Bluetooth-based electronic cigarette state and user behavior monitoring method and system

Abstract

The invention relates to the technical field of electronic cigarettes, in particular to an electronic cigarette state and user behavior monitoring method and system based on Bluetooth, and the method comprises the following steps: establishing stable connection between an electronic cigarette and a mobile phone through BLE communication, collecting starting and closing signals of the electronic cigarette, recording the duration of each time of smoking, and transmitting data to a mobile phone APP in real time. And synchronously carrying out timestamp marking on the acquired data to form an electronic cigarette use time record. According to the electronic cigarette, real-time collection and transmission of use data of the electronic cigarette are achieved through BLE communication, the smoking starting and stopping time and the duration of each time of smoking are accurately collected, time analysis of the smoking behavior is more detailed through synchronous marking of the timestamps, and by monitoring the electric quantity of a battery of the electronic cigarette and the working state of an atomizer in real time, the user experience is improved. And the output and the temperature are dynamically adjusted according to the use condition, so that the energy efficiency and the safety of the electronic cigarette are effectively improved, and the user experience and the equipment maintenance cost are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic cigarettes, and in particular to a method and system for monitoring the status of electronic cigarettes and user behavior based on Bluetooth. Background Art

[0002] The field of electronic cigarette technology includes various manufacturing and use technologies related to electronic cigarettes, mainly focusing on the development of safer and more effective electronic smoking devices, including the design of electronic cigarettes, the formulation of smoke liquids, and the electronic control system of electronic cigarettes. With the advancement of science and technology and the changes in market demand, the field of electronic cigarette technology continues to innovate to meet users' increasing demands for health, convenience and personalized experience, covering a full range of products from basic electronic cigarette devices to smart electronic cigarettes with integrated advanced functions.

[0003] Among them, the electronic cigarette status and user behavior monitoring method refers to a set of technical methods for detecting the electronic cigarette usage status and user smoking behavior. By using the sensors used for recording and analysis in the electronic cigarette device, it can monitor the activation status, smoking frequency, smoking duration and other parameters of the electronic cigarette. In addition, it can also identify the user's smoking pattern, thereby helping to develop a personalized user experience and possible smoking cessation assistance functions.

[0004] Although existing technologies include the basic design and control systems of e-cigarettes, they generally lack in-depth monitoring of user behavior and real-time data feedback, limiting the development of personalized experience and health-assistance functions. Technologies often focus on processing the hardware performance of e-cigarettes, such as the chemical composition of the e-liquid and basic electronic control, and fail to use advanced data analysis technology to optimize the user experience. For example, the lack of tracking and analysis of the details of smoking behavior results in the failure of the adjustment function of e-cigarettes to accurately match the actual needs of users, which not only reduces user satisfaction, but also may increase the risk of e-cigarette failure and operating costs. In addition, insufficient analysis of smoking behavior data makes it difficult for existing technologies to provide users with effective smoking cessation support or health advice, reducing the potential health promotion value of e-cigarettes. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose a method for monitoring the status of an electronic cigarette and user behavior based on Bluetooth.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a method for monitoring the status and user behavior of an electronic cigarette based on Bluetooth, comprising the following steps:

[0007] S1: Establish a stable connection between the electronic cigarette and the mobile phone through BLE communication, collect the start and shutdown signals of the electronic cigarette, record the duration of each smoking, transmit the data to the mobile phone APP in real time, and simultaneously timestamp the collected data to form a record of the electronic cigarette usage time;

[0008] S2: Analyze the smoking frequency through the electronic cigarette usage time record, count the total daily smoking time, calculate the average duration of smoking activities in different time periods, and classify the smoking behaviors in different time periods to obtain the smoking pattern analysis results;

[0009] S3: using the smoking pattern analysis results, performing data sorting, identifying the daily smoking peak period through time tags, analyzing the change in the user's smoking frequency from morning to night, calculating the interval between each smoking according to the analysis results, and simultaneously comparing the smoking behavior on weekdays and weekends to determine the time distribution of the smoking behavior, and drawing a smoking behavior time distribution graph;

[0010] S4: According to the smoking pattern analysis results, the battery power and atomizer status of the electronic cigarette are monitored, the relationship between the electronic cigarette battery consumption rate and the atomization frequency is captured, and the electronic cigarette battery output is adjusted to match the peak period of the smoking behavior according to the captured results. The temperature of the atomizer is simultaneously adjusted to ensure that it does not exceed the set heat range during continuous use, and the device adjustment parameters are recorded to obtain the electronic cigarette operation status monitoring adjustment record.

