Functional product digital design platform based on birch sap component-function association

Abstract

The embodiment of the application discloses a functional product digital design platform based on birch sap component-function correlation, and relates to the technical field of digital research and development of functional food. Taking the quantitative correlation between the lipids such as betulin and betulinic acid in birch sap and the anti-inflammatory function as the core, a four-level architecture system including a data storage module, an intelligent analysis and calculation module, a business function execution module and an interactive application module is constructed. Through standardized database construction, intelligent correlation modeling, multi-dimensional simulation verification and visual design interaction, the whole-process digital research and development from component screening, formula optimization, efficacy prediction to production adaptation is realized. The application solves the problems of fuzzy correlation between components and efficacy, long research and development period and high trial and error cost in the traditional research and development of birch sap functional products, converts the research and development mode from experience-driven to data-driven, greatly improves the product research and development efficiency and efficacy accuracy, and provides technical support for the innovative design of anti-inflammatory birch sap functional products.

Description

Technical Field

[0001] This application relates to the field of digital R&D technology for functional foods, and includes, but is not limited to, a digital design platform for functional products based on the component-function relationship of birch sap. Background Technology

[0002] Birch sap, as a natural beverage, contains lipid components such as betulin and betulinic acid, which have significant anti-inflammatory activity and broad application prospects in the fields of functional foods and health products. However, the research and development of anti-inflammatory functional products made from birch sap still faces many challenges. First, the relationship between components and efficacy is unclear. The content of lipid components in birch sap is greatly affected by factors such as place of origin and processing methods. Existing research mostly focuses on verifying the anti-inflammatory effects of single components, lacking quantitative data on the correlation between multi-component combinations and anti-inflammatory efficacy, leading to reliance on experience-based judgment in research and development. Second, the research and development process is inefficient. Traditional research and development adopts a cyclical model of formula trial production, physical testing, and adjustment and optimization, resulting in a lengthy development cycle, high trial-and-error costs, and a disconnect between design and production, which easily leads to manufacturability problems. Third, data management is fragmented. Component data, efficacy data, and production data are stored in different systems, forming data silos and making it difficult to achieve full-process data traceability and knowledge reuse.

[0003] With the development of digital design technology, data-driven and model-centric R&D models have been widely used in the manufacturing industry. Through the integration of toolchains such as CAD, CAE, and PLM, R&D efficiency can be significantly improved. However, existing digital design platforms are mostly applicable to fields such as machinery and automobiles. For functional products made from natural raw materials such as birch sap, there is a lack of dedicated architecture design based on the relationship between ingredients and efficacy, which cannot meet the needs of precise R&D for anti-inflammatory functional products.

[0004] Therefore, there is an urgent need for a digital design system specifically adapted to the characteristics of lipid components in birch sap and the requirements for anti-inflammatory functions. This system would connect the entire data chain from component screening, formulation design, efficacy verification to production adaptation, enabling the intelligent transformation of the R&D model and significantly improving the accuracy of formulation design and the stability and reliability of the anti-inflammatory efficacy of the product. Summary of the Invention

[0005] This application provides a digital design platform for functional products based on the component-function association of birch sap.

[0006] The technical solution of this application embodiment is implemented as follows: In a first aspect, embodiments of this application provide a digital design platform for functional products based on the component-function association of birch sap, the platform comprising: The system includes a data storage module for storing data on the lipid components of birch sap, anti-inflammatory function evaluation data, raw material attribute data, and quality standard data for anti-inflammatory products. An intelligent analysis and calculation module is used to determine the nonlinear correlation between lipid component combinations and anti-inflammatory efficacy based on a component-efficacy correlation algorithm, and output a quantitative correlation equation. Based on the quantitative correlation equation, a multi-objective optimization algorithm is used to determine candidate formulation schemes. A simulation verification algorithm is then used to perform simulation verification on the candidate formulation schemes. A business function execution module receives user requirement parameters, filters target lipid components and their content ranges, and displays matching raw material information. In intelligent generative design mode, it calls the candidate formulation schemes to determine the design formulation scheme. In manual adjustment mode, it receives user component ratio adjustment operations and provides real-time feedback on efficacy changes. The module performs stability simulation, anti-inflammatory efficacy simulation, and production adaptability simulation on the design formulation scheme and outputs a simulation report. An interactive application module receives user design requirement input, visualizes the design process and results, and exports the final design formulation scheme for integration with the production system.

[0007] Secondly, embodiments of this application provide a digital design method for functional products based on the component-function association of birch sap, applied to a digital design platform for functional products based on the component-function association of birch sap, including: The interactive application module receives user-inputted design requirements, including product type, target anti-inflammatory efficacy, cost budget, and production constraints. The data storage module automatically extracts birch sap lipid component data, anti-inflammatory function evaluation data, and raw material attribute data based on the target anti-inflammatory efficacy and constraints, and transmits them to the intelligent analysis and calculation module. The intelligent analysis and calculation module trains the extracted data using a component-efficacy correlation algorithm to establish a quantitative correlation equation. Based on this quantitative correlation equation, a multi-objective optimization algorithm is used to optimize anti-inflammatory efficacy, minimize component cost, and maximize production feasibility, while considering the constraints. Multiple candidate formulation schemes are generated. Through the business function execution module, the simulation verification algorithm of the intelligent analysis and calculation module is called to perform stability simulation, anti-inflammatory efficacy simulation, and production adaptability simulation on the candidate formulation schemes. Schemes that do not meet the constraints are eliminated, and the parameters of the retained schemes are optimized. If the simulation verification fails, the multi-objective optimization algorithm is driven by the optimization suggestions to regenerate the formulation scheme until the simulation verification is successful. Through the business function execution module, the verified formulation schemes are subject to compliance verification. After the scheme is confirmed to be compliant, the interactive application module outputs a complete design scheme including formulation parameters, production process, efficacy prediction report, and compliance verification report.

