a person skilled in the art within one hour. When the temperature T drops to the optimal fermentation temperature T2, the temperature T is reduced to the stress temperature T3 preset by a person skilled in the art within ten minutes. The nutrient restriction action is to reduce the carbon source feeding rate to 50% of the original carbon source feeding rate. After one hour, the cold stress and nutrient restriction actions are ended, and the temperature T is increased from the stress temperature T3 to the optimal fermentation temperature T2 within one hour, and the carbon source feeding rate is restored to the original carbon source feeding rate.

[0142] The optimal fermentation temperature T2 was determined through multiple parallel fermentation experiments. The principle was to find a temperature point that allowed the current strain to reach the expected growth rate within a unit of time. At the same time, this temperature point should ensure that the current strain has sufficient growth rate and metabolic activity to complete production within a reasonable time, while avoiding the inhibition of low-temperature specific enzyme synthesis or premature cell aging due to excessively high temperature. For the strain in this embodiment, the preferred range of the optimal fermentation temperature T2 is 12℃ to 15℃. In this embodiment, the optimal fermentation temperature T2 is 12℃. The stress temperature T3 was determined through a cold stress gradient experiment. The principle was to find the temperature point that could maximally induce the expression of the target enzyme gene without causing a large number of cells to become inactive by detecting enzyme activity and the number of surviving cells after stress at different low temperatures. For the strain in this embodiment, the preferred range of the stress temperature T3 is 6℃ to 8℃. In this embodiment, the stress temperature T3 is 6℃.

[0143] If the stage identifier ID(t) at time t is 3, execute the growth stabilization period strategy, maintain the temperature T at the optimal fermentation temperature T2, switch the carbon source feeding mode to open the carbon source feeding valve for five minutes every hour, supplement the carbon source at a feeding rate of 300% of the original carbon source feeding rate, and open the inducer valve to add inducer to the fermentation broth.

[0144] If the stage identifier ID(t) at time t is 4, the growth decline strategy is executed. Within four hours, the temperature T is reduced from the optimal fermentation temperature T2 to the refrigeration temperature T4 preset by professionals in the field. The carbon source feeding valve is closed and the protective agent valve is opened. A protective agent is added to the fermentation broth to promote the synthesis of extracellular polysaccharides by the cells to encapsulate themselves. The sterile air main pipeline is adjusted to reduce the gas flow rate F(t) entering the fermenter at time t by 50% until the operator confirms the material collection operation.

[0145] The refrigeration temperature T4 is determined based on the standard practice of industrial microbial agent preservation, providing a low-cost environment that can effectively inhibit residual metabolism and ensure the long-term stability of the product. The refrigeration temperature T4 is set to 4℃.

