Smoker smoking cessation skill learning and smoking cessation intervention system

Abstract

The invention provides a smoker smoking cessation skill learning and smoking cessation intervention system, and belongs to the technical field of medical care information, the method is applied to a mobile terminal, and the system realizes accurate smoking cessation intervention through multi-terminal cooperation. The wearable device end collects physiological indexes, sleep quality, motion data, psychological data and carbon monoxide concentration of a user in real time; the server constructs a neural network prediction model based on a deep learning framework, analyzes the collected data, and predicts the smoking probability of the user in a future period; the mobile phone application end pushes grading intervention content according to a prediction result; when the probability exceeds a threshold value, visual smoking hazard science popularization is pushed; when the probability is medium, real-time smoke control prompts such as acupuncture point pressing are pushed; and pushing interest transfer guidance when the probability is low. Multi-source data are integrated through an AI model, a'data acquisition-risk prediction-hierarchical intervention 'closed-loop system is constructed, a personalized smoking cessation strategy is dynamically generated, and a skill learning module and a smoking cessation process management function are synchronously provided.

Description

Technical Field

[0001] The present invention relates to the field of medical care information technology, and in particular to a system for learning and intervening in smoking cessation skills for smokers and a method for learning and intervening in smoking cessation skills for smokers. Background Art

[0002] Smoking is a major health hazard. Tobacco dependence is a chronic addictive disease, and quitting smoking is an extremely challenging process for smokers, requiring continuous motivation, effective supervision, and personalized guidance. Traditional smoking cessation methods have limited effectiveness, lack of real-time support, and difficulty in long-term adherence. For example, nicotine replacement therapy relies on external medication, which can easily lead to drug dependence and has wide individual differences in adaptability. Psychological counseling and intervention services are difficult to guarantee in terms of frequency and continuity, lacking a real-time dynamic adjustment mechanism. With the popularization of smartphones and the development of artificial intelligence technology, leveraging the powerful functions of smartphones and AI algorithms to assist in smoking cessation has become a new possibility.

[0003] Existing smartphones have relatively simple functions in assisting people to quit smoking. They rely solely on users to actively input smoking behavior data and lack objective monitoring methods. Most of them provide simple smoking cessation reminders or information push notifications, lacking comprehensive monitoring and personalized intervention of smokers' behavior, physiological and psychological states, making it difficult to provide accurate and effective smoking cessation support.

[0004] Therefore, how to achieve multi-dimensional data collection and improve the effectiveness of smoking cessation support has become a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The present invention provides a system for smokers to learn smoking cessation skills and provide smoking cessation intervention, which is used to solve the defects of the existing technology in terms of single data collection dimension and insufficient accuracy of smoking cessation assistance intervention, realize multi-dimensional data collection, and improve the effectiveness of smoking cessation support.

[0006] The present invention provides a smoking cessation skill learning and smoking cessation intervention system for smokers, comprising: The wearable device is used to collect the user's physiological data, sleep data, exercise data, psychological data and carbon monoxide concentration data; the physiological data includes blood pressure, blood oxygen and heart rate; the sleep data includes sleep duration, deep sleep time and light sleep time; The server is configured to use a pre-trained LSTM neural network model to predict the probability of the user smoking within a preset future time period based on the user's historical smoking history, the user's real-time environmental data, and physiological data; The mobile phone application is used to push multimedia content introducing the dangers of smoking, or push prompt information for controlling smoking urges, or push reminders of methods to divert smoking urges based on the smoking probability predicted by the server.

[0007] According to a smoking cessation skill learning and smoking cessation intervention system provided by the present invention, the mobile application end is further configured to: Collect the location data and voice data of the user in real time.

[0008] According to a smoking cessation skill learning and smoking cessation intervention system provided by the present invention, the mobile application end includes a smoking hazard motivation intervention module, and the smoking hazard motivation intervention module is configured to: Display health indicators related to smoking and the impact of smoking on the health indicators; Based on the physical examination data of the user, mark the health indicators with abnormal values.

[0009] According to a smoking cessation skill learning and smoking cessation intervention system provided by the present invention, the mobile application end further includes a smoking cessation process assistance module, and the smoking cessation process assistance module is configured to: Provide a smoking log template; the smoking log template includes smoking time, smoking place type, number of cigarettes smoked, and smoking trigger factors.

[0010] Based on the smoking log template and the filling input of the user, generate the smoking log of the user.

[0011] According to a smoking cessation skill learning and smoking cessation intervention system provided by the present invention, the mobile application end further includes a smoking cessation method guidance module, and the smoking cessation method guidance module is configured to: Present the smoking cessation method guidance content in a multimedia form.

[0012] According to a smoking cessation skill learning and smoking cessation intervention system provided by the present invention, the mobile application end further includes an incentive and social module, and the incentive and social module is configured to: Issue dynamic rewards based on the smoking cessation behavior data of the user; Push user cases to the user where the smoking cessation success rate is greater than a preset threshold and is in the same smoking cessation stage as the user.

