User drinking amount intervention management method and related equipment

Abstract

The invention provides a user drinking amount intervention management method and related equipment, and is applied to the technical field of data processing. The method comprises the following steps: processing drinking purpose information of a target user and real-time physiological status information of the target user to generate a drinking training factor; the last physical examination report information of the target user is processed, and the initial drinking amount of the target user and the initial limit drinking amount of the target user are generated; processing the identity information of the target user and the real-time physiological status information of the target user to generate a drinking amount adjustment factor; 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, and generating the target drinking amount of the target user and the target limit drinking amount of the target user; and processing the target drinking amount and the target limit drinking amount of the target user based on the drinking training factor, generating drinking feedback information of the target user, processing the drinking feedback information, and generating early warning threshold information.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intervention management method for a user's drinking amount and related equipment. Background Art

[0002] As an important drink in the lives of some people, alcohol has spawned various alcohol cultures and has become an indispensable presence in certain occasions. However, studies have shown that not everyone is suitable for drinking, and excessive drinking is extremely harmful to the body; and different people have different drinking abilities, so it is necessary to have a correct understanding of one's own alcohol metabolism ability. In addition, alcohol dependence syndrome is a chronic and easily relapsing encephalopathy, and easy relapse is an important feature of the disease. For alcohol-dependent people who have quit drinking, relapse after stopping drinking is one of the characteristics of alcohol dependence. More than 60% of alcohol-dependent patients relapse within 6 months after quitting. There are many factors that lead to relapse, and the most important factor for relapse is "mental addiction", also known as psychological craving, that is, the craving for alcohol or "the desire or impulse to drink". It is related to serious drinking problems. Whether the psychological craving can be successfully dealt with will affect whether the individual will continue to drink or remain abstinent.

[0003] Similar products currently on the market all test the user's alcohol metabolism ability, but do not quantify the drinking ability. They do not provide strong guidance to users and cannot give targeted drinking suggestions based on the user's physical condition.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of this application is to provide a method for intervening and managing the amount of alcohol consumed by a user and related equipment, which at least to some extent overcomes the problems existing in the prior art, and generates personalized drinking training factors and drinking adjustment factors by analyzing the user's drinking purposes, real-time physiological status and physical examination reports, so as to guide the user to drink within a safe range. The target amount of alcohol consumed and the limit amount of alcohol consumed are calculated based on these factors, and real-time feedback information is provided. When the user's alcohol consumption approaches the preset warning threshold, a warning will be issued and the emergency contact will be notified to prevent the health risks caused by excessive drinking.

[0006] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present invention.

[0007] According to one aspect of the present application, a method for intervention management of user alcohol consumption is provided, comprising: obtaining a target user's alcohol use information, a target user's real-time physiological state information, a target user's most recent physical examination report information, a target user's identity information, and a target alcohol consumption warning model; processing the target user's alcohol use information and the target user's real-time physiological state information to generate a drinking training factor, wherein the drinking training factor is used to process the target user's drinking habits; processing the target user's most recent physical examination report information to generate the target user's initial alcohol consumption and the target user's initial limit alcohol consumption; processing the target user's identity information and the target user's real-time physiological state information to generate a drinking adjustment factor, wherein the drinking adjustment factor is used to adjust the target user's drinking habits. The target user's drinking amount information is processed; the target user's initial drinking amount and the target user's initial limit drinking amount are processed based on the drinking amount adjustment factor to generate the target user's target drinking amount and the target user's target limit drinking amount; the target user's target drinking amount and the target limit drinking amount are processed based on the drinking training factor to generate the target user's drinking feedback information, wherein the target user's drinking feedback information is used to characterize the target user's physiological state information generated based on the real-time drinking amount; the target user's drinking feedback information is processed based on the target drinking amount warning model to generate warning threshold information; if the target user's real-time drinking amount reaches the warning threshold information, a warning message is generated, wherein the warning message includes sending a drinking reminder message to an emergency contact preset by the target user.

[0008] In one embodiment of the present application, obtaining a target drinking amount warning model includes: obtaining a preset training set; preprocessing the preset training set to generate a preset training set with identification information, wherein the identification information is used to characterize that the user cannot drink; processing the preset training set with the identification information based on preset processing rules 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.

[0009] In one embodiment of the present application, the preset training set with identification information is processed based on preset processing rules 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 physical examination report information of different users, 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 physiological state factor information and the degree of influence of the clinical data; processing the degree of influence of the physiological state factor information and the case information based on risk classification rules to generate a risk abnormality indicator; classifying the preset training set with identification information based on the risk abnormality indicator to generate a training set and a test set.

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

[0011] In one 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 characterize the historical drinking amount information of the target user in different drinking scenes; acquiring target relapse prevention information matching the drinking scene information of the target user, wherein the target relapse prevention information is used to characterize the physiological parameters of the target user's aversion to drinking; processing the drinking scene information of the target user based on the target relapse prevention 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.

[0012] In one embodiment of the present application, obtaining target relapse prevention information that matches the target user's drinking scene information includes: obtaining a preset relapse prevention information set, wherein the preset relapse prevention information is used to characterize physiological parameters that cause different users to have aversion to drinking; processing the target user's drinking scene information based on the preset relapse prevention 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 volume information of the target user; if the real-time drinking volume information of the target user is lower than a preset drinking volume threshold, processing the preset relapse prevention information set to generate target relapse prevention information, wherein the target relapse prevention information is used to characterize the physiological parameters that cause the target user to have aversion to drinking.

