Preparation method of a hypoglycemic health product based on marine microbial glucose isomerase

Abstract

The present application relates to the technical fields of bioengineering and health food preparation, and particularly relates to a method for preparing a glucose-reducing health product based on marine microbial glucose isomerase, which comprises the following steps: collecting original material physical and chemical indexes and dynamic reaction data of a target batch, generating a basic enzymolysis scheme according to the original material physical and chemical indexes, performing temperature compensation correction on the basic enzymolysis scheme according to real-time reaction environment data in the dynamic reaction data to obtain a first enzymolysis scheme, and obtaining a target enzymolysis scheme according to the first enzymolysis scheme; performing enzymolysis reaction on the original material of the target batch according to the target enzymolysis scheme to obtain an enzymolysis reaction liquid; performing concentration and drying treatment on the enzymolysis reaction liquid to obtain a concentrated enzymolysis product; generating a target quality control strategy according to the target enzymolysis scheme; and performing feedback regulation on a preparation forming process of the concentrated enzymolysis product according to the target quality control strategy to obtain a standardized glucose-reducing health product.

Description

Technical Field

[0001] This invention relates to the fields of bioengineering and health food preparation technology, and in particular to a method for preparing a hypoglycemic health product based on marine microbial glucose isomerase. Background Technology

[0002] Existing enzymatic methods for preparing hypoglycemic health supplements suffer from the following main drawbacks: First, purely empirical formulation systems rely on fixed enzymatic hydrolysis parameters, failing to consider reaction drift caused by differences in substrate concentration and purity between batches of raw materials. In multi-batch continuous production scenarios, the isomerization rate diverges exponentially, resulting in a false compliance phenomenon where the surface meets the standards but the core is inaccurate, severely affecting the stability of the hypoglycemic efficacy of the finished product. Second, the finished product quality control system relies on single offline sampling inspections, lacking dynamic assistance in scenarios with fluctuating reaction environments (such as sudden changes in circulating water temperature and sudden drops in dissolved oxygen in the fermenter). Quality control failures lead to missed detection of non-conforming products and batch scrapping, triggering the risk of production interruptions. Third, the quality control strategy is disconnected from real-time efficacy deviations and the heat transfer status of the reaction vessel, failing to consider temperature field unevenness caused by dissolution fluctuations, sudden changes in the release rate of active ingredients, and scaling on the heat transfer surface, resulting in waste of coating materials and model drift. After scaling, the efficacy and economy of the finished product significantly decrease.

[0003] For example, Chinese patent CN108434315A discloses a method for preparing a blood sugar-lowering traditional Chinese medicine health product. However, it still uses only traditional physical pulverization and static mixing processes, does not establish a basic enzymatic hydrolysis scheme based on the physicochemical indicators of raw materials to suppress long-term drift when multiple batches of raw materials are introduced, does not introduce marine microbial glucose isomerase for biocatalytic isomerization, and does not perform dynamic temperature compensation calibration of the enzymatic hydrolysis process based on real-time reaction environment data. This results in severe reaction jumps in the enzymatic preparation scenario, and lacks a mechanism to optimize the activity decay of the calibration process based on the batch activity decay parameters of the enzyme preparation. Under highly dynamic conditions, it is prone to isomerization divergence.

[0004] Chinese patent CN120732943A discloses a blood sugar lowering health product, but it still only involves the static ratio optimization of the finished product components. It does not establish a substrate mapping-based enzymatic hydrolysis scheme based on the physicochemical indicators of raw materials, lacks an activity bottom-up mechanism when the catalytic activity of glucose isomerase from marine microorganisms degrades, and does not consider the cumulative impact of the scaling coefficient of the heat transfer surface of the reactor on quality control errors. This leads to poor applicability of enzymatic hydrolysis in multi-source raw material mutation scenarios and the decline in the efficacy of the finished product throughout its entire life cycle.

[0005] Chinese patent CN104800595A discloses a blood sugar-lowering health product and its preparation method. However, it still uses a static pulverization and mixing process for a single raw material formula. It does not establish a concentration fluctuation correction level based on real-time efficacy deviation data, nor does it introduce dissolution deviation and active ingredient release deviation as dynamic optimization variables into the formulation gain adjustment. Furthermore, it lacks a hierarchical optimization logic that adaptively corrects the quality control strategy based on real-time efficacy deviation data and the scaling coefficient of the heat transfer surface of the reactor. This results in problems such as large fluctuations in the efficacy of the finished product, loss of control due to environmental changes and failure to meet efficacy standards, and poor adaptability of quality control throughout the entire life cycle. Summary of the Invention

[0006] Therefore, this invention provides a method for preparing hypoglycemic health products based on marine microbial glucose isomerase, in order to overcome the problems of inaccurate enzymatic hydrolysis reaction, uncontrolled coupling of activity decay, and waste of coating material caused by the disconnect between quality control strategy and real-time efficacy and the scaling state of the heat transfer surface of the reaction vessel in the prior art.

[0007] To achieve the above objectives, the present invention provides a method for preparing a hypoglycemic health product based on marine microbial glucose isomerase, comprising: The physicochemical properties and dynamic reaction data of the target batch of raw materials are collected, and the dynamic reaction data includes real-time reaction environment data and enzyme batch activity decay parameters. A basic enzymatic hydrolysis scheme is generated based on the physicochemical properties of the raw materials. The basic enzymatic hydrolysis scheme is then temperature-compensated and calibrated based on the real-time reaction environment data to obtain a first enzymatic hydrolysis scheme. The activity attenuation of the temperature-compensated calibration process is then optimized based on the batch activity attenuation parameters of the enzyme preparation to obtain a target enzymatic hydrolysis scheme. The target batch of raw materials is subjected to enzymatic hydrolysis according to the target enzymatic hydrolysis scheme to obtain an enzymatic hydrolysis reaction solution; The enzymatic hydrolysis solution was concentrated and dried to obtain concentrated enzymatic hydrolysate; A basic quality control strategy is generated based on the target enzymatic hydrolysis scheme. The concentration fluctuation of the basic quality control strategy is calibrated based on real-time efficacy deviation data to obtain a first quality control strategy. The thermal resistance drift of the concentration fluctuation calibration process is optimized based on the scaling coefficient of the heat transfer surface of the reactor to obtain the target quality control strategy. The formulation process of the concentrated enzymatic hydrolysate is controlled by feedback according to the target quality control strategy to obtain a standardized hypoglycemic health product, which is then pushed to the finished product packaging line for tableting and coating.

[0008] Furthermore, the step of generating a basic enzymatic hydrolysis scheme based on the physicochemical properties of the raw materials includes: The reaction time constant was calculated based on the substrate concentration coefficient, substrate purity index, enzyme loading coefficient, enzyme specific activity coefficient, and substrate molecular weight score in the physicochemical properties of the raw materials. The reaction temperature is calculated based on the reaction time constant, target isomerization rate, environmental isomerization rate, and expected reaction time. The steady-state reaction temperature is set based on the reaction temperature, and the reaction temperature, target isomerization rate, expected reaction time, reaction time constant, and steady-state reaction temperature are output as the basic enzymatic hydrolysis scheme.

[0009] Further, the step of performing temperature compensation calibration on the basic enzymatic hydrolysis scheme based on the real-time reaction environment data to obtain the first enzymatic hydrolysis scheme includes: The temperature hysteresis index is calculated based on the reaction temperature deviation rate and pH offset in the real-time reaction environment data. The basic enzymatic hydrolysis scheme was then calibrated for temperature compensation based on the temperature hysteresis index.

[0010] Furthermore, the temperature compensation calibration of the basic enzymatic hydrolysis scheme based on the temperature hysteresis index includes: The temperature hysteresis index Y is compared with the preset hysteresis index Yb. The reaction state is judged based on the comparison result, and the basic enzymatic hydrolysis scheme is calibrated for temperature compensation based on the judgment result, wherein: When Y≤Yb, the reaction state is determined to be normal, and no temperature compensation calibration is performed on the basic enzymatic hydrolysis scheme. When Y > Yb, the reaction state is determined to be delayed. Temperature compensation is performed on the basic enzymatic hydrolysis scheme to obtain the corrected reaction temperature T1. The corrected reaction temperature T1 is then replaced in the basic enzymatic hydrolysis scheme to obtain the first enzymatic hydrolysis scheme.

[0011] Further, the step of optimizing the activity decay of the temperature compensation calibration process based on the batch activity decay parameters of the enzyme preparation to obtain the target enzymatic hydrolysis scheme includes: The transient activity gain coefficient is calculated based on the relative enzyme activity retention rate and cofactor shedding rate in the batch activity decay parameters of the enzyme preparation. The activity attenuation of the temperature compensation calibration process is optimized based on the transient activity gain coefficient to obtain the target enzymatic hydrolysis scheme.

