Personalized medicated diet tea drink preparation system and method based on physique recognition

Abstract

The invention discloses a personalized medicated diet tea drink configuration system and method based on constitution recognition, particularly relates to the technical field of medicated diet tea drink configuration, and aims at solving the problem that personalized medicated diet tea drink configuration is not clear. According to the method, the static physical data, the real-time physiological data and the environment data of the user are fused, the user state vector is generated, the features are extracted, the relation between the physical condition and the environment is analyzed, the personalized medicated diet tea drink formula and the medicinal material proportion are generated, and the user can rapidly and accurately drink the medicated diet tea drink by monitoring the environment change and physical condition fluctuation in real time. According to the method, the proportion of medicinal materials in a tea drink formula is dynamically adjusted to adapt to different health states and environmental conditions, meanwhile, user feedback data is collected, an incremental learning technology is combined, the formula is continuously optimized, self-adaptive adjustment is conducted according to the demand change and feedback of a user, the individuation and dynamic adaptive capacity of medicated diet tea drink recommendation are improved, and the user experience is improved. The user is helped to obtain a tea drink formula better meeting the health requirement and taste preference of the user, and the health management experience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medicinal food and tea beverage configuration, and more specifically, to a personalized medicinal food and tea beverage configuration system and method based on constitution recognition. Background Art

[0002] Medicinal tea is a health care method that combines traditional Chinese medicine theory with modern dietary concepts. Medicinal tea combines herbs with tea or other natural ingredients to regulate the body, nourish health and prevent diseases according to the theories of yin and yang, cold and heat in traditional Chinese medicine.

[0003] With the acceleration of modern life and the diversification of health needs, people are no longer only concerned about the taste and effect of medicinal tea drinks, but are increasingly inclined to personalized customization and targeted conditioning. The medicinal tea drink market is also constantly developing, gradually moving towards personalization and intelligence.

[0004] Deficiencies in existing technologies: Existing medicinal tea recommendation systems mostly rely on static personalized recommendation models, and usually only recommend recipes based on the user's physical characteristics (such as body temperature, heart rate, etc.) or environmental data (such as temperature, humidity, etc.). They lack a real-time response mechanism to environmental changes and user physical fluctuations, are unable to process users' dynamic feedback, and have a long update cycle, resulting in the recommendation results being unable to adapt to changes in user needs in a timely manner, and unable to continuously optimize recipe recommendations through real-time feedback, limiting the flexibility and accuracy of the personalized recommendation system. The system usually only provides a single recommended recipe, while ignoring the immediate changes in user needs under different health conditions and environmental conditions. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a personalized medicinal tea beverage configuration system and method based on constitution identification, so as to solve the problem of unclear personalized medicinal tea beverage configuration in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for preparing personalized medicinal tea based on constitution identification comprises the following steps: Collect the user's static physical data and real-time physiological data, obtain environmental data through sensors, fuse physical and environmental data through nonlinear mapping functions, and generate a user state vector that includes the user's physical and environmental changes; Extract features based on the fused user state vector, analyze the relationship between physical constitution and environmental data, output personalized medicinal tea recipes and calculate the proportion of medicinal materials to form a user-customized recipe; Calculate the change amount based on real-time environmental data and user's physical condition fluctuations, and adjust the proportion of medicinal materials in the formula to form an optimized medicinal tea formula; The taste and health effect data of medicinal teas are obtained through user feedback, and the tea formula is optimized through incremental learning.

[0007] In a preferred embodiment, static physical data and real-time physiological data of the user are collected, and environmental data is obtained through sensors. The specific process is as follows: The user's physical information is filled in through health records or questionnaires. The physical information includes physical type, age, gender, body shape, allergy history, and family medical history. The physical information is converted into structured data to form static physical data. Users provide real-time physiological data through smart bracelets and smart watches. Real-time physiological data includes heart rate, body temperature, blood pressure, blood sugar, sleep quality, and activity level. Real-time physiological data is stored in the form of time series; Collect environmental data, deploy temperature and humidity sensors, and air quality monitors to collect real-time climate conditions of the user's environment, including temperature, humidity, air quality, PM2.5 concentration, and air pressure. Combined with the weather API interface, obtain real-time weather information in the user's area. Real-time weather information includes current temperature, humidity, and wind speed.