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

[0014] Another aspect of the present application is a device for intervening and managing a user's alcohol consumption, characterized in that it includes: an acquisition module for acquiring a target user's alcohol use information, a target user's real-time physiological state information, a target user's most recent physical examination report information, a target user's identity information, and a target alcohol consumption warning model; a processing module for processing the target user's alcohol use information and the target user's real-time physiological state information to generate a drinking training factor, wherein the drinking training factor is used to process the target user's drinking habits; processing the target user's most recent physical examination report information to generate the target user's initial alcohol consumption and the target user's initial limit alcohol consumption; processing the target user's identity information and the target user's real-time physiological state information to generate a drinking amount adjustment factor, wherein the drinking amount adjustment factor is used to process the target user's drinking habits; processing the target user's most recent physical examination report information to generate the target user's initial alcohol consumption and the target user's initial limit alcohol consumption; processing the target user's identity information and the target user's real-time physiological state information to generate a drinking amount adjustment factor, wherein the drinking amount adjustment factor is used to process the target user's drinking habits; The integral factor is used to process the target user's drinking amount information; based on the drinking amount adjustment factor, the target user's initial drinking amount and the target user's initial limit drinking amount are processed to generate the target user's target drinking amount and the target user's target limit drinking amount; based on the drinking training factor, the target user's target drinking amount and the target limit drinking amount are processed to generate the target user's drinking feedback information, wherein the target user's drinking feedback information is used to characterize the target user's physiological state information generated based on the real-time drinking amount; based on the target drinking amount warning model, the target user's drinking feedback information is processed to generate warning threshold information; if the target user's real-time drinking amount reaches the warning threshold information, a warning message is generated, wherein the warning message includes sending a drinking reminder message to an emergency contact preset by the target user.

[0015] According to another aspect of the present application, an electronic device is characterized in that it includes: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the above-mentioned method for intervention management of user drinking amount by executing the executable instructions.

[0016] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a second processor, the above-mentioned method for intervention management of user drinking amount is implemented.

[0017] According to another aspect of the present application, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a third processor, the above-mentioned method for intervention management of user drinking amount is implemented.

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

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart showing a method for intervening and managing a user's drinking amount provided by an embodiment of the present application;

[0021] Figure 2 A schematic diagram showing the structure of a device for intervening and managing the drinking amount of a user provided in an embodiment of the present application is shown;

[0022] Figure 3 A schematic diagram of the structure 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 in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0025] Combine the following Figure 1 To describe the intervention management method of the user's drinking amount according to the exemplary embodiment of the present application. It should be noted that the following application scenarios are only shown to facilitate understanding of the spirit and principle of the present application, and the embodiments of the present application are not limited in this regard. On the contrary, the embodiments of the present application can be applied to any applicable scenario.

[0026] In one implementation, the present application also proposes a method for intervening and managing a user's alcohol consumption and related equipment. Figure 1 The flowchart of a method for intervening and managing a user's drinking amount according to an embodiment of the present application is schematically shown. Figure 1 As shown, the method is applied to a server, comprising:

[0027] S101, obtaining the target user's drinking usage information, the target user's real-time physiological status information, the target user's most recent physical examination report information, the target user's identity information and the target drinking amount warning model.

[0028] In one embodiment, drinking usage information, which usually requires active input from the user or is inferred by monitoring drinking behavior through smart devices. For example, the system allows users to record their drinking habits, including the occasions and purposes of drinking. Real-time physiological status can be obtained through wearable devices or health monitoring systems, such as smart watches or health monitoring belts, which can track vital signs such as heart rate, blood pressure, and body temperature in real time.

[0029] The target user's physical examination report information includes but is not limited to liver function (total protein, albumin, globulin, albumin-globulin ratio, total bilirubin, direct and indirect bilirubin, transaminase); blood lipids (total cholesterol, triglycerides, high and low density lipoproteins, apolipoproteins); fasting blood sugar; renal function (creatinine, urea nitrogen); uric acid; lactate dehydrogenase; creatine creatinase, peripheral blood count, red blood cell count, hematocrit, etc. In addition, the target user's identity information includes the target user's subsequent dinner information and whether there are other matters to be processed in the future. Among them, the dinner information includes the purpose and importance of the subsequent dinner, which can be obtained through the user's input in the relevant application or through the integrated calendar and social functions.

[0030] In another embodiment, the generation of the target alcohol consumption warning model can be obtained in the following manner: specifically, a preset training set is obtained, and the preset training set is preprocessed to generate a preset training set with identification information, wherein the identification information is used to indicate that the user cannot drink alcohol. The preset training set with identification information is processed based on a preset processing rule to generate a training set and a test set, and the preset alcohol consumption prediction model is trained based on the training set and the test set to generate a target alcohol consumption prediction model.

