A nicotine delivery control method, system, electronic device, and storage medium

Abstract

This invention discloses a nicotine delivery control method, system, electronic device, and storage medium. The method includes: acquiring baseline physiological signal data of a user in a resting state via a biosignal acquisition module; establishing an individual physiological baseline value for the user based on the baseline physiological signal data via an edge computing processing module; acquiring the user's current physiological signal data in real time via the biosignal acquisition module during user use; determining the user's current emotional state based on the current physiological signal data and the individual physiological baseline value via the edge computing processing module, and generating a heating strategy instruction corresponding to the current emotional state; and receiving and executing the heating strategy instruction via a dynamic heating execution module to adjust the heating method of the cigarette, thereby achieving nicotine delivery control. The technical solution of this invention achieves personalized nicotine delivery by identifying the user's emotional state during use and dynamically adjusting the nicotine delivery rate accordingly.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a nicotine delivery control method, system, electronic device and storage medium. Background Technology

[0002] With the advancement of the global tobacco control movement and the increasing health awareness of consumers, heated cigarettes (heated non-combustible tobacco products), as a new type of tobacco product that can significantly reduce the release of harmful substances, have seen a continuous increase in market share in recent years. However, nicotine, the core component of heated cigarettes, remains highly addictive, and users will still develop physiological and psychological dependence after long-term use. How to meet users' needs while achieving scientific management of nicotine intake has become a technical problem to be solved in this field.

[0003] Currently, nicotine delivery control methods in existing heated cigarette devices mainly fall into two categories. The first category uses a constant power or constant temperature heating mode, where the device heats the cigarette with a fixed heating curve, and the nicotine release rate remains constant. The second category provides limited power level adjustments (such as low, medium, and high), allowing the user to manually select the heating intensity, but its adjustment logic is independent of the user's real-time physiological state.

[0004] The aforementioned existing technologies have significant drawbacks. Users (especially those transitioning from traditional cigarettes) often unconsciously overuse nicotine during emotional fluctuations, leading to a short-term surge in nicotine intake. This not only exacerbates health risks but also further strengthens dependence. Therefore, there is an urgent need for a method that can dynamically regulate the nicotine delivery rate. Summary of the Invention

[0005] This invention provides a nicotine delivery control method, system, electronic device, and storage medium, which identifies the user's emotional state during use and dynamically adjusts the nicotine delivery rate accordingly to achieve personalized nicotine delivery.

[0006] In a first aspect, embodiments of the present invention provide a nicotine delivery control method, applied to heated cigarette devices, comprising: The biosignal acquisition module collects baseline physiological signal data of the user in a resting state and sends the baseline physiological signal data to the edge computing processing module. The edge computing processing module establishes the user's individual physiological baseline values ​​based on the baseline physiological signal data. During user operation, the biosignal acquisition module collects the user's current physiological signal data in real time and sends the current physiological signal data to the edge computing processing module. The edge computing processing module determines the user's current emotional state based on the current physiological signal data and the individual physiological baseline value. The edge computing processing module generates a heating strategy instruction corresponding to the current emotional state. The dynamic heating execution module receives and executes the heating strategy instructions to adjust the heating method of the cigarette, thereby controlling the delivery of nicotine.

[0007] Optionally, the biosignal acquisition module includes a PPG sensor array embedded in the grip area of ​​the cigarette; the edge computing processing module has a built-in AI chip, deploys an emotion recognition neural network model, and is connected to the biosignal acquisition module and the dynamic heating execution module respectively; the dynamic heating execution module includes a heating element and its driving circuit for heating the cigarette.

[0008] Optionally, the baseline physiological signal data includes at least one of heart rate, heart rate variability, and blood oxygen saturation; the individual physiological baseline values ​​include at least one of the following: the frequency domain index ratio of heart rate variability, the mean resting heart rate, and the mean resting blood oxygen saturation.

[0009] Optionally, the method further includes: if the edge computing processing module detects that the current physiological baseline value corresponding to the current physiological signal data is higher than the individual physiological baseline value and exceeds a first preset threshold, then the user's current emotional state is determined to be a high arousal stress state; if the edge computing processing module detects that the current physiological baseline value corresponding to the current physiological signal data is lower than the individual physiological baseline value and exceeds a second preset threshold, then the user's current emotional state is determined to be a low arousal fatigue state.

[0010] Optionally, the method further includes: if the current emotional state is a high arousal pressure state, the edge computing processing module generates a slow-release mode instruction; the slow-release mode instruction is used to control the dynamic heating execution module to reduce the heating power or use pulse heating; if the current emotional state is a low arousal fatigue state, the edge computing processing module generates a fast response mode instruction; the fast response mode instruction is used to control the dynamic heating execution module to increase the initial heating rate.

[0011] Optionally, the method further includes: using the edge computing processing module to calculate the user's total nicotine intake within a preset time window; the total nicotine intake is determined by the heating time and the number of puffs; using the edge computing processing module to determine whether the user is about to exceed a preset individual safety threshold based on the real-time deviation trend between the current physiological signal data and the individual physiological baseline; the individual safety threshold is pre-stored based on the user's individual physiological baseline; when it is determined that the individual safety threshold is about to be exceeded, the edge computing processing module sends a reminder command to the feedback and warning module, and / or sends a command to the dynamic heating execution module to reduce the heating temperature.

[0012] Secondly, embodiments of the present invention provide a nicotine delivery control system applied to heated cigarette devices, comprising: The biosignal acquisition module is used to acquire baseline physiological signal data of the user in a resting state and send the baseline physiological signal data to the edge computing processing module; The edge computing processing module is used to establish the user's individual physiological baseline value based on the baseline physiological signal data; During user use, the biosignal acquisition module is used to collect the user's current physiological signal data in real time and send the current physiological signal data to the edge computing processing module; The edge computing processing module is used to determine the user's current emotional state based on the current physiological signal data and the individual physiological baseline value; The edge computing processing module is used to generate heating strategy instructions corresponding to the current emotional state; The dynamic heating execution module is used to receive and execute the heating strategy instructions to adjust the heating method of the cigarette in order to achieve nicotine delivery control.