[0008] In a preferred embodiment, the physical condition and environmental data are fused through a nonlinear mapping function to generate a user state representation including the user's physical condition and environmental changes. The specific process is as follows: Synchronize the user's physical data and environmental data in time. According to the timestamp of each data point, align the user's physical data and environmental data through the timestamp to correctly match the physical and environmental data at each moment. After the physical data and the environmental data are aligned in time, the state alignment is performed to form two time series, namely the physical data series and the environmental data series. The physical data series and the environmental data series are merged to form a unified input stream. For each pair of user physical data and environmental data, a nonlinear function is used to calculate the interaction effect, and the generation of interaction features is represented by a nonlinear function: ,in, is the physical data at time t; is the environmental data at time t; , is the learning weight of the model, which indicates the relative influence of physical fitness and environmental data; b is the bias term, which is used to adjust the output of the model; is the activation function; All interactive features Aggregate and get the user state vector U(t), expressed as: ,in, is the interaction feature of each pair of physical and environmental data, and n is the number of feature pairs.

[0009] In a preferred embodiment, features are extracted based on the fused user state vector, the relationship between the physical constitution and the environmental data is analyzed, and a personalized medicinal tea formula is output and the proportion of medicinal materials is calculated to form a user-customized formula. The specific steps are as follows: The chi-square test was used for feature selection to identify the features that had a significant impact on tea recipe recommendations; Through chi-square test, the physical constitution and environmental characteristics that were positively correlated with the effects of the medicinal tea formula were selected; Based on the results of feature selection, the most relevant features for generating medicinal tea recipes are selected; The selected features are standardized and normalized and then passed to the recommendation engine; According to the requirements of the task, select a deep neural network model or a gradient boosting decision tree as the recommendation model; Based on the characteristics of the given input, a personalized medicinal tea recipe is obtained through the recommendation model. For each medicinal material, the recommended ratio is calculated according to the user's physical condition and environmental conditions, and the recommended ratio of each medicinal material is output as the preliminary recipe; The preliminary formula is used, and a user-customized formula is generated based on user feedback of the preliminary formula.

[0010] In a preferred embodiment, the preliminary formula is used, and a user-customized formula is generated based on user feedback of the preliminary formula. The specific process is as follows: After generating the preliminary recipe, feedback is provided based on the user’s taste preferences and actual needs, and the parameters of the recommendation model are dynamically adjusted through feedback-based reinforcement learning; The reinforcement learning update formula is: ,in, For the status Take action The current value of is the learning rate, is the discount factor; For immediate rewards; For the next state The best value under During the recipe generation process, the recommended medicinal food ingredients are adjusted according to real-time feedback. The adaptive adjustment formula is: ,in, is the adjustment factor of the formula due to the current environment or user's physical condition change, P(t) represents the generated tea formula, It represents the formula of medicinal tea after adaptive adjustment; Real-time feedback includes the user’s health goals, preferences, and current physical changes; The final recommended medicinal tea recipe is output to the user again as a user-customized recipe.

[0011] In a preferred embodiment, the change amount is calculated based on the real-time environmental data and the user's physical condition fluctuations, and the proportion of the medicinal materials in the formula is adjusted to form an optimized medicinal tea formula. The specific process is as follows: Monitor environmental changes and obtain real-time environmental change data through sensors installed in the user's environment; analyze environmental change data to determine the degree of change in the current environment; By obtaining the environmental changes in real time, the proportion of medicinal materials in the medicinal tea can be adjusted according to the degree of change of the current environment; Analyze the user's physical condition fluctuations and make real-time adjustments, and adjust the formula of medicinal tea drinks according to the immediate changes in the user's physical condition; Analyze the user's physical condition change data, identify physical condition change trends, determine the amount of physical condition change, and adjust the proportion of medicinal materials in the medicinal tea formula according to the amount of physical condition change; The comprehensive adjustment of the medicinal tea formula is carried out by combining the environmental changes and the physical changes. The comprehensive fine-tuning formula of the medicinal tea formula is: , where P(t) is the initially generated medicinal tea formula; For the final adjusted formula, It is the adjustment factor for fine-tuning the formula due to environmental changes. It is the adjustment factor for fine-tuning the formula due to changes in physical constitution. is the change in body mass, is the environmental change.

[0012] A personalized medicinal tea beverage configuration system based on constitution recognition, used to implement the above-mentioned personalized medicinal tea beverage configuration method based on constitution recognition, comprising: The physical fitness and environment analysis module is used to collect the user's static physical fitness data and real-time physiological data, obtain environmental data through sensors, fuse physical fitness and environmental data through nonlinear mapping functions, and generate a user state vector containing user physical fitness and environmental changes; The preliminary formula generation module is used to extract features based on the fused user state vector, analyze the relationship between physical constitution and environmental data, output personalized medicinal tea formulas and calculate the proportion of medicinal materials to form a user-customized formula; The formula adjustment module is used to calculate the change amount according to the real-time environmental data and the user's physical fluctuations, and adjust the proportion of the medicinal materials in the formula to form an optimized medicinal tea formula; The feedback optimization module is used to obtain the taste and health effect data of medicinal tea through user feedback, and optimize the tea formula through incremental learning.

