Neurological acid extraction and analysis method and system based on multi-source data prediction

Abstract

The invention discloses a nervonic acid extraction analysis method and system based on multi-source data prediction, and relates to the technical field of nervonic acid analysis, and the method comprises the steps: obtaining extraction raw material information, analyzing the nervonic acid extraction condition of an extraction raw material, obtaining the state information of the extraction raw material, and obtaining the nervonic acid extraction result based on the state information of the extraction raw material. And judging whether the extracted raw materials meet nervonic acid extraction requirements or not. According to the method, the stability and reliability of the nervonic acid quality are ensured by extracting the state index of the raw material and evaluating the quality of the extracted raw material, the accuracy of nervonic acid extraction quality prediction is ensured through the nervonic acid extraction prediction model, and the nervonic acid extraction efficiency and the product quality are improved through the nervonic acid extraction quality index; the nervonic acid extraction quality reduction caused by nervonic acid extraction process faults is avoided through the nervonic acid extraction difference index, the extraction process is convenient to optimize, the production period is shortened, and the nervonic acid extraction efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of neuraminic acid analysis, and specifically to a neuraminic acid extraction and analysis method and system based on multi-source data prediction. Background Art

[0003] Neuroacid is an important polyunsaturated fatty acid with multiple physiological functions, such as cell membrane structure, nervous system development and protection. Neuroacid was first discovered in the nervous tissue of mammals, so it was named as neuroacid. Foreign studies have shown that neuroacid is the core natural component of brain nerve cells and nervous tissue. It is the only special substance in the world that can promote the repair and regeneration of damaged nerve tissue. It is an essential "high-level nutrient" for the growth, redevelopment and maintenance of nerve cells, especially brain cells, optic nerve cells and peripheral nerve cells. It plays a great role in improving the activity of brain nerves and preventing brain nerve aging. Therefore, the requirements for the extraction process of neuroacid are getting higher and higher.

[0004] At present, there are still problems in the extraction and analysis of neuraminic acid, such as the inability to accurately analyze the status of the extraction raw materials, the inability to accurately adjust the extraction raw materials based on the extraction raw material status information, which reduces the extraction efficiency and quality of neuraminic acid, the inability to set appropriate neuraminic acid extraction parameters based on the extraction reagents, and the inability to accurately evaluate the quality of the extracted neuraminic acid. Summary of the invention

[0006] In order to solve the above-mentioned technical problems, a method and system for neuraminic acid extraction and analysis based on multi-source data prediction are provided. The technical solution solves the problems raised in the above-mentioned background technology that the state of the extraction raw materials cannot be accurately analyzed, the extraction raw materials cannot be accurately adjusted according to the extraction raw material state information, thereby reducing the neuraminic acid extraction efficiency and extraction quality, and it is impossible to set appropriate neuraminic acid extraction parameters according to the extraction reagents, and it is impossible to accurately evaluate the quality of the extracted neuraminic acid.

[0007] In order to achieve the above purpose, the technical solution adopted by the present invention is: A method for extracting and analyzing neuraminic acid based on multi-source data prediction, comprising: Acquire extraction raw material information, wherein the extraction raw material information includes extraction raw material type information and extraction raw material parameter information; Analyze the extraction status of nervonic acid of the extraction raw materials to obtain the status information of the extraction raw materials; Based on the extraction raw material status information, determine whether the extraction raw material meets the extraction requirements of neuraminic acid. If not, adjust the extraction raw material according to the extraction raw material status information. If yes, obtain the extraction reagent parameter information. Obtain historical data on neuraminic acid extraction and establish a neuraminic acid extraction prediction model based on model training; According to the prediction model of neuraminic acid extraction and the parameter information of extraction reagents, obtaining neuraminic acid extraction parameter information; Extracting neuraminic acid according to the neuraminic acid extraction parameter information to obtain an initial neuraminic acid extract; Based on liquid chromatography, the initial extract of neuraminic acid is separated and purified to obtain a purified neuraminic acid solution; Conduct concentration analysis on the purified neuraminic acid solution to determine whether the neuraminic acid concentration meets the production standards. If not, the neuraminic acid extraction is unqualified; if so, the neuraminic acid extraction is qualified.

[0008] Preferably, judging whether the extraction raw material meets the extraction requirements of neuraminic acid based on the extraction raw material status information specifically includes: Pre-treating the extracted raw material to obtain a basic processed raw material, wherein the pre-treatment includes washing, drying and crushing; If the raw material for extraction is a plant material, the pretreatment of the raw material for extraction also includes: According to the information of the type of the extracted raw material, the cell wall lytic enzyme information is obtained, wherein the cell wall lytic enzyme information includes the type information of the cell wall lytic enzyme and the concentration information of the cell wall lytic enzyme; According to the cell wall lytic enzyme information, the extraction raw materials are enzymatically hydrolyzed based on the enzymatic method; Conduct status analysis on basic processing raw materials and obtain status information of extracted raw materials; According to the extracted raw material state information, the raw material moisture content information and raw material material composition information are obtained; According to the raw material component information, obtaining information related to the moisture content of the raw material, wherein the information related to the moisture content of the raw material indicates the type and content of components of the raw material component having hydrolysis characteristics; According to the extraction raw material type information and based on the neuraminic acid extraction requirements, the extraction reagent parameter information is obtained, wherein the extraction reagent parameter information includes the extraction reagent type information and the extraction reagent concentration information; Obtaining an extraction raw material state index according to raw material moisture content information, raw material material composition information, raw material material moisture related information and extraction reagent parameter information; Based on the extraction raw material status index, determine whether the extraction raw material meets the requirements for neuraminic acid extraction.

