A method for intervention management of a user's alcohol consumption and related devices

Abstract

The application provides a user drinking amount intervention management method and related equipment applied to the technical field of data processing. The application processes drinking purpose information of a target user and real-time physiological state information of the target user to generate a drinking training factor; processes a recent medical examination report information of the target user to generate an initial drinking amount of the target user and an initial limit drinking amount of the target user; processes identity information of the target user and the real-time physiological state information of the target user to generate a drinking amount adjustment factor; processes the initial drinking amount of the target user and the initial limit drinking amount of the target user based on the drinking amount adjustment factor to generate a target drinking amount of the target user and a target limit drinking amount of the target user; processes the target drinking amount of the target user and the target limit drinking amount of the target user based on the drinking training factor to generate drinking feedback information of the target user and processes the drinking feedback information to generate early warning threshold information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a user drinking amount intervention management method and related equipment. BACKGROUND

[0002] As an important drink in people's life, wine has derived various wine cultures and become an indispensable existence in specific occasions. However, research shows that not everyone is suitable for drinking, and excessive drinking is extremely harmful to the body; and the drinking capacity of different people also has great differences, so it is necessary to have a correct understanding of one's own alcohol metabolism capacity. In addition, alcohol dependence syndrome is a chronic and easily relapsed brain disease, and easy recurrence is an important feature of the disease. After withdrawal, the alcohol-dependent person resumes drinking after stopping drinking, which is one of the characteristics of alcohol dependence, and more than 60% of alcohol-dependent patients relapse within 6 months after withdrawal. There are many factors for resuming drinking, and the most important factor is "heart addiction", also known as psychological craving, that is, the desire or impulse to drink alcohol, which is related to serious drinking problems. Whether the psychological craving can be successfully coped with will affect whether the individual will continue to drink or maintain a state of stopping drinking.

[0003] At present, similar products on the market are all for detecting the alcohol metabolism capacity of the user, without quantifying the drinking capacity, and the guiding effect on the user is not strong, and cannot give targeted drinking suggestions according to the physical condition of the user.

[0004] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The purpose of the present application is to provide a user drinking amount intervention management method and related equipment, which at least to some extent overcomes the problems existing in the prior art, generates personalized drinking training factors and drinking amount adjustment factors by analyzing the drinking purpose, real-time physiological state and medical examination report of the user, to guide the user to drink within a safe range. According to these factors, the target drinking amount and the limit drinking amount are calculated, and real-time feedback information is provided. When the user's drinking amount approaches the preset warning threshold, a warning will be issued and the emergency contact person will be notified to prevent the health risks brought by excessive drinking.

[0006] Other characteristics and advantages of the present application will become apparent from the following detailed description, or will be learned partly through practice of the present application.

[0007] According to an aspect of the present application, a method for intervention management of a user's alcohol consumption is provided, comprising: obtaining alcohol use information of a target user, real-time physiological state information of the target user, a recent medical report information of the target user, identity information of the target user, and a target alcohol consumption early warning model; processing the alcohol use information of the target user and the real-time physiological state information of the target user to generate a drinking training factor, wherein the drinking training factor is used to process the drinking habits of the target user; processing the recent medical report information of the target user to generate an initial alcohol consumption of the target user and an initial limit alcohol consumption of the target user; processing the identity information of the target user and the real-time physiological state information of the target user to generate an alcohol consumption adjustment factor, wherein the alcohol consumption adjustment factor is used to process the alcohol consumption information of the target user; processing the initial alcohol consumption of the target user and the initial limit alcohol consumption of the target user based on the alcohol consumption adjustment factor to generate a target alcohol consumption of the target user and a target limit alcohol consumption of the target user; processing the target alcohol consumption of the target user and the target limit alcohol consumption based on the drinking training factor to generate drinking feedback information of the target user, wherein the drinking feedback information of the target user is used to represent the physiological state information generated by the target user based on the real-time alcohol consumption; processing the drinking feedback information of the target user based on the target alcohol consumption early warning model to generate early warning threshold information; if the real-time alcohol consumption of the target user reaches the early warning threshold information, generating early warning information, wherein the early warning information includes sending alcohol reminder information to a pre-set emergency contact of the target user.

[0008] In an embodiment of the present application, the target alcohol consumption early warning model is obtained, comprising: obtaining a pre-set training set; pre-processing the pre-set training set to generate a pre-set training set with identification information, wherein the identification information is used to represent that the user cannot drink; processing the pre-set training set with identification information based on a pre-set processing rule to generate a training set and a test set; training a pre-set alcohol consumption prediction model based on the training set and the test set to generate a target alcohol consumption prediction model.

[0009] In an embodiment of the present application, the preset training set with the identification information is processed based on a preset processing rule to generate a training set and a test set, including: obtaining clinical data and physiological state factor information of the preset training set based on the physical examination report information of different users respectively, wherein the clinical data includes factors affecting the user's alcohol consumption; processing the clinical data based on a deep learning model to generate the influence degree of the physiological state factor information and the clinical data; processing the influence degree of the physiological state factor information and the case information based on a risk classification rule to generate a risk abnormality index; and classifying the preset training set with the identification information based on the risk abnormality index to generate the training set and the test set.

[0010] In an embodiment of the present application, the drinking purpose information of the target user and the real-time physiological state information of the target user are processed to generate a drinking training factor, including: processing the drinking purpose information of the target user to generate preset drinking amount information of the target user, wherein the preset drinking amount information of the target user is used to represent the alcohol consumption to be drunk by the target user; and processing the preset drinking amount information of the target user based on the real-time physiological state information of the target user to generate a drinking training factor, wherein the drinking training factor is used to represent the physiological state information matched by different drinking amount information of the target user.

[0011] In an embodiment of the present application, the identity information of the target user and the real-time physiological state information of the target user are processed to generate a drinking amount adjustment factor, including: processing the identity information of the target user to generate drinking scene information of the target user, wherein the drinking scene information of the target user is used to represent historical drinking amount information of the target user in different drinking scenes; obtaining target anti-repeated drinking information matched with the drinking scene information of the target user, wherein the target anti-repeated drinking information is used to represent physiological parameters of the target user to generate aversion to drinking; processing the drinking scene information of the target user based on the target anti-repeated drinking information to generate estimated drinking amount information of the target user; and processing the estimated drinking amount information of the target user based on the real-time physiological state information of the target user to generate a drinking amount adjustment factor.