[0013] Technical effects and advantages of the present invention: The present invention collects static physical data and real-time physiological data of the user, combines them with environmental data, and fuses them through a nonlinear mapping function to generate a unified state vector that includes changes in the user's physical condition and environment, providing a data basis for personalized medicinal tea formula recommendations; Based on the fused user state vector, the system extracts key features and analyzes the relationship between physique and environment, thereby generating and calculating personalized medicinal tea formulas and medicinal material ratios to ensure that the recommended tea meets the user's health needs. By real-time monitoring of environmental changes and user physical fluctuations, the formula medicinal material ratio can be dynamically adjusted, and the tea formula can be optimized in real time to adapt to different environments and physical changes; further, the system continuously optimizes the tea formula by collecting user feedback data and combining incremental learning technology to ensure that the recommendation system can make adaptive adjustments as user needs and feedback change, thereby effectively improving the personalization, accuracy, and dynamic adaptability of medicinal tea recommendations, providing users with tea formulas that are more in line with their health needs and taste preferences, and improving the user's health management experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The present invention is a flow chart of a method for preparing personalized medicinal tea drinks based on constitution identification.

[0015] Figure 2 The present invention is a structural schematic diagram of a personalized medicinal tea beverage configuration system based on constitution identification. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Example 1: Figure 1 As shown, a method for preparing a personalized medicinal tea drink based on constitution identification comprises the following steps: Collect the user's static physical data and real-time physiological data, obtain environmental data through sensors, fuse physical and environmental data through nonlinear mapping functions, and generate a user state vector that includes the user's physical and environmental changes; Extract features based on the fused user state vector, analyze the relationship between physical constitution and environmental data, output personalized medicinal tea recipes and calculate the proportion of medicinal materials to form a user-customized recipe; Calculate the change amount based on real-time environmental data and user's physical condition fluctuations, and adjust the proportion of medicinal materials in the formula to form an optimized medicinal tea formula; The taste and health effect data of medicinal teas are obtained through user feedback, and the tea formula is optimized through incremental learning.

[0018] Step 1: Collect and integrate user's physical condition and environmental data. In the configuration of personalized medicinal tea, user's physical condition and environmental status are the key factors to determine the formula recommendation. First, it is necessary to collect and integrate the user's physical condition information and the changes in the environment in real time. Through this process, we can accurately understand the health status of each user and the impact of the external environment on the user's health, so as to generate the most suitable personalized medicinal tea formula. The specific steps are as follows: Collect basic physical information of users. The basic physical information of users can be filled in through health records or questionnaires. The physical information includes physical type (such as cold, hot, damp, weak, etc.), age, gender, body shape, allergy history, family medical history, etc. The definition of physical type can provide a preliminary understanding of the health characteristics of users; Convert physical information into structured data, that is, the user's static physical data, such as: body shape (thin / medium / obese); allergy history (yes / no); basic health status (such as whether there is diabetes, high blood pressure, etc.), etc.

[0019] Users provide real-time physiological data through wearable devices (such as smart bracelets, smart watches, etc.), usually including heart rate, body temperature, blood pressure, blood sugar, sleep quality, activity level, etc. Physiological data reflects the user's current physical state and helps understand the user's dynamic physical state. Real-time physiological data is stored in the form of time series, for example: ,in, Represents the heart rate, body temperature and other data at the i-th moment; The static physical data and the real-time physiological data are combined as the user's physical data to be used as input data and converted into a multi-dimensional feature vector, which contains both static physical information and dynamic physiological data.

[0020] Collect environmental data by deploying temperature and humidity sensors, air quality monitors and other equipment to collect real-time climate conditions of the user's environment, including temperature, humidity, air quality, PM2.5 concentration, air pressure, etc. At the same time, combined with the weather API interface, obtain real-time weather information in the user's area, such as current temperature, humidity, wind speed, etc.; Standardize environmental data to ensure that data from different devices and sensors are on the same scale. For example, temperature data is standardized to the range of [0, 1]. Use filters (such as Kalman filtering) to smooth environmental data and remove invalid fluctuations caused by noise interference in sensor data. Assume that the environmental data is ,in represents environmental variables (temperature, humidity, air quality, etc.), then each Normalization will be performed to ensure that the data of each environmental variable are consistent and comparable; Environmental data is considered as a dynamic input stream, and its impact on the user's physique is constantly changing, so the data needs to be updated in real time.

[0021] The user's physical data is integrated with the environmental data to generate a unified user status representation. The specific steps are as follows: For time synchronization, the user's physical data and environmental data are collected in real time through different devices and sensors, and time synchronization is performed. Each data point has a timestamp t, for example: the user's body temperature data At time t, the ambient temperature data is collected It is also collected at time t; the two data sources are aligned through the timestamp t to ensure that the physical and environmental data at each moment can be correctly matched; After the physical data and environmental data are aligned in time, two time series are formed, namely the physical data series and environmental data series , these time series are merged to form a unified input stream, where n is the total number of time series.

