AI-driven bird's nest precise nutritional formula design and high-value processing methods and systems

Abstract

The present invention discloses an AI-driven bird's nest precise nutritional formula design and high-value processing method and system, which relates to the field of bird's nest high-value processing, including: establishing a bird's nest nutritional formula data set; optimizing the data in the bird's nest nutritional formula data set based on the OPLS algorithm; establishing a bird's nest precise nutritional formula and high-value processing AI-driven design scheme; extracting characteristic words of bird's nest high-value processing, establishing a bird's nest precise nutritional formula and high-value processing AI-driven library; establishing an AI-driven bird's nest high-value processing model, querying the bird's nest precise nutritional formula and high-value processing AI-driven design scheme; establishing an AI-driven bird's nest high-value processing model evaluation coefficient, and comprehensively evaluating the AI-driven bird's nest high-value processing model. By retrieving characteristic words to query the bird's nest precise nutritional formula and high-value processing AI-driven design scheme, the bird's nest is accurately processed with this, thereby improving the intelligence and efficiency of bird's nest processing.

Description

Technical Field

[0001] The present invention relates to the field of high-value processing of bird's nests, and specifically to an AI-driven precise nutritional formula design and high-value processing method and system for bird's nests. Background Art

[0002] High-value processing of bird's nests is a multi-level and complex process. The environmental control, time control and ingredient control corresponding to different types of bird's nests will result in different final quality of the bird's nest and the applicable population. For example, for people with high blood sugar, it is necessary to configure a bird's nest processing process that does not contain sugar. Therefore, during the configuration process, it is necessary to avoid adding sugar during the processing process, and to remove the sugar content of the bird's nest itself by chemical or physical means. Therefore, it is necessary to study the precise nutritional formula design and high-value processing methods of bird's nests from multiple aspects. With the development of AI technology, the application of AI technology to the nutritional formula design and high-value processing of bird's nests is of great significance to improving the efficiency and nutritional balance of bird's nest production.

[0003] The traditional bird's nest processing technology mainly relies on subjective experience to control the smooth progress of each step in different processing links. However, this method is time-consuming and labor-intensive, and is easily affected by subjective experience, resulting in errors in the processing process and affecting the final quality of the bird's nest. Secondly, with the development of automation technology, machines are used instead of traditional manual operations. Although the refinement of bird's nest processing has been improved and errors in the processing process have been effectively improved, such methods can only be used for a single type of bird's nest processing and lack intelligent and refined management across multiple ranges and applicable populations. Summary of the Invention

[0004] In order to solve the above technical problems, an AI-driven bird's nest precise nutritional formula design and high-value processing method and system are provided. This technical solution solves the problems proposed in the above background technology, that is, the traditional bird's nest processing technology is time-consuming and labor-intensive, and is easily affected by subjective experience, resulting in errors in the processing process, which affects the final quality of the bird's nest. It also uses machines instead of traditional manual operations. Although it improves the refinement of the bird's nest processing process, this type of method can only be used for a single type of bird's nest processing, and lacks intelligent and refined management of multiple ranges and multiple applicable populations.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] An AI-driven bird's nest precise nutritional formula design and high-value processing method, including:

[0007] Based on big data or bird's nest formula research data, obtain the corresponding environmental control quantities, time control quantities, and ingredient control quantities for different bird's nest types, and establish a bird's nest nutritional formula dataset;

[0008] According to the bird's nest nutritional formula data set, the data in the bird's nest nutritional formula data set is optimized based on the OPLS algorithm;

[0009] Based on the optimized bird's nest nutritional formula data set, establish a precise nutritional formula for bird's nest and an AI-driven design plan for high-value processing;

[0010] According to the AI-driven design of bird's nest precise nutritional formula and high-value processing, the characteristic words of bird's nest high-value processing are extracted, and a bird's nest precise nutritional formula and high-value processing AI-driven library is established;

[0011] According to the AI-driven library of bird's nest precise nutritional formula and high-value processing, an AI-driven bird's nest high-value processing model is established based on the decision tree model. The AI-driven design scheme of bird's nest precise nutritional formula and high-value processing is searched through the characteristic words of bird's nest high-value processing, and the AI-driven bird's nest high-value processing is driven according to the scheme.

[0012] Based on the AI-driven bird's nest high-value processing model, an AI-driven bird's nest high-value processing model evaluation coefficient is established, and a comprehensive evaluation of the AI-driven bird's nest high-value processing model is carried out.

[0013] Preferably, the step of obtaining the environmental control amount, time control amount, and ingredient control amount corresponding to different bird's nest types based on big data or bird's nest formula research data, and establishing a bird's nest nutritional formula data set specifically includes:

[0014] According to the type of high-value processed bird's nest and the target population, through big data or bird's nest formula research data, the corresponding environmental control amount, time control amount and ingredient control amount of different bird's nest types are obtained;

[0015] Among them, environmental control includes: temperature control, humidity control, odor control and drying control; ingredient control includes: sweetness, nutritional elements and taste;

[0016] According to the environmental control quantity, the changes of environmental control quantity in the whole process of high-value bird's nest processing are obtained, and the range of environmental control quantity in different links is limited;

[0017] A bird's nest nutritional formula dataset was established based on the characteristics of bird's nest types, environmental control quantities, time control quantities, and ingredient control quantities in different links of bird's nest high-value processing.

