A fermentation monitoring and early warning system and method

Abstract

This invention discloses a fermentation monitoring and early warning system and method, relating to the field of monitoring and early warning technology. The fermentation monitoring and early warning system includes a data acquisition module, a data analysis module, an early warning monitoring module, and an alarm module. The data acquisition module acquires monitoring data in real time. The data analysis module calculates the dissolved carbon dioxide concentration in the liquid, corrects the dissolved carbon dioxide concentration to obtain an effective dissolved carbon dioxide concentration, and calculates an early warning index based on temperature, pH value, dissolved oxygen concentration, and effective dissolved carbon dioxide concentration. The early warning monitoring module acquires the early warning index, compares the early warning index value with a preset threshold range, and triggers a corresponding early warning level to generate a corresponding early warning signal based on the comparison result. The alarm module receives the early warning signal and outputs an alarm prompt corresponding to the early warning signal. This invention can reduce the risk of false alarms or missed alarms, enabling operators to intervene in a timely manner before the microbial metabolic balance is disrupted.

Description

Technical Field

[0001] This invention relates to the field of monitoring and early warning technology, and in particular to a fermentation monitoring and early warning system and method. Background Technology

[0002] Currently, monitoring of the Polygonatum fermentation process still relies primarily on traditional methods, which involve setting fixed thresholds for single parameters and triggering alarms when these thresholds are exceeded through offline or online monitoring equipment. However, this monitoring method has the following drawbacks: the parameters in the Polygonatum fermentation process do not act independently. For example, the concentration of dissolved carbon dioxide in the liquid is simultaneously regulated by temperature, pH, and the metabolic intensity of microorganisms. Its inhibitory effect on cell activity is a dynamic result of the interaction of multiple factors, and monitoring a single parameter with a fixed threshold can easily lead to misjudgments of the fermentation status. Furthermore, the early warning system of traditional monitoring methods has a significant lag. By the time a parameter shows a significant abnormality, the abnormal state of the fermentation system has already occurred, and the microbial metabolic balance has been disrupted. Summary of the Invention

[0003] Therefore, in order to address the above-mentioned shortcomings, the present invention provides a fermentation monitoring and early warning system and method to reduce the risk of false alarms or missed alarms, enabling operators to intervene in a timely manner before the microbial metabolic balance is disrupted.

[0004] On one hand, the present invention provides a fermentation monitoring and early warning system, comprising:

[0005] The data acquisition module is used to acquire monitoring data in real time, including temperature, pH value, dissolved oxygen concentration, and carbon dioxide release rate.

[0006] The data analysis module is communicatively connected to the data acquisition module. The data analysis module is used to dynamically calculate the dissolved carbon dioxide concentration in the liquid based on the carbon dioxide release rate, correct the dissolved carbon dioxide concentration based on the pH value, calculate the effective dissolved carbon dioxide concentration, and calculate the early warning index based on temperature, pH value, dissolved oxygen concentration, and effective dissolved carbon dioxide concentration.

[0007] The early warning monitoring module is communicatively connected to the data analysis module. It is used to acquire an early warning index, compare the early warning index value with a preset threshold range, trigger the corresponding early warning level based on the comparison result, and generate a corresponding early warning signal based on the triggered early warning level.

[0008] An alarm module, which is communicatively connected to the early warning monitoring module, is used to receive early warning signals and output alarm prompts corresponding to the early warning signals. The alarm prompts are at least one of sound, light, and text message.

[0009] Furthermore, the data acquisition module includes:

[0010] Temperature detection unit, used to acquire liquid temperature in real time;

[0011] The pH detection unit is used to acquire the pH value of liquids in real time.

[0012] Dissolved oxygen concentration detection unit, used to obtain the dissolved oxygen concentration of liquid in real time;

[0013] A carbon dioxide release rate detection unit is used to acquire the carbon dioxide release rate in real time.

