AI-driven cubilose precise nutrition formula design and high-value processing method and system

Abstract

The invention discloses an AI-driven cubilose precise nutrition formula design and high-value processing method and system, and relates to the field of cubilose high-value processing, and the method comprises the steps: building a cubilose nutrition formula data set; based on an OPLS algorithm, performing optimization processing on data in the cubilose nutrition formula data set; establishing a cubilose accurate nutrition formula and high-value processing AI-driven design scheme; feature words of cubilose high-value processing are extracted, and a cubilose precise nutrition formula and high-value processing AI drive library is established; an AI-driven cubilose high-value processing model is established, and a cubilose precise nutrition formula and a high-value processing AI-driven design scheme are inquired; and establishing an evaluation coefficient of the AI-driven cubilose high-value processing model, and comprehensively evaluating the AI-driven cubilose high-value processing model. The cubilose accurate nutrition formula and the high-value processing AI-driven design scheme are inquired by retrieving the feature words, high-value processing is accurately carried out on the cubilose, and the intelligence and the high efficiency of cubilose processing are improved.

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 nutrition formula design and high-value processing method and system for bird's nests. Background Art

[0002] The high-value processing of bird's nests is a multi-level and complex process. The environmental control amount, time control amount, and ingredient control amount corresponding to different types of bird's nests will result in different final product qualities and applicable populations of bird's nests. For example, when targeting people with high blood sugar, a bird's nest processing process without sugar content needs to be configured. Therefore, during the configuration process, it is necessary to avoid adding sugar during the processing process and remove the sugar content in the bird's nest itself through chemical or physical methods. Therefore, it is necessary to study the precise nutrition formula design and high-value processing method of bird's nests from multiple aspects. With the development of AI technology, applying AI technology to the nutrition formula design and high-value processing of bird's nests is of great significance for improving the efficiency and nutritional balance of bird's nest production.

[0003] Traditional bird's nest processing techniques mainly rely on subjective experience to control the smooth progress of each step in different processing links. However, this method is time-consuming and laborious, and is easily affected by subjective experience, resulting in mistakes during the processing process and affecting the final quality of the bird's nest. Secondly, with the development of automation technology, using machines to replace traditional manual operations has improved the refinement level of bird's nest processing and effectively reduced mistakes during the processing process. However, these methods can only be applied to the processing of single-type bird's nests and lack intelligent and refined management for multiple ranges and applicable populations. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides an AI-driven precise nutrition formula design and high-value processing method and system for bird's nests. This technical solution solves the problems in the above background art that traditional bird's nest processing techniques are time-consuming and laborious, easily affected by subjective experience, resulting in mistakes during the processing process and affecting the final quality of the bird's nest, and that using machines to replace traditional manual operations, although improving the refinement level of bird's nest processing, can only be applied to the processing of single-type bird's nests and lack intelligent and refined management for multiple ranges and applicable populations.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] An AI-driven precise nutrition formula design and high-value processing method for bird's nests, comprising:

[0007] Obtaining the environmental control amount, time control amount, and ingredient control amount corresponding to different types of bird's nests according to big data or bird's nest formula research data, and establishing a bird's nest nutrition formula dataset;

[0008] Based on the bird's nest nutritional formula dataset and the OPLS algorithm, optimize the data in the bird's nest nutritional formula dataset;

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

[0010] According to the AI-driven design scheme for precise bird's nest nutritional formula and high-value processing, extract the characteristic words of high-value processing of bird's nest and establish an AI-driven library for precise bird's nest nutritional formula and high-value processing;

[0011] According to the AI-driven library for precise bird's nest nutritional formula and high-value processing, based on the decision tree model, establish an AI-driven high-value processing model for bird's nest. Through the characteristic words of high-value processing of bird's nest, query the AI-driven design scheme for precise bird's nest nutritional formula and high-value processing, and drive the high-value processing of bird's nest according to the scheme;

[0012] According to the AI-driven high-value processing model for bird's nest, establish an evaluation coefficient for the AI-driven high-value processing model for bird's nest and conduct a comprehensive evaluation of the AI-driven high-value processing model for bird's nest.