[0031] Specifically, data related to drinking is collected, which may include the user's drinking habits, health data, physiological reactions, etc. This data can be obtained from health monitoring applications, medical records, or user input. The above data is processed, including cleaning data, handling missing values, standardizing formats, etc. For example, make sure that all data is numerical so that the model can process it. This can be achieved through data cleaning and transformation, such as using data processing libraries such as pandas and numpy. After preprocessing the data, we need to add identification information to the dataset, which may include a label whether the user is suitable for drinking. These identifiers can be determined by analyzing the user's physical examination report, physiological state, and drinking purpose. For example, if the user's liver function indicators are abnormal, it may be necessary to add an identifier indicating that the user should not drink.

[0032] Using preprocessing rules, split the dataset into training and test sets. This usually involves random sampling or using cross-validation to ensure uniform data distribution. A common split ratio might be 70% training set and 30% test set, or using a more complex k-fold cross-validation method. This will be discussed later and will not be repeated here.

[0033] Using the training set, train a machine learning model to predict the target drinking amount. This may involve choosing an appropriate algorithm, such as a decision tree, support vector machine, or neural network, and adjusting the model parameters. During the training process, we can use the validation set to adjust the model parameters to prevent overfitting. When the model training is complete, use the test set to evaluate the performance of the model. This can be achieved by calculating accuracy, recall, and other metrics. Depending on the test results, it may be necessary to go back to the model training step and further optimize the model. Finally, when the model performance reaches a satisfactory level, it can be deployed as an application or service for users to query their drinking predictions.

[0034] In another implementation, the preset training set with identification information is processed based on a preset processing rule to generate a training set and a test set, which specifically includes:

[0035] The clinical data and physiological status factor information of the preset training set are obtained based on the physical examination report information of different users, where the clinical data includes factors that affect the user's drinking amount. Clinical data related to drinking are 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.), renal function indicators (such as creatinine, urea nitrogen), blood lipid levels (such as total cholesterol, triglycerides, high and low density lipoproteins, apolipoproteins), fasting blood sugar, uric acid, lactate dehydrogenase, creatine creatinase, and peripheral blood counts. The collected data is preprocessed, including data cleaning, formatting and standardization. This step is crucial because it ensures the consistency and accuracy of the data and lays the foundation for subsequent model training. After preprocessing, we need to add identification information to the data set, which may include labels on whether the user is suitable for drinking. These identifications can be determined by analyzing the user's physical examination report, physiological state and drinking purpose.

[0036] Clinical data can be processed based on deep learning models to generate information about physiological state factors and the degree of influence 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 information about physiological state factors and the degree of influence of clinical data. These models can learn complex patterns from the data and predict the impact of each factor on alcohol consumption.

[0037] When building a deep learning model to process clinical data and generate information about physiological state factors and the impact of clinical data, the following example can be used to understand the process:

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

[0039] The relationship between blood lipid levels (such as total cholesterol and triglycerides) and drinking habits. Deep learning models may reveal that individuals with higher blood lipid levels are more likely to reduce their drinking or, in some cases, may increase their drinking 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 drinking recommendations. For example, the model may indicate that individuals with high creatinine levels (which may indicate renal insufficiency) should avoid drinking.

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

[0042] In more complex cases, deep learning models can take into account multiple physiological state factors (such as liver function, kidney function, blood lipids, and blood sugar levels) to predict an individual's drinking. The model may assign a weight to each factor, indicating their relative importance in predicting drinking. These examples show 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 influence of physiological status factor information and case information is processed to generate risk abnormality indicators. Based on the risk abnormality indicators, the preset training set with identification information is classified and processed to generate training sets and test sets. Based on the risk classification rules, the influence of physiological status factor information and case information can be processed to generate risk abnormality indicators. These rules may be based on medical research and clinical guidelines to determine which indicators indicate that users should not drink. The preset training set with identification information can be classified and processed using risk abnormality indicators to generate training sets and test sets. These data sets will be used to train and evaluate your alcohol consumption prediction model.

[0044] S102: Process the target user's drinking usage information and the target user's real-time physiological state information to generate a drinking training factor.

[0045] In one implementation,.

[0046] The target user's drinking purpose information is processed to generate the target user's preset drinking amount information, wherein the target user's preset drinking amount information is used to characterize the amount of alcohol that the target user is going to drink. The user's drinking purpose information is collected through questionnaires, interviews, or user input in the application. This may include drinking occasions (such as social gatherings, relaxing alone, holiday celebrations, etc.), the frequency of drinking, and the user's psychological expectations of drinking (such as the hope to improve mood and relieve stress through drinking). Next, the relationship between different drinking purposes and drinking amount is analyzed. For example, social occasions may be associated with higher drinking amounts, while relaxing alone may be associated with lower drinking amounts. Combined with the user's health status, such as liver function, kidney function, and heart health status obtained from a physical examination report, the preset drinking amount is adjusted. Certain health conditions may require users to limit or avoid drinking.

