An intelligent recommendation system and method for an alcoholism-prevention and liver-protection scheme based on genetic polymorphism detection

Abstract

This application discloses an intelligent recommendation system and method for hangover relief and liver protection based on gene polymorphism detection, belonging to the field of intelligent dietary assistance technology. The system includes: a gene polymorphism acquisition module configured to acquire gene polymorphism site typing data related to the user's alcohol metabolism and liver damage repair; an alcohol metabolism capacity assessment module configured to determine the user's quantitative assessment result based on the key gene polymorphism site typing data and a preset alcohol metabolism capacity scoring model; and a hangover relief and liver protection recommendation module configured to generate a hangover relief and liver protection recommendation plan based on the quantitative assessment result and pre-collected basic physiological characteristic data of the user, using a rule engine combined with a machine learning recommendation model, including safe drinking limits, dietary and lifestyle guidance, and suggestions for intervention products. This technical solution can construct a gene data-driven intelligent recommendation system, realizing customized recommendations for hangover relief and liver protection plans tailored to each individual.

Description

Technical Field

[0001] This application belongs to the field of intelligent hangover relief and liver protection recommendation technology, specifically involving an intelligent recommendation system and method for hangover relief and liver protection solutions based on gene polymorphism detection. Background Technology

[0002] With the expansion of the alcohol-drinking population, the incidence of alcoholic liver disease continues to rise, and the differences in alcohol metabolism capacity caused by individual differences have become the core challenge of liver protection intervention.

[0003] Current hangover relief and liver protection technologies primarily focus on the development of active ingredients. For example, they enhance the activity of alcohol-metabolizing enzymes by screening specific bioactive peptides and constructing complex delivery carriers, or improve the stability and bioavailability of active ingredients through formulation processes. However, these solutions often employ generic formulation designs, failing to consider the decisive impact of user genetic differences on alcohol metabolism efficiency. Since different populations exhibit significant differences in alcohol tolerance thresholds and susceptibility to liver damage, and because existing technologies lack individualized quantitative assessment mechanisms, they cannot provide differentiated safe drinking limits and intervention strategies for different users. Consequently, generic products offer limited protection for most people and may even exacerbate metabolic burden due to inappropriate dosage, making effective dietary guidance and intervention difficult to achieve. Summary of the Invention

[0004] This application provides an intelligent recommendation system and method for hangover relief and liver protection based on gene polymorphism detection. The aim is to solve the technical problems of existing technologies that cannot perform quantitative assessment based on individual genetic differences and are difficult to output precise and personalized intervention strategies. By constructing a gene data-driven intelligent recommendation system, it is possible to customize hangover relief and liver protection solutions for each individual.

[0005] In a first aspect, embodiments of this application provide an intelligent recommendation system for hangover relief and liver protection based on gene polymorphism detection, the system comprising: The gene polymorphism acquisition module is configured to acquire gene polymorphism site typing data of users' alcohol metabolism and liver damage repair. The alcohol metabolism capacity assessment module is connected to the gene polymorphism acquisition module and is configured to determine the quantitative assessment results of the user in at least one of the four dimensions: ethanol decomposition rate, acetaldehyde accumulation risk, oxidative stress level and liver damage susceptibility, based on the key gene polymorphism site typing data and a preset alcohol metabolism capacity scoring model. The hangover relief and liver protection recommendation module is connected to the alcohol metabolism capacity assessment module. It is configured to generate a hangover relief and liver protection recommendation plan based on the quantitative assessment results and the user's basic physiological characteristic data collected in advance, through a rule engine combined with a machine learning recommendation model. The plan includes at least one of the following: safe drinking limits, dietary and lifestyle guidance, and intervention product suggestions.

[0006] Furthermore, the gene polymorphism acquisition module is also configured as follows: The obtained key gene polymorphism site genotyping data were subjected to integrity verification and outlier identification. When missing or abnormal data is detected, a data correction prompt is generated, and the verification operation is re-executed after the corrected data is received.

[0007] Furthermore, the alcohol metabolism capacity scoring model in the alcohol metabolism capacity assessment module is constructed in the following manner: Collect key gene polymorphism site typing data, alcohol metabolism-related physiological index data, and clinical data on liver injury from multiple samples; The multi-sample data was trained using machine learning algorithms to determine the scoring weights of each gene locus under different population characteristics. Based on the scoring weights, the quantitative evaluation results for the four dimensions are output.

[0008] Furthermore, the alcohol metabolism capacity scoring model is also configured as follows: The scoring weights are dynamically adjusted based on the user's geographical distribution and age range to accommodate the genetic background differences among different groups.

[0009] Furthermore, the hangover relief and liver protection recommendation module is also configured as follows: Receive user feedback data on the use of the generated intervention plan and subsequent liver function monitoring data; Based on the feedback data and liver function monitoring data, the recommended model parameters are iteratively updated to dynamically optimize the subsequently generated personalized hangover relief and liver protection intervention plan.

[0010] Furthermore, it also includes: The data storage module is configured to encrypt and store the key gene polymorphism site typing data, quantitative evaluation results, and personalized hangover relief and liver protection intervention plans.

[0011] Furthermore, the key gene polymorphism sites include at least one or more of ADH1B, ALDH2, CYP2E1, GSTM1, and UGT1A1; The alcohol metabolism capacity assessment module is also configured to: The key gene polymorphism site genotyping data are mapped to standardized wild-type, heterozygous mutant, or homozygous mutant risk genotypes.

[0012] Furthermore, the hangover relief and liver protection recommendation module is also configured as follows: When generating the personalized hangover relief and liver protection intervention plan, a pre-set intervention product database is called, and the corresponding intervention product type and dosage range are matched according to the quantitative evaluation results; The intervention products in the intervention product database include bioactive ingredients with the ability to activate alcohol metabolism enzymes, and the screening and recommendation of the bioactive ingredients are dynamically determined based on the quantitative evaluation results.

[0013] Furthermore, the hangover relief and liver protection recommendation module is also configured as follows: When the quantitative assessment results indicate that the risk level of acetaldehyde accumulation exceeds a preset threshold, a priority recommendation instruction for interventional products that enhance acetaldehyde dehydrogenase activity is generated.

[0014] Secondly, embodiments of this application provide an intelligent recommendation method for hangover relief and liver protection based on gene polymorphism detection, the method comprising: Obtain genotyping data of gene polymorphism sites related to alcohol metabolism and liver damage repair in users; Based on the key gene polymorphism site typing data, combined with the preset alcohol metabolism capacity scoring model, the quantitative assessment results of users in at least one of the four dimensions of ethanol decomposition rate, acetaldehyde accumulation risk, oxidative stress level and liver damage susceptibility are determined. Based on the quantitative assessment results and the pre-collected basic physiological characteristic data of users, a hangover relief and liver protection recommendation plan is generated by combining a rule engine with a machine learning recommendation model. This plan includes at least one of the following: safe drinking limits, dietary and lifestyle guidance, and suggestions for intervention products.

[0015] Thirdly, embodiments of this application provide an electronic device, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described above.

[0016] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described above.

[0017] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method described above.