[0018] Preferably, the optimizing process of the data in the bird's nest nutritional formula data set based on the OPLS algorithm specifically includes:

[0019] Based on the bird's nest nutritional formula dataset, the characteristics of bird's nest types, environmental control quantities, time control quantities, and ingredient control quantities in different links of bird's nest high-value processing in the dataset were digitized;

[0020] The bird's nest nutritional formula dataset after numerical processing is normalized to eliminate the influence of data dimension and abnormal value;

[0021] According to the optimized bird's nest nutritional formula dataset, the data in the bird's nest nutritional formula dataset is further optimized based on the OPLS algorithm.

[0022] Preferably, the design scheme for establishing precise nutritional formula of bird's nest and high-value processing AI-driven design based on the optimized bird's nest nutritional formula data set specifically includes:

[0023] Determine different nutritional formulas for bird's nests based on different combinations of bird's nest types, environmental control quantities, time control quantities, and ingredient control quantities in different stages of high-value bird's nest processing in the dataset;

[0024] Based on data experiments or big data analysis, determine the edible effects of different nutritional formulas of bird's nest and the corresponding applicable populations;

[0025] Based on the different nutritional formulas, edible effects and corresponding applicable populations of bird's nests, a precise nutritional formula and high-value processing AI-driven design plan for bird's nests is established.

[0026] Preferably, the design scheme of bird's nest precise nutritional formula and high-value processing AI-driven, extracting characteristic words of bird's nest high-value processing, and establishing a bird's nest precise nutritional formula and high-value processing AI-driven library specifically includes:

[0027] Based on the precise nutritional formula of bird's nest and the AI-driven design of high-value processing, we extract the characteristic words that can be used to call the specific design scheme of bird's nest high-value processing;

[0028] Refine the precise nutritional formula and high-value processing AI-driven design plan for each bird's nest, accurately match the characteristic words of bird's nest high-value processing with the design plan, and establish a precise nutritional formula and high-value processing AI-driven library for bird's nest.

[0029] Preferably, according to the bird's nest precise nutritional formula and high-value processing AI-driven library, based on the decision tree model, an AI-driven bird's nest high-value processing model is established, and the bird's nest precise nutritional formula and high-value processing AI-driven design scheme is queried through the bird's nest high-value processing feature words, and the AI-driven bird's nest high-value processing according to the scheme specifically includes:

[0030] Based on the bird's nest precise nutritional formula and high-value processing AI-driven library, a decision tree model training set was established, in which the characteristic words of bird's nest high-value processing were used as input, and the bird's nest precise nutritional formula and high-value processing AI-driven design plan was used as output;

[0031] Based on the decision tree model, an AI-driven high-value processing model for bird's nests was established. Through the characteristic words of bird's nest high-value processing, precise nutritional formulas for bird's nests and AI-driven design solutions for high-value processing were found.

[0032] Through the precise nutritional formula of bird's nest and the AI-driven design plan for high-value processing, we guide each link of bird's nest processing to carry out high-value processing.

[0033] Preferably, the AI-driven bird's nest high-value processing model is used to establish an evaluation coefficient for the AI-driven bird's nest high-value processing model, and the comprehensive evaluation of the AI-driven bird's nest high-value processing model specifically includes:

[0034] Based on the training results of the AI-driven bird's nest high-value processing model, an optimized training sample set for the AI-driven bird's nest high-value processing model was established;

[0035] Among them, the AI-driven bird's nest high-value processing model optimization training sample set includes: the target data of the AI-driven bird's nest high-value processing model and the training results of the AI-driven bird's nest high-value processing model;

[0036] The target data of the AI-driven bird's nest high-value processing model is used as reference data and defined as accurate data;

[0037] The training data of the AI-driven bird's nest high-value processing model contains both accurate and erroneous data, which is mixed data.

[0038] Optimize the training sample set based on the AI-driven bird's nest high-value processing model and establish the evaluation coefficient of the AI-driven bird's nest high-value processing model;

[0039] Based on the feedback of the evaluation coefficient of the AI-driven bird's nest high-value processing model, a comprehensive evaluation of the AI-driven bird's nest high-value processing model is conducted, and data optimization is carried out.