[0047] Based on the usage information and health factors, set a reasonable preset drinking amount for the user. This amount should be safe and consistent with 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 a relaxing effect but does not cause health risks. The preset drinking amount information is provided to the user to let the user confirm whether it meets their expectations and needs. The user can adjust it according to personal preferences. The final preset drinking amount information should be an amount within a range, not 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 status and needs. Create a personalized preset drinking amount information for the user to help the user better control their drinking behavior while reducing 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 characterize the physiological state information matched by the different drinking amount information of the target user, and the drinking training factor is used to process the drinking habits of the target user. The user's physiological state information is collected in real time, which may include indicators such as heart rate, blood pressure, blood sugar level, body temperature, etc. These data can be obtained through smart watches, health monitoring devices or user self-reports. The collected physiological state information is used to evaluate the user's current health status. For example, if the user's current blood pressure is high, the preset drinking amount may need to be adjusted to avoid health risks that may be caused by drinking.

[0049] According to the user's physiological state assessment results, the preset drinking amount is adjusted. If the user's physiological state indicators show that drinking is not appropriate, the preset drinking amount should be reduced accordingly or the user should be advised not to drink temporarily. Combined with the user's preset drinking amount and real-time physiological state information, drinking training factors are generated. These factors can be a set of parameters that guide 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 to users. These suggestions can help users adjust their drinking habits according to their physiological state and reduce the risk of excessive drinking. Continuously monitor the user's physiological state and drinking behavior, and adjust the drinking training factors based on feedback. This can be achieved through mobile applications or health management platforms to provide users with real-time drinking guidance. Provide users with educational information on healthy drinking, help them understand the relationship between drinking and health, and encourage them to adopt healthier drinking behaviors. Drinking training factors not only help users understand the degree of match between their drinking amount and physiological state, but also promote users to form healthy drinking habits and reduce health risks caused by improper drinking.

[0050] Based on the prediction results of the model, the system provides users with personalized drinking recommendations. For example, if the model predicts that the user's drinking in the current state may lead to health risks, the system can suggest that the user reduce the amount of alcohol consumed or choose low-alcohol drinks. The system continuously monitors the user's physiological state and drinking behavior, and adjusts the preset drinking amount and drinking training factors based on real-time data to ensure that the user's drinking behavior matches their health status. It can help users establish healthy drinking habits and reduce health risks caused by improper drinking. At the same time, this method also helps medical institutions and health managers better understand and manage patients' drinking behavior.

[0051] S103: Process the target user's most recent physical examination report information to generate the target user's initial alcohol consumption and the target user's initial alcohol consumption limit.

[0052] In one embodiment, due to the fat-soluble characteristics of ethanol, it is easy to cause adverse effects on the functions of various systems of the body, and the changes in peripheral blood in the blood system are the most sensitive. Since red blood cells account for 40% of the blood, changes in parameters such as the number of red blood cells and the specific volume have a greater impact on the physiological function of the blood. Long-term and large-scale alcohol abuse can lead to a decrease in the deformability of red blood cells, an increase in the mean corpuscular volume (MCV) and the mean corpuscular hemoglobin content (MCH), a decrease in the mean corpuscular hemoglobin concentration (MCHC), leukopenia, abnormal platelet count and function, etc. The obvious increase in red blood cell MCV and MCH after drinking is due to the fact that the high concentration of ethanol in the blood may affect the lipid structure of the red blood cell membrane and the protein configuration on the membrane, thereby directly affecting the deformability of red blood cells. At the same time, ethanol can also cause folic acid absorption disorders, slowing down the division and proliferation of red blood cells, so MCHC is reduced. Ethanol intake causes a decrease in the number of white blood cells, which may be related to the direct inhibition of colony stimulating factor production by ethanol, resulting in 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 alcohol intake group were lower than those in the control group, while the lymphocyte ratio, MCV, and MCH values ​​were significantly higher than those in the control group. The difference between the two groups was statistically significant, confirming that ethanol has a significant impact on peripheral blood counts. Therefore, the target user will be judged based on the values ​​in the target's latest physical examination report to determine whether he or she can drink alcohol, and if so, what is the range of the amount of alcohol he or she can drink.

[0053] The specific examples are as follows. The target user's initial alcohol consumption and the target user's initial maximum alcohol consumption are set based on the type of alcohol consumed by the target user. 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 greater than the normal value, then if the target user drinks liquor, the initial alcohol consumption is 110ml and the initial maximum alcohol consumption is 300ml; if the target user drinks beer, the initial alcohol consumption is 300ml and the initial maximum alcohol consumption is 500ml; if the target user drinks wine, the initial alcohol consumption is 50ml and the initial maximum alcohol consumption is 800ml.

[0054] S104: Process the identity information of the target user and the real-time physiological status information of the target user to generate a drinking amount adjustment factor.

[0055] In one embodiment, the target user's identity information is processed to generate the target user's drinking scene information, wherein the target user's drinking scene information is used to characterize the target user's historical drinking volume information in different drinking scenes. Target relapse prevention information matching the target user's drinking scene information is obtained, wherein the target relapse prevention information is used to characterize the target user's physiological parameters that cause aversion to drinking, and the target user's drinking scene information is processed based on the target relapse prevention information to generate the target user's estimated drinking volume information. First, by analyzing the target user's identity information, such as age, gender, occupation, etc., combined with their living habits and social activities, determine the drinking scenes they may participate in, such as business entertainment, family gatherings, friends' gatherings, etc. For each determined drinking scene, collect and analyze the target user's historical drinking data, including the time, frequency, type and amount of drinking, which can be obtained through user surveys or drinking records in mobile applications.