[0011] As a further solution of the present invention, the steps for obtaining the electronic cigarette usage time record are:

[0012] S111: Establish a connection between the electronic cigarette and the mobile phone through BLE communication, record the number of successful connection attempts and the success rate, perform status evaluation simultaneously, and generate a preliminary connection status evaluation result;

[0013] S112: According to the preliminary connection status evaluation result, activate the signal collection of the electronic cigarette, record the signal timestamps of starting and shutting down the electronic cigarette, and use the formula:

[0014] T s =S off -S on ;

[0015] Calculate the duration T of a single smoking event s , generate smoking duration records, where S on Represents the start signal timestamp, S off Represents the closing signal timestamp;

[0016] S113: Based on the smoking duration record, extract the data of each smoking event, transmit it to the mobile phone APP in real time, and timestamp each data to reveal the user's smoking habits and generate an electronic cigarette usage time record.

[0017] As a further solution of the present invention, the steps of obtaining the smoking pattern analysis result are:

[0018] S211: Using the electronic cigarette usage time record, extract the record from each startup to shutdown, using the formula:

[0019]

[0020] Calculate the daily smoking frequency frequency and generate the smoking frequency and total duration statistics, where N represents the number of times you smoke in a day, T represents the total number of days counted, D represents the number of effective monitoring hours in a day, and H represents the average number of minutes each smoking session lasts;

[0021] S212: Analyze the total duration of smoking in different time periods according to the statistical results of smoking frequency and total duration, and generate statistical data of smoking duration in each time period by accumulating the smoking duration in all time periods;

[0022] S213: Analyze the smoking patterns of different time periods according to the smoking duration statistics of the time periods, and classify the smoking behaviors of the different time periods by comparing the durations of the different time periods to obtain a smoking pattern analysis result.

[0023] As a further embodiment of the present invention, the steps of analyzing the change in smoking frequency are:

[0024] S311: using the smoking pattern analysis result, performing data sorting, organizing the user's smoking records in a day in chronological order, and generating time-sorted smoking records;

[0025] S312: Analyzing the smoking records sorted by time, performing time window analysis, identifying the smoking frequency in each time period, marking the peak smoking period of each day, and generating the peak smoking period data of each day;

[0026] S313: Analyze the overall smoking frequency trend of the user using the daily smoking peak time period data, and generate a user smoking frequency change analysis result by calculating the number of smoking events in all time periods.

[0027] As a further solution of the present invention, the steps of drawing the smoking behavior time distribution diagram are:

[0028] S321: based on the analysis result of the user's smoking frequency change, extract the user's smoking time point, calculate the time difference between two consecutive smoking times, and obtain the interval time length data of each smoking time;

[0029] S322: performing classification statistics based on the interval time data of each smoking, distinguishing between weekdays and weekends, comparing the difference in smoking intervals between weekdays and weekends, and generating comparison data of smoking intervals between weekdays and weekends;

[0030] S323: Using the comparison data of the smoking intervals on weekdays and weekends, perform time series analysis to determine the time distribution of smoking behavior, perform data visualization processing to reveal the time distribution of smoking behavior, and draw a time distribution graph of smoking behavior.

[0031] As a further solution of the present invention, the steps for capturing the relationship between the battery consumption rate and the atomization frequency are:

[0032] S411: extracting the initial and final data of the battery power each time the electronic cigarette is used according to the smoking pattern analysis result, recording the battery power change, and generating the electronic cigarette battery usage record;

[0033] S412: Based on the electronic cigarette battery usage record, the formula is used:

[0034]

[0035] Analyze and calculate the average battery consumption rate E avg , where E start and E end Respectively represent the battery's starting and ending power levels, and N represents the number of cigarettes smoked;

[0036] S413: Monitor the frequency of use of the atomizer of the electronic cigarette f vap , the average battery consumption rate E avg With atomization frequency f vap For comparison, use the formula:

[0037]

[0038] Calculate the ratio R of battery consumption rate to atomization frequency relation , generate data on the relationship between the e-cigarette battery consumption rate and the atomization frequency.

