Nano-encapsulation method for stabilizing wine

Abstract

This invention relates to the field of tonic wine stabilization technology and discloses a nano-encapsulation method for stabilizing tonic wine. The method includes constructing a digital encapsulation matrix model of a tonic wine system containing multiple active components, extracting the molecular structural characteristics and physicochemical parameters of the active substances, and, in conjunction with preset encapsulation target requirements, analyzing the key limiting factors for the stability of the tonic wine system and generating an encapsulation demand vector. Then, based on this vector, a multi-level screening operation is performed in the model to locate the target encapsulation domain. Within this domain, relevant data of the nanocarrier material are integrated, and the compatibility between it and the active substance units is evaluated using multi-parameter fusion calculation rules to obtain an optimal sequence of nano-encapsulation materials. Based on this sequence, a nano-encapsulation operation instruction set is generated to perform nano-encapsulation processing on the tonic wine system, obtaining an encapsulated composite system. The dispersion uniformity and sedimentation rate of the system are monitored in real time, and its stability is verified using preset standards, outputting verification result data.

Description

Technical Field

[0001] This invention relates to the field of tonic wine stabilization technology, specifically a nano-encapsulation method for stabilizing tonic wine. Background Technology

[0002] Tonic wine, a traditional beverage that blends traditional Chinese medicine's health-preserving concepts with wine culture, has a long history in my country. By adding various Chinese medicinal herbs to the wine, it combines the blood-circulating and medicinal properties of the wine with the nourishing and regulating effects of the herbs, offering numerous health benefits and making it popular among consumers. However, tonic wine systems face many stability challenges during actual production and storage.

[0003] Tonic wines typically contain a variety of active ingredients, which are widely sourced and diverse in nature. The active components in traditional Chinese medicinal herbs, such as polysaccharides, saponins, and flavonoids, have complex and varied molecular structures. Within the tonic wine system, different active substances may undergo chemical reactions, such as oxidation, polymerization, and hydrolysis, leading to a decrease in the content of active ingredients or even the production of harmful byproducts, affecting the quality and efficacy of the tonic wine. For example, ginsenosides in some tonic wines may undergo structural changes due to oxidation during long-term storage, significantly reducing their original tonic effects.

[0004] The stability of tonic wines is also affected by physical factors. Because the active substances in tonic wines vary in particle size, they are prone to sedimentation and stratification under gravity, severely impacting the uniformity of their appearance and taste. Some tonic wines, after a period of storage, will develop noticeable sediment at the bottom of the bottle, while the upper layer becomes relatively clear. This not only reduces consumer willingness to purchase but may also lead to uneven distribution of active ingredients, affecting the stability of the tonic wine's efficacy.

[0005] Traditional tonic wine production processes have limitations in addressing stability issues. Common methods such as filtration and clarification, while improving the appearance of the tonic wine to some extent, cannot fundamentally solve the problems of chemical and physical stability of active substances. Adding chemical stabilizers, although improving stability to some degree, may introduce new safety risks and adversely affect the flavor and taste of the tonic wine. With consumers' increasing demands for the quality and safety of tonic wines, developing an efficient, safe, and environmentally friendly method for stabilizing tonic wines has become an urgent priority. Summary of the Invention

[0006] The purpose of this invention is to provide a method for stabilizing alcohol by nano-encapsulation, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a method for stabilizing alcohol by nano-encapsulation, the method comprising:

[0008] Construct a digital embedding matrix model for a tonic wine system containing multiple active components;

[0009] The molecular structure features and physicochemical parameters of the active substances in the digital embedding matrix model are extracted. Combined with the preset embedding target requirements, the key factors restricting the stability of the tonic wine system are analyzed, and an embedding demand vector is generated based on the key factors restricting the stability.

[0010] Based on the embedding requirement vector, a multi-level screening operation is performed in the digital embedding matrix model to locate the target embedding domain that matches the stability improvement requirement. The multi-level screening operation includes a layered detection process that gradually expands from the core active unit to the auxiliary stabilizing unit.

[0011] Within the target embedding domain, the surface property data and interfacial energy parameters of the nanocarrier material are integrated, and the compatibility between the nanocarrier material and the active material unit is quantitatively evaluated using multi-parameter fusion calculation rules, resulting in an optimal sequence of nano-embedding materials.

[0012] Based on the preferred sequence of the nano-embedding material, a nano-embedding operation instruction set is generated for the wine replenishment system. The wine replenishment system is subjected to nano-embedding treatment according to the nano-embedding operation instruction set to obtain the treated encapsulated composite system.

[0013] The dispersion uniformity and sedimentation rate of the embedded composite system are monitored in real time. The stability of the embedded composite system is verified in combination with the preset stability verification standard, and the stability verification result data is output.

[0014] Preferably, the construction of the digital encapsulation matrix model for the tonic wine system containing multiple active components includes:

[0015] The digital embedding matrix model represents active substance units through nodes and physical or chemical interactions between active substances through connections. The node attributes and connection strength in the digital embedding matrix model are dynamically adjusted based on the real-time changes in the concentration of the tonic wine components.

[0016] The molecular weight distribution spectrum and functional group characteristic spectrum of each active component in the tonic wine system are collected, each active component is mapped to an independent node in the digital embedding matrix model, and a corresponding molecular structure code is assigned to each node.

[0017] The hydrogen bonding energy and hydrophobic interaction energy between active components are measured, and the connection relationship between nodes in the digital embedding matrix model is established based on the hydrogen bonding energy and hydrophobic interaction energy, and the connection relationship weight coefficient is initialized.

