Intelligent control system and method for enzymolysis animal protein production process

Abstract

The invention relates to the technical field of protein enzymolysis, and discloses an intelligent control system and method for an animal protein enzymolysis production process, and the method comprises the steps: obtaining key attribute parameters of animal protein raw materials, building a raw material grading model, and obtaining initial grades of the raw materials; matching a preset grading corresponding relation library according to the initial grading to obtain an initial stage optimal production parameter range; collecting a first production parameter before enzymolysis to obtain an adjusted production parameter; starting enzymolysis according to the adjusted parameters, collecting process key parameters in real time, inputting an optimal production parameter prediction model, and predicting an optimal production parameter at the next moment; if the optimal production parameters exceed the safety range, correction is carried out; continuously performing enzymolysis according to the corrected parameters, and continuously predicting and correcting the parameters until the enzymolysis is finished; through raw material grading and parameter real-time optimization, the process parameters can be dynamically regulated and controlled according to different batches of raw materials, the production efficiency is improved to the maximum extent while the product quality is guaranteed, and remarkable intelligence and adaptability are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of protein enzymolysis, and more specifically to an intelligent control system and method for enzymolysis animal protein production process. Background Art

[0002] Protein enzymatic hydrolysis is an important technical means to prepare functional foods and bioactive peptides. Traditional enzymatic hydrolysis process control mainly relies on experience and manual operation, lacks intelligent management and precise regulation, resulting in problems such as low production efficiency and unstable product quality. With the development of biotechnology and information technology, the introduction of intelligent control methods into the protein enzymatic hydrolysis production process to achieve real-time optimization and dynamic adjustment of process parameters has become an important way to improve enzymatic hydrolysis efficiency and ensure product quality.

[0003] The Chinese patent application with publication number CN118550263A discloses a control system and method for silicone sealant production. The method collects key production parameters through real-time monitoring equipment, dynamically calibrates parameters based on historical data, and predicts future state transitions and key defects using hidden Markov models and support vector machines. The method focuses on predicting and intervening in production defects through data-driven modeling, but does not adequately consider the dynamic optimization of production parameters caused by differences in raw material properties, and lacks intelligent grading and parameter matching mechanisms based on raw material characteristics.

[0004] The Chinese patent application with publication number CN104419697A discloses a method for efficiently regulating enzymatic reactions in aqueous phases by temperature. The method uses the temperature response characteristics of the intelligent polymer gel PNIPAM to control the rate of enzymatic reactions through temperature change cycles. The method cleverly uses external temperature stimulation to achieve "on-off" regulation of enzymatic reactions, but only considers a single temperature factor and lacks comprehensive optimization of other key parameters such as pH and enzyme concentration. In addition, the method is aimed at enzymatic reactions in aqueous phase systems, and the process characteristics of solid-liquid multiphase enzymatic hydrolysis systems are not sufficiently considered.

[0005] The Chinese patent application with publication number CN115078298A discloses an intelligent control system and method for the proteolysis process based on spectral monitoring. The system uses near-infrared spectroscopy to monitor the enzymatic hydrolysis process in situ, and uses physical and chemical indicators such as biological activity to determine the end point of the enzymatic hydrolysis and the timing of multi-enzyme switching. This method can monitor the enzymatic hydrolysis process in real time and reduce the enzymatic hydrolysis time, but it mainly focuses on the end point control of the enzymatic hydrolysis process, and pays insufficient attention to the optimization of production parameters in the raw material feeding stage and the enzymatic hydrolysis startup stage, and lacks a dynamic parameter optimization strategy throughout the entire enzymatic hydrolysis process.

[0006] In summary, most of the existing enzymatic production control methods focus on the optimization control of a specific link or a certain type of parameter, and lack the integrated analysis of multi-source heterogeneous information such as raw material properties, process parameters, and enzymatic process, as well as the optimization of the entire process. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art, the present invention provides an intelligent control system and method for the production process of enzymatic animal protein, which realizes dynamic optimization and precise control of the entire production process from the raw material end to the finished product end, in view of the large differences in the characteristics of animal protein raw materials and the complex and changeable enzymatic process. The intelligent control method for the production process of enzymatic animal protein proposed in the present invention obtains the key attribute parameters of the raw materials to be enzymatically hydrolyzed and establishes a raw material grading model, matches the optimal production parameter range, collects the key parameters of the enzymatic process, and dynamically predicts and corrects the optimal production parameters until the end of the enzymatic process, thereby realizing intelligent grading processing of different batches of raw materials and dynamic optimization of parameters of the entire enzymatic process, has significant intelligence and adaptability, can significantly improve production efficiency and product quality, and overcomes the shortcomings of the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The intelligent control method of the enzymatic animal protein production process includes:

[0010] Obtain key attribute parameters of the animal protein raw material to be hydrolyzed, establish a raw material grading model, input the collected key attribute parameters into the raw material grading model, and obtain the initial grading of the raw material; according to the initial grading of the raw material, match the preset grading correspondence library to obtain the optimal production parameter range of different batches of raw materials in the initial stage; collect the first production parameter before hydrolysis, and obtain the adjusted production parameter according to the first production parameter and the optimal production parameter range;

[0011] The enzymatic hydrolysis reaction is started according to the adjusted production parameters, and the key parameters of the enzymatic hydrolysis process are collected in real time to form a dynamically updated process parameter sequence; the process parameter sequence collected in real time is input into the pre-trained optimal production parameter prediction model, and the optimal production parameter combination at the next moment t+Δt is dynamically predicted; the optimal production parameters in the optimal production parameter combination are compared with the preset process safety threshold value, and when they exceed the safety range, the optimal production parameters are corrected for safety to obtain a corrected production parameter combination; the enzymatic hydrolysis process is continued according to the corrected production parameter combination; and during the execution process, the key parameters of the enzymatic hydrolysis process are continuously monitored and collected, and the production parameters are predicted and corrected until the end of the enzymatic hydrolysis process, t is the current moment, and Δt is the prediction time step.

[0012] Furthermore, the key attribute parameters of the animal protein raw material to be hydrolyzed include the freshness, fat content and tissue structure of the raw material;

[0013] The freshness of the raw material is calculated by weighting the pH value and volatile basic nitrogen content of the raw material.

[0014] Furthermore, the adjusted production parameters obtained according to the first production parameter and the optimal production parameter range include: real-time collection of the first production parameter P1=(pH1, T1, E1) after the raw material is fed and before the enzymatic hydrolysis begins, where pH1 is the measured pH value of the enzymatic hydrolysis system, T1 is the measured temperature of the enzymatic hydrolysis system, and E1 is the measured enzyme concentration of the enzymatic hydrolysis system;

[0015] Taking the median of the optimal range of each parameter in the optimal production parameter range as a reference value, respectively calculating the deviations of pH1, T1, E1 from the reference values, and calculating the first deviation value Δδ according to the deviations of pH1, T1, E1 from the reference values;

[0016] The first deviation value Δδ is compared with a preset first deviation threshold ε1, and when Δδ>ε1, the first adjustment of the production parameters is triggered to obtain the adjusted production parameters.