[0013] Preferably, the obtaining of the environmental control quantity, time control quantity, and ingredient control quantity corresponding to different bird's nest types based on big data or bird's nest formula research data and the establishment of the bird's nest nutritional formula dataset specifically include:

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

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

[0016] According to the environmental control quantity, obtain the change of the environmental control quantity in the whole process of high-value processing of bird's nest and limit the range of the environmental control quantity in different links;

[0017] According to the characteristics of the bird's nest type, environmental control quantity, time control quantity, and ingredient control quantity in different links of high-value processing of bird's nest, establish a bird's nest nutritional formula dataset.

[0018] Preferably, the optimizing the data in the bird's nest nutritional formula dataset based on the OPLS algorithm according to the bird's nest nutritional formula dataset specifically includes:

[0019] According to the bird's nest nutritional formula dataset, digitally process the characteristics of the bird's nest type, environmental control quantity, time control quantity, and ingredient control quantity in different links of high-value processing of bird's nest in the dataset;

[0020] Normalize the dataset of the nutritional formula of bird's nest after numerical processing to eliminate the influence of data dimension and abnormal values;

[0021] Based on the optimized dataset of the nutritional formula of bird's nest, further optimize the data in the dataset of the nutritional formula of bird's nest based on the OPLS algorithm.

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

[0023] Determine different nutritional formulas of bird's nest according to different combinations of the characteristics of the type of bird's nest, environmental control quantity, time control quantity, and ingredient control quantity in different links of high-value processing of bird's nest in the dataset;

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

[0025] Establish the AI-driven design scheme for the precise nutritional formula and high-value processing of bird's nest according to different nutritional formulas of bird's nest, edible effects, and corresponding applicable populations.

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

[0027] Extract the characteristic words of high-value processing of bird's nest that can retrieve specific design schemes according to the AI-driven design scheme for the precise nutritional formula and high-value processing of bird's nest;

[0028] Refine each AI-driven design scheme for the precise nutritional formula and high-value processing of bird's nest to make the characteristic words of high-value processing characteristics of bird's nest match the design scheme precisely, and establish the AI-driven library for the precise nutritional formula and high-value processing of bird's nest.

[0029] Preferably, establishing the AI-driven high-value processing model of bird's nest based on the decision tree model according to the AI-driven library for the precise nutritional formula and high-value processing of bird's nest, querying the AI-driven design scheme for the precise nutritional formula and high-value processing of bird's nest through the characteristic words of high-value processing of bird's nest, and driving the high-value processing of bird's nest according to the scheme specifically includes:

[0030] According to the AI-driven library for the precise nutritional formula and high-value processing of bird's nest, establish a training set for the decision tree model, where the characteristic words of high-value processing of bird's nest are used as the input quantity, and the AI-driven design scheme for the precise nutritional formula and high-value processing of bird's nest is used as the output quantity;

[0031] Based on the decision tree model, an AI-driven high-value processing model for bird's nest is established. Through the feature words of high-value processing of bird's nest, the precise nutritional formula of bird's nest and the design scheme driven by AI for high-value processing are queried;

[0032] Guided by the precise nutritional formula of bird's nest and the design scheme driven by AI for high-value processing, high-value processing is carried out in each link of bird's nest processing.

[0033] Preferably, according to the AI-driven high-value processing model of bird's nest, an evaluation coefficient of the AI-driven high-value processing model of bird's nest is established. The comprehensive evaluation of the AI-driven high-value processing model of bird's nest specifically includes:

[0034] According to the training results of the AI-driven high-value processing model of bird's nest, an optimized training sample set of the AI-driven high-value processing model of bird's nest is established;

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

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

[0037] The training results of the AI-driven high-value processing model of bird's nest are used as the training data, which contains accurate data and error data and is mixed data;

[0038] According to the optimized training sample set of the AI-driven high-value processing model of bird's nest, an evaluation coefficient of the AI-driven high-value processing model of bird's nest is established;

[0039] According to the feedback of the evaluation coefficient of the AI-driven high-value processing model of bird's nest, the AI-driven high-value processing model of bird's nest is comprehensively evaluated and data optimization is carried out.