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

[0057] Provide users with personalized drinking adjustment suggestions to help them control their drinking in different scenarios. These suggestions can be updated in real time and adjusted according to the user's latest physiological state and relapse prevention information, continuously monitor the user's drinking behavior and physiological reactions, and collect feedback information to optimize the estimated drinking model. This can be achieved through mobile applications, smart devices or regular user interviews. It can help target users better understand and control their drinking behavior while reducing the negative effects that drinking may bring. By collecting physiological data such as EEG, skin electricity, blood volume and pulse, the physiological craving of alcohol-dependent people can be assessed in real time. After the subject's craving is fully induced, relaxation training, relapse prevention technology learning and other content are given, ultimately achieving the effect of assisting long-term rehabilitation and correction training for alcohol abstinence.

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

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

[0060] In another implementation, obtaining target relapse prevention information that matches the target user's drinking scene information specifically includes:

[0061] Obtain a preset anti-relapse information set, where the preset anti-relapse information is used to characterize the physiological parameters that cause different users to have an aversion to drinking. Based on the preset anti-relapse information set, the target user's drinking scene information is processed to generate the target user's physiological parameter change information. First, establish an information set containing the physiological parameters that cause different users to have an aversion to drinking. This information set can be constructed through research and user feedback, including but not limited to common discomfort symptoms after drinking, such as headaches, nausea, palpitations, etc. Utilize the preset anti-relapse information set, combined with the target user's behavioral data in different drinking scenarios, analyze and generate the user's physiological parameter change information, which may involve monitoring and analyzing the user's drinking habits, frequency, and amount in specific scenarios.

[0062] According to the user's physiological parameter change information, combined with real-time monitored physiological data (such as heart rate, blood pressure, blood sugar, etc.), the real-time drinking information of the target user is generated. This information can help us understand the appropriate drinking amount for the user in the current state. If the real-time drinking amount information of the target user is lower than the preset drinking amount threshold, we need to reprocess the preset anti-relapse drinking information set to generate a drinking amount adjustment factor. This adjustment factor will be used to fine-tune the user's drinking amount to ensure that his drinking behavior does not have an adverse effect on his health. By continuously monitoring the user's physiological state and drinking behavior, the drinking amount adjustment factor is updated in real time to ensure that it always reflects the user's optimal health state. Users are encouraged to provide feedback, such as subjective feelings after drinking and any discomfort symptoms, to further adjust and improve the preset anti-relapse drinking information set and drinking amount adjustment factor. Users' drinking behavior can be managed more effectively, the risk of excessive drinking can be reduced, and the overall health of users can be promoted.

[0063] The target user's physiological parameter change information is processed to generate the target user's real-time drinking amount information. If the target user's real-time drinking amount information is lower than the preset drinking amount threshold, the preset anti-relapse drinking information set is processed to generate the target anti-relapse drinking information, wherein the target anti-relapse drinking information is used to characterize the physiological parameters that cause the target user to have an aversion to drinking. The user's physiological parameters, such as heart rate, blood pressure, blood sugar, etc., are monitored in real time through smart devices. These parameters are essential for assessing the user's health status and drinking reaction. The user's physiological parameter changes in different drinking scenarios are analyzed to determine their tolerance to drinking and possible adverse reactions. For example, if the user has an increased heart rate or blood pressure after drinking, these changes may indicate that the user is highly sensitive to alcohol. According to the user's physiological parameter changes, combined with the preset drinking amount threshold, the user's real-time drinking amount information is generated. This information will help the user understand the appropriate amount of drinking for himself in the current state. If the user's real-time drinking amount is lower than the preset threshold, the effectiveness of the preset anti-relapse drinking information set needs to be evaluated. This includes analyzing the user's physiological parameters that cause an aversion to drinking, such as whether nausea, headache, etc. occur.

[0064] Based on the evaluation results, target relapse prevention information is generated, which will be used to help users control their drinking in different drinking scenarios and avoid excessive drinking. If the existing relapse prevention information fails to effectively help users control their drinking, or the user's drinking habits and physiological reactions have changed, the relapse prevention information needs to be adjusted or replaced to ensure its effectiveness. Continue to track the user's drinking behavior and physiological reactions, and optimize the relapse prevention information based on real-time data to improve the user's self-control over drinking. It can help users better manage their drinking behavior, reduce the health risks brought by drinking, and at the same time improve users' awareness and control over their own drinking habits.

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

[0066] In one embodiment, the target user's initial drinking amount and the target user's initial limit drinking amount are processed based on the drinking amount adjustment factor to generate the target user's drinking amount difference information, and the target user's drinking amount difference information is processed to generate the target user's historical drinking amount information, historical physiological parameter information matching the historical drinking amount information, the target user's real-time drinking amount information, and real-time physiological parameter information matching the real-time drinking amount information. First, it is necessary to determine the user's initial drinking amount, which can be based on the user's self-report or historical drinking record. At the same time, it is also necessary to determine the user's limit drinking amount, which is usually based on health guidelines or medical professionals' advice. By subtracting the initial drinking amount from the limit drinking amount, we can get the user's drinking amount difference information, which can represent the amount of drinking that the user can safely increase within a specific time, or the amount of drinking that the user needs to reduce to reach the healthy drinking standard. By analyzing the user's past drinking records, the user's historical drinking amount information can be generated, which includes the user's average drinking amount, drinking frequency, and drinking pattern over the past period of time.