[0012] In an embodiment of the present application, the target anti-drinking information matched with the drinking scene information of the target user is obtained, comprising: obtaining a preset anti-drinking information set, wherein the preset anti-drinking information is used to represent physiological parameters of different users to generate aversion to drinking; processing the drinking scene information of the target user based on the preset anti-drinking information set to generate physiological parameter change information of the target user; processing the physiological parameter change information of the target user to generate real-time drinking amount information of the target user; if the real-time drinking amount information of the target user is lower than a preset drinking amount threshold, processing the preset anti-drinking information set to generate the target anti-drinking information, wherein the target anti-drinking information is used to represent physiological parameters of the target user to generate aversion to drinking.

[0013] In an embodiment of the present application, the initial drinking amount of the target user and the initial limit drinking amount of the target user are processed based on the drinking amount adjustment factor to generate the target drinking amount of the target user and the target limit drinking amount of the target user, comprising: processing the initial drinking amount of the target user and the initial limit drinking amount of the target user based on the drinking amount adjustment factor to generate drinking amount difference information of the target user; processing the drinking amount difference information of the target user to generate historical drinking amount information of the target user, historical physiological parameter information matched with the historical drinking amount information, real-time drinking amount information of the target user, and real-time physiological parameter information matched with the real-time drinking amount information; processing the real-time drinking amount information of the target user and the real-time physiological parameter information matched with the real-time drinking amount information based on the historical drinking amount information of the target user and the historical physiological parameter information matched with the historical drinking amount information to generate the target drinking amount of the target user and the target limit drinking amount of the target user.

[0014] In another aspect of the present application, an intervention management device for a user's alcohol consumption amount includes: an acquisition module configured to acquire alcohol consumption purpose information of a target user, real-time physiological state information of the target user, a most recent physical examination report of the target user, identity information of the target user, and a target alcohol consumption amount early warning model; a processing module configured to process the alcohol consumption purpose information of the target user and the real-time physiological state information of the target user to generate an alcohol consumption training factor, wherein the alcohol consumption training factor is used to process the alcohol consumption habit of the target user; process the most recent physical examination report of the target user to generate an initial alcohol consumption amount of the target user and an initial limit alcohol consumption amount of the target user; process the identity information of the target user and the real-time physiological state information of the target user to generate an alcohol consumption amount adjustment factor, wherein the alcohol consumption amount adjustment factor is used to process the alcohol consumption amount information of the target user; process the initial alcohol consumption amount of the target user and the initial limit alcohol consumption amount of the target user based on the alcohol consumption amount adjustment factor to generate a target alcohol consumption amount of the target user and a target limit alcohol consumption amount of the target user; process the target alcohol consumption amount of the target user and the target limit alcohol consumption amount based on the alcohol consumption training factor to generate alcohol consumption feedback information of the target user, wherein the alcohol consumption feedback information of the target user is used to represent the physiological state information generated by the target user based on the real-time alcohol consumption amount; process the alcohol consumption feedback information of the target user based on the target alcohol consumption amount early warning model to generate early warning threshold information; and generate early warning information if the real-time alcohol consumption amount of the target user reaches the early warning threshold information, wherein the early warning information includes sending alcohol consumption reminder information to a pre-set emergency contact person of the target user.

[0015] According to still another aspect of the present application, an electronic device includes: a first processor; and a memory configured to store executable instructions of the first processor; wherein the first processor is configured to execute the executable instructions to implement the above-mentioned intervention management method for a user's alcohol consumption amount.

[0016] According to yet another aspect of the present application, a computer readable storage medium is provided, and a computer program is stored on the computer readable storage medium, wherein the computer program is executed by a second processor to implement the above-mentioned intervention management method for a user's alcohol consumption amount.

[0017] According to still another aspect of the present application, a computer program product is provided, and includes a computer program, wherein the computer program is executed by a third processor to implement the above-mentioned intervention management method for a user's alcohol consumption amount.

[0018] The user alcohol consumption intervention management method and related device provided by the present application generate personalized alcohol consumption training factors and alcohol consumption adjustment factors through the server by analyzing the user's alcohol consumption purpose, real-time physiological state and physical examination report, to guide the user to drink within a safe range. According to these factors, the target alcohol consumption and the limit alcohol consumption are calculated, and real-time feedback information is provided. When the user's alcohol consumption approaches the preset warning threshold, the system will issue a warning and notify the emergency contact person to prevent the health risks caused by excessive alcohol consumption. This comprehensive intervention management system aims to help users understand and control their own drinking behavior and ensure health and safety.

[0019] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A flow chart of a user alcohol consumption intervention management method provided by an embodiment of the present application is shown;

[0021] Figure 2 A structural schematic diagram of a user alcohol consumption intervention management device provided by an embodiment of the present application is shown;

[0022] Figure 3 A structural schematic diagram of an electronic device provided by an embodiment of the present application is shown;

[0023] Figure 4 A schematic diagram of a storage medium provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described herein are only for illustrating and explaining the present application, and are not intended to limit the present application.

[0025] The user alcohol consumption intervention management method according to the exemplary embodiments of the present application is described below in conjunction with Figure 1 It should be noted that the following application scenarios are only shown for the purpose of facilitating the understanding and principle of the present application, and the embodiments of the present application are not limited in this respect. On the contrary, the embodiments of the present application can be applied to any applicable scenario.

[0026] In one embodiment, the present application also provides a user alcohol consumption intervention management method and related device. Figure 1 A flow schematic diagram of a user alcohol consumption intervention management method according to an embodiment of the present application is shown schematically. As Figure 1 shown, the method is applied to a server, comprising:

[0027] S101, obtain the drinking purpose information of the target user, the real-time physiological state information of the target user, the recent medical examination report information of the target user, the identity information of the target user, and a target drinking amount early warning model.

[0028] In an implementation, the drinking purpose information, which usually needs to be actively input by the user or inferred through intelligent device monitoring of drinking behavior. For example, the system allows the user to record their drinking habits, including the occasion and purpose of drinking. Real-time physiological state can be obtained through wearable devices or health monitoring systems, such as smart watches or health monitoring bands, which can track vital signs such as heart rate, blood pressure, body temperature, etc. in real time.

[0029] The medical examination report information of the target user includes but is not limited to liver function (total protein, albumin, globulin, albumin-globulin ratio, total bilirubin, direct and indirect bilirubin, transaminase); blood lipid (total cholesterol, triglyceride, high and low density lipoprotein, apolipoprotein); fasting blood glucose; renal function (creatinine, urea nitrogen); uric acid; lactate dehydrogenase; creatine kinase, peripheral blood, red blood cell count, hematocrit, etc. In addition, the identity information of the target user includes the subsequent banquet information of the target user and whether there are other transactions to be processed, wherein the banquet information includes the purpose and importance of the subsequent banquet, which can be obtained through the input of the user in the related application program or through the integrated calendar and social functions.