[0009] Preferably, judging whether the extraction raw material meets the extraction requirements of neuraminic acid according to the extraction raw material state index specifically includes: Based on the extraction reagent concentration requirements, obtain the standard value of raw material moisture content; According to the standard value of raw material moisture content and the relevant information of raw material moisture, obtain the characteristic value of raw material moisture content: In the formula, is the characteristic value of raw material moisture content, is the standard value of moisture content in raw materials, n is the total number of material components with hydrolysis characteristics in the raw materials, and m is the total number of material components in the raw materials; Based on the extraction efficiency requirements of nervonic acid, the threshold value of the extraction raw material status index is obtained ; According to the characteristic value of raw material moisture content, extraction raw material state index and extraction raw material state index threshold, it is judged whether the extraction raw material meets the extraction requirements of neuraminic acid; Among them, if , then the extraction raw material meets the requirements for neuraminic acid extraction. , then the extraction raw material does not meet the extraction requirements of neuraminic acid, and the extraction raw material is adjusted according to the extraction raw material state index; The calculation formula of the extraction raw material state index is: Where Q is the extraction raw material state index, is the standard extraction rate of neuraminic acid of the extraction reagent, is the concentration of the extraction reagent, Indicates the characteristic value of neuraminic acid extraction. If the extraction raw material is animal cells, then If the extraction material is plant cells, then , is the concentration of cell wall lytic enzymes, is the particle size of the raw material, P is the moisture content of the raw material, is the hydrolysis ratio of the i-th substance component with hydrolysis characteristics in the raw material, It is the content of the i-th substance with hydrolysis characteristics in the raw material.

[0010] Preferably, the step of obtaining the neuraminic acid extraction parameter information based on the neuraminic acid extraction prediction model and the extraction reagent parameter information specifically includes: According to the historical extraction data of neuraminic acid, historical information of extraction raw materials, historical parameter information of extraction reagents, historical extraction parameter information of neuraminic acid and historical data of neuraminic acid are obtained; According to the historical extraction parameter information of neuraminic acid, the historical extraction centrifugation data of neuraminic acid and the historical extraction environment information of neuraminic acid are obtained; Wherein, the historical extraction centrifugation data of neuraminic acid includes historical centrifugation speed information and historical centrifugation time information, and the historical extraction environment information of neuraminic acid includes historical environment temperature information and historical extraction pH value information; Acquire historical characteristic data of neuraminic acid according to the historical data of neuraminic acid, wherein the historical characteristic data of neuraminic acid includes neuraminic acid extraction rate information, neuraminic acid extraction concentration information and neuraminic acid structure information; Matching the nervonic acid extraction rate information with the historical parameter information of the extraction reagent, the historical centrifugation time information, and the historical ambient temperature information to obtain extraction rate historical matching data; Matching the nervonic acid extraction concentration information with the historical centrifugation speed information, the historical centrifugation time information and the historical ambient temperature information to obtain extraction concentration historical matching data; Matching the nervonic acid structure information with the historical parameter information of the extraction reagent, the historical ambient temperature information, and the historical extraction pH value information to obtain the nervonic acid quality historical matching data; Performing data aggregation on the extraction rate historical matching data, the extraction concentration historical matching data and the neuraminic acid mass historical matching data to obtain the neuraminic acid historical matching data; According to the historical matching data of neuraminic acid, a prediction model for neuraminic acid extraction is obtained; According to the neuraminic acid extraction prediction model and the extraction reagent parameter information, the neuraminic acid extraction parameter information is obtained.

[0011] Preferably, the step of obtaining the neuraminic acid extraction parameter information based on the neuraminic acid extraction prediction model and the extraction reagent parameter information specifically includes: According to the historical matching data of neuraminic acid, the data were divided into training set and test set; Based on the training set and the set model hyperparameters, the deep learning model is trained to obtain the basic model; According to the test set, the basic model was verified and optimized based on the cross-validation method to obtain the prediction model of neuraminic acid extraction; According to the prediction model of neuraminic acid extraction and the parameter information of extraction reagents, the neuraminic acid extraction quality index is obtained; Adjusting the neuraminic acid extraction parameters until the neuraminic acid extraction quality index reaches a maximum value, and obtaining neuraminic acid extraction parameter information; Among them, the calculation formula of nervonic acid extraction quality index is: Where, E is the extraction quality index of nervonic acid, is the temperature influence coefficient of nervonic acid extraction rate, is the ambient temperature, is the standard ambient temperature, is the standard extraction rate of neuraminic acid of the extraction reagent, is the centrifugation time, is the centrifugal speed, is the centrifugation time influence coefficient of neuraminic acid extraction concentration, is the centrifugal speed influence coefficient of neuraminic acid extraction concentration, is the temperature influence coefficient of nervonic acid decomposition, The standard quality index of nervonic acid for the extraction reagent, is the pH sensitivity coefficient of nervonic acid, is the pH value, The optimal pH value.

[0012] Preferably, the concentration analysis of the purified neuraminic acid solution to determine whether the neuraminic acid concentration meets the production standards specifically includes: Performing concentration analysis on the purified neuraminic acid solution to obtain data on the purified neuraminic acid solution, wherein the data on the purified neuraminic acid solution includes information on the types of material components in the purified solution and information on the content of material components in the purified solution; According to the data of purified solution of neuraminic acid, the content information of neuraminic acid is obtained; According to the data of purified neuraminic acid solution and the content information of neuraminic acid, the neuraminic acid extraction difference index was obtained based on the neuraminic acid extraction quality index; Neuroacid extraction difference index , then the neuraminic acid extraction is qualified, if , then there is a problem in the mixing process of the extraction reagent and the extraction raw material during the neuraminic acid extraction process, and the neuraminic acid extraction process is adjusted according to the neuraminic acid extraction difference index; The calculation formula of the neuraminic acid extraction difference index is: Where R is the difference index of neuraminic acid extraction, is the mass of nervonic acid in the purified nervonic acid solution, is the total mass of purified neuraminic acid solution, is the content of the jth component in the purified solution of nervonic acid, is the influence coefficient of the jth component in the purified neuraminic acid solution on neuraminic acid, and E is the neuraminic acid extraction quality index.