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

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

[0042] Model establishment and evaluation module, which is used to establish an AI-driven high-value processing model for bird's nest based on the decision tree model according to the precise nutrition formula of bird's nest and the AI-driven library for high-value processing. Through the characteristic words of high-value processing of bird's nest, query the design scheme of the precise nutrition formula of bird's nest and AI-driven high-value processing, and drive the high-value processing of bird's nest according to the scheme; according to the AI-driven high-value processing model of bird's nest, establish an evaluation coefficient for the AI-driven high-value processing model of bird's nest, and comprehensively evaluate the AI-driven high-value processing model of bird's nest.

[0043] Preferably, the data processing module includes:

[0044] Data acquisition unit, which is used to obtain the environmental control quantity, time control quantity and ingredient control quantity corresponding to different types of bird's nest according to big data or bird's nest formula research data, and establish a bird's nest nutrition formula dataset;

[0045] Data optimization unit, which is used to optimize the data in the bird's nest nutrition formula dataset based on the OPLS algorithm according to the bird's nest nutrition formula dataset;

[0046] Design scheme unit, which is used to establish a design scheme of precise nutrition formula of bird's nest and AI-driven high-value processing according to the optimized bird's nest nutrition formula dataset;

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

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

[0049] High-value processing model unit, which is used to establish an AI-driven high-value processing model for bird's nest based on the decision tree model according to the precise nutrition formula of bird's nest and the AI-driven library for high-value processing. Through the characteristic words of high-value processing of bird's nest, query the design scheme of the precise nutrition formula of bird's nest and AI-driven high-value processing, and drive the high-value processing of bird's nest according to the scheme;

[0050] Model evaluation unit, which is used to establish an evaluation coefficient for the AI-driven high-value processing model of bird's nest according to the AI-driven high-value processing model of bird's nest, and comprehensively evaluate the AI-driven high-value processing model of bird's nest.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] According to big data or research data on bird's nest formulas, obtain the environmental control amounts, time control amounts, and ingredient control amounts corresponding to different types of bird's nests, and establish a bird's nest nutritional formula dataset. Based on the OPLS algorithm, optimize the data in the bird's nest nutritional formula dataset, effectively remove redundant information, retain effective data features, and based on the optimized bird's nest nutritional formula dataset, establish a precise bird's nest nutritional formula and a high-value processing AI-driven design scheme. Secondly, according to the precise bird's nest nutritional formula and the high-value processing AI-driven design scheme, extract the characteristic words of high-value bird's nest processing, establish a precise bird's nest nutritional formula and a high-value processing AI-driven library, and based on the decision tree model, establish an AI-driven high-value bird's nest processing model. Through the characteristic words of high-value bird's nest processing, query the precise bird's nest nutritional formula and the high-value processing AI-driven design scheme, and drive the high-value bird's nest processing according to the scheme. Finally, according to the AI-driven high-value bird's nest processing model, establish an evaluation coefficient for the AI-driven high-value bird's nest processing model, comprehensively evaluate the AI-driven high-value bird's nest processing model, and optimize the AI-driven high-value bird's nest processing model to improve the accuracy and reliability of model classification, thereby achieving the purpose of intelligent and efficient bird's nest processing. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0054] Figure 2 It is a flowchart of optimizing the data in the bird's nest nutritional formula dataset based on the OPLS algorithm according to the bird's nest nutritional formula dataset of the present invention;

[0055] Figure 3 It is a flowchart of establishing an AI-driven high-value bird's nest processing model based on the decision tree model according to the precise bird's nest nutritional formula and the high-value processing AI-driven library of the present invention, querying the precise bird's nest nutritional formula and the high-value processing AI-driven design scheme through the characteristic words of high-value bird's nest processing, and driving the high-value bird's nest processing according to the scheme;