[0040] Furthermore, this solution proposes an AI-driven bird's nest precise nutritional formula design and high-value processing system, which is used to implement the above-mentioned AI-driven bird's nest precise nutritional formula design and high-value processing method, including:

[0041] A data processing module, the data processing module is used to obtain the environmental control amount, time control amount and ingredient control amount corresponding to different bird's nest types based on big data or bird's nest formula research data, and establish a bird's nest nutritional formula data set; based on the bird's nest nutritional formula data set, based on the OPLS algorithm, optimize the data in the bird's nest nutritional formula data set; based on the optimized bird's nest nutritional formula data set, establish a bird's nest precise nutritional formula and high-value processing AI-driven design scheme; based on the bird's nest precise nutritional formula and high-value processing AI-driven design scheme, extract the characteristic words of bird's nest high-value processing, and establish a bird's nest precise nutritional formula and high-value processing AI-driven library;

[0042] The model establishment and evaluation module is used to establish an AI-driven bird's nest high-value processing model based on the AI-driven library of bird's nest precise nutritional formula and high-value processing and the decision tree model, query the bird's nest precise nutritional formula and high-value processing AI-driven design plan through the bird's nest high-value processing feature words, and drive the bird's nest high-value processing according to the plan; according to the AI-driven bird's nest high-value processing model, establish an AI-driven bird's nest high-value processing model evaluation coefficient, and conduct a comprehensive evaluation of the AI-driven bird's nest high-value processing model.

[0043] Preferably, the data processing module includes:

[0044] A data acquisition unit, the data acquisition unit being used to acquire environmental control quantities, time control quantities, and ingredient control quantities corresponding to different bird's nest types based on big data or bird's nest formula research data, and to establish a bird's nest nutritional formula data set;

[0045] A data optimization unit, wherein the data optimization unit is used to optimize the data in the bird's nest nutritional formula data set based on the OPLS algorithm;

[0046] A design solution unit, which is used to establish a precise nutritional formula and high-value processing AI-driven design solution for bird's nest based on the optimized bird's nest nutritional formula data set;

[0047] An AI-driven library unit is used to extract characteristic words of high-value processing of bird's nests according to the AI-driven design scheme of precise nutritional formula and high-value processing of bird's nests, and to establish an AI-driven library of precise nutritional formula and high-value processing of bird's nests.

[0048] Preferably, the model building and evaluation module includes:

[0049] A high-value processing model unit, which is used to establish an AI-driven bird's nest high-value processing model based on the bird's nest precise nutritional formula and high-value processing AI-driven library and a decision tree model, query the bird's nest precise nutritional formula and high-value processing AI-driven design plan through the bird's nest high-value processing feature words, and drive the bird's nest high-value processing according to the AI ​​plan;

[0050] The model evaluation unit is used to establish an AI-driven bird's nest high-value processing model evaluation coefficient based on the AI-driven bird's nest high-value processing model, and to conduct a comprehensive evaluation of the AI-driven bird's nest high-value processing model.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] According to big data or bird's nest formula research data, the environmental control quantity, time control quantity and ingredient control quantity corresponding to different bird's nest types are obtained, and a bird's nest nutritional formula data set is established. Based on the OPLS algorithm, the data in the bird's nest nutritional formula data set is optimized to effectively remove redundant information and retain valid data features. According to the optimized bird's nest nutritional formula data set, a bird's nest precise nutritional formula and high-value processing AI-driven design scheme is established. Secondly, according to the bird's nest precise nutritional formula and high-value processing AI-driven design scheme, the characteristic words of bird's nest high-value processing are extracted to establish a bird's nest precise nutritional formula. Formula and high-value processing AI-driven library, and based on the decision tree model, establish an AI-driven bird's nest high-value processing model, through the bird's nest high-value processing feature words, query the bird's nest precise nutritional formula and high-value processing AI-driven design plan, and drive the bird's nest high-value processing according to the plan. Finally, according to the AI-driven bird's nest high-value processing model, establish the AI-driven bird's nest high-value processing model evaluation coefficient, conduct a comprehensive evaluation of the AI-driven bird's nest high-value processing model, and optimize the AI-driven bird's nest high-value processing model to improve the accuracy and reliability of the model classification, thereby achieving the purpose of intelligent and efficient bird's nest processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of the AI-driven bird's nest precise nutritional formula design and high-value processing method of the present invention;

[0054] Figure 2 This is a flow chart of optimizing the data in the bird's nest nutritional formula data set based on the OPLS algorithm according to the present invention;

[0055] Figure 3 According to the AI-driven library of bird's nest precise nutritional formula and high-value processing of the present invention, an AI-driven bird's nest high-value processing model is established based on the decision tree model. The AI-driven design scheme of bird's nest precise nutritional formula and high-value processing is queried through the feature words of bird's nest high-value processing, and the AI-driven bird's nest high-value processing flow chart is driven according to the scheme;

[0056] Figure 4 The present invention is based on the AI-driven bird's nest high-value processing model, establishes the AI-driven bird's nest high-value processing model evaluation coefficient, and performs a comprehensive evaluation flow chart of the AI-driven bird's nest high-value processing model. DETAILED DESCRIPTION

[0057] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0058] Reference Figure 1As shown, an AI-driven bird's nest precise nutritional formula design and high-value processing method includes:

[0059] Based on big data or bird's nest formula research data, obtain the corresponding environmental control quantities, time control quantities, and ingredient control quantities for different bird's nest types, and establish a bird's nest nutritional formula dataset;

[0060] According to the bird's nest nutritional formula data set, the data in the bird's nest nutritional formula data set is optimized based on the OPLS algorithm;

[0061] Based on the optimized bird's nest nutritional formula data set, establish a precise nutritional formula for bird's nest and an AI-driven design plan for high-value processing;

[0062] According to the AI-driven design of bird's nest precise nutritional formula and high-value processing, the characteristic words of bird's nest high-value processing are extracted, and a bird's nest precise nutritional formula and high-value processing AI-driven library is established;

[0063] According to the AI-driven library of bird's nest precise nutritional formula and high-value processing, an AI-driven bird's nest high-value processing model is established based on the decision tree model. The AI-driven design scheme of bird's nest precise nutritional formula and high-value processing is searched through the characteristic words of bird's nest high-value processing, and the AI-driven bird's nest high-value processing is driven according to the scheme.