[0030] In another implementation, the generation of the target drinking amount early warning model can be obtained by the following method. Specifically, a preset training set is obtained, the preset training set is preprocessed, and a preset training set with identification information is generated, wherein the identification information is used to represent that the user cannot drink. The preset training set with identification information is processed based on a preset processing rule to generate a training set and a test set, the preset drinking amount prediction model is trained based on the training set and the test set, and the target drinking amount prediction model is generated.

[0031] Specifically, collect data related to drinking, which may include user drinking habits, health data, physiological responses, etc. These data can be obtained from health monitoring applications, medical records, or user input. Process the above data, including data cleaning, missing value processing, and standardization. For example, ensure that all data is numerical so that the model can process it. This can be achieved through data cleaning and conversion, such as using data processing libraries like pandas and numpy. After preprocessing the data, we need to add identification information to the data set, which may include labels indicating whether the user is suitable for drinking. These identifications can be determined by analyzing the user's medical examination report, physiological state, and drinking purpose. For example, if the user's liver function indicators are abnormal, an identification may be added indicating that the user should not drink.

[0032] The dataset is split into training and testing sets using pre-processing rules. This usually involves random sampling or using cross-validation methods to ensure uniformity of data distribution. Common split ratios might be 70% training and 30% testing, or using more complex k-fold cross-validation methods. Specifics will be expanded upon later, and will not be repeated here.

[0033] A machine learning model is trained to predict target alcohol consumption using the training set. This might involve selecting an appropriate algorithm, such as a decision tree, support vector machine, or neural network, and adjusting model parameters. During training, a validation set can be used to adjust model parameters and prevent overfitting. When the model is trained, it is evaluated on the test set, which can be done by calculating accuracy, recall, and other metrics. Based on the test results, the model may need to be further optimized by going back to the training step. Finally, when the model performance is satisfactory, it can be deployed as an application or service for users to query their alcohol consumption predictions.

[0034] In another embodiment, the pre-set training set with identification information is processed based on pre-set processing rules to generate a training set and a test set, and specifically further comprising:

[0035] Clinical data and physiological state factor information of the pre-set training set are obtained based on the physical examination report information of different users, respectively. The clinical data includes factors that affect the user's alcohol consumption. The clinical data related to alcohol consumption is extracted from the user's physical examination report. These data may include but are not limited to liver function indicators (such as total protein, albumin, globulin, albumin-globulin ratio, total bilirubin, direct and indirect bilirubin, transaminase, etc.), kidney function indicators (such as creatinine, urea nitrogen), blood lipid levels (such as total cholesterol, triglycerides, high and low density lipoprotein, apolipoprotein), fasting blood glucose, uric acid, lactate dehydrogenase, creatine kinase, and peripheral blood count, etc. The collected data is pre-processed, including data cleaning, formatting and standardization. This step is crucial as it ensures the consistency and accuracy of the data, laying the foundation for subsequent model training. After pre-processing, we need to add identification information to the data set, which may include labels indicating whether the user is suitable for drinking. These identifications can be determined by analyzing the user's physical examination report, physiological state, and drinking purposes.

[0036] The clinical data is processed based on a deep learning model to generate physiological state factor information and the influence degree of clinical data. Deep learning models such as convolutional neural networks (CNN) or recurrent neural networks (RNN) can be used to process clinical data and generate physiological state factor information and the influence degree of clinical data. These models can learn complex patterns from data and predict the influence of each factor on alcohol consumption.

[0037] In building a deep learning model to process clinical data and generate physiological state factor information and the extent of clinical data impact, this process can be understood through the following examples:

[0038] Suppose there is a dataset containing individuals' liver function indicators (such as ALT levels) and their alcohol consumption. By training a deep learning model, it can learn how ALT levels affect alcohol consumption. For example, the model may find that individuals with higher ALT levels tend to reduce their alcohol consumption, as high levels of ALT may indicate liver damage, requiring reduced alcohol intake to avoid further damage.

[0039] The relationship between blood lipid levels (such as total cholesterol and triglycerides) and drinking habits. A deep learning model may reveal that individuals with higher blood lipid levels are more likely to reduce their alcohol consumption, or in some cases, may increase their alcohol consumption as a coping mechanism.

[0040] Kidney function indicators (such as creatinine and urea nitrogen levels) are also important clinical data for assessing an individual's health status. Deep learning models can help us understand how these indicators affect alcohol recommendations. For example, the model may indicate that individuals with higher creatinine levels (which may indicate kidney dysfunction) should avoid alcohol.

[0041] For diabetic patients, blood glucose levels are a key health indicator. Deep learning models can analyze the relationship between blood glucose control and drinking behavior and provide personalized alcohol recommendations for diabetic patients.

[0042] In more complex cases, deep learning models can consider multiple physiological state factors (such as liver function, kidney function, blood lipid, and blood glucose levels) to predict an individual's alcohol consumption. The model may assign a weight to each factor, indicating their relative importance in predicting alcohol consumption. These examples demonstrate how deep learning models can be used to analyze and understand the relationship between clinical data and alcohol consumption. Through these models, more personalized and data-driven health recommendations can be provided to individuals.

[0043] Based on the risk classification rules, the impact of physiological state factor information and case information is processed to generate risk anomaly indicators, and the preset training set with identification information is classified based on the risk anomaly indicators to generate training and test sets. Based on the risk classification rules, the impact of physiological state factor information and case information can be processed to generate risk anomaly indicators, which may be based on medical research and clinical guidelines to determine which indicators indicate that users should not drink alcohol. The risk anomaly indicators can be used to classify the preset training set with identification information to generate training and test sets. These data sets will be used to train and evaluate your alcohol consumption prediction model.

[0044] S102, processing the drinking purpose information of the target user and the real-time physiological state information of the target user to generate a drinking training factor.

[0045] In one implementation,

[0046] The drinking purpose information of the target user is processed to generate preset drinking amount information of the target user, wherein the preset drinking amount information of the target user is used to represent the drinking amount to be drunk by the target user. The drinking purpose information of the user is collected through a questionnaire, an interview or the input of the user in the application. This may include the occasion of drinking (such as social gatherings, relaxing alone, holiday celebrations, etc.), the frequency of drinking, and the user's psychological expectation of drinking (such as hoping to improve mood and relieve stress through drinking, etc.). Next, the relationship between different drinking purposes and drinking amounts is analyzed, for example, social occasions may be associated with higher drinking amounts, while relaxing alone may be associated with lower drinking amounts. The preset drinking amount is adjusted in combination with the user's health status, such as liver function, kidney function and heart health status obtained from a medical report. Certain health conditions may require the user to limit or avoid drinking.