[0056] Figure 4 It is a flowchart of establishing an evaluation coefficient for the AI-driven high-value bird's nest processing model and comprehensively evaluating the AI-driven high-value bird's nest processing model according to the AI-driven high-value bird's nest processing model of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0057] 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 in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0058] Refer to Figure 1As shown in the figure, an AI-driven precise nutrition formula design and high-value processing method for bird's nest includes:

[0059] According to big data or bird's nest formula research data, obtain the environmental control amount, time control amount, and ingredient control amount corresponding to different bird's nest types, and establish a bird's nest nutrition formula dataset;

[0060] Based on the OPLS algorithm, optimize the data in the bird's nest nutrition formula dataset according to the bird's nest nutrition formula dataset;

[0061] According to the optimized bird's nest nutrition formula dataset, establish an AI-driven design scheme for precise bird's nest nutrition formula and high-value processing;

[0062] According to the AI-driven design scheme for precise bird's nest nutrition formula and high-value processing, extract the characteristic words of high-value bird's nest processing, and establish an AI-driven library for precise bird's nest nutrition formula and high-value processing;

[0063] According to the AI-driven library for precise bird's nest nutrition formula and high-value processing, based on the decision tree model, establish an AI-driven high-value bird's nest processing model. Through the characteristic words of high-value bird's nest processing, query the AI-driven design scheme for precise bird's nest nutrition formula and high-value processing, and drive the high-value processing of bird's nest according to the scheme;

[0064] According to the AI-driven high-value bird's nest processing model, establish an evaluation coefficient for the AI-driven high-value bird's nest processing model, and comprehensively evaluate the AI-driven high-value bird's nest processing model.

[0065] It can be explained that in this scheme, according to big data or bird's nest formula research data, the environmental control amount, time control amount, and ingredient control amount corresponding to different bird's nest types are obtained, and a bird's nest nutrition formula dataset is established. Based on the OPLS algorithm, the data in the bird's nest nutrition formula dataset is optimized to effectively remove redundant information and retain effective data features. According to the optimized bird's nest nutrition formula dataset, an AI-driven design scheme for precise bird's nest nutrition formula and high-value processing is established. Secondly, according to the AI-driven design scheme for precise bird's nest nutrition formula and high-value processing, the characteristic words of high-value bird's nest processing are extracted, an AI-driven library for precise bird's nest nutrition formula and high-value processing is established, and an AI-driven high-value bird's nest processing model is established based on the decision tree model. Through the characteristic words of high-value bird's nest processing, the AI-driven design scheme for precise bird's nest nutrition formula and high-value processing is queried, and the high-value processing of bird's nest is driven according to the scheme. Finally, according to the AI-driven high-value bird's nest processing model, an evaluation coefficient for the AI-driven high-value bird's nest processing model is established, the AI-driven high-value bird's nest processing model is comprehensively evaluated, and the AI-driven high-value bird's nest processing model is optimized to improve the accuracy and reliability of model classification, so as to achieve 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 nutrition formula dataset based on the OPLS algorithm specifically includes:

[0067] Digitally process the characteristics of the bird's nest type, environmental control quantity, time control quantity, and ingredient control quantity in different high-value processing links in the bird's nest nutrition formula dataset according to the bird's nest nutrition formula dataset;

[0068] Perform normalization processing on the numerically processed bird's nest nutrition formula dataset to eliminate the influence of data dimension and abnormal values;

[0069] Based on the optimized bird's nest nutrition formula dataset, further optimize the data in the bird's nest nutrition formula dataset based on the OPLS algorithm.