[0067] Match the user's historical drinking information with historical physiological parameter information, which may include physiological indicators such as blood pressure, heart rate, and blood sugar level at different drinking amounts. Use smart devices or applications to monitor the user's drinking behavior in real time and generate the user's real-time drinking information. Match the user's real-time drinking information with the real-time physiological parameter information to understand whether the user's current physiological state is suitable for his or her drinking amount. If the user's real-time drinking information is lower than the preset drinking threshold, target relapse prevention information needs to be generated, which includes physiological parameters that cause the user to have a disgusting reaction in a specific drinking scenario, such as nausea, headache, etc. If it is found that the current disgust physiological parameters do not have the expected anti-relapse effect, the preset anti-relapse information set needs to be processed, and these parameters may need to be replaced or adjusted to improve their effectiveness and help users better control their drinking behavior while reducing the health risks that may be caused by drinking.

[0068] Based on the target user's historical drinking information and the historical physiological parameter information that matches the historical drinking information, the target user's real-time drinking information and the real-time physiological parameter information that matches the real-time drinking information are processed to generate the target user's target drinking amount and the target limit drinking amount of the target user. Based on the user's historical and real-time drinking information, the drinking amount difference is calculated, and this difference can be used to adjust the user's drinking amount to make it closer to the recommended range of healthy drinking. Based on the user's drinking amount difference information and physiological parameter changes, the user's target drinking amount and target limit drinking amount are generated. The target drinking amount should be the amount that the user can drink safely without causing adverse reactions, while the limit drinking amount is the amount that the user should avoid exceeding to prevent potential health risks. Based on the user's feedback and continuously monitored physiological parameters, the target drinking amount and limit drinking amount are continuously adjusted to ensure that they always match the user's health status and drinking habits.

[0069] S106: Process the target drinking amount 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.

[0070] In one implementation, the target user's drinking feedback information is used to characterize the target user's physiological state information generated based on the real-time drinking amount. In order to generate the target user's drinking feedback information, we need to comprehensively process the user's drinking training factors, historical drinking amount information, historical physiological parameter information, and real-time drinking amount information and real-time physiological parameter information. First, we need to understand how drinking training factors affect the user's drinking behavior. These factors may include the user's drinking habits, preferred alcohol, and physical reactions after drinking. Using the user's historical drinking data, we can analyze the user's average drinking amount in different scenarios. These data can help us understand the user's drinking pattern and serve as a basis for adjusting the real-time drinking amount.

[0071] Match the user's historical drinking volume with historical physiological parameters (such as heart rate and blood pressure changes after drinking) to determine the user's physiological response to different drinking volumes, and obtain the user's current drinking volume information through real-time monitoring equipment or user input. This information is essential for generating real-time drinking feedback. Match the user's real-time drinking volume with real-time physiological parameters to assess whether the user's current physiological state is suitable for continuing to drink. Combine the above information to generate the user's drinking feedback information. This feedback information will characterize the user's physiological state information generated based on the real-time drinking volume, helping the user understand the impact of their drinking behavior on the body. If the user's real-time drinking volume information is lower than the preset drinking volume threshold, the user's drinking volume needs to be adjusted according to the target anti-relapse information, which may involve reducing the user's drinking volume or advising the user to avoid drinking in certain circumstances. Continuously monitor the user's drinking behavior and physiological response, and optimize the drinking feedback information based on real-time data to improve its accuracy and personalization. It can help users better control their drinking behavior while reducing the health risks that may be caused by drinking.

[0072] S107: Process the target user's drinking feedback information based on the target drinking amount warning model to generate warning threshold information.

[0073] In one implementation, first, it is necessary to analyze the drinking training factors, which may include the user's drinking habits, preferred alcohol, and physical reactions after drinking, etc. This information helps to understand the user's drinking behavior in different drinking scenarios. Based on the user's drinking training factors, the user's initial drinking amount and limit drinking amount are processed, which includes analyzing the user's drinking history and drinking amount in different scenarios, so as to determine a safe and reasonable drinking range.

[0074] Calculate the user's drinking difference, that is, the difference between the target limit drinking amount and the current drinking amount. This difference information will be used to provide real-time feedback to users to help them adjust their drinking behavior: by analyzing the user's drinking difference information and real-time physiological parameters, generate warning threshold information. This warning threshold will represent the user's physiological state information. For example, when the user's drinking amount approaches or exceeds this threshold, the system will remind the user to pay attention to the amount of drinking to avoid adverse reactions. Use smart devices or applications to monitor the user's drinking behavior and physiological state in real time, and provide real-time feedback to users based on the warning threshold information. This can help users understand their drinking status in a timely manner and make corresponding adjustments. Based on user feedback and continuously monitored 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 that may be caused by drinking.

[0075] S108: If the target user's real-time drinking amount reaches the warning threshold information, a warning message is generated.

[0076] In one embodiment, when the target user's real-time drinking volume reaches the warning threshold, the system will automatically generate a warning message. The generation of this warning message is a key function of the drinking volume monitoring system, and its purpose is to promptly alert the user and his emergency contacts to prevent possible health risks caused by excessive drinking.