[0018] A dynamic update protocol for the digital embedded matrix model is established. When a temperature fluctuation or pH shift in the wine replenishment system is detected, the topology reconstruction mechanism of the digital embedded matrix model is triggered to recalculate the molecular structure encoding of the affected nodes and update the relevant connection weight coefficients.

[0019] Preferably, the extraction of molecular structural features and physicochemical parameters of active substances from the digitally embedded matrix model includes:

[0020] Identify the polarity index and steric hindrance parameter corresponding to each node in the digital embedding matrix model, and normalize and transform the polarity index and steric hindrance parameter to form a standardized molecular feature vector;

[0021] Obtain the maximum binding energy and minimum dissociation energy corresponding to the connection relationships in the digital embedding matrix model, and calculate the energy difference threshold between the maximum binding energy and the minimum dissociation energy;

[0022] By integrating the standardized molecular feature vectors and the energy difference threshold, a stability constraint analysis report of the tonic wine system is generated.

[0023] Preferably, generating the embedding requirement vector based on the key stability constraints includes:

[0024] Decompose the thermosensitivity and oxidation sensitivity indices in the stability limiting factors analysis report, and extract the critical temperature points corresponding to the thermosensitivity indices and the free radical capture requirements corresponding to the oxidation sensitivity indices;

[0025] The critical temperature point is compared with a preset temperature tolerance benchmark value, and the temperature deviation coefficient is calculated.

[0026] The free radical capture requirement is matched with a preset antioxidant capacity benchmark value to determine the antioxidant gap value;

[0027] The temperature deviation coefficient and the antioxidant gap value are integrated using a demand vector synthesis algorithm to form a multidimensional embedded demand vector.

[0028] Preferably, the multi-level screening operation performed in the digital embedding matrix model includes:

[0029] The digital embedding matrix model is divided into spatial grids, and the multidimensional embedding requirement vector is mapped to the grid space coordinate system.

[0030] Calculate the spatial distance between the multidimensional embedding requirement vector and the center point of each grid cell, and select the N grid cells with the smallest distance values ​​as the initial scope.

[0031] Extract the set of adjacent nodes of the nodes within the initial scope, and detect the matching degree between the molecular polarity parameters of the nodes in the set of adjacent nodes and the multidimensional embedding requirement vector.

[0032] For adjacent nodes whose matching degree reaches the set threshold, a recursive expansion operation is performed, extending layer by layer to the third-level associated nodes to form a complete target embedding scope.

[0033] Preferably, the quantitative evaluation of the compatibility between the nanocarrier material and the active substance unit using multi-parameter fusion calculation rules includes:

[0034] Obtain the charge distribution cloud map of the active material within the target embedding domain and the surface potential spectrum of the nanocarrier material;

[0035] Calculate the electrostatic potential matching value between the charge distribution cloud map and the surface potential spectrum;

[0036] The pore size distribution curves of nanocarrier materials and the molecular size spectra of active substances are measured to generate size fit indices.

[0037] A gradient adjustment mechanism is used to fuse the electrostatic potential matching value and the size fit index to generate a comprehensive fit score for the nanocarrier material.

[0038] Preferably, the output nano-embedded material preferably includes the following sequence:

[0039] Set a grading threshold range for the comprehensive adaptation score, and divide the nanocarrier materials within the target embedding domain into three priority groups;

[0040] Extract biocompatibility data and process cost parameters of nanocarrier materials from high-priority groups;

[0041] The biocompatibility data and the process cost parameters are weighted and sorted to generate an optimal sequence of nano-embedded materials.

[0042] Preferably, the generation of the nano-encapsulation operation instruction set for the tonic wine system includes:

[0043] Analysis of the encapsulation mechanism and dispersion conditions of the first material in the preferred sequence of nano-encapsulation materials;

[0044] Determine the target encapsulation concentration and phase transition temperature control point of the tonic wine system;

[0045] Based on the aforementioned embedding mechanism and the aforementioned dispersion processing conditions, phased embedding process control parameters are formulated;

[0046] Based on the target embedding concentration and the phase transition temperature control point, a set of nano-embedding operation instructions, including time gradient control and temperature gradient control, is generated.

[0047] Preferably, the stability verification of the embedded composite system includes:

[0048] Transmittance data of the embedded composite system at multiple time points were collected under a constant temperature oscillation environment.

[0049] Calculate the attenuation slope of transmittance data at adjacent time points;

[0050] Measure the phase separation time points of the embedded composite system under a centrifugal force field;

[0051] The decay slope value and the phase separation time point are input into the stability verification model, and the stability verification result data is output.

[0052] Preferably, after outputting the stability verification result data, the following is also included:

[0053] When the stability verification result data is lower than the preset standard threshold, the process control parameter adjustment item in the nano-embedding operation instruction set is extracted;

[0054] The dynamic update protocol for updating the digital embedding matrix model is based on the process control parameter adjustment items;

[0055] The topology reconstruction mechanism of the digital embedding matrix model is triggered to regenerate the embedding requirement vector.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] By constructing a digitally embedded matrix model, multiple active components in the tonic wine system can be precisely analyzed within a unified framework. This digital approach breaks through the limitations of traditional methods that rely on experience to judge and handle the stability of tonic wines, ensuring that the entire stabilization process is based on scientific and precise data from the outset. Compared to previous vague and unsystematic processing methods, this significantly improves the comprehensiveness and accuracy of our understanding of the tonic wine system.