[0017] Further, the optimal production parameters in the optimal production parameter combination include optimal temperature, optimal pH value and optimal enzyme concentration;

[0018] The comparing the optimal production parameter in the optimal production parameter combination with the preset process safety threshold comprises:

[0019] When T' <T min or T'>T max When , it is determined that T' is beyond the safety range;

[0020] When pH <pH min or pH'>pH max When pH' is beyond the safe range;

[0021] When E' <E min or E'>E max When , it is determined that E' is beyond the safety range;

[0022] Among them, T' is the optimal temperature, pH' is the optimal pH value, E' is the optimal enzyme concentration, T min is the lower limit of the temperature safety threshold range, T max is the upper limit of the temperature safety threshold range, pH min is the lower limit of the pH safety threshold range, pH max is the upper limit of the pH safety threshold range, E min is the lower limit of the enzyme concentration safety threshold range, E max It is the upper limit of the enzyme concentration safety threshold range.

[0023] Furthermore, the safety correction of the optimal production parameters includes safety correction of the optimal temperature T', safety correction of the optimal pH value pH', and safety correction of the optimal enzyme concentration E';

[0024] The safety correction of the optimal temperature T' comprises:

[0025] T*=min(max(T',T min ),T max );

[0026] Where T* is the corrected temperature.

[0027] Furthermore, the safety correction of the optimal pH value pH' comprises:

[0028] pH*=min(max(pH',pH min ),pH max );

[0029] Wherein, pH* is the corrected pH value.

[0030] Furthermore, the safety correction of the optimal enzyme concentration E' comprises:

[0031] E*=min(max(E',E min ),E max );

[0032] Where E* is the corrected enzyme concentration.

[0033] An intelligent control system for an enzymatic animal protein production process, which is used to implement the above-mentioned intelligent control method for an enzymatic animal protein production process, and the system comprises:

[0034] Raw material classification module: used to obtain key attribute parameters of the animal protein raw materials to be enzymatically hydrolyzed, establish a raw material classification model, input the collected key attribute parameters into the raw material classification model, and obtain the initial classification of the raw materials;

[0035] Parameter adjustment module: used to match the preset classification correspondence library according to the initial classification of raw materials, and obtain the optimal production parameter range of different batches of raw materials in the initial stage; collect the first production parameters before enzymatic hydrolysis, and obtain the adjusted production parameters according to the first production parameters and the optimal production parameter range;

[0036] Parameter prediction module: used to start the enzymatic hydrolysis reaction according to the adjusted production parameters, collect the key parameters of the enzymatic hydrolysis process in real time, and form a dynamically updated process parameter sequence; input the real-time collected process parameter sequence into the pre-trained optimal production parameter prediction model, and dynamically predict the optimal production parameter combination at the next moment t+Δt, where t is the current moment and Δt is the prediction time step;

[0037] Parameter correction module: used to compare the optimal production parameters in the optimal production parameter combination with the preset process safety threshold. When the optimal production parameters exceed the safety range, the optimal production parameters are corrected for safety to obtain the corrected production parameter combination;

[0038] Cycle module: used to continue the enzymatic hydrolysis process according to the revised production parameter combination; and continuously monitor and collect key parameters of the enzymatic hydrolysis process during the execution process, predict and correct production parameters until the enzymatic hydrolysis process is completed.

[0039] An electronic device comprises a memory, a central processing unit and a computer program stored in the memory and executable on the central processing unit. When the central processing unit executes the computer program, the intelligent control method for the production process of enzymatic animal protein is implemented.

[0040] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the above-mentioned intelligent control method for the production process of enzymatic animal protein.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The invention establishes a raw material grading model, comprehensively considers key attributes such as freshness, fat content, and tissue structure of the raw materials, realizes accurate grading of the raw materials, and provides a basis for differentiated control of the enzymatic hydrolysis process; matches a preset optimal production parameter range for raw materials of different grades, provides a reference for the initial setting of process parameters, and reduces the trial and error cost; collects key parameters of the enzymatic hydrolysis process in real time, inputs a pre-trained parameter prediction model, and dynamically predicts the optimal production parameter combination at the next moment, so as to realize real-time optimization control of the enzymatic hydrolysis process; compares the predicted optimal parameters with the safety threshold, and corrects them when they exceed the range, and continuously optimizes the production parameters and improves production efficiency under the premise of ensuring process safety; continuously collects key parameters and predicts the corrected parameters during the enzymatic hydrolysis process until the enzymatic hydrolysis is completed, so as to realize dynamic optimization of the whole process, and has strong adaptability; combines grading processing with real-time optimization, dynamically adjusts the enzymatic hydrolysis parameters for raw materials of different qualities, and shortens the production cycle to the maximum extent while ensuring product yield and quality, and improves equipment utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 The figure is a principle flow chart of the intelligent control method for the enzymatic animal protein production process of the present invention;

[0045] Figure 2 A flow chart of a method for obtaining adjusted production parameters in the intelligent control method for the enzymatic animal protein production process of the present invention;

[0046] Figure 3 A flow chart of a method for obtaining a corrected production parameter combination in the intelligent control method for the enzymatic animal protein production process of the present invention;

[0047] Figure 4 It is a functional module diagram of the intelligent control system of the enzymatic animal protein production process in the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Example 1

[0050] See also Figure 1 As shown, this embodiment provides an intelligent control method for the enzymatic animal protein production process, comprising:

[0051] Step S1000, obtain key attribute parameters of the animal protein raw material to be hydrolyzed, establish a raw material grading model, input the collected key attribute parameters into the raw material grading model, and obtain the initial classification of the raw material; according to the initial classification of the raw material, match the preset grading correspondence library to obtain the optimal production parameter range of different batches of raw materials in the initial stage; collect the first production parameter before hydrolysis, calculate the first deviation value between the first production parameter and the optimal production parameter range, and trigger the first adjustment when the first deviation value exceeds the preset first deviation threshold to obtain the adjusted production parameter.

[0052] Further, step S1000 includes:

[0053] Step S1100, obtaining basic information and key attribute parameters of the animal protein raw material to be enzymatically hydrolyzed;

[0054] Furthermore, step S1100 includes:

[0055] Step S1110, obtaining basic information such as batch identification ID, source information and timestamp of the animal protein raw material to be hydrolyzed;

[0056] Step S1120, collecting key attribute parameters such as freshness, fat content, and tissue structure of different batches of raw materials;

[0057] The method for obtaining the freshness of the raw material is: measuring the pH value and volatile basic nitrogen content of the raw material, and weightedly calculating the pH value and volatile basic nitrogen content of the raw material to obtain the freshness of the raw material;

[0058] Step S1130, storing the key attribute parameters and basic information of the raw materials into the raw material information database.