[0013] Furthermore, a neuraminic acid extraction and analysis system based on multi-source data prediction is proposed, which is used to implement the above-mentioned extraction and analysis method, including: A main control module, wherein the main control module is used to determine whether the extraction raw material meets the extraction requirements of neuraminic acid according to the characteristic value of the raw material moisture content, the extraction raw material state index and the extraction raw material state index threshold, determine whether the neuraminic acid concentration meets the production standard according to the neuraminic acid extraction difference index, obtain cell wall lysing enzyme information according to the extraction raw material type information, obtain raw material moisture related information according to the raw material material component information, obtain extraction reagent parameter information based on the neuraminic acid extraction requirements according to the extraction raw material type information, obtain neuraminic acid historical characteristic data according to the neuraminic acid historical data, adjust the extraction raw material according to the extraction raw material state information, and adjust the neuraminic acid extraction process according to the neuraminic acid extraction difference index; An information acquisition module, wherein the information acquisition module is used to acquire extraction raw material information, extraction raw material type information and extraction raw material parameter information, analyze the neuraminic acid extraction status of the extraction raw material, acquire extraction raw material status information, acquire neuraminic acid historical extraction data, and acquire extraction raw material historical information, extraction reagent historical parameter information, neuraminic acid historical extraction parameter information and neuraminic acid historical data based on the neuraminic acid historical extraction data; An evaluation module, wherein the evaluation module is used to obtain an extraction raw material state index based on raw material moisture content information, raw material material composition information, raw material material moisture-related information and extraction reagent parameter information, obtain a raw material moisture content characteristic value based on a raw material moisture content standard value and raw material material moisture-related information, divide the data into a training set and a test set based on historical matching data of neuraminic acid, train a deep learning model based on the training set and set model hyperparameters to obtain a basic model, verify and optimize the basic model based on the test set based on a cross-validation method, obtain a neuraminic acid extraction prediction model, obtain a neuraminic acid extraction quality index based on the neuraminic acid extraction prediction model and extraction reagent parameter information, and obtain a neuraminic acid extraction difference index based on the neuraminic acid extraction quality index based on neuraminic acid purified liquid data and neuraminic acid content information; A display module interacts with the main control module and is used to output and display the extraction raw material status index, the neuraminic acid extraction prediction model, the neuraminic acid extraction quality index and the neuraminic acid extraction difference index.

[0014] Optionally, the main control module specifically includes: A control unit, the control unit being used to obtain cell wall lytic enzyme information according to extraction raw material type information, obtain raw material moisture related information according to raw material composition information, obtain extraction reagent parameter information based on neuraminic acid extraction requirements according to extraction raw material type information, obtain neuraminic acid historical characteristic data according to neuraminic acid historical data, adjust the extraction raw materials according to extraction raw material status information, obtain neuraminic acid extraction difference index, and adjust the neuraminic acid extraction process; An information receiving unit, which interacts with the information acquisition module and the evaluation module to receive data and transmit it to the judgment unit; A judgment unit, wherein the judgment unit is used to judge whether the extraction raw material meets the requirements of neuraminic acid extraction according to the characteristic value of the raw material moisture content, the extraction raw material state index and the extraction raw material state index threshold, and to judge whether the neuraminic acid concentration meets the production standard according to the neuraminic acid extraction difference index.

[0015] Optionally, the information acquisition module specifically includes: A first acquisition unit, the first acquisition unit is used to acquire extraction raw material information, extraction raw material type information and extraction raw material parameter information, analyze the neuraminic acid extraction status of the extraction raw material, and acquire extraction raw material status information; The second acquisition unit is used to acquire the historical extraction data of neuraminic acid, and acquire the historical information of extraction raw materials, historical parameter information of extraction reagents, historical extraction parameter information of neuraminic acid and historical data of neuraminic acid based on the historical extraction data of neuraminic acid.

[0016] Optionally, the evaluation module specifically includes: A raw material evaluation unit, the raw material evaluation unit is used to obtain an extraction raw material state index according to the raw material moisture content information, the raw material material composition information, the raw material material moisture related information and the extraction reagent parameter information, and obtain the raw material moisture content characteristic value according to the raw material moisture content standard value and the raw material material moisture related information; A neuraminic acid extraction evaluation unit, wherein the neuraminic acid extraction evaluation unit is used to divide the data into a training set and a test set according to the neuraminic acid historical matching data, train the deep learning model based on the training set and the set model hyperparameters, obtain the basic model, verify and optimize the basic model based on the test set and the cross-validation method, obtain the neuraminic acid extraction prediction model, and obtain the neuraminic acid extraction quality index according to the neuraminic acid extraction prediction model and the extraction reagent parameter information; A neuraminic acid quality assessment unit, which is used to obtain a neuraminic acid extraction difference index based on the neuraminic acid extraction quality index according to the neuraminic acid purified liquid data and the neuraminic acid content information.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes a method and system for neuraminic acid extraction and analysis based on multi-source data prediction. The quality of the extracted raw materials is evaluated by extracting the raw material state index to ensure the stability and reliability of the neuraminic acid quality. The accuracy of the neuraminic acid extraction quality prediction is ensured by the neuraminic acid extraction prediction model. The neuraminic acid extraction quality index is used to improve the neuraminic acid extraction efficiency and product quality. The neuraminic acid extraction difference index is used to avoid neuraminic acid extraction process failures that cause reduced neuraminic acid extraction quality, thereby facilitating the optimization of the extraction process, shortening the production cycle, and improving the neuraminic acid extraction efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of a method for extracting and analyzing neuraminic acid based on multi-source data prediction proposed by the present invention; Figure 2 Flow chart for obtaining raw material state index in the present invention Figure 3 This is a flow chart for obtaining the extraction parameter information of nervonic acid in the present invention; Figure 4 This is a structural block diagram of the neuraminic acid extraction and analysis system based on multi-source data prediction proposed by the present invention. DETAILED DESCRIPTION