[0013] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the nicotine delivery control method according to any embodiment of the present invention.

[0014] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the nicotine delivery control method according to any embodiment of the present invention.

[0015] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the nicotine delivery control method described in any embodiment of the present invention.

[0016] The technical solution of this invention uses a biosignal acquisition module to collect baseline physiological signal data of the user in a resting state and sends this baseline physiological signal data to an edge computing processing module. This lays the foundation for establishing a personalized physiological baseline and accurate emotion recognition, solving the problem of general thresholds not matching individual differences. The edge computing processing module establishes the user's individual physiological baseline value based on the baseline physiological signal data, ensuring that each emotion judgment is a deviation analysis relative to the user's normal level, significantly improving recognition accuracy. During user use, the biosignal acquisition module collects the user's current physiological signal data in real time and sends this data to the edge computing processing module, achieving continuous and imperceptible monitoring of the user's physiological state and providing real-time data support for dynamic adjustment. The edge computing processing module determines the user's current emotional state based on the current physiological signal data and the individual physiological baseline value; it also generates a heating strategy instruction corresponding to the current emotional state, converting the emotion recognition result into executable control parameters, achieving an automated closed loop from perception to decision-making. The dynamic heating execution module receives and executes the heating strategy instructions to adjust the heating method of the cigarette, thereby controlling the delivery of nicotine. This ultimately achieves personalized nicotine delivery, effectively avoiding excessive intake caused by emotional fluctuations, while optimizing the user experience in different scenarios and improving the scientific nature and safety of health management.

[0017] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a nicotine delivery control method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a nicotine delivery control method provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of a nicotine delivery control system provided in Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the nicotine delivery control method of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] Example 1 Figure 1 The flowchart illustrates a nicotine delivery control method according to Embodiment 1 of the present invention. This embodiment is applicable to situations involving dynamic adjustment of nicotine delivery. This method can be executed by a nicotine delivery control system, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes: S110. The biosignal acquisition module collects the user's baseline physiological signal data at rest and sends the baseline physiological signal data to the edge computing processing module.

[0023] In this embodiment, the heated cigarette device refers to a portable electronic device that uses an electric heating element to heat a specially made cigarette at a low temperature (usually below 350°C), causing the nicotine, flavorings, and other components in the cigarette to volatilize and form an aerosol for the user to inhale. Unlike traditional combustible cigarettes, heated cigarette devices do not produce tar or a large number of harmful substances. The biosignal acquisition module refers to a sensor unit embedded inside the device for non-invasively acquiring the user's physiological signals. In this embodiment, the module is preferably a photoplethysmography (PPG) sensor array, which can be attached to the grip area of ​​the device to emit light of a specific wavelength (such as green or red light) and receive changes in reflected light intensity to extract signals such as heart rate, heart rate variability, and blood oxygen saturation in real time. The user refers to an individual using the heated cigarette device, typically a smoker transitioning from traditional cigarettes or a consumer who wishes to manage their nicotine intake scientifically.

[0024] In this embodiment, the resting state refers to a state where the user is relaxed, without significant movement or emotional fluctuations, such as when waking up in the morning before activity, or sitting quietly with eyes closed for more than 5 minutes. In the resting state, the user's autonomic nervous system tends to be balanced, and the physiological signals collected at this time can serve as a personalized baseline. The baseline physiological signal data can refer to the raw physiological parameters collected in the resting state, including at least one of heart rate, heart rate variability, and blood oxygen saturation. These data represent the normal physiological level of an individual user and will be used to construct individual physiological baseline values. The edge computing processing module can refer to the AI ​​chip and supporting processing unit built into the main control board of the smoking device, which is equipped with a lightweight emotion recognition neural network model. This module is responsible for receiving and processing physiological signals, executing emotion recognition algorithms, and generating heating strategy instructions. Edge computing means that all calculations are completed locally in the smoking device, without the need for a network connection, protecting user privacy and reducing latency.

[0025] Specifically, when a user uses the device for the first time or actively enters calibration mode, the system prompts the user to remain still (e.g., by flashing an indicator light or vibration, indicating 2 minutes of stillness). The biosignal acquisition module (such as a PPG sensor array) begins acquiring reflected light signals from the user's fingers or palm at a sampling rate of 50-100Hz. The raw light signal is processed by a built-in amplifier and analog-to-digital converter, and then an adaptive filtering algorithm removes motion artifacts and ambient light interference to obtain a clean PPG waveform. From this waveform, the system can extract the following features.

[0026] Instantaneous heart rate (HR) is calculated by detecting the intervals between adjacent peaks of the PPG waveform (i.e., the heartbeat interval, RR interval). Time-domain and frequency-domain analyses are performed on a continuous RR interval sequence (e.g., 60 seconds) to obtain heart rate variability (HRV). Frequency-domain analysis uses Fast Fourier Transform to extract low-frequency power (LF, 0.04-0.15Hz) and high-frequency power (HF, 0.15-0.4Hz), and the LF / HF ratio is calculated, reflecting the balance between the sympathetic and parasympathetic nervous systems. Blood oxygen saturation is estimated by the ratio of the DC to AC components of the PPG waveform. After acquisition, all baseline physiological signal data are encapsulated into data packets and sent to the edge computing processing module via an internal bus (such as I2C or SPI).

[0027] By collecting personalized physiological signal baselines from users in a resting state, all subsequent emotion judgments are referenced to this individual baseline value, avoiding misjudgments caused by universal thresholds and significantly improving the accuracy and adaptability of emotion recognition. Meanwhile, the PPG sensor array is embedded in the grip area of ​​the smoking device, eliminating the need for additional wearable equipment and making it natural and convenient to use.

[0028] As an optional implementation of this disclosure, the baseline physiological signal data includes at least one of heart rate, heart rate variability, and blood oxygen saturation; the individual physiological baseline values ​​include at least one of the following: the frequency domain index ratio of heart rate variability, the mean resting heart rate, and the mean resting blood oxygen saturation.