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

[0071] Take the characteristic data of the numerically processed bird's nest type, environmental control quantity, time control quantity, and ingredient control quantity in different high-value processing links as the independent variable matrix , and take the final finished product quality of the bird's nest as the response variable matrix . According to the matrix standardization formula, standardize the independent variable matrix and the response variable matrix to obtain and . According to the covariance matrix formula, obtain the covariance and of, extract the weight matrix between and through the covariance of and , thereby obtaining the score matrix and the loading matrix between . Decompose the independent variable matrix into the relevant and irrelevant formulas of the response variable matrix : . In the formula, and are the score matrix and loading matrix related to the response variable matrix , and are the orthogonal score matrix and loading matrix unrelated to the response variable matrix , and .​​​​​​​​​ is the residual matrix, which is used to represent the independent variable matrix the difference between the actual model and the real data, and the independent variable matrix is removed by orthogonalization the response variable matrix in the decomposition formula Irrelevant variations are removed, so as to obtain the characteristic data of the bird's nest type, environmental control quantity, time control quantity, and ingredient control quantity in different high-value processing links of the bird's nest, which have a relatively high correlation with the final finished product quality of the bird's nest, simplify the redundancy of the data, extract important influencing characteristic factors, and ensure the acceptance ability and interpretability of the system for the characteristic data.

[0072] Refer to Figure 3 As shown, based on the decision tree model, an AI-driven high-value processing model for bird's nest is established according to the accurate nutrition formula and high-value processing AI-driven library of bird's nest. Through the high-value processing characteristic words of bird's nest, the design scheme of the accurate nutrition formula and high-value processing AI-driven of bird's nest is queried, and the specific steps of AI-driven high-value processing of bird's nest according to the scheme include:

[0073] According to the accurate nutrition formula and high-value processing AI-driven library of bird's nest, a training set of the decision tree model is established, where the characteristic words of high-value processing of bird's nest are used as the input quantity, and the design scheme of the accurate nutrition formula and high-value processing AI-driven of bird's nest is used as the output quantity;

[0074] Based on the decision tree model, an AI-driven high-value processing model for bird's nest is established, and the design scheme of the accurate nutrition formula and high-value processing AI-driven of bird's nest is queried through the high-value processing characteristic words of bird's nest;

[0075] Through the design scheme of the accurate nutrition formula and high-value processing AI-driven of bird's nest, guide the high-value processing of each link of bird's nest processing.

[0076] It can be explained that the conventional decision tree model calculates the information entropy and information gain of each feature item, and according to the comparison of the information gain values, the maximum value is selected 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 likely to occur during the training process, resulting in weak generalization ability and reduced intelligence of the model. The traditional method is to process through traditional pruning operations, but the effect is average. 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 training set of the decision tree model, obtain the information entropy and information gain of the characteristics of the bird's nest type, environmental control quantity, time control quantity, and ingredient control quantity in different high-value processing links of the bird's nest. The information entropy expression is:

[0078] In the formula, is the information entropy value of the total data of the decision tree model training set, refers to the type of AI-driven design scheme for precise nutrition formula and high-value processing of bird's nest, is the number of data groups in the decision tree model training set, is the th AI-driven design scheme for precise nutrition formula and high-value processing of bird's nest, is the probability of occurrence in the

[0079] The information gain expression is:

[0080]

[0081] In the formula, is the information gain value of the th feature item of bird's nest type, environmental control amount, time control amount, and ingredient control amount in different links of high-value processing of bird's nest, is the th information entropy value corresponding to the feature item of bird's nest type, environmental control amount, time control amount, and ingredient control amount in different links of high-value processing of bird's nest, and its calculation formula is the same as the information entropy expression;

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

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

[0084] According to the data processed by the OPLS algorithm, the input coefficient of the irrelevant variation term in the removed response variable matrix is set to zero, and by multiplying the coefficients, the information gain value of the feature item corresponding to the removed response variable matrix is reduced;

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

[0086]

[0087] In the formula, is the The information gain values of the characteristics of the bird's nest types, environmental control amounts, time control amounts, and ingredient control amounts in different high-value processing links of the bird's nest is a limit constant to prevent the information gain value from being too large and exceeding the limit value.