[0012] Further, the step of optimizing the activity decay of the temperature compensation calibration process based on the transient activity gain coefficient to obtain the target enzymatic hydrolysis scheme includes: The transient active gain coefficient Z is compared with the preset gain coefficient Zb. Based on the comparison result, the active coupling state is determined, and the active attenuation is optimized in the temperature compensation calibration process based on the determination result, wherein: When Z≤Zb, the active coupling state is determined to be stable, and no active attenuation optimization is performed on the temperature compensation calibration process; When Z > Zb, the active coupling state is determined to be strong coupling. The activity attenuation optimization is performed on the temperature compensation calibration process to obtain the target reaction temperature T2. The target reaction temperature T2 is then replaced in the first enzymatic hydrolysis scheme to obtain the target enzymatic hydrolysis scheme. The holding time W is set according to the reaction conditions. When the batch enzymatic hydrolysis mode is identified, W=3600s is set. When the continuous feeding mode is identified, W=600s is set. When the intermittent feeding mode is identified, W=1800s is set. The holding time W is then added to the target enzymatic hydrolysis scheme.

[0013] Furthermore, the step of generating a basic quality control strategy based on the target enzymatic hydrolysis scheme includes: The basic quality control level Q0 is calculated based on the steady-state reaction temperature Ts, the incubation duration W, the current enzyme residual rate Ve, and the current isomerization compliance rate Ue in the target enzymatic hydrolysis scheme. Based on the basic quality control level Q0, the tableting start / stop threshold Fp and the coating auxiliary concentration X0 are set to obtain a basic quality control strategy that includes the tableting start / stop threshold Fp and the coating auxiliary concentration X0.

[0014] Further, the step of adjusting the concentration fluctuation of the basic quality control strategy based on real-time efficacy deviation data to obtain the first quality control strategy includes: The real-time efficacy deviation data is acquired, including dissolution deviation and active ingredient release deviation. The efficacy deviation index is calculated based on the dissolution deviation, active ingredient release deviation, first efficacy weight, and second efficacy weight. The concentration fluctuation is then calibrated against the basic quality control strategy based on the efficacy deviation index to obtain the first quality control strategy.

[0015] Further, the step of adjusting the concentration fluctuation of the basic quality control strategy based on the efficacy deviation index to obtain the first quality control strategy includes: The efficacy deviation index P is compared with the preset efficacy index Pb. The efficacy status is judged based on the comparison result, and the concentration fluctuation is calibrated for the quality control strategy based on the judgment result. When P≤Pb, the efficacy status is determined to be up to standard, and no concentration fluctuation calibration is performed on the quality control strategy. When P > Pb, the efficacy status is determined to be substandard. The concentration fluctuation is calibrated for the quality control strategy to obtain the calibrated coating auxiliary concentration X1. The calibrated coating auxiliary concentration X1 is then replaced in the basic quality control scheme to obtain the first quality control strategy.

[0016] Furthermore, the step of optimizing the concentration fluctuation correction process based on the fouling coefficient of the reactor heat transfer surface to obtain the target quality control strategy includes: The fouling coefficient of the heat transfer surface of the reactor is obtained, and the fouling coefficient of the heat transfer surface of the reactor includes the heat transfer coefficient decay rate and the thermal resistance growth rate. The scaling correction factor is calculated based on the heat transfer coefficient decay rate and the thermal resistance growth rate. The scaling correction coefficient K is compared with the preset scaling coefficient Kb. The scaling state is judged based on the comparison result, and the thermal resistance drift is optimized for the concentration fluctuation correction process based on the judgment result. When K≤Kb, the scaling condition is determined to be mild, and thermal resistance drift optimization is not performed on the concentration fluctuation correction process. When K > Kb, the scaling state is determined to be severe. Thermal resistance drift optimization is performed on the concentration fluctuation calibration process to obtain the corrected coating auxiliary concentration X2. The corrected coating auxiliary concentration X2 is then replaced in the first quality control strategy to obtain the target quality control strategy.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: The method achieves synergistic optimization of enzymatic hydrolysis reaction precision and formulation quality through multi-dimensional dynamic coupling analysis of raw material physicochemical indicators, dynamic reaction data, real-time reaction environment data, and enzyme batch activity decay parameters. The method collects raw material physicochemical indicators and dynamic reaction data for the target batch, solving the technical problem of existing enzymatic hydrolysis strategies lacking raw material attribute benchmarks and real-time reaction state perception data. This provides a complete data foundation for subsequent enzymatic hydrolysis decision-making and quality control optimization, realizing a shift from static benchmarking to dynamic perception. The method generates enzymatic hydrolysis schemes based on the raw material physicochemical indicators, solving the problem of static setting deviations caused by relying on fixed empirical values ​​and not considering raw material attribute differences in existing technologies, making the initial enzymatic hydrolysis parameters adaptable to operating conditions. The method generates basic enzymatic hydrolysis schemes based on the raw material physicochemical indicators and performs temperature compensation calibration on the basic enzymatic hydrolysis schemes using real-time reaction environment data from the dynamic reaction data. This solves the problem of enzymatic hydrolysis inaccuracies caused by only monitoring surface isomerization rate and ignoring the lag in core reaction precision in existing technologies, achieving synchronous control of surface isomerization rate and core reaction precision, eliminating false positives and negative negatives. The method addresses the issue of pseudo-enzymatic hydrolysis. By optimizing the enzyme activity decay parameters in the dynamic reaction data for the temperature compensation calibration process, it resolves the overshoot problem caused by the lack of consideration for dynamic enzyme activity decay, temperature compensation, and internal activity resonance in existing technologies. This achieves dynamic decoupling between external temperature compensation and enzyme activity, ensuring hydrolysis accuracy. The method executes the hydrolysis reaction of the target batch of raw materials using the basic hydrolysis scheme to obtain the hydrolysis reaction solution. This solves the problems of disconnection between hydrolysis commands and reaction execution mechanisms, and response lag in existing technologies, achieving seamless integration from the hydrolysis scheme to the reaction flow. The closed-loop execution ensures that the isomerization rate remains stable within the target range. The method generates a quality control strategy based on the target enzymatic hydrolysis scheme, solving the problems of disconnect between enzymatic hydrolysis reaction requirements and quality control equipment selection, and inaccurate efficacy prediction in existing technologies. This achieves a reasonable mapping from enzymatic hydrolysis reaction requirements to quality control equipment configuration. The method generates a basic quality control strategy based on the target enzymatic hydrolysis scheme, and uses real-time efficacy deviation data to calibrate the basic quality control strategy for concentration fluctuations. This solves the problem of waste of coating materials caused by failure to consider dissolution fluctuations and sudden changes in active ingredient release in existing technologies, achieving adaptive coating optimization based on operating conditions.The method optimizes the concentration fluctuation calibration process by using the scaling coefficient of the reactor heat transfer surface to address the technical problems in existing technologies, such as temperature field inhomogeneity and model drift caused by scaling on the reactor heat transfer surface, and inflated efficacy figures after scaling. This achieves adaptive and precise control of the efficacy model throughout the entire lifecycle. Furthermore, the method uses the target quality control strategy to provide feedback regulation of the formulation process of the concentrated enzymatic hydrolysate to obtain standardized hypoglycemic health products. This solves the coating loss problem caused by independent control and lack of collaborative optimization of formulation equipment in existing technologies. It achieves collaborative operation of the enzymatic hydrolysis reaction and formulation under optimal efficacy boundary conditions, completing the optimal closed-loop control of the preparation system, and pushing the standardized hypoglycemic health products to the finished product packaging line for tableting and coating applications. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of the method for preparing hypoglycemic health products based on marine microbial glucose isomerase in this embodiment; Figure 2 This is a flowchart illustrating step S2; Figure 3 This is a flowchart illustrating step S4. Detailed Implementation

[0019] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0020] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0021] Please see Figure 1 The diagram shown is a flowchart illustrating the method for preparing a hypoglycemic health product based on marine microbial glucose isomerase in this embodiment. The method includes: Please see Figure 1 The diagram shown is a flowchart illustrating the method for preparing a hypoglycemic health product based on marine microbial glucose isomerase in this embodiment. The method includes: Step S1: Collect the physicochemical properties and dynamic reaction data of the raw materials for the target batch. The dynamic reaction data includes real-time reaction environment data and enzyme batch activity decay parameters. Step S2: Generate a basic enzymatic hydrolysis scheme based on the physicochemical indicators of the raw materials; perform temperature compensation calibration on the basic enzymatic hydrolysis scheme based on the real-time reaction environment data to obtain a first enzymatic hydrolysis scheme; and optimize the activity decay of the temperature compensation calibration process based on the batch activity decay parameters of the enzyme preparation to obtain a target enzymatic hydrolysis scheme. Step S3: Perform enzymatic hydrolysis on the target batch of raw materials according to the target enzymatic hydrolysis scheme to obtain enzymatic hydrolysis reaction solution; Step S4: The enzymatic hydrolysis reaction solution is concentrated and dried to obtain concentrated enzymatic hydrolysate; Step S5: Generate a basic quality control strategy based on the target enzymatic hydrolysis scheme; perform concentration fluctuation calibration on the basic quality control strategy based on real-time efficacy deviation data to obtain a first quality control strategy; and optimize the concentration fluctuation calibration process by thermal resistance drift based on the scaling coefficient of the heat transfer surface of the reactor to obtain the target quality control strategy. Step S6: The formulation process of the concentrated enzymatic hydrolysate is controlled by feedback according to the target quality control strategy to obtain a standardized hypoglycemic health product, and the standardized hypoglycemic health product is pushed to the finished product packaging line for tableting and coating application.