[0014] Furthermore, the specific method for dynamically calculating the concentration of dissolved carbon dioxide in a liquid based on the carbon dioxide release rate is as follows:

[0015] ;

[0016] in, K H The Henry's constant is related to temperature, and its unit is 1000 ppm. mol / (L·atm) ; P 1 represents the partial pressure of carbon dioxide in the gas phase, calculated from the carbon dioxide release rate and ventilation conditions using material balance, in units of 1. atm ; c 1 represents the concentration of dissolved carbon dioxide, in units of... mol / L .

[0017] Furthermore, the specific method for calculating the effective dissolved carbon dioxide concentration is as follows:

[0018] ;

[0019] in, c 2 represents the effective dissolved carbon dioxide concentration, in units of... mol / L ; α This represents the mole fraction of dissolved carbon dioxide.

[0020] Furthermore, the mole fraction of dissolved carbon dioxide is calculated as follows:

[0021] ;

[0022] in, δ pH value; pKa 1 is the first-order dissociation constant; pKa 2 is the second-order dissociation constant.

[0023] Furthermore, the specific method for calculating the early warning index based on temperature, pH value, dissolved oxygen concentration, and effective dissolved carbon dioxide concentration is as follows:

[0024] ;

[0025] in, τ This is an early warning index; i The first parameter; j The second parameter; D i The normalized deviation of the first parameter from its optimal value for the process; D j Normalized deviation of the second parameter relative to its optimal value set by the process; w i The base weights for the first parameter; ε ij The first parameter i With the second parameter j The interaction coupling coefficient between them;

[0026] The first parameter and the second parameter include temperature, pH value, dissolved oxygen concentration, and effective dissolved carbon dioxide concentration.

[0027] Furthermore, the specific method for comparing the warning index value with a preset threshold range and triggering the corresponding warning signal based on the comparison result is as follows:

[0028] If the warning index value is less than the first threshold, it is judged as a normal state and no warning is triggered;

[0029] If the warning index value is greater than or equal to the first threshold and less than the second threshold, a Level 1 warning is triggered.

[0030] If the warning index value is greater than or equal to the second threshold, a level-two warning is triggered;

[0031] The second threshold is greater than the first threshold.

[0032] On the other hand, the present invention also provides a fermentation monitoring and early warning method, which employs the aforementioned fermentation monitoring and early warning system, and the monitoring and early warning method includes:

[0033] Real-time acquisition of monitoring data, including temperature, pH value, dissolved oxygen concentration, and carbon dioxide release rate;

[0034] The concentration of dissolved carbon dioxide in the liquid is dynamically calculated based on the carbon dioxide release rate. The concentration of dissolved carbon dioxide is corrected based on pH value to obtain the effective concentration of dissolved carbon dioxide. The warning index is calculated based on temperature, pH value, dissolved oxygen concentration and effective dissolved carbon dioxide concentration.

[0035] The system acquires a warning index, compares the warning index value with a preset threshold range, triggers the corresponding warning level based on the comparison result, generates a corresponding warning signal based on the triggered warning level, and sends it to the alarm module so that the alarm module can output an alarm prompt corresponding to the warning signal.

[0036] The present invention has the following advantages:

[0037] This invention reduces the risk of false alarms or missed alarms by collecting multi-dimensional data such as temperature, pH value, dissolved oxygen concentration and carbon dioxide release rate in real time, and calculates a comprehensive early warning index based on the interaction between parameters, so that operators can intervene in time before the microbial metabolic balance is disrupted. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the logical structure of the fermentation monitoring system;

[0039] Figure 2 yes Figure 1 A schematic diagram of the data acquisition module in the fermentation monitoring system shown;

[0040] Figure 3 yes Figure 2 The diagram shows the layout of the data acquisition module within the fermentation module.