[0064] Based on the AI-driven bird's nest high-value processing model, an AI-driven bird's nest high-value processing model evaluation coefficient is established, and a comprehensive evaluation of the AI-driven bird's nest high-value processing model is carried out.

[0065] It can be explained that this scheme obtains the environmental control quantity, time control quantity and ingredient control quantity corresponding to different types of bird's nests based on big data or bird's nest formula research data, and establishes a bird's nest nutritional formula data set. Based on the OPLS algorithm, the data in the bird's nest nutritional formula data set is optimized to effectively remove redundant information and retain valid data features. According to the optimized bird's nest nutritional formula data set, a bird's nest precise nutritional formula and high-value processing AI-driven design scheme is established. Secondly, according to the bird's nest precise nutritional formula and high-value processing AI-driven design scheme, the characteristic words of bird's nest high-value processing are extracted to establish a bird's nest nutritional formula data set. The AI-driven library of bird's nest precise nutritional formula and high-value processing is established, and based on the decision tree model, an AI-driven bird's nest high-value processing model is established. Through the bird's nest high-value processing feature words, the AI-driven design scheme of bird's nest precise nutritional formula and high-value processing is queried, and AI-driven bird's nest high-value processing is carried out according to the scheme. Finally, according to the AI-driven bird's nest high-value processing model, an AI-driven bird's nest high-value processing model evaluation coefficient is established, a comprehensive evaluation of the AI-driven bird's nest high-value processing model is carried out, and the AI-driven bird's nest high-value processing model is optimized to improve the accuracy and reliability of model classification, thereby achieving the purpose of intelligent and efficient bird's nest processing.

[0066] Reference Figure 2 As shown, the optimization process of the data in the bird's nest nutritional formula data set based on the OPLS algorithm specifically includes:

[0067] Based on the bird's nest nutritional formula dataset, the characteristics of bird's nest types, environmental control quantities, time control quantities, and ingredient control quantities in different links of bird's nest high-value processing in the dataset were digitized;

[0068] The bird's nest nutritional formula dataset after numerical processing is normalized to eliminate the influence of data dimension and abnormal value;

[0069] According to the optimized bird's nest nutritional formula dataset, the data in the bird's nest nutritional formula dataset is further optimized based on the OPLS algorithm.

[0070] It can be explained that the specific steps of further optimizing the data in the bird's nest nutritional formula dataset based on the OPLS algorithm are as follows:

[0071] The digitized bird's nest type, the environmental control quantity, time control quantity and ingredient control quantity of different links in the high-value processing of bird's nest are used as the independent variable matrix , and the final product quality of the bird's nest is used as the response variable matrix , according to the matrix standardization formula, the independent variable matrix and the response variable matrix Standardize and get and , according to the covariance matrix formula, we can obtain and The covariance of ,pass and The covariance of and The weight matrix between the two , thereby obtaining and The score matrix between the two and the load matrix , the independent variable matrix Decompose into response variable matrix The formulas for correlation and non-correlation are: , where and is the matrix of response variables The associated score matrix and loading matrix, and is the matrix of response variables Uncorrelated orthogonal score and loading matrices, is the residual matrix, which is used to represent the independent variable matrix The difference between the actual model and the real data is removed by orthogonalization of the independent variable matrix Response variable matrix in decomposition formula Unrelated variations are eliminated, so the characteristic data of bird's nest types, environmental control quantities, time control quantities and ingredient control quantities in different links of bird's nest high-value processing, which are highly correlated with the final quality of the bird's nest, are obtained. This simplifies the redundancy of the data, extracts important influencing characteristic factors, and ensures the system's acceptance and interpretability of characteristic data.

[0072] Reference Figure 3 As shown, according to the AI-driven library of bird's nest precise nutritional formula and high-value processing, an AI-driven bird's nest high-value processing model is established based on the decision tree model. The AI-driven design scheme of bird's nest precise nutritional formula and high-value processing is queried through the feature words of bird's nest high-value processing, and the AI-driven bird's nest high-value processing is specifically included in accordance with the scheme:

[0073] Based on the bird's nest precise nutritional formula and high-value processing AI-driven library, a decision tree model training set was established, in which the characteristic words of bird's nest high-value processing were used as input, and the bird's nest precise nutritional formula and high-value processing AI-driven design plan was used as output;

[0074] Based on the decision tree model, an AI-driven high-value processing model for bird's nests was established. Through the characteristic words of bird's nest high-value processing, precise nutritional formulas for bird's nests and AI-driven design solutions for high-value processing were found.