[0008] The beneficial effects of the technical solutions provided in this application include at least the following: This application provides a digital design platform for functional products based on the component-function correlation of birch sap. By constructing a data storage module integrating multi-dimensional data and an intelligent analysis and calculation module integrating deep learning models, it establishes a database of lipid components and an anti-inflammatory function evaluation database for birch sap. Utilizing a component-efficacy correlation algorithm, it mines the nonlinear correlation between multi-component combinations and anti-inflammatory efficacy, outputting a quantitative correlation equation. This quantitative correlation equation can accurately predict the anti-inflammatory activity of specific component ratios and automatically identify key influencing components and their optimal content ranges, transforming formulation design from experience-based judgment to data-driven approaches, thereby significantly improving the stability and reliability of the product's anti-inflammatory efficacy. Through a closed-loop R&D process of data input, model construction, simulation verification, and design output, it achieves full-process digital integration from demand input to solution output. Specifically, the multi-objective optimization algorithm automatically generates multiple candidate formulation schemes with three optimization objectives: optimal anti-inflammatory efficacy, lowest component cost, and strongest production feasibility. The simulation verification algorithm constructs a digital twin of the product to simulate the stability of lipid components in the product system, the absorption efficiency in the human gastrointestinal tract, and the process of anti-inflammatory efficacy. The error between the simulation results and physical experiments is controlled below 5%, significantly reducing the number of physical experiments. Meanwhile, the data traceability function, through the product lifecycle management system, accumulates information such as formula parameters, simulation results, and process adjustments into a structured design knowledge base for reuse in subsequent R&D. Through these technologies, the R&D cycle can be shortened by more than 50%, trial-and-error costs reduced by more than 40%, and knowledge reuse rate increased to over 70%. The solution verification function performs production adaptability simulations on the formula, verifying whether the formula is compatible with the mixing, filling, and sterilization process parameters of existing production lines. The compliance verification function automatically compares the formula with the product standard database to verify whether the formula's component content, heavy metal indicators, and functional claims comply with relevant regulations and quality standards. Pre-verification of production feasibility and compliance is completed during the design phase, avoiding changes to the design due to process incompatibility or non-compliance after the design enters the production stage. Complete design solutions can be directly exported and integrated with the production system, achieving seamless integration between design and production. Through these technologies, the design change rate can be reduced to below 10%, effectively avoiding production rework and compliance risks, and significantly improving the efficiency of transformation from R&D to industrialization. Utilizing a cloud-based deployment model, this system supports cross-terminal access via computers and tablets, providing visual interactive functions such as 3D model display, data chart analysis, solution comparison, and collaborative editing, making the R&D process intuitive, visual, and easy to operate. Integrating AR health guidance functionality, users can scan virtual products to trigger dynamic content such as animations explaining the anti-inflammatory principles of lipid components and product usage suggestions, providing technical support for subsequent product marketing and promotion. Therefore, this application is applicable to various user groups, including enterprise R&D personnel, research institutions, and makers, and has broad application prospects.Furthermore, blockchain technology is used to protect the copyright of original data and a user contribution mechanism is established. This mechanism receives user-uploaded R&D data, which is then professionally reviewed and incorporated into the corresponding database, enabling dynamic iteration and sharing of data. While protecting the intellectual property rights of data contributors, this mechanism promotes the continuous enrichment of data resources, providing a sustainable data foundation for the long-term innovative R&D of birch sap functional products. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 A schematic diagram of a digital design platform for functional products based on the component-function association of birch sap, provided in an embodiment of this application; Figure 2 This is a schematic diagram of the component-efficacy correlation model provided in the embodiments of this application; Figure 3 This is a flowchart illustrating the digital design method for functional products based on the component-function association of birch sap, as provided in this application embodiment. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0011] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0012] It should be noted that the terms "first, second, and third" used in the embodiments of this application are merely to distinguish similar objects and do not represent a specific order of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0013] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of this application pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have a meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0014] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0015] In view of the current problems in the design of birch sap functional products in the field of digital R&D technology for functional foods, this application provides a digital design platform for functional products based on the component-function correlation of birch sap.

[0016] The technical solution of this application is described below, starting with the system implementation of this application.

[0017] Please refer to Figure 1 It illustrates a schematic diagram of a digital design platform for functional products based on the component-function association of birch sap, as provided in an embodiment of this application. Figure 1 As shown, the platform includes a data storage module, an intelligent analysis and calculation module, a business function execution module, and an interactive application module. The data storage module stores data on the lipid components of birch sap, anti-inflammatory function evaluation data, raw material attribute data, and quality standard data for anti-inflammatory products. The intelligent analysis and calculation module determines the nonlinear correlation between lipid component combinations and anti-inflammatory efficacy based on a component-efficacy correlation algorithm, outputting a quantitative correlation equation. Based on the quantitative correlation equation, a multi-objective optimization algorithm is used to determine candidate formulation schemes. Simulation verification algorithms are then used to verify the candidate formulation schemes. The business function execution module receives user requirement parameters, filters target lipid components and their content ranges, and displays matching raw material information. In intelligent generative design mode, it calls the candidate formulation schemes to determine the design formulation scheme. In manual adjustment mode, it receives user component ratio adjustment operations and provides real-time feedback on efficacy changes. It performs stability simulation, anti-inflammatory efficacy simulation, and production adaptability simulation on the designed formulation scheme and outputs a simulation report. The interactive application module receives user design requirement input, visualizes the design process and results, and exports the final design formulation scheme for integration with the production system.

[0018] In this embodiment, the data storage module serves as the system's data foundation, primarily used to construct unified data standards and interface specifications, and to integrate multi-dimensional data resources. This module comprises four sub-databases: a birch sap lipid component database, an anti-inflammatory function evaluation database, a raw material attribute database, and a product standard database. The birch sap lipid component database stores the content, purity, and stability parameters of triterpenoid lipid components such as betulinol and betulinic acid. Data dimensions cover origin, harvesting season, processing technology, and detection methods. Specifically, this database collects birch sap samples from different origins (e.g., Northeast China forest region, Siberian forest region), different harvesting seasons (spring budding period, summer growing season), and different processing technologies (low-temperature pressing, vacuum concentration). Component data is obtained using detection techniques such as high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS), while simultaneously correlating corresponding storage conditions and shelf-life data to construct a correlation between component attributes and environmental factors.