[0146] In this embodiment, based on the stage identifier ID(t) received in real time at time t, the system calls and executes the fine-grained control strategy AIR, which uniquely matches the stage identifier ID(t) received at time t. Each strategy is a programmed sequence of operations. When ID(t) is 0, the growth preparation phase strategy is executed. Under this strategy, the control system maintains all environmental parameters in a stable and unchanged state before the fermenter is inoculated or started. This operation aims to provide a stable, uniform, and repeatable initial environment for subsequent biological reactions to ensure consistency in different batch production processes. When ID(t) is 1, the growth adaptation phase strategy is executed: This strategy aims to shorten the lag period of slow start-up of low-temperature microorganisms. The system adjusts the temperature T from the basic fermentation temperature to a preset activation temperature T1, while increasing the carbon source feeding rate to 110% of the original rate. This method applies the principle of biochemical reaction kinetics, increasing the intracellular enzymatic reaction rate by moderately raising the temperature and providing sufficient nutrition to accelerate the start-up of cell metabolism, thereby effectively shortening non-productive time and improving equipment utilization. When ID(t) is 2, the growth ramp-up phase strategy is executed: This strategy aims to maximize the induced expression of the target enzyme system. The system executes a combined sequence of actions involving cold stress and nutrient restriction: First, the temperature T is gradually reduced from the activation temperature T1 to the optimal fermentation temperature T2 within one hour; then, within ten minutes, the temperature T is rapidly reduced from T2 to a lower stress temperature T3; simultaneously, the carbon source feeding rate is reduced to 50% of its original rate. This combined stress state is maintained for one hour, after which the temperature T and carbon source feeding rate are programmed to be restored. The technical principle of this strategy is that the synergistic effect of the two stress signals—the sudden drop in temperature T and nutrient restriction—strongly induces the overexpression of specific genes related to cold adaptation in the low-temperature microorganisms. When ID(t) is 3, a growth stationary phase strategy is executed: this strategy aims to prolong the duration of high enzyme production efficiency. The temperature T is maintained at the optimal fermentation temperature T2, and the carbon source feeding mode is switched to pulsed mode, i.e., feeding at a rate of 300% of the original rate for five minutes every hour, while adding specific inducers as needed for the process. The rationale behind this strategy is that at low temperatures, continuous micro-feeding may be insufficient to maintain cellular metabolic activity, leading to dormancy. Pulsed feeding maintains the overall nutrient supply limitation to continuously relieve carbon metabolite repression, while periodically activating cells through high-concentration substrates, preventing them from entering a dormant state with low productivity. When ID(t) is 4, a growth decline strategy is implemented: this strategy aims to optimize the storage stability and application efficiency of the final inoculum product. The temperature T will be linearly reduced from the optimal fermentation temperature T2 to the refrigeration temperature T4 within four hours, while carbon source feeding is stopped, a preservative is added, and the gas flow rate F(t) entering the fermenter at time t is reduced by 50%. This strategy is based on the principle of microbial cryopreservation, reducing physical damage to cells and inducing the synthesis of protective substances such as extracellular polysaccharides through programmed slow cooling and the addition of preservatives.This operation can significantly improve the survival rate of the final microbial agent product and its activity in low-temperature application environments.

[0147] Example 5

[0148] A control system for the preparation of low-temperature, high-efficiency straw decomposition-promoting microbial agents, please refer to... Figure 2 Specifically, it includes a data acquisition module, a metabolic characteristic analysis module, an enzyme synthesis potential calculation module, an evaluation module, and an execution module;

[0149] The data acquisition module uses multiple types of sensors deployed in the fermenter to collect the inoculum preparation dataset RAW(t) at time t in the fermenter according to the preset acquisition time window △t.

[0150] The metabolic characteristic analysis module calculates the oxygen uptake rate Ro of microorganisms in the fermenter at time t using the RAW(t) dataset of microbial agent preparation at time t, combined with the material balance principle of biochemical reactors. O (t) and the carbon dioxide release rate R at time t C (t), and construct the metabolic feature vector Vm(t) at time t.

[0151] The enzyme synthesis potential calculation module calculates the respiratory entropy Q at time t using the metabolic feature vector Vm(t) based on time t. R (t), combined with the optimal respiratory entropy Q of the bacteria when entering the growth ramp-up phase obtained through experiments, an enzyme synthesis potential assessment model is constructed to calculate the enzyme synthesis potential E of microorganisms in the fermenter at time t. S (t);

[0152] The evaluation module assesses the enzyme synthesis potential E of microorganisms in the fermenter at time t. S (t), calculate the rate of change of enzyme synthesis potential dE within the preset collection time window Δt. S (t), and construct the diagnostic state vector Ds(t) at time t, and compare the diagnostic state vector Ds(t) at time t with the preset potential threshold θ and trend stability interval ϵ, and generate the stage identifier ID(t) at time t based on the comparison result.

[0153] The execution module automatically invokes and executes the fine-grained control strategy AIR based on the stage identifier ID(t) at time t.