[0075] Through the precise nutritional formula of bird's nest and the AI-driven design plan for high-value processing, we guide each link of bird's nest processing to carry out high-value processing.

[0076] It can be explained that the conventional decision tree model calculates the information entropy and information gain of each feature item, and based on the comparison of the information gain value, selects the maximum value as the leaf node of the next layer of the decision tree model. However, when establishing the decision tree model, due to too many feature items, overfitting is prone to occur during the training process, resulting in weak generalization ability of the model and reduced intelligence ability. The traditional method is to process it through traditional pruning operations, but the effect is general. Therefore, by combining the decision tree model with the data processed by the OPLS algorithm, the overfitting phenomenon of the decision tree model can be reduced and the generalization ability of the model can be improved. The specific steps of combining the decision tree model with the data processed by the OPLS algorithm are as follows:

[0077] According to the decision tree model training set, the information entropy and information gain of the characteristics of bird's nest type, environmental control quantity, time control quantity and ingredient control quantity in different links of bird's nest high-value processing are obtained. The information entropy expression is:

[0078] Where, is the information entropy value of the total data for the decision tree model training set, It refers to the bird's nest precise nutritional formula and high-value processing AI-driven design solution type, is the number of data sets in the decision tree model training set, For the A bird's nest precise nutritional formula and high-value processing AI-driven design solution, is the probability of the i-th AI-driven design of bird's nest precise nutritional formula and high-value processing occurring;

[0079] The information gain expression is:

[0080]

[0081] Where, For the The information gain value of the characteristic items of the environmental control quantity, time control quantity and ingredient control quantity of each bird's nest type and different links of bird's nest high-value processing is For the The information entropy value corresponding to the characteristic items of environmental control quantity, time control quantity and ingredient control quantity of each type of bird's nest and different links of high-value bird's nest processing is calculated with the formula equivalent to the information entropy expression;

[0082] A decision tree model was established based on the information entropy and information gain of the characteristics of the environmental control quantity, time control quantity and ingredient control quantity of the different links of bird's nest high-value processing;

[0083] According to the data processed by the OPLS algorithm, the decision tree model is pruned. The specific steps are as follows:

[0084] According to the data processed by OPLS algorithm, the response variable matrix is ​​eliminated The input coefficients of irrelevant variables are set to zero, and the response variable matrix is ​​reduced and eliminated by multiplying the coefficients. The information gain value of the feature item corresponding to the irrelevant variation item;

[0085] According to the OPLS algorithm, and The weight between the two , by multiplying the coefficients, the response variable matrix is ​​improved The correlation of related feature items, the specific formula is:

[0086]

[0087] Where, To combine the OPLS algorithm The information gain value of the characteristic items of the environmental control quantity, time control quantity and ingredient control quantity of each bird's nest type and different links of bird's nest high-value processing is To limit the constant and prevent the interest gain value from being too large and exceeding the limit value.

[0088] Reference Figure 4 As shown, according to the AI-driven bird's nest high-value processing model, an AI-driven bird's nest high-value processing model evaluation coefficient is established, and a comprehensive evaluation of the AI-driven bird's nest high-value processing model is conducted, specifically including:

[0089] Based on the training results of the AI-driven bird's nest high-value processing model, an optimized training sample set for the AI-driven bird's nest high-value processing model was established;

[0090] Among them, the AI-driven bird's nest high-value processing model optimization training sample set includes: the target data of the AI-driven bird's nest high-value processing model and the training results of the AI-driven bird's nest high-value processing model;

[0091] The target data of the AI-driven bird's nest high-value processing model is used as reference data and defined as accurate data;

[0092] The training data of the AI-driven bird's nest high-value processing model contains both accurate and erroneous data, which is mixed data.

[0093] Optimize the training sample set based on the AI-driven bird's nest high-value processing model and establish the evaluation coefficient of the AI-driven bird's nest high-value processing model;

[0094] Based on the feedback of the evaluation coefficient of the AI-driven bird's nest high-value processing model, a comprehensive evaluation of the AI-driven bird's nest high-value processing model is conducted, and data optimization is carried out.

[0095] It can be explained that in order to improve the accuracy of the classification of the AI-driven bird's nest high-value processing model, it is necessary to evaluate the correctness of the classification results of the model. This solution establishes an evaluation coefficient of the AI-driven bird's nest high-value processing model to comprehensively evaluate the classification quality of the AI-driven bird's nest high-value processing model. By setting a boundary threshold, it is determined whether the evaluation coefficient of the AI-driven bird's nest high-value processing model exceeds the boundary threshold. If so, it is considered that the classification quality of the AI-driven bird's nest high-value processing model meets the standard. If not, it means that the classification quality of the AI-driven bird's nest high-value processing model does not meet the standard and the model needs further optimization.