[0019] In this embodiment, the anti-inflammatory function evaluation database integrates in vitro, animal, and clinical data to store the quantitative relationships between different lipid component concentrations and ratios and the inhibition rates of inflammatory factors and the degree of activation of anti-inflammatory pathways, thus constructing a standardized efficacy evaluation index system. The in vitro experiments include cell experiments such as macrophage inflammation models, animal experiments include data from mouse ear swelling models, and clinical data comes from pilot studies. The database records that a TNF-α inhibition rate of 50 μg / mL betulinol can reach 45%, and that a 2:1 ratio of betulinol to betulinic acid... The database includes specific quantitative data such as optimal pathway inhibition effect. The raw material attribute database stores the physicochemical properties, compatibility, and safety indicators of birch sap raw materials and excipients. Physicochemical properties include parameters such as pH value and viscosity, while safety indicators include microbial limits and heavy metal content. Excipient information covers compatibility data for commonly used excipients such as stabilizers and flavoring agents, ensuring the safety and stability of the formulation design. The product standard database embeds relevant regulations for food and health products, as well as quality standards for anti-inflammatory functional products. Specifically, it includes regulatory requirements such as the "Administrative Measures for Registration and Filing of Health Foods" and the "General Hygiene Standards for Food Production," as well as quality standards and labeling specifications for anti-inflammatory functional products, providing a basis for comparison in subsequent compliance verification.

[0020] It's worth noting that the data storage module uses blockchain technology to protect the copyright of original data and establishes a user contribution mechanism. This module receives R&D data uploaded by users, and after review by a professional team, newly added data that passes the review is included in the corresponding database. For example, component data under new processing techniques and new anti-inflammatory efficacy verification data can be added to the database after review, realizing dynamic iteration and sharing of data.

[0021] In this embodiment, the intelligent analysis and calculation module is the core technology of the system, including a component-efficacy correlation unit, a multi-objective optimization unit, a simulation verification unit, and a formula optimization unit. This module receives data from the data storage module and performs quantitative correlation modeling between components and anti-inflammatory efficacy, multi-objective formula optimization, and digital simulation verification for product performance. Specifically, the component-efficacy correlation unit trains a deep learning model on lipid component data and anti-inflammatory function evaluation data to mine the nonlinear correlation between multi-component combinations and anti-inflammatory efficacy, outputting a quantitative correlation equation. The deep learning model uses a BP neural network or random forest algorithm, taking lipid component data (betulinol concentration, betulinic acid concentration, and their ratio) from the birch sap lipid component database in the data storage module as input variables, and anti-inflammatory efficacy data (TNF-α inhibition rate, ...) from the anti-inflammatory function evaluation database as input variables. The unit is trained using pathway activation level as the output variable. The resulting quantitative correlation equation is used to predict anti-inflammatory activity based on the input component ratios and can automatically identify key influencing components and their optimal content ranges. For example, this unit can identify that the anti-inflammatory activity peaks when betulinol content is 40-60 μg / mL and betulinic acid content is 20-30 μg / mL. Please refer to [reference needed]. Figure 2 It illustrates a schematic diagram of the component-efficacy correlation model provided in the embodiments of this application, such as... Figure 2 As shown, the horizontal axis represents the lipid composition ratio (betulinol:betulinic acid), and the vertical axis represents the TNF-α inhibition rate. The curves illustrate the changes in anti-inflammatory efficacy under different ratios. The optimal ratio of 2:1 is marked in the figure, corresponding to a TNF-α inhibition rate of 52%, and the confidence interval is also displayed to demonstrate the accuracy of the model's prediction. This schematic diagram visually presents the nonlinear mapping relationship between components and efficacy reflected by the quantitative correlation equation.

[0022] In this embodiment, the multi-objective optimization unit receives the quantitative correlation equation output by the component-efficacy correlation unit and uses it as the efficacy objective function. With optimal anti-inflammatory efficacy (inhibition rate of inflammatory factors ≥50%), lowest component cost, and strongest production feasibility as three optimization objectives, and combining raw material supply constraints and component compatibility constraints, a genetic algorithm performs global optimization within the feasible domain defined by the constraints. The raw material supply constraints include the availability of raw materials from specific origins and the lower limit of raw material purity; the component compatibility constraints include the physicochemical compatibility between lipid components and excipients, and pH matching range. The genetic algorithm encodes the decision variables (concentration of each component) using real-number encoding. Through non-dominated sorting, crowding calculation, tournament selection, simulated binary crossover, and polynomial mutation operations, it iteratively evolves in the solution space, ultimately extracting the Pareto optimal solution set. This unit automatically generates multiple candidate formulation schemes and prioritizes each candidate formulation scheme. The ranking can use a weighted sum method, comprehensively scoring the schemes according to preset weights or user preferences, and outputting an ordered list of candidate schemes. Each scheme includes component ratios, predicted efficacy values, and cost values.

[0023] In this embodiment, the simulation verification unit combines finite element analysis and multiphysics simulation technology to construct a digital twin of the product based on the candidate formulation schemes generated by the multi-objective optimization unit. The digital twin is a virtual mapping of the product formulation's behavior in a real environment, simulating the stability of lipid components in the product system, the absorption efficiency in the human gastrointestinal tract, and the anti-inflammatory efficacy process. Stability simulation covers changes in component retention rates under different storage environments (room temperature, low temperature, high temperature); absorption efficiency simulation is based on a physiological pharmacokinetic model to predict the dissolution and absorption processes of lipid components in the human gastrointestinal tract; and the anti-inflammatory efficacy process simulation predicts the inhibition rate of inflammatory factors and the degree of activation of anti-inflammatory pathways. The error between the simulation verification results and physical experiments is controlled below 5%, which can simulate the changing patterns of the product's anti-inflammatory efficacy under different storage environments and usage scenarios, providing a basis for product packaging design and instruction manual formulation. The formulation optimization unit is used to iteratively optimize the candidate formulation schemes based on the simulation verification results. When the simulation verification results are unsatisfactory (for example, the loss rate of betulinic acid in a certain formula exceeds the acceptable range under high temperature sterilization process), the unit automatically feeds back the optimization suggestions to the multi-objective optimization unit, driving it to re-perform the genetic algorithm optimization under the adjusted constraints, automatically adjust the allocation ratio and process parameters, and regenerate the optimized formula scheme until the simulation verification results are satisfactory, forming a closed loop iteration of optimization-simulation-re-optimization.