[0041] Figure 4 This is a flowchart illustrating the fermentation monitoring method;

[0042] In the picture:

[0043] 100. Fermentation module;

[0044] 200. Data acquisition module; 210. Temperature detection unit; 220. pH detection unit; 230. Dissolved oxygen concentration detection unit; 240. Carbon dioxide release rate detection unit;

[0045] 300. Data Analysis Module;

[0046] 400. Early warning and monitoring module;

[0047] 500, Alarm Module. Detailed Implementation

[0048] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0049] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0050] As described in the background section, monitoring a single parameter at a fixed threshold can easily lead to misjudgments of the fermentation state. Furthermore, traditional monitoring methods suffer from significant delays in early warning; by the time a parameter shows a marked abnormality, the abnormal state of the fermentation system has already occurred, and the microbial metabolic balance has been disrupted.

[0051] Example 1:

[0052] Therefore, in order to solve the above-mentioned technical problems existing in the prior art, this embodiment provides a fermentation monitoring and early warning system, which is suitable for monitoring and early warning of the fermentation process of Polygonatum odoratum, such as... Figure 1 As shown, the monitoring and early warning system includes:

[0053] The data acquisition module 200 is used to acquire monitoring data in real time, including temperature, pH value, dissolved oxygen concentration and carbon dioxide release rate.

[0054] The data analysis module 300 is communicatively connected to the data acquisition module. The data analysis module is used to dynamically calculate the dissolved carbon dioxide concentration in the liquid based on the carbon dioxide release rate, correct the dissolved carbon dioxide concentration based on the pH value, calculate the effective dissolved carbon dioxide concentration, and calculate the early warning index based on temperature, pH value, dissolved oxygen concentration, and effective dissolved carbon dioxide concentration.

[0055] The early warning monitoring module 400 is communicatively connected to the data analysis module. It is used to acquire an early warning index, compare the early warning index value with a preset threshold range, trigger the corresponding early warning level based on the comparison result, and generate a corresponding early warning signal based on the triggered early warning level.

[0056] An alarm module 500 is communicatively connected to an early warning monitoring module. It is used to receive early warning signals and output alarm prompts corresponding to the early warning signals. The alarm prompts are at least one of sound, light, and text messages.

[0057] This embodiment collects multi-dimensional data such as temperature, pH value, dissolved oxygen concentration and carbon dioxide release rate in real time, and calculates a comprehensive early warning index based on the interaction between parameters, reducing the risk of false alarms or missed alarms, so that operators can intervene in time before the microbial metabolic balance is disrupted.

[0058] For example, such as Figure 2 As shown, the data acquisition module includes:

[0059] Temperature detection unit 210 is used to acquire liquid temperature in real time;

[0060] pH detection unit 220 is used to acquire the pH value of liquid in real time;

[0061] Dissolved oxygen concentration detection unit 230 is used to acquire the dissolved oxygen concentration of liquid in real time;

[0062] The carbon dioxide release rate detection unit 240 is used to acquire the carbon dioxide release rate in real time.

[0063] Specifically, such as Figure 3 As shown, the temperature detection unit, pH detection unit, dissolved oxygen concentration detection unit, and carbon dioxide release rate detection unit are all located on the fermentation module 100. The temperature detection unit can be a contact temperature sensor, such as, but not limited to, a platinum resistance temperature sensor (e.g., Pt100) or a thermocouple temperature sensor; a non-contact infrared temperature sensor can also be used. The pH detection unit can be an industrial online pH sensor suitable for insertion installation, such as, but not limited to, a composite glass electrode, antimony electrode, or optical pH sensor that can withstand high-temperature steam sterilization. The dissolved oxygen concentration detection unit can be an industrial online dissolved oxygen sensor that can be inserted installation, such as, but not limited to, an optical dissolved oxygen sensor based on the fluorescence quenching principle or a membrane electrode dissolved oxygen sensor based on the electrochemical principle. The carbon dioxide release rate detection unit can be an online gas analyzer or a dedicated fermentation exhaust gas analyzer. In this embodiment, the temperature, pH, and dissolved oxygen sensors can be inserted into the fermenter; the carbon dioxide release rate detection unit is installed on the exhaust pipe and equipped with gas pretreatment components (such as filters and condensers).