[0059] Specifically, in step S1110, the unique identification ID of the raw material batch is obtained by scanning the QR code, RFID and other automatic identification technologies, and the source information such as the origin, supplier and the time of entry into the factory of the raw material are manually registered to establish a traceability file of the raw material batch. Step S1120 is to obtain the key attribute parameters reflecting the quality of the raw material through sensor technology and physical and chemical testing methods. The pH value and volatile basic nitrogen content of the raw material are measured by pH meter and volatile basic nitrogen tester respectively, and a weighted algorithm is designed to calculate the freshness of the raw material. The fat content adopts the standard Soxhlet extraction method, and the fat in the raw material is extracted by Soxhlet extractor and weighed to calculate the content percentage. The characterization of the tissue structure is relatively complicated. It is necessary to prepare the raw material tissue slices first, observe its fiber structure under a microscope, and then use the image processing algorithm to extract the characteristic parameters such as fiber orientation, thickness, density, etc. Step S1130 summarizes and stores the collected information, and "files and cards" for the raw materials, so that each batch of raw materials can be accurately traced and queried for attributes, providing data support for subsequent grading and optimization control; through information digitization and associated storage, refined management of raw materials is achieved.

[0060] Step S1200, establishing a raw material classification model, inputting the collected key attribute parameters into the raw material classification model, and obtaining an initial classification of the raw materials.

[0061] The method for establishing a raw material grading model is as follows: obtaining raw material historical data, wherein the raw material historical data includes key attribute parameters of different batches of raw materials in historical enzymatic hydrolysis processes, including freshness, fat content and tissue structure; using a K-means clustering algorithm to perform cluster analysis on the raw material historical data, with the number of clusters set to 3, and dividing the raw materials into three grades: high-quality material A, medium-quality material B and low-quality material C, to obtain a raw material grading model.

[0062] The process of clustering analysis of raw material historical data using K-means clustering algorithm is as follows:

[0063] Determine the number of clusters K: the raw materials are divided into 3 levels, so K=3.

[0064] Initialize cluster centers: Randomly select 3 data points as initial cluster centers.

[0065] Divide the data: Calculate the distance from each data point to the three cluster centers and divide it into the nearest class.

[0066] Update center: Calculate the data centers of the three clusters respectively as the new cluster centers.

[0067] Repeat the above two steps until the cluster center no longer changes significantly or the maximum number of iterations is reached.

[0068] The necessary data cleaning and preprocessing are performed on the historical data of raw materials, and the data is converted into a numerical matrix form suitable for cluster analysis. The values ​​of different indicators may vary greatly, and standardization is required to make them comparable. High-quality material A has the best quality, such as high freshness, moderate fat content, and loose texture; low-quality material C has the worst quality, such as poor freshness, too high or too low fat content, and dense texture. Therefore, the grade of the raw material can be quickly determined based on the clustering results, laying the foundation for subsequent differentiated processing. K-means and other clustering algorithms are used to automatically cluster the existing raw material data. The clustering process uses freshness, fat content, and tissue structure as feature vectors, and automatically classifies the raw material samples by calculating the similarity between each vector. According to the clustering results, the raw materials are divided into three levels: high-quality material A, medium material B, and low-quality material C. When a batch of new raw materials arrives at the factory, only its key attribute parameters need to be input into the trained raw material grading model, and the model will automatically determine the most similar clustering cluster of the raw material, thereby determining its initial classification. The establishment of the raw material grading model makes full use of historical big data to form an intelligent decision-making tool for raw material grading, which can significantly improve the accuracy and automation level of grading.

[0069] Step S1300, according to the initial classification of the raw materials, matching the preset classification correspondence library, and obtaining the optimal production parameter range of different batches of raw materials in the initial stage;

[0070] Specifically, in the practice of enzymatic hydrolysis production, there are differences in the appropriate production parameters for raw materials of different quality grades. For high-quality raw materials, efficient enzymatic hydrolysis can be achieved within a wider parameter range; while for inferior raw materials, the enzymatic hydrolysis process is more sensitive to production parameters and the parameter range is relatively strict. The practical experience of enzymatic hydrolysis production is manually summarized and sorted out, the optimal production parameter value range under different raw material classification conditions is summarized, and a classification correspondence library of "raw material classification-optimal production parameter range" is constructed; the classification correspondence library adopts a structured representation, such as "IF raw material classification = A THEN pH range = [6.5,7.5], temperature range = [50,60]℃, enzyme concentration range = [1.5,2.0]mg / g". These correspondences can be used to guide the initial setting and dynamic adjustment of production parameters. According to the initial classification of raw materials, pattern matching is performed in the classification correspondence library to quickly retrieve the optimal value range of each production parameter such as pH value, temperature, enzyme concentration, etc. under the corresponding classification. The matched ranges of production parameters such as pH value, temperature, enzyme concentration, etc. are used as constraints for production control in the initial stage to ensure that the parameters are set within the optimal range, creating favorable initial conditions for obtaining efficient enzymatic hydrolysis. The establishment of a grading correspondence library enables the correlation matching between raw material grading and production parameter settings, and differentiated control strategies can be adopted for raw materials of different qualities to improve the adaptability and accuracy of the enzymatic hydrolysis process. At the same time, the introduction of the grading correspondence library also reduces the blindness of empirical manual parameter adjustment, providing theoretical support for intelligent production control.

[0071] Step S1400, collecting the first production parameter before enzymatic hydrolysis, calculating the first deviation value between the first production parameter and the optimal production parameter range, triggering the first adjustment when the first deviation value exceeds the preset first deviation threshold, and obtaining the adjusted production parameter.

[0072] Furthermore, if Figure 2 As shown, step S1400 includes:

[0073] Step S1410, real-time collection of the first production parameter P1=(pH1, T1, E1) after the raw material is added and before the enzymatic hydrolysis begins, where pH1 is the measured pH value of the enzymatic hydrolysis system, T1 is the measured temperature of the enzymatic hydrolysis system, and E1 is the measured enzyme concentration of the enzymatic hydrolysis system;

[0074] Step S1420, taking the median of the optimal range of each parameter in the optimal production parameter range as a reference value, respectively calculating the deviations of pH1, T1, E1 from the reference values, and calculating a first deviation value Δδ according to the deviations of pH1, T1, E1 from the reference values;

[0075] Step S1430, comparing the first deviation value Δδ with a preset first deviation threshold ε1, when Δδ>ε1, triggering the first adjustment of the production parameters to obtain the adjusted production parameters.