[0024] In this embodiment, the business function execution module is a functional service layer oriented towards the product development process, including an ingredient screening unit, a formula design unit, a scheme verification unit, a compliance verification unit, and a data traceability unit. The ingredient screening unit receives user input of requirement parameters based on target anti-inflammatory efficacy and cost budget. The target anti-inflammatory efficacy can be divided into different levels (e.g., mild anti-inflammatory, moderate anti-inflammatory), and the cost budget is the upper limit of the target cost per bottle of product. Based on the above requirement parameters, this unit automatically matches the birch sap lipid component database and raw material attribute database in the data storage module, filters target lipid components and their content ranges that meet the requirement parameters, and displays the origin, processing technology, and purity information of the matching raw materials for user confirmation.

[0025] In this embodiment, the formulation design unit provides two modes: intelligent generative design and manual adjustment. In intelligent generative design mode, the formulation design unit calls upon candidate formulation schemes output by the multi-objective optimization unit in the intelligent analysis and calculation module to generate a designed formulation scheme including ingredient ratios, predicted efficacy values, and cost values. Users can adjust the target weights (e.g., prioritizing efficacy or cost), and the formulation design unit updates the formulation content in real time. In manual adjustment mode, the formulation design unit receives user adjustments to the ingredient ratios and, by calling the component-efficacy correlation unit in the intelligent analysis and calculation module, inputs the adjusted ingredient ratios into the quantitative correlation equation, calculates and feeds back the anti-inflammatory efficacy change value in real time, assisting users in optimizing the formulation. The two modes can be flexibly switched to meet the R&D needs of different users.

[0026] In this embodiment, the scheme verification unit calls the simulation verification unit in the intelligent analysis and calculation module to perform three-dimensional simulation verification on the design formula scheme generated by the formula design unit. Specifically, stability simulation simulates changes in component retention rate and product form under different storage environments, such as simulating a betulinic acid retention rate of 82% after 6 months of storage at room temperature; anti-inflammatory efficacy simulation predicts the inhibition rate of inflammatory factors and the degree of activation of anti-inflammatory pathways; and production compatibility simulation verifies whether the formula is compatible with the mixing, filling, and sterilization process parameters of the existing production line. After the simulation is completed, the scheme verification unit outputs a simulation report containing existing problems and optimization suggestions. For example, when the betulinic acid loss rate of a formula reaches 18% under high-temperature sterilization, exceeding the acceptable range, the simulation report marks the problem and provides optimization suggestions: "It is recommended to adjust the sterilization temperature to 85℃, which can reduce the betulinic acid loss rate to 12%." If the simulation verification fails, the optimization suggestions are fed back to the formula optimization unit of the intelligent analysis and calculation module to drive formula iteration; if the simulation verification is successful, the scheme enters the compliance verification stage.

[0027] In this embodiment, the compliance verification unit receives verified design formulation schemes and automatically compares them with the product standard database in the data storage module. Verification includes checking whether the content of formulation ingredients exceeds regulatory limits, whether heavy metal levels exceed standards, and whether functional claims comply with the "Administrative Measures for the Registration and Filing of Health Foods," among other regulations. After verification, a compliance conclusion is output to ensure the design scheme meets market compliance requirements. The data traceability unit manages data throughout the entire R&D process through a product lifecycle management system. The data recorded in this unit covers ingredient sources (origin, batch), formulation parameters (content of each ingredient), simulation results (stability, efficacy, and production compatibility simulation reports), production process adjustment records, and compliance verification conclusions. All data constitutes a traceable, structured R&D dataset and is stored as a design knowledge base for subsequent R&D projects to query and reuse, improving knowledge reuse efficiency.

[0028] In this embodiment, the interactive application module adopts a cloud deployment mode, supporting cross-terminal access, including computers and tablets. This module provides a visual interactive interface, including a 3D model display interface, a data chart analysis interface, a scheme comparison interface, and a collaborative editing interface. The 3D model display interface shows the product form and component distribution, allowing users to observe the product's appearance and the spatial distribution of its internal components from multiple angles through rotation and zoom. The data chart analysis interface displays the component-efficacy correlation, formulation parameters, and simulation results output by the data storage module and intelligent analysis and calculation module in the form of line graphs and bar charts. For example, a line graph shows the TNF-α inhibition rate change curves under different ratios of betulinol and betulinic acid, while a bar chart compares the efficacy indicators of different formulation schemes. The scheme comparison interface displays the efficacy indicators, cost data, and feasibility scores of different formulations side-by-side, allowing users to compare the advantages and disadvantages of different schemes to assist in the final decision. The collaborative editing interface supports real-time synchronous collaboration among multiple teams on the same design scheme, allowing R&D personnel in different regions to simultaneously edit and discuss formulation parameters.

[0029] In addition, the interactive application module includes an AR health guidance unit. This unit receives scans of the virtual product generated from the designed formula and triggers dynamic content display. The dynamic content includes animations explaining the anti-inflammatory mechanism of lipid components and product usage suggestions, providing technical support for subsequent product marketing and promotion. After the R&D process is completed, the interactive application module exports the complete design scheme, including formula parameters, production processes, efficacy prediction reports, and compliance verification reports, into a standardized document, directly connecting to the production system to achieve seamless integration between design and R&D and manufacturing.

[0030] The above is a description of the system embodiments of this application. Based on the foregoing embodiments, the method embodiments of this application are described below.

[0031] Please refer to Figure 2 It illustrates a flowchart of a digital design method for functional products based on the component-function association of birch sap, provided in an embodiment of this application. This method is applied to, for example... Figure 1 The digital design platform for functional products based on the component-function association of birch sap shown is illustrated. For details not disclosed in the method embodiment, please refer to the system embodiment. The method includes the following steps S210 to S250.

[0032] Step S210: Receive design requirements input by the user through the interactive application module. The design requirements include product type, target anti-inflammatory efficacy, cost budget, and production constraints.