[0039] As a further solution of the present invention, the steps for obtaining the electronic cigarette operation status monitoring and adjustment record are:

[0040] S421: Analyze the peak period of smoking behavior by using the data on the relationship between the electronic cigarette battery consumption rate and the atomization frequency, adjust the battery output parameters to match the energy demand during the peak period of smoking, and generate a battery output adjustment result;

[0041] S422: According to the battery output adjustment result, synchronously adjust the temperature setting of the atomizer, using the formula:

[0042]

[0043] Calculate and set the new atomizer temperature T new , generate the adjusted atomizer temperature setting result, where T setRepresents the set target temperature, T max and T min Represent the maximum and minimum temperature limits, T obs is the currently observed atomizer temperature;

[0044] S423: The battery output adjustment result and the adjusted atomizer temperature setting result are integrated to summarize the battery output and atomizer temperature settings, and generate an electronic cigarette operation status monitoring adjustment record.

[0045] A Bluetooth-based electronic cigarette status and user behavior monitoring system, comprising:

[0046] The connection and synchronization module establishes a connection between the electronic cigarette and the mobile phone through BLE communication, evaluates the connection status, synchronously activates the signal collection of the electronic cigarette, records the signal timestamps of the electronic cigarette startup and shutdown, calculates the duration of a single smoking event, and transmits it to the mobile phone APP in real time, and timestamps each data to generate a record of the electronic cigarette usage time;

[0047] The behavior analysis module uses the electronic cigarette usage time record to extract the record from each startup to shutdown, calculates the daily smoking frequency, analyzes the total duration of smoking in different time periods, and accumulates the smoking duration of all time periods. By comparing the duration of different time periods, the smoking behavior in different time periods is classified to obtain the smoking pattern analysis result;

[0048] The trend recognition module uses the smoking pattern analysis results to organize the user's smoking records in a day in chronological order, identify the smoking frequency in each time period, and mark the peak smoking period of each day, analyze the user's overall smoking frequency trend, calculate the time difference between two consecutive smokings, compare the difference in smoking intervals between weekdays and weekends, determine the time distribution of smoking behavior and perform data visualization processing to draw a smoking behavior time distribution diagram;

[0049] The battery monitoring module records the battery power changes according to the smoking pattern analysis results, analyzes and calculates the average battery consumption rate, analyzes the relationship between the battery consumption rate and the atomization frequency, adjusts the battery output parameters to match the energy demand during the smoking peak period, synchronously adjusts the temperature setting of the atomizer, summarizes the battery output and the atomizer temperature setting, and generates an electronic cigarette operation status monitoring adjustment record.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are:

[0051] In the present invention, real-time collection and transmission of electronic cigarette usage data are achieved through BLE communication, the time of smoking start and stop and the duration of each smoking session are accurately collected, and the time analysis of smoking behavior is made more detailed through the synchronization marking of timestamps. By real-time monitoring of the battery power of the electronic cigarette and the working status of the atomizer, and dynamically adjusting the output and temperature according to the usage, the energy efficiency and safety of the electronic cigarette are effectively improved, which brings significant improvements in user experience and equipment maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a main step flow chart of the present invention;

[0053] Figure 2 This is a flow chart for obtaining the usage time record of the electronic cigarette of the present invention;

[0054] Figure 3 A flowchart for obtaining the smoking pattern analysis results of the present invention;

[0055] Figure 4 is a flow chart for analyzing the change of smoking frequency of the present invention;

[0056] Figure 5 A flow chart for drawing a time distribution diagram of smoking behavior according to the present invention;

[0057] Figure 6 It is a flow chart for capturing the relationship between battery consumption rate and atomization frequency of the present invention;

[0058] Figure 7 This is a flow chart for obtaining the monitoring and adjustment records of the electronic cigarette operation status of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0060] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are 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 therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0061] See also Figure 1, a method for monitoring the status and user behavior of an electronic cigarette based on Bluetooth, comprising the following steps:

[0062] S1: Establish a stable connection between the electronic cigarette and the mobile phone through BLE communication, collect the start and shutdown signals of the electronic cigarette, record the duration of each smoking, transmit the data to the mobile phone APP in real time, and simultaneously timestamp the collected data to form a record of the electronic cigarette usage time;

[0063] S2: By recording the time of electronic cigarette use, analyzing the smoking frequency, counting the total daily smoking time, calculating the average duration of smoking activities in different time periods, and classifying the smoking behaviors in different time periods to obtain the results of the smoking pattern analysis;