[0064] Specifically, the method for dynamically calculating the concentration of dissolved carbon dioxide in a liquid based on the carbon dioxide release rate is as follows:

[0065] ;

[0066] in, K H The Henry's constant is related to temperature, and its unit is 1000 ppm. mol / (L·atm) ; P 1 represents the partial pressure of carbon dioxide in the gas phase, calculated from the carbon dioxide release rate and ventilation conditions using material balance, in units of 1. atm ; c 1 represents the concentration of dissolved carbon dioxide, in units of... mol / L .

[0067] In this embodiment, K H The calculation method is as follows:

[0068] ;

[0069] in, K H0 Reference temperature T Henry's constant at 0, in units of mol / (L·atm) ;∆E The enthalpy change of carbon dioxide at solution, in units of 1000 ppm. J / mol ; R It is the ideal gas constant; T This refers to the real-time fermentation temperature, in units of... K In this embodiment, the reference temperature T 0 is usually 298.15 K .

[0070] Because the total dissolved inorganic carbon in the fermentation broth exists in multiple forms, including dissolved carbon dioxide, carbonate ions, and bicarbonate ions, and their distribution is determined by pH, the calculated dissolved carbon dioxide concentration needs to be corrected based on the real-time pH to obtain a more accurate effective dissolved carbon dioxide concentration.

[0071] Specifically, the method for calculating the effective dissolved carbon dioxide concentration is as follows:

[0072] ;

[0073] in, c 2 represents the effective dissolved carbon dioxide concentration, in units of... mol / L ; α This represents the mole fraction of dissolved carbon dioxide.

[0074] Specifically, the mole fraction of dissolved carbon dioxide is calculated as follows:

[0075] ;

[0076] in, δ pH value; pKa 1 is the first-order dissociation constant; pKa 2 is the second-order dissociation constant.

[0077] In this embodiment, the effective dissolved carbon dioxide concentration dynamically reflects the molecular concentration of carbon dioxide with actual bioinhibitory effects in the fermentation broth. When fermentation causes a decrease in pH due to acid-producing metabolism (such as the growth of contaminating microorganisms), even if the total carbon dioxide production does not increase significantly, the carbonic acid balance will shift towards the generation of more dissolved carbon dioxide molecules, leading to an increase in the effective dissolved carbon dioxide concentration. These high concentrations of electrically neutral carbon dioxide molecules can freely diffuse into microbial cells, causing intracellular acidification and inhibiting the activity of key enzymes, thus providing an early warning of the risk of "rancidity" or "metabolic inhibition" before the pH index becomes significantly abnormal.

[0078] Specifically, the method for calculating the early warning index based on temperature, pH value, dissolved oxygen concentration, and effective dissolved carbon dioxide concentration is as follows:

[0079] ;

[0080] in, τ This is an early warning index; i The first parameter; j The second parameter; D i The normalized deviation of the first parameter from its optimal value for the process; D j Normalized deviation of the second parameter relative to its optimal value set by the process; w i The base weights for the first parameter; ε ij The first parameter i With the second parameter j The interaction coupling coefficient between them;

[0081] When calculating the early warning index, the first summation is performed on all parameters. i Perform; the second double summation on all parameter pairs ( i , j ) to carry out (usually) i ≠ j ).

[0082] The first parameter and the second parameter include temperature, pH value, dissolved oxygen concentration, and effective dissolved carbon dioxide concentration.

[0083] In this embodiment, the specific calculation method for the normalized deviation of the first parameter from its optimal process setting value is as follows:

[0084] ;

[0085] in, M i The first parameter is measured or calculated in real time. O i This is the optimal setting value for the first parameter in the current process stage; U i This represents the upper or lower limit of the risk threshold for the first parameter in the direction of deviation. n It is a non-linear weighting exponent. n It is a constant greater than 1, used to amplify high-risk deviations.