[0033] In this embodiment, the user inputs structured design requirement parameters through the visual interactive interface of the interactive application module. The design requirements include product type, target anti-inflammatory efficacy, cost budget, and production constraints. The product type can be oral liquid, solid beverage, functional beverage, etc.; the target anti-inflammatory efficacy can be divided into different levels such as mild anti-inflammatory and moderate anti-inflammatory, and quantified as specific inflammatory factor inhibition rate indicators (e.g., TNF-α inhibition rate ≥50%); the cost budget is the upper limit of the target cost per bottle; and the production constraints are adapting to existing production line process parameters, such as the upper limit of sterilization temperature and the lower limit of filling speed. After receiving the above design requirements, the interactive application module transmits them as a design task package to the data storage module and intelligent analysis and calculation module through a bidirectional data interface, initiating a closed-loop R&D process.

[0034] In step S220, the data storage module automatically extracts data on lipid components of birch sap, evaluation data on anti-inflammatory function, and raw material properties based on the target anti-inflammatory efficacy and constraints, and transmits them to the intelligent analysis and calculation module.

[0035] In this embodiment, after receiving the design task package, the data storage module performs multidimensional data query and extraction based on the target anti-inflammatory efficacy and constraints. The extracted birch sap lipid component data comes from a birch sap lipid component database, covering the content, purity, and stability parameters of triterpenoid lipid components such as betulinol and betulinic acid in birch sap samples from different origins, harvesting seasons, and processing techniques. Screening conditions are linked to the target efficacy requirements, prioritizing raw material data with compliant component content and high batch stability. For example, for moderate anti-inflammatory needs, raw material data with betulinol content ≥50 μg / mL and betulinic acid content ≥25 μg / mL are screened; the origin is prioritized in the Northeast forest region; the harvesting season is prioritized in the spring budding period; and the processing technique is prioritized in low-temperature pressing. The extracted anti-inflammatory function evaluation data comes from an anti-inflammatory function evaluation database, containing quantitative relationships between different lipid component concentrations, ratios, and the inhibition rates of inflammatory factors and the degree of activation of anti-inflammatory pathways. This data provides training samples for subsequent component-efficacy correlation modeling, specifically including in vitro cell experiments (macrophage inflammation model), animal experiments (mouse ear swelling model), and clinical pilot data. The extracted raw material attribute data comes from a raw material attribute database, containing the physicochemical properties, compatibility, and safety indicators of birch sap raw materials and excipients. The physicochemical properties include parameters such as pH value and viscosity, while the safety indicators include microbial limits and heavy metal content. After extraction, the above structured dataset is transmitted to the intelligent analysis and computing module via a bidirectional data interface, serving as the data foundation for subsequent modeling and optimization.

[0036] Step S230: The extracted data is trained by the intelligent analysis and calculation module using the component-efficacy correlation algorithm to establish a quantitative correlation equation. Based on the quantitative correlation equation, a multi-objective optimization algorithm is used to generate multiple candidate formulation schemes with the objectives of optimal anti-inflammatory efficacy, lowest component cost, and strongest production feasibility, combined with constraints.

[0037] In this embodiment, after receiving data transmitted by the data storage module, the intelligent analysis and calculation module sequentially performs modeling and optimization through the component-efficacy correlation unit and the multi-objective optimization unit. First, the component-efficacy correlation unit trains on the extracted lipid component data and anti-inflammatory function evaluation data based on a deep learning model. The deep learning model employs a BP neural network or random forest algorithm, using lipid component data (betulin concentration, betulinic acid concentration, and their ratio) as input variables and anti-inflammatory efficacy data (TNF-α inhibition rate, ... Using pathway activation level as the output variable, this study explores the nonlinear correlation between multi-component combinations and anti-inflammatory efficacy, outputting a quantitative correlation equation. This equation is used to predict anti-inflammatory activity based on the input component ratios and can automatically identify key influencing components and their optimal content ranges. For example, the equation can identify that the anti-inflammatory activity reaches its peak when betulinol content is 40-60 μg / mL and betulinic acid content is 20-30 μg / mL.

[0038] Furthermore, the multi-objective optimization unit receives the quantified correlation equation output by the component-efficacy correlation unit and uses it as the efficacy objective function. With optimal anti-inflammatory efficacy, lowest component cost, and strongest production feasibility as the three optimization objectives, and combined with raw material supply constraints and component compatibility constraints, a genetic algorithm performs global optimization in the solution space. The raw material supply constraints include the availability of raw materials from specific origins and the lower limit of raw material purity; the component compatibility constraints include the physicochemical compatibility between lipid components and excipients, and pH matching ranges. The genetic algorithm uses real-number encoding to encode the decision variables (concentration of each component), and through iterative evolution via selection, crossover, and mutation operations, extracts the Pareto optimal solution set, automatically generates multiple candidate formulation schemes, and prioritizes each candidate formulation scheme. The ranking can use a weighted sum method, calculating a comprehensive score based on default weights or user preferences and then arranging them in descending order. Each candidate formulation scheme includes specific component ratios, predicted efficacy values, and cost values.

[0039] In step S240, the business function execution module calls the simulation verification algorithm of the intelligent analysis and calculation module to perform stability simulation, anti-inflammatory efficacy simulation, and production adaptability simulation on the candidate formulation schemes. Schemes that do not meet the constraints are eliminated, and the parameters of the retained schemes are optimized. If the simulation verification fails, the multi-objective optimization algorithm is driven by the optimization suggestions to regenerate the formulation scheme until the simulation verification is successful.

[0040] In this embodiment, the scheme verification unit of the business function execution module receives candidate formulation schemes and calls the simulation verification unit of the intelligent analysis and calculation module to construct a digital twin of the product for each candidate formulation scheme and perform three-dimensional simulation verification. Specifically, stability simulation is used to simulate the retention rate of components and changes in product morphology under different storage environments. For example, it simulates the retention rate of betulin after a formulation is stored at room temperature for 6 months, and whether morphological changes such as stratification and precipitation occur. Anti-inflammatory efficacy simulation is used to predict the inhibition rate of inflammatory factors and the degree of activation of anti-inflammatory pathways. Based on a physiological pharmacokinetic model, it simulates the dissolution and absorption process of lipid components in the human gastrointestinal tract to predict the inhibitory effect of inflammatory factors in vivo. Production suitability simulation is used to verify whether the formulation is suitable for the stirring, filling, and sterilization process parameters of the existing production line. For example, it simulates the heat loss rate of each component of a formulation under specific sterilization temperatures and holding times to determine whether it meets production requirements.