[0013] The present invention also provides a smoking cessation skill learning and smoking cessation intervention method, which is applied to the smoking cessation skill learning and smoking cessation intervention system as described in any one of the above, and the smoking cessation skill learning and smoking cessation intervention method includes the following steps: The wearable device end collects the physiological data, sleep data, exercise data, psychological data, and carbon monoxide concentration data of the user; the physiological data includes blood pressure, blood oxygen, and heart rate; the sleep data includes sleep duration, deep sleep duration, and light sleep duration; The server predicts the probability of the user smoking within a preset future time period by using a pre-trained LSTM neural network model based on the user's historical smoking records, the user's real-time environmental data, and physiological data; Based on the probability of smoking predicted by the server, the mobile application terminal pushes multimedia content introducing the hazards of smoking, or pushes control smoking craving reminder information, or pushes a method reminder for diverting smoking craving.

[0014] According to a smoking cessation skill learning and smoking cessation intervention method provided by the present invention, the smoking cessation skill learning and smoking cessation intervention method further includes: The server regularly generates a smoking cessation achievement report based on the user's historical smoking records and smoking cessation behavior data; The server pushes the smoking cessation achievement report to the mobile application terminal; The mobile application terminal stores and displays the smoking cessation achievement report.

[0015] According to a smoking cessation skill learning and smoking cessation intervention method provided by the present invention, the mobile application terminal based on the probability of smoking predicted by the server, pushes multimedia content introducing the hazards of smoking, or pushes no-smoking reminder information, or pushes a smoking cessation alternative activity reminder, including: Based on the probability of smoking predicted by the server, determining the intervention strategy type, intervention intensity, and information push timing; the intervention strategy type includes pushing multimedia content introducing the hazards of smoking, pushing control smoking craving reminder information, and pushing a method reminder for diverting smoking craving; Based on the intervention strategy type and the intervention intensity, perform an intervention at the information push timing.

[0016] According to a smoking cessation skill learning and smoking cessation intervention method provided by the present invention, the smoking cessation skill learning and smoking cessation intervention method further includes: After the mobile application terminal receives the user's response to the intervention, it feeds back the response to the server; The server optimizes the LSTM neural network model based on the user's response.

[0017] The smoking cessation skill learning and smoking cessation intervention system provided by the present invention includes a wearable device end for collecting the user's physiological data, sleep data, exercise data, psychological data, and carbon monoxide concentration data; the physiological data includes blood pressure, blood oxygen, and heart rate; the sleep data includes sleep duration, deep sleep time, and light sleep time; a server end for predicting the probability of the user smoking within a preset period in the future using a pre-trained LSTM neural network model based on the user's historical smoking records, the user's real-time environmental data, and physiological data; and a mobile application end for pushing multimedia content introducing the hazards of smoking, or pushing control smoking craving reminder information, or pushing methods for diverting smoking craving reminders based on the probability of smoking predicted by the server end. The present invention collects multi-modal data through wearable devices, integrates the user's physiological data, sleep data, exercise data, psychological data, and carbon monoxide concentration data, providing a reliable data basis for smoking cessation assistance; the LSTM neural network model can capture long-distance dependencies, selectively retain the user's historical smoking cessation information, accurately predict the probability of the user smoking within a certain period in the future through the LSTM neural network model, and then perform precise auxiliary intervention in advance, improving the smoking cessation success rate and user compliance. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 is a schematic structural diagram of the smoking cessation skill learning and smoking cessation intervention system provided by the present invention; Figure 2 is one of the schematic flowcharts of the smoking cessation skill learning and smoking cessation intervention method provided by the present invention; Figure 3 is another schematic flowchart of the smoking cessation skill learning and smoking cessation intervention method provided by the present invention; Figure 4 is a schematic flowchart of the prediction model provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0021] It should be noted that in the description of the embodiments of the present invention, 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 not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element. The orientation or positional relationship indicated by terms such as "upper", "lower", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "mount", "connect" and "couple" shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection or an integral connection; it may be a mechanical connection or an electrical connection; it may be a direct connection or an indirect connection through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0022] The terms "first", "second", etc. in the present application are used to distinguish similar objects and are not used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same category, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and / or" means at least one of the connected objects, and the character " / " generally means an "or" relationship between the associated objects before and after.

[0023] Figure 1 is a schematic structural diagram of a smoking cessation skill learning and smoking cessation intervention system provided by the present invention. As Figure 1 shown, the smoking cessation skill learning and smoking cessation intervention system includes the following: The wearable device side is used to collect the user's physiological data, sleep data, exercise data, psychological data, and carbon monoxide concentration data; the physiological data includes blood pressure, blood oxygen, and heart rate; the sleep data includes sleep duration, deep sleep time, and light sleep time; The server side is used to predict the probability of the user smoking within a preset future period by using a pre-trained LSTM neural network model based on the user's historical smoking records, the user's real-time environmental data, and physiological data; The mobile application side is used to push multimedia content introducing the hazards of smoking, or push smoking craving control prompt information, or push reminders of methods to divert smoking cravings based on the probability of smoking predicted by the server side.