[0058] In the stage of analyzing key factors limiting stability, this method combines the molecular structural characteristics, physicochemical parameters, and pre-set encapsulation targets of active substances for in-depth analysis. This means that key factors affecting the stability of tonic wines can be precisely located at the microscopic level. Whether it is the potential chemical reaction risk between active substances or the instability caused by differences in physical properties, these factors can be clearly identified. Unlike previous approaches that addressed stability issues in a general way, this precise analysis makes subsequent treatment measures more targeted, avoiding the waste of resources and poor results caused by blind attempts.

[0059] A key highlight of this method is its multi-layered screening process. The hierarchical detection process, expanding outward from the core active unit to the auxiliary stabilizing unit, is akin to a precise "CT scan" of the tonic wine system. This approach allows for the accurate localization of target encapsulation domains within complex systems that match the needs for stability enhancement. Previous methods often struggled to comprehensively and hierarchically consider the interrelationships of different parts of the tonic wine system, easily overlooking critical areas. This method, through its scientific screening mechanism, ensures that stability enhancement efforts cover the most critical regions, providing precise targets for the effective action of subsequent nano-encapsulation materials.

[0060] Regarding the selection of nano-embedding materials, this invention integrates surface characteristic data and interfacial energy parameters of nanocarrier materials, and uses multi-parameter fusion calculation rules to quantitatively evaluate their compatibility with active substance units, outputting an optimal sequence of nano-embedding materials. This quantitative evaluation method completely changes the subjectivity and arbitrariness of previous nano-embedding material selection. Through precise calculation and scientific evaluation, it can be ensured that the selected nano-embedding materials achieve optimal compatibility with the active substances in the tonic wine. This not only maximizes the protective and stabilizing effect of the nano-embedding materials on the active substances, but also reduces resource waste and potential negative effects caused by poor material compatibility.

[0061] The nano-embedding operation instruction set, generated based on the optimized sequence of nano-embedding materials, makes the nano-embedding treatment of the tonic wine system efficient and standardized. In actual production, operators can perform the nano-embedding operation methodically according to these precise instructions, greatly improving production efficiency and product quality consistency. Compared with the previous manual operation or production process lacking precise guidance, this effectively reduces the uncertainty and human error in the production process, ensuring that each batch of tonic wine products meets high standards of stability.

[0062] Real-time monitoring of the dispersion uniformity and sedimentation rate of the encapsulated composite system, combined with pre-set stability verification standards, provides continuous assurance of the stability of the tonic wine. Throughout the production and storage process, any issues that may affect stability can be promptly identified, and targeted adjustments can be made based on the verification results. This ensures that the tonic wine undergoes rigorous stability testing before leaving the factory, and its stability changes can be monitored at any time during storage. Compared to previous tonic wine production methods lacking real-time monitoring and effective verification mechanisms, this significantly improves the stability and reliability of the tonic wine product in the market, enhancing consumer confidence. Attached Figure Description

[0063] Figure 1 This is a schematic diagram illustrating the working principle of the nano-encapsulation stabilization method for alcohol as described in this invention.

[0064] Figure 2 Flowchart for constructing a digital embedding matrix model;

[0065] Figure 3 A flowchart for the extraction of molecular structure features and physicochemical parameters;

[0066] Figure 4 A flowchart for evaluating the compatibility of nanocarrier materials with active substance units. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] Please see Figure 1 This invention provides a method for stabilizing alcohol by nano-encapsulation, the method comprising:

[0069] Construct a digital embedding matrix model for a tonic wine system containing multiple active components;

[0070] The molecular structure characteristics and physicochemical parameters of active substances in the digital embedding matrix model are extracted. Combined with the preset embedding target requirements, the key factors restricting the stability of the wine replenishment system are analyzed, and an embedding demand vector is generated based on the key factors restricting the stability.

[0071] Based on the embedding requirement vector, a multi-level screening operation is performed in the digital embedding matrix model to locate the target embedding domain that matches the stability improvement requirements. This screening operation includes a layered detection process that gradually expands from the core active unit to the auxiliary stabilizing unit.

[0072] Within the target embedding domain, the surface property data and interfacial energy parameters of the nanocarrier material are integrated, and the compatibility between the nanocarrier material and the active material unit is quantitatively evaluated using multi-parameter fusion calculation rules, outputting the optimal sequence of nano-embedding materials.

[0073] A set of nano-embedding operation instructions for the tonic wine system is generated based on the optimized sequence of nano-embedding materials. The nano-embedding process is performed according to the instruction set to obtain the embedded composite system.

[0074] The dispersion uniformity and settling rate of the embedded composite system are monitored in real time, and the stability status is verified in combination with the preset stability verification standard, and the stability verification result data is output.

[0075] Example 1: See Figure 2Regarding the construction and implementation of the digital embedding matrix model, the core of this process lies in establishing a structured digital framework capable of dynamically simulating the complex components and interactions of the tonic wine. Nodes represent each specific active substance unit in the tonic wine system, each node possessing an independent identifier and carrying characteristic attribute data of that active substance. The connections between nodes are used to characterize the physical forces or chemical reaction connections existing between different active substances; these connections are not static but rather dynamic associations with quantifiable strength. To reflect the inherent dynamic characteristics of the tonic wine system, the model design includes a self-updating mechanism based on real-time monitoring data.