[0022] Specifically, the method for preparing hypoglycemic health products based on marine microbial glucose isomerase is applied to a production line for hypoglycemic health products derived from marine microorganisms, equipped with an enzymatic hydrolysis reactor and formulation molding equipment. This method achieves synergistic optimization of enzymatic hydrolysis accuracy and formulation quality through multi-dimensional dynamic coupling analysis of raw material physicochemical indicators, dynamic reaction data, real-time reaction environment data, and batch-by-batch enzyme activity decay parameters. Furthermore, by collecting raw material physicochemical indicators and dynamic reaction data for the target batch, this method addresses the technical problem in existing enzymatic hydrolysis strategies of lacking raw material attribute benchmarks and real-time reaction state perception data, thus providing a foundation for subsequent enzyme hydrolysis... The method provides a complete data foundation for strategy and quality control optimization, enabling a shift from static calibration to dynamic perception. It generates enzymatic hydrolysis schemes based on the physicochemical indicators of the raw materials, addressing the static setting deviations caused by the reliance on fixed empirical values ​​and failure to consider differences in raw material properties in existing technologies. This ensures that the initial enzymatic hydrolysis parameters are adaptable to various operating conditions. Furthermore, it generates basic enzymatic hydrolysis schemes using the physicochemical indicators of the raw materials and performs temperature compensation calibration on these schemes using real-time reaction environment data from the dynamic reaction data. This addresses the inaccuracies caused by existing technologies that only monitor surface isomerization rates and neglect the lag in core reaction precision. The method achieves simultaneous control of surface isomerization rate and core reaction precision, eliminating false enzymatic hydrolysis. It optimizes the temperature compensation calibration process by using enzyme batch activity decay parameters from the dynamic reaction data, addressing the precision overshoot problem caused by the lack of consideration for dynamic enzyme activity decay, temperature compensation, and internal activity resonance in existing technologies. This achieves dynamic decoupling of external temperature compensation and enzyme activity, ensuring enzymatic hydrolysis precision. The method executes the enzymatic hydrolysis reaction of the target batch of raw materials using the basic enzymatic hydrolysis scheme to obtain the enzymatic hydrolysis reaction solution, solving the problems of disconnection between enzymatic hydrolysis commands and reaction execution mechanisms, and response lag in existing technologies. This method achieves closed-loop execution from enzymatic hydrolysis scheme to reaction flow, ensuring that the isomerization rate remains stable within the target range. It generates a quality control strategy based on the target enzymatic hydrolysis scheme, addressing the problems of disconnect between enzymatic hydrolysis reaction requirements and quality control equipment selection, as well as inaccurate efficacy predictions in existing technologies. This achieves a reasonable mapping between enzymatic hydrolysis reaction requirements and quality control equipment configuration. Furthermore, it generates a basic quality control strategy based on the target enzymatic hydrolysis scheme and uses real-time efficacy deviation data to calibrate the basic quality control strategy for concentration fluctuations. This addresses the problems of wasted coating materials caused by dissolution fluctuations and sudden changes in active ingredient release, which are not considered in existing technologies, enabling adaptive coating optimization based on operating conditions.The method optimizes the concentration fluctuation calibration process by using the scaling coefficient of the reactor heat transfer surface to address the technical problems in existing technologies, such as temperature field inhomogeneity and model drift caused by scaling on the reactor heat transfer surface, and inflated efficacy figures after scaling. This achieves adaptive and precise control of the efficacy model throughout the entire lifecycle. Furthermore, the method uses the target quality control strategy to provide feedback regulation of the formulation process of the concentrated enzymatic hydrolysate to obtain standardized hypoglycemic health products. This solves the coating loss problem caused by independent control and lack of collaborative optimization of formulation equipment in existing technologies. It achieves collaborative operation of the enzymatic hydrolysis reaction and formulation under optimal efficacy boundary conditions, completing the optimal closed-loop control of the preparation system, and pushing the standardized hypoglycemic health products to the finished product packaging line for tableting and coating applications.

[0023] Specifically, the target batch refers to a raw material assembly with independent enzymatic hydrolysis circuits, composed of multiple monomeric raw materials in a marine microbial-derived hypoglycemic health product production line. The physicochemical indicators of the raw materials include substrate concentration coefficient, substrate purity index, enzyme loading coefficient, enzyme specific activity coefficient, and substrate molecular weight score. The substrate concentration coefficient is a quantitative indicator of the structural complexity of isomerizable substrates per unit of raw material, calculated by comprehensively considering substrate chain length, branching degree, glycosidic bond type diversity, and the proportion of reducing ends, with a value ranging from 1 to 10. In this embodiment, the substrate concentration coefficient is collected using a substrate structure analysis algorithm. This algorithm involves placing the raw material sample in an enzymatic hydrolysis reactor and measuring the ratio of the substrate hydrolysis time to the reference substrate hydrolysis time under constant temperature conditions. The substrate concentration coefficient value is obtained through normalization. The substrate purity index refers to the target substrate's molecular weight at a given time. The percentage of purity appearing in the istorical raw material dataset, ranging from 0 to 1, characterizes the common purity level of the substrate. In this embodiment, the substrate purity index is collected through historical data statistics. The historical data statistics refer to a full scan of the raw material database over the past 12 months, calculating the ratio of the number of times a specific substrate purity appears to the total number of batches. The enzyme loading coefficient refers to the proportion of all enzyme molecules in the target batch that can be loaded onto the substrate active site, ranging from 0 to 1. In this embodiment, the enzyme loading coefficient is collected through enzyme loading coverage calculation. The enzyme loading coverage calculation refers to comparing the current raw material substrate with the standard active site library of marine microbial glucose isomerase, and calculating the ratio of the number of successfully loaded enzyme molecules to the total number of enzyme molecules. The specific activity coefficient refers to the catalytic success rate of glucose isomerase in historical data, ranging from 0.8 to 0.999 represents the performance level of the enzyme preparation. In this embodiment, the specific activity coefficient of the enzyme is collected through offline test calibration. The offline test calibration refers to running glucose isomerase on a standard test set and calculating the ratio of the number of correct isomerizations to the total number of tests. The substrate molecular weight score refers to the uniformity and polymerization stability score of the substrate molecule in historical transport, with a value range of 0 to 1. In this embodiment, the substrate molecular weight score is collected through substrate monitoring logs. The substrate monitoring logs record the success status, molecular weight distribution, and coefficient of variation of each raw material test. The weighted score of success rate and molecular weight variation coefficient is calculated through a sliding window. The dynamic reaction data includes real-time reaction environment data and enzyme batch activity decay parameters. The real-time reaction environment data includes reaction temperature deviation rate and pH offset. The reaction temperature deviation rate is the normalized ratio of the difference between the actual reaction temperature monitored by the temperature sensor and the set reaction temperature. The success rate monitored by the temperature sensor is calculated as the ratio of the number of successfully matched temperature points to the total number of temperature points, with a sampling frequency of 1Hz. The pH offset refers to the actual pH value monitored by the pH electrode. The normalized ratio of the difference between the pH value and the set pH value, the batch activity decay parameters of the enzyme preparation include relative enzyme activity retention rate and cofactor shedding rate. The relative enzyme activity retention rate refers to the ratio of the remaining enzyme activity of the current batch of enzyme preparation measured under standard test conditions to the initial enzyme activity, calculated by the formula Rr=Ea / E0, where Ea is the current enzyme activity measurement value and E0 is the initial enzyme activity calibration value, with a value range of 0.5 to 1.0. In this embodiment, the relative enzyme activity retention rate is obtained by timed sampling offline measurement. The timed sampling offline measurement refers to sampling from the reaction every 30 minutes. 5 mL of reaction solution was drawn from the sampling port of the reactor. After centrifugation to remove the substrate, the glucose isomerase activity was measured under standard substrate concentration and pH conditions. The cofactor shedding rate refers to the proportion of essential cofactors of the enzyme molecule dissociated from the active site, calculated using the formula Fd = (C0 - Ca) / C0, where C0 is the initial cofactor concentration and Ca is the current cofactor concentration. In this embodiment, the cofactor shedding rate was monitored using an ion-selective electrode. The ion-selective electrode is a dedicated electrode for real-time potentiometry determination of the Mg²⁺ concentration in the reaction solution, with a sampling frequency of 0.1 Hz.