[0088] Refer to Figure 4 As shown, based on the AI-driven high-value processing model of bird's nest, establishing an evaluation coefficient for the AI-driven high-value processing model of bird's nest and comprehensively evaluating the AI-driven high-value processing model of bird's nest specifically includes:

[0089] According to the training results of the AI-driven high-value processing model of bird's nest, establish an optimized training sample set for the AI-driven high-value processing model of bird's nest;

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

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

[0092] The training results of the AI-driven high-value processing model of bird's nest are used as training data, which contains accurate data and error data and is mixed data;

[0093] According to the optimized training sample set for the AI-driven high-value processing model of bird's nest, establish an evaluation coefficient for the AI-driven high-value processing model of bird's nest;

[0094] According to the feedback of the evaluation coefficient of the AI-driven high-value processing model of bird's nest, comprehensively evaluate the AI-driven high-value processing model of bird's nest and optimize the data.

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

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

[0097]

[0098] In the formula, is the evaluation coefficient of the AI-driven high-value processing model of bird's nest, The number of positive samples in the classification results of the AI - driven high - value processing model for bird's nest for the number of times of classification, The number of false positive samples in the classification results of the AI - driven high - value processing model for bird's nest for the number of times of classification. Among them, false positive samples refer to samples in which incorrect data is correctly identified as reference data, and false negative samples refer to samples in which reference data is identified as incorrect data.

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

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

[0101] A model establishment and evaluation module, which is used to establish an AI - driven high - value processing model for bird's nest based on the decision - tree model according to the AI - driven library for precise nutrition formula and high - value processing of bird's nest, query the AI - driven design scheme for precise nutrition formula and high - value processing of bird's nest through the feature words of high - value processing of bird's nest, and drive the high - value processing of bird's nest according to the scheme; establish an evaluation coefficient for the AI - driven high - value processing model for bird's nest according to the AI - driven high - value processing model, and comprehensively evaluate the AI - driven high - value processing model for bird's nest;

[0102] The data processing module includes:

[0103] A data acquisition unit, which is used to obtain the environmental control quantity, time control quantity, and ingredient control quantity corresponding to different bird's nest types according to big data or bird's nest formula research data, and establish a bird's nest nutrition formula dataset;

[0104] A data optimization unit, which is used to optimize the data in the bird's nest nutrition formula dataset based on the OPLS algorithm according to the bird's nest nutrition formula dataset;

[0105] A design solution unit, which is used to establish an AI-driven design solution for precise bird's nest nutrition formula and high-value processing based on the optimized bird's nest nutrition formula dataset.

[0106] An AI-driven library unit, which is used to extract feature words for high-value processing of bird's nest according to the AI-driven design solution for precise bird's nest nutrition formula and high-value processing, and establish an AI-driven library for precise bird's nest nutrition formula and high-value processing.

[0107] The model establishment and evaluation module includes:

[0108] A high-value processing model unit, which is used to establish an AI-driven high-value processing model of bird's nest based on the decision tree model according to the AI-driven library for precise bird's nest nutrition formula and high-value processing, query the AI-driven design solution for precise bird's nest nutrition formula and high-value processing through the feature words for high-value processing of bird's nest, and drive the high-value processing of bird's nest according to the solution.

[0109] A model evaluation unit, which is used to establish an evaluation coefficient for the AI-driven high-value processing model of bird's nest according to the AI-driven high-value processing model of bird's nest, and comprehensively evaluate the AI-driven high-value processing model of bird's nest.

[0110] In summary, the advantages of the present invention are as follows: By retrieving feature words, querying the AI-driven design solution for precise bird's nest nutrition formula and high-value processing, and precisely carrying out high-value processing on the bird's nest, the intelligence and efficiency of bird's nest processing are improved.

[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 by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. 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 amount, time control amount and ingredient control amount for different bird's nest types, and establish a bird's nest nutritional formula data set; 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 scheme for high-value processing; According to the design scheme of bird's nest precise nutritional formula and high-value processing AI-driven, extract the characteristic words of bird's nest high-value processing, and establish the bird's nest precise nutritional formula and high-value processing AI-driven library; 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 characteristic words of bird's nest high-value processing, and AI-driven bird's nest high-value processing is driven according to the scheme; Based on the AI ​​driven bird's nest high-value processing model, an evaluation coefficient of the AI ​​driven bird's nest high-value processing model is established, and a comprehensive evaluation of the AI ​​driven bird's nest high-value processing model is carried out.