[0076] The specific construction process involves the following detailed operations: The first step is to collect detailed chemical composition data of each active component in the tonic wine system. This involves using analytical equipment to obtain precise molecular weight distribution spectra, clarifying the molecular weight range of each component and its relative abundance in the system. Simultaneously, the functional group characteristic spectra on the molecules of each active component are determined through spectral analysis or other structural characterization methods to identify the reactive or binding sites on the molecular surface, such as hydroxyl, carboxyl, carbonyl, or aromatic rings. After obtaining this basic data, a unique node is created for each identified active component in the digital model. Each node is assigned a unique molecular structure code, which is a digital summary of its core chemical properties. For example, a component with a flavonoid core structure might be coded as FLV followed by a serial number (e.g., FLV-001), and a component containing a saponin structure might be coded as SAP followed by a serial number (e.g., SAP-002). This coding rule must cover all identified effective active substance categories within the system.

[0077] After nodes are established, the next step is to detect and quantify the interactions between them. The hydrogen bond energies that may form between adjacent active components are determined using specific experimental methods or molecular simulation techniques. The magnitude of the interaction energy is quantified in energy units (e.g., kJ / mol). Similarly, the hydrophobic interaction energies driven by hydrophobic effects between different components involved in association are also measured. These specific energy values ​​form the basis for establishing connections between nodes in the digital model. When a valid hydrogen bond or hydrophobic interaction energy is detected between the active components represented by two nodes, and this energy value exceeds a set minimum threshold (e.g., a hydrogen bond energy greater than or equal to 5 kJ / mol), the system establishes a connection between the two nodes. Initially, each connection is assigned an initial weighting coefficient, which intuitively reflects the level of interaction strength currently observed. For example, a first-order connection generated above the aforementioned hydrogen bond energy threshold is assigned an initial weighting coefficient of 0.8.

[0078] To ensure the model faithfully reflects the response of the tonic wine system to changes in environmental factors during actual storage or processing, a dynamic update protocol is crucial. The core of this protocol is defining a set of sensitive trigger thresholds for changes in environmental variables and corresponding model update rules. Specific temperature fluctuation thresholds (e.g., the system detects an absolute temperature change in the tonic wine system exceeding ±2℃) and pH offset thresholds (e.g., the system detects an absolute pH shift exceeding ±0.5). When online sensors or feedback systems detect that the temperature or pH of the tonic wine system exceeds the aforementioned preset threshold range, the dynamic update protocol is activated, triggering the model's topology reconstruction mechanism.

[0079] The operation of the topology reconstruction mechanism involves a series of precise computational steps: First, it identifies which active material nodes' key structural properties (such as the protonation state of specific functional groups and the stability of molecular conformations) might be potentially affected under specific temperature or pH shifts. For these identified affected nodes, the system recalculates their property states under the current new environmental conditions according to physicochemical rules and updates the node's molecular structure code accordingly. For example, a specific suffix is ​​appended to the original code to indicate the node's current state. A typical example is that if a significant increase in temperature is detected, leading to the expected breakage or relaxation of hydrogen bonds in the molecule represented by a node, the original code suffix of that node will be appended with the "_TD" tag (e.g., FLV-001_TD). Second, it re-evaluates and updates the weight coefficients of relevant connections. The topology reconstruction algorithm recalculates the new weight coefficients of affected connections using predefined function rules based on the type and magnitude of the environmental variables that triggered the change. For example, if an increase in temperature is expected to enhance the hydrophobic interaction between two nodes, the new weighting coefficient connecting these two nodes will be increased by a calculated increment (e.g., within the range of 0.1 to 0.3) on top of its original value. After all calculations are updated, the model's topology (node ​​attributes and connection strengths) is refreshed to reflect the latest state of the current environment. This dynamic update mechanism allows the digital model to function as a living simulation system, continuously tracking and mapping the intrinsic physicochemical state changes of the actual winemaking system. The entire construction process aims to provide a realistic, reliable, and environmentally responsive digital twin foundation for subsequent embedding strategy analysis. Its structure consists of nodes and weighted edges, attribute data derived from direct physicochemical measurements, and update rules rooted in inferences from relevant scientific principles.

[0080] Example 2: See Figure 3Based on the completed construction of the digital embedding matrix model, the process of extracting active substance features and generating embedding requirements is initiated. This process focuses on deeply analyzing the molecular characteristic information contained within the model and transforming it into operable embedding guidance parameters. The first stage of the operation involves molecular-level feature mining of the active substance nodes in the model. The system automatically identifies and retrieves the basic attribute data stored in each node, mainly including polarity index and steric hindrance parameters. The polarity index is determined with reference to the internationally accepted solvent polarity scaling system, and is assigned a value by calculating the asymmetry of the charge distribution within the molecule. The steric hindrance parameters are obtained based on the molecular conformation simulation results, quantifying the degree of molecular extension in three-dimensional space and the possible spatial barrier effects. The system performs a standardization transformation on these raw parameters, converting them into dimensionless pure numerical forms to eliminate computational interference caused by different dimensions. The standardized molecular feature vectors formed after the transformation are stored in a fixed length, with each dimension corresponding to a normalized molecular attribute.

[0081] Simultaneously, the system processes the node connection data defined in the model in parallel. For each existing connection, its energy characteristic parameters are obtained: maximum binding energy and minimum dissociation energy. The maximum binding energy reflects the maximum interaction strength that can occur between two active material units, obtained through molecular docking simulations or quantum chemical calculations. The minimum dissociation energy indicates the minimum energy threshold required to break the connection, determined based on molecular dynamics stretching simulations. The system calculates the difference between these two energy values, which is defined as the energy difference threshold, characterizing the ease with which a specific connection is maintained. This energy difference threshold is considered a key physical quantity for assessing the stability of interactions within the system.

