CN-121995884-A - Lucid ganoderma spore oil exosome production and preparation control method and system

CN121995884ACN 121995884 ACN121995884 ACN 121995884ACN-121995884-A

Abstract

The invention discloses a method and a system for controlling the production and preparation of ganoderma lucidum spore oil exosomes, which belong to the technical field of biological medicines, wherein the method comprises the following specific steps of (I) collecting and preprocessing multi-type variable data of ganoderma lucidum spore oil exosomes, extracting the characteristics of various types of variable data after processing, and forming a multi-dimensional data set; the method can avoid the negative influence of single production link adjustment on the whole production, effectively reduce production fluctuation, provide a sufficient time window for timely intervention, ensure the stability and batch consistency of the quality of the ganoderma lucidum spore oil exosome products, effectively avoid the production risk caused by unreasonable parameter adjustment, improve the feasibility and safety of the scheme, ensure the stable quality of the ganoderma lucidum spore oil exosome products, and flexibly cope with the parameter adjustment requirements in different production scenes.

Inventors

ZHOU YAJIE
XIA HUI
SUI JING
FENG PENG
CHEN XIAOHONG
WANG YING
CHEN YUTONG
LI MI

Assignees

南京中科药业有限公司

Dates

Publication Date: 20260508
Application Date: 20260203

Claims (8)