[0024] It should be noted that, Figure 1 The schematic diagram of an example architecture is shown. For the purpose of description, the drawn architecture is only an example of a suitable environment, and does not put forward any limitations on the scope of use or functions of this application. Nor should the system be interpreted as having any dependencies or requirements on Figure 1 any component shown or its combination.

[0025] To further enrich the data collection dimension, optionally, the mobile application side is further used to: Collect the user's location data and voice data in real time.

[0026] As Figure 1 shown, the wearable device side may include but is not limited to devices such as watches, bracelets, glasses, etc.; the wearable device side collects the wearer's physiological data (including but not limited to blood pressure data, blood oxygen data, heart rate data), sleep data (including but not limited to sleep duration, deep sleep time, light sleep time), exercise data, psychological data (including but not limited to stress value), and carbon monoxide concentration data through sensors. At the same time, the mobile application side also includes a multi-source data collection module, which collects data through mobile phone sensors (including but not limited to cameras, GPS / Beidou, microphones, accelerometers), collects environmental data (including but not limited to time, activity place type), voice data, behavior data (smoking action recognition); at the same time, through the interface connected to the wearable device, it receives the collection data transmitted back by the wearable device side; in addition, it can also receive data manually input by the user, such as the user's craving intensity for tobacco, emotion logs, smoking logs, etc. data.

[0027] The smoker's smoking cessation skill learning and smoking cessation intervention system provided by the embodiments of the present invention collects the user's exercise situation, sleep situation, activity place type, and interconnects with the wearable device to collect physiological data, sleep data, exercise data, psychological data, carbon monoxide concentration data, etc., enriches the data dimension, and provides targeted smoking cessation for smokers based on this data.

[0028] In an embodiment of the present invention, carbon monoxide concentration data is collected to calculate the nicotine concentration. The nicotine concentration includes environmental nicotine concentration and human body nicotine concentration. Among them, the environmental nicotine concentration is the nicotine concentration in the air, which is obtained by back-calculating the carbon monoxide concentration in the air. Specifically: Environmental nicotine concentration (ppm) = [CO]_{ppm} × K × MW_{nicotine} / MW_{CO}× CF; Wherein, [CO]_{ppm} represents the actually measured CO concentration (ppm) by the sensor at the wearable device end; K represents the tobacco release ratio coefficient (default 0.08, range 0.06 - 0.12); MW_{nicotine} represents the nicotine molecular weight (162.23 g / mol); MW_{CO} represents the CO molecular weight (28 g / mol); CF represents the environmental calibration factor (ventilated space = 0.3, enclosed space = 1.2).

[0029] The human body nicotine concentration is the nicotine concentration in the human body, which is obtained by back-calculating the carbon monoxide concentration exhaled by the human body.

[0030] In the specific implementation process, based on a preset detection frequency, the mobile application end or the wearable device end regularly reminds the user to measure the exhaled carbon monoxide concentration. After the user confirms to measure the carbon monoxide concentration, the user is prompted to blow air for 5 - 10 seconds, and a blowing countdown is displayed. When the countdown ends, it is displayed that the blowing is completed, and then the carbon monoxide concentration exhaled by the user is detected to calculate the nicotine concentration in the user's body.

[0031] When the human body metabolizes nicotine, the proportion of CO bound to hemoglobin is strongly correlated with the nicotine intake: {Exhaled CO (ppm)} ≈ 0.92 {Blood carboxyhemoglobin (COHb\%)}; and the relationship between COHb% and nicotine dose is as follows: COHb\% = 0.16 Nicotine intake (mg); then the in-vivo nicotine concentration (ppm) is calculated based on the following formula: In-vivo nicotine concentration (ppm) = (Exhaled CO - Environmental CO) ÷ (0.92 0.16) × α; Wherein, Exhaled CO represents the end-segment average value (anti-interference processing) of the user's 10-second blowing; Environmental CO represents the simultaneously measured air CO concentration (background deduction); α represents the individual metabolism factor (default 1.0, smokers = 1.2 - 1.5).

[0032] In one embodiment, a test reminder is pushed one hour after waking up each morning to avoid the morning CO peak. Specifically, the wearable device vibrates and displays a test reminder, such as "Please blow into the sensor." After the user confirms the start of the blow, a countdown animation is displayed, such as a progress bar with a 10-second countdown and a bar chart showing real-time airflow intensity feedback. After successful data collection, the user is notified of the completion of data collection through flashing lights and vibrations, and the corresponding calculated nicotine concentration data is displayed. If data collection fails, the user is prompted that insufficient airflow is present and needs to repeat the blow test. Optionally, after the wearable device collects the user's nicotine concentration, it is uploaded to and stored by the mobile application, which then generates weekly / monthly nicotine concentration curves to motivate the user.