[0029] In this embodiment of the disclosure, heart rate (HR) can refer to the number of times the heart beats per minute, measured in bpm. A normal adult resting heart rate is approximately 60-100 bpm. Heart rate increases after emotional excitement, anxiety, or nicotine intake. Heart rate variability (HRV) can refer to the small fluctuations between successive heartbeat cycles. Heart rate variability reflects the autonomic nervous system's ability to regulate the heart. High HRV generally indicates good physiological regulation, while low HRV is associated with stress and fatigue. Blood oxygen saturation (… Blood oxygen saturation can refer to the percentage of oxyhemoglobin in the total hemoglobin in the blood, with a normal range of 95%-100%. Long-term heavy smoking may lead to a slight decrease in blood oxygen levels. Frequency domain ratios can refer to the LF / HF ratio. LF (low-frequency power) primarily represents sympathetic nerve activity, while HF (high-frequency power) primarily represents parasympathetic nerve activity. An elevated LF / HF ratio indicates sympathetic dominance, corresponding to stress and anxiety. A decreased LF / HF ratio indicates parasympathetic dominance, corresponding to relaxation and fatigue. Mean resting heart rate refers to the average heart rate measured continuously for 1-3 minutes at rest. Mean resting blood oxygen saturation refers to the average blood oxygen saturation measured continuously at rest.

[0030] Specifically, after the PPG sensor array acquires the raw signal, the edge computing processing module runs signal processing algorithms to calculate the aforementioned indicators. For example, for a 60-second RR interval sequence, median filtering is used to remove outliers, and then the average heart rate is calculated. A frequency domain transformation is performed on the same sequence to obtain LF and HF power values, and then the LF / HF ratio is calculated. All indicators are stored in the module's non-volatile memory as components of individual physiological baseline values.

[0031] S120. The edge computing processing module establishes the user's individual physiological baseline value based on the baseline physiological signal data.

[0032] In this embodiment of the disclosure, individual physiological baseline values ​​can refer to a set of personalized physiological reference values ​​established for a specific user in a resting state. Individual physiological baseline values ​​serve as a benchmark for subsequent comparisons of emotional states. For example, a user's resting LF / HF ratio is 1.2, resting heart rate is 72 bpm, and resting blood oxygen saturation is 98%; these values ​​constitute their individual physiological baseline values.

[0033] Specifically, after receiving the baseline physiological signal data, the edge computing processing module performs the following sub-steps: Outlier removal (e.g., removing sudden changes caused by instantaneous actions) is performed on the data from multiple samplings (e.g., three consecutive samplings, each lasting one minute with a 30-second interval). The mean or median of each indicator is calculated. For example, the baseline value is obtained by averaging the LF / HF ratio from the three samplings. The baseline value is obtained by averaging the heart rate data. The baseline value is obtained by averaging the blood oxygen data. These baseline values ​​are packaged into an individual physiological baseline configuration file, stored in the module's embedded flash memory, and marked with the creation time. If the user subsequently resets the smoking device or actively requests recalibration, the original baseline values ​​are overwritten.

[0034] The establishment of individual physiological baseline values ​​means that subsequent emotion recognition is no longer a comparison of absolute values, but a deviation analysis relative to the user's own normal level, which greatly reduces misjudgment caused by physiological differences between users and realizes personalized control for each user.

[0035] S130. During user operation, the biosignal acquisition module collects the user's current physiological signal data in real time and sends the current physiological signal data to the edge computing processing module.

[0036] In this embodiment of the disclosure, the user's usage process can refer to the stage where the user inserts the cigarette into the smoking device, turns on the heating, and begins to smoke. This stage may last from several minutes to more than ten minutes (the heating cycle of one cigarette). The current physiological signal data can refer to physiological parameters collected in real time during use (data of the same type as in S110, such as heart rate, HRV, blood oxygen, etc.). Due to smoking behavior and emotional fluctuations, these data will change dynamically.

[0037] Specifically, when the user inserts a cigarette and presses the start button, or when it is automatically triggered by the inhalation sensor, the biosignal acquisition module begins to continuously acquire PPG signals at a high frequency (e.g., 100Hz), and calculates the current heart rate, current HRV frequency domain index (LF / HF ratio), and current blood oxygen saturation in real time within a sliding window (e.g., updated every 10 seconds). After each window's calculation is completed, this current data is packaged and sent to the edge computing processing module. The sampling process continues throughout the entire usage cycle until the user stops inhaling or the cigarette heating ends. Real-time and continuous monitoring of the user's physiological changes provides a timely and reliable data foundation for dynamically adjusting the heating strategy, ensuring that the recognition of emotional state can keep up with the rapid changes in the user's state.

[0038] S140. The current emotional state of the user is determined by the edge computing processing module based on the current physiological signal data and the individual physiological baseline value.

[0039] In this embodiment, the current emotional state can refer to the user's psychological or physiological arousal level inferred by the system through analysis of physiological signal deviations. This embodiment mainly distinguishes between two categories: high arousal stress states (such as anxiety, irritability, tension) and low arousal fatigue states (such as drowsiness, fatigue, over-relaxation). It may also include neutral states (no significant deviation).

[0040] Specifically, the edge computing processing module reads individual physiological baseline values ​​(such as frequency domain index ratios, mean resting heart rate, and mean resting blood oxygen saturation) from memory and compares them with the real-time received current data. The comparison strategy employs a multi-parameter weighted judgment. The rate of change of the current LF / HF ratio relative to the baseline value is calculated. If this rate of change is greater than 20% (e.g., a first preset threshold, which can be adaptively adjusted based on the user's historical data) and lasts for more than 10 seconds (to prevent transient noise interference), and is further supported by a rate of change of the current heart rate relative to the baseline value greater than 5%, then it is determined to be a high arousal pressure state. If this rate of change is less than -20% (e.g., a second preset threshold) and lasts for more than 10 seconds, and is further supported by a decrease in heart rate or a slight decrease in blood oxygen saturation, then it is determined to be a low arousal fatigue state. If the deviation does not exceed the threshold, it is determined to be a neutral state, and the current heating strategy remains unchanged. For example, a user's resting LF / HF = 1.2, and the current LF / HF = 1.6, representing a change of +33%. If the heart rate rises from 72 to 78 for 20% of the duration for 15 seconds, the system determines this as a high arousal stress state. By comparing real-time data with individual baseline values ​​and incorporating time duration and auxiliary parameter verification, the system can accurately identify users' emotional smoking needs, avoiding false triggers caused by accidental actions or sensor noise.