[0024] Please see Figure 2 As shown, this is a flowchart illustrating step S2 of this embodiment, which includes: Step S21: Generate a basic enzymatic hydrolysis scheme based on the physicochemical properties of the raw materials; Step S22: Perform temperature compensation calibration on the basic enzymatic hydrolysis scheme based on the real-time reaction environment data to obtain the first enzymatic hydrolysis scheme; Step S23: Optimize the activity decay of the temperature compensation calibration process according to the batch activity decay parameters of the enzyme preparation to obtain the target enzymatic hydrolysis scheme.

[0025] Specifically, the step of generating a basic enzymatic hydrolysis scheme based on the physicochemical properties of the raw materials includes: The reaction time constant τ is calculated based on the substrate concentration coefficient Cc, substrate purity index Fc, enzyme loading coefficient Mc, enzyme specific activity coefficient Ma, and substrate molecular weight score Rs in the physicochemical indicators of the raw materials, and is set as τ=(Cc×Fc×Mc) / (Ma×Rs). The reaction temperature C0 is calculated based on the reaction time constant τ, the target isomerization rate Ttarget, the environmental isomerization rate Tamb, and the expected reaction time texp, and is set as C0 = (Ttarget - Tamb) × (Cc × Fc × Mc) / (τ × (1 - exp(-texp / τ))). The steady-state reaction temperature Cs is set based on the reaction temperature C0, with Cs=C0. ​​The reaction temperature C0, the target isomerization rate Ttarget, the expected reaction time texp, the reaction time constant τ, and the steady-state reaction temperature Cs are output as the basic enzymatic hydrolysis scheme.

[0026] Specifically, the target isomerization rate refers to the core target working isomerization rate of the substrate set by the enzymatic hydrolysis reaction management system, which is set to 99.9%. The reason for this value is that this isomerization rate is located in the middle of the optimal working isomerization rate range of 99.5% to 99.95% for marine microbial glucose isomerase. At this isomerization rate, the substrate conversion rate is the highest, the enzyme cycle life is the longest, and the reaction efficiency is the highest. The environmental isomerization rate refers to the current substrate isomerization rate collected in real time through the substrate monitoring log. The expected reaction time refers to the set time required for the current environmental isomerization rate to rise to the target isomerization rate, which is set to 300s to 600s. The reason for this value is that it corresponds to the industry-standard rapid preheating index of 5min to 10min, takes into account the user's waiting experience when starting the reaction and the load limit of the enzymatic hydrolysis reactor, and takes the lower value of 300s for the low isomerization rate cold start condition and the higher value of 600s for the normal preheating condition.

[0027] Specifically, the step of performing temperature compensation calibration on the basic enzymatic hydrolysis scheme based on the real-time reaction environment data to obtain the first enzymatic hydrolysis scheme includes: The temperature hysteresis index Y is calculated based on the reaction temperature deviation rate Tc, pH offset Tm, first precision weight wc, and second precision weight wm in the real-time reaction environment data. Y is set as Y = wc × (Tm - Tc) / ΔTbase + wm × (dTc / dt) / Rbase, where ΔTbase is the reference precision difference and Rbase is the reference precision change rate. The temperature hysteresis index Y is compared with the preset hysteresis index Yb. The reaction state is judged based on the comparison result, and the basic enzymatic hydrolysis scheme is calibrated for temperature compensation based on the judgment result, wherein: When Y≤Yb, the reaction state is determined to be normal, and no temperature compensation calibration is performed on the basic enzymatic hydrolysis scheme. When Y > Yb, the reaction state is determined to be delayed. Temperature compensation calibration is performed on the basic enzymatic hydrolysis scheme. The temperature compensation calibration includes: calculating the temperature compensation amount ΔC according to the temperature gain coefficient Kh, setting ΔC = Kh × (Y - Yb), and calibrating the reaction temperature C0 according to the temperature compensation amount ΔC to obtain the calibrated reaction temperature T1. T1 = C0 + ΔC is set, and the reaction temperature C0 in the basic enzymatic hydrolysis scheme is replaced with the calibrated reaction temperature T1 to obtain the first enzymatic hydrolysis scheme.

[0028] Specifically, the first precision weight refers to the weighting coefficient assigned to the difference between the reaction temperature deviation rate and the pH deviation when calculating the temperature hysteresis index, with a value of 0.6. The reason for this value is that in the marine microbial glucose isomerase reaction model, the difference between the pH deviation and the reaction temperature deviation rate under steady-state conditions, ΔT=Tm-Tc, reflects the structural deviation caused by pH resolution hysteresis, which accounts for 60% to 70% of the total temperature effect. The marine microbial glucose isomerase reaction model simplifies the global isomerase reaction to a substrate-level structure, describing the resolution process of pH information mapping from the reaction surface to the core standard. The model is based on information theory, setting the information entropy H=-Σp(x)logp(x), where p(x) is the probability distribution of the substrate value. The second precision weight refers to the weighting coefficient assigned to the rate of change of the reaction temperature deviation rate when calculating the temperature hysteresis index, with a value of 0.4. The reason for this value is that the rate of change of the reaction temperature deviation rate, dTc / dt, reflects the transient response hysteresis of the overall temperature stability of the reaction, determining the mapping... The response speed and overshoot suppression capability of the management system are considered. This transient effect accounts for 30% to 40% of the total temperature effect. The preset hysteresis index refers to the normalized critical threshold for determining whether the reaction state exceeds the limit, with a value of 1.0. The reason for this value is that it is a dimensionless normalized threshold. When Y=1.0, the difference between the pH offset and the reaction temperature deviation rate exactly reaches the superimposed critical state of 5% of the reference accuracy difference and the change rate of the reaction temperature deviation rate reaches the reference change rate of 2% / min. Exceeding this value indicates that the reaction has exceeded the standard response capability of the mapping management system. The temperature gain coefficient refers to the proportional conversion coefficient between the temperature hysteresis index deviation and the temperature adjustment amount when calculating the temperature compensation amount, with a value of 5℃ / unit Y deviation to 10℃ / unit Y deviation. The reason for this value is that, based on the temperature adjustment accuracy of the enzymatic hydrolysis reactor, the minimum temperature adjustment step of this type of reactor is 5℃, and according to the temperature elimination time requirement, the system needs to reduce the difference between the pH offset and the reaction temperature deviation rate by 50% within 30 seconds. When the deviation of the temperature hysteresis index Y is 1.At unit 0, an increase of 5 to 10 reaction temperatures is required to generate sufficient reaction capacity within 30 seconds to offset pH lag. For small batches with fewer than 100 substrates, the lag is smaller, hence a lower value of 5°C. For large batches with 100 or more substrates, the lag is larger, hence a higher value of 10°C. The reaction state refers to the classification and judgment result of the degree of pH resolution lag within the reaction based on the comparison between the temperature lag index Y and the preset lag index Yb. The reaction state includes two types: normal and lag. The reference accuracy difference refers to the reference accuracy difference normalized by the difference between pH deviation and reaction temperature deviation rate when calculating the temperature lag index, and is set to 5%. The reason for this value is: based on the industry standard for marine microbial glucose isomerase reaction, combined with the standard mapping conditions, i.e., the mapping engine. Under conditions of 10 concurrent threads and a cache hit rate of 90%, the steady-state pH-temperature difference of conventional raw materials at standard reaction rates typically remains between 3% and 5%. A difference exceeding 5% indicates mapping anomalies or excessive reaction load. Therefore, 5% is used as the normalization benchmark. The benchmark accuracy change rate refers to the reference change rate value, normalized to the change rate of reaction temperature deviation, when calculating the temperature hysteresis exponent. This value is set at 2% / min. The rationale is as follows: according to the safety operation specifications of the enzymatic hydrolysis reaction system, the change rate of reaction temperature deviation should not exceed 2% / min under normal reaction conditions. Exceeding this value indicates excessive reaction concurrency and a risk of system overload. Therefore, 2% / min is used as the normalization benchmark for the response speed of the mapping management system to ensure that the temperature compensation calibration process does not trigger isomerization overshoot.