[0033] Figure 4 It is a flow chart of the prediction model provided by the present invention, such as Figure 4 As shown, in this embodiment of the present invention, the mobile application uploads its own collected data, as well as data collected by the wearable device, to the server for prediction and calculation. The server's smoking cessation AI analysis and prediction engine uses historical smoking records, real-time environmental data, and physiological data uploaded by the mobile application to predict the probability that the user will crave a cigarette in the future. For example, if real-time environmental data indicates that the user is located in a bar and physiological data indicates an elevated heart rate, the pre-trained LSTM neural network model predicts an 85% probability that the user will smoke in the next five minutes.

[0034] In one embodiment, the neural network prediction model includes an input layer, a hidden layer, and an output layer. The input layer receives ten feature data including heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), stress index (Stress), ambient nicotine concentration (AirNic), body nicotine concentration (BodyNic), emotion, scene, location, and time period. Among them, emotion, scene, location, and time period need to be encoded. The specific encoding method used is not limited here. For example, one-hot encoding can be used. The hidden layer includes at least two fully connected layers and uses the ReLU activation function. The output layer contains a neuron that uses the sigmoid activation function to output the probability of smoking between 0 and 1. Specifically: The input features of the neural network prediction model include: heart rate x1, systolic blood pressure x2, diastolic blood pressure x3, stress index x4, environmental nicotine concentration x5, in-body nicotine concentration x6, mood x7 (categorical variable, M moods are encoded as M-dimensional vectors), scene x8 (categorical variable, P scenes are encoded as P-dimensional vectors), location x9 (categorical variable, Q locations are encoded as Q-dimensional vectors), time period x10 (categorical variable, R time periods are encoded as R-dimensional variables). If one-hot encoding is used, the actual input vector includes the features of the continuous part X = [x1, x2, x3, x4, x5, x6] and the encoded vectors of the categorical part X_class = [x7_onehot, x8_onehot, x9_onehot, x10_onehot]. The total input vector X_total = [X, X_class], and the dimension is D = 6 + M + P + Q + R. That is to say, the input layer contains D neurons; Hidden layer 1 contains H1 neurons (e.g., 128), and the ReLU activation function is used: Z1 = W1 X_total + b1, A1 = ReLU(Z1) = max(0, Z1); Hidden layer 2 contains H2 neurons (e.g., 64), and the ReLU activation function is used: Z2 = W2 A1 + b2, A2 = ReLU(Z2); The output layer contains 1 neuron, and the sigmoid activation function is used to map the prediction result to (0, 1): Z3 = W3 A2 + b3, the smoking probability Y_hat = sigmoid(Z3) = 1 / (1 + exp(-Z3)).

[0035] Optionally, the neural network prediction model uses binary cross-entropy as the loss function: L = - [ Y log(Y_hat) + (1 - Y) log(1 - Y_hat) ]; Among them, Y is the true label, that is, the true smoking probability value, which is normalized to a value between 0 and 1 through real-time reports by users or experimental settings in the specific implementation process.

[0036] Optionally, the Adam optimizer is used to accelerate the model learning process.

[0037] Optionally, the smoking cessation AI analysis and prediction engine is also used to identify the smoking cessation stage. Specifically, through a pre-trained SVM classifier, based on the user's behavioral data for 7 consecutive days, the smoking cessation stage the user is in is identified, such as the preparatory stage (preparing to quit smoking), the action stage (implementing smoking cessation behaviors), or the maintenance stage (the stage of successfully quitting smoking and preventing relapse).

[0038] Optionally, the smoking cessation AI analysis and prediction engine is also used to generate a smoking cessation evaluation. Specifically, AI algorithms such as time series analysis and prediction, natural language processing, classification, and clustering are used to evaluate the smoking cessation results for the day, this week, and this month.

[0039] Optionally, the smoking cessation AI analysis and prediction engine is also used for smoking cessation guidance. Specifically, deep learning algorithms are used to give reasonable smoking cessation guidance.

[0040] Optionally, the smoking cessation AI analysis and prediction engine is also used for smoking cessation optimization. Specifically, deep learning algorithms are used to evaluate the deficiencies in smoking cessation and make improvements.

[0041] In the embodiments of the present invention, according to the probability of smoking predicted by the server, multimedia content introducing the hazards of smoking, or smoking craving control prompt information, or a reminder of methods to divert smoking cravings is pushed to the user.

[0042] In the specific implementation process, an intervention strategy is determined based on a preset threshold. Specifically, if the predicted probability is greater than the first threshold, multimedia content introducing the hazards of smoking, such as a lung pathology animation, is pushed to the user; if the predicted probability is less than the first threshold and greater than the second threshold, smoking craving control prompt information, such as instant smoking control prompt information like mindful breathing and acupoint pressure, is pushed to the user; if the predicted probability is less than the second threshold, a reminder of methods to divert smoking cravings, such as an interest diversion guidance course like a sports challenge or a meditation course, is pushed to the user.