[0041] S150. Through the edge computing processing module, generate heating strategy instructions corresponding to the current emotional state.

[0042] In this embodiment of the disclosure, the heating strategy instruction may refer to the control code sent by the edge computing processing module to the dynamic heating execution module, which includes parameters such as target heating power, heating curve type (such as continuous heating or pulse heating), and heating rate.

[0043] Specifically, in a high-wake-up-pressure state, a slow-release mode command is generated. This command includes reducing the heating power to 60%-80% of normal power, or using pulse heating (e.g., a 2-second heating, 1-second pause cycle) to slow the evaporation rate of nicotine in the cigarette. In a low-wake-up-fatigue state, a fast-response mode command is generated. This command includes increasing the initial heating rate to 1.5-2 times the normal rate, quickly reaching the preset temperature within the first 10 seconds of heating, so that the user can obtain a higher nicotine concentration on the first puff to meet their alertness needs. In a neutral state, a standard mode command is generated, which uses the factory-preset constant power or constant temperature heating curve. After the command is generated, it is sent to the dynamic heating execution module via the internal control bus of the smoking device.

[0044] Based on real-time emotional states, a targeted heating strategy is generated, achieving a leap from passive, constant heating to proactive, adaptive delivery. Automatic slow release during high arousal prevents users from ingesting excessive nicotine in a short period due to emotional smoking. Rapid response during low arousal provides a better energizing experience.

[0045] S160: The dynamic heating execution module receives and executes heating strategy instructions to adjust the heating method of the cigarette to achieve nicotine delivery control.

[0046] In this embodiment, the dynamic heating execution module includes at least one heating element (such as a ceramic heating plate, a metal heating needle, or a surrounding heating film) and its driving circuit (such as a MOSFET switch or a PWM control circuit). The dynamic heating execution module is used to directly contact the cigarette and is responsible for converting electrical energy into heat energy. The heating method for the cigarette can include a combination of parameters such as heating power, heating time, temperature control curve, and pulse frequency. Different heating methods result in different nicotine release rates in the cigarette. Nicotine delivery control can refer to controlling the amount of nicotine released from the cigarette and inhaled by the user per unit time by adjusting the heating method. For example, in slow-release mode, the delivery amount per unit time is reduced. In fast-response mode, the initial delivery amount is increased.

[0047] Specifically, after receiving the command, the dynamic heating execution module adjusts the power supply voltage or PWM duty cycle of the heating element according to the command parameters. If the command is for a slow-release mode, the drive circuit reduces the heating power, such as from 12W to 8W, or switches to pulse heating, such as supplying power at a 10Hz frequency and 50% duty cycle, causing the heating element to heat up intermittently. This operation keeps the internal temperature of the cigarette at a lower level (approximately 220-250℃), slowing down the nicotine evaporation rate and reducing the amount of nicotine inhaled per puff, but prolonging the entire smoking process and providing a gradual and sustained sense of satisfaction. If the command is for a fast-response mode, the drive circuit applies overpower (e.g., 15W) for 5-10 seconds during the startup phase, rapidly raising the heating element to 280-320℃, and then returning to normal power. This operation results in a higher nicotine release concentration during the first puff, providing a rapid energizing effect. If the command is for a standard mode, a constant temperature (e.g., 270℃) is maintained according to the default PID algorithm. During operation, the thermistor built into the heating element provides real-time temperature feedback, forming a closed-loop control.

[0048] By precisely executing strategy instructions through the dynamic heating execution module, the emotion recognition results are transformed into actual changes in nicotine delivery rate, allowing users to naturally obtain a more scientific and healthier intake management experience without changing their smoking habits.

[0049] The technical solution of this invention uses a biosignal acquisition module to collect baseline physiological signal data of the user in a resting state and sends this data to an edge computing processing module. This lays the foundation for establishing a personalized physiological baseline and accurate emotion recognition, solving the problem of general thresholds not matching individual differences. The edge computing processing module establishes the user's individual physiological baseline value based on the baseline physiological signal data, ensuring that each emotion judgment is a deviation analysis relative to the user's normal level, significantly improving recognition accuracy. During user operation, the biosignal acquisition module collects the user's current physiological signal data in real time and sends it to the edge computing processing module, achieving continuous and imperceptible monitoring of the user's physiological state and providing real-time data support for dynamic adjustment. The edge computing processing module determines the user's current emotional state based on the current physiological signal data and the individual physiological baseline value; it also generates heating strategy instructions corresponding to the current emotional state, converting the emotion recognition result into executable control parameters, achieving an automated closed loop from perception to decision-making. The dynamic heating execution module receives and executes heating strategy instructions to adjust the heating method of the cigarette, thereby controlling the delivery of nicotine. This ultimately achieves personalized nicotine delivery, effectively avoiding excessive intake caused by emotional fluctuations, while optimizing the user experience in different scenarios and improving the scientific nature and safety of health management.

[0050] As an optional embodiment of this disclosure, the biosignal acquisition module includes a PPG sensor array embedded in the grip area of ​​the smoking device; an edge computing processing module with a built-in AI chip and an emotion recognition neural network model, which are connected to the biosignal acquisition module and the dynamic heating execution module respectively; and the dynamic heating execution module includes a heating element and its driving circuit for heating the cigarette.

[0051] In this embodiment, the grip area of ​​the smoking device refers to the area naturally contacted by the user's hand when using a heated cigarette device, typically the middle or lower surface of the device's casing. This location facilitates stable contact between the PPG sensor and the skin. The PPG sensor array can refer to an array composed of multiple photoplethysmography (PPG) sensor units. Each unit includes an LED (e.g., green or infrared) and a photodetector. The array layout can cover a larger contact area and reduce signal loss due to grip position shift. For example, three sensors are arranged in a triangle, ensuring that at least one sensor receives a valid signal even with slight finger movement. The AI ​​chip can refer to a low-power, high-efficiency neural network processor designed specifically for edge computing, such as an ARM Cortex-M series MCU with DSP extensions, or a more dedicated AI accelerator chip (e.g., the TinyML chip). This chip runs the emotion recognition model locally without requiring a network connection.