[0029] Specifically, the step of optimizing the activity decay of the temperature compensation calibration process based on the batch activity decay parameters of the enzyme preparation to obtain the target enzymatic hydrolysis scheme includes: The transient activity gain coefficient Z is calculated based on the relative enzyme activity retention rate Rr and cofactor shedding rate Fd in the enzyme preparation batch activity decay parameters. Z is set as Z = β1 × (Rr / Rbase) + β2 × (Fd / Fdbase), where Rbase is the baseline retention rate, Fdbase is the baseline shedding rate, β1 is the first activity weight, and β2 is the second activity weight. The transient active gain coefficient Z is compared with the preset gain coefficient Zb. Based on the comparison result, the active coupling state is determined, and the active attenuation is optimized in the temperature compensation calibration process based on the determination result, wherein: When Z≤Zb, the active coupling state is determined to be stable, and no active attenuation optimization is performed on the temperature compensation calibration process; When Z > Zb, the active coupling state is determined to be strong coupling. The activity attenuation optimization is performed on the temperature compensation calibration process. The activity attenuation optimization includes: adjusting the temperature gain coefficient Kh to Kh', setting Kh' = Kh × (1 + ν × (Z - Zb) / Zmax), where ν is the coupling gain coefficient and Zmax is the maximum activity gain. The calibrated reaction temperature T1 is recalculated based on the optimized gain coefficient Kh' to obtain the target reaction temperature T2. T2 is set to [Kh' × (Y - Yb)] + C0. The calibrated reaction temperature T1 in the first enzymatic hydrolysis scheme is replaced with the target reaction temperature T2 to obtain the target enzymatic hydrolysis scheme. The holding time W is set according to the reaction conditions. When the batch enzymatic hydrolysis mode is identified, W=3600s is set. When the continuous feeding mode is identified, W=600s is set. When the intermittent feeding mode is identified, W=1800s is set. The holding time W is then added to the target enzymatic hydrolysis scheme.

[0030] Specifically, the reaction condition refers to the current enzymatic hydrolysis processing mode and system load status of the target batch, including batch hydrolysis mode, continuous feed mode, and intermittent feeding mode. In this embodiment, the reaction condition is acquired through a condition identification algorithm in the enzymatic hydrolysis management system. The condition identification algorithm receives task status signals sent by the scheduler through the system monitoring interface. The task status signals include task queue depth, number of concurrent requests, data connection signals, and synchronization cycle configuration information. Combined with the current reaction direction and reaction amplitude of the raw material package, the current reaction condition is determined by a lookup table method or a state machine. The batch hydrolysis mode refers to the operating condition in which large-scale data import of the target batch is performed through the batch hydrolysis interface. The continuous feed mode refers to the system... In a high-concurrency, real-time request processing environment, the intermittent feeding mode refers to a synchronous operation where the system runs at fixed and quasi-fixed cycles. The heat preservation duration refers to the predicted duration for which the peak reaction temperature above the steady-state reaction temperature Cs needs to be maintained under the current reaction conditions. The baseline retention rate refers to a reference value of the unit enzyme activity retention rate measured under standard reaction rate and normal load conditions through standard testing, with a value of 1%. The reason for this value is that it corresponds to the typical retention level of conventional glucose isomerase under standard conditions, and serves as a normalization baseline to make the transient activity gain coefficient Z a dimensionless parameter, facilitating the comparison of retention deviations under different conditions. The first activity weight refers to the weight used when calculating the transient activity gain coefficient. The weighting coefficient for the relative enzyme activity retention rate is set to 0.7. The rationale is that the relative enzyme activity retention rate directly reflects the accuracy of enzyme catalysis; a lower retention rate indicates a higher enzyme load, posing over 70% of the threat to system stability, and this threat increases monotonically with the load. Therefore, it has a decisive and dominant contribution to the mapped load, hence the highest weighting. The second activity weighting refers to the weighting coefficient for the cofactor shedding rate when calculating the transient activity gain coefficient, set to 0.3. The rationale is that the cofactor shedding rate mainly reflects the deviation of the enzyme molecule cofactor from the standard cofactor. Although it also affects reaction quality, it is less significant than the retention rate itself. Furthermore, the shedding rate includes acceptable normal cofactor fluctuations, hence a lower weighting is given to create a complementary diagnostic effect. The activity coupling state refers to the classification and judgment result of the degree of coupling between enzyme catalytic errors and cofactor shedding based on the comparison result of transient activity gain coefficient Z and preset gain coefficient Zb. The activity coupling state includes two types: stable and strong coupling. The preset gain coefficient is the critical threshold for determining whether the activity coupling state has entered the strong coupling region, and its value is 2.0. The reason for this value is that when the transient activity gain coefficient Z is 2.0, the corresponding actual retention rate is twice the baseline retention rate, that is, high load or abnormal data conditions. At this time, the enzyme error is on the same order of magnitude as the external verification load. The coupling gain coefficient is the adjustment parameter for controlling the attenuation of the temperature gain coefficient during the activity attenuation optimization process, and its value is 0.3. The rationale for this value is as follows: According to the gain scheduling strategy in control theory, it is necessary to ensure that under the maximum activity gain condition, the temperature gain coefficient Kh' is not less than 0.7 times the original value Kh. This multiple is calculated by subtracting the difference between the coupling gain coefficient multiplied by the transient activity gain coefficient Z and the preset gain coefficient Zb from Formula 1, and then dividing by the maximum activity gain. Substituting the coupling gain coefficient as 0.3, Z as the maximum activity gain, and Zb as 2.0, the calculation result is 0.7. This effectively prevents isomerization runaway caused by a sudden drop in reaction temperature due to excessive adjustment, while retaining sufficient response sensitivity. The maximum activity gain refers to the upper limit that the transient activity gain coefficient Z can reach, which is set to 5.0. The rationale for this value is that under extreme conditions such as abnormal data influx or tax law mutations, the sum of the relative enzyme activity retention rate and cofactor shedding rate can reach 5 times the benchmark value. This value corresponds to the extreme load boundary of the raw material package reaction management system. Using this as the maximum activity gain can cover the activity fluctuation range of all normal operating conditions.

[0031] Specifically, step S3 involves performing an enzymatic hydrolysis reaction on the target batch of raw materials according to the target enzymatic hydrolysis scheme to obtain an enzymatic hydrolysis reaction solution, specifically including: The enzymatic hydrolysis circuit of the target batch is switched to active enzymatic hydrolysis mode. The initial heating power of the enzymatic hydrolysis reactor is set according to the target reaction temperature T2 in the target enzymatic hydrolysis scheme. The initial heating power is set as P0 = T2 / Tmax × 100%, where Tmax is the maximum heating capacity of the enzymatic hydrolysis reactor. The initial heating power is output to the enzymatic hydrolysis reactor through the power regulator to control the reactor to start enzymatic hydrolysis at the target reaction temperature T2. The strength of the verification rules is initially adjusted based on the comparison between the target isomerization rate Ttarget and the reaction temperature deviation rate Tc in the target enzymatic hydrolysis scheme. When Ttarget > Tc, the enhanced verification mode is enabled and semantic verification rules are added. When Ttarget ≤ Tc, the standard verification mode is enabled and the number of verification rules is reduced. After the enzymatic hydrolysis reaction is completed, the reaction solution is centrifuged to remove insoluble impurities, thus obtaining the enzymatic hydrolysis reaction solution.

[0032] Specifically, step S4 involves concentrating and drying the enzymatic hydrolysis solution to obtain concentrated enzymatic hydrolysate, specifically including: The enzymatic hydrolysis solution was concentrated under reduced pressure using a vacuum rotary evaporator. The vacuum level was set to 0.08 MPa to 0.095 MPa, and the concentration temperature was set to 45°C to 55°C. The concentration was carried out until the solid content reached 30% to 40%, and the concentrate was obtained. The concentrate is dried in a spray drying tower with an inlet air temperature of 160°C to 180°C and an outlet air temperature of 70°C to 80°C to obtain a dried enzymatic hydrolysate with a moisture content of less than or equal to 5%. The dried enzymatic hydrolysate from Susonghu is then output as a concentrated enzymatic hydrolysate.

[0033] Please see Figure 3 As shown, this is a flowchart illustrating step S5 of this embodiment, which includes: Step S51: Generate a basic quality control strategy based on the target enzymatic hydrolysis scheme; Step S52: Based on the real-time efficacy deviation data, the basic quality control strategy is adjusted for concentration fluctuations to obtain the first quality control strategy; Step S53: Optimize the thermal resistance drift of the concentration fluctuation calibration process based on the fouling coefficient of the reactor heat transfer surface to obtain the target quality control strategy.

[0034] Specifically, the generation of a basic quality control strategy based on the target enzymatic hydrolysis scheme includes: Based on the steady-state reaction temperature Ts, the holding time W, the current enzyme residual rate Ve, and the current isomerization compliance rate Ue in the target enzymatic hydrolysis scheme, the basic quality control level Q0 is calculated and set as Q0=Ts×W×(1 / Avac)×(Mh0 / Ue), where Avac is the availability correction coefficient and Mh0 is the baseline isomerization compliance rate. Based on the basic quality control level Q0, the tableting start / stop threshold Fp and the coating auxiliary concentration X0 are set to obtain a basic quality control strategy that includes the tableting start / stop threshold Fp and the coating auxiliary concentration X0.