[0154] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the preparation of a low-temperature, high-efficiency straw decomposition-promoting microbial agent, characterized in that: Includes the following steps: S1. By deploying multiple types of sensors in the fermenter, the inoculant preparation dataset RAW(t) at time t in the fermenter is collected according to the preset acquisition time window △t. S2. Based on the RAW(t) dataset of microbial agent preparation at time t, and combined with the material balance principle of biochemical reactors, calculate the oxygen uptake rate R of microorganisms in the fermenter at time t. O (t) and the carbon dioxide release rate R at time t C (t), and construct the metabolic feature vector Vm(t) at time t. S3. Based on the metabolic feature vector Vm(t) at time t, calculate the respiratory entropy Q at time t. R (t), combined with the optimal respiratory entropy Q of the cells when entering the growth ramp-up phase obtained through experiments, and the enzyme synthesis potential E of the microorganisms in the fermenter at time t was calculated. S (t); S4. Enzyme synthesis potential E of microorganisms in the fermenter at time t S (t), calculate the rate of change of enzyme synthesis potential dE within the preset collection time window Δt. S (t), and construct the diagnostic state vector Ds(t) at time t, and combine the diagnostic state vector Ds(t) at time t with the preset potential threshold θ and trend stability interval. Compare the results and generate the stage identifier ID(t) at time t. S5. Based on the stage identifier ID(t) at time t, automatically invoke and execute the matching fine-grained control strategy AIR; S1 includes: By deploying multiple types of sensors in the fermenter, the inoculant preparation dataset RAW(t) at time t in the fermenter is collected according to the preset acquisition time window △t. The RAW(t) dataset for inoculant preparation at time t includes the following: The oxygen mole fraction O(t) and carbon dioxide mole fraction C(t) in the gas discharged from the fermenter at time t are collected by an online multi-component gas analyzer deployed on the main exhaust pipe at the top of the fermenter. The gas velocity F(t) entering the fermenter at time t is collected by a thermal mass flow controller deployed on the sterile air main pipe entering the fermenter. The pressure P(t) inside the fermenter at time t is collected by a pressure transmitter deployed in the gas phase space at the top of the fermenter. The temperature T(t) at time t is collected by a Pt100 platinum resistance temperature sensor deployed in a sanitary blind pipe on the side wall of the fermenter. The total weight W1(t) of the entire fermenter at time t is measured by a weighing sensor deployed under the support feet of the fermenter. The net weight W3(t) of the fermentation liquid at time t is obtained by subtracting the known empty equipment plus the unloaded weight of multiple sensors W2. The volume V(t) of the fermentation liquid at time t is calculated according to the liquid volume-weight calculation formula. Generate the bacterial agent preparation dataset at time t: RAW(t) = [O(t), C(t), F(t), P(t), T(t). V(t)]; S2 includes S21: S21. Based on the RAW(t) dataset of inoculum preparation at time t, and combined with the material balance principle of biochemical reactors, the gas flow rate F(t) entering the fermenter at time t and the oxygen mole fraction O(t) in the gas discharged at time t are measured, and a preset nitrogen mole fraction N that does not participate in the microbial fermentation reaction is introduced. in A formula for estimating oxygen uptake rate was constructed, and the oxygen uptake rate RO(t) of microorganisms in the fermenter at time t was calculated. The formula for calculating the oxygen uptake rate is as follows: ； In the formula, R represents the preset ideal gas constant, with a value of 8.314, and O in This indicates the preset mole fraction of oxygen in the gas entering the fermenter, with a value of 0.2034 N. in This represents the preset mole fraction of nitrogen in the gas entering the fermenter, with a value of 0.7902. S2 includes S22: S22. Based on the RAW(t) dataset of inoculum preparation at time t, and combined with the material balance principle of biochemical reactors, the gas flow rate F(t) entering the fermenter at time t and the carbon dioxide mole fraction C(t) in the gas discharged at time t are measured, and a preset nitrogen mole fraction N that does not participate in the microbial fermentation reaction is introduced. in A formula for estimating carbon dioxide release rate was constructed, and the carbon dioxide release rate RC(t) of microorganisms in the fermenter at time t was calculated. The formula for calculating the carbon dioxide release rate is as follows: ； The metabolic feature vector Vm(t) = [RO(t), RC(t)] at time t is constructed by combining the oxygen uptake rate RO(t) and the carbon dioxide release rate RC(t) at time t; S3 includes S31: S31. Based on the metabolic feature vector Vm(t) at time t, calculate the respiratory entropy Q of the microorganisms in the fermenter at time t according to the ratio of the carbon dioxide release rate RC(t) to the oxygen uptake rate RO(t) at time t. R (t), through multiple experiments in the early stage, the fermentation of microorganisms in the fermenter was analyzed, and the optimal respiratory entropy Q of the cells when they entered the growth and climbing phase was obtained; S3 includes S32: S32. Calculate the respiratory entropy Q of microorganisms in the fermenter at time t. R The difference between (t) and the optimal respiratory entropy Q at the start of the growth ramp-up phase is used, and a nonlinear mapping is performed using the hyperbolic tangent function tanh to calculate the enzyme synthesis potential E of the microorganisms in the fermenter