[0018] The technical solution provided in this application, through the correlation of gene polymorphism acquisition, multidimensional quantitative assessment of alcohol metabolism, and intelligent solution recommendation, achieves the technical effect of accurately determining the user's alcohol metabolism capacity and liver damage risk. Based on multidimensional quantitative results such as ethanol decomposition rate and acetaldehyde accumulation risk, it dynamically generates safe drinking limits, dietary and lifestyle guidance, and intervention prompts that are suitable for individual genotypes. This not only avoids increasing the metabolic burden due to inappropriate dosage, but also fills the gap in the lack of individualized quantitative assessment mechanisms in existing technologies, significantly improving the pertinence and effectiveness of alcohol-related liver protection interventions. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the intelligent recommendation system for hangover relief and liver protection based on gene polymorphism detection provided in Embodiment 1 of this application; Figure 2 This is a flowchart illustrating the intelligent recommendation method for hangover relief and liver protection based on gene polymorphism detection provided in Embodiment 2 of this application. Figure 3 This is a schematic diagram of the structure of the electronic device provided in Embodiment 3 of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0021] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0022] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0023] The intelligent recommendation system for hangover relief and liver protection based on gene polymorphism detection provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0024] Example 1 Figure 1 This is a schematic diagram of the intelligent recommendation system for hangover relief and liver protection based on gene polymorphism detection provided in Embodiment 1 of this application. Figure 1 As shown, the system specifically includes: The gene polymorphism acquisition module 101 is configured to acquire the user's gene polymorphism site typing data for alcohol metabolism and liver damage repair. The alcohol metabolism capacity assessment module 102 is connected to the gene polymorphism acquisition module 101 and is configured to determine the quantitative assessment results of the user in at least one of the four dimensions of ethanol decomposition rate, acetaldehyde accumulation risk, oxidative stress level and liver damage susceptibility based on the key gene polymorphism site typing data and the preset alcohol metabolism capacity scoring model. The hangover relief and liver protection recommendation module 103 is connected to the alcohol metabolism capacity assessment module 102. It is configured to generate a hangover relief and liver protection recommendation plan based on the quantitative assessment results and the user's basic physiological characteristic data collected in advance, through a rule engine combined with a machine learning recommendation model. The plan includes at least one of the following: safe drinking limit, dietary and rest guidance, and intervention product suggestions.

[0025] Genotyping data for polymorphic loci related to alcohol metabolism and liver damage repair refers to the genotyping results of gene loci associated with human alcohol breakdown, acetaldehyde detoxification, and liver damage repair. Examples include genotyping data for alcohol dehydrogenase, acetaldehyde dehydrogenase, cytochrome P450 enzymes, glutathione S-transferase, and so on.

[0026] This solution can receive and read users' original genotyping information through data import, interface integration, or file parsing. Specifically, it can parse user-uploaded gene sequencing reports and gene chip results, or it can automatically synchronize data by connecting to a third-party testing platform interface to complete data collection.

[0027] Key gene polymorphism genotyping data refers to the genotype results of core gene loci that have a decisive impact on alcohol metabolism capacity and liver damage risk. Examples include genotyping data for ADH1B (alcohol dehydrogenase 1B) and ALDH2 (acetaldehyde dehydrogenase 2), as well as genotyping data for CYP2E1 (cytochrome P450 2E1), GSTM1 (glutathione S-transferase M1), and UGT1A1 (uridine diphosphate glucuronide transferase 1A1).

[0028] An alcohol metabolism capacity scoring model is a pre-constructed mathematical model that converts genotypes into quantitative scores of metabolic capacity. It can be a scoring model trained using logistic regression or random forest algorithms, or a fusion scoring model that adapts to different populations and dynamically adjusts weights.

[0029] Ethanol decomposition rate refers to the rate at which the human liver breaks down ethanol (alcohol) into acetaldehyde, such as rapid, medium, and slow decomposition, and the change in ethanol metabolic concentration per unit time.

[0030] Acetaldehyde accumulation risk refers to the probability that acetaldehyde will accumulate in the body after drinking alcohol and cannot be eliminated in time. For example, it can be classified as low, medium or high accumulation risk level, peak concentration of acetaldehyde in the body, and duration of metabolic half-life.

[0031] Oxidative stress level can be an indicator of the degree to which alcohol consumption causes excessive free radicals in the body, leading to oxidative damage to cells, such as low, medium, and high stress levels, the concentration of reactive oxygen species in the body, and the degree of decline in antioxidant enzyme activity.

[0032] Susceptibility to liver injury can be an indicator of an individual's liver's sensitivity to alcohol toxicity. This can be categorized as low, moderate, or high susceptibility, or it can be a level representing the probability of liver injury or a risk value for hepatocellular damage.

[0033] In this solution, the system can call a scoring model, input genotyping data to calculate scores for each dimension, and output numerical or graded evaluation results. Specifically, the model can automatically match the weights of each gene locus, calculate individual and overall scores, and generate a standardized quantitative report.

[0034] Quantitative assessment results refer to numerical or graded conclusions calculated from dimensions such as ethanol breakdown rate and acetaldehyde accumulation risk. Examples include comprehensive metabolic capacity score, high-risk level of liver damage, sub-item scores for each dimension, and intoxication risk level.

[0035] Basic physiological characteristics data refer to personal health-related data such as user gender, weight, age, frequency of alcohol consumption, and history of underlying liver disease. For example, this could include gender, weight, age, weekly alcohol consumption frequency, and history of fatty liver disease.

[0036] A rule engine is a logical unit that stores preset intervention rules and automatically matches risk levels with intervention strategies. It can be a tiered alcohol consumption limit rule base, a dietary taboo rule base, or others such as a sleep regulation rule base or an intervention product matching rule base.

[0037] Machine learning recommendation models refer to algorithmic models that are trained based on genetic, evaluation, and solution effectiveness data to output personalized recommendations. Examples include collaborative filtering recommendation models, gradient boosting tree recommendation models, and multi-label classification models adapted to individual metabolic characteristics.

[0038] Safe drinking limits refer to the upper limit of alcohol intake per instance or per day, determined based on metabolic capacity. This means that the daily alcohol intake should not exceed 25 grams, high-risk individuals should not drink alcohol, or the amount of alcohol consumed at one time should not exceed 100 ml of low-alcohol beverages.

[0039] Dietary and lifestyle guidance refers to recommendations on dietary structure, meal times, and lifestyle arrangements that are appropriate for metabolic risk, such as eating high-protein foods before meals, supplementing with vitamin C after drinking alcohol, or avoiding staying up late and ensuring 7-8 hours of sleep per day.

[0040] Intervention product recommendations refer to suggestions for liver-protecting foods, health products, or dietary supplements tailored to individual metabolic characteristics, such as kudzu root and jujube fruit compound preparations, B vitamin supplements, or wolfberry and blueberry antioxidant drinks, and silymarin liver-protecting preparations.

[0041] In this solution, the system can integrate evaluation results, user characteristics, rule matching, and model predictions to output structured and actionable personalized solutions. For example, the rule engine outputs basic intervention strategies, the machine learning model optimizes the fit, and finally, a text-based, itemized recommendation plan for hangover relief and liver protection is generated.