[0021] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0022] Reference Figure 1 - Figure 3 As shown, a method for extracting and analyzing neuraminic acid based on multi-source data prediction in an embodiment of the present invention includes: Acquire extraction raw material information, wherein the extraction raw material information includes extraction raw material type information and extraction raw material parameter information; Analyze the extraction status of nervonic acid of the extraction raw materials to obtain the status information of the extraction raw materials; Based on the extraction raw material status information, determine whether the extraction raw material meets the extraction requirements of neuraminic acid. If not, adjust the extraction raw material according to the extraction raw material status information. If yes, obtain the extraction reagent parameter information. Specifically, based on the extraction raw material status information, it is judged whether the extraction raw material meets the extraction requirements of neuraminic acid, including: Pre-treating the extracted raw material to obtain a basic processed raw material, wherein the pre-treatment includes washing, drying and crushing; If the raw material for extraction is a plant material, the pretreatment of the raw material for extraction also includes: According to the information of the type of the extracted raw material, the cell wall lytic enzyme information is obtained, wherein the cell wall lytic enzyme information includes the type information of the cell wall lytic enzyme and the concentration information of the cell wall lytic enzyme; According to the cell wall lytic enzyme information, the extraction raw materials are enzymatically hydrolyzed based on the enzymatic method; Conduct status analysis on basic processing raw materials and obtain status information of extracted raw materials; According to the extracted raw material state information, the raw material moisture content information and raw material material composition information are obtained; According to the raw material component information, obtaining information related to the moisture content of the raw material, wherein the information related to the moisture content of the raw material indicates the type and content of components of the raw material component having hydrolysis characteristics; According to the extraction raw material type information and based on the neuraminic acid extraction requirements, the extraction reagent parameter information is obtained, wherein the extraction reagent parameter information includes the extraction reagent type information and the extraction reagent concentration information; Obtaining an extraction raw material state index according to raw material moisture content information, raw material material composition information, raw material material moisture related information and extraction reagent parameter information; Based on the extraction reagent concentration requirements, obtain the standard value of raw material moisture content; According to the standard value of raw material moisture content and the relevant information of raw material moisture, obtain the characteristic value of raw material moisture content: In the formula, is the characteristic value of raw material moisture content, is the standard value of moisture content in raw materials, n is the total number of material components with hydrolysis characteristics in the raw materials, and m is the total number of material components in the raw materials; Based on the extraction efficiency requirements of nervonic acid, the threshold value of the extraction raw material status index is obtained ; According to the characteristic value of raw material moisture content, extraction raw material state index and extraction raw material state index threshold, it is judged whether the extraction raw material meets the extraction requirements of neuraminic acid; Among them, if , then the extraction raw material meets the requirements for neuraminic acid extraction. , then the extraction raw material does not meet the extraction requirements of neuraminic acid, and the extraction raw material is adjusted according to the extraction raw material state index; The calculation formula of the extraction raw material state index is: Where Q is the extraction raw material state index, is the standard extraction rate of neuraminic acid of the extraction reagent, is the concentration of the extraction reagent, Indicates the characteristic value of neuraminic acid extraction. If the extraction raw material is animal cells, then If the extraction material is plant cells, then , is the concentration of cell wall lytic enzymes, is the particle size of the raw material, P is the moisture content of the raw material, is the hydrolysis ratio of the i-th substance component with hydrolysis characteristics in the raw material, It is the content of the i-th substance with hydrolysis characteristics in the raw material.

[0023] In this scheme, the basic processing raw materials are obtained by pre-treating the extraction raw materials, such as washing, drying and crushing, and the surface impurities of the extraction raw materials are removed. The state analysis of the basic processing raw materials is performed to obtain the extraction raw material state information, and the extraction raw material state index is obtained based on the raw material moisture content information, raw material material composition information, raw material moisture-related information and extraction reagent parameter information. Based on the raw material moisture content characteristic value, the extraction raw material state index and the extraction raw material state index threshold, it is judged whether the extraction raw materials meet the extraction requirements of neuraminic acid to ensure the stability and reliability of neuraminic acid quality; It is understandable that before extracting neuraminic acid, the raw materials are often fractionated to increase the contact area between the raw materials and the extraction reagents and improve the extraction efficiency of neuraminic acid. In this embodiment, the characteristic value of the raw material moisture content is obtained through the standard value of the raw material moisture content and the information related to the moisture content of the raw material. The moisture content of the extracted raw material is further analyzed through the characteristic value of the raw material moisture content to avoid excessive influence of moisture on the extraction of neuraminic acid. For example, when using a 95% concentration ethanol solution for neuraminic acid extraction, the moisture content of the raw material should be around 10%. This can ensure the effectiveness of ethanol and reduce the interference of moisture on the extraction process. Too high moisture may affect the solubility and extraction effect of ethanol. In the actual extraction process, some chemical substances in the raw material may undergo hydrolysis or degradation reactions under the action of water, which will affect the extraction of neuraminic acid. Therefore, the moisture content needs to be further limited; It should be noted that when extracting neuraminic acid from plant raw materials, the cell walls need to be enzymatically hydrolyzed. This is because the hard structure of the plant cell walls will hinder the release of neuraminic acid. By using enzymes such as cellulase or pectinase, the cell walls can be effectively destroyed to help release and extract neuraminic acid. The threshold value of the extraction raw material state index is the extraction raw material state index under the standard state of the extraction raw material.

[0024] Obtain historical data on neuraminic acid extraction and establish a neuraminic acid extraction prediction model based on model training; According to the prediction model of neuraminic acid extraction and the parameter information of extraction reagents, obtaining neuraminic acid extraction parameter information; Specifically, according to the prediction model of neuraminic acid extraction and the extraction reagent parameter information, neuraminic acid extraction parameter information is obtained, including: According to the historical extraction data of neuraminic acid, historical information of extraction raw materials, historical parameter information of extraction reagents, historical extraction parameter information of neuraminic acid and historical data of neuraminic acid are obtained; According to the historical extraction parameter information of neuraminic acid, the historical extraction centrifugation data of neuraminic acid and the historical extraction environment information of neuraminic acid are obtained; Wherein, the historical extraction centrifugation data of neuraminic acid includes historical centrifugation speed information and historical centrifugation time information, and the historical extraction environment information of neuraminic acid includes historical environment temperature information and historical extraction pH value information; Acquire historical characteristic data of neuraminic acid according to the historical data of neuraminic acid, wherein the historical characteristic data of neuraminic acid includes neuraminic acid extraction rate information, neuraminic acid extraction concentration information and neuraminic acid structure information; Matching the nervonic acid extraction rate information with the historical parameter information of the extraction reagent, the historical centrifugation time information, and the historical ambient temperature information to obtain extraction rate historical matching data; Matching the nervonic acid extraction concentration information with the historical centrifugation speed information, the historical centrifugation time information and the historical ambient temperature information to obtain extraction concentration historical matching data; Matching the nervonic acid structure information with the historical parameter information of the extraction reagent, the historical ambient temperature information, and the historical extraction pH value information to obtain the nervonic acid quality historical matching data; Performing data aggregation on the extraction rate historical matching data, the extraction concentration historical matching data and the neuraminic acid mass historical matching data to obtain the neuraminic acid historical matching data; According to the historical matching data of neuraminic acid, a prediction model for neuraminic acid extraction is obtained; According to the neuraminic acid extraction prediction model and the extraction reagent parameter information, the neuraminic acid extraction parameter information is obtained.