[0082] Based on the above work, the system performs data integration and report generation steps. All molecular feature information contained in the standardized molecular feature vectors is correlated and aggregated with the calculated energy difference threshold. All collected and processed data is input into a specially designed analysis engine, which performs multi-dimensional cross-checks according to preset stability assessment rules. The output of the analysis engine is a structured stability constraint analysis report, which uses an industry-standard data format and clearly lists the identified main constraints and their associated parameters.

[0083] After the report is generated, the system enters the stage of synthesizing the encapsulation demand vector. First, the system analyzes the thermosensitivity data in the report, extracting key critical temperature values. These critical temperatures mark the lower limit at which active substances begin to undergo thermal decomposition or structural denaturation. This temperature point is then mathematically compared with a preset temperature tolerance benchmark value to calculate the relative deviation and derive the temperature deviation coefficient. The temperature tolerance benchmark value is comprehensively set based on the typical storage temperature range of alcoholic beverages. Simultaneously, the system analyzes the oxidation sensitivity indicators in the report, extracting specific requirements for their free radical scavenging ability. This parameter reflects the minimum antioxidant capacity required to maintain component stability. This requirement parameter is then matched with a preset antioxidant capacity benchmark value. The antioxidant capacity benchmark value is determined based on the typical antioxidant levels of similar products. The matching result is used to calculate the difference, generating an antioxidant gap value that directly reflects the difference in antioxidant capacity.

[0084] In the final stage, the system initiates the demand vector synthesis algorithm. The two core parameters obtained from the aforementioned calculations—temperature deviation coefficient and antioxidant gap value—are input into the synthesis algorithm module. This module employs a multi-layer network-based data fusion mechanism to achieve deep integration of multi-source heterogeneous data. During the synthesis process, not only are the original parameter values ​​retained, but necessary cross-parameters and statistical characteristic values ​​are automatically derived according to physicochemical rules. After a complete information fusion process, a compact and meaningful embedded demand vector is finally output. This vector encapsulates all key embedded demand information in the form of a numerical sequence; its dimensions and data structure are rigorously designed to ensure direct input into the subsequent screening and analysis system. The entire process is fully automated; parameter transfer and calculation are completed internally, eliminating the risk of bias introduced by manual intervention. Data from all intermediate processing stages are recorded in detail in the system database, achieving complete traceability of the processing.

[0085] Example 3: See Figure 4 Given a digital embedding matrix model and embedding requirement vector, a multi-level screening operation is performed to locate the target embedding domain. The initial operation divides the entire model space into equal three-dimensional grid cells at a fixed scale, with the grid spacing set to a reasonable value based on the average molecular size. The generated multi-dimensional embedding requirement vector is projected onto this grid coordinate system, and its position is automatically determined by the vector's feature components. The system calculates the geometric distance between the center point of each grid cell and the projected position, using the Euclidean norm formula for distance measurement. The N grid cells with the smallest distance values ​​(N is a preset integer value) are selected to form the initial domain set. For each node in this set, its directly connected adjacent node information is retrieved, constructing an adjacent node set library.

[0086] The process then proceeds to the adjacent node feature matching stage. The molecular polarity parameter (specifically, the dipole moment value) stored in each node of the adjacent node set is detected. The relative deviation of this parameter from the preset target value in the embedding requirement vector is calculated. A specific allowable deviation threshold range is set; only when the calculated deviation value is below the lower limit of the threshold is the node considered a matching node. A first-level expansion operation is performed on the matching adjacent nodes, incorporating them into the current scope coverage. The newly formed node set after expansion triggers the adjacent node retrieval process again, but this time the retrieval scope is expanded to secondary associated nodes (i.e., adjacent nodes of adjacent nodes) at a specific step distance from the original node. The same matching degree detection mechanism is applied to the newly retrieved node set, further filtering out matching nodes and performing a second-level expansion. Then, a third-level retrieval and expansion process is initiated, extending the node association depth to a third-level adjacency relationship layer. After three layers of recursive expansion operations, a complete spherical scope region covering the core active unit and its outer auxiliary stabilizing units is finally formed, its spatial boundary defined by the coordinate range of the outermost node.

[0087] Entering the quantitative evaluation stage of compatibility, the electrostatic interaction parameters are processed first. Raw data on the electron density distribution of the active material within the target embedding domain is retrieved; this data is obtained through density functional theory calculations and stored in the node attribute library. The spatial electron density values ​​are converted into a charge density distribution function, constructing a three-dimensional charge distribution cloud map data array. Simultaneously, surface potential distribution data of the candidate nanocarrier material is acquired; this data is measured using atomic force microscopy in electrical mode and generated as a continuous surface spectrum using an image reconstruction algorithm. The electrostatic potential matching value between the two is calculated according to the following formula:

[0088]

[0089] in, This represents the electrostatic potential energy matching value between the two. This represents the charge density value of the active substance at a point in space. This refers to the surface potential value of the nanomaterial at the same spatial point. This represents the integration volume of the entire domain. The triple integral is achieved through numerical integration over a spatially discrete grid.

[0090] Simultaneously perform spatial size adaptation analysis. Measure the pore size distribution curve of the nanocarrier material to obtain the lower limit, upper limit, and peak probability data of the pore size. Measure the molecular hydrodynamic size spectrum of the active substance within the target domain, recording its average size value and standard deviation of size fluctuation. Construct a quantifiable index for size adaptation: this index is a quantitative measure of the ability of a candidate carrier material to accommodate a group of active substances within the current domain. The specific calculation is achieved by statistically analyzing the effective proportion of the carrier pore size distribution covering the molecular size range, and additionally considering the overlap factor and standard deviation weighting coefficient of the size distribution.