[0076] Specifically, due to the fluctuation of raw material properties and the existence of production errors, the actual production parameters may deviate from the optimal range. In order to ensure that the enzymolysis is carried out in the best state, the parameters need to be monitored and dynamically adjusted before the enzymolysis begins. Step S1410 sets online sensors for parameters such as pH, temperature, and enzyme concentration in the enzymolysis device, and collects the first production parameter P1 after the raw material is fed in real time through the online sensor. pH sensors, temperature sensors, concentration sensors, etc. are installed on the enzymolysis tank to continuously monitor the pH value, temperature, and enzyme concentration of the enzymolysis system. These parameters are key factors affecting the efficiency of enzymolysis. Step S1420 uses the median of the optimal range of each parameter (such as the median 7.0 of the pH range [6.5,7.5]) as a reference benchmark to calculate the deviation of the measured parameters, thereby calculating the first deviation value. In step S1430, the first deviation value Δδ is compared with the preset first deviation threshold ε1. When Δδ≤ε1, the first adjustment of the production parameter is not triggered; when Δδ>ε1, that is, the deviation exceeds the allowable range, the adjustment mechanism is triggered. The first adjustment is based on the positive and negative and size of the deviations of pH1, T1, E1 from the reference values, and the correction amount of each parameter is calculated using the PID control algorithm, and the actuator (such as acid pump, alkali pump, heater, cooler, etc.) is controlled to adjust each parameter accordingly to return to the optimal range. For example, when the pH measured value is lower than the lower limit of the range, the alkali pump is controlled to continue to add alkali solution until the pH reaches the median of the range. The first adjustment realizes the rapid migration of production parameters from the initial state to the optimal state, reduces the adverse effects of parameter deviation on the enzymolysis effect, and ensures that the enzymolysis is started under optimal conditions. At the same time, the introduction of sensor monitoring and PID control realizes the automation and intelligence of parameter adjustment, reduces the labor intensity of manual operation, and improves the accuracy and reliability of process control.

[0077] The method for calculating the first deviation value Δδ according to the deviation of pH1, T1, E1 from the reference value includes:

[0078]

[0079] in:

[0080] Δδ represents the first deviation value and is a scalar;

[0081] pH1 represents the measured pH value of the enzymatic hydrolysis system;

[0082] T1 represents the measured temperature of the enzymatic hydrolysis system;

[0083] E1 represents the measured enzyme concentration of the enzymatic hydrolysis system;

[0084] pH0 represents the reference value of pH in the optimal production parameter range (median of the optimal range);

[0085] T0 represents the reference value of temperature in the optimal production parameter range;

[0086] E0 represents the reference value of enzyme concentration in the optimal production parameter range;

[0087] α is the weight coefficient of pH, which indicates the contribution of pH deviation to the total deviation;

[0088] β is the weight coefficient of temperature, which indicates the contribution of temperature deviation to the total deviation;

[0089] γ is the weight coefficient of enzyme concentration, which indicates the contribution of enzyme concentration deviation to the total deviation; α, β, and γ are determined by those skilled in the art based on a large number of experiments;

[0090] λ is a positive scaling parameter that controls the decay rate of the exponential function in the denominator and can be optimized based on historical data fitting;

[0091] e is the base of natural logarithms, approximately equal to 2.71828.

[0092] The numerator reflects the cumulative effect of the deviation of each parameter on the total deviation by linearly superimposing the absolute values ​​of the deviations of the three parameters. The denominator introduces an exponential decay function, which makes Δδ larger when the deviation is small and smaller when the deviation is large, thereby improving the sensitivity of the formula to small deviations. The setting of the weight coefficient allows the importance of the deviation of each parameter to be adjusted according to process requirements. The scaling parameter λ controls the speed of the exponential decay of the denominator. The larger the λ, the faster the Δδ value decays, which can adjust the nonlinearity of the formula.

[0093] As pH1, T1, E1 deviate more from the reference values ​​pH0, T0, E0, the molecule increases linearly and the Δδ value increases accordingly.

[0094] When the deviation is small, the denominator is close to 1, and the Δδ value is mainly determined by the numerator, showing an approximately linear growth.

[0095] When the deviation is large, the denominator decreases rapidly, the growth rate of the Δδ value slows down, and the value gradually stabilizes.

[0096] By adjusting the relative sizes of α, β, and γ, the weight of the influence of pH, temperature, and enzyme concentration deviation on Δδ can be adjusted.

[0097] Increasing the value of λ can speed up the decay of the Δδ value, making it respond more quickly to deviations.

[0098] This formula has a good balance between computational efficiency and nonlinear sensitivity and is suitable for real-time monitoring and feedback control.

[0099] Step S1000 starts with raw material information collection, raw material classification, optimal range matching, deviation correction, etc., to finely classify the enzymatic raw materials and set initial parameters. Information collection realizes the digital characterization of raw material quality, classification correspondence library matching realizes the association between classification and parameter range, and online monitoring realizes real-time correction of deviations. The seamless connection of these links together constitutes an intelligent closed loop of raw material classification and parameter setting, which significantly improves the raw material adaptability and process accuracy of enzymatic production. At the same time, by constructing digital twins of raw materials, classification, and parameters, this method has also accumulated rich big data for enzymatic optimization, which can be used to continuously improve the classification model and control strategy, and promote the intelligent level of enzymatic production to a new level.

[0100] Step S2000, start the enzymatic hydrolysis reaction according to the adjusted production parameters, collect the key parameters of the enzymatic hydrolysis process in real time, and form a dynamically updated process parameter sequence P(t); input the real-time collected process parameter sequence into the pre-trained optimal production parameter prediction model, and dynamically predict the optimal production parameter combination at the next moment; compare the optimal production parameters in the optimal production parameter combination with the preset process safety threshold, and when they exceed the safety range, perform safety correction on the predicted optimal production parameters to obtain a corrected production parameter combination (T*, pH*, E*); execute the enzymatic hydrolysis process according to the corrected production parameter combination (T*, pH*, E*); and continuously monitor and collect the key parameters of the enzymatic hydrolysis process during the execution process, and continuously predict and correct the production parameters until the enzymatic hydrolysis process is completed.

[0101] Furthermore, step S2000 includes:

[0102] Step S2100, starting the enzymatic hydrolysis reaction, collecting key parameters of the enzymatic hydrolysis process in real time, and forming a dynamically updated process parameter sequence P(t); the key parameters of the enzymatic hydrolysis process include temperature, pH value and enzyme concentration;

[0103] Specifically, after starting the enzymatic hydrolysis reaction, various sensors are installed on the enzymatic hydrolysis device to achieve real-time online monitoring of the enzymatic hydrolysis process. The temperature sensor is used to collect the enzymatic hydrolysis temperature, the pH sensor is used to collect the pH value of the enzymatic hydrolysis system, and the concentration sensor is used to collect the real-time concentration of the substrate and enzyme. These sensors continuously sample at a certain frequency to obtain time series data that reflects the dynamic changes of the enzymatic hydrolysis process. The collected parameter values ​​are matched one by one with the timestamp to form structured time series data P(t), which fully records the changes in key parameters of the entire enzymatic hydrolysis process and provides a data basis for subsequent process optimization and control. The dynamic monitoring of the enzymatic hydrolysis process avoids the traditional offline detection mode, and can promptly detect abnormal fluctuations in parameters, creating conditions for precise regulation. At the same time, the accumulation of massive process parameter time series data also provides data support for process optimization and product quality traceability, which has important application value.