at time t. S (t); E(t) = E0 * exp(-kt) (1) S (t) is calculated as follows: ； S4 includes S41: S41, based on the enzyme synthesis potential E of the microorganism in the fermentor at time t S (t), the change rate dE of the enzyme synthesis potential of the microorganism in the fermentor within the preset collection time window At at time t is calculated S (t); wherein the rate of change of enzyme synthesis potential dE at time t S (t) is calculated as follows: ； According to the enzyme synthesis potential E of the microorganism in the fermenter at time t S (t) and the rate of change of the enzyme synthesis potential dE at time t S (t), the diagnostic state vector Ds(t) at time t is constructed as Ds(t) = [E S (t), dE S (t)]; S4 includes S42: S42. The diagnostic state vector Ds(t) at time t will be compared with the preset potential threshold θ and the trend stability interval. A comparison is performed, and a stage identifier ID(t) is generated at time t based on the comparison results, where the preset trend stability interval is defined. Including trend stability upper limit 1 and trend stability lower limit 2; If the enzyme synthesis potential E of the microorganisms in the fermenter at time t S (t) < the preset potential threshold θ, and the rate of change of enzyme synthesis potential dE of microorganisms in the fermenter at time t. S (t) < Upper limit of trend stability If 1 is found, it is determined that the current stage is in the growth preparation period, and a stage identifier ID(t) is generated at time t and assigned a value of 0. If the enzyme synthesis potential E of the microorganisms in the fermenter at time t S (t) < the preset potential threshold θ, and the rate of change of enzyme synthesis potential dE of microorganisms in the fermenter at time t. S (t) ≥ Trend Stability Upper Limit If 1 is found, it is determined that the current stage is in the growth adaptation period, and a stage identifier ID(t) is generated at time t and assigned the value 1. If the enzyme synthesis potential E of the microorganisms in the fermenter at time t S (t) ≥ the preset potential threshold θ, and the rate of change of enzyme synthesis potential dE of microorganisms in the fermenter at time t. S (t) ≥ Trend Stability Upper Limit If 1 is found, it is determined that the current stage is in the growth and climbing phase, and a stage identifier ID(t) is generated at time t and assigned the value 2. If the enzyme synthesis potential E of the microorganisms in the fermenter at time t S (t) ≥ the preset potential threshold θ, and the lower limit of trend stability ϵ2 ≤ the rate of change of enzyme synthesis potential of microorganisms in the fermenter at time t, dE S (t) < Upper limit of trend stability If 1 is found, it is determined that the current growth is in a stable period, and a stage identifier ID(t) is generated at time t and assigned the value 3; If the rate of change of enzyme synthesis potential of microorganisms in the fermenter at time t is dE S (t) < trend stability lower bound 2. If the stage identifier ID(t-△t) value of the previous monitoring round is 3, then it is determined that the current stage is in the growth and decline phase, and the stage identifier ID(t) at time t is generated and assigned a value of 4. S5 includes: Based on the stage identifier ID(t) at time t, the matching fine-grained control strategy AIR is automatically invoked and executed; If the stage identifier ID(t) at time t is 0, execute the growth preparation period strategy and maintain the current growth environment; If the stage identifier ID(t) is 1 at time t, the growth adaptation strategy is executed, the temperature T is adjusted to the activation temperature T1 preset by professionals in the field, and the carbon source feeding rate is increased to 110% of the original carbon source feeding rate; If the stage identifier ID(t) at time t is 2, the growth ramp-up strategy is executed, including cold stress and nutrient restriction actions. The cold stress action is to reduce the temperature T from the activation temperature T1 to the optimal fermentation temperature T2 preset by a person skilled in the art within one hour. When the temperature T drops to the optimal fermentation temperature T2, the temperature T is reduced to the stress temperature T3 preset by a person skilled in the art within ten minutes. The nutrient restriction action is to reduce the carbon source feeding rate to 50% of the original carbon source feeding rate. After one hour, the cold stress and nutrient restriction actions are ended, and the temperature T is increased from the stress temperature T3 to the optimal fermentation temperature T2 within one hour, and the carbon source feeding rate is restored to the original carbon source feeding rate. If the stage identifier ID(t) at time t is 3, execute the growth stabilization period strategy, maintain the temperature T at the optimal fermentation temperature T2, switch the carbon source feeding mode to open the carbon source feeding valve for five minutes every hour, supplement the carbon source at a feeding rate of 300% of the original carbon source feeding rate, and open the inducer valve to add inducer to the fermentation broth. If the stage identifier ID(t) at time t is 4, the growth decline strategy is executed. Within four hours, the temperature T is reduced from the optimal fermentation temperature T2 to the refrigeration temperature T4 preset by professionals in the field. The carbon source feeding valve is closed and the protective agent valve is opened. A protective agent is added to the fermentation broth to promote the synthesis of extracellular polysaccharides by the cells to encapsulate themselves. The sterile air main pipeline is adjusted to reduce the gas flow rate F(t) entering the fermenter at time t by 50% until the operator confirms the material collection operation.