[0091] A gradient adjustment mechanism is employed to fuse the two types of parameters mentioned above. The electrostatic potential matching value and size fit index are input into a dynamic weight allocation module. This module automatically adjusts the weight contribution ratio based on the numerical distribution range of the two parameters: when the size fit is in a low value range, its weight ratio is increased; conversely, when the electrostatic matching value is low, its weight coefficient is increased. Smooth switching control of the weights is achieved through a nonlinear function. The final output comprehensive fit score is mapped to a percentage value, and the score result comprehensively reflects the compatibility level of the nanomaterial carrier with the active substances within the target domain.

[0092] The entire screening and evaluation process is fully automated, with a streamlined data processing chain. Spatial grid parameters can be adjusted by the user according to the material system characteristics, and the recursive expansion depth can be set to two or four levels to accommodate different system complexities. The weight adjustment rules for the scoring algorithm are implemented through a pre-defined fuzzy logic function library, eliminating the need for manual intervention in parameter adjustment. The completed comprehensive adaptation score and intermediate process data are all written to the system audit log for subsequent traceability analysis.

[0093] Example 4: After completing the fit scoring, the generation of the preferred sequence of nano-embedded materials is achieved through a classification and sorting mechanism. The system divides candidate materials within the target domain into three groups based on preset grading thresholds: the first-level priority group includes materials with a comprehensive fit score higher than 85, the second-level group includes materials with scores between 75 and 85, and the third-level group covers materials with scores lower than 75. Taking chitosan nanoparticles, mesoporous silica, and liposomes as examples: chitosan with a score of 92 is assigned to the first-level group, mesoporous silica with a score of 80 is assigned to the second-level group, and liposomes with a score of 70 are assigned to the third-level group.

[0094] For materials in the first priority group, the system automatically extracts key parameters from its biocompatibility database. Biocompatibility data includes the material's degradation cycle in vivo, acute toxicity test results, and cell compatibility reports. Taking chitosan as an example, the system extracts the hepatocyte survival rate measured by the standard MTT assay (e.g., HepG2 cell survival rate recorded as 93% after 96 hours of culture). Simultaneously, the system retrieves the material process cost database to obtain comprehensive cost parameters for each batch of materials, including raw material costs, equipment energy consumption, and labor hours. The cost of chitosan raw material might be listed as 500 yuan per kilogram, while the cost of mesoporous silica might be 1200 yuan per kilogram.

[0095] The system performs a multi-dimensional weighted ranking operation. A fixed weight is assigned to biocompatibility data items, and a different weight is assigned to process cost items. The weighting coefficients can be dynamically configured based on product positioning (e.g., health wines use a higher biocompatibility weight). The ranking function generates a temporary calculation table in memory: it multiplies the cell viability value of chitosan by the biocompatibility weighting coefficient and its cost value by the cost weighting coefficient. The difference between the two results yields the material's priority index. The system iteratively calculates the priority indices of all materials within the first-level group, generating an ordered sequence in descending order, such as chitosan priority index 85 > silica priority index 78.

[0096] The system generates an embedding operation instruction set based on the first material in the sequence. It analyzes the specific embedding mechanism of the material: for example, chitosan is a cationic polymer, and its main embedding mechanism is electrostatic adsorption. Key parameters include: 1) Potential difference threshold (the zeta potential difference between the carrier and the active material must be greater than 20 mV); 2) Critical ion concentration (the upper limit of the solution's ionic strength is 0.15 mol / L). Simultaneously, standard dispersion conditions for the material are applied: during the pre-dispersion stage, the shear rate should be controlled within the range of 200-400 s⁻¹, and a 0.1% acetic acid solution is recommended as the dispersion medium.

[0097] The encapsulation concentration and temperature control point were determined. The target encapsulation concentration was calculated using an adsorption isotherm model, with input parameters including the total molar number of active molecules within the target domain and the specific surface area of ​​the carrier. For example, when the active molecule concentration was 0.5 mM, the system output a target encapsulation concentration of 15 mg / mL. The phase transition temperature control point was determined using differential scanning calorimetry: taking the chitosan-flavonoid composite system as an example, it exhibited a significant heat flux peak in the 40-45℃ range, and the system set the phase transition temperature control point to 42℃ accordingly.

[0098] Three-stage encapsulation process control parameters were established: In the premixing stage, the temperature was maintained at 25±2℃, the shear rate was set at 350s⁻¹, and the duration was 8 minutes; in the main encapsulation stage, a linear gradient heating strategy was adopted: the temperature increased from 25℃ to 42℃ within 10 minutes, with a stirring frequency of 60Hz; in the curing stage, two-stage cooling was performed: first, a rapid drop to 10℃ within 2 minutes, followed by a constant drop to 4℃ within 15 minutes. All parameters were integrated into structured instruction data, with typical instruction items including: time axis marker (t=0-8min), temperature setpoint (25℃), and equipment control parameters (stirrer speed 1500rpm). The final nano-encapsulation operation instruction set contains more than 40 precise control items, output to the execution equipment control system via an industrial bus protocol. The instruction set configuration version number is bound to the material code, and the instruction regeneration module is automatically triggered to regenerate the parameter set when the preferred sequence is updated.