[0042] The technical solution provided in this embodiment, through a three-tier architecture of gene data collection, multi-dimensional metabolic quantitative assessment, and dual-engine personalized solution generation, deeply integrates gene polymorphism detection and intelligent recommendation technology. It breaks through the limitations of the traditional "one-size-fits-all" approach to hangover and liver protection solutions, achieving an upgrade from general guidance to precise individual adaptation. It matches the user's alcohol metabolism gene characteristics from the root, effectively reducing the risk of liver damage caused by drinking, and improving the scientific, personalized, and practical nature of health management.

[0043] In one embodiment, optionally, the gene polymorphism acquisition module 101 is further configured to: The obtained key gene polymorphism site genotyping data were subjected to integrity verification and outlier identification. When missing or abnormal data is detected, a data correction prompt is generated, and the verification operation is re-executed after the corrected data is received.

[0044] Among them, key gene polymorphism genotyping data refers to the genotype results of core gene loci that have a decisive impact on alcohol metabolism capacity and liver damage risk. For example, it can be genotyping data of ADH1B and ALDH2 gene loci, or genotyping data of CYP2E1, GSTM1, and UGT1A1 gene loci.

[0045] Integrity verification refers to the system checking whether the gene data contains all preset key sites and whether there are any missing sites. Specifically, the system can compare the imported data against a preset list of gene sites, checking the site coverage one by one and marking any missing sites.

[0046] Outlier identification refers to the system's identification of invalid data such as incorrectly formatted classification results or values ​​exceeding reasonable ranges. Specifically, this can involve the system comparing the classification results with standard classification codes and filtering out non-standard, duplicate, or contradictory classification data.

[0047] Detection refers to the system scanning and verifying results in real time to determine whether there are problems such as missing loci or genotyping anomalies. Specifically, this may involve the system running a verification algorithm, outputting a data quality report, and identifying non-compliant data items.

[0048] Data correction prompts refer to text or interface prompts pushed by the system to users, informing them of data problems and how to correct them. For example, it could be "ALDH2 gene locus data is missing, please upload the missing data" or "ADH1B genotyping results are abnormal, please re-upload the report," etc.

[0049] In this solution, the system can automatically match a preset prompt template based on the detected problem type and output targeted correction information. Specifically, the system can associate missing / abnormal sites, generate standardized prompt text, and display it to the user.

[0050] Supplementary data refers to genotyping data that has been uploaded and corrected by the user. For example, it could be a genotyping report with missing sites or a file after correcting abnormal genotyping results.

[0051] Specifically, after receiving the corrected data, the system can run the integrity verification and outlier identification process again. This can be achieved by the system automatically triggering a second verification to ensure that the corrected data meets quality requirements.

[0052] This technical solution effectively eliminates invalid data and fills in missing information by verifying the integrity of gene data, identifying outliers, and providing correction prompts. This ensures the accuracy and reliability of the data foundation for subsequent metabolic assessments, avoids assessment bias and insufficient adaptability of the solution due to data defects, and improves the accuracy and credibility of the system's output results.

[0053] In one embodiment, optionally, the alcohol metabolism capacity scoring model in the alcohol metabolism capacity assessment module 102 is constructed in the following manner: Collect key gene polymorphism site typing data, alcohol metabolism-related physiological index data, and clinical data on liver injury from multiple samples; The multi-sample data was trained using machine learning algorithms to determine the scoring weights of each gene locus under different population characteristics. Based on the scoring weights, the quantitative evaluation results for the four dimensions are output.

[0054] Among them, "multiple samples" refers to a large-scale population sample collection covering different regions, ages, genders, and health conditions. For example, it could be a sample of thousands of healthy people, a sample of hundreds of patients with alcoholic liver disease, or a sample of people from different ethnic groups and regions.

[0055] Physiological indicators related to alcohol metabolism refer to physiological test data that reflect the state of alcohol metabolism in the human body. These include ethanol breakdown rate, acetaldehyde blood concentration, liver enzyme activity data, concentration of oxidative stress markers, and antioxidant enzyme activity data.

[0056] Clinical data on liver injury refers to clinical testing and diagnostic data that reflects the degree of liver damage. Examples include alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels, diagnostic results for alcoholic fatty liver disease, degree of liver fibrosis, and pathological data on hepatocellular damage.

[0057] Specifically, the system can collect genetic, physiological, and clinical data from various samples through multi-center collaboration, database integration, and clinical surveys. Specifically, it can connect to medical institution databases to obtain clinical data and collaborate with gene testing institutions to aggregate genetic data from samples across multiple regions.

[0058] Machine learning algorithms refer to artificial intelligence algorithms used for data training, weight fitting, and model optimization. Examples include logistic regression, random forest, gradient boosting tree algorithms, as well as support vector machines and neural network algorithms.

[0059] Specifically, multiple sample data can be input into the algorithm to iteratively optimize model parameters and fit the association between gene loci and metabolic indicators. This can be achieved by systematically dividing the system into training and validation sets, iterating repeatedly to reduce errors, and determining the optimal model parameters.

[0060] The scoring weight refers to the coefficient of influence of each gene locus on different metabolic dimensions. For example, it could be the high weight of the ALDH2 gene locus on the risk of acetaldehyde accumulation, the high weight of the ADH1B gene locus on the rate of ethanol breakdown, or the differentiated weight of the same gene locus in different age groups.

[0061] This solution uses algorithm training to quantify the contribution of each gene locus and population characteristic to the metabolic assessment results, outputting weight values. Specifically, the model can automatically calculate the importance of each variable and generate a standardized weight matrix. Substituting user gene data with the weight matrix, the solution calculates quantitative results across four dimensions, including ethanol breakdown rate and acetaldehyde accumulation risk. Specifically, the model can automatically calculate individual scores and a comprehensive score, generating a structured assessment report.

[0062] This technical solution uses large-scale, multi-dimensional sample data collection and machine learning algorithm training to determine differentiated weights, constructing a scientific and accurate scoring model. It overcomes the limitations of poor adaptability of traditional static models, making metabolic assessment more consistent with the characteristics of real populations and significantly improving the scientific nature, accuracy, and universality of the assessment results.

[0063] In one embodiment, optionally, the alcohol metabolism capacity scoring model is further configured as follows: The scoring weights are dynamically adjusted based on the user's geographical distribution and age range to accommodate the genetic background differences among different groups.

[0064] Geographical distribution characteristics refer to the geographical region, ethnic group, and other regional genetic background characteristics to which a user belongs. For example, it could be East Asian populations, European and American populations, or Han Chinese from the south, Han Chinese from the north, or ethnic minority groups.

[0065] The age range refers to the age group in which the user belongs. For example, it could be 18-30 years old (young people), 31-50 years old (middle-aged people), 51 years old and above (elderly people), or it could be the basic age range of minors, adults, and the elderly.

[0066] Dynamic adjustment refers to the model updating its scoring parameters in real time by calling corresponding weight subsets based on user location and age information. Specifically, this can be achieved by having the model built into different population weight libraries, automatically matching and replacing weight parameters after identifying user characteristics.

[0067] Scoring weights refer to the coefficients representing the degree of influence of each gene locus on different metabolic dimensions. For example, the high weight of the ALDH2 gene in East Asian populations, the differentiated weight of the CYP2E1 gene in young people, or the adaptation weight of the same gene locus in different age groups.