[0035] Specifically, the current enzyme residual rate refers to the ratio of successful substrate catalysis to total catalytic requests at a given moment, expressed as a percentage or decimal, ranging from 0% to 100%. In this embodiment, the current enzyme residual rate is obtained through sliding window statistics, which involves statistically analyzing catalytic requests over the past 5 minutes and calculating the ratio of successful catalysis to total requests. The current isomerization compliance rate refers to the ratio of successful isomerizations to total isomerizations in the target isomerization reaction, characterizing the degree of performance degradation of the isomerization reaction, ranging from 50% to 100%. In this embodiment, the current isomerization compliance rate is obtained through isomerization accuracy testing, which involves running isomerization reactions on a standard test set and calculating the ratio of correct isomerizations to total tests. The availability correction coefficient refers to the nonlinear correction applied to the current enzyme residual rate (Ve) when calculating the basic quality control level to reflect the energy consumption penalty coefficient caused by increased reaction delay under low residual rate conditions, where Avac = Ve is set. 0.5The benchmark isomerization compliance rate refers to the reference compliance rate value used as the normalization benchmark for the isomerization compliance rate when calculating the basic quality control level, and its value is 100%. Setting the tableting start / stop threshold Fp and coating auxiliary concentration X0 based on the basic quality control level Q0 refers to configuring parameters for the collaborative work of tableting and coating according to the quality control level segmentation strategy. Specifically, this includes: when the basic quality control level Q0 is less than or equal to 1000, it is determined to be a low quality control condition, the tableting start / stop threshold Fp is set to 10% and the coating auxiliary concentration X0 is set to 0 g / L, and tableting is prioritized to maximize the efficacy ratio; when the basic quality control level Q0 is greater than 1000 and ... the tableting start / stop threshold Fp is set to 10% and the coating auxiliary concentration X0 is set to 0 g / L, and tableting is prioritized to maximize the efficacy ratio; when the basic quality control level Q0 is greater than 1000 and less than or equal to 1000, the tableting start / stop threshold Fp is set to 10% and the coating auxiliary concentration X0 is set to 0 g / L, and tableting is prioritized to maximize the efficacy ratio. When the value equals 3000, it is determined to be a medium quality control condition. The tableting start / stop threshold Fp is set to 50% and the coating auxiliary concentration X0 is Q0×0.3 / W. Coating assistance is enabled to supplement the peak concentration. When the basic quality control level Q0 is greater than 3000, it is determined to be a high quality control condition. The tableting start / stop threshold Fp is set to 90% and the coating auxiliary concentration X0 is Q0×0.6 / W. Coating is enabled first to ensure a rapid response under extreme loads. The higher the percentage value of Fp, the more stringent the tableting activation conditions are. The setting value of X0 is proportional to the basic quality control level Q0 and the heat preservation duration W to ensure that sufficient coating concentration is provided during the heat preservation period.

[0036] Specifically, the step of performing concentration fluctuation calibration on the basic quality control strategy based on real-time efficacy deviation data to obtain a first quality control strategy includes: The real-time efficacy deviation data is acquired, including dissolution deviation ΔS and active ingredient release deviation ΔR; The efficacy deviation index P is calculated based on the dissolution deviation ΔS, the active ingredient release deviation ΔR, the first efficacy weight ws, and the second efficacy weight wr. P is set as P = ws × (ΔS / Smax) + wr × (ΔR / Rmax), where Smax is the maximum permissible dissolution deviation and Rmax is the maximum permissible active ingredient release deviation. The efficacy deviation index P is compared with the preset efficacy index Pb. The efficacy status is judged based on the comparison result, and the concentration fluctuation is calibrated for the quality control strategy based on the judgment result. When P≤Pb, the efficacy status is determined to be up to standard, and no concentration fluctuation calibration is performed on the quality control strategy. When P > Pb, the efficacy status is determined to be substandard. The quality control strategy is then subjected to concentration fluctuation calibration. The concentration fluctuation calibration includes: calibrating the coating auxiliary concentration X0 in the basic quality control strategy according to the efficacy decay coefficient λ to obtain the calibrated coating auxiliary concentration X1. X1 is set as X0 × (1 - λ × (P - Pb) / Pmax), where Pmax is the maximum efficacy deviation. The coating auxiliary concentration X0 in the basic quality control scheme is replaced with the calibrated coating auxiliary concentration X1 to obtain the first quality control strategy.

[0037] Specifically, acquiring the real-time efficacy deviation data refers to real-time monitoring of the dissolution rate of wet granules and the release of active ingredients after tableting using an online near-infrared spectroscopy monitoring module in the formulation equipment. This online near-infrared spectroscopy monitoring module acquires spectral data in reflection mode using a fiber optic probe, with a wavelength range of 1000nm to 2500nm and a sampling frequency of 0.5Hz. The actual dissolution rate and actual active ingredient release value obtained are then compared with the theoretically calculated target dissolution rate and target active ingredient release value under target conditions to obtain the instantaneous values ​​of dissolution deviation and active ingredient release deviation. The dissolution deviation refers to the absolute difference between the actual dissolution rate value and the target dissolution rate value. The active ingredient release deviation refers to the absolute difference between the actual active ingredient release value and the target active ingredient release value. The first efficacy weight is the weighting coefficient assigned to dissolution deviation when calculating the efficacy deviation index, with a value of 0.6. The reason for this value is that the user experience loss caused by dissolution deviation is proportional to the square of the dissolution rate, and its impact on system performance increases exponentially under high-concurrency conditions, accounting for more than 60% of the total energy efficiency loss. Therefore, a higher weight is assigned to prioritize suppressing performance degradation under high dissolution conditions. The second efficacy weight is the weighting coefficient assigned to active ingredient release deviation when calculating the efficacy deviation index, with a value of 0.4. The reason for this value is that active ingredient release deviation mainly reflects task backlog caused by insufficient system processing capacity. Performance loss and release reduction are linearly related. Although they affect processing efficiency, the relationship is less significant than the square relationship of dissolution rate. Furthermore, the active ingredient release deviation includes acceptable normal load fluctuations; therefore, a weight of 0.4 is assigned to it to complement the first efficacy weight. The maximum permissible dissolution rate deviation refers to the critical threshold of the maximum dissolution rate deviation from the target value allowed for steady-state operation of the system, which is set at 20% of the target dissolution rate. The rationale for this value is as follows: According to the service level agreement for enzymatic hydrolysis systems, when the dissolution rate deviates from the target value by more than 20%, it indicates that the system is under high load or network jitter, or other non-steady-state extreme conditions. At this time, the user experience will significantly deteriorate; therefore, this is used as the normalization benchmark. The maximum permissible active ingredient release deviation refers to the maximum permissible active ingredient release deviation allowed for steady-state operation of the system. The critical threshold for release deviating from the target value is set at 10% of the release of the target active ingredient. The rationale for this value is as follows: According to the system capacity management standard, when the release of the active ingredient deviates from the target value by more than 10%, it indicates that there is resource contention, memory leakage, or connection pool exhaustion within the system. At this point, the system performance has significantly decreased and there is a risk of system crash. Therefore, this value is used as the normalization benchmark. The efficacy status refers to the classification and judgment result of the efficiency level of the current system's computing resources in converting into effective concurrent efficacy based on the comparison result of efficacy deviation index P and preset efficacy threshold Pb. The efficacy status includes two types: meeting the target and failing to meet the target. The efficacy decay coefficient is an adjustment parameter that controls the reduction of the coating auxiliary concentration during the concentration fluctuation calibration process, and its value is 0.3. The rationale for this value is as follows: According to the conservative efficacy control strategy, it is necessary to ensure that the coating auxiliary concentration is not completely shut off and the basic tableting capacity is retained under the condition of maximum efficacy deviation. When the efficacy deviation index P reaches its maximum value, subtracting the efficacy attenuation coefficient of 0.3 from Formula 1 can ensure that the coating concentration retains 70% of the original value. This effectively suppresses the waste of coating materials under inefficient conditions and avoids tableting interruption caused by a sudden drop in concentration. The maximum efficacy deviation refers to the theoretical upper limit that the efficacy deviation index P may reach, which is set to 3.0. The rationale for this value is that under extreme operating conditions, such as network jitter combined with data source failure, dissolution deviation and active ingredient release deviation may simultaneously reach the limit of the maximum allowable value. At this time, the maximum value of the efficacy deviation index P is... The sum of the normalized values ​​of the first efficacy weight (0.6) and the second efficacy weight (0.4) is 3.0. This value covers all normal operating conditions and extreme boundary conditions. The preset efficacy index is the critical threshold for determining whether the efficacy status is within the acceptable range, and it is set to 1.0. The reason for this value is that it is a dimensionless normalized threshold. When the efficacy deviation index P equals 1.0, it corresponds to a critical state where both the dissolution deviation and the active ingredient release deviation have reached 50% of their respective maximum allowable deviations and are superimposed according to their weights. Exceeding this value indicates that the combined deviation of dissolution and active ingredient release has caused the system efficacy to drop below 80% of the theoretical optimal value, and the total ineffective calculation loss exceeds 20% of the input power, requiring a reduction in coating concentration to reduce ineffective energy consumption.