[0096] The evaluation coefficient expression of the AI-driven bird's nest high-value processing model is:

[0097]

[0098] Where, Evaluation coefficient for AI-driven bird's nest high-value processing model, The classification results of AI-driven bird's nest high-value processing model The number of positive samples of the sub-classification, The classification results of AI-driven bird's nest high-value processing model The number of false positive samples of the sub-classification, The classification results of AI-driven bird's nest high-value processing model The number of false negative samples in the sub-classification, where a false positive sample refers to a sample in which the incorrect data is correctly identified as the reference data, and a false negative sample refers to a sample in which the reference data is identified as the incorrect data.

[0099] Furthermore, based on the same inventive concept as the above-mentioned AI-driven bird's nest precise nutritional formula design and high-value processing method, this solution proposes an AI-driven bird's nest precise nutritional formula design and high-value processing system, including:

[0100] A data processing module, the data processing module is used to obtain the environmental control amount, time control amount and ingredient control amount corresponding to different bird's nest types based on big data or bird's nest formula research data, and establish a bird's nest nutritional formula data set; based on the bird's nest nutritional formula data set, based on the OPLS algorithm, optimize the data in the bird's nest nutritional formula data set; based on the optimized bird's nest nutritional formula data set, establish a bird's nest precise nutritional formula and high-value processing AI-driven design scheme; based on the bird's nest precise nutritional formula and high-value processing AI-driven design scheme, extract the characteristic words of bird's nest high-value processing, and establish a bird's nest precise nutritional formula and high-value processing AI-driven library;

[0101] A model building and evaluation module is used to establish an AI-driven bird's nest high-value processing model based on the AI-driven library of bird's nest precise nutritional formula and high-value processing and a decision tree model, query the AI-driven design scheme of bird's nest precise nutritional formula and high-value processing through the bird's nest high-value processing feature words, and drive the AI-driven bird's nest high-value processing according to the scheme; establish an AI-driven bird's nest high-value processing model evaluation coefficient based on the AI-driven bird's nest high-value processing model, and conduct a comprehensive evaluation of the AI-driven bird's nest high-value processing model;

[0102] The data processing module includes:

[0103] A data acquisition unit, the data acquisition unit being used to acquire environmental control quantities, time control quantities, and ingredient control quantities corresponding to different bird's nest types based on big data or bird's nest formula research data, and to establish a bird's nest nutritional formula data set;

[0104] A data optimization unit, wherein the data optimization unit is used to optimize the data in the bird's nest nutritional formula data set based on the OPLS algorithm;

[0105] A design solution unit, which is used to establish a precise nutritional formula and high-value processing AI-driven design solution for bird's nest based on the optimized bird's nest nutritional formula data set;

[0106] An AI-driven library unit, which is used to extract characteristic words of bird's nest high-value processing according to the AI-driven design scheme of bird's nest precise nutritional formula and high-value processing, and establish an AI-driven library of bird's nest precise nutritional formula and high-value processing;

[0107] The model building and evaluation module includes:

[0108] A high-value processing model unit, which is used to establish an AI-driven bird's nest high-value processing model based on the bird's nest precise nutritional formula and high-value processing AI-driven library and a decision tree model, query the bird's nest precise nutritional formula and high-value processing AI-driven design plan through the bird's nest high-value processing feature words, and drive the bird's nest high-value processing according to the AI ​​plan;

[0109] The model evaluation unit is used to establish an AI-driven bird's nest high-value processing model evaluation coefficient based on the AI-driven bird's nest high-value processing model, and to conduct a comprehensive evaluation of the AI-driven bird's nest high-value processing model.

[0110] To sum up, the advantages of the present invention are: by searching for characteristic words to query the precise nutritional formula of bird's nest and the AI-driven design scheme for high-value processing, the bird's nest can be accurately processed in a high-value manner, thereby improving the intelligence and efficiency of bird's nest processing.