[0043] The smoking cessation skill learning and smoking cessation intervention system provided by the embodiments of the present invention includes a wearable device terminal for collecting the user's physiological data, sleep data, exercise data, psychological data, and carbon monoxide concentration data; the physiological data includes blood pressure, blood oxygen, and heart rate; the sleep data includes sleep duration, deep sleep duration, and light sleep duration; a server for predicting the probability of the user smoking within a preset period in the future based on the user's historical smoking records, the user's real-time environmental data, and physiological data using a pre-trained LSTM neural network model; and a mobile application terminal for pushing multimedia content introducing the hazards of smoking, or pushing control smoking craving prompt information, or pushing method reminders for diverting smoking cravings based on the probability of smoking predicted by the server. The present invention collects multi-modal data through wearable devices, integrates the user's physiological data, sleep data, exercise data, psychological data, and nicotine concentration data, providing a reliable data basis for smoking cessation assistance; the LSTM neural network model can capture long-distance dependencies, selectively retain the user's historical smoking cessation information, accurately predict the probability of the user smoking within a period of time in the future through the LSTM neural network model, and then perform precise auxiliary intervention in advance to improve the smoking cessation success rate and user compliance.

[0044] In an alternative embodiment, the mobile application terminal includes a smoking hazard motivation intervention module, and the smoking hazard motivation intervention module is used for: Displaying health indicators related to smoking and the impact of smoking on the health indicators; Marking the health indicators with abnormal values based on the user's physical examination data.

[0045] In the embodiments of the present invention, after the user completes the physical examination, the smoking hazard motivation intervention module updates the physical examination data in real time, displays the health indicators related to smoking, and explains the impact of smoking on the corresponding health indicators, marks the health indicators with abnormal values such as blood pressure, blood oxygen, blood lipids, heart, and endocrine in the physical examination, and can indicate whether the health indicator is higher or lower than the normal value through an upward or downward arrow, and can identify the abnormal indicators through highlighting / different colors.

[0046] Optionally, the smoking hazard motivation intervention module is further used for pushing content related to the hazards of smoking. Specifically, in the forms of short videos, animations, audios, pictures, texts, etc., using "World No Tobacco Day", authoritative expert lectures, real stories, successful smoking cessation cases, etc., to spread the hazards of smoking.

[0047] The smoking cessation skill learning and smoking cessation intervention system provided by the embodiments of the present invention prompts the user about the impact of smoking on health through the smoking hazard motivation intervention module, thereby further promoting the user to quit smoking and providing precise and effective smoking cessation support for the user.

[0048] In an alternative embodiment, the mobile application further includes a smoking cessation process assistance module, which is configured to: Provide a smoking log template; the smoking log template includes smoking time, smoking location type, number of cigarettes smoked, and smoking trigger factors.

[0049] Generate the user's smoking log based on the smoking log template and the user's filled-in input.

[0050] In the embodiment of the present invention, the smoking cessation process assistance module provides a smoking log template, and the user can fill in each smoking record based on the smoking log template, thereby providing a data basis for predicting the user's smoking probability in the future. Specifically, fill in the daily smoking record according to the standardized smoking log template, including but not limited to smoking time, location type, number of cigarettes smoked, degree of psychological addiction before smoking, smoking trigger factors, etc., to generate a smoking log record.

[0051] Optionally, the smoking cessation process assistance module is further configured to record the user's smoking cessation cycle. Specifically, record information such as the user's smoking cessation start time, smoking cessation cycle, and smoking cessation plan.

[0052] Optionally, the smoking cessation process assistance module is further configured to assist the user in establishing a good smoking cessation environment. For example, prompt the user to stay away from smoking environments, such as public smoking areas, dinners, bars, KTVs, people who are smoking, etc.; prompt the user to remove smoking items, such as ashtrays, lighters, cigarette cases, etc.

[0053] Optionally, the smoking cessation process assistance module is further configured to link with other users to supervise the smoker. Specifically, based on the set smoking cessation supervisor and effective supervision strategy, allow the smoker's relatives and friends to obtain the smoker's smoking records, health indicators, physiological data, psychological data, etc. through the associated account, thereby assisting in the supervision and psychological counseling of the smoker.

[0054] Optionally, the smoking cessation process assistance module is further configured to prompt the user to learn smoking cessation skills. Specifically, based on the set frequency and time, prompt and assist the user in learning smoking cessation skills.

[0055] Optionally, the smoking cessation process assistance module is further configured to prepare alternative activities. For example, through mobile phone prompts, reminders, etc., prompt the user to take alternative activities (such as drinking ice water, taking deep breaths) to keep the brain busy, distract and reduce the craving for smoking.

[0056] The smoker smoking cessation skill learning and smoking cessation intervention system provided by the embodiment of the present invention generates a smoking log through the assistance of the smoking cessation process assistance module, thereby providing a data basis for predicting the smoking probability in the future.

[0057] In an alternative embodiment, the mobile application further includes a smoking cessation method guidance module, which is configured to: Present the content of smoking cessation method guidance in a multimedia form.