[0104] Step S2200, inputting the real-time collected process parameter sequence into the pre-trained optimal production parameter prediction model to dynamically predict the optimal production parameter combination at the next moment;

[0105] Further, step S2200 includes:

[0106] Step S2210, constructing and training an optimal production parameter prediction model;

[0107] The method for constructing and training an optimal production parameter prediction model comprises:

[0108] A large amount of historical production data is obtained, including historical process parameter sequences and corresponding optimal production parameter combinations, and the historical production data is preprocessed by cleaning and normalization, and divided into training sets and test sets according to the time series; an LSTM network structure is designed, including an input layer, an LSTM hidden layer, a fully connected layer, and an output layer; the historical process parameter sequence is used as input and the corresponding optimal production parameter combination is used as a label to train the LSTM network, and the network parameters are optimized through the back propagation algorithm; the model performance is evaluated using the test set, and the model is tuned until the expected prediction accuracy is achieved; the trained LSTM model is saved as the final optimal production parameter prediction model for online prediction of the optimal production parameter combination for the enzymatic hydrolysis process.

[0109] Step S2220, input the process parameter sequence P(t) collected in real time into the trained optimal production parameter prediction model, and dynamically predict the optimal production parameter combination (T', pH', E') at the next time t+Δt; where T' is the optimal temperature, pH' is the optimal pH value, E' is the optimal enzyme concentration, t is the current time, and Δt is the prediction time step.

[0110] Specifically, based on historical production data, the long short-term memory network (LSTM) in deep learning is used to build an optimal production parameter prediction model. LSTM is a recursive neural network suitable for processing time series data. By introducing a gating mechanism, it can effectively capture long-distance dependencies in time series. The historical production data is organized according to the time series, the process parameter sequence is used as the model input, and the optimal production parameter combination at each moment is used as the model output to train the LSTM network. By designing a reasonable network structure and training strategy, the model can accurately establish the nonlinear mapping relationship between the process parameters and the optimal production parameter combination, and has the ability to dynamically predict the optimal control strategy in the future. During the enzymatic hydrolysis process, the process parameter sequence collected in real time is input into the trained optimal production parameter prediction model. The model can dynamically predict the optimal production parameter combination at future moments according to the parameter change trend. Deep learning predictive control transforms the optimal control problem of the enzymatic hydrolysis process into a time series prediction problem, makes full use of the inherent laws of historical production data, and realizes the self-optimization of the control strategy. This predictive control method can effectively improve the accuracy and timeliness of control, and opens up a new path for realizing intelligent control of the enzymatic hydrolysis process.

[0111] Step S2300, compare the optimal production parameters predicted by the optimal production parameter prediction model with the preset process safety threshold. When they exceed the safety range, the predicted optimal production parameters are corrected for safety to obtain a corrected production parameter combination (T*, pH*, E*); if they do not exceed the safety range, the correction is not triggered.

[0112] Furthermore, if Figure 3 As shown, step S2300 includes:

[0113] Step S2310, for the key parameters of the enzymatic hydrolysis process, temperature, pH value and enzyme concentration, respectively set the temperature safety threshold range [T min ,T max ]、pH safety threshold range [pH min ,pH max ], enzyme concentration safety threshold range [E min ,E max ]; T min is the lower limit of the temperature safety threshold range, T max is the upper limit of the temperature safety threshold range, pH min is the lower limit of the pH safety threshold range, pH max is the upper limit of the pH safety threshold range, E min is the lower limit of the enzyme concentration safety threshold range, E max It is the upper limit of the enzyme concentration safety threshold range.

[0114] Step S2320, determining whether the optimal production parameters T', pH', and E' exceed the corresponding safety threshold range;

[0115] When T' <T min or T'>T max When , it is determined that T' is beyond the safety range;

[0116] When pH <pH min or pH'>pH max When pH' is beyond the safe range;

[0117] When E' <E min or E'>E max When , it is determined that E' is beyond the safety range.

[0118] Step S2330, when the optimal production parameters exceed the safety range, the predicted optimal production parameters are corrected for safety and a corrected production parameter combination (T*, pH*, E*) is generated; wherein T* is the corrected temperature, pH* is the corrected pH value, and E* is the corrected enzyme concentration.

[0119] The method of performing safety correction on the optimal temperature T' to obtain the corrected temperature T* includes:

[0120] T*=min(max(T',T min ),T max );

[0121] The method for making safety correction to the optimal pH value pH' to obtain the corrected pH value pH* includes:

[0122] pH*=min(max(pH',pH min ),pH max );

[0123] The method of performing safety correction on the optimal enzyme concentration E' to obtain the corrected enzyme concentration E* includes:

[0124] E*=min(max(E',E min ),E max ).

[0125] Specifically, after the optimal production parameter prediction model predicts the optimal production parameters, the predicted values ​​need to be checked for safety. According to the performance parameters of the process equipment and the production operating procedures, the safety threshold range is set for key parameters such as temperature, pH value, and enzyme concentration. The predicted value is compared with the threshold range to determine whether it exceeds the safety boundary. Once it is found that the predicted parameter exceeds the standard, an emergency adjustment strategy is taken in time to correct it and limit the exceeded parameter within the safety range. The emergency adjustment can adopt a simple upper and lower limit truncation method, that is, the value less than the lower limit is adjusted to the lower limit, and the value greater than the upper limit is adjusted to the upper limit. More complex logical judgment and multi-objective optimization methods can also be used to try to make the corrected parameters close to the predicted values ​​while ensuring safety, thereby reducing the impact on control performance. The corrected safety parameters will be sent to the actuator as the final control command. The safety correction of control parameters is a necessary part of the intelligent control system, which can avoid parameter loss of control due to model prediction errors, thereby causing safety accidents such as equipment damage and product scrapping. This shows that intelligent control is safer and more reliable than traditional control. At the same time, the safety threshold data in the production process can be used to continuously optimize the deep learning prediction model, so that it can learn the safety boundary of the parameter value, reduce the probability of the model predicting an over-limit, and improve the reliability of intelligent control. For example, when the pH value prediction result is pH'=5.2, and the pH value safety threshold range is [5.5,7.5], an emergency adjustment is triggered to correct pH' to pH*=min(max(5.2,5.5),7.5)=5.5, ensuring that the enzymatic hydrolysis process is carried out within a safe pH range.

[0126] Step S2400, continue the enzymatic hydrolysis process according to the corrected production parameter combination (T*, pH*, E*), and return to step S2100, and loop through steps S2100 to S2400 until the enzymatic hydrolysis process is completed.

[0127] Specifically, in step S2400, the enzymatic hydrolysis process is accurately performed according to the production parameter combination (T*, pH*, E*) obtained after the safety threshold is corrected. The heating power of the enzymatic hydrolysis tank is adjusted according to T*, and the temperature is accurately controlled by the PID algorithm with a deviation within ±0.5°C. Acid-base regulators are automatically added according to pH*, and the dosage is calculated in real time by the PID algorithm, which can control the pH within ±0.1 units. The flow rate of the enzyme preparation dosing pump is adjusted according to E* to achieve dynamic regulation of the enzyme concentration and ensure that its fluctuation is within 5% of the optimal concentration. Through the above process, the dynamic stability of the optimal production parameter combination is jointly maintained, providing the necessary conditions for the efficient implementation of the enzymatic hydrolysis process.