[0068] This technical solution allows the model to adjust weights to match the evaluation results with the genetic background and physiological characteristics of different populations. Specifically, the model can optimize the evaluation logic to address gene expression differences caused by region and age, and output results that are more tailored to individuals.

[0069] Differences in genetic background refer to inherent differences in gene frequency and gene expression levels among different geographical regions and age groups. For example, East Asian populations may have a higher ALDH2 mutation rate, older adults may have lower liver metabolic enzyme activity, and there may be differences in the distribution of CYP2E1 gene polymorphisms among different geographical populations.

[0070] This technical solution dynamically adjusts the weights of region and age dimensions to accurately adapt to the genetic background differences of different groups, avoiding the problem of evaluation bias in different groups by a single weight model, further improving the individual fit and accuracy of metabolic assessment results, and making the assessment more in line with the actual physiological characteristics of users.

[0071] In one embodiment, optionally, the hangover relief and liver protection recommendation module 103 is further configured as follows: Receive user feedback data on the use of the generated intervention plan and subsequent liver function monitoring data; Based on the feedback data and liver function monitoring data, the recommended model parameters are iteratively updated to dynamically optimize the subsequently generated personalized hangover relief and liver protection intervention plan.

[0072] The use of feedback data refers to user feedback on the effectiveness of the recommended plan, their physical reactions, and their experience with its adaptation. Examples include improvements in physical discomfort after drinking alcohol, difficulty in adhering to the recommended diet and lifestyle, and feelings about using intervention products. It can also include written feedback such as plan effectiveness ratings and adaptation evaluations.

[0073] Liver function monitoring data refers to liver function-related indicators that users will periodically monitor. These may include alanine aminotransferase (ALT), aspartate aminotransferase (AST), bilirubin levels, changes in fatty liver severity, liver enzyme activity, and liver ultrasound results.

[0074] The system can collect user feedback and subsequent test data through user interfaces and data interfaces. Specifically, the system can provide feedback forms for users to fill out and automatically synchronize liver function test reports through interfaces with medical institutions.

[0075] Recommendation model parameters refer to the core variables used in machine learning recommendation models to calculate the suitability of solutions and generate solutions. These can include, for example, gene feature weights, correlation coefficients of evaluation results, solution effect fitting parameters, user feature matching thresholds, and intervention strategy priority parameters.

[0076] Iterative updates refer to the process where the system retrains the model and optimizes its parameters using new data to improve the model's prediction accuracy. Specifically, this can involve adding new feedback and monitoring data to the training set, iterating through the training process, and updating the model's parameter matrix.

[0077] Dynamic optimization refers to updating model parameters so that subsequent generated plans better reflect the user's actual physical changes and user experience. Specifically, this can involve the system adjusting the priority of intervention strategies, details of dietary and lifestyle recommendations, and a list of recommended intervention products based on the optimized model.

[0078] Personalized hangover relief and liver protection intervention programs refer to customized dietary, lifestyle, and liver protection guidance programs generated based on user genes, assessment results, and feedback data. Examples include hangover relief diet programs specifically for individuals at high risk of acetaldehyde accumulation, liver protection lifestyle programs for middle-aged individuals with fatty liver, and even intervention product recommendations tailored to individual metabolic characteristics.

[0079] This technical solution constructs a dynamic closed loop of "assessment-recommendation-feedback-optimization" through closed-loop collection of user feedback and liver function monitoring data, and iterative updates of model parameters. This allows the solution to continuously adapt to changes in the user's body, breaking through the limitations of traditional one-time recommendations and continuously improving the practicality, adaptability, and long-term effectiveness of the solution.

[0080] In one embodiment, optionally, the system further includes: The data storage module is configured to encrypt and store the key gene polymorphism site typing data, quantitative evaluation results, and personalized hangover relief and liver protection intervention plans.

[0081] Encrypted storage refers to the system using encryption algorithms to encode data before storing it in a database to prevent data leakage and tampering. Specifically, this could involve the system using the AES (Advanced Encryption Standard) algorithm to encrypt genetic data and evaluation reports, storing them in a privacy database, and allowing only authorized users to decrypt and view them.

[0082] This technical solution, through encrypted storage of core privacy data, strictly protects the privacy and security of user genetic information, evaluation results, and personalized plans, prevents data leakage and tampering risks, complies with health data privacy protection standards, and enhances users' sense of security and trust in using the system.

[0083] In one embodiment, optionally, the key gene polymorphism sites include at least one or more of ADH1B, ALDH2, CYP2E1, GSTM1, and UGT1A1; The alcohol metabolism capacity assessment module 102 is also configured to: The key gene polymorphism site genotyping data are mapped to standardized wild-type, heterozygous mutant, or homozygous mutant risk genotypes.

[0084] Mapping refers to the system's conversion of raw genotyping results into standardized risk genotyping labels. Specifically, this can involve the system pre-setting genotyping mapping rules to match specific genotype results into three categories: wild-type, heterozygous mutant, and homozygous mutant.

[0085] Standardization refers to unifying the naming rules for genotyping and defining risk levels to ensure consistency in genotyping results for different genes and samples. For example, a normal genotype can be uniformly defined as wild-type, a single-copy mutation as a heterozygous mutation, and a double-copy mutation as a homozygous mutation.

[0086] Wild-type refers to a genotype with no gene mutations and normal gene function. For example, it could be ADH1B wild-type or ALDH2 wild-type, which typically corresponds to a stronger ability to metabolize alcohol and a lower risk of liver damage.

[0087] Heterozygous mutants are genotypes characterized by a single-copy mutation at a gene locus, resulting in partial functional impairment. Examples include heterozygous ALDH2 mutants and heterozygous CYP2E1 mutants, which typically correspond to moderate alcohol metabolism capacity and moderate risk of liver damage.

[0088] Homozygous mutants refer to genotypes with double-copy mutations at a gene locus, resulting in significant functional impairment. Examples include homozygous ALDH2 mutants and homozygous GSTM1 mutants, which typically correspond to weaker alcohol metabolism and a higher risk of liver damage.

[0089] Risk genotyping refers to a classification based on genotype mutations that reflects the level of metabolic capacity and risk of liver damage. For example, it could be low-risk wild-type, medium-risk heterozygous mutant, or high-risk homozygous mutant.

[0090] This technical solution simplifies the understanding of complex gene data by identifying core key gene loci, standardizing typing mapping rules, and unifying the standards for interpreting gene typing. It makes the risk level classification clear and intuitive, providing a unified and reliable typing basis for subsequent accurate assessment and personalized solution generation, and improving the readability and consistency of system results.

[0091] In one embodiment, optionally, the hangover relief and liver protection recommendation module 103 is further configured as follows: When generating the personalized hangover relief and liver protection intervention plan, a pre-set intervention product database is called, and the corresponding intervention product type and dosage range are matched according to the quantitative evaluation results; The intervention products in the intervention product database include bioactive ingredients with the ability to activate alcohol metabolism enzymes, and the screening and recommendation of the bioactive ingredients are dynamically determined based on the quantitative evaluation results.

[0092] Personalized hangover relief and liver protection intervention programs refer to customized dietary, lifestyle, and liver protection guidance programs generated based on user genes and assessment results. Examples include hangover relief diet programs specifically for individuals at high risk of acetaldehyde accumulation, liver protection lifestyle programs for middle-aged individuals with fatty liver, and even intervention product recommendations tailored to individual metabolic characteristics.