1. A method for controlling the production and preparation of ganoderma lucidum spore oil exosomes is characterized by comprising the following specific steps: Collecting and preprocessing multi-type variable data of ganoderma lucidum spore oil exosomes, and extracting characteristics of various types of variable data after processing to form a multi-dimensional data set; II, predicting the production trend of the current ganoderma lucidum spore oil exosomes based on a multi-dimensional dataset, and analyzing the corresponding production deviation direction and degree; III, according to the prediction result, coordinating the control directions and adjustment requirements of different production links to generate corresponding global optimal control instructions; IV, collecting adjusted production data of the ganoderma lucidum spore oil exosomes, judging the matching degree of the current control parameters and a preset production target, and adjusting the control parameters according to the judging result; And V, collecting all control data, prediction data and feedback data in the production process of the corresponding batch to form a complete production data file.
2. The method for controlling the production and preparation of ganoderma lucidum spore oil exosomes according to claim 1, wherein the specific steps of forming the multi-dimensional dataset in step I are as follows: S1.1, collecting multi-type variable data in the production process of ganoderma lucidum spore oil exosomes, removing data exceeding a preset value range in various types of variable data, supplementing missing data in the various types of variable data by adopting an interpolation method, uniformly mapping the various types of variable data into [0,1], and simultaneously extracting and integrating the characteristics of the various types of variable data to form a multi-dimensional data set, wherein the multi-type variable data comprise raw material extraction purity, workshop temperature and pressure, stirring rate, heating power, feeding rate and discharging pressure of a reaction kettle.
3. The method for controlling the production and preparation of ganoderma lucidum spore oil exosomes according to claim 1, wherein the specific steps of predicting the production trend of the current ganoderma lucidum spore oil exosomes in step II are as follows: S2.1, acquiring historical production data of ganoderma lucidum spore oil exosomes, raw material characteristics and production process parameters of a current production batch, calculating similarity between the current production batch data and the historical production data, screening out the historical production data with similarity exceeding a preset standard, matching multi-type variable data with the screened historical production data according to production time nodes and variable types to form a fusion data set, and constructing and training a variable prediction model; S2.2, inputting the fusion data set into a trained variable prediction model, performing multidimensional association analysis on various variable data in the fusion data set based on preset algorithm logic, mining an inherent mapping relation between different variables, simultaneously combining trend rules in historical production data, performing fitting analysis on a change track of the current production data, and generating a production trend prediction curve of corresponding indexes in the variable data of each type; S2.3, comparing and analyzing various production trend prediction curves with a preset standard production trend curve, determining the fit degree of the current production trend and the standard trend, and predicting the change direction and the change rate of various variables in the subsequent production process to form a production trend prediction report.
4. The method for controlling the production and preparation of ganoderma lucidum spore oil exosomes according to claim 3, wherein the specific steps of analyzing the corresponding production deviation direction and degree in step II are as follows: S3.1, calculating numerical value differences between all production trend prediction curves and preset standard production trend curves at all time nodes, identifying corresponding production deviations, obtaining standard numerical value ranges, standard trend curves and corresponding process control thresholds of various indexes of ganoderma lucidum spore oil exosome production, comparing each index prediction value output by a variable prediction model with the corresponding standard value one by one, and judging positive deviation if the prediction value is higher than the standard value, judging negative deviation if the prediction value is lower than the standard value, and judging no deviation if the prediction value is within a standard value interval; S3.2, calculating the absolute value of the difference between the predicted value of each index and the corresponding standard value, calculating the ratio of the absolute value of the deviation to the corresponding standard value, converting the ratio into a percentage form, calculating and recording two quantization parameters of each index to form a deviation quantization parameter table, setting a deviation grade division rule according to the production process requirement of ganoderma lucidum spore oil exosomes, judging the deviation as slight deviation if the relative proportion of the deviation is in a range of 0-5%, judging the deviation as medium deviation if the relative proportion of the deviation is in a range of 5-15%, judging the deviation as serious deviation if the relative proportion of the deviation exceeds 15%, and then carrying out association binding on the deviation grade, the corresponding index and the quantization parameter; S3.3, according to the deviation grade judging result, corresponding data of the same kind of deviation grade in a preset production database are obtained, the influence rule of different grade deviation on the quality and production efficiency of the final ganoderma lucidum spore oil exosome product is analyzed, if the deviation is slight, no obvious influence is caused on the quality and production efficiency of the product, urgent intervention is not needed, if the deviation is moderate, fluctuation of the quality or reduction of the production efficiency of the product is caused, control parameters are required to be adjusted in time, if the deviation is serious, the quality of the product is not up to standard or the production flow is interrupted, intervention measures are required to be started immediately, and finally deviation direction, deviation quantization parameter table and deviation grade judging result are estimated to influence degree evaluation data, and deviation evaluation report is compiled according to a standardized format.
5. The method for controlling the production and preparation of ganoderma lucidum spore oil exosomes according to claim 3, wherein the specific training steps of the variable prediction model of S2.2 are as follows: P1.1, collecting historical production data of ganoderma lucidum spore oil exosomes, preprocessing each group of collected historical production data to form a standardized characteristic data set, and dividing the standardized characteristic data set into a training set, a verification set and a test set according to a preset proportion; P1.2, acquiring time sequence characteristics and multivariate correlation characteristics of ganoderma lucidum spore oil exosome production, constructing a variable prediction model framework according to the time sequence characteristics and the multivariate correlation characteristics, setting core parameters and super parameters of model training, selecting a smooth L1 loss function, and configuring an Adam optimizer and an initial learning rate; inputting data in a training set into a built variable prediction model according to preset batches, carrying out forward propagation, carrying out feature fusion and nonlinear conversion on the data in each batch sequentially through an input layer, a normalization layer, a convolution layer, a circulation layer, a full-connection layer and an output layer, outputting each index prediction result, calculating the deviation between a predicted value and a true value through a smooth L1 loss function, then solving each layer of parameter gradient in a chain mode through a reverse propagation algorithm, updating model weight and bias by combining an Adam optimizer and setting learning rate, simultaneously evaluating model performance by using verification set data of the current batch, and recording a smooth L1 loss function value and each error index; p1.4, recording a smooth L1 loss function value, a verification set prediction error and a parameter state of the model after each round of iteration is completed, comparing the current performance index of the model with a preset termination condition, analyzing a failure to reach the standard if the smooth L1 loss function value is not converged, the verification set error is higher than the preset termination condition or an ascending trend occurs, adjusting the model super-parameter through an Adam optimizer, and repeating the iterative training process until the smooth L1 loss function is converged and the verification set prediction error is converged within a preset range; And P1.5, inputting the test set data into an optimized variable prediction model, calculating error indexes between a model prediction value and a test set actual value, comparing each error index with a preset threshold, judging that the model performance meets the standard if all indexes are within the preset threshold, otherwise, returning to a model architecture adjustment or super-parameter optimization step, and re-carrying out training and verification.