[0128] The intelligent control of the enzymatic hydrolysis process is a dynamic closed-loop process, which requires continuous monitoring of process parameters, prediction of optimal parameters, and execution of parameter adjustments until the end of the enzymatic hydrolysis process. Therefore, after executing step S2400, it automatically returns to step S2100, re-collects process parameters, inputs the optimal production parameter prediction model, and performs parameter correction and execution based on the prediction results, and repeats this cycle. This closed-loop control method relies on online sensing technology and data-driven models, and can perform adaptive adjustments to fluctuations in raw materials and processes. On the basis of ensuring process safety, it always maintains production parameters at the dynamic optimum, thereby achieving the purpose of improving quality and increasing efficiency.

[0129] Step S3000, after the enzymatic hydrolysis is completed, the quality of the output protein product is tested to obtain quality attribute data, and the quality attribute data is associated with the initial classification of the batch of raw materials, the process parameter sequence, the optimal production parameter combination, and the corrected production parameter combination to form a structured batch production file; the optimal production parameter prediction model is optimized, and the classification correspondence library is updated.

[0130] Specifically, high performance liquid chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS), gel electrophoresis and other analytical methods were used to qualitatively and quantitatively detect the protein products produced by enzymatic hydrolysis. The protein yield was determined by the Coomassie brilliant blue method, and the yield percentage was calculated. The amino acid composition and content of the protein product were detected by an automatic amino acid analyzer. The molecular weight distribution characteristics of the peptide segment were analyzed by liquid chromatography-mass spectrometry (LC-MS). These test data characterize the key quality attributes of the protein product from both macro and micro levels. The quality attribute data obtained by the test were associated with production factors such as raw material information (grading, attribute parameters), process parameters (temperature, pH value, etc.), and control decisions (adjustment records, predicted values) to build a data chain of "raw materials-process-product" and form a structured and traceable batch production archive. As the result of the production process, quality attributes must have some inherent connection with each production factor. Data analysis methods such as association rule mining and causal inference were used to deeply explore the implicit relationship between quality attributes and production factors, and reveal the influence mechanism of each production link on the final quality. Through data collection and correlation analysis, quality feedback and production process form a closed loop, which can not only guide process optimization, but also accumulate optimization knowledge, and continuously iterate and upgrade in the cycle of "production-evaluation-optimization", so as to continuously improve the refinement and intelligence level of the production process. This link introduces product quality testing into the feedback loop of production control, integrates all production links through digital means, and forms a data-driven production optimization closed loop, which is the key to realizing intelligent regulation of the enzymatic hydrolysis process.

[0131] Adopt machine learning methods such as incremental learning and online learning, and use newly collected closed-loop data of production optimization to retrain the existing models. Divide the new data into training set and validation set, and fine-tune the model parameters to adapt to the dynamic changes of data distribution. For example, for the raw material grading model, new raw material samples can be added to optimize the cluster center; for the optimal production parameter prediction model, more process parameter sequences can be accumulated to expand the training data. The continuous learning of the model enables its prediction ability to keep pace with the times and better meet the actual needs of production. On the basis of model retraining, cross-validation and other methods are used to evaluate the prediction performance of each model. Optimize the model hyperparameters through grid search and other methods, control the model complexity, and improve its generalization ability. For models with poor performance, analyze their limitations and improve the model algorithm, such as using new neural network structures and loss functions. After continuous iterative optimization, an intelligent algorithm model with high accuracy and strong robustness is finally obtained. For the "raw material grading-optimal production parameter range" grading correspondence library, expert review, statistical analysis and other methods are used to regularly maintain and update the grading correspondence library. Enzymatic production experts summarize and refine new production experience, summarize new classification rules and optimization ranges, and add them to the classification correspondence library. At the same time, data statistical methods are used to analyze the confidence and support of the rules in the classification correspondence library, and outdated and invalid entries are eliminated. The dynamic evolution of the classification correspondence library ensures the advancement and effectiveness of its knowledge, and provides reliable theoretical guidance for intelligent production control.

[0132] Example 2

[0133] This embodiment provides an intelligent control method for the enzymatic animal protein production process based on embodiment 1, including:

[0134] Step S2330, when the optimal production parameters exceed the safety range, the predicted optimal production parameters are corrected for safety, and a corrected production parameter combination (T*, pH*, E*) is generated;

[0135] The calculation formula of the corrected temperature T* is:

[0136]

[0137] in:

[0138] T * : Corrected temperature;

[0139] T': optimal temperature predicted by the optimal production parameter prediction model;

[0140] T opt : The median of the temperature safety threshold range, used as a reference value for parameter adjustment, derived from empirical data of the production process or through statistical analysis of historical production data;

[0141] T mid : The middle value of the temperature safety threshold range, used to determine the size of the deviation, usually taken as T min +T max -T min ) / 2;

[0142] λ T : The scaling parameter of the exponential function controls the decay rate of the correction term. A larger λ T Values ​​will make corrections more rapid;

[0143] δ T : Correction coefficient, the correction strength of the control temperature, usually determined by historical data or experience;

[0144] k T : Attenuation coefficient, controls the attenuation speed of the nonlinear offset, the larger the k T Values ​​increase the sensitivity of the correction;

[0145] Δ T : The nonlinear offset added after correction, indicating further adjustment of parameters when the temperature exceeds the safety threshold range.

[0146] exp(·): exponential function with the base e of the natural logarithm as base.

[0147] The formula uses exponential decay and nonlinear correction methods to ensure that when the predicted optimal temperature exceeds the temperature safety threshold, the production parameters can be adjusted quickly and over-correction can be avoided to ensure the stability of the system. T and δ T Provides flexible control of the correction amplitude and speed to meet different process requirements. T The formula gently corrects the temperature over-limit situation, avoiding drastic parameter fluctuations. The hierarchical structure of the formula ensures accurate correction of small deviations and rapid response to large deviations, realizing intelligent and automated control of the enzymatic hydrolysis process.

[0148] When T′ approaches T opt When the correction term (T′-T opt ) is very small, the overall correction force is low, T * It is not much different from the predicted value T′.

[0149] As T′ increases beyond the threshold range, the correction term increases accordingly, but due to exponential decay, the growth rate of the correction term gradually slows down to avoid over-adjustment.

[0150] By adjusting the parameter λ T and δ T , which controls the smoothness and sensitivity of the correction. A larger λ Tand a smaller δ T will make the correction faster, and a smaller λ T This will make the correction smoother, meaning that the speed and magnitude of the correction will be reduced, making the change in parameters more gradual and stable.

[0151] This correction method can balance response speed and stability, and is particularly suitable for complex enzymatic hydrolysis processes, ensuring that production parameters are always within a safe range and improving production efficiency through precise control.