[0086] In this implementation, the interaction coupling coefficient between the first and second parameters reflects the synergistic or antagonistic effects between the parameters. For example, the coupling coefficient between temperature and effective dissolved carbon dioxide concentration is negative, reflecting the inhibitory risk of low temperature and high dissolved carbon dioxide on microbial metabolism. When the temperature is low, the solubility of carbon dioxide increases, leading to an increase in the effective dissolved carbon dioxide concentration, thereby enhancing the inhibition of cell activity. Conversely, the coupling coefficient between pH and effective dissolved carbon dioxide concentration is positive, characterizing the nonlinear amplification effect of low pH environment on the toxicity of high dissolved carbon dioxide. A decrease in pH drives the carbonic acid balance towards the generation of more dissolved carbon dioxide molecules, resulting in a stronger biological inhibitory effect at the same carbon dioxide release rate, thus providing an early warning of metabolic rancidity risk. Specifically, the optimal parameter settings, risk thresholds, and interaction coupling coefficients in the early warning index calculation are all stored in the system's process knowledge base. This knowledge base is constructed based on characteristic experiments of Polygonatum sibiricum fermentation strains, a database of historical successful batches, and a mechanistic model. It can also undergo adaptive iterative optimization through a statistical learning model after obtaining new successful batch data. For example, the interaction coupling coefficient can be determined in one of the following ways: 1. by fitting based on multivariate experimental design (such as response surface methodology) and regression analysis; 2. by derivation based on fermentation process mechanism model; 3. by using historical abnormal batch data and calibrating after feature importance analysis through machine learning algorithms (such as random forest, neural network).

[0087] In this embodiment, the specific method for comparing the warning index value with a preset threshold range and triggering a warning signal of the corresponding warning level based on the comparison result is as follows:

[0088] If the warning index value is less than the first threshold, it is judged as a normal state and no warning is triggered;

[0089] If the warning index value is greater than or equal to the first threshold and less than the second threshold, a Level 1 warning is triggered.

[0090] If the warning index value is greater than or equal to the second threshold, a level-two warning is triggered;

[0091] The second threshold is greater than the first threshold.

[0092] Specifically, the thresholds can be determined using statistical process control methods. For example, the first threshold can be set as the mean of the warning index for normal batches plus two standard deviations, and the second threshold can be set as the mean plus three standard deviations. The thresholds can be adaptively optimized and updated periodically based on newly added successful batch data (for example, a rolling time window can be built into the system to continuously collect time-series data of the warning index for batches marked as successful. After accumulating N new successful batches, the system automatically recalculates the overall mean and standard deviation of the warning index and updates the first and second thresholds accordingly). In this embodiment, if a Level 1 warning is triggered, the system outputs a yellow warning indicator and a visual alarm to prompt the operator to conduct a manual inspection. If a Level 2 warning is triggered, the system outputs a red warning indicator and an audible and visual alarm. The warning signals can be displayed in real time through a human-machine interface and can be sent to workstations or mobile terminals through the centralized control system.

[0093] Example 2:

[0094] This embodiment provides a fermentation monitoring and early warning method, which employs a fermentation monitoring and early warning system described in Embodiment 1. The monitoring and early warning method includes:

[0095] S100: Real-time acquisition of monitoring data, including temperature, pH value, dissolved oxygen concentration, and carbon dioxide release rate;

[0096] Specifically, sensor units installed on the fermenter and its exhaust pipe synchronously and in real time collect direct monitoring data of the fermentation process. This direct monitoring data includes: fermentation broth temperature, fermentation broth pH value, dissolved oxygen concentration in the fermentation broth, and carbon dioxide release rate in the fermentation exhaust gas. In practice, before the data enters subsequent analysis, data preprocessing can be performed, including filtering the raw signal to eliminate noise, validating the range and mutation rate, and repairing missing values ​​caused by brief signal interruptions using time-series-based linear interpolation or nearest-neighbor imputation to ensure the integrity and reliability of the data stream.