[0093] The system can automatically read and retrieve pre-stored intervention supplies database data when generating a treatment plan. Specifically, after the system triggers the treatment plan generation logic, it connects to the database interface and reads information such as the type, ingredients, and dosage of the intervention supplies.

[0094] The pre-built intervention product database refers to a structured database that the system pre-constructs and stores information on various liver protection intervention products. For example, it could be a database containing information on food, health products, and dietary supplements, or it could be a standardized database containing active ingredients, applicable populations, and recommended dosages.

[0095] Quantitative assessment results refer to numerical or graded conclusions calculated from dimensions such as ethanol breakdown rate and acetaldehyde accumulation risk. Examples include a comprehensive metabolic capacity score, a high-risk level for liver damage, or individual scores for each dimension, and a level of intoxication risk.

[0096] The system can screen suitable intervention product types and dosages based on the risk level and metabolic characteristics of the assessment results. Specifically, the system can preset risk-product matching rules, matching acetaldehyde accumulation high-risk products with acetaldehyde dehydrogenase activators and low-risk products with basic liver protection products.

[0097] The dosage range refers to the recommended dosage range of intervention products tailored to individual characteristics. For example, it could be 1-2 B vitamin tablets daily, 1 sachet of kudzu root compound preparation each time, 200-500 ml of antioxidant drink daily, or 1-2 grams of silymarin daily.

[0098] Bioactive components refer to functional ingredients that possess specific physiological activities, can activate alcohol metabolism enzymes, and protect liver cells. Examples include puerarin, Hovenia dulcis extract, silymarin, as well as B vitamins, glutathione, and Lycium barbarum polysaccharides.

[0099] The ability to activate alcohol metabolism enzymes refers to the ability of bioactive components to enhance the activity of alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH2) and accelerate alcohol metabolism. For example, puerarin can activate ADH, Hovenia dulcis extract can activate ALDH2, and complex peptides can enhance the overall activity of metabolic enzymes.

[0100] In this solution, the system can screen suitable ingredients from the database and include them in the recommended solutions based on the assessment results. Specifically, for those with a high risk of acetaldehyde accumulation, the system may prioritize screening ALDH2-activating ingredients; for those with a high risk of liver damage, it may prioritize screening hepatocellular protective ingredients. The recommendations for bioactive ingredients are not fixed and vary depending on the user's quantitative assessment results. Specifically, for those with weak metabolism and a high risk of acetaldehyde accumulation, highly active metabolic enzyme activating ingredients may be recommended; for those with strong metabolism and a low risk, basic antioxidant and hepatoprotective ingredients may be recommended.

[0101] This technical solution, through a pre-built database of intervention products and dynamic matching of active ingredients and dosages based on assessment results, ensures that intervention product recommendations are tailored to individual metabolic risk characteristics, avoids blindly recommending generic products, achieves precise adaptation of intervention products, further enhances the pertinence and effectiveness of the solution, and at the same time ensures the scientific validity and safety of the recommendations.

[0102] In one embodiment, optionally, the hangover relief and liver protection recommendation module 103 is further configured as follows: When the quantitative assessment results indicate that the risk level of acetaldehyde accumulation exceeds a preset threshold, a priority recommendation instruction for interventional products that enhance acetaldehyde dehydrogenase activity is generated.

[0103] Acetaldehyde accumulation risk level refers to the classification of the risk of acetaldehyde accumulating in the body after drinking alcohol. For example, it can be classified as low, medium, and high, or as mild, moderate, and severe accumulation risk levels.

[0104] A preset threshold refers to a risk level threshold that the system pre-sets to trigger the priority recommendation logic. For example, it could be a high-risk threshold or a moderate accumulation risk threshold.

[0105] After identifying a risk level exceeding the threshold, the system automatically outputs an instruction to prioritize and recommend specific intervention products. Specifically, the system may trigger risk assessment logic, automatically assigning a recommendation priority once the threshold is exceeded, and outputting a priority recommendation instruction.

[0106] Aldehyde dehydrogenase 2 (ALDH2) activity-enhancing intervention products refer to liver-protecting products that can increase ALDH2 activity and accelerate the decomposition and clearance of acetaldehyde. Examples include kudzu root and jujube fruit compound preparations, rose, black bean, and chuanxiong combination preparations, as well as functional drinks containing ALDH2 activating peptides and highly active curcumin compound preparations.

[0107] The priority recommendation instruction is a system-defined instruction that prioritizes specific intervention products, placing them at the top of the recommendation list. For example, it could prioritize listing acetaldehyde dehydrogenase activators in the treatment plan and displaying them at the top of the recommendation details page, or it could be a default selection of such products, prioritizing the push of related recommendation information.

[0108] This technical solution precisely targets the high-risk pain point of acetaldehyde accumulation by setting a risk threshold for acetaldehyde accumulation and prioritizing the recommendation of acetaldehyde dehydrogenase activators when the threshold is exceeded. It addresses the core metabolic disorder first, specifically reduces the toxic damage caused by acetaldehyde, and significantly improves the intervention effect for people at high risk of acetaldehyde accumulation, making the solution more focused on the core risk and more targeted.

[0109] To enable those skilled in the art to better understand this solution, this application also provides a preferred embodiment.

[0110] This invention discloses an intelligent recommendation system for personalized hangover relief and liver protection plans based on gene polymorphism detection. The system mainly includes a gene polymorphism acquisition module, an alcohol metabolism capacity assessment module, and a hangover relief and liver protection plan recommendation module, as well as a data storage module to ensure user data security. The gene polymorphism acquisition module obtains polymorphic site information of key genes related to alcohol metabolism and liver damage repair, such as ADH1B and ALDH2, and completes gene data import, standardized annotation, and risk typing. Based on the gene typing results, the alcohol metabolism capacity assessment module uses a preset scoring model to quantitatively analyze dimensions such as the user's ethanol breakdown rate and acetaldehyde accumulation risk, outputting assessment results for alcohol metabolism, intoxication risk, and liver damage susceptibility, and generating a visual report. The hangover relief and liver protection plan recommendation module combines the assessment results with basic information such as the user's gender and weight, and intelligently generates personalized intervention plans through a rule engine and machine learning model. This invention relies on gene polymorphism to achieve precise guidance for hangover relief and liver protection, effectively solving the technical pain points of traditional plans lacking individual targeting and having limited intervention effects. It can significantly reduce the risk of liver damage caused by alcohol consumption and improve the scientific, personalized, and practical nature of health management.

[0111] This invention provides an intelligent recommendation system for personalized hangover relief and liver protection plans based on gene polymorphism detection. The system includes a gene polymorphism acquisition module, an alcohol metabolism capacity assessment module, and a hangover relief and liver protection plan recommendation module. It may also optionally include a data storage module. All modules work collaboratively, and the specific structure is as follows: A personalized intelligent recommendation system for hangover relief and liver protection based on gene polymorphism detection includes: Gene polymorphism acquisition: This module is used to obtain information on key gene polymorphism sites related to alcohol metabolism and liver damage repair. It supports the automatic import of gene data from gene sequencing reports, gene chip results, or third-party testing platform interfaces.