[0077] The system monitors the user's alcohol consumption in real time through smart devices or applications, and compares the user's real-time alcohol consumption with the preset warning threshold. Once the real-time alcohol consumption reaches or exceeds the warning threshold, the system will generate a warning message, which will be sent to the user's preset emergency contact via SMS, application push or other instant messaging methods. The system will record warning events and provide a feedback mechanism for users or medical professionals to follow up. The warning system can not only remind users to pay attention to their drinking, but also notify emergency contacts when necessary, thereby improving the safety and health protection of users.

[0078] By comprehensively analyzing the target user's drinking habits and physiological state, an effective alcohol consumption intervention management system is established. The core steps include obtaining the user's drinking purpose information, real-time physiological state information, physical examination report, identity information and drinking amount warning model. This information will be used to generate a drinking training factor to help users adjust their drinking habits. In this application, the server generates a drinking training factor by processing the user's drinking purpose information and real-time physiological state information. This factor will be used to characterize the user's physiological state at different drinking amounts, help the user understand the impact of their own drinking behavior on health, and generate the user's initial drinking amount and initial limit drinking amount based on the user's physical examination report. These values ​​will serve as a reference standard for the user's drinking to ensure that their drinking behavior 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 adjustment factor. This factor will dynamically adjust the user's drinking amount according to the user's drinking scene and physiological response to ensure that their drinking behavior meets health standards. On this basis, the system will generate the target drinking amount and target limit drinking amount for 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 generated 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 and generate warning threshold information. If the user's real-time drinking 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 to ensure the user's safety. It can not only 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, if Figure 2 As shown, the present application also provides a device for intervening and managing a user's drinking amount, comprising:

[0081] The acquisition module 201 is used to acquire the target user's drinking usage information, the target user's real-time physiological status information, the target user's most recent physical examination report information, the target user's identity information and the target drinking amount warning model;

[0082] The processing module 202 is used to process the target user's drinking usage information and the target user's real-time physiological state information to generate a drinking training factor, wherein the drinking training factor is used to process the target user's drinking habits; process the target user's most recent physical examination report information to generate the target user's initial drinking amount and the target user's initial limit drinking amount; process the target user's identity information and the target user's real-time physiological state information to generate a drinking amount adjustment factor, wherein the drinking amount adjustment factor is used to process the target user's drinking amount information; adjust the target user's initial drinking amount and the target user's real-time physiological state information based on the drinking amount adjustment factor. The target user's initial limit drinking amount is processed to generate a target drinking amount and a target limit drinking amount of the target user; the target drinking amount and the target limit drinking amount of the target user are processed 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 characterize the physiological state information generated by the target user based on the real-time drinking amount; the drinking feedback information of the target user is processed based on the target drinking amount warning model to generate warning threshold information; if the real-time drinking amount of the target user reaches the warning threshold information, warning information is generated, wherein the warning information includes sending drinking reminder information to the emergency contact preset by the target user.

[0083] The present application embodiment provides an electronic device, such as Figure 3 As shown, the electronic device 3 includes 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 via the bus 302; the memory 301 stores a computer program that can be run on the first processor 300, and when the first processor 300 runs the computer program, the intervention management method for the user's drinking amount provided in any of the aforementioned embodiments of the present application is executed.

[0084] The memory 301 may include a high-speed random access memory (RAM), and may 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 may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0085] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may 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 method for intervening and managing the drinking amount of a user disclosed in any implementation of the embodiment of the present application may be applied to the first processor 300, or implemented by the first processor 300.

[0086] The first processor 300 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the first processor 300. The above-mentioned first processor 300 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 readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor to be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 301, and the first processor 300 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.

[0087] The electronic device provided in the above-mentioned embodiment of the present application and the method for intervention management of user alcohol consumption provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application stored therein.

[0088] The present application embodiment provides a computer-readable storage medium, such as Figure 4 As shown, the computer-readable storage medium 401 stores a computer program, and when the computer program is read and executed by the second processor 402, the aforementioned method for intervening and managing the user's drinking amount is implemented.

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

[0090] It should be noted that, in this application, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or still includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0091] Each embodiment in the present application is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the intervention management method, electronic device, electronic device, and readable storage medium embodiment for evaluating the user's drinking amount, since they are basically similar to the embodiment of the intervention management method for the user's drinking amount, the description is relatively simple, and the relevant parts can be referred to the partial description of the embodiment of the intervention management method for the user's drinking amount.

[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 changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application shall be subject to the scope defined by the claims.

Claims

1. A method for intervening and managing a user's alcohol consumption, characterized in that: include: Obtaining target user's drinking usage information, target user's real-time physiological status information, target user's most recent physical examination report information, target user's identity information and target drinking volume warning model; Processing the target user's drinking usage information and the target user's real-time physiological state information to generate a drinking training factor, wherein the drinking training factor is used to process the target user's drinking habits; Processing the target user's most recent physical examination report information to generate the target user's initial alcohol consumption and the target user's initial alcohol consumption limit; 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 to process the drinking amount information of the target user; Processing the target user's initial drinking amount and the target user's initial drinking limit based on the drinking amount adjustment factor to generate the target user's target drinking amount and the target user's target drinking limit; Processing the target drinking amount 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, wherein the drinking feedback information of the target user is used to represent physiological state information generated by the target user based on the real-time drinking amount; Processing the target user's drinking feedback information based on the target drinking amount warning model to generate warning threshold information; If the target user's real-time drinking amount reaches the warning threshold information, a warning message is generated, wherein the warning message includes sending a drinking reminder message to the target user's preset emergency contact.