[0099] Example 5: The stability verification operation of the embedded composite system was initiated in a controlled environmental chamber. The sample to be tested was quantitatively dispensed into a standard optical path cuvette array and fixedly mounted on a constant-temperature oscillation platform. The temperature control system maintained the environmental chamber at 25±0.2℃. The oscillator adopted a vertical dual-axis composite motion mode, with the axial amplitude adjusted to cover the fluctuations of the liquid surface, and the oscillation frequency maintained at a periodic oscillation of 100 times per minute. The optical detection system initiated the scanning process at fixed time intervals (30 minutes / time): a monochromatic light source emitted a 600±2nm wavelength light beam that penetrated the sample, and an array photoelectric sensor simultaneously recorded the raw voltage value of the transmitted light intensity. The data acquisition system converted the voltage value into a transmittance percentage value and stored it in a time-series database.

[0100] The time series data processing module is automatically triggered after each acquisition cycle. It calls the data from two adjacent time points (…). and Recorded transmittance values and The instantaneous attenuation slope value is obtained based on a differential calculation algorithm. This slope value quantifies the rate of change of transmittance per unit time. An additional data anomaly filtering is implemented during the calculation process: if a transmittance jump exceeds a preset tolerance threshold (e.g., a single change greater than 10%), the data point is marked and the instrument's self-test program is initiated. After confirming that there are no errors, the data point is re-acquired.

[0101] Independent centrifugation analysis units operate synchronously. Equal amounts of sample are aliquoted into high-speed centrifuge tubes, and the centrifuge executes a gradient acceleration operation according to a preset program: initially rotating at 1000g for 300 seconds, then stepping to 2000g and maintaining it for 200 seconds, finally stabilizing at a constant centrifugal force field of 3000g. A camera system installed in the centrifuge chamber continuously records the liquid surface state at a frequency of five frames per second. The image processing engine identifies the phase interface formation process through an edge detection algorithm: when a stable liquid-liquid boundary line is observed for more than ten consecutive frames, the system records that moment as the phase separation time point. The entire operation is completed under inert gas protection to avoid the influence of oxidation on the results.

[0102] The collected multidimensional validation data is input into the stability validation model. This model employs a three-input, single-output architecture: the input layer receives the transmittance time series vector, the attenuation slope array, and the phase separation time point values. The data preprocessing module performs standardization transformation and feature extraction on the input data: the transmittance sequence is transformed by wavelet to obtain the low-frequency energy coefficients; the variance statistics are calculated from the attenuation slope array; and the phase separation time points are associated with centrifugal force gradient parameters. The feature-processed data is input into the core computation unit, which incorporates a regression predictor based on a radial basis function kernel. The kernel parameters are determined through training and optimization using historical samples. The computation unit outputs a stability index value in the range of 0 to 100, accurate to one decimal place.

[0103] The system automatically compares the stability index output value with a preset standard threshold. When the index value is detected to be lower than the set acceptable limit (e.g., less than 80 points), the iterative optimization program module is triggered. The optimization program first parses and generates the process instruction set used by the embedded composite system, accurately locating the control parameter entries in the instruction set that contain dynamic adjustment functions. Taking the temperature gradient control stage as an example: the duration and heating rate variables of the linear heating segment are extracted as adjustable parameters. The system generates parameter adjustment schemes based on the stability defect mode: if a thermally sensitive defect is identified, the heating duration is shortened and the heating rate is reduced; if insufficient dispersion is detected, the initial stirring intensity parameter is increased.

[0104] The parameter adjustment scheme is imported into the dynamic update protocol of the digital embedding matrix model. Adjustment coefficients are embedded in the threshold determination rules of the protocol; for example, the temperature fluctuation determination threshold is reconstructed as a function expression of the original threshold and duration. The data synchronization module activates the model's topology reconstruction signal, and the model initiates node attribute recalculation: molecular structure encoding is regenerated for all temperature-sensitive nodes, and dynamic parameters influencing the labels are added. The connection relation database updates the weight coefficient library according to the new environmental parameters: the system establishes a mapping table between time-temperature coupling parameters and hydrogen bond weights, and retrieves and updates coefficient values ​​based on the adjusted heating parameters. Finally, the model is driven to re-execute the embedding demand vector generation program, forming an updated material selection and embedding instruction set.

[0105] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0106] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for stabilizing alcohol by nano-encapsulation, characterized in that, The method includes: Construct a digital embedding matrix model for a tonic wine system containing multiple active components; The molecular structure features and physicochemical parameters of the active substances in the digital embedding matrix model are extracted. Combined with the preset embedding target requirements, the key factors restricting the stability of the tonic wine system are analyzed, and an embedding demand vector is generated based on the key factors restricting the stability. Based on the embedding requirement vector, a multi-level screening operation is performed in the digital embedding matrix model to locate the target embedding domain that matches the stability improvement requirement. The multi-level screening operation includes a layered detection process that gradually expands from the core active unit to the auxiliary stabilizing unit. Within the target embedding domain, the surface property data and interfacial energy parameters of the nanocarrier material are integrated, and the compatibility between the nanocarrier material and the active material unit is quantitatively evaluated using multi-parameter fusion calculation rules, outputting an optimal sequence of nano-embedding materials. Based on the preferred sequence of the nano-embedding material, a nano-embedding operation instruction set is generated for the wine replenishment system. The wine replenishment system is subjected to nano-embedding treatment according to the nano-embedding operation instruction set to obtain the treated encapsulated composite system. The dispersion uniformity and sedimentation rate of the embedded composite system are monitored in real time. The stability of the embedded composite system is verified in combination with the preset stability verification standard, and the stability verification result data is output. The digital embedding matrix model represents active substance units through nodes and physical or chemical interactions between active substances through connections. The node attributes and connection strength in the digital embedding matrix model are dynamically adjusted based on the real-time changes in the concentration of the tonic wine components. The molecular weight distribution spectrum and functional group characteristic spectrum of each active component in the tonic wine system are collected, each active component is mapped to an independent node in the digital embedding matrix model, and a corresponding molecular structure code is assigned to each node. The hydrogen bonding energy and hydrophobic interaction energy between active components are measured, and the connection relationship between nodes in the digital embedding matrix model is established based on the hydrogen bonding energy and hydrophobic interaction energy, and the connection relationship weight coefficient is initialized. A dynamic update protocol for the digital embedded matrix model is established. When a temperature fluctuation or pH shift in the wine replenishment system is detected, the topology reconstruction mechanism of the digital embedded matrix model is triggered to recalculate the molecular structure encoding of the affected nodes and update the relevant connection weight coefficients.