[0022] The user's physical data and environmental data are combined through high-order feature extraction methods to ensure that the complex interaction between the two can be captured. Different physical types respond to the environment nonlinearly, so a nonlinear mapping function is used to process the interaction between the data. The specific steps are as follows: For each pair of physique and environment data, a nonlinear function is used to calculate the interaction effect. That is, a nonlinear function is used to calculate the interaction effect for each pair of user physique data and environment data. The generation of interaction features can be achieved through the following nonlinear function: ,in, is the physical data at time t, such as body temperature, heart rate, etc.; is the environmental data at time t, such as temperature, humidity, etc.; , is the learning weight of the model, which indicates the relative influence of physical fitness and environmental data; b is the bias term, which is used to adjust the output of the model; It is an activation function, commonly used are ReLU, Sigmoid or Tanh, which helps capture nonlinear relationships; All interactive features After aggregation, the final user state vector U(t) is obtained, which can be expressed as: ,in, is the interaction feature of each pair of physical fitness and environmental data, and n is the number of feature pairs; This final user state representation vector not only contains the static information of the user's physique, but also integrates the dynamic impact of environmental changes on the physique. For example, some physique types (such as cold physique) are more sensitive to changes in external temperature. By analyzing the user state representation to adjust the recommended medicinal tea formula, that is, the complete data flow from the user's physique to the environment, an accurate user state vector is formed, which can provide a data basis for subsequent personalized medicinal tea recommendations.

[0023] Step 2: Personalized medicinal tea formula recommendation, that is, by using AI technology, intelligent analysis is performed based on the fused data to generate medicinal tea formulas that meet the user's physical and environmental needs. By understanding the changes in physical and environmental conditions, the recommendation engine can flexibly adjust the recommended medicinal ingredients to ensure that the recommended tea not only meets the user's health needs, but also has practical effects. The specific steps are as follows: Extract the most important features for recommendation from the fused user state vector and perform necessary preprocessing, i.e. the user state vector fused in step 1. The user state vector contains the comprehensive influence of the user's physical data and environmental data and is a multi-dimensional feature vector. This state vector will be passed as input to the recipe recommendation engine. Extract the most influential features for the generation of medicinal tea recipes from the fused user state vector. Since the user state vector usually contains multiple features, the purpose of feature selection is to reduce redundant features, remove irrelevant features, and retain features that are critical to recipe recommendation. The steps are as follows: Use statistical methods such as information gain and chi-square test to perform feature selection and identify features that have a significant impact on tea recipe recommendations. For example, the relationship between the user's body temperature and the external temperature has an important impact on the recommended recipe. The system will select key features based on this information: The chi-square test formula is: ,in, is the frequency of observing information i, is the frequency of the expected information i, and n is the total number of information; Through the chi-square test, the physical and environmental characteristics with strong correlation (positive correlation) with the target variable (the effect of the medicinal tea formula) were selected; Based on the results of feature selection, the k features most relevant to the generation of medicinal tea recipes are finally selected. , these characteristics can be considered the most critical factors for recommending medicinal teas.

[0024] At the same time, data standardization and normalization are preprocessing steps in machine learning. The purpose is to eliminate the dimensional differences between features, ensure that the impact of different features on the model is at the same level, and avoid some features dominating due to their large numerical range. Assume that the selected eigenvector is , where the data may have different scales (for example, the temperature range is 36-38 degrees, the air quality range is 0-100), through standardization, each feature is converted to zero mean and unit variance, so that different features can be compared on the same scale. The formula for standardization is: , is the original eigenvalue; is the mean of the i-th feature; is the standard deviation of the i-th feature; Limit the eigenvalues ​​to a uniform range (e.g. [0, 1]) and perform normalization. The normalization formula is: ,in, is the original eigenvalue; and are the minimum and maximum values ​​of the i-th feature respectively; Standardized and normalized data The normalized input features are passed to the recommendation engine.

[0025] Based on the input user feature vector, generate the personalized medicinal tea formula that best meets the user's needs, and convert the user's physical condition and environmental conditions into specific medicinal tea formula recommendations; Different models are selected to handle recommendation problems according to the requirements of the task. Commonly used recommendation models include: Deep Neural Network (DNN), which learns the complex relationship between physical fitness and environmental data through a multi-layer perceptron network; Gradient Boosting Decision Tree (GBDT), which captures the relationship between user status and tea recipes through a tree structure model; The recommendation model is trained through the training data set (historical records of constitution, environment and recipes), so that the model can learn the complex nonlinear relationship between the recipe of medicinal tea and the user's constitution and environment; Given the input feature vector X(t), the personalized medicinal tea formula P(t) is obtained through the recommendation model M, that is: , where P(t) represents the generated tea recipe, including the ingredient ratio of each medicinal material.