[0111] 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 merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An AI-driven bird's nest precise nutritional formula design and high-value processing method, characterized in that: include: Based on big data or bird's nest formula research data, obtain the corresponding environmental control quantities, time control quantities, and ingredient control quantities for different bird's nest types, and establish a bird's nest nutritional formula dataset; According to the bird's nest nutritional formula data set, the data in the bird's nest nutritional formula data set is optimized based on the OPLS algorithm; Based on the optimized bird's nest nutritional formula data set, establish a precise nutritional formula for bird's nest and an AI-driven design plan for high-value processing; According to the AI-driven design of bird's nest precise nutritional formula and high-value processing, the characteristic words of bird's nest high-value processing are extracted, and a bird's nest precise nutritional formula and high-value processing AI-driven library is established; According to the AI-driven library of bird's nest precise nutritional formula and high-value processing, an AI-driven bird's nest high-value processing model is established based on the decision tree model. The AI-driven design scheme of bird's nest precise nutritional formula and high-value processing is searched through the characteristic words of bird's nest high-value processing, and the AI-driven bird's nest high-value processing is driven according to the scheme. According to the AI-driven bird's nest high-value processing model, an AI-driven bird's nest high-value processing model evaluation coefficient is established to conduct a comprehensive evaluation of the AI-driven bird's nest high-value processing model; According to the AI-driven library of bird's nest precise nutritional formula and high-value processing, an AI-driven bird's nest high-value processing model is established based on a decision tree model. The AI-driven design scheme of bird's nest precise nutritional formula and high-value processing is searched through the feature words of bird's nest high-value processing, and the AI-driven bird's nest high-value processing is specifically driven according to the scheme, including: According to the data processed by the OPLS algorithm, the decision tree model is pruned. The specific steps are as follows: According to the data processed by OPLS algorithm, the response variable matrix is ​​eliminated The input coefficients of irrelevant variables are set to zero, and the response variable matrix is ​​reduced and eliminated by multiplying the coefficients. The information gain value of the feature item corresponding to the irrelevant variation item; According to the OPLS algorithm, and The weight between the two , by multiplying the coefficients, the response variable matrix is ​​improved The correlation of related feature items, the specific formula is: ； Where, To combine the OPLS algorithm The information gain value of the characteristic items of the environmental control quantity, time control quantity and ingredient control quantity of each bird's nest type and different links of bird's nest high-value processing is To limit the constant and prevent the information gain value from being too large and exceeding the limit value, For the The information gain value of the characteristic items of the environmental control quantity, time control quantity and ingredient control quantity of each bird's nest type and different links of bird's nest high-value processing is for and The weight between the two; Among them, the numerical bird's nest type, the environmental control quantity, time control quantity and ingredient control quantity of different links in the high-value processing of bird's nest are used as the independent variable matrix , and the final product quality of the bird's nest is used as the response variable matrix , according to the matrix standardization formula, the independent variable matrix and the response variable matrix Standardize and get and , according to the covariance matrix formula, we can obtain and The covariance of ,pass and The covariance of and The weight between the two .

2. The AI-driven bird's nest precise nutritional formula design and high-value processing method according to claim 1, characterized in that: The method of obtaining the environmental control amount, time control amount, and ingredient control amount corresponding to different bird's nest types based on big data or bird's nest formula research data, and establishing a bird's nest nutritional formula data set specifically includes: According to the type of high-value processed bird's nest and the target population, through big data or bird's nest formula research data, the corresponding environmental control amount, time control amount and ingredient control amount of different bird's nest types are obtained; Among them, environmental control includes: temperature control, humidity control, odor control and drying control; ingredient control includes: sweetness, nutritional elements and taste; According to the environmental control quantity, the changes of environmental control quantity in the whole process of high-value bird's nest processing are obtained, and the range of environmental control quantity in different links is limited; A bird's nest nutritional formula dataset was established based on the characteristics of bird's nest types, environmental control quantities, time control quantities, and ingredient control quantities in different links of bird's nest high-value processing.

3. The AI-driven bird's nest precise nutritional formula design and high-value processing method according to claim 2, characterized in that: The optimizing process of the data in the bird's nest nutritional formula data set based on the OPLS algorithm specifically includes: Based on the bird's nest nutritional formula dataset, the characteristics of bird's nest types, environmental control quantities, time control quantities, and ingredient control quantities in different links of bird's nest high-value processing in the dataset were digitized; The bird's nest nutritional formula dataset after numerical processing is normalized to eliminate the influence of data dimension and abnormal value; According to the optimized bird's nest nutritional formula dataset, the data in the bird's nest nutritional formula dataset is further optimized based on the OPLS algorithm.

4. The AI-driven bird's nest precise nutritional formula design and high-value processing method according to claim 3, characterized in that: The design scheme for establishing precise nutritional formula and high-value processing AI-driven bird's nest based on the optimized bird's nest nutritional formula data set specifically includes: Determine different nutritional formulas for bird's nests based on different combinations of bird's nest types, environmental control quantities, time control quantities, and ingredient control quantities in different stages of high-value bird's nest processing in the dataset; Based on data experiments or big data analysis, determine the edible effects of different nutritional formulas of bird's nest and the corresponding applicable populations; Based on the different nutritional formulas, edible effects and corresponding applicable populations of bird's nests, a precise nutritional formula and high-value processing AI-driven design plan for bird's nests is established.

5. The AI-driven bird's nest precise nutritional formula design and high-value processing method according to claim 4, characterized in that: The design scheme based on the precise nutritional formula and high-value processing AI-driven bird's nest extracts the characteristic words of high-value processing of bird's nest and establishes the precise nutritional formula and high-value processing AI-driven library of bird's nest, specifically including: Based on the precise nutritional formula of bird's nest and the AI-driven design of high-value processing, we extract the characteristic words that can be used to call the specific design scheme of bird's nest high-value processing; Refine the precise nutritional formula and high-value processing AI-driven design plan for each bird's nest, accurately match the characteristic words of bird's nest high-value processing with the design plan, and establish a precise nutritional formula and high-value processing AI-driven library for bird's nest.