[0152] Example 3

[0153] This embodiment provides an intelligent control system for the enzymatic animal protein production process based on embodiment 1, such as Figure 4 As shown, including:

[0154] Raw material classification module: used to obtain key attribute parameters of the animal protein raw materials to be enzymatically hydrolyzed, establish a raw material classification model, input the collected key attribute parameters into the raw material classification model, and obtain the initial classification of the raw materials;

[0155] Parameter adjustment module: used to match the preset classification correspondence library according to the initial classification of raw materials, and obtain the optimal production parameter range of different batches of raw materials in the initial stage; collect the first production parameters before enzymatic hydrolysis, and obtain the adjusted production parameters according to the first production parameters and the optimal production parameter range;

[0156] Parameter prediction module: used to start the enzymatic hydrolysis reaction according to the adjusted production parameters, collect the key parameters of the enzymatic hydrolysis process in real time, and form a dynamically updated process parameter sequence; input the real-time collected process parameter sequence into the pre-trained optimal production parameter prediction model, and dynamically predict the optimal production parameter combination at the next moment t+Δt, where t is the current moment and Δt is the prediction time step;

[0157] Parameter correction module: used to compare the optimal production parameters in the optimal production parameter combination with the preset process safety threshold. When the optimal production parameters exceed the safety range, the optimal production parameters are corrected for safety to obtain the corrected production parameter combination;

[0158] Cycle module: used to continue the enzymatic hydrolysis process according to the revised production parameter combination; and continuously monitor and collect key parameters of the enzymatic hydrolysis process during the execution process, predict and correct production parameters until the enzymatic hydrolysis process is completed.

[0159] The raw material classification module comprises:

[0160] Data acquisition unit: used to obtain basic information and key attribute parameters of the animal protein raw materials to be enzymatically hydrolyzed;

[0161] Grading model building unit: used to establish a raw material grading model, input the collected key attribute parameters into the raw material grading model, and obtain the initial classification of the raw materials.

[0162] In the data acquisition unit, the basic information and key attribute parameters of the animal protein raw material to be hydrolyzed are obtained, including:

[0163] Step S1110, obtaining basic information such as batch identification ID, source information and timestamp of the animal protein raw material to be hydrolyzed;

[0164] Step S1120, collecting key attribute parameters such as freshness, fat content, and tissue structure of different batches of raw materials;

[0165] The method for obtaining the freshness of the raw material is: measuring the pH value and volatile basic nitrogen content of the raw material, and weightedly calculating the pH value and volatile basic nitrogen content of the raw material to obtain the freshness of the raw material;

[0166] Step S1130, storing the key attribute parameters and basic information of the raw materials into the raw material information database.

[0167] The parameter adjustment module comprises:

[0168] Matching unit: used to match the preset classification correspondence library according to the initial classification of raw materials, and obtain the optimal production parameter range of different batches of raw materials in the initial stage;

[0169] Adjustment unit: used to collect the first production parameter before enzymatic hydrolysis, calculate the first deviation value between the first production parameter and the optimal production parameter range, trigger the first adjustment when the first deviation value exceeds the preset first deviation threshold, and obtain the adjusted production parameter.

[0170] The parameter prediction module comprises:

[0171] Real-time data acquisition unit: used to start the enzymatic hydrolysis reaction, collect key parameters of the enzymatic hydrolysis process in real time, and form a dynamically updated process parameter sequence;

[0172] Prediction unit: used to input the real-time collected process parameter sequence into the pre-trained optimal production parameter prediction model to dynamically predict the optimal production parameter combination at the next moment.

[0173] Example 4

[0174] This embodiment discloses an electronic device, which may include one or more processors and one or more memories, wherein the memories store computer-readable codes, which, when executed by the one or more processors, may execute the above-mentioned intelligent control method for the production process of enzymatic animal protein.

[0175] The method or system according to the embodiment of the present application can also be implemented with the help of the architecture of an electronic device. The electronic device may include a bus, one or more CPUs, a read-only memory (ROM), a random access memory (RAM), a communication port connected to a network, an input / output component, a hard disk, etc. A storage device in the electronic device, such as a ROM or a hard disk, can store the enzymatic animal protein production process intelligent control method provided in the present application. The intelligent control method of the enzymatic animal protein production process may, for example, include: obtaining key attribute parameters of the animal protein raw material to be enzymatically hydrolyzed, establishing a raw material grading model, inputting the collected key attribute parameters into the raw material grading model, and obtaining the initial grading of the raw material; according to the initial grading of the raw material, matching a preset grading correspondence library to obtain the optimal production parameter range of different batches of raw materials in the initial stage; collecting the first production parameter before enzymatic hydrolysis, and obtaining the adjusted production parameter according to the first production parameter and the optimal production parameter range; starting the enzymatic hydrolysis reaction according to the adjusted production parameter, collecting the key parameters of the enzymatic hydrolysis process in real time, and forming a dynamically updated process parameter sequence; inputting the real-time collected process parameter sequence into a pre-trained optimal production parameter prediction model, and dynamically predicting the optimal production parameter combination at the next moment t+Δt; comparing the optimal production parameter in the optimal production parameter combination with the preset process safety threshold, and when it exceeds the safety range, performing safety correction on the optimal production parameter to obtain the corrected production parameter combination; continuing to execute the enzymatic hydrolysis process according to the corrected production parameter combination; and continuously monitoring and collecting the key parameters of the enzymatic hydrolysis process during the execution process, predicting and correcting the production parameters until the end of the enzymatic hydrolysis process, t is the current moment, and Δt is the prediction time step.

[0176] Furthermore, the electronic device may also include a user interface. Of course, the architecture disclosed in the present invention is only exemplary, and when implementing different devices, one or more components in the electronic device disclosed in the present invention may be omitted according to actual needs.

[0177] Example 5

[0178] The present embodiment discloses a computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the intelligent control method for the enzymatic animal protein production process according to the embodiment of the present application described with reference to the above drawings can be executed. The storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may, for example, include random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0179] In addition, according to the embodiments of the present application, the process described in the above reference flowchart can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, which stores machine-readable instructions, and the machine-readable instructions can be executed by a processor to execute instructions corresponding to the method steps provided in the present application, such as: obtaining key attribute parameters of the animal protein raw material to be enzymatically hydrolyzed, establishing a raw material grading model, inputting the collected key attribute parameters into the raw material grading model, and obtaining the initial grading of the raw material; according to the initial grading of the raw material, matching a preset grading correspondence library, and obtaining the optimal production parameter range for the initial stage of different batches of raw materials; collecting the first production parameters before enzymatic hydrolysis, and obtaining the adjusted production parameters according to the first production parameters and the optimal production parameter range; according to the adjusted production parameters, obtaining the optimal production parameter range; according to the adjusted production parameters, obtaining the optimal production parameter range; according to the adjusted production parameters, obtaining the optimal production parameter range; according to the adjusted production parameters, obtaining the optimal production parameter range. The enzymolysis reaction is started by using production parameters, and the key parameters of the enzymolysis process are collected in real time to form a dynamically updated process parameter sequence; the process parameter sequence collected in real time is input into the pre-trained optimal production parameter prediction model, and the optimal production parameter combination at the next moment t+Δt is dynamically predicted; the optimal production parameters in the optimal production parameter combination are compared with the preset process safety threshold, and when the safety range is exceeded, the optimal production parameters are corrected for safety to obtain the corrected production parameter combination; the enzymolysis process is continued according to the corrected production parameter combination; and the key parameters of the enzymolysis process are continuously monitored and collected during the execution process, and the production parameters are predicted and corrected until the end of the enzymolysis process, t is the current moment, and Δt is the prediction time step. When the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are executed.