[0026] For each medicinal ingredient, the system will calculate the recommended ratio based on the user's physical condition and environmental conditions, and this recommended ratio will be output as a preliminary formula.

[0027] The preliminary formula is used, and a user-customized formula is generated based on user feedback of the preliminary formula; After generating the preliminary recipe, the system also needs to adjust it according to the user's taste preferences and actual needs. Through the adaptive algorithm, the recommendation system can dynamically adjust the tea recipe based on user feedback to ensure that the recommended recipe meets both health needs and taste preferences. The specific steps are as follows: User feedback on medicinal teas (such as taste and effect) will help the system learn the user's taste preferences. Through feedback-based reinforcement learning, the parameters of the recommendation model can be dynamically adjusted. For example, if the user prefers certain medicinal materials (such as wolfberry, chrysanthemum, etc.), the system will automatically increase the proportion of such medicinal materials. The reinforcement learning update formula is: ,in, For the status Take action The current value of is the learning rate, is the discount factor; For immediate rewards (such as taste ratings from user feedback); For the next state The best value under Adaptive adjustment is performed, that is, during the recipe generation process, the system not only considers the user's physique and environment, but also adjusts the recommended medicinal ingredients based on real-time feedback (such as the user's health goals, preferences, and current physical changes). For example, when the climate changes, the system may automatically adjust the proportion of warming or dehumidifying medicinal materials. The adaptive adjustment formula is: ,in, The adjustment factor of the formula due to the current environment or the user's physical condition changes determines the adjustment ratio of the medicinal ingredients; The final recommended medicinal tea formula is output to the user, which contains the specific proportion of each medicinal ingredient and its function (such as replenishing qi, clearing heat, etc.).

[0028] After using the medicinal tea, users submit feedback information (such as taste, effect, satisfaction, etc.) through the application or interface. These feedbacks will be passed back to the recommendation system as a basis for future formula optimization.

[0029] Step 3: After generating a personalized medicinal tea formula based on dynamic environmental adaptation and real-time fine-tuning, real-time fine-tuning is performed according to changes in the environment (such as temperature, humidity, air quality, etc.) and changes in the user's physical condition (such as body temperature, heart rate, etc.), thereby ensuring that the recommended medicinal tea can be flexibly adjusted according to the current environment and the user's immediate health status, and the recommendation can be continuously optimized to make it more in line with the user's health needs and actual situation. The specific steps are as follows: Monitor environmental changes and obtain external environmental data in real time through sensors installed in the user's environment (such as temperature and humidity sensors, air quality monitors, etc.). Assume that the environmental data is ,in, is the current temperature; is the current humidity; is the current humidity; By analyzing the environmental change data, the degree of environmental change can be determined. For example, when the external temperature drops sharply, the system may need to add warming and tonic herbs (such as ginger, wolfberry, etc.); when the humidity is too high, the system may recommend more dampness-removing herbs (such as coix seed, red bean, etc.); Hypothesized factors affecting environmental change is the magnitude of environmental change at the current moment t (environmental change amount), and the formula is: , where f is a nonlinear function that represents the impact of environmental changes on recipe adjustment; is the environmental data at time t, is the environmental data at time t−1; By obtaining real-time environmental changes The system adjusts the proportion of medicinal materials in medicinal tea according to the degree of change in the current environment. It can be adjusted according to the degree of environmental changes: , where P(t) is the initially generated medicinal tea formula; It is a formula fine-tuned according to environmental changes. It is the adjustment factor for fine-tuning the formula due to environmental changes.

[0030] Analyze the user's physical condition fluctuations and make real-time adjustments. Fine-tune the formula of medicinal tea according to the user's immediate physical condition changes (such as temperature fluctuations, fatigue, etc.). The user's physical condition changes dynamically, so it is necessary to monitor the user's physical condition in real time and make adaptive adjustments to the formula; Physical fitness change monitoring, the physical fitness changes of users are usually reflected by real-time health data (such as heart rate, body temperature, etc.), which are collected by wearable devices and transmitted to the system. For example, assuming that the user's physical fitness data is ,in, Indicates the user's current body temperature at time t, Indicates the user's current heart rate at time t; Analyze the user's physical condition change data and identify its change trend. For example, when the body temperature rises, the system may recommend more herbs with heat-clearing effects (such as chrysanthemum, honeysuckle, etc.); when the user is tired, the system may recommend herbs with qi-replenishing effects (such as ginseng, astragalus, etc.); Assume the influencing factors of user's physical condition changes is the change in health indicators such as body temperature, and the formula is: , where gx is a nonlinear function, which represents the effect of physical changes on tea formula; is the user's physical fitness data at the current moment, It is the user's physical fitness data at the last moment; According to the change of body , the system will adjust the proportion of medicinal materials in the medicinal tea formula, and the formula after fine-tuning is: , P(t) is the initially generated medicinal tea formula; It is a formula that has been fine-tuned based on changes in the user’s physique; It is the adjustment coefficient of tea formula due to changes in physical constitution.