[0047] According to the purpose information and health factors, a reasonable preset drinking amount is set for the user. This amount should be safe and meet the user's drinking purpose. For example, if the user's purpose of drinking is to relax, the preset drinking amount should be an amount that can achieve the effect of relaxation but does not cause health risks. The preset drinking amount information is provided to the user to confirm whether it meets their expectations and needs, and the user can adjust it according to personal preferences. The final preset drinking amount information should be a range of amounts, rather than a fixed number. This range can be dynamically adjusted based on different drinking occasions and the user's health status. The user's drinking purpose and health status may change over time, so the preset drinking amount information should be updated regularly to ensure that it always reflects the user's current state and needs. A personalized preset drinking amount information is created for the user to help them better control their drinking behavior and reduce potential health risks.

[0048] The preset drinking amount information of the target user is processed based on the real-time physiological state information of the target user to generate a drinking training factor, wherein the drinking training factor is used to represent the physiological state information matched by different drinking amount information of the target user, and the drinking training factor is used to process the drinking habit of the target user. Real-time physiological state information of the user is collected, which may include heart rate, blood pressure, blood glucose level, body temperature and other indicators. These data can be obtained through a smart watch, a health monitoring device or user self-reporting. Using the collected physiological state information, the current health status of the user is evaluated, for example, if the user's current blood pressure is high, the preset drinking amount may need to be adjusted to avoid potential health risks caused by drinking.

[0049] According to the physiological state evaluation result of the user, the preset drinking amount is adjusted. If the physiological state index of the user shows that it is not suitable to drink, the preset drinking amount should be reduced accordingly or the user is suggested to temporarily not drink. Combining the preset drinking amount of the user and the real-time physiological state information, the drinking training factors are generated. These factors can be a set of parameters guiding the user's drinking behavior, such as the recommended drinking speed, drinking interval time, etc. According to the drinking training factors, personalized drinking suggestions are provided for the user. These suggestions can help the user adjust their drinking habits according to their physiological state and reduce the risk of excessive drinking. The physiological state and drinking behavior of the user are continuously monitored, and the drinking training factors are adjusted according to the feedback. This can be achieved through a mobile application or a health management platform, providing real-time drinking guidance for the user. Education information about healthy drinking is provided for the user, helping them understand the relationship between drinking and health, and encouraging them to adopt healthier drinking behaviors. The drinking training factors not only help the user understand the matching degree of their drinking amount and physiological state, but also promote the user to form healthy drinking habits and reduce the health risks caused by improper drinking.

[0050] According to the prediction results of the model, personalized drinking suggestions are provided for the user. For example, if the model predicts that the user may experience health risks under the current state after drinking, the system can suggest the user to reduce the drinking amount or choose low-alcohol drinks. The physiological state and drinking behavior of the user are continuously monitored, and the preset drinking amount and drinking training factors are adjusted according to real-time data to ensure that the user's drinking behavior matches their health condition. This can help the user establish healthy drinking habits and reduce the health risks caused by improper drinking. At the same time, this method also helps medical institutions and health managers better understand and manage the drinking behavior of patients.

[0051] S103, processing the physical examination report information of the target user in the last time to generate the initial drinking amount of the target user and the initial limit drinking amount of the target user.

[0052] In an embodiment, since ethanol has the characteristics of fat solubility, it can easily cause adverse effects on the functions of various systems of the body, and among them, the changes in the peripheral blood in the blood system are the most sensitive. Since red blood cells account for 40% in the blood, changes in the number of red blood cells, hematocrit and other parameters have a greater impact on the physiological function of the blood. Long-term heavy drinking can lead to a decrease in the deformability of red blood cells, an increase in the mean corpuscular volume (MCV) and mean corpuscular hemoglobin content (MCH) values, a decrease in the mean corpuscular hemoglobin concentration (MCHC) value, a decrease in white blood cells, and abnormalities in platelet number and function. The significant increase in MCV and MCH of red blood cells after drinking is due to the high concentration of ethanol in the blood, which can affect the lipid structure of the red blood cell membrane and the protein conformation on the membrane, thereby directly affecting the deformability of red blood cells. At the same time, ethanol can also cause folate absorption disorders, and the proliferation rate of red blood cells slows down, so MCHC decreases. The decrease in the number of white blood cells caused by ethanol intake may be related to the direct inhibition of colony-stimulating factor production, leading to a decrease in granulocyte production, and an increase in the relative value of lymphocytes. Related studies have shown that the white blood cell count and platelet count in the blood of the alcohol intake group are lower than those in the control group, and the lymphocyte ratio, MCV, and MCH values are significantly higher than those in the control group, and the difference between the two groups is statistically significant, confirming that ethanol has a greater impact on peripheral blood. Therefore, the values in the target latest physical examination report are used to determine whether the target user can drink, and the interval range of the drinking amount of various wines if the target user can drink.

[0053] For example, the initial drinking amount of the target user and the initial limit drinking amount of the target user are set based on the type of wine the target user drinks. If the target user's physical examination report indicates that the target user has gout or other diseases or the value of the uric acid parameter is much higher than the normal value, if the target user drinks baijiu, the initial drinking amount is 110ml, and the initial limit drinking amount is 300ml; if the target user drinks beer, the initial drinking amount is 300ml, and the initial limit drinking amount is 500ml; if the target user drinks wine, the initial drinking amount is 50ml, and the initial limit drinking amount is 800ml.

[0054] S104, processing the identity information of the target user and the real-time physiological state information of the target user to generate a drinking amount adjustment factor.

[0055] In one implementation, the identity information of the target user is processed to generate the drinking scene information of the target user, wherein the drinking scene information of the target user is used to represent the historical drinking amount information of the target user in different drinking scenes. The target anti-re-drinking information matched with the drinking scene information of the target user is obtained, wherein the target anti-re-drinking information is used to represent the physiological parameters of the target user to produce aversion to drinking. The drinking scene information of the target user is processed based on the target anti-re-drinking information to generate the estimated drinking amount information of the target user. First, by analyzing the identity information of the target user, such as age, gender, occupation, etc., combined with their living habits and social activities, the drinking scenes they may participate in are determined, such as business entertainment, family gatherings, friend gatherings, etc. For each determined drinking scene, the historical drinking data of the target user is collected and analyzed, including the time, frequency, type and quantity of drinking, which can be obtained through user survey or drinking records in mobile application.