[0180] The methods, systems, and devices of the present application may be implemented in many ways. For example, the methods, systems, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present application. Therefore, the present application also covers a recording medium storing a program for executing the method according to the present application.

[0181] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0182] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent control method for enzymatic animal protein production process, characterized in that: The method comprises: Obtain key attribute parameters of the animal protein raw material to be hydrolyzed, establish a raw material grading model, input the collected key attribute parameters into the raw material grading model, and obtain the initial grading of the raw material; according to the initial grading of the raw material, match the preset grading correspondence library to obtain the optimal production parameter range of different batches of raw materials in the initial stage; collect the first production parameter before hydrolysis, and obtain the adjusted production parameter according to the first production parameter and the optimal production parameter range; The enzymatic hydrolysis reaction is started according to the adjusted production parameters, and the key parameters of the enzymatic hydrolysis process are collected in real time to form a dynamically updated process parameter sequence; the process parameter sequence collected in real time is input into the pre-trained optimal production parameter prediction model, and the optimal production parameter combination at the next moment t+Δt is dynamically predicted; the optimal production parameters in the optimal production parameter combination are compared with the preset process safety threshold value, and when they exceed the safety range, the optimal production parameters are corrected for safety to obtain a corrected production parameter combination; the enzymatic hydrolysis process is continued according to the corrected production parameter combination; and during the execution process, the key parameters of the enzymatic hydrolysis process are continuously monitored and collected, and the production parameters are predicted and corrected until the end of the enzymatic hydrolysis process, t is the current moment, and Δt is the prediction time step.

2. The intelligent control method for enzymatic animal protein production process according to claim 1, characterized in that: The key attribute parameters of the animal protein raw material to be enzymatically hydrolyzed include the freshness, fat content and tissue structure of the raw material; The freshness of the raw material is calculated by weighting the pH value and volatile basic nitrogen content of the raw material.

3. The intelligent control method for enzymatic animal protein production process according to claim 1, characterized in that: The adjusted production parameters obtained according to the first production parameter and the optimal production parameter range include: real-time collection of the first production parameter P1=(pH1, T1, E1) after the raw material is fed and before the enzymatic hydrolysis begins, where pH1 is the measured pH value of the enzymatic hydrolysis system, T1 is the measured temperature of the enzymatic hydrolysis system, and E1 is the measured enzyme concentration of the enzymatic hydrolysis system; Taking the median of the optimal range of each parameter in the optimal production parameter range as a reference value, respectively calculating the deviations of pH1, T1, E1 from the reference values, and calculating the first deviation value Δδ according to the deviations of pH1, T1, E1 from the reference values; The first deviation value Δδ is compared with a preset first deviation threshold ε1, and when Δδ>ε1, the first adjustment of the production parameters is triggered to obtain the adjusted production parameters.

4. The intelligent control method for enzymatic animal protein production process according to claim 1, characterized in that: The optimal production parameters in the optimal production parameter combination include optimal temperature, optimal pH value and optimal enzyme concentration; The comparing the optimal production parameter in the optimal production parameter combination with the preset process safety threshold comprises: When T' <T min or T'>T max When , it is determined that T' is beyond the safety range; When pH <pH min or pH'>pH max When pH' is beyond the safe range; When E' <E min or E'>E max When , it is determined that E' exceeds the safety range; Among them, T' is the optimal temperature, pH' is the optimal pH value, E' is the optimal enzyme concentration, T min is the lower limit of the temperature safety threshold range, T max is the upper limit of the temperature safety threshold range, pH min is the lower limit of the pH safety threshold range, pH max is the upper limit of the pH safety threshold range, E min is the lower limit of the enzyme concentration safety threshold range, E max It is the upper limit of the enzyme concentration safety threshold range.

5. The intelligent control method for enzymatic animal protein production process according to claim 1, characterized in that: The safety correction of the optimal production parameters includes safety correction of the optimal temperature T', safety correction of the optimal pH value pH', and safety correction of the optimal enzyme concentration E'; The safety correction of the optimal temperature T' comprises: T*=min(max(T',T min ),T max ); Where T* is the corrected temperature.

6. The intelligent control method for enzymatic animal protein production process according to claim 5, characterized in that: The safety correction of the optimal pH value pH' comprises: pH*=min(max(pH',pH min ),pH max ); Wherein, pH* is the corrected pH value.

7. The intelligent control method for enzymatic animal protein production process according to claim 5, characterized in that: The safety correction of the optimal enzyme concentration E' comprises: E*=min(max(E',E min ),AND max ); Where E* is the corrected enzyme concentration.

8. An intelligent control system for enzymatic animal protein production process, which is used to implement the intelligent control method for enzymatic animal protein production process according to any one of claims 1 to 7, characterized in that: The system comprises: Raw material classification module: used to obtain key attribute parameters of the animal protein raw materials to be enzymatically hydrolyzed, establish a raw material classification model, input the collected key attribute parameters into the raw material classification model, and obtain the initial classification of the raw materials; Parameter adjustment module: used to match the preset classification correspondence library according to the initial classification of raw materials, and obtain the optimal production parameter range of different batches of raw materials in the initial stage; collect the first production parameters before enzymatic hydrolysis, and obtain the adjusted production parameters according to the first production parameters and the optimal production parameter range; Parameter prediction module: used to start the enzymatic hydrolysis reaction according to the adjusted production parameters, collect the key parameters of the enzymatic hydrolysis process in real time, and form a dynamically updated process parameter sequence; input the real-time collected process parameter sequence into the pre-trained optimal production parameter prediction model, and dynamically predict the optimal production parameter combination at the next moment t+Δt, where t is the current moment and Δt is the prediction time step; Parameter correction module: used to compare the optimal production parameters in the optimal production parameter combination with the preset process safety threshold. When the optimal production parameters exceed the safety range, the optimal production parameters are corrected for safety to obtain the corrected production parameter combination; Cycle module: used to continue the enzymatic hydrolysis process according to the revised production parameter combination; and continuously monitor and collect key parameters of the enzymatic hydrolysis process during the execution process, predict and correct production parameters until the enzymatic hydrolysis process is completed.

9. An electronic device comprising a memory, a central processing unit, and a computer program stored in the memory and executable on the central processing unit, characterized in that: When the central processing unit executes the computer program, the intelligent control method for the enzymatic animal protein production process described in any one of claims 1-7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the intelligent control method for the enzymatic animal protein production process according to any one of claims 1 to 7 is implemented.

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