[0025] Specifically, the step of obtaining neuraminic acid extraction parameter information according to the neuraminic acid extraction prediction model and the extraction reagent parameter information specifically includes: According to the historical matching data of neuraminic acid, the data were divided into training set and test set; Based on the training set and the set model hyperparameters, the deep learning model is trained to obtain the basic model; According to the test set, the basic model was verified and optimized based on the cross-validation method to obtain the prediction model of neuraminic acid extraction; According to the prediction model of neuraminic acid extraction and the parameter information of extraction reagents, the neuraminic acid extraction quality index is obtained; Adjusting the neuraminic acid extraction parameters until the neuraminic acid extraction quality index reaches a maximum value, and obtaining neuraminic acid extraction parameter information; Among them, the calculation formula of nervonic acid extraction quality index is: Where, E is the extraction quality index of nervonic acid, is the temperature influence coefficient of nervonic acid extraction rate, is the ambient temperature, is the standard ambient temperature, is the standard extraction rate of neuraminic acid of the extraction reagent, is the centrifugation time, is the centrifugal speed, is the centrifugation time influence coefficient of neuraminic acid extraction concentration, is the centrifugal speed influence coefficient of neuraminic acid extraction concentration, is the temperature influence coefficient of nervonic acid decomposition, The standard quality index of nervonic acid for the extraction reagent, is the pH sensitivity coefficient of nervonic acid, is the pH value, The optimal pH value.

[0026] In this scheme, historical characteristic data of neuraminic acid is obtained through historical data of neuraminic acid, and the historical characteristic data of neuraminic acid is matched with historical centrifugation data of neuraminic acid extraction and historical environmental information of neuraminic acid extraction. A neuraminic acid extraction prediction model is obtained through historical matching data of neuraminic acid, and neuraminic acid extraction parameter information is obtained based on the neuraminic acid extraction prediction model and extraction reagent parameter information, thereby ensuring the accuracy of neuraminic acid extraction quality prediction. Through the neuraminic acid extraction quality index, the neuraminic acid extraction efficiency and product quality are improved; It is understandable that setting different extraction parameters will lead to different extraction results of neuraminic acid. Temperature will affect the solubility of neuraminic acid and reagents. Too high temperature may lead to thermal decomposition of neuraminic acid, while too low temperature may affect the dissolution and reaction efficiency of reagents. The higher the centrifugal speed, the better the separation effect, but too high a centrifugal speed may cause cell membrane rupture, release more impurities, and affect the purity of neuraminic acid. The pH value of the solution during the extraction process will affect the stability and solubility of neuraminic acid. The appropriate pH value helps to maintain the quality of neuraminic acid. By matching the characteristic data, a prediction model is obtained based on historical data; It should be noted that the optimal pH value is the pH value at which the neuraminic acid extraction quality index is maximum when other neuraminic acid extraction parameters are the same.

[0027] Extracting neuraminic acid according to the neuraminic acid extraction parameter information to obtain an initial neuraminic acid extract; Based on liquid chromatography, the initial extract of neuraminic acid is separated and purified to obtain a purified neuraminic acid solution; Conduct concentration analysis on the purified neuraminic acid solution to determine whether the neuraminic acid concentration meets the production standards. If not, the neuraminic acid extraction is unqualified; if so, the neuraminic acid extraction is qualified.

[0028] Specifically, the concentration analysis of the purified neuraminic acid solution to determine whether the neuraminic acid concentration meets the production standards specifically includes: Performing concentration analysis on the purified neuraminic acid solution to obtain data on the purified neuraminic acid solution, wherein the data on the purified neuraminic acid solution includes information on the types of material components in the purified solution and information on the content of material components in the purified solution; According to the data of purified solution of neuraminic acid, the content information of neuraminic acid is obtained; According to the data of purified neuraminic acid solution and the content information of neuraminic acid, the neuraminic acid extraction difference index was obtained based on the neuraminic acid extraction quality index; Neuroacid extraction difference index , then the neuraminic acid extraction is qualified, if , then there is a problem in the mixing process of the extraction reagent and the extraction raw material during the neuraminic acid extraction process, and the neuraminic acid extraction process is adjusted according to the neuraminic acid extraction difference index; The calculation formula of the neuraminic acid extraction difference index is: Where R is the difference index of nervonic acid extraction, is the mass of nervonic acid in the purified nervonic acid solution, is the total mass of purified neuraminic acid solution, is the content of the jth component in the purified solution of nervonic acid, is the influence coefficient of the jth component in the purified neuraminic acid solution on neuraminic acid, and E is the neuraminic acid extraction quality index.

[0029] In this scheme, the neuraminic acid extraction difference index is obtained based on the neuraminic acid extraction quality index through the data of neuraminic acid purified liquid and the neuraminic acid content information. The neuraminic acid extraction difference index avoids the failure of the neuraminic acid extraction process and the reduction of the neuraminic acid extraction quality, facilitates the optimization of the extraction process, shortens the production cycle, and improves the extraction efficiency of neuraminic acid. It is understandable that improper mixing ratio may lead to incomplete extraction of neuraminic acid or reduced purity. If there are too many reagents, the extraction of impurities may increase, affecting the purity of neuraminic acid. If there are too few reagents, it may not be enough to fully dissolve or extract neuraminic acid. Uneven mixing may lead to inconsistent extraction efficiency in local areas. Some areas may be over-extracted, while some areas may be under-extracted. Therefore, when the neuraminic acid extraction difference index is lower than the set threshold, it indicates that there is a problem in the mixing process during the neuraminic acid extraction process. The neuraminic acid extraction difference index is used to adjust the neuraminic acid extraction process, reducing equipment testing costs and improving production efficiency.