2. According to claim 1, an AI-driven bird's nest precise nutritional formula design and high-value processing method is characterized in that: The method of obtaining the environment control amount, time control amount and ingredient control amount corresponding to different types of bird's nests 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 types of high-value processed bird's nests and the target population, obtain the corresponding environmental control amount, time control amount and ingredient control amount for different types of bird's nests through big data or bird's nest formula research data; Among them, the environmental control includes: temperature control, humidity control, odor control and drying control, and the 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 high-value bird's nest 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: According to the bird's nest nutritional formula data set, 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 data set are digitized; The bird's nest nutritional formula data set after numerical processing is normalized to eliminate the influence of data dimension and abnormal value; According to the optimized bird's nest nutritional formula data set, the data in the bird's nest nutritional formula data set is further optimized based on the OPLS algorithm.

4. The AI-driven precise nutritional formula design and high-value processing method for bird's nest according to claim 3, characterized in that: The design scheme of establishing precise nutrition formula of bird's nest and high-value processing AI-driven according to the optimized bird's nest nutrition formula data set specifically includes: Determine different nutritional formulas for bird's nests based on different combinations of characteristics of bird's nest types, environmental control quantities, time control quantities, and ingredient control quantities in different links of high-value processing of bird's nests in the data set; Based on data experiments or big data analysis, determine the edible effects of different nutritional formulas of bird's nest and the corresponding applicable population; According to the different nutritional formulas, edible benefits 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 precise nutritional formula design and high-value processing method for bird's nest 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, which specifically includes: According to the precise nutritional formula of bird's nest and the AI-driven design of high-value processing, the characteristic words of bird's nest high-value processing that can call up a specific design solution are extracted; Refine the precise nutritional formula and high-value processing AI-driven design of each bird's nest, accurately match the characteristic words of high-value processing of bird's nest 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 bird's nest precise nutritional formula and high-value processing AI driving 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 is specifically included according to the scheme: According to the bird's nest precise nutritional formula and high-value processing AI-driven library, a decision tree model training set is established, in which the characteristic words of bird's nest high-value processing are used as input, and the bird's nest precise nutritional formula and high-value processing AI-driven design scheme is used as output; Based on the decision tree model, an AI-driven high-value processing model for bird's nests was established. Through the high-value processing feature words of bird's nests, the precise nutritional formula of bird's nests and the AI-driven design scheme for high-value processing were queried; Through the precise nutritional formula of bird's nest and the AI-driven design of 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 AI-driven bird's nest high-value processing model evaluation coefficient, and the AI-driven bird's nest high-value processing model is comprehensively evaluated, specifically including: According to the training results of the AI-driven bird's nest high-value processing model, an optimized training sample set of the AI-driven bird's nest high-value processing model is 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 results of the AI-driven bird's nest high-value processing model are used as training data, which contains accurate data and erroneous data, which is mixed data; According to the AI-driven bird's nest high-value processing model, the training sample set is optimized and the evaluation coefficient of the AI-driven bird's nest high-value processing model is established; 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 carried out, and data optimization is performed.

8. An AI-driven bird's nest precise nutritional formula design and high-value processing system, characterized in that: 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 according to big data or bird's nest formula research data, and establish a bird's nest nutritional formula data set; according to 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; according to 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; according to 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; 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 based on 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 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 comprises: A data acquisition unit, the data acquisition unit is used to obtain the environment control amount, time control amount and ingredient control amount corresponding to different types of bird's nests according to big data or bird's nest formula research data, and establish a bird's nest nutrition 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 scheme unit, which is used to establish a precise nutritional formula of bird's nest and a high-value processing AI-driven design scheme 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, wherein the high-value processing model unit is used to establish an AI-driven bird's nest high-value processing model based on a bird's nest precise nutritional formula and a high-value processing AI-driven library, based on a decision tree model, query a bird's nest precise nutritional formula and a high-value processing AI-driven design plan through a bird's nest high-value processing feature word, and drive the bird's nest high-value processing according to the AI-driven plan; A 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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