2. The nano-encapsulation method for wine stabilization according to claim 1, characterized in that, The extraction of molecular structural features and physicochemical parameters of active substances from the digitally embedded matrix model includes: Identify the polarity index and steric hindrance parameter corresponding to each node in the digital embedding matrix model, and normalize and transform the polarity index and steric hindrance parameter to form a standardized molecular feature vector; Obtain the maximum binding energy and minimum dissociation energy corresponding to the connection relationships in the digital embedding matrix model, and calculate the energy difference threshold between the maximum binding energy and the minimum dissociation energy; By integrating the standardized molecular feature vectors and the energy difference threshold, a stability constraint analysis report of the tonic wine system is generated.

3. The nano-encapsulation method for wine stabilization according to claim 2, characterized in that, The generation of the embedding requirement vector based on the key stability constraints includes: Decompose the thermosensitivity and oxidation sensitivity indices in the stability limiting factors analysis report, and extract the critical temperature points corresponding to the thermosensitivity indices and the free radical capture requirements corresponding to the oxidation sensitivity indices; The critical temperature point is compared with a preset temperature tolerance benchmark value, and the temperature deviation coefficient is calculated. The free radical capture requirement is matched with a preset antioxidant capacity benchmark value to determine the antioxidant gap value; The temperature deviation coefficient and the antioxidant gap value are integrated using a demand vector synthesis algorithm to form a multidimensional embedded demand vector.

4. The nano-encapsulation method for wine stabilization according to claim 3, characterized in that, The multi-level screening operation performed in the digital embedded matrix model includes: The digital embedding matrix model is divided into spatial grids, and the multidimensional embedding requirement vector is mapped to the grid space coordinate system. Calculate the spatial distance between the multidimensional embedding requirement vector and the center point of each grid cell, and select the N grid cells with the smallest distance values ​​as the initial scope. Extract the set of adjacent nodes of the nodes within the initial scope, and detect the matching degree between the molecular polarity parameters of the nodes in the set of adjacent nodes and the multidimensional embedding requirement vector. For adjacent nodes whose matching degree reaches the set threshold, a recursive expansion operation is performed, extending layer by layer to the third-level associated nodes to form a complete target embedding scope.

5. The nano-encapsulation method for wine stabilization according to claim 4, characterized in that, The quantitative evaluation of the compatibility between the nanocarrier material and the active substance unit using multi-parameter fusion calculation rules includes: Obtain the charge distribution cloud map of the active material within the target embedding domain and the surface potential spectrum of the nanocarrier material; Calculate the electrostatic potential matching value between the charge distribution cloud map and the surface potential spectrum; The pore size distribution curves of nanocarrier materials and the molecular size spectra of active substances are measured to generate size fit indices. A gradient adjustment mechanism is used to fuse the electrostatic potential matching value and the size fit index to generate a comprehensive fit score for the nanocarrier material.

6. The nano-encapsulation stabilization method for alcohol according to claim 5, characterized in that, The preferred sequence of the output nano-embedded materials includes: Set a grading threshold range for the comprehensive adaptation score, and divide the nanocarrier materials within the target embedding domain into three priority groups; Extract biocompatibility data and process cost parameters of nanocarrier materials from high-priority groups; The biocompatibility data and the process cost parameters are weighted and sorted to generate an optimal sequence of nano-embedded materials.

7. The nano-encapsulation method for wine stabilization according to claim 6, characterized in that, The generation of the nano-embedding operation instruction set for the tonic wine system includes: Analysis of the encapsulation mechanism and dispersion conditions of the first material in the preferred sequence of nano-encapsulation materials; Determine the target encapsulation concentration and phase transition temperature control point of the tonic wine system; Based on the aforementioned embedding mechanism and the aforementioned dispersion processing conditions, phased embedding process control parameters are formulated; Based on the target embedding concentration and the phase transition temperature control point, a set of nano-embedding operation instructions, including time gradient control and temperature gradient control, is generated.

8. The nano-encapsulation method for alcohol stabilization according to claim 7, characterized in that, The stability verification of the embedded composite system includes: Transmittance data of the embedded composite system at multiple time points were collected under a constant temperature oscillation environment. Calculate the attenuation slope of transmittance data at adjacent time points; Measure the phase separation time points of the embedded composite system under a centrifugal force field; The decay slope value and the phase separation time point are input into the stability verification model, and the stability verification result data is output.

9. The nano-encapsulation method for alcohol stabilization according to claim 8, characterized in that, The output stability verification result data also includes: When the stability verification result data is lower than the preset standard threshold, the process control parameter adjustment item in the nano-embedding operation instruction set is extracted; The dynamic update protocol for updating the digital embedding matrix model is based on the process control parameter adjustment items; The topology reconstruction mechanism of the digital embedding matrix model is triggered to regenerate the embedding requirement vector.

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