[0041] The error between the simulation verification results and the physical experiments is controlled below 5%. After the simulation is completed, the scheme verification unit outputs a simulation report containing existing problems and optimization suggestions. Schemes that pass the simulation are retained and proceed to subsequent steps; schemes that fail the simulation are discarded, and optimization suggestions are fed back to the formulation optimization unit of the intelligent analysis and calculation module. The formulation optimization unit drives the multi-objective optimization unit to re-optimize using a genetic algorithm under adjusted constraints, automatically adjusting the allocation ratio and process parameters, regenerating the optimized formulation scheme, and resubmitting it for simulation verification. This closed-loop iteration of "optimization-simulation-re-optimization" continues until the simulation verification results of all retained schemes are qualified.

[0042] Step S250: The business function execution module performs compliance verification on the qualified formula scheme. After confirming the compliance of the scheme, the interactive application module outputs a complete design scheme including formula parameters, production process, efficacy prediction report and compliance verification report.

[0043] In this embodiment, the business function execution module receives the formula scheme that has passed simulation verification and automatically compares it with the product standard database in the data storage module. The verification includes checking whether the content of the formula ingredients exceeds the permitted range by regulations, whether the heavy metal index exceeds the standard, and whether the functional claims comply with relevant regulations such as the "Administrative Measures for the Registration and Filing of Health Foods." After verification, a compliance conclusion is output. For schemes that pass the compliance verification, the interactive application module provides a visual display and exports the scheme. Users can view the efficacy indicators, cost data, and feasibility scores of different schemes side-by-side through the scheme comparison interface to make a final decision. After confirming the scheme, the interactive application module exports the complete design scheme, including formula parameters, production process, efficacy prediction report, and compliance verification report, as a standardized document, directly connecting to the production system to achieve seamless integration of design and development with manufacturing.

[0044] Throughout the execution of the methodology, the data traceability unit of the business function execution module manages data across the entire process through the product lifecycle management system. It records ingredient sources, formula parameters, simulation results, production process adjustments, and compliance verification conclusions, forming a traceable, structured R&D dataset, which is then stored in a design knowledge base for subsequent R&D projects to query and reuse. Simultaneously, the data storage module employs blockchain technology to protect the copyright of original data, establishes a user contribution mechanism, receives user-uploaded R&D data, and incorporates it into the corresponding database after professional review, enabling dynamic data iteration.

[0045] The following description uses a specific embodiment to illustrate the above-mentioned digital design platform for functional products based on the component-function association of birch sap. However, it is worth noting that this specific embodiment is only for better illustration of this application and does not constitute an improper limitation of this application.

[0046] Example: Digital Design of an Anti-inflammatory Oral Solution Based on Birch Sap The system and method described in this embodiment are used for the digital design of birch sap anti-inflammatory oral liquid, as detailed below: Users input design requirements through the interactive application module: the product type is oral liquid, the target anti-inflammatory effect is moderate anti-inflammatory (TNF-α inhibition rate ≥50%), the cost budget is ≤5 yuan per bottle, and the production constraints are to adapt to the existing oral liquid production line (sterilization temperature ≤90℃, filling speed ≥50 bottles / minute).

[0047] The data storage module automatically extracts data based on demand. The birch sap lipid composition database filters raw material data collected in spring in Northeast China using a low-temperature pressing process, with betulinol content ≥50μg / mL and betulinic acid content ≥25μg / mL. The anti-inflammatory function evaluation database extracts anti-inflammatory activity data for different ratios of betulinol and betulinic acid. The raw material property database extracts physicochemical properties, compatibility, and safety data for commonly used stabilizers and flavorings in oral liquids. After extraction, the data is transmitted to the intelligent analysis and calculation module via a bidirectional data interface.

[0048] The component-efficacy correlation unit of the intelligent analysis and calculation module is trained on extracted lipid component data and anti-inflammatory efficacy data using a BP neural network to establish a quantitative correlation equation. This equation can predict the TNF-α inhibition rate under any component ratio and identify that the anti-inflammatory activity reaches its peak when the betulin content is 40-60 μg / mL and the betulinic acid content is 20-30 μg / mL. The multi-objective optimization unit uses the quantitative correlation equation as the efficacy objective function, while also introducing a cost function and a production feasibility penalty function. A genetic algorithm is used to perform global optimization under constraints (cost ≤ 5 yuan, sterilization temperature ≤ 90℃), generating three candidate formulation schemes: Scheme 1 (betulinol 55μg / mL, betulinic acid 25μg / mL, stabilizer 0.1%, flavoring agent 0.2%), predicting a TNF-α inhibition rate of 52% and a cost per bottle of 4.8 yuan; Scheme 2 (betulinol 50μg / mL, betulinic acid 30μg / mL, stabilizer 0.12%, flavoring agent 0.18%), predicting a TNF-α inhibition rate of 53% and a cost per bottle of 4.9 yuan; Scheme 3 (betulinol 60μg / mL, betulinic acid 20μg / mL, stabilizer 0.09%, flavoring agent 0.22%), predicting a TNF-α inhibition rate of 51% and a cost per bottle of 4.7 yuan.

[0049] The scheme verification unit of the business function execution module calls the simulation verification unit of the intelligent analysis and calculation module to construct digital twins for simulation of three candidate schemes. Scheme 1 has a betulinic acid loss rate of 12% under 85℃ sterilization, which meets the requirement (loss rate ≤14%). Scheme 2 has a component loss rate of 10% after sterilization, but the stabilizer dosage is slightly high, which may affect the taste. Scheme 3 has a component loss rate of 15% after sterilization, which exceeds the acceptable range. The simulation verification unit outputs a simulation report, providing optimization suggestions for Scheme 3: it is recommended to adjust the sterilization temperature to 82℃, which can reduce the betulinic acid loss rate to 13%. The formula optimization unit feeds back this optimization suggestion to the multi-objective optimization unit. After readjusting the process parameters of Scheme 3, the loss rate is reduced to 13%, and the simulation verification is qualified.