[0064] S3: Use the results of the smoking pattern analysis to perform data sorting, identify the peak smoking period of each day through time tags, analyze the changes in the user's smoking frequency from morning to night, calculate the interval between each smoking according to the analysis results, and simultaneously compare the smoking behavior on weekdays and weekends to determine the time distribution of smoking behavior and draw a time distribution map of smoking behavior;

[0065] S4: Based on the results of the smoking pattern analysis, the battery power and atomizer status of the electronic cigarette are monitored, the relationship between the electronic cigarette battery consumption rate and the atomization frequency is captured, and the electronic cigarette battery output is adjusted to match the peak period of the smoking behavior according to the captured results. The temperature of the atomizer is simultaneously adjusted to ensure that it does not exceed the set heat range during continuous use, and the device adjustment parameters are recorded to obtain the electronic cigarette operation status monitoring adjustment record.

[0066] The e-cigarette usage time record includes the startup timestamp, shutdown timestamp and continuous smoking time record; the smoking pattern analysis results include the total daily smoking time, average smoking interval and smoking frequency distribution records; the smoking behavior time distribution chart includes the peak time chart, frequency change chart and daytime behavior comparison chart; the e-cigarette operation status monitoring and adjustment record includes battery power adjustment record, atomizer temperature adjustment record and energy efficiency data.

[0067] See also Figure 2 ,The steps to obtain the e-cigarette usage time record are:

[0068] S111: Establish a connection between the electronic cigarette and the mobile phone through BLE communication, record the number of successful connection attempts and the success rate, perform status evaluation simultaneously, and generate a preliminary connection status evaluation result;

[0069] When establishing a connection between an e-cigarette and a mobile phone through BLE communication technology, the first step is the device pairing and authentication process, which depends on whether the two devices can recognize and respond to each other within the Bluetooth signal range. The number of connection attempts and the success rate are calculated by real-time monitoring of the strength and quality of the Bluetooth signal. At each attempt to connect, the success of the connection and the time of the attempt are recorded to evaluate the connection quality in the current environment. If the connection fails, the transmission power is automatically adjusted or the communication channel is changed, and the attempt is made again until it succeeds or the maximum number of attempts is reached. Dynamic adjustment ensures that a stable connection can be established as much as possible even in an environment with weak signals or high interference. The success rate is calculated by dividing the number of successful connections by the total number of attempts. This ratio reflects the reliability of the connection and the communication efficiency of the current device. In this way, not only can the connection between devices be established quickly and efficiently, but also data on the stability of the connection can be collected to provide a reference for subsequent use.

[0070] S112: Based on the preliminary connection status evaluation result, activate the electronic cigarette signal collection, record the electronic cigarette start and shut down signal timestamps, using the formula:

[0071] T s =S off -S on ;

[0072] Calculate the duration T of a single smoking event s , generate smoking duration records, where S on Represents the start signal timestamp, S off Represents the closing signal timestamp;

[0073] During a specific use by a user, the e-cigarette is used at time S on = Start at 10:00, at S off =10:05 off. Then, the smoking time is T s The calculation is as follows:

[0074] T s =10:05-10:00=5 minutes

[0075] The results showed that the user smoked for 5 minutes during this use, which is important for tracking the user's smoking habits and health effects over time, and can be used to adjust personal smoking behavior or provide data support for smoking cessation.

[0076] S113: Based on the smoking duration record, extract the data of each smoking event, transmit it to the mobile phone APP in real time, and timestamp each data to reveal the user's smoking habits and generate the electronic cigarette use time record;

[0077] After obtaining the smoking duration record, the data is formatted into a format that can be transmitted in real time and sent to the mobile phone APP through the established BLE connection. During this process, each data point is accompanied by a timestamp to ensure the timeliness and order of the information. After the data arrives at the mobile phone, the APP will sort and store the received data, and each smoking event will be recorded in the user's activity log in chronological order. The record is then used to generate a timeline of smoking behavior, which can show the user's smoking frequency, duration and pattern. In addition, it can also be used to generate health reports or for smoking cessation support programs, in which the APP can provide personalized smoking cessation advice and support by analyzing smoking patterns and frequencies. The real-time data transmission and processing mechanism not only improves the utilization of data, but also enhances users' awareness and control of their own behavior, supporting the goal of health management.