[0030] Reference Figure 4 As shown, further, in combination with the above-mentioned method for extracting and analyzing neuraminic acid based on multi-source data prediction, a system for extracting and analyzing neuraminic acid based on multi-source data prediction is proposed, including: A main control module, wherein the main control module is used to determine whether the extraction raw material meets the extraction requirements of neuraminic acid according to the characteristic value of the raw material moisture content, the extraction raw material state index and the extraction raw material state index threshold, determine whether the neuraminic acid concentration meets the production standard according to the neuraminic acid extraction difference index, obtain cell wall lysing enzyme information according to the extraction raw material type information, obtain raw material moisture related information according to the raw material material component information, obtain extraction reagent parameter information based on the neuraminic acid extraction requirements according to the extraction raw material type information, obtain neuraminic acid historical characteristic data according to the neuraminic acid historical data, adjust the extraction raw material according to the extraction raw material state information, and adjust the neuraminic acid extraction process according to the neuraminic acid extraction difference index; An information acquisition module, wherein the information acquisition module is used to acquire extraction raw material information, extraction raw material type information and extraction raw material parameter information, analyze the neuraminic acid extraction status of the extraction raw material, acquire extraction raw material status information, acquire neuraminic acid historical extraction data, and acquire extraction raw material historical information, extraction reagent historical parameter information, neuraminic acid historical extraction parameter information and neuraminic acid historical data based on the neuraminic acid historical extraction data; An evaluation module, wherein the evaluation module is used to obtain an extraction raw material state index based on raw material moisture content information, raw material material composition information, raw material material moisture-related information and extraction reagent parameter information, obtain a raw material moisture content characteristic value based on a raw material moisture content standard value and raw material material moisture-related information, divide the data into a training set and a test set based on historical matching data of neuraminic acid, train a deep learning model based on the training set and set model hyperparameters to obtain a basic model, verify and optimize the basic model based on the test set based on a cross-validation method, obtain a neuraminic acid extraction prediction model, obtain a neuraminic acid extraction quality index based on the neuraminic acid extraction prediction model and extraction reagent parameter information, and obtain a neuraminic acid extraction difference index based on the neuraminic acid extraction quality index based on neuraminic acid purified liquid data and neuraminic acid content information; A display module interacts with the main control module and is used to output and display the extraction raw material status index, the neuraminic acid extraction prediction model, the neuraminic acid extraction quality index and the neuraminic acid extraction difference index.

[0031] Main control module, specifically including: A control unit, the control unit being used to obtain cell wall lytic enzyme information according to extraction raw material type information, obtain raw material moisture related information according to raw material composition information, obtain extraction reagent parameter information based on neuraminic acid extraction requirements according to extraction raw material type information, obtain neuraminic acid historical characteristic data according to neuraminic acid historical data, adjust the extraction raw materials according to extraction raw material status information, obtain neuraminic acid extraction difference index, and adjust the neuraminic acid extraction process; An information receiving unit, which interacts with the information acquisition module and the evaluation module to receive data and transmit it to the judgment unit; A judgment unit, wherein the judgment unit is used to judge whether the extraction raw material meets the requirements of neuraminic acid extraction according to the characteristic value of the raw material moisture content, the extraction raw material state index and the extraction raw material state index threshold, and to judge whether the neuraminic acid concentration meets the production standard according to the neuraminic acid extraction difference index.

[0032] Information acquisition module, specifically including: A first acquisition unit, the first acquisition unit is used to acquire extraction raw material information, extraction raw material type information and extraction raw material parameter information, analyze the neuraminic acid extraction status of the extraction raw material, and acquire extraction raw material status information; The second acquisition unit is used to acquire the historical extraction data of neuraminic acid, and acquire the historical information of extraction raw materials, historical parameter information of extraction reagents, historical extraction parameter information of neuraminic acid and historical data of neuraminic acid based on the historical extraction data of neuraminic acid.

[0033] Assessment modules include: A raw material evaluation unit, the raw material evaluation unit is used to obtain an extraction raw material state index according to the raw material moisture content information, the raw material material composition information, the raw material material moisture related information and the extraction reagent parameter information, and obtain the raw material moisture content characteristic value according to the raw material moisture content standard value and the raw material material moisture related information; A neuraminic acid extraction evaluation unit, wherein the neuraminic acid extraction evaluation unit is used to divide the data into a training set and a test set according to the neuraminic acid historical matching data, train the deep learning model based on the training set and the set model hyperparameters, obtain the basic model, verify and optimize the basic model based on the test set and the cross-validation method, obtain the neuraminic acid extraction prediction model, and obtain the neuraminic acid extraction quality index according to the neuraminic acid extraction prediction model and the extraction reagent parameter information; A neuraminic acid quality assessment unit, which is used to obtain a neuraminic acid extraction difference index based on the neuraminic acid extraction quality index according to the neuraminic acid purified liquid data and the neuraminic acid content information.

[0034] In summary, the advantages of the present invention are: by extracting the raw material state index, the quality of the extracted raw material is evaluated to ensure the stability and reliability of the quality of neuraminic acid; by collecting the extraction rate historical matching data, the extraction concentration historical matching data and the neuraminic acid quality historical matching data, the neuraminic acid historical matching data is obtained; through the neuraminic acid historical matching data, a neuraminic acid extraction prediction model is obtained to ensure the accuracy of the neuraminic acid extraction quality prediction; through the neuraminic acid extraction prediction model and the extraction reagent parameter information, the neuraminic acid extraction quality index is obtained; through the neuraminic acid extraction quality index, the neuraminic acid extraction efficiency and product quality are improved; through the neuraminic acid purified liquid data and the neuraminic acid content information, based on the neuraminic acid extraction quality index, the neuraminic acid extraction difference index is obtained; through the neuraminic acid extraction difference index, the failure of the neuraminic acid extraction process and the resulting reduction in the neuraminic acid extraction quality are avoided, which facilitates the optimization of the extraction process, shortens the production cycle, and improves the neuraminic acid extraction efficiency.

[0035] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for extracting and analyzing neuraminic acid based on multi-source data prediction, characterized in that: include: Acquire extraction raw material information, wherein the extraction raw material information includes extraction raw material type information and extraction raw material parameter information; Analyze the extraction status of nervonic acid of the extraction raw materials to obtain the status information of the extraction raw materials; Based on the extraction raw material status information, determine whether the extraction raw material meets the extraction requirements of neuraminic acid. If not, adjust the extraction raw material according to the extraction raw material status information. If yes, obtain the extraction reagent parameter information. Obtain historical data on neuraminic acid extraction and establish a neuraminic acid extraction prediction model based on model training; According to the prediction model of neuraminic acid extraction and the parameter information of extraction reagents, obtaining neuraminic acid extraction parameter information; Extracting neuraminic acid according to the neuraminic acid extraction parameter information to obtain an initial neuraminic acid extract; Based on liquid chromatography, the initial extract of neuraminic acid is separated and purified to obtain a purified neuraminic acid solution; Conduct concentration analysis on the purified neuraminic acid solution to determine whether the neuraminic acid concentration meets the production standards. If not, the neuraminic acid extraction is unqualified; if so, the neuraminic acid extraction is qualified.