[0058] Specifically, present the content of smoking cessation method guidance from authoritative institutions and experts in the forms of short videos, animations, audios, pictures, texts, etc. It includes avoiding public smoking environments and people, changing the life and work states and scenes associated with smoking, how to avoid temporarily facing smoking scenes, delaying the craving for smoking actions, and unbinding the craving for smoking.

[0059] Optionally, the mobile application end further includes a smoking cessation assistance resource module, which is used to provide smoking cessation assistance resources, filter out the content that triggers the craving for smoking according to the preferences of smokers, and the smoking cessation AI analysis and prediction engine on the server side intelligently recommends suitable high-quality content. For example, three-minute breathing exercises, three-minute Baduanjin, three-minute Taijiquan, music, cross talk, talk shows, and the suitable content jumps to the corresponding APP for display.

[0060] The smoking cessation skill learning and smoking cessation intervention system provided by the embodiments of the present invention guides users on specific smoking cessation methods through the smoking cessation method guidance module, and provides users with rich and scientific smoking cessation method guidance.

[0061] In an optional embodiment, the mobile application end further includes an incentive and social module, and the incentive and social module is used for: Issuing dynamic rewards based on the smoking cessation behavior data of the user; Pushing user cases with a smoking cessation success rate greater than a preset threshold and in the same smoking cessation stage as the user.

[0062] In the embodiments of the present invention, according to the user's smoking cessation behavior data, determine the user's smoking cessation milestones, such as 24 hours without smoking, and then issue dynamic rewards, such as virtual titles, point redemption, etc.

[0063] In the embodiments of the present invention, based on similar smoking cessation stages / population characteristics, push user cases with high success rates, so as to motivate users to quit smoking.

[0064] The smoking cessation skill learning and smoking cessation intervention system provided by the embodiments of the present invention motivates users to quit smoking by issuing dynamic rewards and pushing user cases with high success rates, and further improves the user's smoking cessation success rate.

[0065] In an optional embodiment, the mobile application end further includes a dynamic real-time smoking cessation intelligent intervention module, including: Real-time intervention library: including but not limited to deep breathing guidance videos, cognitive behavioral therapy (CBT) micro-courses, alternative behavior suggestions (such as drinking ice water), emergency contact calls, etc.; Intervention methods: Provide targeted smoking cessation measures to smokers in real time based on their location, physical and mental condition through mobile phones or wearable devices; make full use of holographic means (sound, image, video, vibration, information interaction, etc.) of mobile phones or wearable devices (watches, bracelets, glasses) to intervene and provide guidance; Strategy matching rules: If the probability of smoking is greater than a preset threshold (such as 80%), an instant stress-relieving game will be pushed; if it is detected that the user is at a dinner party, a prompt message to control the urge to smoke will pop up on the mobile phone or wearable device; if it is identified that the user is in the "action period" and the user's hand-held smoking action is detected, the mobile phone camera will be activated to guide the behavior interruption.

[0066] Figure 3 This is the second flow chart of the method for smokers to learn smoking cessation skills and to intervene in smoking cessation provided by the present invention. Figure 3 As shown, in an optional embodiment, the mobile app also includes a smoking cessation behavior learning and evaluation module, which uses LSTM or Transformer models to analyze user behavior data and improve AI smoking cessation measures. Specifically, it conducts periodic smoking cessation behavior assessments, such as hourly assessments; extracts and generates appropriate smoking cessation measures for smokers; and updates smoking cessation measures based on smoking cessation results to address imperfections in smoking cessation plans.

[0067] In an optional embodiment, the server also includes a smoking cessation knowledge base for storing knowledge content related to smoking cessation, classifying and labeling it, regularly updating the content of the smoking cessation knowledge base, incorporating the latest research results, eliminating outdated methods, and continuously improving the knowledge base in iterations. Specifically, the knowledge base content includes basic cognitive knowledge, such as popular science on the hazards of smoking and scientific principles of smoking cessation; practical method knowledge, such as personalized smoking cessation plans, behavioral intervention techniques, and relapse prevention strategies; resource support knowledge, medical resource navigation, psychological support channels, auxiliary content, and tool recommendations. The knowledge base content also includes knowledge base classification, which classifies and labels the smoking cessation knowledge base according to male smoking cessation, female smoking cessation, young and middle-aged smoking cessation, elderly smoking cessation, and youth prevention.

[0068] The following describes the smoker quitting smoking skills learning and smoking cessation intervention method provided in the embodiment of the present application. The smoker quitting smoking skills learning and smoking cessation intervention method described below is applied to any of the smoker quitting smoking skills learning and smoking cessation intervention systems described above.