[0052] In this embodiment, the emotion recognition neural network model can refer to a lightweight deep learning model. The input to the emotion recognition neural network model is HRV time-frequency features extracted from the PPG signal (such as LF / HF ratio, SDNN, RMSSD, etc.), and the output is the probability of the emotion category. The model can employ a 1D convolutional neural network (1D-CNN) or a recurrent neural network (RNN), with the number of parameters controlled within 100k to adapt to the memory and computing power limitations of embedded devices. The heating element can be a metal or ceramic-based resistance heating element. The heating element can be sheet-like, needle-like, or tubular. The heating element contacts the outer surface or interior of the cigarette for heat transfer. The driving circuit can include a power MOSFET, a PWM controller, and a temperature feedback circuit (such as an NTC thermistor). The driving circuit can be used to adjust the current flowing through the heating element according to instructions from the edge computing module.

[0053] Specifically, when a user holds the smoking device, the green LEDs (wavelength 530nm) of the PPG sensor array emit light that penetrates the skin and is absorbed and reflected by the blood in the vessels. A photodetector receives the reflected light intensity. Because blood volume changes periodically with heartbeats, the reflected light intensity also exhibits a pulsating waveform. This waveform is amplified, filtered, and then input into the AI ​​chip. A lightweight neural network model deployed on the chip (pre-trained using a large amount of HRV data and corresponding emotion labels) calculates the current HRV features in real time and outputs the emotion classification result. For example, when the LF / HF ratio is detected to rise from the baseline of 1.2 to 1.6 and remain there for more than 10 seconds, the model outputs a high arousal stress state probability of 0.92. The edge computing processing module then generates a slow-release mode command based on this, reducing the heating power from 12W to 8W via a PWM drive circuit. The delay from signal acquisition to control output is less than 200ms, achieving real-time closed-loop regulation.

[0054] By using the hardware design of the PPG sensor array and a lightweight AI model, complex monitoring technologies can be integrated into portable smoking devices, thereby significantly reducing power consumption and cost while ensuring real-time performance and accuracy.

[0055] As an optional implementation of this disclosure, the method further includes: using an edge computing processing module to calculate the user's total nicotine intake within a preset time window; the total nicotine intake is determined by the heating time and the number of suction ports; using the edge computing processing module to determine whether the user is about to exceed a preset individual safety threshold based on the real-time deviation trend between the current physiological signal data and the individual physiological baseline value; the individual safety threshold is pre-stored based on the user's individual physiological baseline value; when it is determined that the individual safety threshold is about to be exceeded, the edge computing processing module sends a reminder command to the feedback and warning module, and / or sends a command to the dynamic heating execution module to reduce the heating temperature.

[0056] In this embodiment, the preset time window can be 1 hour, 2 hours, or 24 hours. The preset time window can be determined according to user settings or system defaults. The preset time window is used to measure the cumulative intake over a short period. Total nicotine intake can refer to the actual mass of nicotine inhaled by the user, expressed in milligrams (mg). Since the amount deposited in the lungs cannot be directly measured, this system uses an indirect assessment method. For example, if the total nicotine content of each cigarette is known (e.g., 0.5 mg), empirical models of heating time versus cigarette consumption ratio and number of puffs versus release per puff can be used to calculate the intake. For example, when the heating time reaches 80% of the total cigarette time and the number of puffs reaches 12, the estimated intake is 0.4 mg. Heating time can refer to the cumulative time from the start of heating to the current moment, which can be obtained from the system timer.

[0057] In this embodiment, the number of puffs can refer to the number of times the user inhales, detected by the airflow sensor within the smoking device. The real-time deviation trend can refer to the continuous change curve of current physiological signal data (especially the LF / HF ratio of HRV) relative to an individual's physiological baseline. For example, if the LF / HF ratio continuously rises within 10 minutes with a slope greater than a certain value, it indicates that the user's sympathetic nervous system is continuously excited, potentially indicating a tendency towards overuse. "About to exceed" can refer to a warning being triggered when the current total nicotine intake plus the predicted future short-term intake (based on recent puffing rate) reaches more than 90% of the individual's safety threshold.

[0058] In this embodiment, the individual safety threshold can refer to an upper limit preset based on the user's individual physiological baseline (such as resting heart rate, HRV baseline) and health recommendations. For example, for users with a high resting heart rate, the individual safety threshold is set lower (e.g., 0.8 mg nicotine per hour). For users with a normal resting heart rate, it can be set to 1.2 mg. This threshold can also be dynamically adjusted based on the user's historical usage data through machine learning. The feedback and warning module includes: a vibration motor, an indicator light, or a miniature screen. The feedback and warning module is used to provide tactile or visual feedback to the user. The vibration motor can generate different modes of vibration (e.g., two short vibrations indicate attention, and one long vibration indicates a warning). The indicator light can use red / yellow / green tri-color LEDs. The miniature screen can display text, "Please use appropriately." The reminder command can refer to a data command that controls the feedback and warning module to execute a reminder action. The command to reduce the heating temperature can be a command sent to the dynamic heating execution module to reduce the current heating temperature by a certain amount (e.g., reduce by 10°C) or force entry into standby mode.

[0059] Specifically, the edge computing processing module maintains a sliding time window (e.g., the past 60 minutes), accumulating the estimated nicotine intake each time the user completes a vape session or every minute of heating. Simultaneously, it monitors the rate of change in the LF / HF ratio in real time. The system pre-stores individual safety thresholds. For example, using resting HRV and heart rate data collected during initial use, combined with a built-in safety model (e.g., "resting heart rate + 20%" as a warning heart rate), a maximum recommended intake rate is calculated. Assuming a user's resting LF / HF = 1.0, the system sets the maximum hourly nicotine intake to 1.0 mg. When the user continuously vapes, and the cumulative intake reaches 0.9 mg (90% of the threshold) and the LF / HF ratio increases by more than 30% from the baseline, the system determines that the individual safety threshold is about to be exceeded. The edge computing processing module immediately sends a warning command to the vibration motor (e.g., two moderate-intensity vibrations) and simultaneously sends a command to the dynamic heating execution module to reduce the heating temperature (e.g., lowering the target temperature from 270℃ to 250℃). If the user continues to use the device, when the intake reaches 1.0mg, the system will forcibly shut off heating or enter lock mode until the intake decreases after the time window slides. The feedback and warning module will issue reminders, such as the vibration motor vibrating, the indicator light turning yellow or red, and the miniature screen displaying "Approaching today's recommended upper limit," etc.