[0031] The environment and physical condition work together to affect the needs of tea formulas. Therefore, taking into account the influence of both can more accurately adjust the proportion of medicinal materials in tea. For example, in cold weather and when the body temperature is high, the system will increase the proportion of warming and tonic medicinal materials; while in a humid environment and when the body is tired, the system will increase the proportion of dehumidifying medicinal materials; Taking into account the dual changes of environment and physical condition, the formula is fine-tuned to make it more accurately adapt to the current health status and external conditions. Through the dual adaptation mechanism, the system can provide accurate tea formula in a complex dynamic environment. The impact of environmental changes and physical changes on the medicinal tea formula needs to be combined for comprehensive adjustment, setting two adjustment factors and , respectively represent the impact of environmental changes and physical changes on formula adjustment, It is the adjustment factor for fine-tuning the formula due to environmental changes. It is the adjustment factor for fine-tuning the formula due to physical changes, which can be used to represent the fine-tuning coefficient for environmental and physical changes. The comprehensive fine-tuning formula is: , where P(t) is the initially generated medicinal tea formula; This is the final adjusted recipe.

[0032] Through dynamic environmental adaptation and real-time fine-tuning, the system can flexibly adjust the recipe of medicinal tea drinks according to changes in the environment and user's physical condition. The combination of environmental change monitoring, physical condition fluctuation monitoring and dual adaptation mechanism makes the recipe recommendation more accurate and in line with user needs.

[0033] Step 4: Intelligently optimize based on user feedback. By collecting real-time feedback from users, intelligently optimize the personalized medicinal tea recommendation system to ensure that it can continue to provide the best formula under the user's changing physical condition and needs, and adjust and improve the recommendation accuracy. The specific steps are as follows: Collect user feedback after using herbal teas, understand users' true feelings about tea recipes, and provide new input data for the recommendation engine. Feedback information may include taste, effect, satisfaction, changes in health status, etc. Users can provide feedback through various means (e.g., apps, smart devices, voice assistants, etc.). Feedback types include but are not limited to: taste (sweet, bitter, sour, etc.), physical reaction (whether you feel comfortable, tired, indigestion, etc.), overall satisfaction, etc.; The content of each feedback includes user ID, feedback type, feedback value, feedback time, etc., forming a structured feedback data set; Assuming that user feedback contains information such as taste satisfaction and health effects, sentiment analysis methods can be used to extract sentiment tendencies from the feedback. For example, the taste feedback can be used to extract satisfaction scores from descriptions such as "sweet" or "bitter". Use word vector models (such as Word2Vec) to process users’ natural language feedback and obtain a sentiment value, which indicates the user’s overall satisfaction with the tea drink. Extract key features (such as health effect scores) from feedback data, convert these features into numerical form, and pass them to the recommendation system as new input features; According to user feedback data, the parameters of the recommendation model are automatically adjusted, so that the recommendation engine can continuously adapt to user needs. Through adaptive optimization, the system can quickly adjust the recommendation strategy according to the feedback of different users, thereby improving the personalization of recommendations; Use incremental learning algorithms to update the recommendation model based on each user feedback. For example, in deep learning, online learning methods are used to dynamically update the weights of the neural network so that the model can adjust the recommendation strategy based on the latest user feedback. The incremental learning formula is: ,in, is the parameter of the model at time t; is the loss function, which calculates the error between the model prediction and the user feedback; is the learning rate; ) is the loss function parameter The gradient of , which indicates the impact of feedback on model update; Compare user feedback with the predicted value output by the current model to generate an adjustment factor. Adjust the parameters of the recommendation system based on the health effect or satisfaction of the feedback; Through incremental learning and feedback-driven adaptive optimization, the recommendation system can adjust the recommendation strategy in real time based on the actual feedback of each user, so that the system can continuously improve its responsiveness to user needs. This adaptive optimization can greatly improve the accuracy of recommendations and ensure that the system always provides users with tea recipes that meet their health needs.

[0034] To summarize, step 4 is to achieve continuous optimization of personalized medicinal tea formulas through user feedback and intelligent optimization mechanisms. Through the collection and processing of feedback data, incremental learning and online optimization, and feedback-driven adjustment mechanisms, the system can dynamically adjust the recommendation strategy based on the user's actual needs, changes in health status, and taste preferences. Ultimately, the system can provide more personalized and accurate tea recommendations, and through continuous adaptive optimization, continuously improve recommendation effects and user satisfaction.