2. The method for extracting and analyzing neuraminic acid based on multi-source data prediction according to claim 1 is characterized in that: The step of judging whether the raw materials meet the extraction requirements of nervonic acid based on the raw materials state information specifically includes: Pre-treating the extracted raw material to obtain a basic processed raw material, wherein the pre-treatment includes washing, drying and crushing; If the raw material for extraction is a plant material, the pretreatment of the raw material for extraction also includes: According to the information of the type of the extracted raw material, the cell wall lytic enzyme information is obtained, wherein the cell wall lytic enzyme information includes the type information of the cell wall lytic enzyme and the concentration information of the cell wall lytic enzyme; According to the cell wall lytic enzyme information, the extraction raw materials are enzymatically hydrolyzed based on the enzymatic method; Conduct status analysis on basic processing raw materials and obtain status information of extracted raw materials; According to the extracted raw material state information, the raw material moisture content information and raw material material composition information are obtained; According to the raw material component information, obtaining information related to the moisture content of the raw material, wherein the information related to the moisture content of the raw material indicates the type and content of components of the raw material component having hydrolysis characteristics; According to the extraction raw material type information and based on the neuraminic acid extraction requirements, the extraction reagent parameter information is obtained, wherein the extraction reagent parameter information includes the extraction reagent type information and the extraction reagent concentration information; Obtaining an extraction raw material state index according to raw material moisture content information, raw material material composition information, raw material material moisture related information and extraction reagent parameter information; Based on the extraction raw material status index, determine whether the extraction raw material meets the requirements for neuraminic acid extraction.

3. The method for extracting and analyzing neuraminic acid based on multi-source data prediction according to claim 2 is characterized in that: The method of judging whether the extraction raw material meets the extraction requirements of nervonic acid according to the extraction raw material state index specifically includes: Based on the extraction reagent concentration requirements, obtain the standard value of raw material moisture content; According to the standard value of raw material moisture content and the relevant information of raw material moisture, obtain the characteristic value of raw material moisture content: In the formula, is the characteristic value of raw material moisture content, is the standard value of moisture content in raw materials, n is the total number of material components with hydrolysis characteristics in the raw materials, and m is the total number of material components in the raw materials; Based on the extraction efficiency requirements of nervonic acid, the threshold value of the extraction raw material status index is obtained ; According to the characteristic value of raw material moisture content, extraction raw material state index and extraction raw material state index threshold, it is judged whether the extraction raw material meets the extraction requirements of neuraminic acid; Among them, if , then the extraction raw material meets the requirements for neuraminic acid extraction. , then the extraction raw material does not meet the extraction requirements of neuraminic acid, and the extraction raw material is adjusted according to the extraction raw material state index; The calculation formula of the extraction raw material state index is: Where Q is the extraction raw material state index, is the standard extraction rate of neuraminic acid of the extraction reagent, is the concentration of the extraction reagent, Indicates the characteristic value of neuraminic acid extraction. If the extraction raw material is animal cells, then If the extraction material is plant cells, then , is the concentration of cell wall lytic enzymes, is the particle size of the raw material, P is the moisture content of the raw material, is the hydrolysis ratio of the i-th substance component with hydrolysis characteristics in the raw material, It is the content of the i-th substance with hydrolysis characteristics in the raw material.

4. The method for extracting and analyzing neuraminic acid based on multi-source data prediction according to claim 1, characterized in that: The step of obtaining neuraminic acid extraction parameter information according to the neuraminic acid extraction prediction model and the extraction reagent parameter information specifically includes: According to the historical extraction data of neuraminic acid, historical information of extraction raw materials, historical parameter information of extraction reagents, historical extraction parameter information of neuraminic acid and historical data of neuraminic acid are obtained; According to the historical extraction parameter information of neuraminic acid, the historical extraction centrifugation data of neuraminic acid and the historical extraction environment information of neuraminic acid are obtained; Wherein, the historical extraction centrifugation data of neuraminic acid includes historical centrifugation speed information and historical centrifugation time information, and the historical extraction environment information of neuraminic acid includes historical environment temperature information and historical extraction pH value information; Acquire historical characteristic data of neuraminic acid according to the historical data of neuraminic acid, wherein the historical characteristic data of neuraminic acid includes neuraminic acid extraction rate information, neuraminic acid extraction concentration information and neuraminic acid structure information; Matching the nervonic acid extraction rate information with the historical parameter information of the extraction reagent, the historical centrifugation time information, and the historical ambient temperature information to obtain extraction rate historical matching data; Matching the nervonic acid extraction concentration information with the historical centrifugation speed information, the historical centrifugation time information and the historical ambient temperature information to obtain extraction concentration historical matching data; Matching the nervonic acid structure information with the historical parameter information of the extraction reagent, the historical ambient temperature information, and the historical extraction pH value information to obtain the nervonic acid quality historical matching data; Performing data aggregation on the extraction rate historical matching data, the extraction concentration historical matching data and the neuraminic acid mass historical matching data to obtain the neuraminic acid historical matching data; According to the historical matching data of neuraminic acid, a prediction model for neuraminic acid extraction is obtained; According to the neuraminic acid extraction prediction model and the extraction reagent parameter information, the neuraminic acid extraction parameter information is obtained.

5. The method for extracting and analyzing neuraminic acid based on multi-source data prediction according to claim 4 is characterized in that: The step of obtaining neuraminic acid extraction parameter information according to the neuraminic acid extraction prediction model and the extraction reagent parameter information specifically includes: According to the historical matching data of neuraminic acid, the data were divided into training set and test set; Based on the training set and the set model hyperparameters, the deep learning model is trained to obtain the basic model; According to the test set, the basic model was verified and optimized based on the cross-validation method to obtain the prediction model of neuraminic acid extraction; According to the prediction model of neuraminic acid extraction and the parameter information of extraction reagents, the neuraminic acid extraction quality index is obtained; Adjusting the neuraminic acid extraction parameters until the neuraminic acid extraction quality index reaches a maximum value, and obtaining neuraminic acid extraction parameter information; Among them, the calculation formula of nervonic acid extraction quality index is: Where, E is the extraction quality index of nervonic acid, is the temperature influence coefficient of nervonic acid extraction rate, is the ambient temperature, is the standard ambient temperature, is the standard extraction rate of neuraminic acid of the extraction reagent, is the centrifugation time, is the centrifugal speed, is the centrifugation time influence coefficient of neuraminic acid extraction concentration, is the centrifugal speed influence coefficient of neuraminic acid extraction concentration, is the temperature influence coefficient of nervonic acid decomposition, The standard quality index of nervonic acid for the extraction reagent, is the pH sensitivity coefficient of nervonic acid, is the pH value, The optimal pH value.