[0050] The compliance verification unit receives simulated and qualified solutions, automatically compares them with the product standard database, and confirms that the component content, heavy metal index, and functional claims of each solution comply with the "Administrative Measures for the Registration and Filing of Health Foods" and other regulations. Users select Solution 1 after comprehensive comparison through the solution comparison interface of the interactive application module. The interactive application module exports Solution 1 as a complete design scheme including formula parameters (betulinol 55μg / mL, betulinic acid 25μg / mL, stabilizer 0.1%, flavoring agent 0.2%), production process (sterilization temperature 85℃, holding time 15 minutes, filling speed 55 bottles / minute), efficacy prediction report (TNF-α inhibition rate predicted value 52%), and compliance verification report, directly connecting to the production system. Data from the entire R&D process is recorded by the data traceability unit through the product lifecycle management system and stored in the design knowledge base.

[0051] The birch sap anti-inflammatory oral liquid designed using this system and method has shortened the R&D cycle from the traditional 3 months to 15 days, reduced trial and error costs by 40%, and ensured that the product's anti-inflammatory efficacy accurately meets the target (the measured TNF-α inhibition rate is 51.8%, which is basically consistent with the predicted value of 52%). It is also fully compatible with existing production processes, achieving a dual improvement in R&D efficiency and product quality.

[0052] In summary, the functional product digital design platform based on the component-function correlation of birch sap provided in this application establishes a birch sap lipid component database and an anti-inflammatory function evaluation database by constructing a data storage module integrating multi-dimensional data and an intelligent analysis and calculation module integrating deep learning models. It utilizes a component-efficacy correlation algorithm to mine the nonlinear correlation between multi-component combinations and anti-inflammatory efficacy, outputting a quantitative correlation equation. This quantitative correlation equation can accurately predict the anti-inflammatory activity of specific component ratios and automatically identify key influencing components and their optimal content ranges, transforming formulation design from experience-based judgment to data-driven approaches, thereby significantly improving the stability and reliability of the product's anti-inflammatory efficacy. Through a closed-loop R&D process of data input, model construction, simulation verification, and design output, it achieves full-process digital integration from demand input to solution output. The multi-objective optimization algorithm automatically generates multiple candidate formulation schemes with three optimization objectives: optimal anti-inflammatory efficacy, lowest ingredient cost, and strongest production feasibility. The simulation verification algorithm constructs a digital twin of the product to simulate the stability of lipid components in the product system, the absorption efficiency in the human gastrointestinal tract, and the process of anti-inflammatory efficacy. The error between simulation results and physical experiments is controlled below 5%, significantly reducing the number of physical experiments. Simultaneously, the data traceability function, through the product lifecycle management system, stores information such as formulation parameters, simulation results, and process adjustments into a structured design knowledge base for reuse in subsequent R&D. Through these technologies, the R&D cycle can be shortened by more than 50%, trial-and-error costs reduced by more than 40%, and knowledge reuse rate increased to over 70%. The scheme verification function performs production adaptability simulations on the formulation schemes to verify whether the formulation is suitable for the mixing, filling, and sterilization process parameters of existing production lines. The compliance verification function automatically compares the formulation with the product standard database to verify whether the content of formulation ingredients, heavy metal indicators, and functional claims comply with relevant regulations and quality standards. Pre-verification of production feasibility and compliance is completed during the design phase, avoiding changes due to process incompatibility or non-compliance after the design scheme enters the production stage. Complete design schemes can be directly exported and integrated with the production system, achieving seamless integration between design and production. Through these technical means, the design change rate can be reduced to below 10%, effectively avoiding production rework and compliance risks, and significantly improving the efficiency of transformation from R&D to industrialization. Adopting a cloud deployment model, it supports cross-terminal access such as computers and tablets, providing visual interactive functions such as 3D model display, data chart analysis, scheme comparison, and collaborative editing, making the R&D process intuitive, visual, and easy to operate. Integrating AR health guidance functions, users can scan virtual products to trigger dynamic content such as animations explaining the anti-inflammatory principles of lipid components and product usage suggestions, providing technical support for subsequent product marketing and promotion. Therefore, this application is applicable to different user groups such as enterprise R&D personnel, research institutions, and makers, and has broad application prospects.Furthermore, blockchain technology is used to protect the copyright of original data and a user contribution mechanism is established. This mechanism receives user-uploaded R&D data, which is then professionally reviewed and incorporated into the corresponding database, enabling dynamic iteration and sharing of data. While protecting the intellectual property rights of data contributors, this mechanism promotes the continuous enrichment of data resources, providing a sustainable data foundation for the long-term innovative R&D of birch sap functional products.

[0053] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A digital design platform for functional products based on the component-function correlation of birch sap, characterized in that, The platform includes: The data storage module is used to store data on lipid components of birch sap, anti-inflammatory function evaluation data, raw material property data, and quality standard data for anti-inflammatory products. The intelligent analysis and calculation module is used to determine the nonlinear correlation between lipid component combinations and anti-inflammatory effects based on the component-efficacy correlation algorithm, and output a quantitative correlation equation; based on the quantitative correlation equation, candidate formulation schemes are determined through a multi-objective optimization algorithm; and the candidate formulation schemes are simulated and verified by a simulation verification algorithm. The business function execution module is used to receive user requirement parameters, filter target lipid components and content ranges, and display matching raw material information; in the intelligent generative design mode, it calls the candidate formulation scheme to determine the design formulation scheme; in the manual adjustment mode, it receives the user's component ratio adjustment operation and provides real-time feedback on efficacy change values; it performs stability simulation, anti-inflammatory efficacy simulation, and production adaptability simulation on the design formulation scheme and outputs a simulation report. The interactive application module is used to receive user design requirements input, visualize the design process and results, and export the final design formula to connect with the production system.

2. The system of claim 1, wherein, The data storage module includes: The birch sap lipid component database is used to store the content, purity, and stability parameters of triterpenoid lipid components such as betulinol and betulinic acid. The data dimensions cover the place of origin, harvesting season, processing technology, and detection methods, and are associated with the corresponding storage conditions and shelf life data to build the relationship between component properties and environmental factors. An anti-inflammatory function evaluation database is used to integrate in vitro experimental, animal experimental and clinical data, store the quantitative relationship between the concentration and ratio of different lipid components and the inhibition rate of inflammatory factors and the degree of activation of anti-inflammatory pathways, and construct a standardized efficacy evaluation index system. The raw material property database is used to store the physicochemical properties, compatibility, and safety indicators of birch sap raw materials and excipients. The product standards database is used to embed relevant regulations for food / health products and quality standards for anti-inflammatory products.