[0035] It should be noted that the thresholds involved in the embodiments can be determined according to specific scenarios and requirements.

[0036] The present invention collects static physical data and real-time physiological data of the user, combines them with environmental data, and fuses them through a nonlinear mapping function to generate a unified state vector that includes changes in the user's physical condition and environment, providing a data basis for personalized medicinal tea formula recommendations; Based on the fused user state vector, the system extracts key features and analyzes the relationship between physique and environment, thereby generating and calculating personalized medicinal tea formulas and medicinal material ratios to ensure that the recommended tea meets the user's health needs. By real-time monitoring of environmental changes and user physical fluctuations, the formula medicinal material ratio can be dynamically adjusted, and the tea formula can be optimized in real time to adapt to different environments and physical changes; further, the system continuously optimizes the tea formula by collecting user feedback data and combining incremental learning technology to ensure that the recommendation system can make adaptive adjustments as user needs and feedback change, thereby effectively improving the personalization, accuracy, and dynamic adaptability of medicinal tea recommendations, providing users with tea formulas that are more in line with their health needs and taste preferences, and improving the user's health management experience.

[0037] Embodiment 2: A personalized medicinal tea beverage configuration system based on constitution recognition, such as Figure 2 As shown, specifically including: The physical fitness and environment analysis module is used to collect the user's static physical fitness data and real-time physiological data, obtain environmental data through sensors, fuse physical fitness and environmental data through nonlinear mapping functions, and generate a user state vector containing user physical fitness and environmental changes; The preliminary formula generation module is used to extract features based on the fused user state vector, analyze the relationship between physical constitution and environmental data, output personalized medicinal tea formulas and calculate the proportion of medicinal materials to form a user-customized formula; The formula adjustment module is used to calculate the change amount according to the real-time environmental data and the user's physical fluctuations, and adjust the proportion of the medicinal materials in the formula to form an optimized medicinal tea formula; The feedback optimization module is used to obtain the taste and health effect data of medicinal tea through user feedback, and optimize the tea formula through incremental learning.

[0038] The above formulas are all dimensionless and calculated numerically. Specific dimension removal can be achieved by various means such as standardization, which will not be elaborated here. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0039] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, an ATA hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state ATA hard disk.

[0040] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0041] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0042] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0043] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0044] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0045] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for preparing personalized medicinal tea based on constitution identification, characterized in that: The steps include: Collect the user's static physical data and real-time physiological data, obtain environmental data through sensors, fuse physical and environmental data through nonlinear mapping functions, and generate a user state vector that includes the user's physical and environmental changes; Extract features based on the fused user state vector, analyze the relationship between physical constitution and environmental data, output personalized medicinal tea recipes and calculate the proportion of medicinal materials to form a user-customized recipe; Calculate the change amount based on real-time environmental data and user's physical condition fluctuations, and adjust the proportion of medicinal materials in the formula to form an optimized medicinal tea formula; The taste and health effect data of medicinal teas are obtained through user feedback, and the tea formula is optimized through incremental learning.

2. The method for preparing personalized medicinal tea based on constitution identification according to claim 1, characterized in that: Collect the user's static physical data and real-time physiological data, and obtain environmental data through sensors. The specific process is as follows: The user's physical information is filled in through health records or questionnaires. The physical information includes physical type, age, gender, body shape, allergy history, and family medical history. The physical information is converted into structured data to form static physical data. Users provide real-time physiological data through smart bracelets and smart watches. Real-time physiological data includes heart rate, body temperature, blood pressure, blood sugar, sleep quality, and activity level. Real-time physiological data is stored in the form of time series; Collect environmental data, deploy temperature and humidity sensors, and air quality monitors to collect real-time climate conditions of the user's environment, including temperature, humidity, air quality, PM2.5 concentration, and air pressure. Combined with the weather API interface, obtain real-time weather information in the user's area. Real-time weather information includes current temperature, humidity, and wind speed.

3. The method for preparing personalized medicinal tea based on constitution identification according to claim 2, characterized in that: The physical fitness and environmental data are fused through a nonlinear mapping function to generate a user state representation that includes the user's physical fitness and environmental changes. The specific process is as follows: Synchronize the user's physical data and environmental data in time. According to the timestamp of each data point, align the user's physical data and environmental data through the timestamp to correctly match the physical and environmental data at each moment. After the physical data and the environmental data are aligned in time, the state alignment is performed to form two time series, namely the physical data series and the environmental data series. The physical data series and the environmental data series are merged to form a unified input stream. For each pair of user physical data and environmental data, a nonlinear function is used to calculate the interaction effect, and the generation of interaction features is represented by a nonlinear function: ,in, is the physical data at time t; is the environmental data at time t; , is the learning weight of the model, which indicates the relative influence of physical fitness and environmental data; b is the bias term, which is used to adjust the output of the model; is the activation function; All interactive features Aggregate and get the user state vector U(t), expressed as: ,in, is the interaction feature of each pair of physical and environmental data, and n is the number of feature pairs.