[0097] S200: Dynamically calculates the concentration of dissolved carbon dioxide in the liquid based on the carbon dioxide release rate, corrects the concentration of dissolved carbon dioxide based on pH value, calculates the effective concentration of dissolved carbon dioxide, and calculates the early warning index based on temperature, pH value, dissolved oxygen concentration, and effective dissolved carbon dioxide concentration.

[0098] Specifically, this embodiment first calculates the partial pressure of carbon dioxide in the gas phase based on the real-time collected carbon dioxide release rate and the material balance equation of the gas phase of the fermenter. Then, applying Henry's Law and considering the current fermentation temperature, the dissolved carbon dioxide concentration is calculated. The specific calculation method can be found in the method described in Example 1 for calculating the dissolved carbon dioxide concentration in the liquid. Since the inorganic carbon system in the fermentation broth exists in ionization equilibrium, the dissolved carbon dioxide concentration needs to be morphologically corrected based on the real-time pH value to obtain the concentration of dissolved carbon dioxide molecules that truly have a biological inhibitory effect. The specific correction method can be found in the method described in Example 1 for calculating the effective dissolved carbon dioxide concentration. This effective dissolved carbon dioxide concentration reflects the concentration of dissolved carbon dioxide molecules that can freely diffuse into microbial cells and directly induce intracellular acidification; it is an indicator for early warning of metabolic inhibition. For each parameter (i.e., temperature, pH, dissolved oxygen concentration, and effective dissolved carbon dioxide concentration), its normalized deviation from the optimal set value of the current process stage is calculated. The specific calculation method can be found in the method described in Example 1 for calculating the normalized deviation of the first parameter from its optimal process set value. The early warning index is obtained by weighted summation of the deviation of each parameter and the interaction between each pair. The calculation method can refer to the specific method for calculating the early warning index described in Example 1.

[0099] S300: Obtain the warning index, compare the warning index value with a preset threshold range, trigger the corresponding warning level based on the comparison result, generate the corresponding warning signal based on the triggered warning level and send it to the alarm module so that the alarm module can output an alarm prompt corresponding to the warning signal.

[0100] Specifically, the calculated real-time warning index is compared with a preset dynamic threshold range to achieve state classification: if τ If the value is less than the first threshold, the fermentation process is considered normal, and no warning is triggered. If the first threshold is less than or equal to... τ If the second threshold is exceeded, a slight deviation in the judgment process will trigger a Level 1 warning. τ≥ The second threshold indicates a serious risk of anomaly during the judgment process, triggering a Level 2 warning. Based on the judgment result, corresponding warning and alarm operations are executed: When a Level 1 warning is triggered, the system generates a yellow warning icon, prominently displayed on the human-machine interface, and may trigger a continuous light indicator signal to notify operators to conduct inspections and initial intervention. When a Level 2 warning is triggered, the system generates a red warning icon and simultaneously triggers an audible and visual alarm for a strong warning. The alarm information can also be pushed to relevant personnel's mobile terminals or workstations via SMS or the centralized control system, requiring immediate corrective action.