6. The method for preparing and controlling the production of ganoderma lucidum spore oil exosomes according to claim 3, wherein the specific steps of generating the corresponding global optimal control command in step III are as follows: S4.1, receiving a prediction result output by a variable prediction model in real time, carrying out layered analysis on the prediction result through a built-in distributed cooperative control center, extracting index deviation information, trend change rules and potential deviation risks corresponding to each production link, determining a production link range to be adjusted, and distributing the analyzed prediction information to corresponding responsible sub-controllers according to preset division of a distributed control architecture; S4.2, after each sub-controller receives the distributed prediction information, acquiring each index data of the current link, comparing and analyzing the index data with corresponding indexes in a prediction result, combing the fit degree of the current working condition and the prediction trend, determining the preliminary requirement of the self-control link to be adjusted, and then synchronously feeding back the current working condition and the preliminary adjustment requirement to a distributed cooperative control center through a communication network by each sub-controller to form a working condition-prediction comparison data set; s4.3, based on a working condition-prediction comparison data set and in combination with a distributed consensus algorithm, each sub-controller exchanges data in real time through a communication network, the working condition current situation, the preliminary adjustment requirement and the potential influence on other links of the sub-controllers are reported one by one, and then the distributed cooperative control center coordinates the adjustment requirement of each sub-controller according to a global optimization target and negotiates and determines a uniform control direction aiming at the production links with deviation; S4.4, invoking a global optimization algorithm to carry out cooperative operation according to the control direction determined by negotiation, aiming at minimizing deviation influence, guaranteeing product quality stability and improving production efficiency, constructing a corresponding objective function, inputting the current condition fed back by each sub-controller, deviation parameters in a prediction result and production process constraint conditions, solving the objective function through iterative operation to obtain specific adjustment parameters of each production link, simultaneously distributing specific execution tasks of each sub-controller, and generating a global optimal control instruction for each production link according to the cooperative operation result; And S4.5, dividing the global optimal control instructions into work according to the sub-controllers, sorting the packaged control instructions to form a dedicated instruction list of each sub-controller, issuing the dedicated instruction list, adjusting corresponding production equipment parameters after each sub-controller receives the corresponding global optimal control instructions, continuously collecting the adjusted parameters of the self-control link, recording a dynamic change curve of parameter adjustment, and transmitting the dynamic change curve back to the cooperative control module.
7. A ganoderma lucidum spore oil exosome production and preparation control system for realizing the ganoderma lucidum spore oil exosome production and preparation control method according to any one of claims 1-6, which is characterized by comprising an acquisition processing module, a variable prediction module, a cooperative control module, a control execution module, a real-time monitoring module, an adjustment optimization module, a storage management module and a standard configuration module; The collection processing module is used for collecting various variable data in the production process of the ganoderma lucidum spore oil exosome and preprocessing the collected various variable data; The variable prediction module fits the current production data change track according to the processed variable data to generate a production trend prediction curve of each index; The cooperative control module is used for receiving the prediction result, extracting deviation information and risk points of each production link and generating a corresponding global optimal control instruction; The control execution module is used for receiving and analyzing the corresponding global optimal control instruction and adjusting the execution parameters of the corresponding production equipment according to the analysis information; The real-time monitoring module is used for collecting the index adjusted by each control link in real time and recording the corresponding parameter dynamic change curve; The adjusting and optimizing module is used for analyzing the matching degree of the current control parameter and a preset production target and generating an optimized parameter scheme according to an analysis result; The storage management module is used for storing the whole process data of the ganoderma lucidum spore oil exosome production and classifying and archiving according to production batches, data types and time nodes; The standard configuration module is used for storing a standard numerical range, a standard production trend curve and a process control threshold of each index.
8. The ganoderma lucidum spore oil exosome production and preparation control system according to claim 7, wherein the specific steps of the adjustment optimization module for generating an optimized parameter scheme according to the analysis result are as follows: S5.1, receiving monitoring data collected by each sub-controller in real time and decision information of a cooperative control module, extracting current actual control parameters of each production link, real-time characteristic indexes of ganoderma lucidum spore oil exosomes and parameter change trend curves from the monitoring data, extracting global production targets, control threshold values of each link, deviation grades and control parameter reference values executed currently from the decision information, classifying and integrating the analyzed information according to the production links, and establishing a corresponding relation table of 'control parameters-actual indexes-production targets'; S5.2, acquiring corresponding standard parameters of ganoderma lucidum spore oil exosome production, determining standard target intervals and optimal target values of all control parameters by combining with a global production target, calculating deviation quantization indexes of current actual control parameters and the optimal target values, simultaneously analyzing the fit degree of the real-time characteristic indexes of the ganoderma lucidum spore oil exosome and the target indexes, evaluating the influence effect of the current control parameters on the product quality, and dividing the matching states of all the control parameters into corresponding grades according to a preset matching degree dividing rule; S5.3, locking deviation control parameters in a matching state lower than a preset state based on a matching degree analysis result, analyzing influence factors generated by the deviation control parameters, simultaneously carding linkage influence of the deviation control parameters on other control parameters, production efficiency and product quality, selecting a corresponding self-adaptive learning algorithm according to the type of the deviation parameters, coupling characteristics and optimization requirements, and setting corresponding constraint conditions; s5.4, inputting the locked deviation control parameters, current actual working condition data, production target parameters and constraint conditions into a self-adaptive learning algorithm which is configured to be completed, taking the minimized deviation of the control parameters and the maximized matching degree of the production target as an objective function, adjusting the deviation parameters through multiple iterations, calculating the matching degree of the optimized parameters and the target values after each iteration, comparing the deviation change conditions before and after the iteration, and if the matching degree does not reach the preset requirement, adjusting the algorithm parameters, and then continuing the iteration, otherwise, stopping iterative operation, and outputting a preliminary optimization parameter scheme, wherein the specific calculation formula of the matching degree is as follows: ; in the formula, Represent the first The production target matching degree evaluation value after the round of iteration; representing the total number of production target indexes; Represent the first Post-round iteration Standard reaching rate of individual production target indexes; Represent the first Weight coefficients of the individual production target indexes; S5.5, comparing the preliminary optimization parameter scheme with production process constraint conditions and equipment operation limit parameters, removing parameter values exceeding a normal constraint range, inputting the preliminary optimization parameter scheme into a variable prediction model to perform simulation operation, predicting parameter change trend of each production link, ganoderma lucidum spore oil exosome quality index and generated new deviation after the scheme is implemented, then judging that the optimization scheme is feasible if the simulation result meets the requirement, otherwise, returning to iterative optimization, adjusting algorithm parameters to perform re-operation, integrating the verified optimization scheme, and synchronously transmitting the integrated optimization scheme to a cooperative control module.