6. The method for extracting and analyzing neuraminic acid based on multi-source data prediction according to claim 1, characterized in that: The concentration analysis of the purified neuraminic acid solution to determine whether the neuraminic acid concentration meets the production standards specifically includes: Performing concentration analysis on the purified neuraminic acid solution to obtain data on the purified neuraminic acid solution, wherein the data on the purified neuraminic acid solution includes information on the types of material components in the purified solution and information on the content of material components in the purified solution; According to the data of purified solution of neuraminic acid, the content information of neuraminic acid is obtained; According to the data of purified neuraminic acid solution and the content information of neuraminic acid, the neuraminic acid extraction difference index was obtained based on the neuraminic acid extraction quality index; Neuroacid extraction difference index , then the neuraminic acid extraction is qualified, if , then there is a problem in the mixing process of the extraction reagent and the extraction raw material during the neuraminic acid extraction process, and the neuraminic acid extraction process is adjusted according to the neuraminic acid extraction difference index; The calculation formula of the neuraminic acid extraction difference index is: Where R is the difference index of neuraminic acid extraction, is the mass of nervonic acid in the purified nervonic acid solution, is the total mass of purified neuraminic acid solution, is the content of the jth component in the purified solution of nervonic acid, is the influence coefficient of the jth component in the purified neuraminic acid solution on neuraminic acid, and E is the neuraminic acid extraction quality index.

7. A neuraminic acid extraction and analysis system based on multi-source data prediction, used to implement the extraction and analysis method according to any one of claims 1 to 6, characterized in that: include: A main control module, wherein the main control module is used to determine whether the extraction raw material meets the extraction requirements of neuraminic acid according to the characteristic value of the raw material moisture content, the extraction raw material state index and the extraction raw material state index threshold, determine whether the neuraminic acid concentration meets the production standard according to the neuraminic acid extraction difference index, obtain cell wall lysing enzyme information according to the extraction raw material type information, obtain raw material moisture related information according to the raw material material component information, obtain extraction reagent parameter information based on the neuraminic acid extraction requirements according to the extraction raw material type information, obtain neuraminic acid historical characteristic data according to the neuraminic acid historical data, adjust the extraction raw material according to the extraction raw material state information, and adjust the neuraminic acid extraction process according to the neuraminic acid extraction difference index; An information acquisition module, wherein the information acquisition module is used to acquire extraction raw material information, extraction raw material type information and extraction raw material parameter information, analyze the neuraminic acid extraction status of the extraction raw material, acquire extraction raw material status information, acquire neuraminic acid historical extraction data, and acquire extraction raw material historical information, extraction reagent historical parameter information, neuraminic acid historical extraction parameter information and neuraminic acid historical data based on the neuraminic acid historical extraction data; An evaluation module, wherein the evaluation module is used to obtain an extraction raw material state index based on raw material moisture content information, raw material material composition information, raw material material moisture-related information and extraction reagent parameter information, obtain a raw material moisture content characteristic value based on a raw material moisture content standard value and raw material material moisture-related information, divide the data into a training set and a test set based on historical matching data of neuraminic acid, train a deep learning model based on the training set and set model hyperparameters to obtain a basic model, verify and optimize the basic model based on the test set based on a cross-validation method, obtain a neuraminic acid extraction prediction model, obtain a neuraminic acid extraction quality index based on the neuraminic acid extraction prediction model and extraction reagent parameter information, and obtain a neuraminic acid extraction difference index based on the neuraminic acid extraction quality index based on neuraminic acid purified liquid data and neuraminic acid content information; A display module interacts with the main control module and is used to output and display the extraction raw material status index, the neuraminic acid extraction prediction model, the neuraminic acid extraction quality index and the neuraminic acid extraction difference index.

8. The neuraminic acid extraction and analysis system based on multi-source data prediction according to claim 7 is characterized in that: The main control module specifically includes: A control unit, the control unit being used to obtain cell wall lytic enzyme information according to extraction raw material type information, obtain raw material moisture related information according to raw material composition information, obtain extraction reagent parameter information based on neuraminic acid extraction requirements according to extraction raw material type information, obtain neuraminic acid historical characteristic data according to neuraminic acid historical data, adjust the extraction raw materials according to extraction raw material status information, obtain neuraminic acid extraction difference index, and adjust the neuraminic acid extraction process; An information receiving unit, which interacts with the information acquisition module and the evaluation module to receive data and transmit it to the judgment unit; A judgment unit, wherein the judgment unit is used to judge whether the extraction raw material meets the requirements of neuraminic acid extraction according to the characteristic value of the raw material moisture content, the extraction raw material state index and the extraction raw material state index threshold, and to judge whether the neuraminic acid concentration meets the production standard according to the neuraminic acid extraction difference index.

9. The neuraminic acid extraction and analysis system based on multi-source data prediction according to claim 7 is characterized in that: The information acquisition module specifically includes: A first acquisition unit, the first acquisition unit is used to acquire extraction raw material information, extraction raw material type information and extraction raw material parameter information, analyze the extraction status of nervonic acid in the extraction raw material, and acquire extraction raw material status information; The second acquisition unit is used to acquire the historical extraction data of neuraminic acid, and acquire the historical information of extraction raw materials, historical parameter information of extraction reagents, historical extraction parameter information of neuraminic acid and historical data of neuraminic acid based on the historical extraction data of neuraminic acid.

10. The neuraminic acid extraction and analysis system based on multi-source data prediction according to claim 7 is characterized in that: The evaluation module specifically includes: A raw material evaluation unit, the raw material evaluation unit is used to obtain an extraction raw material state index according to the raw material moisture content information, the raw material material composition information, the raw material material moisture related information and the extraction reagent parameter information, and obtain a raw material moisture content characteristic value according to the raw material moisture content standard value and the raw material material moisture related information; A neuraminic acid extraction evaluation unit, wherein the neuraminic acid extraction evaluation unit is used to divide the data into a training set and a test set according to the neuraminic acid historical matching data, train the deep learning model based on the training set and the set model hyperparameters, obtain the basic model, verify and optimize the basic model based on the test set and the cross-validation method, obtain the neuraminic acid extraction prediction model, and obtain the neuraminic acid extraction quality index according to the neuraminic acid extraction prediction model and the extraction reagent parameter information; A neuraminic acid quality assessment unit, which is used to obtain a neuraminic acid extraction difference index based on the neuraminic acid extraction quality index according to the neuraminic acid purified liquid data and the neuraminic acid content information.

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