[0101] The above description of the disclosed embodiments enables those skilled in the art to make or use the 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 invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fermentation monitoring and early warning system, characterized in that, include: The data acquisition module is used to acquire monitoring data in real time, including temperature, pH value, dissolved oxygen concentration, and carbon dioxide release rate. The data analysis module is communicatively connected to the data acquisition module. The data analysis module is used to dynamically calculate the dissolved carbon dioxide concentration in the liquid based on the carbon dioxide release rate, correct the dissolved carbon dioxide concentration based on the pH value, calculate the effective dissolved carbon dioxide concentration, and calculate the early warning index based on temperature, pH value, dissolved oxygen concentration, and effective dissolved carbon dioxide concentration. The early warning monitoring module is communicatively connected to the data analysis module. It is used to acquire an early warning index, compare the early warning index value with a preset threshold range, trigger the corresponding early warning level based on the comparison result, and generate a corresponding early warning signal based on the triggered early warning level. An alarm module, which is communicatively connected to the early warning monitoring module, is used to receive early warning signals and output alarm prompts corresponding to the early warning signals. The alarm prompts are at least one of sound, light, and text messages. The specific method for dynamically calculating the concentration of dissolved carbon dioxide in a liquid based on the carbon dioxide release rate is as follows: ； in, K H The Henry's constant is related to temperature, and its unit is 1000 ppm. mol / (L·atm) ; P 1 represents the partial pressure of carbon dioxide in the gas phase, calculated from the carbon dioxide release rate and ventilation conditions using material balance, in units of 1. atm ; c 1 represents the concentration of dissolved carbon dioxide, in units of... mol / L ; Specifically, the method for calculating the effective dissolved carbon dioxide concentration is as follows: ； in, c 2 represents the effective dissolved carbon dioxide concentration, in units of... mol / L ; α This represents the mole fraction of dissolved carbon dioxide. The mole fraction of dissolved carbon dioxide is calculated as follows: ； in, δ pH value; pKa 1 is the first-order dissociation constant; pKa 2 is the second-order dissociation constant; The specific method for calculating the early warning index based on temperature, pH value, dissolved oxygen concentration, and available dissolved carbon dioxide concentration is as follows: ； in, τ This is an early warning index; i The first parameter; j This is the second parameter; D i The normalized deviation of the first parameter from its optimal value for the process; D j Normalized deviation of the second parameter relative to its optimal value set by the process; w i The base weights for the first parameter; ε ij The first parameter i With the second parameter j The interaction coupling coefficient between parameters reflects the synergistic or antagonistic effect between parameters. It is determined by multivariate experimental design and regression analysis fitting, fermentation process mechanism model derivation, or by using machine learning algorithms to perform feature importance analysis on historical abnormal batch data. The first and second parameters include temperature, pH value, dissolved oxygen concentration, and effective dissolved carbon dioxide concentration; The specific calculation method for the normalized deviation of the first parameter from its optimal process setting value is as follows: ； in, M i The first parameter is measured or calculated in real time. O i This is the optimal setting value for the first parameter in the current process stage; U i This represents the upper or lower limit of the risk threshold for the first parameter in the direction of deviation. n It is a non-linear weighting exponent. n It is a constant greater than 1, used to amplify high-risk deviations.

2. The fermentation monitoring and early warning system according to claim 1, characterized in that, The data acquisition module includes: Temperature detection unit, used to acquire liquid temperature in real time; The pH detection unit is used to acquire the pH value of liquids in real time. Dissolved oxygen concentration detection unit, used to obtain the dissolved oxygen concentration of liquid in real time; A carbon dioxide release rate detection unit is used to acquire the carbon dioxide release rate in real time.

3. The fermentation monitoring and early warning system according to claim 1, characterized in that, The specific method for comparing the warning index value with a preset threshold range and triggering a warning signal of the corresponding warning level based on the comparison result is as follows: If the warning index value is less than the first threshold, it is judged as a normal state and no warning is triggered; If the warning index value is greater than or equal to the first threshold and less than the second threshold, a Level 1 warning is triggered. If the warning index value is greater than or equal to the second threshold, a level-two warning is triggered; The second threshold is greater than the first threshold.

4. A fermentation monitoring and early warning method, using a fermentation monitoring and early warning system as described in any one of claims 1 to 3, characterized in that, Includes the following steps: Real-time acquisition of monitoring data, including temperature, pH value, dissolved oxygen concentration, and carbon dioxide release rate; The concentration of dissolved carbon dioxide in the liquid is dynamically calculated based on the carbon dioxide release rate. The concentration of dissolved carbon dioxide is corrected based on pH value to obtain the effective concentration of dissolved carbon dioxide. The warning index is calculated based on temperature, pH value, dissolved oxygen concentration and effective dissolved carbon dioxide concentration. The system acquires a warning index, compares the warning index value with a preset threshold range, triggers the corresponding warning level based on the comparison result, generates a corresponding warning signal based on the triggered warning level, and sends it to the alarm module so that the alarm module can output an alarm prompt corresponding to the warning signal.

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