[0069] Figure 2 This is one of the flow charts of the method for smokers to learn smoking cessation skills and to intervene in smoking cessation provided by the present invention, such as Figure 2 As shown, the smoker's smoking cessation skills learning and smoking cessation intervention methods may include but are not limited to: S210, the wearable device collects the user's physiological data, sleep data, exercise data, psychological data, and carbon monoxide concentration data; the physiological data includes blood pressure, blood oxygen, and heart rate; the sleep data includes sleep duration, deep sleep time, and light sleep time; S220, based on the user's historical smoking records, the user's real-time environmental data, and physiological data, the server uses a pre-trained LSTM neural network model to predict the probability of the user smoking within a preset future period; S230, based on the probability of smoking predicted by the server, the mobile application pushes multimedia content introducing the harms of smoking, or pushes prompt information for controlling the craving to smoke, or pushes reminders of methods for diverting the craving to smoke.

[0070] In the smoker smoking cessation skill learning and smoking cessation intervention method provided by the embodiments of the present invention, the wearable device collects the user's physiological data, sleep data, exercise data, psychological data, and carbon monoxide concentration data; the physiological data includes blood pressure, blood oxygen, and heart rate; the sleep data includes sleep duration, deep sleep time, and light sleep time; based on the user's historical smoking records, the user's real-time environmental data, and physiological data, the server uses a pre-trained LSTM neural network model to predict the probability of the user smoking within a preset future period; based on the probability of smoking predicted by the server, the mobile application pushes multimedia content introducing the harms of smoking, or pushes prompt information for controlling the craving to smoke, or pushes reminders of methods for diverting the craving to smoke. Through the wearable device in the embodiments of the present invention, multi-modal data is collected, integrating the user's physiological data, sleep data, exercise data, psychological data, and nicotine concentration data, providing a reliable data basis for smoking cessation assistance; the LSTM neural network model can capture long-distance dependencies, selectively retain the user's historical smoking cessation information, accurately predict the probability of the user smoking within a certain period in the future through the LSTM neural network model, and then perform precise auxiliary intervention in advance, improving the smoking cessation success rate and user compliance.

[0071] In an optional embodiment, the smoker smoking cessation skill learning and smoking cessation intervention method further includes: The server regularly generates a smoking cessation achievement report based on the user's historical smoking records and smoking cessation behavior data; The server pushes the smoking cessation achievement report to the mobile application; The mobile application stores and displays the smoking cessation achievement report.

[0072] In an embodiment of the present invention, the AI analysis and prediction engine of the server performs AI operations regularly. For example, AI operations are performed at 0:00 every day to evaluate the smoking cessation results of each smoker on the previous day. The server pushes the smoking cessation results to the application system on the mobile device, and the smoking cessation result report is presented on the mobile device / wearable device. For example, "Congratulations! Today is your 3rd smoke-free day! You reported 5 strong urges today, but successfully coped with 4 of them (success rate 80%), which is an improvement compared to yesterday (success rate 60%)! The stress level increased around 3 pm. It is recommended to try deep breathing exercises at this time tomorrow. Keep it up!"

[0073] The method for smokers to learn smoking cessation skills and smoking cessation intervention provided by the embodiment of the present invention regularly generates a smoking cessation result report based on the user's smoking log records and behavior data, intuitively reflects the user's smoking cessation results, and effectively promotes the user's smoking cessation.

[0074] In an alternative embodiment, the mobile application based on the probability of smoking predicted by the server pushes multimedia content introducing the hazards of smoking, or pushes prompt messages for controlling the craving for smoking, or pushes reminders of methods for diverting the craving for smoking, including: Based on the probability of smoking predicted by the server, determine the type of intervention strategy, the intensity of intervention, and the timing of information push; the types of intervention strategies include pushing multimedia content introducing the hazards of smoking, pushing prompt messages for controlling the craving for smoking, and pushing reminders of methods for diverting the craving for smoking; Based on the type of intervention strategy and the intensity of intervention, perform intervention at the timing of information push.

[0075] The method for smokers to learn smoking cessation skills and smoking cessation intervention provided by the embodiment of the present invention adopts different intervention strategies according to different smoking probabilities, so as to perform precise auxiliary intervention in advance, improve the smoking cessation success rate and user compliance.

[0076] In an alternative embodiment, the method for smokers to learn smoking cessation skills and smoking cessation intervention further includes: After the mobile application receives the user's response to the intervention, it feeds back the response to the server; The server optimizes the LSTM neural network model based on the user's response.

[0077] In an embodiment of the present invention, based on the user's response to the intervention strategy (such as ignoring or completing), the LSTM neural network model is optimized, so as to continuously optimize the prediction accuracy.

[0078] For example, when the user is detected to enter a bar through the mobile phone GPS / Beidou positioning and the sound of a lighter is detected through the mobile phone microphone, the server calculates based on the comprehensive historical data that the probability of the user's craving for smoking is increased to 85%, exceeding the set threshold, and immediately pushes a reminder for a smoking cessation alternative activity, such as a 15-second deep breathing animation, and retrieves relevant knowledge about "the health benefits after successful smoking cessation" from the knowledge base. If the user chooses to perform deep breathing, the user's effective response is recorded and corresponding points are rewarded.