[0060] By combining intake statistics with physiological signal trends, it not only achieves quantity control but also incorporates state perception, enabling earlier intervention for emotional overuse, helping users establish healthy usage habits, and reducing the risk of nicotine dependence.

[0061] Example 2 Figure 2 This is a flowchart of a nicotine delivery control method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment describes in detail the specific conditions for determining emotional states and the process of generating differentiated heating strategies. Explanations of terms that are the same as or corresponding to those in the above embodiments are not repeated here. Figure 2 As shown, the method includes: S210. The biosignal acquisition module collects the user's baseline physiological signal data at rest and sends the baseline physiological signal data to the edge computing processing module.

[0062] S220: Through the edge computing processing module, establish the user's individual physiological baseline value based on the baseline physiological signal data.

[0063] S230. During user operation, the biosignal acquisition module collects the user's current physiological signal data in real time and sends the current physiological signal data to the edge computing processing module.

[0064] S240. If the edge computing processing module detects that the current physiological baseline value corresponding to the current physiological signal data is higher than the individual physiological baseline value and exceeds the first preset threshold, then the user's current emotional state is determined to be a high arousal stress state.

[0065] In this embodiment, the current physiological baseline can refer to key indicators extracted from the currently collected physiological signals, such as the current LF / HF ratio and the current average heart rate. The first preset threshold can refer to a pre-set relative deviation percentage, such as +20% or +30%. The first preset threshold can be dynamically adjusted by the system based on the user's historical data, or the user can manually set the sensitivity (high, medium, and low levels). For example, the default first preset threshold is +20%. A high arousal stress state can refer to a state in which the user is in a highly active sympathetic nervous system state, such as anxiety, irritability, tension, or excitement. Under high arousal stress, users often have a strong emotional urge to smoke and are prone to unconscious overuse.

[0066] Specifically, the edge computing processing module continuously calculates the current LF / HF ratio and compares it with the frequency domain index ratio in the individual's physiological baseline. It calculates the relative rate of change. If this relative rate of change is greater than a first preset threshold (e.g., 20%), and the state persists for more than a preset duration (e.g., 10 seconds, which can be achieved by counting consecutive sampling windows using a timer), it further verifies whether the heart rate has increased synchronously (optional, to increase robustness). If the verification passes, the system outputs a judgment result: the current emotional state is a high arousal stress state. For example, if the user's resting LF / HF = 1.2, the current LF / HF = 1.56, the relative rate of change is 30%, which is greater than 20%, and it lasts for 12 seconds, with the heart rate rising from 72 to 80, the system determines it to be a high arousal stress state.

[0067] By employing explicit numerical comparisons and time-duration conditions, misjudgments caused by random noise are avoided. Using relative rates of change instead of absolute values ​​accommodates physiological differences among users. Heart rate-assisted verification further improves accuracy. This decision logic is simple and efficient, suitable for real-time operation on embedded edge devices.

[0068] S250. If the edge computing processing module detects that the current physiological baseline value corresponding to the current physiological signal data is lower than the individual physiological baseline value and exceeds the second preset threshold, then the user's current emotional state is determined to be a low-arousal fatigue state.

[0069] In this embodiment, the second preset threshold can be a negative deviation, such as -20% or -25%. Similar to the first preset threshold, it can be dynamically adjusted. Low arousal fatigue state can refer to a state where the user is drowsy, tired, unfocused, or overly relaxed. In this situation, the user may want to quickly refresh themselves with nicotine.

[0070] Specifically, the edge computing processing module continuously calculates the current LF / HF ratio and compares it with the frequency domain index ratio in the individual's physiological baseline. It then calculates the relative rate of change. If this relative rate of change is less than a second preset threshold (e.g., -20%) and lasts for more than a preset duration (e.g., 10 seconds), accompanied by a decrease in heart rate or a slight decrease in blood oxygen, it is determined to be a low-arousal fatigue state. For example, if a user's resting LF / HF = 1.2, the current LF / HF = 0.9, the relative rate of change is -25%, which is less than -20%, and this lasts for 15 seconds, with the heart rate dropping from 72 to 68, the system determines this to be a low-arousal fatigue state.

[0071] Complementing the high-arousal judgment, it can identify scenarios where users need to be refreshed, thereby triggering a rapid response mode to meet the user's actual physiological needs rather than simply suppressing use. In this way, the system provides both protection (slow release) and convenience (rapid refresh), achieving intelligent two-way regulation.

[0072] S260. Through the edge computing processing module, generate heating strategy instructions corresponding to the current emotional state.

[0073] As an optional implementation of this disclosure, the edge computing processing module generates a heating strategy instruction corresponding to the current emotional state. Specifically, this includes: if the current emotional state is a high arousal pressure state, the edge computing processing module generates a slow-release mode instruction; the slow-release mode instruction is used to control the dynamic heating execution module to reduce the heating power or use pulse heating; if the current emotional state is a low arousal fatigue state, the edge computing processing module generates a fast response mode instruction; the fast response mode instruction is used to control the dynamic heating execution module to increase the initial heating rate.

[0074] In this embodiment, the slow-release mode command can cause the heating element to operate at a lower steady-state power or in an intermittent pulse mode, slowing down the volatilization rate of nicotine in the cigarette. Reducing the heating power can be, but is not limited to, reducing it from the rated 12W to 8W, or from the preset temperature of 270°C to 240°C. Pulse heating can refer to intermittent power supply with a certain duty cycle (e.g., 50%, period of 2 seconds), causing the temperature to fluctuate between 220-250°C, rather than a constant high temperature. The fast-response mode command can cause the heating element to output high power for a short period during the startup phase, quickly reaching a higher temperature, and then returning to normal. Increasing the initial heating rate can be, but is not limited to, increasing the initial temperature rise rate from 10°C / second to 20°C / second, so that the temperature reaches above 280°C within the first 5 seconds.