[0056] Anti-re-drinking information refers to physiological or psychological feedback that can encourage users to reduce or stop drinking. This may include uncomfortable symptoms after drinking, such as headache, nausea, stomach pain, etc., or emotional changes after drinking, such as anxiety, depression, etc. These information can be collected through user self-reporting, medical records or wearable health monitoring devices. The collected anti-re-drinking information is matched with the drinking scene information of the user to analyze the physiological parameters of the user's aversion to drinking in specific scenes, for example, if the user often has a headache after business entertainment, then headache may be an effective anti-re-drinking indicator. Based on the drinking scene information and anti-re-drinking information of the user, an algorithm model is used to estimate the appropriate drinking amount of the user in different scenes. This estimated drinking amount should be lower than or equal to the threshold value of the user's aversion reaction to help the user avoid discomfort and reduce the amount of drinking.

[0057] Provide personalized drinking amount adjustment suggestions for users to help them control their drinking amount in different scenes. These suggestions can be updated in real time, adjusted according to the user's latest physiological state and anti-re-drinking information, continuously monitor the user's drinking behavior and physiological response, collect feedback information to optimize the estimated drinking amount model. This can be achieved through mobile applications, smart devices or regular user interviews. It can help the target user better understand and control their drinking behavior while reducing the negative effects of drinking. By collecting physiological data such as brain waves, skin electricity, blood volume and pulse, the physiological craving degree of alcohol-dependent individuals is evaluated in real time. After the subject's craving is fully induced, relaxation training, anti-re-drinking technology learning, etc. are given, and finally the effect of assisting long-term rehabilitation correction training for alcohol abstinence is achieved.

[0058] The estimated alcohol consumption information of the target user is processed based on the real-time physiological state information of the target user to generate an alcohol consumption adjustment factor, which is used to process the alcohol consumption information of the target user. The physiological data of the user is analyzed using a machine learning algorithm to identify physiological patterns and trends related to alcohol consumption, for example, certain physiological indicators may exhibit specific change patterns after drinking. Based on the real-time physiological state and historical drinking data of the user, personalized alcohol consumption adjustment factors are generated, which can quantify the relationship between the user's alcohol consumption and physiological state, such as the relationship between alcohol consumption and blood pressure increase. Based on the adjustment factors, real-time alcohol consumption adjustment suggestions are provided to the user, if the user's physiological state indicators show that drinking may have adverse effects on their health, the system can suggest the user to reduce alcohol consumption or avoid drinking.

[0059] As the user's drinking behavior and physiological state data continue to accumulate, the model is continuously optimized to improve the accuracy and personalization of the alcohol consumption adjustment factors, encouraging users to provide feedback such as subjective feelings after drinking and any discomfort symptoms to further adjust and refine the alcohol consumption adjustment factors. Help users better understand the relationship between their drinking habits and physiological state, and make healthier drinking decisions. This process not only helps to reduce the risk of excessive drinking, but also promotes the overall health level of the user.

[0060] In another embodiment, target anti-re-drinking information matching the drinking scene information of the target user is obtained, specifically further comprising:

[0061] A preset anti-re-drinking information set is obtained, wherein the preset anti-re-drinking information is used to represent the physiological parameters of different users who develop aversion to drinking. The drinking scene information of the target user is processed based on the preset anti-re-drinking information set to generate the physiological parameter change information of the target user. First, an information set containing the physiological parameters of different users who develop aversion to drinking is established. This information set can be constructed through research and user feedback, including but not limited to common discomfort symptoms after drinking, such as headache, nausea, palpitations, etc. Using the preset anti-re-drinking information set, combined with the behavior data of the target user in different drinking scenes, the physiological parameter change information of the user is analyzed and generated, which may involve monitoring and analyzing the drinking habits, frequency and amount of the user in specific scenes.

[0062] Based on the user's physiological parameter change information, combined with real-time monitoring of physiological data (such as heart rate, blood pressure, blood sugar, etc.), real-time drinking amount information of the target user is generated. These information can help us understand the user's appropriate drinking amount in the current state, if the target user's real-time drinking amount information is lower than the preset drinking amount threshold, we need to reprocess the preset anti-drinking information set, generate a drinking amount adjustment factor. This adjustment factor will be used to fine-tune the user's drinking amount to ensure that their drinking behavior does not adversely affect their health. By continuously monitoring the user's physiological state and drinking behavior, real-time updating of the drinking amount adjustment factor ensures that it always reflects the user's best health status. Encourage users to provide feedback, such as subjective feelings after drinking and any discomfort symptoms, to further adjust and improve the preset anti-drinking information set and drinking amount adjustment factor. It can more effectively manage the user's drinking behavior, reduce the risk of excessive drinking, and promote the user's overall health.

[0063] The physiological parameter change information of the target user is processed to generate real-time drinking amount information of the target user, and if the real-time drinking amount information of the target user is lower than the preset drinking amount threshold, the preset anti-drinking information set is processed to generate target anti-drinking information, wherein the target anti-drinking information is used to represent the physiological parameters of the target user that produce aversion to drinking. Real-time monitoring of the user's physiological parameters such as heart rate, blood pressure, blood sugar, etc. through intelligent devices is crucial for assessing the user's health status and drinking response. Analyze the user's physiological parameter changes in different drinking scenarios to determine their tolerance to drinking and possible adverse reactions. For example, if the user's heart rate increases or blood pressure rises after drinking, these changes may indicate that the user is more sensitive to alcohol. Based on the user's physiological parameter changes, combined with the preset drinking amount threshold, generate the user's real-time drinking amount information, which will help the user understand their appropriate drinking amount in the current state. If the user's real-time drinking amount is lower than the preset threshold, the effectiveness of the preset anti-drinking information set needs to be evaluated. This includes analyzing the user's physiological parameters that produce aversion to drinking, such as whether nausea, headache, etc. reactions occur.

[0064] Based on the evaluation results, generate target anti-drinking information, which will be used to help users control their drinking amount in different drinking scenarios and avoid excessive drinking. If the existing anti-drinking information fails to effectively help users control their drinking, or the user's drinking habits and physiological reactions have changed, the anti-drinking information needs to be adjusted or replaced to ensure its effectiveness. Continuously track the user's drinking behavior and physiological reactions, and optimize the anti-drinking information based on real-time data to improve the user's self-control ability for drinking. It can help users better manage their drinking behavior, reduce the health risks associated with drinking, and improve their awareness and control of their own drinking habits.

[0065] S105, processing the initial drinking amount of the target user and the initial limit drinking amount of the target user based on the drinking amount adjustment factor to generate the target drinking amount of the target user and the target limit drinking amount of the target user.