[0038] Specifically, the optimization of the concentration fluctuation correction process based on the fouling coefficient of the reactor heat transfer surface to obtain the target quality control strategy includes: The fouling coefficient of the heat transfer surface of the reactor is obtained, and the fouling coefficient of the heat transfer surface of the reactor includes the heat transfer coefficient decay rate and the thermal resistance growth rate. The fouling correction factor K is calculated based on the heat transfer coefficient decay rate and the thermal resistance growth rate. K is set as K=(1 / Ua)×(Ra / R0), where Ua is the heat transfer coefficient decay rate, Ra is the thermal resistance growth rate, and R0 is the reference thermal resistance. The scaling correction coefficient K is compared with the preset scaling coefficient Kb. The scaling state is judged based on the comparison result, and the thermal resistance drift is optimized for the concentration fluctuation correction process based on the judgment result. When K≤Kb, the scaling condition is determined to be mild, and thermal resistance drift optimization is not performed on the concentration fluctuation correction process. When K > Kb, the scaling state is determined to be severe. Thermal resistance drift optimization is performed on the concentration fluctuation calibration process. The thermal resistance drift optimization includes: correcting the efficacy attenuation coefficient λ to obtain the corrected efficacy attenuation coefficient λ', setting λ' = λ × (1 + γ × (K - Kb) / Kmax), where γ is the scaling sensitivity coefficient and Kmax is the maximum scaling coefficient. The calibrated coating auxiliary concentration is recalculated based on the corrected efficacy attenuation coefficient λ' to obtain the corrected coating auxiliary concentration X2, setting X2 = X0 × (1 - λ' × (P - Pb) / Pmax). The calibrated coating auxiliary concentration X1 in the first quality control strategy is replaced with the corrected coating auxiliary concentration X2 to obtain the target quality control strategy.

[0039] Specifically, obtaining the fouling coefficient of the reactor's heat transfer surface refers to dynamically identifying the current heat transfer coefficient and thermal resistance through periodic heat transfer testing and online performance monitoring of the enzymatic hydrolysis management system. The heat transfer coefficient decay rate is obtained through standard heat transfer detection testing, and the thermal resistance growth rate is obtained through historical thermal resistance trend analysis. The testing cycle is set to be performed once every 1000 reactions. The baseline thermal resistance is archived and saved through reactor connection testing or stable state testing after initial connection. The heat transfer coefficient decay rate is the ratio of the current successful heat transfer count to the total heat transfer count, calculated using the formula Ua=Unow / Utotal, where Unow is the current successful heat transfer count and Utotal is the total heat transfer count. The heat transfer coefficient decay rate ranges from 0.5 to 1.0, with a value closer to 1.0 indicating a higher heat transfer rate. The closer the heat transfer surface health status is to the newly connected state, the lower the value (below 0.8) indicates that the heat transfer surface has entered the end of its lifespan. The thermal resistance growth rate refers to the ratio of the current thermal resistance to the initial reference thermal resistance, calculated using the formula Ra=Rnow / R0, where Rnow is the average thermal resistance obtained through performance testing or online monitoring in the current state, and R0 is the initial reference thermal resistance. The thermal resistance growth rate ranges from 1.0 to 2.5. A value closer to 1.0 indicates that the heat transfer surface connection and server processing status are intact, while a value exceeding 2.0 indicates that network congestion or excessive server load has significantly increased transmission resistance. The reference thermal resistance refers to the thermal resistance test value when the reactor enters a stable state after 5 standardized probes following connection, with a value ranging from 100ms to 500ms. The reason for this value is that it corresponds to the standard RESTful... The typical response latency of the API in a standard network environment is used as a normalized benchmark to establish the relative proportional relationship of thermal resistance growth, eliminating the influence of individual differences in different reactors on the determination of scaling. The preset scaling coefficient refers to the critical threshold for determining whether the reactor has entered a state of severe scaling and triggering the correction of the efficiency attenuation coefficient. The value is set to 2.0. The reason for this value is as follows: According to the reactor maintenance standard, when the heat transfer coefficient attenuation rate Ua is 0.8 and the thermal resistance growth rate Ra is 1.25R0, the scaling correction coefficient K = (1 / 0.8) × (1.25R0 / R0) = 1.5625. After rounding and leaving a margin, Kb is set to 2.0. Exceeding this value indicates that the heat transfer performance of the reactor has significantly deviated from the initial characteristics. The scaling state refers to the classification and judgment result of the degree of health degradation during the entire life cycle of the reactor based on the comparison result of the scaling correction coefficient K and the preset scaling coefficient Kb. The scaling state includes two types: mild and severe. The scaling sensitivity coefficient is a gain parameter that controls the growth rate of the efficiency decay coefficient during the thermal resistance drift optimization process. It is set to 0.5. The reason for this value is: according to the marginal efficiency loss analysis of the scaling reactor, when the scaling correction coefficient K increases from the critical value of 2.0 to the maximum value of 4.0, the efficiency decay coefficient λ needs to be increased by about 50% to compensate for the surge in calculation loss caused by the delay square relationship. Setting γ to 0.5 can make λ'=λ×(1+0.5) when K=4.0.5×(4-2) / 4)=λ×1.25, which avoids over-correction for mild scaling while ensuring sufficient concentration reduction for severe scaling. The maximum scaling coefficient refers to the theoretical upper limit of the scaling correction coefficient K at the end of the reactor's lifespan, which is set to 4.0. The reason for this value is as follows: According to the reactor's design lifespan termination standard, when the heat transfer coefficient decay rate Ua decreases to 50% and the thermal resistance growth rate Ra increases to twice the initial value, the scaling correction coefficient K=(1 / 0.5)×(2R0 / R0)=4.0. This value corresponds to the extreme scaling boundary before the reactor is completely decommissioned, covering the scaling degree fluctuation range throughout the entire usable lifespan.

[0040] Specifically, step S6 involves feedback regulation of the formulation process of the concentrated enzymatic hydrolysate according to the target quality control strategy to obtain a standardized hypoglycemic health product, and then pushing the standardized hypoglycemic health product to the finished product packaging line for tableting and coating application, specifically including: The modified coating auxiliary concentration X2 in the target quality control strategy is compared with the preset concentration threshold Xb. Based on the comparison result, a feedback control signal is generated and output to the formulation equipment, wherein: When X2 > Xb, the output enhancement signal controls the formulation forming equipment to increase the coating spray rate to enhance the stability of the finished product, and at the same time increases the drying temperature to accelerate the evaporation of moisture; When X2≤Xb, the output frequency reduction signal controls the formulation forming equipment to reduce the coating spray rate to reduce the consumption of coating material, and at the same time reduce the drying temperature to reduce the heat loss of active ingredients; The corrected data quality covariance is converted into a smoothing window setting parameter for the formulation equipment. The smoothing window setting parameter is output to the smoothing processor of the formulation equipment, and the formulation equipment is controlled to operate with the smoothing window to suppress particle size fluctuation. The standardized blood sugar lowering health product is pushed to the finished product packaging line via a conveyor belt. The finished product packaging line includes a tablet press, a coating machine, and a filling and sealing machine. The tablet press compresses the concentrated enzymatic hydrolysate into a tablet core. The coating machine sprays a coating liquid onto the surface of the tablet core to form a coating layer. The filling and sealing machine fills the finished capsules into containers and seals them.

[0041] Specifically, the preset concentration threshold refers to the critical threshold for determining whether the coating spray rate needs to be enhanced, with a value of Xb = 50 g / L. The rationale for this value is as follows: when the corrected coating auxiliary concentration X2 exceeds 50 g / L, it indicates that the system is under high load and the coating spray rate needs to be increased to meet complex stability requirements; when X2 is less than or equal to 50 g / L, it indicates that the system is under normal operating conditions and the coating spray rate can be reduced to save coating material. The trace of the data quality covariance matrix refers to the sum of the elements on the main diagonal of the matrix, representing the overall energy level of the data quality covariance matrix of the formulation equipment. The calculation of the smoothing window can realize the average measurement of the noise level of the particle state vector, ensuring that the smoothing processor window setting matches the noise characteristics of the current system. The particle state vector dimension refers to the number of independent particle states in the current formulation system, with a value range of 6 to 20, corresponding to the particle dimension of standard hypoglycemic health products. The smoothing processor refers to the software module that performs sliding average de-jittering on the formulation results, controlling the output stability by adjusting the window size.