[0079] In an alternative embodiment, the server also performs risk early warning based on the user's historical physiological data, so as to perform auxiliary intervention in advance. For example, when it is detected that the user's sleep quality has declined for 3 consecutive days and the user's work schedule is marked with "project deadline", the AI generates a high-risk early warning, pushes a "stress management CBT course" in advance and recommends contacting a smoking cessation counselor.

[0080] Through the description of the above implementation manners, those skilled in the art can clearly understand that each implementation manner can be realized by means of software plus a necessary general hardware platform, and of course, it can also be realized by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A smoking cessation skill learning and smoking cessation intervention system, characterized in that, Including: The wearable device side is used to collect the user's physiological data, sleep data, exercise data, psychological data, and carbon monoxide concentration data; The physiological data includes blood pressure, blood oxygen, and heart rate; the sleep data includes sleep duration, deep sleep time, and light sleep time; The server side is used to predict the probability of the user smoking within a preset future period by using a pre-trained LSTM neural network model based on the user's historical smoking records, the user's real-time environmental data, and physiological data; The mobile application side is used to push multimedia content introducing the hazards of smoking, or push control smoking craving reminder information, or push a reminder of methods to divert smoking cravings based on the probability of smoking predicted by the server side.

2. The smoking cessation skill learning and smoking cessation intervention system according to claim 1, characterized in that The mobile application side is further used for: Real-time collection of the user's location data and voice data.

3. The smoking cessation skill learning and smoking cessation intervention system according to claim 1, wherein The mobile application side includes a smoking hazard motivation intervention module, and the smoking hazard motivation intervention module is used for: Displaying health indicators related to smoking, and the impact of smoking on the health indicators; Marking health indicators with abnormal values based on the user's physical examination data.

4. The smoker smoking cessation skill learning and smoking cessation intervention system according to claim 1, characterized in that, The mobile application side further includes a smoking cessation process assistance module, and the smoking cessation process assistance module is used for: Providing a smoking log template; the smoking log template includes smoking time, smoking place type, number of cigarettes smoked, and smoking trigger factors; Generating the user's smoking log based on the smoking log template and the user's filled-in input.

5. The smoking cessation skill learning and smoking cessation intervention system according to claim 1, characterized in that, The mobile application side further includes a smoking cessation method guidance module, and the smoking cessation method guidance module is used for: Presenting smoking cessation method guidance content in a multimedia form.

6. The smoking cessation skill learning and smoking cessation intervention system according to claim 1, wherein The mobile application side further includes an incentive and social module, and the incentive and social module is used for: Issuing dynamic rewards based on the user's smoking cessation behavior data; Pushing user cases with a smoking cessation success rate greater than a preset threshold and in the same smoking cessation stage as the user to the user.

7. A method for smokers to learn smoking cessation skills and smoking cessation intervention, characterized in that, Applied to the smoker smoking cessation skill learning and smoking cessation intervention system as described in claim 1, the smoker smoking cessation skill learning and smoking cessation intervention method includes: The wearable device side collects the user's physiological data, sleep data, exercise data, psychological data, and carbon monoxide concentration data; the physiological data includes blood pressure, blood oxygen, and heart rate; the sleep data includes sleep duration, deep sleep time, and light sleep time; The server side predicts the probability of the user smoking within a preset future period by using a pre-trained LSTM neural network model based on the user's historical smoking records, the user's real-time environmental data, and physiological data; The mobile application side pushes multimedia content introducing the hazards of smoking, or pushes control smoking craving reminder information, or pushes a reminder of methods to divert smoking cravings based on the probability of smoking predicted by the server side.

8. The smoking cessation skill learning and smoking cessation intervention method according to claim 7, characterized in that, The smoker smoking cessation skill learning and smoking cessation intervention method further includes: The server side regularly generates a smoking cessation achievement report based on the user's historical smoking records and smoking cessation behavior data; The server side pushes the smoking cessation achievement report to the mobile application side; The mobile application side stores and displays the smoking cessation achievement report.

9. The method for smokers to learn smoking cessation skills and smoking cessation intervention according to claim 7, characterized in that, Based on the probability of smoking predicted by the server, the mobile application terminal pushes multimedia content introducing the hazards of smoking, or pushes smoking craving control prompt information, or pushes reminders of methods to divert smoking cravings, including: Based on the probability of smoking predicted by the server, determine the type of intervention strategy, the intensity of intervention, and the information push timing; the types of intervention strategies include pushing multimedia content introducing the hazards of smoking, pushing smoking craving control prompt information, and pushing reminders of methods to divert smoking cravings; Based on the type of intervention strategy and the intensity of intervention, conduct an intervention at the information push timing.

10. The smoking cessation skill learning and smoking cessation intervention method according to claim 9, characterized in that, The smoker's smoking cessation skill learning and smoking cessation intervention method further includes: After the mobile application terminal receives the user's response to the intervention, it feeds back the response to the server; Based on the user's response, the server optimizes the LSTM neural network model.

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