[0112] Alcohol metabolism capacity assessment: Based on the genotyping and risk genotyping results output by the gene polymorphism acquisition module, combined with the preset alcohol metabolism capacity scoring model, the system quantifies and analyzes four dimensions of the user: ethanol breakdown rate, acetaldehyde accumulation risk, oxidative stress level, and susceptibility to liver damage. Recommended hangover relief and liver protection plan: Receives the assessment results output by the alcohol metabolism capacity assessment module, and collects basic information such as user gender, weight, drinking frequency, and basic liver disease history. Through a rule engine combined with a machine learning recommendation model, it intelligently generates a personalized hangover relief and liver protection intervention plan.

[0113] Furthermore, the gene polymorphism acquisition module also includes a data verification unit, which is used to verify the completeness and accuracy of the imported gene data, remove abnormal data, and provide prompts for missing gene locus information to support users in supplementing and improving the data.

[0114] Furthermore, the alcohol metabolism capacity scoring model is obtained by collecting a large amount of genotyping data, alcohol metabolism-related physiological index data, and liver injury case data, and is optimized through machine learning training. The scoring weights can be dynamically adjusted according to the regional and age characteristics of different populations.

[0115] Furthermore, the machine learning recommendation model in the hangover relief and liver protection plan recommendation module can continuously optimize the accuracy of the plan recommendation based on user feedback and subsequent liver function monitoring data, thereby achieving dynamic updates of the plan.

[0116] Furthermore, it also includes a data storage module for encrypting and storing users' genetic data, evaluation results, basic personal information, and recommendation schemes, ensuring user data privacy and security, and allowing users to query and export their own relevant data at any time.

[0117] Furthermore, the specific health product or medication usage tips mentioned are for health guidance and reference only, and do not constitute medical advice. They also clearly indicate the contraindications and dosage ranges.

[0118] Gene polymorphism acquisition module: The core function of this module is to obtain information on key gene polymorphism sites related to alcohol metabolism and liver damage repair. These key genes include at least one of ADH1B, ALDH2, CYP2E1, GSTM1, and UGT1A1. These genes are directly or indirectly involved in the alcohol metabolism process or the liver damage repair process. The typing of their polymorphism sites directly determines the human body's ability to metabolize alcohol and its susceptibility to liver damage.

[0119] This module supports multiple gene data import methods. It can extract genotyping data from user-uploaded gene sequencing reports and gene chip results, and can also automatically synchronize gene data through third-party testing platform interfaces, eliminating the need for manual input and improving operational convenience. Simultaneously, the module includes a data verification unit to check the completeness and accuracy of imported gene data, removing abnormal data (such as missing gene loci or incorrect genotyping results), and providing clear prompts for missing gene loci information, allowing users to supplement and improve the data, ensuring the accuracy of subsequent evaluation results.

[0120] After the data is imported and verified, the module performs standardized annotation on the gene loci, specifying the gene name and genotype of each locus, and classifies them into three risk types based on the genotyping results: wild type, heterozygous mutant, and homozygous mutant. Among them, the homozygous mutant has the weakest alcohol metabolism capacity and the highest risk of liver damage, the wild type has the strongest alcohol metabolism capacity and the lowest risk of liver damage, and the heterozygous mutant is in between.

[0121] Alcohol Metabolism Assessment Module: This module uses the genotyping and risk genotyping results output by the gene polymorphism acquisition module as core input data, combined with a pre-set alcohol metabolism capacity scoring model, to quantitatively analyze users' alcohol metabolism-related indicators. The alcohol metabolism capacity scoring model is obtained by collecting genotyping data from a large number of samples, alcohol metabolism-related physiological indicator data (such as ethanol breakdown rate, acetaldehyde concentration, etc.), and liver injury case data, and is trained and optimized using machine learning algorithms. It can dynamically adjust the scoring weights according to the geographical and age characteristics of different populations, adapting to the physiological differences of different groups.

[0122] In the specific assessment process, the model quantifies and scores the user's ethanol metabolism rate, acetaldehyde accumulation risk, oxidative stress level, and susceptibility to liver damage across four dimensions. Each dimension is assigned a corresponding score based on the genotyping results, and the scores are aggregated to obtain a comprehensive score, which in turn outputs three core assessment results: alcohol metabolism strength level (divided into strong, moderate, and weak), intoxication risk level (divided into low, moderate, and high), and liver damage genetic susceptibility level (divided into low, moderate, and high). Simultaneously, the module generates a visual assessment report, clearly displaying the scores for each dimension, assessment levels, level descriptions, and genotyping details, allowing users to intuitively understand their own alcohol metabolism and liver damage risk status.

[0123] Recommended hangover remedies and liver protection modules: This module is the core execution module of the system. It mainly receives the assessment results output by the alcohol metabolism capacity assessment module, and at the same time collects the user's basic personal information, including gender, weight, drinking frequency (such as the number of times drinking per week and the amount of alcohol consumed each time), basic liver disease history (such as whether the user has fatty liver, hepatitis, etc.), dietary habits, etc., to build a personalized feature database for the user.

[0124] The module uses a rule engine combined with a machine learning recommendation model to generate personalized hangover relief and liver protection intervention plans based on a user feature database. The rule engine presets basic intervention principles corresponding to different assessment levels, while the machine learning recommendation model optimizes and adjusts the plans based on a large amount of user case data and feedback data, ensuring the plans are targeted and practical. The intervention plan includes at least the following two items and contents: (1) Safe drinking limits and drinking taboos: Based on the strength of alcohol metabolism and the level of drunkenness, the safe drinking amount for each user per day / each time is specified (e.g., the daily alcohol intake of low-risk people should not exceed 25g, and high-risk people are advised not to drink alcohol). At the same time, drinking taboos are reminded (e.g., drinking on an empty stomach, mixing different types of alcohol, etc.). (2) Food and nutrient ratio scheme for relieving hangover before / drinking / after drinking: Recommend suitable foods for people with different metabolic abilities (such as eating high-protein foods before meals, drinking warm water during drinking, and eating fruits rich in vitamins after drinking), and specify the ratio and consumption method of nutrients (such as B vitamins, glutathione, etc.). (3) Dietary and lifestyle guidance for liver protection: Based on the susceptibility level of liver damage, recommend daily diets for liver protection (such as eating more foods rich in dietary fiber and high-quality protein, and reducing the intake of high-fat and high-sugar foods), as well as reasonable lifestyle plans (such as avoiding staying up late and ensuring 7-8 hours of sleep every day). (4) Targeted health care products or drug usage tips: Recommend liver-protecting health care products (such as silymarin, liver protection tablets, etc.) or drugs that are suitable for the user's genetic characteristics, clearly indicate the dosage range, usage time and contraindications, and emphasize that this tip is only for health guidance and reference and does not constitute medical advice; (5) Alcohol abstinence / limitation reminders and liver function monitoring recommendations for high-risk groups: For people with high susceptibility to liver damage, issue mandatory alcohol abstinence / limitation reminders and recommend regular (e.g., every 3-6 months) liver function monitoring to detect liver damage in a timely manner and intervene.

[0125] In addition, the machine learning recommendation model can continuously optimize the accuracy of the recommended plans based on user feedback (such as the effectiveness of the plan implementation and the body's reaction after drinking) and subsequent liver function monitoring data, so as to achieve dynamic updates of the plans and ensure that the plans are always adapted to the user's physical condition.