[0075] Specifically, the edge computing processing module generates instructions based on the judgment results of S240 or S250 through table lookup or rule engine. If the state is under high wake-up pressure, a slow-release mode instruction (instruction code 0x01) is generated, with parameters such as a target power of 8W or a pulse mode duty cycle of 50%. If the state is under low wake-up fatigue, a fast response mode instruction (instruction code 0x02) is generated, with parameters such as an initial overpower duration of 8 seconds and an overpower value of 15W. If the state is neutral, a standard mode instruction (instruction code 0x00) is generated. The instructions are sent to the drive circuit of the dynamic heating execution module via the internal bus.

[0076] By directly mapping emotional states to specific, actionable heating parameters, a seamless transition from perception to control is achieved. The slow-release mode helps prevent excessive emotional intake, while the rapid response mode provides a better user experience. The two modes complement each other, covering the main usage scenarios.

[0077] S270: The dynamic heating execution module receives and executes heating strategy instructions to adjust the heating method of the cigarette to achieve nicotine delivery control.

[0078] The technical solution of this invention, through an edge computing processing module, determines that the user's current emotional state is a high arousal stress state if the current physiological baseline value corresponding to the current physiological signal data is higher than the individual's physiological baseline value and exceeds a first preset threshold. This accurately identifies the user's emotional smoking needs caused by anxiety, tension, or other emotions, providing precise triggering conditions for subsequent slow-release control. Furthermore, if the edge computing processing module detects that the current physiological baseline value corresponding to the current physiological signal data is lower than the individual's physiological baseline value and exceeds a second preset threshold, it determines that the user's current emotional state is a low arousal fatigue state. This identifies scenarios where the user needs to refresh themselves, providing a basis for a rapid response mode and achieving bidirectional intelligent regulation of positive needs and negative behaviors.

[0079] Overall, this embodiment, through explicit threshold comparison and state determination, combined with differentiated heating strategies, enables heated cigarette devices to automatically switch between slow-release and rapid-response modes based on the user's real-time emotional state. This effectively reduces excessive nicotine intake caused by emotional fluctuations while meeting the need for alertness during fatigue, significantly improving the scientific rigor, safety, and user experience of nicotine intake management. Furthermore, this method operates entirely within the device's local edge computing unit, requiring no network connection, thus protecting user privacy. Its low latency and low power consumption make it suitable for integration into portable devices.

[0080] The following are embodiments of the nicotine delivery control system provided by the present invention. This system and the nicotine delivery control methods of the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the nicotine delivery control system, please refer to the embodiments of the above nicotine delivery control methods.

[0081] Example 3 Figure 3 This is a schematic diagram of a nicotine delivery control system provided in Embodiment 3 of the present invention. Figure 3 As shown, the system includes: a biosignal acquisition module 310, an edge computing processing module 320, and a dynamic heating execution module 330.

[0082] The biosignal acquisition module 310 is used to collect baseline physiological signal data of the user in a resting state and send the baseline physiological signal data to the edge computing processing module 320. The edge computing processing module 320 is used to establish the user's individual physiological baseline value based on the baseline physiological signal data. During user use, the biosignal acquisition module 310 is used to collect the user's current physiological signal data in real time and send the current physiological signal data to the edge computing processing module 320. The edge computing processing module 320 is used to determine the user's current emotional state based on the current physiological signal data and the individual physiological baseline value. The edge computing processing module 320 is used to generate heating strategy instructions corresponding to the current emotional state. The dynamic heating execution module 330 is used to receive and execute the heating strategy instructions to adjust the heating method of the cigarette to achieve nicotine delivery control.

[0083] The technical solution of this invention uses a biosignal acquisition module to collect baseline physiological signal data of the user in a resting state and sends this data to an edge computing processing module. This lays the foundation for establishing a personalized physiological baseline and accurate emotion recognition, solving the problem of general thresholds not matching individual differences. The edge computing processing module establishes the user's individual physiological baseline value based on the baseline physiological signal data, ensuring that each emotion judgment is a deviation analysis relative to the user's normal level, significantly improving recognition accuracy. During user operation, the biosignal acquisition module collects the user's current physiological signal data in real time and sends it to the edge computing processing module, achieving continuous and imperceptible monitoring of the user's physiological state and providing real-time data support for dynamic adjustment. The edge computing processing module determines the user's current emotional state based on the current physiological signal data and the individual physiological baseline value; it also generates heating strategy instructions corresponding to the current emotional state, converting the emotion recognition result into executable control parameters, achieving an automated closed loop from perception to decision-making. The dynamic heating execution module receives and executes heating strategy instructions to adjust the heating method of the cigarette, thereby controlling the delivery of nicotine. This ultimately achieves personalized nicotine delivery, effectively avoiding excessive intake caused by emotional fluctuations, while optimizing the user experience in different scenarios and improving the scientific nature and safety of health management.

[0084] Based on the above technical solution, the biosignal acquisition module includes a PPG sensor array embedded in the grip area of ​​the smoking device; an edge computing processing module with a built-in AI chip and an emotion recognition neural network model, which are connected to the biosignal acquisition module and the dynamic heating execution module respectively; and the dynamic heating execution module includes a heating element and its driving circuit for heating the cigarette.

[0085] Based on the above technical solutions, the baseline physiological signal data shall include at least one of the following: heart rate, heart rate variability, and blood oxygen saturation; the individual physiological baseline values ​​shall include at least one of the following: the frequency domain index ratio of heart rate variability, the mean resting heart rate, and the mean resting blood oxygen saturation.

[0086] Based on the above technical solution, the edge computing processing module is used to determine that the user's current emotional state is a high arousal pressure state if the current physiological baseline value corresponding to the current physiological signal data is higher than the individual physiological baseline value and exceeds a first preset threshold; the edge computing processing module is used to determine that the user's current emotional state is a low arousal fatigue state if the current physiological baseline value corresponding to the current physiological signal data is lower than the individual physiological baseline value and exceeds a second preset threshold.