2. The method according to claim 1, characterized in that Obtain target drinking amount warning model, including: Get the preset training set; Preprocessing the preset training set to generate a preset training set with identification information, wherein the identification information is used to indicate that the user cannot drink alcohol; Processing the preset training set with identification information based on preset processing rules to generate a training set and a test set; The preset alcohol consumption prediction model is trained based on the training set and the test set to generate a target alcohol consumption prediction model.

3. The method according to claim 2, characterized in that The processing of the preset training set with identification information based on the preset processing rules to generate a training set and a test set includes: Acquire clinical data and physiological status factor information of a preset training set based on the physical examination report information of different users, wherein the clinical data includes factors that affect the amount of alcohol consumed by the user; Processing the clinical data based on a deep learning model to generate the physiological state factor information and the degree of influence of the clinical data; Processing the influence degree of the physiological status factor information and the case information based on the risk classification rules to generate a risk abnormality indicator; The preset training set with identification information is classified based on the risk anomaly index to generate a training set and a test set.

4. The method according to claim 1, characterized in that Processing the target user's drinking usage information and the target user's real-time physiological state information to generate a drinking training factor includes: Processing the target user's drinking usage information 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 amount of alcohol to be consumed by the target user; 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 characterize the physiological state information matched by different drinking amount information of the target user.

5. The method according to claim 1, characterized in that The identity information of the target user and the real-time physiological status information of the target user are processed to generate a drinking 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 the historical drinking amount information of the target user in different drinking scenes; Acquire target relapse prevention information that matches the target user's drinking scene information, wherein the target relapse prevention information is used to characterize physiological parameters that cause the target user to have aversion to drinking; Processing the target user's drinking scene information based on the target relapse prevention information to generate estimated drinking volume information of the target user; The estimated 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 amount adjustment factor.

6. The method according to claim 5, characterized in that Obtain target relapse prevention information that matches the target user's drinking scenario information, including: Acquire a preset anti-relapse drinking information set, wherein the preset anti-relapse drinking information is used to characterize physiological parameters that cause different users to have aversion to drinking; Processing the target user's drinking scene information based on the preset anti-relapse 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, the preset relapse prevention information set is processed to generate target relapse prevention information, wherein the target relapse prevention information is used to characterize the physiological parameters that cause the target user to have an aversion to drinking.

7. The method according to claim 5, characterized in that The target user's initial drinking amount and the target user's initial drinking limit amount are processed based on the drinking amount adjustment factor to generate the target user's target drinking amount and the target user's target drinking limit amount, including: Processing the target user's initial drinking amount and the target user's initial drinking limit based on the drinking amount adjustment factor to generate drinking amount difference information of the target user; Processing the target user's alcohol consumption difference information to generate the target user's historical alcohol consumption information, historical physiological parameter information matching the historical alcohol consumption information, the target user's real-time alcohol consumption information, and real-time physiological parameter information matching the real-time alcohol consumption information; The target user's real-time drinking amount information and the real-time physiological parameter information matching the real-time drinking amount information are processed based on the target user's historical drinking amount information and the historical physiological parameter information matching the historical drinking amount information to generate the target user's target drinking amount and the target limit drinking amount of the target user.

8. A device for intervening and managing a user's drinking amount, characterized in that: The device comprises: An acquisition module is used to acquire the target user's drinking usage information, the target user's real-time physiological status information, the target user's most recent physical examination report information, the target user's identity information and the target drinking amount warning model; The processing module is used to process the target user's drinking usage information and the target user's real-time physiological state information to generate a drinking training factor, wherein the drinking training factor is used to process the target user's drinking habits; process the target user's most recent physical examination report information to generate the target user's initial drinking amount and the target user's initial limit drinking amount; process the target user's identity information and the target user's real-time physiological state information to generate a drinking amount adjustment factor, wherein the drinking amount adjustment factor is used to process the target user's drinking amount information; adjust the target user's initial drinking amount and the target user's initial limit drinking amount based on the drinking amount adjustment factor. The target user's initial limit drinking amount is processed to generate a target drinking amount and a target limit drinking amount of the target user; the target drinking amount and the target limit drinking amount of the target user are processed 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 characterize the physiological state information generated by the target user based on the real-time drinking amount; the drinking feedback information of the target user is processed based on the target drinking amount warning model to generate warning threshold information; if the real-time drinking amount of the target user reaches the warning threshold information, warning information is generated, wherein the warning information includes sending drinking reminder information to the emergency contact preset by the target user.

9. An electronic device, characterized in that: include: a first processor; and a memory for storing executable instructions of the first processor; The first processor is configured to execute the method for intervening and managing the user's drinking amount according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the second processor, the method for intervening and managing the user's drinking amount according to any one of claims 1 to 7 is implemented.

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