[0042] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for preparing a hypoglycemic health product based on marine microbial glucose isomerase, characterized in that, include: The physicochemical properties and dynamic reaction data of the target batch of raw materials are collected, and the dynamic reaction data includes real-time reaction environment data and enzyme batch activity decay parameters. A basic enzymatic hydrolysis scheme is generated based on the physicochemical properties of the raw materials. The basic enzymatic hydrolysis scheme is then temperature-compensated and calibrated based on the real-time reaction environment data to obtain a first enzymatic hydrolysis scheme. The activity attenuation of the temperature-compensated calibration process is then optimized based on the batch activity attenuation parameters of the enzyme preparation to obtain a target enzymatic hydrolysis scheme. The target batch of raw materials is subjected to enzymatic hydrolysis according to the target enzymatic hydrolysis scheme to obtain an enzymatic hydrolysis reaction solution; The enzymatic hydrolysis solution was concentrated and dried to obtain concentrated enzymatic hydrolysate; A basic quality control strategy is generated based on the target enzymatic hydrolysis scheme. The concentration fluctuation of the basic quality control strategy is calibrated based on real-time efficacy deviation data to obtain a first quality control strategy. The thermal resistance drift of the concentration fluctuation calibration process is optimized based on the scaling coefficient of the heat transfer surface of the reactor to obtain the target quality control strategy. The formulation process of the concentrated enzymatic hydrolysate is controlled by feedback according to the target quality control strategy to obtain a standardized hypoglycemic health product, which is then pushed to the finished product packaging line for tableting and coating.

2. The method for preparing hypoglycemic health products based on marine microbial glucose isomerase according to claim 1, characterized in that, The step of generating a basic enzymatic hydrolysis scheme based on the physicochemical properties of the raw materials includes: The reaction time constant was calculated based on the substrate concentration coefficient, substrate purity index, enzyme loading coefficient, enzyme specific activity coefficient, and substrate molecular weight score in the physicochemical properties of the raw materials. The reaction temperature is calculated based on the reaction time constant, target isomerization rate, environmental isomerization rate, and expected reaction time. The steady-state reaction temperature is set based on the reaction temperature, and the reaction temperature, target isomerization rate, expected reaction time, reaction time constant, and steady-state reaction temperature are output as the basic enzymatic hydrolysis scheme.

3. The method for preparing hypoglycemic health products based on marine microbial glucose isomerase according to claim 1, characterized in that, The step of performing temperature compensation calibration on the basic enzymatic hydrolysis scheme based on the real-time reaction environment data to obtain the first enzymatic hydrolysis scheme includes: The temperature hysteresis index is calculated based on the reaction temperature deviation rate and pH offset in the real-time reaction environment data. The basic enzymatic hydrolysis scheme was then calibrated for temperature compensation based on the temperature hysteresis index.

4. The method for preparing hypoglycemic health products based on marine microbial glucose isomerase according to claim 3, characterized in that, The temperature compensation calibration of the basic enzymatic hydrolysis scheme based on the temperature hysteresis index includes: The temperature hysteresis index Y is compared with the preset hysteresis index Yb. The reaction state is judged based on the comparison result, and the basic enzymatic hydrolysis scheme is calibrated for temperature compensation based on the judgment result, wherein: When Y≤Yb, the reaction state is determined to be normal, and no temperature compensation calibration is performed on the basic enzymatic hydrolysis scheme. When Y > Yb, the reaction state is determined to be delayed. Temperature compensation is performed on the basic enzymatic hydrolysis scheme to obtain the corrected reaction temperature T1. The corrected reaction temperature T1 is then replaced in the basic enzymatic hydrolysis scheme to obtain the first enzymatic hydrolysis scheme.

5. The method for preparing hypoglycemic health products based on marine microbial glucose isomerase according to claim 1, characterized in that, The step of optimizing the activity decay of the temperature compensation calibration process based on the batch activity decay parameters of the enzyme preparation to obtain the target enzymatic hydrolysis scheme includes: The transient activity gain coefficient is calculated based on the relative enzyme activity retention rate and cofactor shedding rate in the batch activity decay parameters of the enzyme preparation. The activity attenuation of the temperature compensation calibration process is optimized based on the transient activity gain coefficient to obtain the target enzymatic hydrolysis scheme.

6. The method for preparing hypoglycemic health products based on marine microbial glucose isomerase according to claim 5, characterized in that, The step of optimizing the activity decay of the temperature compensation calibration process based on the transient activity gain coefficient to obtain the target enzymatic hydrolysis scheme includes: The transient active gain coefficient Z is compared with the preset gain coefficient Zb. Based on the comparison result, the active coupling state is determined, and the active attenuation is optimized in the temperature compensation calibration process based on the determination result, wherein: When Z≤Zb, the active coupling state is determined to be stable, and no active attenuation optimization is performed on the temperature compensation calibration process; When Z > Zb, the active coupling state is determined to be strong coupling. The activity attenuation optimization is performed on the temperature compensation calibration process to obtain the target reaction temperature T2. The target reaction temperature T2 is then replaced in the first enzymatic hydrolysis scheme to obtain the target enzymatic hydrolysis scheme. The holding time W is set according to the reaction conditions. When the batch enzymatic hydrolysis mode is identified, W=3600s is set. When the continuous feeding mode is identified, W=600s is set. When the intermittent feeding mode is identified, W=1800s is set. The holding time W is then added to the target enzymatic hydrolysis scheme.

7. The method for preparing hypoglycemic health products based on marine microbial glucose isomerase according to claim 1, characterized in that, The step of generating a basic quality control strategy based on the target enzymatic hydrolysis scheme includes: The basic quality control level Q0 is calculated based on the steady-state reaction temperature Ts, the incubation duration W, the current enzyme residual rate Ve, and the current isomerization compliance rate Ue in the target enzymatic hydrolysis scheme. Based on the basic quality control level Q0, the tableting start / stop threshold Fp and the coating auxiliary concentration X0 are set to obtain a basic quality control strategy that includes the tableting start / stop threshold Fp and the coating auxiliary concentration X0.

8. The method for preparing hypoglycemic health products based on marine microbial glucose isomerase according to claim 1, characterized in that, The step of adjusting the concentration fluctuation of the basic quality control strategy based on real-time efficacy deviation data to obtain a first quality control strategy includes: The real-time efficacy deviation data is acquired, including dissolution deviation and active ingredient release deviation. The efficacy deviation index is calculated based on the dissolution deviation, active ingredient release deviation, first efficacy weight, and second efficacy weight. The concentration fluctuation is then calibrated against the basic quality control strategy based on the efficacy deviation index to obtain the first quality control strategy.

9. The method for preparing a hypoglycemic health product based on marine microbial glucose isomerase according to claim 8, characterized in that, The step of performing concentration fluctuation calibration on the basic quality control strategy based on the efficacy deviation index to obtain the first quality control strategy includes: The efficacy deviation index P is compared with the preset efficacy index Pb. The efficacy status is judged based on the comparison result, and the concentration fluctuation is calibrated for the quality control strategy based on the judgment result. When P≤Pb, the efficacy status is determined to be up to standard, and no concentration fluctuation calibration is performed on the quality control strategy. When P > Pb, the efficacy status is determined to be substandard. The concentration fluctuation is calibrated for the quality control strategy to obtain the calibrated coating auxiliary concentration X1. The calibrated coating auxiliary concentration X1 is then replaced in the basic quality control scheme to obtain the first quality control strategy.

10. The method for preparing a hypoglycemic health product based on marine microbial glucose isomerase according to claim 1, characterized in that, The process of optimizing the concentration fluctuation correction process based on the fouling coefficient of the reactor heat transfer surface to obtain the target quality control strategy includes: The fouling coefficient of the heat transfer surface of the reactor is obtained, and the fouling coefficient of the heat transfer surface of the reactor includes the heat transfer coefficient decay rate and the thermal resistance growth rate. The scaling correction factor is calculated based on the heat transfer coefficient decay rate and the thermal resistance growth rate. The scaling correction coefficient K is compared with the preset scaling coefficient Kb. The scaling state is judged based on the comparison result, and the thermal resistance drift is optimized for the concentration fluctuation correction process based on the judgment result. When K≤Kb, the scaling condition is determined to be mild, and thermal resistance drift optimization is not performed on the concentration fluctuation correction process. When K > Kb, the scaling state is determined to be severe. Thermal resistance drift optimization is performed on the concentration fluctuation calibration process to obtain the corrected coating auxiliary concentration X2. The corrected coating auxiliary concentration X2 is then replaced in the first quality control strategy to obtain the target quality control strategy.

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