[0066] In an embodiment, the initial drinking amount of the target user and the initial limit drinking amount of the target user are processed based on the drinking amount adjustment factor to generate the drinking amount difference information of the target user, and the drinking amount difference information of the target user is processed to generate the historical drinking amount information of the target user, the historical physiological parameter information matched with the historical drinking amount information, the real-time drinking amount information of the target user, and the real-time physiological parameter information matched with the real-time drinking amount information. First, the initial drinking amount of the user needs to be determined, which can be based on the user's self-report or historical drinking records, and at the same time, the limit drinking amount of the user also needs to be determined, which is usually obtained according to health guidelines or the advice of medical professionals. By subtracting the initial drinking amount from the limit drinking amount, we can get the drinking amount difference information of the user, which can represent the safe increase of the user's drinking amount at a certain time, or the decrease of the user's drinking amount to achieve a healthy drinking standard. By analyzing the user's past drinking records, the historical drinking amount information of the user can be generated, which includes the user's average drinking amount, drinking frequency and drinking pattern in the past period of time.

[0067] The historical drinking amount information of the user is matched with the historical physiological parameter information, which may include the user's blood pressure, heart rate, blood sugar level and other physiological indicators under different drinking amounts. The user's drinking behavior is monitored in real time by using intelligent devices or applications to generate the user's real-time drinking amount information. The real-time drinking amount information of the user is matched with the real-time physiological parameter information to understand whether the user's current physiological state is suitable for his drinking amount. If the user's real-time drinking amount information is lower than the preset drinking amount threshold, the target anti-drinking information needs to be generated, which includes the user's physiological parameters such as nausea, headache, etc. in the specific drinking scene to produce aversion. If it is found that the current aversion physiological parameter does not have the expected anti-drinking effect, the preset anti-drinking information set needs to be processed, which may need to replace or adjust these parameters to improve their effectiveness, help the user better control the drinking behavior, and at the same time reduce the health risks that may be brought by drinking.

[0068] Based on the target user's historical alcohol consumption information and corresponding historical physiological parameter information, the target user's real-time alcohol consumption information and corresponding real-time physiological parameter information are processed to generate the target user's target alcohol consumption level and target maximum alcohol consumption level. Based on the user's historical and real-time alcohol consumption information, an alcohol consumption difference is calculated. This difference can be used to adjust the user's alcohol consumption to be closer to the recommended range for healthy drinking. Based on the user's alcohol consumption difference information and changes in physiological parameters, the target alcohol consumption level and target maximum alcohol consumption level are generated. The target alcohol consumption level should be the amount that the user can safely drink without causing adverse reactions, while the maximum alcohol consumption level is the amount that the user should avoid exceeding to prevent potential health risks. Based on user feedback and continuously monitored physiological parameters, the target alcohol consumption level and target maximum alcohol consumption level are continuously adjusted to ensure that they always conform to the user's health condition and drinking habits.

[0069] S106, Based on the drinking training factor, process the target drinking amount and the target maximum drinking amount of the target user to generate drinking feedback information for the target user.

[0070] In one implementation, the target user's drinking feedback information is used to characterize the physiological state information generated by the target user based on real-time alcohol consumption. To generate this information, we need to comprehensively process the user's drinking training factors, historical alcohol consumption information, historical physiological parameter information, and real-time alcohol consumption and physiological parameter information. First, we need to understand how drinking training factors influence the user's drinking behavior. These factors may include the user's drinking habits, preferred types of alcohol, and post-drinking physical reactions. Using the user's historical drinking data, we can analyze the user's average alcohol consumption in different scenarios. This data helps us understand the user's drinking patterns and serves as a basis for adjusting real-time alcohol consumption.

[0071] The user's historical drinking amount is matched with historical physiological parameters (such as heart rate, blood pressure changes after drinking, etc.) to determine the user's physiological response to different drinking amounts. Real-time monitoring devices or user input are used to obtain the user's current drinking amount information. This information is crucial for generating real-time drinking feedback. The user's real-time drinking amount is matched with real-time physiological parameters to assess whether the user's current physiological state is suitable for continuing to drink. Based on the above information, the user's drinking feedback information is generated. This feedback information will represent the user's physiological state information generated based on real-time drinking amount, helping the user understand the impact of their drinking behavior on their body. If the user's real-time drinking amount information is below the preset drinking amount threshold, the user's drinking amount needs to be adjusted according to the target anti-drinking information, which may involve reducing the user's drinking amount or suggesting that the user avoid drinking in certain situations. Continuous monitoring of the user's drinking behavior and physiological response, based on real-time data, optimizes the drinking feedback information to improve its accuracy and personalization. This can help users better control their drinking behavior while reducing the health risks associated with drinking.

[0072] In one embodiment, first, the drinking training factors need to be analyzed, which may include the user's drinking habits, preferred alcohol, and body reactions after drinking, etc. These information helps to understand the user's drinking behavior in different drinking scenarios. Based on the user's drinking training factors, the initial drinking amount and the limit drinking amount of the user are processed, which includes analyzing the user's drinking history and the drinking amount in different scenarios, so as to determine a safe and reasonable drinking range.

[0073] In one embodiment, first, the drinking training factors need to be analyzed, which may include the user's drinking habits, preferred alcohol, and body reactions after drinking, etc. These information helps to understand the user's drinking behavior in different drinking scenarios. Based on the user's drinking training factors, the initial drinking amount and the limit drinking amount of the user are processed, which includes analyzing the user's drinking history and the drinking amount in different scenarios, so as to determine a safe and reasonable drinking range.

[0074] The difference between the target limit drinking amount and the current drinking amount is calculated, which is the drinking amount difference. This difference information will be used to provide real-time feedback to the user to help them adjust their drinking behavior. By analyzing the user's drinking amount difference information and real-time physiological parameters, the warning threshold information is generated. This warning threshold will represent the user's physiological state information, such as when the user's drinking amount approaches or exceeds this threshold, the system will remind the user to pay attention to the drinking amount to avoid adverse reactions. Real-time monitoring of the user's drinking behavior and physiological state using intelligent devices or applications provides real-time feedback to the user based on the warning threshold information. This can help users understand their drinking status in a timely manner and make appropriate adjustments. Based on the user's feedback and continuous monitoring of physiological parameters, the warning threshold information is continuously optimized to improve its accuracy and personalization, which can help users better control their drinking behavior while reducing the health risks associated with drinking.

[0075] If the target user's real-time drinking amount reaches the warning threshold information, the warning information is generated.

[0076] In one embodiment, the system will automatically generate a warning message when the target user's real-time drinking amount reaches the warning threshold. This generation of warning message is the key function of the drinking amount monitoring system, which aims to timely remind the user and his emergency contact person to prevent possible health risks caused by excessive drinking.