Description

Lucid ganoderma spore oil exosome production and preparation control method and system Technical Field The invention relates to the technical field of biological medicine, in particular to a method and a system for controlling production and preparation of ganoderma lucidum spore oil exosomes. Background The ganoderma lucidum spore oil is a core active product of ganoderma lucidum, is rich in key active ingredients such as triterpenes and polysaccharides, is a core foundation for playing medicinal values, has irreplaceable important roles in the biomedical fields such as immunoregulation, anti-tumor, anti-inflammatory and the like, and can effectively break through a biological barrier by virtue of excellent biocompatibility, low immunogenicity and targeted delivery capability, so that the bioavailability and in-vivo targeted enrichment efficiency of the active ingredients of the ganoderma lucidum spore oil are remarkably improved, and the pharmacological efficacy of the active ingredients is greatly enhanced. The existing ganoderma lucidum spore oil exosome production and preparation control method and system are easy to cause linkage fluctuation of the whole production process when parameters of any production link are regulated, stability and consistency of quality of products in each batch are difficult to ensure, and in addition, the existing ganoderma lucidum spore oil exosome production and preparation control method and system are insufficient in parameter regulation flexibility when coping with different production scenes, often depend on manual experience and have production risks caused by unreasonable regulation, so the ganoderma lucidum spore oil exosome production and preparation control method and system are provided. Disclosure of Invention The invention aims to solve the defects in the prior art, and provides a method and a system for controlling the production and preparation of ganoderma lucidum spore oil exosomes. In order to achieve the above purpose, the present invention adopts the following technical scheme: A method for controlling the production and preparation of ganoderma lucidum spore oil exosomes comprises the following specific steps: Collecting and preprocessing multi-type variable data of ganoderma lucidum spore oil exosomes, and extracting characteristics of various types of variable data after processing to form a multi-dimensional data set; II, predicting the production trend of the current ganoderma lucidum spore oil exosomes based on a multi-dimensional dataset, and analyzing the corresponding production deviation direction and degree; III, according to the prediction result, coordinating the control directions and adjustment requirements of different production links to generate corresponding global optimal control instructions; IV, collecting adjusted production data of the ganoderma lucidum spore oil exosomes, judging the matching degree of the current control parameters and a preset production target, and adjusting the control parameters according to the judging result; And V, collecting all control data, prediction data and feedback data in the production process of the corresponding batch to form a complete production data file. A ganoderma lucidum spore oil exosome production and preparation control system comprises an acquisition processing module, a variable prediction module, a cooperative control module, a control execution module, a real-time monitoring module, an adjustment optimization module, a storage management module and a standard configuration module; The collection processing module is used for collecting various variable data in the production process of the ganoderma lucidum spore oil exosome and preprocessing the collected various variable data; The variable prediction module fits the current production data change track according to the processed variable data to generate a production trend prediction curve of each index; The cooperative control module is used for receiving the prediction result, extracting deviation information and risk points of each production link and generating a corresponding global optimal control instruction; The control execution module is used for receiving and analyzing the corresponding global optimal control instruction and adjusting the execution parameters of the corresponding production equipment according to the analysis information; The real-time monitoring module is used for collecting the index adjusted by each control link in real time and recording the corresponding parameter dynamic change curve; The adjusting and optimizing module is used for analyzing the matching degree of the current control parameter and a preset production target and generating an optimized parameter scheme according to an analysis result; The storage management module is used for storing the whole process data of the ganoderma lucidum spore oil exosome production and classifying and archiving according to production batches, data ty