4. The method for preparing personalized medicinal tea based on constitution identification according to claim 3, characterized in that: Extract features based on the fused user state vector, analyze the relationship between physical constitution and environmental data, output personalized medicinal tea recipes and calculate the proportion of medicinal materials to form a user-customized recipe. The specific steps are as follows: The chi-square test was used for feature selection to identify the features that had a significant impact on tea recipe recommendations; Through chi-square test, the physical constitution and environmental characteristics that were positively correlated with the effects of the medicinal tea formula were selected; Based on the results of feature selection, the most relevant features for generating medicinal tea recipes are selected; The selected features are standardized and normalized and then passed to the recommendation engine; According to the requirements of the task, select a deep neural network model or a gradient boosting decision tree as the recommendation model; According to the characteristics of the given input, a personalized medicinal tea recipe is obtained through the recommendation model. For each medicinal material, the recommended ratio is calculated according to the user's physical condition and environmental conditions, and the recommended ratio of each medicinal material is output as the preliminary recipe; The preliminary formula is used, and a user-customized formula is generated based on user feedback of the preliminary formula.

5. The method for preparing personalized medicinal tea based on constitution identification according to claim 4, characterized in that: The preliminary formula is used, and a user-customized formula is generated based on user feedback of the preliminary formula. The specific process is as follows: After generating the preliminary recipe, feedback is provided based on the user’s taste preferences and actual needs, and the parameters of the recommendation model are dynamically adjusted through feedback-based reinforcement learning; The reinforcement learning update formula is: ,in, For the status Take action The current value of is the learning rate, is the discount factor; For immediate rewards; For the next state The best value under During the recipe generation process, the recommended medicinal food ingredients are adjusted according to real-time feedback. The adaptive adjustment formula is: ,in, is the adjustment factor of the formula due to the current environment or user's physical condition change, P(t) represents the generated tea formula, It represents the formula of medicinal tea after adaptive adjustment; Real-time feedback includes the user’s health goals, preferences, and current physical changes; The final recommended medicinal tea recipe is output to the user again as a user-customized recipe.

6. The method for preparing personalized medicinal tea based on constitution identification according to claim 5, characterized in that: The change is calculated based on real-time environmental data and user's physical condition fluctuations, and the proportion of medicinal materials in the formula is adjusted to form an optimized medicinal tea formula. The specific process is as follows: Monitor environmental changes and obtain real-time environmental change data through sensors installed in the user's environment; analyze environmental change data to determine the degree of change in the current environment; By obtaining the environmental changes in real time, the proportion of medicinal materials in the medicinal tea can be adjusted according to the degree of change of the current environment; Analyze the user's physical condition fluctuations and make real-time adjustments, and adjust the formula of medicinal tea drinks according to the immediate changes in the user's physical condition; Analyze the user's physical condition change data, identify physical condition change trends, determine the amount of physical condition change, and adjust the proportion of medicinal materials in the medicinal tea formula according to the amount of physical condition change; The comprehensive adjustment of the medicinal tea formula is carried out by combining the environmental changes and the physical changes. The comprehensive fine-tuning formula of the medicinal tea formula is: , where P(t) is the initially generated medicinal tea formula; For the final adjusted formula, It is the adjustment factor for fine-tuning the formula due to environmental changes. It is the adjustment factor for fine-tuning the formula due to changes in physical constitution. is the amount of physical change, is the environmental change.

7. A personalized medicinal tea drink preparation system based on constitution identification, used to implement a personalized medicinal tea drink preparation method based on constitution identification as claimed in any one of claims 1 to 6, characterized in that: include: The physical fitness and environment analysis module is used to collect the user's static physical fitness data and real-time physiological data, obtain environmental data through sensors, fuse physical fitness and environmental data through nonlinear mapping functions, and generate a user state vector containing user physical fitness and environmental changes; The preliminary formula generation module is used to extract features based on the fused user state vector, analyze the relationship between physical constitution and environmental data, output personalized medicinal tea formulas and calculate the proportion of medicinal materials to form a user-customized formula; The formula adjustment module is used to calculate the change amount according to the real-time environmental data and the user's physical fluctuations, and adjust the proportion of the medicinal materials in the formula to form an optimized medicinal tea formula; The feedback optimization module is used to obtain the taste and health effect data of medicinal tea through user feedback, and optimize the tea formula through incremental learning.

Images