[0077] The system monitors the user's drinking amount in real time through intelligent devices or applications, compares the user's real-time drinking amount with the preset warning threshold, and once the real-time drinking amount reaches or exceeds the warning threshold, the system will generate a warning message, which will be sent to the user's preset emergency contact person through SMS, application push or other instant messaging methods. The system will record the warning event and provide a feedback mechanism for the user or medical professionals to follow up, the warning system not only reminds the user to pay attention to the drinking amount, but also informs the emergency contact person when necessary, thereby improving the safety and health protection of the user.

[0078] By comprehensively analyzing the drinking habits and physiological state of the target user, an effective drinking amount intervention management system is established. The core steps include obtaining the user's drinking purpose information, real-time physiological state information, medical examination report, identity information and drinking amount warning model, which will be used to generate drinking training factors to help users adjust their drinking habits. In this application, the server generates drinking training factors by processing the user's drinking purpose information and real-time physiological state information, which will be used to represent the user's physiological state under different drinking amounts, helping the user understand the impact of their drinking behavior on health, and based on the user's medical examination report, the initial drinking amount and the initial limit drinking amount of the user are generated. These values will serve as a reference standard for the user's drinking behavior to ensure that it is within a safe range.

[0079] In addition, the user's identity information and real-time physiological state information will be processed to generate a drinking amount adjustment factor. This factor will dynamically adjust the user's drinking amount based on the user's drinking scenario and physiological response to ensure that their drinking behavior meets health standards. On this basis, the system will generate the target drinking amount and the target limit drinking amount of the target user. These values will be further processed in combination with the drinking training factor to generate the user's drinking feedback information, reflecting the user's physiological state information based on the real-time drinking amount. Finally, based on the drinking amount warning model, the system will process the user's drinking feedback information to generate warning threshold information. If the user's real-time drinking amount reaches the warning threshold, the system will automatically generate a warning message and send a drinking reminder message to the user's preset emergency contact person to ensure the user's safety. Not only can it help users effectively manage their drinking behavior, but also reduce the health risks caused by excessive drinking through real-time monitoring and feedback mechanisms.

[0080] In one embodiment, asFigure 2 As shown, the present application also provides a user alcohol consumption intervention management device, comprising:

[0081] The acquisition module 201 is configured to acquire the alcohol use information of the target user, the real-time physiological state information of the target user, the recent medical examination report information of the target user, the identity information of the target user, and a target alcohol consumption early warning model.

[0082] The processing module 202 is configured to process the alcohol use information of the target user and the real-time physiological state information of the target user to generate a drinking training factor, wherein the drinking training factor is used to process the drinking habit of the target user; process the recent medical examination report information of the target user to generate an initial alcohol consumption of the target user and an initial limit alcohol consumption of the target user; process the identity information of the target user and the real-time physiological state information of the target user to generate an alcohol consumption adjustment factor, wherein the alcohol consumption adjustment factor is used to process the alcohol consumption information of the target user; process the initial alcohol consumption of the target user and the initial limit alcohol consumption of the target user based on the alcohol consumption adjustment factor to generate a target alcohol consumption of the target user and a target limit alcohol consumption of the target user; process the target alcohol consumption of the target user and the target limit alcohol consumption based on the drinking training factor to generate drinking feedback information of the target user, wherein the drinking feedback information of the target user is used to represent the physiological state information generated by the target user based on the real-time alcohol consumption; process the drinking feedback information of the target user based on the target alcohol consumption early warning model to generate early warning threshold information; if the real-time alcohol consumption of the target user reaches the early warning threshold information, generate early warning information, wherein the early warning information comprises sending alcohol reminder information to the target user's pre-set emergency contact person.

[0083] The present application provides an electronic device, such as Figure 3 As shown, the electronic device 3 comprises a first processor 300, a memory 301, a bus 302 and a communication interface 303, the first processor 300, the communication interface 303 and the memory 301 are connected through the bus 302; the memory 301 stores a computer program which can run on the first processor 300, and the first processor 300 runs the computer program to execute the user alcohol consumption intervention management method provided by any one of the preceding embodiments of the present application.

[0084] The memory 301 can include a random access memory (RAM) and can also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 303 (which can be wired or wireless), and the Internet, a wide area network, a local network, a metropolitan area network, etc. can be used.

[0085] The bus 302 can be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store a program, and the first processor 300 executes the program after receiving an execution instruction. The user alcohol consumption intervention management method disclosed in any of the embodiments of the present application can be applied to the first processor 300 or implemented by the first processor 300.

[0086] The first processor 300 can be an integrated circuit chip with signal processing capability. In the implementation process, each step of the above method can be completed by integrated logic circuits of hardware in the first processor 300 or instructions in the form of software. The first processor 300 described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a ready-to-program gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. Each method, step and logic block disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be embodied as a hardware code processor for execution, or a combination of hardware and software modules in the code processor for execution. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, or other mature storage media in the art. The storage medium is located in the memory 301, and the first processor 300 reads the information in the memory 301 and combines the hardware to complete the steps of the above method.

[0087] The electronic device provided by the above embodiments of the present application has the same beneficial effects as the method adopted, run or implemented by the application program stored therein, and the user alcohol consumption intervention management method provided by the embodiments of the present application has the same beneficial effects.

[0088] The computer readable storage medium provided by the embodiments of the present application, such as a computer readable storage medium, can be a computer readable storage medium, such as a computer readable storage medium,Figure 4 As shown, the computer readable storage medium 401 stores a computer program, which, when read and run by the second processor 402, implements the intervention management method for user alcohol consumption as described above.

[0089] The computer readable storage medium provided by the above embodiments of the present application has the same beneficial effects as the method adopted, run or implemented by the application program stored therein, based on the same inventive concept as the intervention management method for user alcohol consumption.

[0090] It should be noted that, in the present application, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0091] Each of the embodiments in the present application is described in a relevant manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, the intervention management method for evaluating user alcohol consumption, the electronic device, the electronic equipment, and the readable storage medium are basically similar to the above-mentioned embodiments of the intervention management method for user alcohol consumption, and thus the description is relatively simple, and the relevant parts can be referred to the above-mentioned embodiments of the intervention management method for user alcohol consumption.

[0092] Although the present application is disclosed as above, the present application is not limited thereto. Any person skilled in the art can make various modifications and changes without departing from the spirit and scope of the present application, and therefore the protection scope of the present application should be subject to the scope defined by the claims.