[0126] Data storage module: To ensure user data privacy and security, the system allows users to optionally configure a data storage module for encrypted storage of user genetic data, assessment results, basic personal information, and recommended plans. Encryption algorithms are used to protect the data and prevent leakage or tampering. This module also allows users to query and export their data at any time, facilitating user retention or provision to medical and health management institutions for reference.

[0127] Example of a specific use case: This embodiment provides a personalized hangover relief and liver protection plan intelligent recommendation system based on gene polymorphism detection, including a gene polymorphism acquisition module, an alcohol metabolism capacity assessment module, a hangover relief and liver protection plan recommendation module, and a data storage module.

[0128] 1. Gene Polymorphism Acquisition: Users upload their own gene sequencing reports. The gene polymorphism acquisition module extracts the genotyping data of three key genes, ADH1B, ALDH2, and CYP2E1, from the report. The data is verified by the data verification unit to confirm that the data is complete and accurate. The module performs standardized annotation on the gene loci, determines that the ADH1B gene is wild-type, the ALDH2 gene is heterozygous mutant, and the CYP2E1 gene is homozygous mutant, and completes the risk genotyping labeling.

[0129] 2. Alcohol Metabolism Capacity Assessment: The alcohol metabolism capacity assessment module calls a preset scoring model and, in conjunction with the above genotyping results, quantifies and scores the user's ethanol breakdown rate, acetaldehyde accumulation risk, oxidative stress level, and liver damage susceptibility in four dimensions. After comprehensive scoring, the assessment results are output: weak alcohol metabolism capacity, high risk of intoxication, and high genetic susceptibility to liver damage. A visual assessment report is also generated, clearly indicating that the user is prone to acetaldehyde accumulation after drinking alcohol and has a high risk of liver damage.

[0130] 3. Recommended hangover relief and liver protection plan: The module collects basic user information, revealing that the user is male, weighs 70kg, drinks alcohol 3 times a week, and has no history of underlying liver disease. Based on the assessment results, a personalized plan is generated using a rule engine and machine learning model: (1) Drinking recommendations: It is recommended that the daily alcohol intake should not exceed 10g, avoid drinking on an empty stomach or mixing alcoholic beverages, prioritize low-alcohol beverages, and abstain from alcohol for at least 4 days a week; (2) Dietary guidance: Eat high-protein foods such as eggs and milk before meals, drink 200ml of warm water every 30 minutes while drinking alcohol, eat fruits rich in vitamin C such as oranges and kiwis after drinking alcohol, and supplement with B vitamin tablets daily (dosage is 1 tablet per day). (3) Rest and monitoring: Ensure 7-8 hours of sleep every day, avoid staying up late (do not fall asleep after 11 pm), and monitor liver function every 3 months (the test indicators include alanine aminotransferase, aspartate aminotransferase, etc.). (4) Health Supplement Recommendation: Based on the core needs of users with acetaldehyde accumulation (accelerating acetaldehyde metabolism, reducing accumulation, and protecting the liver and detoxifying), we recommend health supplements that combine ingredients from the research on food and medicine homology. Specifically, we recommend a combination of kudzu root and Japanese raisin tree fruit compound preparation and a combination of rose, black bean, and chuanxiong. The two ingredients work synergistically to match the genetic characteristics of users with acetaldehyde accumulation, aligning with the conclusions of the research on food and medicine homology, and are safe with no side effects. Among them, the kudzu root and Japanese raisin tree fruit compound preparation (the main ingredients are kudzu root and Japanese raisin tree fruit) can significantly increase the activity of ethanol and acetaldehyde dehydrogenase, while rose, black bean, and chuanxiong can activate the activity of acetaldehyde dehydrogenase. The three ingredients work synergistically to accelerate the conversion of acetaldehyde to acetic acid, rapidly metabolize the acetaldehyde accumulated in the body, and at the same time play a role in detoxifying alcohol and protecting the liver. Contraindicated groups are pregnant women and breastfeeding women. It should not be taken at the same time as other liver-protecting drugs.

[0131] 4. Data storage: The data storage module encrypts and stores the user's genotyping data, assessment reports, personal basic information, and recommended plans. Users can query and export relevant data through the system interface.

[0132] Example of a specific use case: This embodiment provides a personalized hangover relief and liver protection plan intelligent recommendation system based on gene polymorphism detection, including a gene polymorphism acquisition module, an alcohol metabolism capacity assessment module, and a hangover relief and liver protection plan recommendation module.

[0133] 1. Acquisition of Gene Polymorphism: Users synchronize their genotyping data for the three genes GSTM1, UGT1A1, and ADH1B through a third-party gene testing platform interface. After receiving the data, the gene polymorphism acquisition module verifies that the GSTM1 gene locus is missing and prompts the user to supplement the genotyping data (wild type). The module completes standardized annotation and determines that ADH1B is wild type, UGT1A1 is heterozygous mutant, and GSTM1 is wild type.

[0134] 2. Alcohol Metabolism Capacity Assessment: The scoring model combines the above genotyping results to quantitatively assess and output: moderate alcohol metabolism capacity, moderate risk of intoxication, and moderate genetic susceptibility to liver damage. The visual report suggests that the user has moderate alcohol metabolism capacity and should pay attention to appropriate intervention after drinking to avoid excessive drinking.

[0135] 3. Recommended hangover relief and liver protection plan: Based on basic user information (female, weight 55kg, drinks alcohol once a week, history of mild fatty liver disease), a plan is generated: (1) Drinking recommendations: Daily alcohol intake should not exceed 15g. Avoid strenuous exercise after drinking. The interval between drinking should be at least 48 hours. (2) Dietary guidance: Eat multigrain porridge and vegetable salad before meals, drink light tea while drinking alcohol, drink millet porridge after drinking alcohol, eat more liver-protecting vegetables such as spinach and broccoli in daily life, and reduce the intake of fried and spicy foods; (3) Daily routine guidance: Go to bed before 22:30 every day, ensure 8 hours of sleep, and avoid overwork; (4) Medication Recommendation: Considering the user's moderate alcohol metabolism and history of mild fatty liver disease, we recommend a health product containing food-grade medicinal ingredients that enhance acetaldehyde dehydrogenase activity, provide antioxidant effects, and protect the liver. Specifically, this product is a kudzu root and Japanese raisin tree fruit preparation combined with a wolfberry and blueberry beverage. Both are made from food-grade medicinal ingredients, providing gentle liver protection, aiding metabolism, and possessing antioxidant properties. The kudzu root and wolfberry compound preparation (mainly composed of kudzu root and wolfberry) and the kudzu root and Japanese raisin tree fruit compound preparation (mainly composed of kudzu root and Japanese raisin tree fruit) can significantly enhance liver health. This product is designed to enhance the activity of ethanol and acetaldehyde dehydrogenases. The main ingredients of this goji berry and blueberry beverage are goji berries and blueberries. Goji berries and blueberries are rich in antioxidants, which can exert antioxidant effects, scavenge liver free radicals, protect liver cells, and reduce liver oxidative stress damage. It also aids digestion and protects liver cells. However, it is clearly stated that this product is contraindicated for pregnant and breastfeeding women, and those with excessive stomach acid should use it with caution. This is for health guidance only and does not constitute medical advice. It is recommended to combine this product with dietary and lifestyle adjustments, and to have regular (every 6 months) liver function tests to monitor changes in fatty liver.