[0087] Based on the above technical solution, the edge computing processing module generates a slow-release mode instruction if the current emotional state is a high arousal pressure state; the slow-release mode instruction is used to control the dynamic heating execution module to reduce the heating power or use pulse heating; the edge computing processing module generates a fast response mode instruction if the current emotional state is a low arousal fatigue state; the fast response mode instruction is used to control the dynamic heating execution module to increase the initial heating rate.

[0088] Based on the above technical solution, the system also includes: a feedback and warning module; The edge computing processing module is used to calculate the user's total nicotine intake within a preset time window. The total nicotine intake is determined by the heating time and the number of puffs. The edge computing processing module is also used to determine whether the user is about to exceed a preset individual safety threshold based on the real-time deviation trend between the current physiological signal data and the individual's physiological baseline value. The individual safety threshold is pre-stored based on the user's individual physiological baseline value. When it is determined that the individual safety threshold is about to be exceeded, the edge computing processing module sends a reminder command to the feedback and warning module and / or sends a command to the dynamic heating execution module to reduce the heating temperature.

[0089] The nicotine delivery control system provided in the embodiments of the present invention can execute the nicotine delivery control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the nicotine delivery control method.

[0090] It is worth noting that the various units and modules included in the above nicotine delivery control embodiments are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0091] Example 4 Figure 4 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0092] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0093] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0094] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as nicotine delivery control methods.

[0095] In some embodiments, the nicotine delivery control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the nicotine delivery control method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the nicotine delivery control method by any other suitable means (e.g., by means of firmware).

[0096] Various implementations of the systems and techniques described above herein can be implemented in digital circuit systems, integrated circuits, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chips (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0097] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0098] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0099] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0100] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0101] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0102] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the nicotine delivery control method provided in any embodiment of this application.

[0103] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider). This program product belongs to the same inventive concept as the nicotine delivery control method disclosed in the embodiments of this application, and therefore will not be described further here.

[0104] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0105] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A nicotine delivery control method, characterized by, Applications in heated cigarette devices, including: The biosignal acquisition module collects baseline physiological signal data of the user in a resting state and sends the baseline physiological signal data to the edge computing processing module. The edge computing processing module establishes the user's individual physiological baseline values ​​based on the baseline physiological signal data. During user operation, the biosignal acquisition module collects the user's current physiological signal data in real time and sends the current physiological signal data to the edge computing processing module. The edge computing processing module determines the user's current emotional state based on the current physiological signal data and the individual physiological baseline value. The edge computing processing module generates a heating strategy instruction corresponding to the current emotional state. The dynamic heating execution module receives and executes the heating strategy instructions to adjust the heating method of the cigarette, thereby controlling the delivery of nicotine.

2. The method of claim 1, wherein, The biosignal acquisition module includes a PPG sensor array embedded in the grip area of ​​the cigarette; the edge computing processing module has a built-in AI chip, deploys an emotion recognition neural network model, and is connected to the biosignal acquisition module and the dynamic heating execution module respectively; the dynamic heating execution module includes a heating element and its driving circuit for heating the cigarette.

3. The method of claim 1, wherein, The baseline physiological signal data includes at least one of heart rate, heart rate variability, and blood oxygen saturation; the individual physiological baseline values ​​include at least one of the following: the frequency domain index ratio of heart rate variability, the mean resting heart rate, and the mean resting blood oxygen saturation.

4. The method of claim 1, wherein, The step of determining the user's current emotional state based on the current physiological signal data and the individual physiological baseline value through the edge computing processing module includes: If the edge computing processing module detects that the current physiological baseline value corresponding to the current physiological signal data is higher than the individual physiological baseline value and exceeds the first preset threshold, then it determines that the user's current emotional state is a high arousal stress state. If the edge computing processing module detects that the current physiological baseline value corresponding to the current physiological signal data is lower than the individual physiological baseline value and exceeds the second preset threshold, then it determines that the user's current emotional state is a low-arousal fatigue state.

5. The method of claim 4, wherein, The step of generating a heating strategy instruction corresponding to the current emotional state through the edge computing processing module includes: If the current emotional state is a high arousal pressure state, the edge computing processing module generates a slow-release mode instruction; the slow-release mode instruction is used to control the dynamic heating execution module to reduce the heating power or use pulse heating. If the current emotional state is a low-arousal fatigue state, the edge computing processing module generates a fast response mode instruction; the fast response mode instruction is used to control the dynamic heating execution module to increase the initial heating rate.

6. The method of claim 1, wherein, The method further includes: The edge computing processing module calculates the user's total nicotine intake within a preset time window; the total nicotine intake is determined by the heating time and the number of puffs. The edge computing processing module determines whether a user is about to exceed a preset individual safety threshold based on the real-time deviation trend between the current physiological signal data and the individual's physiological baseline value. The individual safety threshold is pre-stored based on the user's individual physiological baseline value. When it is determined that the individual safety threshold is about to be exceeded, the edge computing processing module sends a reminder command to the feedback and warning module, and / or sends a command to the dynamic heating execution module to reduce the heating temperature.

7. A nicotine delivery control system, characterized by, Applications in heated cigarette devices, including: The biosignal acquisition module is used to acquire baseline physiological signal data of the user in a resting state and send the baseline physiological signal data to the edge computing processing module; The edge computing processing module is used to establish the user's individual physiological baseline value based on the baseline physiological signal data; During user use, the biosignal acquisition module is used to collect the user's current physiological signal data in real time and send the current physiological signal data to the edge computing processing module; The edge computing processing module is used to determine the user's current emotional state based on the current physiological signal data and the individual physiological baseline value; The edge computing processing module is used to generate heating strategy instructions corresponding to the current emotional state; The dynamic heating execution module is used to receive and execute the heating strategy instructions to adjust the heating method of the cigarette in order to achieve nicotine delivery control.

8. An electronic device, comprising: The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the nicotine delivery control method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the nicotine delivery control method according to any one of claims 1-6.

10. A computer program product comprising a computer program that, when executed by a processor, implements the nicotine delivery control method according to any one of claims 1-6.

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