2. A straw low-temperature high-efficiency rot fungus agent preparation control system applied to the straw low-temperature high-efficiency rot fungus agent preparation control method of claim 1, characterized in that: It includes a data acquisition module, a metabolic characteristic analysis module, an enzyme synthesis potential calculation module, an evaluation module, and an execution module; The data acquisition module uses multiple types of sensors deployed in the fermenter to collect the inoculum preparation dataset RAW(t) at time t in the fermenter according to the preset acquisition time window △t. The metabolic characteristic analysis module calculates the oxygen uptake rate Ro of microorganisms in the fermenter at time t using the RAW(t) dataset of microbial agent preparation at time t, combined with the material balance principle of biochemical reactors. O (t) and the carbon dioxide release rate R at time t C (t), and construct the metabolic feature vector Vm(t) at time t. The enzyme synthesis potential calculation module calculates the respiratory entropy Q at time t using the metabolic feature vector Vm(t) based on time t. R (t), combined with the optimal respiratory entropy Q of the bacteria when entering the growth ramp-up phase obtained through experiments, an enzyme synthesis potential assessment model is constructed to calculate the enzyme synthesis potential E of microorganisms in the fermenter at time t. S (t); The evaluation module assesses the enzyme synthesis potential E of microorganisms in the fermenter at time t. S (t), calculate the rate of change of enzyme synthesis potential dE within the preset collection time window Δt. S (t), and construct the diagnostic state vector Ds(t) at time t, and combine the diagnostic state vector Ds(t) at time t with the preset potential threshold θ and trend stability interval. Compare the results and generate the stage identifier ID(t) at time t. The execution module automatically invokes and executes the fine-grained control strategy AIR based on the stage identifier ID(t) at time t.

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