[0136] The purpose of this invention is to overcome the shortcomings of existing hangover relief and liver protection programs, which lack individual targeting and have insufficient practicality of gene testing results. It provides an intelligent recommendation system for personalized hangover relief and liver protection programs based on gene polymorphism detection. By relying on gene polymorphism, it can achieve precise guidance on hangover relief and liver protection, reduce the damage of alcohol to the liver, improve the scientific and personalized level of health management, and meet the growing demand for health management in modern society.

[0137] Example 2 Figure 2 This is a flowchart illustrating the intelligent recommendation method for hangover relief and liver protection based on gene polymorphism detection provided in Embodiment 2 of this application. Figure 2 As shown, the method includes: S21, Obtain the user's gene polymorphism site typing data on alcohol metabolism and liver damage repair; S22. Based on the key gene polymorphism site typing data and combined with the preset alcohol metabolism capacity scoring model, determine the quantitative assessment results of the user in at least one of the four dimensions: ethanol decomposition rate, acetaldehyde accumulation risk, oxidative stress level and liver damage susceptibility. S23. Based on the quantitative evaluation results and the pre-collected basic physiological characteristic data of users, a hangover relief and liver protection recommendation plan is generated by combining a rule engine with a machine learning recommendation model. This plan includes at least one of the following: safe drinking limits, dietary and lifestyle guidance, and intervention product suggestions.

[0138] The method provided in this embodiment has the same execution process and beneficial effects as the system described above, and will not be repeated here to avoid duplication.

[0139] Example 3 like Figure 3 As shown, this application embodiment also provides an electronic device 300, including a processor 301, a memory 302, and a program or instructions stored in the memory 302 and executable on the processor 301. When the program or instructions are executed by the processor 301, they implement the various processes of the above-described intelligent recommendation system embodiment for hangover relief and liver protection based on gene polymorphism detection, and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0140] It should be noted that the electronic devices in the embodiments of this application include mobile electronic devices and non-mobile electronic devices as described above.

[0141] Example 4 This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described intelligent recommendation system for hangover relief and liver protection based on gene polymorphism detection, and achieve the same technical effect. To avoid repetition, these will not be described again here.

[0142] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0143] Example 5 This application also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described intelligent recommendation system for hangover relief and liver protection based on gene polymorphism detection, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0144] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, device chip, chip system, or system-on-a-chip, etc.

[0145] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element. Furthermore, it should be noted that the scope of the methods and systems in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0146] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0147] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms fall within the scope of protection of this application.

[0148] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein. Various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of this application, and the scope of this application is determined by the scope of the claims.

Claims

1. An intelligent recommendation system for an alcohol detoxification and liver protection program based on genetic polymorphism detection, characterized in that, The system includes: The gene polymorphism acquisition module is configured to acquire gene polymorphism site typing data of users' alcohol metabolism and liver damage repair. The alcohol metabolism capacity assessment module is connected to the gene polymorphism acquisition module and is configured to determine the quantitative assessment results of the user in at least one of the four dimensions: ethanol decomposition rate, acetaldehyde accumulation risk, oxidative stress level and liver damage susceptibility, based on the key gene polymorphism site typing data and a preset alcohol metabolism capacity scoring model. The hangover relief and liver protection recommendation module is connected to the alcohol metabolism capacity assessment module. It is configured to generate a hangover relief and liver protection recommendation plan based on the quantitative assessment results and the user's basic physiological characteristic data collected in advance, through a rule engine combined with a machine learning recommendation model. The plan includes at least one of the following: safe drinking limits, dietary and lifestyle guidance, and intervention product suggestions.

2. The system according to claim 1, characterized in that, The gene polymorphism acquisition module is also configured to: The obtained key gene polymorphism site genotyping data were subjected to integrity verification and outlier identification. When missing or abnormal data is detected, a data correction prompt is generated, and the verification operation is re-executed after the corrected data is received.

3. The system according to claim 1, characterized in that, The alcohol metabolism capacity scoring model in the alcohol metabolism capacity assessment module is constructed in the following way: Collect key gene polymorphism site typing data, alcohol metabolism-related physiological index data, and clinical data on liver injury from multiple samples; The multi-sample data was trained using machine learning algorithms to determine the scoring weights of each gene locus under different population characteristics. Based on the scoring weights, the quantitative evaluation results for the four dimensions are output.

4. The system according to claim 3, characterized in that, The alcohol metabolism capacity scoring model is also configured as follows: The scoring weights are dynamically adjusted based on the user's geographical distribution and age range to accommodate the genetic background differences among different groups.

5. The system according to claim 1, characterized in that, The hangover relief and liver protection recommendation module is also configured as follows: Receive user feedback data on the use of the generated intervention plan and subsequent liver function monitoring data; Based on the feedback data and liver function monitoring data, the recommended model parameters are iteratively updated to dynamically optimize the subsequently generated personalized hangover relief and liver protection intervention plan.

6. The system according to claim 1, characterized in that, Also includes: The data storage module is configured to encrypt and store the key gene polymorphism site typing data, quantitative evaluation results, and personalized hangover relief and liver protection intervention plans.

7. The system according to claim 1, characterized in that, The key gene polymorphism sites include at least one or more of ADH1B, ALDH2, CYP2E1, GSTM1, and UGT1A1; The alcohol metabolism capacity assessment module is also configured to: The key gene polymorphism site genotyping data are mapped to standardized wild-type, heterozygous mutant, or homozygous mutant risk genotypes.

8. The system according to claim 1, characterized in that, The hangover relief and liver protection recommendation module is also configured as follows: When generating the personalized hangover relief and liver protection intervention plan, a pre-set intervention product database is called, and the corresponding intervention product type and dosage range are matched according to the quantitative evaluation results; The intervention products in the intervention product database include bioactive ingredients with the ability to activate alcohol metabolism enzymes, and the screening and recommendation of the bioactive ingredients are dynamically determined based on the quantitative evaluation results.

9. The system according to claim 8, characterized in that, The hangover relief and liver protection recommendation module is also configured as follows: When the quantitative assessment results indicate that the risk level of acetaldehyde accumulation exceeds a preset threshold, a priority recommendation instruction for interventional products that enhance acetaldehyde dehydrogenase activity is generated.

10. A method for intelligently recommending hangover relief and liver protection regimens based on gene polymorphism detection, characterized in that, The method includes: Obtain genotyping data of gene polymorphism sites related to alcohol metabolism and liver damage repair in users; Based on the key gene polymorphism site typing data, combined with the preset alcohol metabolism capacity scoring model, the quantitative assessment results of users in at least one of the four dimensions of ethanol decomposition rate, acetaldehyde accumulation risk, oxidative stress level and liver damage susceptibility are determined. Based on the quantitative assessment results and the pre-collected basic physiological characteristic data of users, a hangover relief and liver protection recommendation plan is generated by combining a rule engine with a machine learning recommendation model. This plan includes at least one of the following: safe drinking limits, dietary and lifestyle guidance, and suggestions for intervention products.

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