3. The system of claim 2, wherein, The data storage module uses blockchain technology to protect the copyright of original data and establishes a user contribution mechanism. It receives R&D data uploaded by users and, after professional review, incorporates the approved new data into the corresponding database, thereby realizing dynamic iteration and sharing of the data layer.

4. The system of claim 1, wherein, The intelligent analysis and calculation module includes: The component-efficacy correlation unit is used to train a deep learning model on lipid component data and anti-inflammatory function evaluation data, mine the nonlinear correlation between multi-component combinations and anti-inflammatory efficacy, and output a quantitative correlation equation. The quantitative correlation equation is used to predict anti-inflammatory activity based on the input component ratio and automatically identify key influencing components and their optimal content range. The multi-objective optimization unit is used to take the quantitative correlation equation as the efficacy objective function, with the goals of optimal anti-inflammatory efficacy, lowest ingredient cost, and strongest production feasibility. Combining raw material supply constraints and ingredient compatibility constraints, it automatically generates multiple candidate formulation schemes through a genetic algorithm and prioritizes each candidate formulation scheme. The simulation verification unit is used to combine finite element analysis and multiphysics simulation technology to construct a digital twin of the product for the candidate formulation scheme, simulate the stability of lipid components in the product system, the absorption efficiency of human gastrointestinal tract and the anti-inflammatory effect, and output simulation verification results. The error between the simulation verification results and the physical test is controlled to be below 5%. The formulation optimization unit is used to iteratively optimize the candidate formulation scheme based on the simulation verification results. When the simulation verification results are unqualified, it automatically adjusts the distribution ratio and process parameters and regenerates the optimized formulation scheme until the simulation verification results are qualified.

5. The system of claim 1, wherein, The business function execution module includes: The ingredient screening unit is used to receive the user's input requirements parameters based on the target anti-inflammatory efficacy and cost budget, automatically match the birch sap lipid component database and raw material attribute database in the data storage module, screen the target lipid components and their content range that meet the requirements parameters, and display the origin, processing technology and ingredient purity information of the matched raw materials. The formulation design unit provides two modes: intelligent generative design and manual adjustment. In the intelligent generative design mode, it calls the candidate formulation schemes output by the multi-objective optimization unit to generate a design formulation scheme that includes ingredient ratios, predicted efficacy values, and cost values, and supports users to adjust the target weights to update the formulation in real time. In the manual adjustment mode, it receives user adjustments to the ingredient ratios and provides real-time feedback on changes in anti-inflammatory efficacy values ​​by calling the ingredient-efficacy correlation unit. The scheme verification unit is used to call the simulation verification unit to perform stability simulation, anti-inflammatory efficacy simulation, and production adaptability simulation on the designed formulation scheme. The stability simulation is used to simulate changes in component retention rate and product form under different storage environments; the anti-inflammatory efficacy simulation is used to predict the inhibition rate of inflammatory factors and the degree of activation of anti-inflammatory pathways; and the production adaptability simulation is used to verify whether the formulation is suitable for the mixing, filling, and sterilization process parameters of the existing production line. After the simulation is completed, a simulation report containing existing problems and optimization suggestions is output.

6. The system of claim 5, wherein, The business function execution module also includes: The compliance verification unit is used to receive verified design formulas, automatically compare them with the product standard database, and verify whether the formula ingredient content, heavy metal index and functional claims comply with relevant regulations and anti-inflammatory product quality standards. The data traceability unit is used to manage data throughout the entire R&D process through the product lifecycle management system, recording ingredient sources, formula parameters, simulation results, production process adjustments, and compliance verification conclusions to build a traceable R&D dataset.

7. The system according to claim 1, characterized in that, The interactive application module adopts a cloud deployment mode and supports cross-terminal access, including computers and tablet devices. The interactive application module provides a visual interactive interface, including: A 3D model display interface is used to showcase the product's form and component distribution; The data chart analysis interface is used to display the component-efficacy relationship, formula parameters and simulation results output by the data storage module and intelligent analysis and calculation module in the form of line charts and bar charts; The scheme comparison interface is used to display the efficacy indicators, cost data, and feasibility scores of different formulas side by side; The collaborative editing interface is designed to support real-time synchronous collaboration among multiple teams on the same design scheme.

8. The system of claim 7, wherein, The interactive application module also includes an AR health guidance unit, which is used to receive the user's scanning operation of the virtual product generated by the design formula and trigger dynamic content display. The dynamic content includes an animation explaining the anti-inflammatory principle of lipid components and product usage advice information.

9. A functional product digital design method based on birch sap ingredient-function association, applied to the system of any one of claims 1-9, characterized in that, include: The interactive application module receives design requirements input by the user, including product type, target anti-inflammatory efficacy, cost budget, and production constraints. The data storage module automatically extracts data on lipid components, anti-inflammatory function evaluation data, and raw material properties of birch sap based on the target anti-inflammatory efficacy and constraints, and transmits them to the intelligent analysis and calculation module. The intelligent analysis and calculation module trains the extracted data using a component-efficacy correlation algorithm to establish a quantitative correlation equation. Based on the quantitative correlation equation, a multi-objective optimization algorithm is used to generate multiple candidate formulation schemes with the objectives of optimal anti-inflammatory efficacy, lowest component cost, and strongest production feasibility, combined with constraints. Through the business function execution module, the simulation verification algorithm of the intelligent analysis and calculation module is called to perform stability simulation, anti-inflammatory efficacy simulation and production adaptability simulation on the candidate formulation schemes. Schemes that do not meet the constraints are eliminated, and the parameters of the retained schemes are optimized. If the simulation verification fails, the multi-objective optimization algorithm is driven by the optimization suggestions to regenerate the formulation scheme until the simulation verification is successful. The business function execution module performs compliance verification on the qualified formula scheme. After confirming the compliance of the scheme, the interactive application module outputs a complete design scheme including formula parameters, production process, efficacy prediction report and compliance verification report.

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