Claims

1. A method for intervention management of a user's alcohol consumption, characterized by, The method comprises the following steps: obtaining the drinking purpose information of a target user, the real-time physiological state information of the target user, the recent medical report information of the target user, the identity information of the target user, and a target drinking amount prediction model; processing the drinking purpose information of the target user and the real-time physiological state information of the target user to generate a drinking training factor, wherein the drinking training factor is used for processing the drinking habit of the target user; processing the recent medical report information of the target user to generate the initial drinking amount of the target user and the initial limit drinking amount of the target user; processing the identity information of the target user and the real-time physiological state information of the target user to generate a drinking amount adjustment factor, wherein the drinking amount adjustment factor is used for processing the drinking amount information of the target user; processing the initial drinking amount of the target user and the initial limit drinking amount of the target user based on the drinking amount adjustment factor to generate the target drinking amount of the target user and the target limit drinking amount of the target user; processing the target drinking amount of the target user and the target limit drinking amount based on the drinking training factor to generate the drinking feedback information of the target user, wherein the drinking feedback information of the target user is used for representing the physiological state information generated by the target user based on the real-time drinking amount; processing the drinking feedback information of the target user based on the target drinking amount prediction model to generate the early warning threshold information; if the real-time drinking amount of the target user reaches the early warning threshold information, generating early warning information, wherein the early warning information comprises sending the drinking reminder information to the target user to the preset emergency contact person.

2. The method of claim 1, wherein, The method for obtaining the target drinking amount prediction model comprises the following steps: obtaining a preset training set; processing the preset training set to generate a preset training set with identification information, wherein the identification information is used for representing that the user cannot drink; processing the preset training set with identification information based on a preset processing rule to generate a training set and a test set; training a preset drinking amount prediction model based on the training set and the test set to generate a target drinking amount prediction model.

3. The method of claim 2, wherein, The method for processing the preset training set with identification information based on the preset processing rule to generate a training set and a test set comprises the following steps: obtaining the clinical data and the physiological state factor information of the preset training set based on the medical report information of different users respectively, wherein the clinical data comprises factors affecting the drinking amount of the user; processing the clinical data based on a deep learning model to generate the influence degree of the physiological state factor information and the clinical data; processing the physiological state factor information and the influence degree of the clinical data based on a risk classification rule to generate a risk abnormality index; classifying the preset training set with identification information based on the risk abnormality index to generate a training set and a test set.

4. The method of claim 1, wherein, The method for processing the drinking purpose information of the target user and the real-time physiological state information of the target user to generate a drinking training factor comprises the following steps: processing the drinking purpose information of the target user to generate preset drinking amount information of the target user, wherein the preset drinking amount information of the target user is used to represent the drinking amount to be drunk by the target user; processing the preset drinking amount information of the target user based on the real-time physiological state information of the target user to generate a drinking training factor, wherein the drinking training factor is used to represent the physiological state information matched by different drinking amount information of the target user.

5. The method of claim 1, wherein, processing the identity information of the target user and the real-time physiological state information of the target user to generate a drinking amount adjustment factor, comprising: processing the identity information of the target user to generate drinking scene information of the target user, wherein the drinking scene information of the target user is used to represent the historical drinking amount information of the target user in different drinking scenes; obtaining target anti-re-drinking information matched with the drinking scene information of the target user, wherein the target anti-re-drinking information is used to represent the physiological parameters of the target user to generate aversion to drinking; processing the drinking scene information of the target user based on the target anti-re-drinking information to generate estimated drinking amount information of the target user; processing the estimated drinking amount information of the target user based on the real-time physiological state information of the target user to generate a drinking amount adjustment factor.

6. The method of claim 5, wherein, obtaining target anti-re-drinking information matched with the drinking scene information of the target user, comprising: obtaining a preset anti-re-drinking information set, wherein the preset anti-re-drinking information is used to represent the physiological parameters of different users to generate aversion to drinking; processing the drinking scene information of the target user based on the preset anti-re-drinking information set to generate physiological parameter change information of the target user; processing the physiological parameter change information of the target user to generate real-time drinking amount information of the target user; if the real-time drinking amount information of the target user is lower than a preset drinking amount threshold, processing the preset anti-re-drinking information set to generate target anti-re-drinking information, wherein the target anti-re-drinking information is used to represent the physiological parameters of the target user to generate aversion to drinking.

7. The method of claim 5, wherein, processing the initial drinking amount of the target user and the initial limit drinking amount of the target user based on the drinking amount adjustment factor to generate the target drinking amount of the target user and the target limit drinking amount of the target user, comprising: processing the initial drinking amount of the target user and the initial limit drinking amount of the target user based on the drinking amount adjustment factor to generate drinking amount difference information of the target user; processing the drinking amount difference information of the target user to generate historical drinking amount information of the target user, historical physiological parameter information matched with the historical drinking amount information, real-time drinking amount information of the target user, and real-time physiological parameter information matched with the real-time drinking amount information; processing the real-time drinking amount information of the target user and the real-time physiological parameter information matched with the real-time drinking amount information based on the historical drinking amount information of the target user and the historical physiological parameter information matched with the historical drinking amount information to generate the target drinking amount of the target user and the target limit drinking amount of the target user.

8. An intervention management device for a user's alcohol consumption, characterized by the device comprises: An acquisition module is configured to acquire drinking purpose information of a target user, real-time physiological state information of the target user, a recent medical examination report of the target user, identity information of the target user, and a target drinking amount prediction model. A processing module is configured to process the drinking purpose information of the target user and the real-time physiological state information of the target user to generate a drinking training factor, wherein the drinking training factor is used to process a drinking habit of the target user; process the recent medical examination report of the target user to generate an initial drinking amount of the target user and an initial limit drinking amount of the target user; process the identity information of the target user and the real-time physiological state information of the target user to generate a drinking amount adjustment factor, wherein the drinking amount adjustment factor is used to process drinking amount information of the target user; process the initial drinking amount of the target user and the initial limit drinking amount of the target user based on the drinking amount adjustment factor to generate a target drinking amount of the target user and a target limit drinking amount of the target user; process the target drinking amount of the target user and the target limit drinking amount based on the drinking training factor to generate drinking feedback information of the target user, wherein the drinking feedback information of the target user is used to represent physiological state information of the target user generated based on a real-time drinking amount; process the drinking feedback information of the target user based on the target drinking amount prediction model to generate early warning threshold information; and generate early warning information if the real-time drinking amount of the target user reaches the early warning threshold information, wherein the early warning information includes sending drinking reminder information to a preset emergency contact person of the target user.

9. An electronic device, comprising: comprise: a first processor; and a memory configured to store executable instructions of the first processor; wherein the first processor is configured to execute the user drinking amount intervention management method of any one of claims 1-7 via execution of the executable instructions.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the second processor to implement the user drinking amount intervention management method of any one of claims 1-7.

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