6. The AI-driven bird's nest precise nutritional formula design and high-value processing method according to claim 5, characterized in that: According to the AI-driven library of bird's nest precise nutritional formula and high-value processing, an AI-driven bird's nest high-value processing model is established based on a decision tree model. The AI-driven design scheme of bird's nest precise nutritional formula and high-value processing is searched through the feature words of bird's nest high-value processing, and the AI-driven bird's nest high-value processing is specifically driven according to the scheme, including: Based on the bird's nest precise nutritional formula and high-value processing AI-driven library, a decision tree model training set was established, in which the characteristic words of bird's nest high-value processing were used as input, and the bird's nest precise nutritional formula and high-value processing AI-driven design plan was used as output; Based on the decision tree model, an AI-driven high-value processing model for bird's nests was established. Through the characteristic words of bird's nest high-value processing, precise nutritional formulas for bird's nests and AI-driven design solutions for high-value processing were found. Through the precise nutritional formula of bird's nest and the AI-driven design plan for high-value processing, we guide each link of bird's nest processing to carry out high-value processing.

7. The AI-driven bird's nest precise nutritional formula design and high-value processing method according to claim 6, characterized in that: The AI-driven bird's nest high-value processing model is used to establish an evaluation coefficient for the AI-driven bird's nest high-value processing model, and a comprehensive evaluation of the AI-driven bird's nest high-value processing model is performed, specifically including: Based on the training results of the AI-driven bird's nest high-value processing model, an optimized training sample set for the AI-driven bird's nest high-value processing model was established; Among them, the AI-driven bird's nest high-value processing model optimization training sample set includes: the target data of the AI-driven bird's nest high-value processing model and the training results of the AI-driven bird's nest high-value processing model; The target data of the AI-driven bird's nest high-value processing model is used as reference data and defined as accurate data; The training data of the AI-driven bird's nest high-value processing model contains both accurate and erroneous data, which is mixed data. Optimize the training sample set based on the AI-driven bird's nest high-value processing model and establish the evaluation coefficient of the AI-driven bird's nest high-value processing model; Based on the feedback of the evaluation coefficient of the AI-driven bird's nest high-value processing model, a comprehensive evaluation of the AI-driven bird's nest high-value processing model is conducted, and data optimization is carried out.

8. An AI-driven bird's nest precise nutritional formula design and high-value processing system, characterized by: The method for realizing the AI-driven precise nutritional formula design and high-value processing of bird's nests as claimed in any one of claims 1 to 7 comprises: A data processing module, the data processing module is used to obtain the environmental control amount, time control amount and ingredient control amount corresponding to different bird's nest types based on big data or bird's nest formula research data, and establish a bird's nest nutritional formula data set; based on the bird's nest nutritional formula data set, based on the OPLS algorithm, optimize the data in the bird's nest nutritional formula data set; based on the optimized bird's nest nutritional formula data set, establish a bird's nest precise nutritional formula and high-value processing AI-driven design scheme; based on the bird's nest precise nutritional formula and high-value processing AI-driven design scheme, extract the characteristic words of bird's nest high-value processing, and establish a bird's nest precise nutritional formula and high-value processing AI-driven library; The model establishment and evaluation module is used to establish an AI-driven bird's nest high-value processing model based on the AI-driven library of bird's nest precise nutritional formula and high-value processing and the decision tree model, query the bird's nest precise nutritional formula and high-value processing AI-driven design plan through the bird's nest high-value processing feature words, and drive the bird's nest high-value processing according to the plan; according to the AI-driven bird's nest high-value processing model, establish an AI-driven bird's nest high-value processing model evaluation coefficient, and conduct a comprehensive evaluation of the AI-driven bird's nest high-value processing model.

9. The AI-driven bird's nest precise nutritional formula design and high-value processing system according to claim 8, characterized in that: The data processing module includes: A data acquisition unit, the data acquisition unit being used to acquire environmental control quantities, time control quantities, and ingredient control quantities corresponding to different bird's nest types based on big data or bird's nest formula research data, and to establish a bird's nest nutritional formula data set; A data optimization unit, wherein the data optimization unit is used to optimize the data in the bird's nest nutritional formula data set based on the OPLS algorithm; A design solution unit, which is used to establish a precise nutritional formula and high-value processing AI-driven design solution for bird's nest based on the optimized bird's nest nutritional formula data set; An AI-driven library unit is used to extract characteristic words of high-value processing of bird's nests according to the AI-driven design scheme of precise nutritional formula and high-value processing of bird's nests, and to establish an AI-driven library of precise nutritional formula and high-value processing of bird's nests.

10. The AI-driven bird's nest precise nutritional formula design and high-value processing system according to claim 9, characterized in that: The model building and evaluation module includes: A high-value processing model unit, which is used to establish an AI-driven bird's nest high-value processing model based on the bird's nest precise nutritional formula and high-value processing AI-driven library and a decision tree model, query the bird's nest precise nutritional formula and high-value processing AI-driven design plan through the bird's nest high-value processing feature words, and drive the bird's nest high-value processing according to the AI ​​plan; The model evaluation unit is used to establish an AI-driven bird's nest high-value processing model evaluation coefficient based on the AI-driven bird's nest high-value processing model, and to conduct a comprehensive evaluation of the AI-driven bird's nest high-value processing model.

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