[0078] See also Figure 3 , the steps to obtain the results of smoking pattern analysis are:

[0079] S211: Using the electronic cigarette usage time record, extract the record from each start-up to shutdown, using the formula:

[0080]

[0081] Calculate the daily smoking frequency frequency and generate the smoking frequency and total duration statistics, where N represents the number of times you smoke in a day, T represents the total number of days counted, D represents the number of effective monitoring hours in a day, and H represents the average number of minutes each smoking session lasts;

[0082] In an observation period, a user smokes 8 times a day (N=8), the effective monitoring hours are 10 hours (D=10), the data comes from a 7-day observation period (T=7), and the average duration of each smoking is 5 minutes (H=5). First calculate the square root of the smoking duration:

[0083]

[0084] Calculate the daily smoking frequency according to the formula:

[0085]

[0086] 5.11 means that after taking into account the average duration and observation period, the average smoking frequency per day is 5.11 times. The results show that based on the data provided, the monitored people smoke about 5 times a day on average, which is crucial for understanding their daily smoking patterns and potential health risks. In this way, smoking behavior can be accurately quantified and the effectiveness of smoking reduction interventions and their long-term health effects can be evaluated accordingly.

[0087] S212: Analyze the total duration of smoking in different time periods according to the statistical results of smoking frequency and total duration, and generate statistical data of smoking duration in each time period by accumulating the smoking duration in all time periods;

[0088] According to the above-mentioned statistics on the frequency and total duration of smoking per day, the data of each time period is recorded and accumulated in detail. First, the number of times and duration of smoking in each observation day are recorded in detail, and a daily time distribution diagram is created based on the data, which shows the smoking behavior in each time period from morning to evening. In this way, the possible concentration of smoking in certain time periods can be observed, such as the time period after getting up in the morning and after lunch. Subsequently, the total duration of smoking in each specific time period is accumulated. This data processing step is completed by superimposing the duration of each smoking event time by time. For example, if a person smokes three times between 9 and 10 in the morning, with durations of 5 minutes, 7 minutes and 6 minutes respectively, then the total duration of smoking in this time period is 18 minutes. Through this analysis, the total duration of smoking in each time period can be accurately calculated, providing data support for further behavior pattern analysis.

[0089] S213: Analyze the smoking patterns of different time periods according to the smoking duration statistics of the time periods, and classify the smoking behaviors of the different time periods by comparing the durations of the different time periods to obtain the smoking pattern analysis results;

[0090] Using the statistical results of smoking duration in each time period, we began to conduct an in-depth analysis of the smoking patterns in each time period. First, the data of all time periods were processed centrally, with the aim of identifying which time periods showed a significant increase or decrease in smoking activity. The processing of data not only includes simple numerical comparisons, but also involves the identification of changing trends in smoking behavior, such as whether there are differences in smoking patterns between weekdays and weekends. In addition, for time periods that show abnormal smoking patterns, further analysis is conducted to determine possible causes, such as the influence of work pressure or social activities. By comprehensively considering the changes in smoking duration and frequency in each time period, different smoking behavior patterns can be effectively classified. For example, if frequent and short-term smoking behavior is observed in a certain period of time, it indicates that the smoker experiences greater stress or anxiety during this period, and the smoking patterns in different time periods are displayed in detail, providing a basis for subsequent health intervention strategies.

[0091] See also Figure 4 , the analysis steps for changes in smoking frequency are:

[0092] S311: using the smoking pattern analysis result, performing data sorting, organizing the user's smoking records in a day in chronological order, and generating a time-sorted smoking record;

[0093] Using the smoking data recorded in the database, covering the users’ smoking time points and frequencies, preliminary data cleaning was performed, including removing invalid timestamps and duplicate records, to ensure the accuracy and completeness of the data. The cleaned smoking records were then sorted in chronological order. This process ensured the serialization of the data and the continuity of the timeline. The sorted data would lay the foundation for the subsequent time window analysis, which relied on precise time markers to ensure that smoking behaviors within each time window could be accurately identified. It could not only efficiently process large amounts of data, but also provide clear and orderly data support for subsequent steps, which was an important basis for evaluating smoking patterns.

[0094] S312: Analyze the smoking records sorted by time, perform time window analysis, identify the smoking frequency in each time period, mark the peak smoking period of each day, and generate the peak smoking period data of each day;

[0095] After obtaining the smoking data sorted by time, the next step is to identify the smoking frequency and peak smoking period in each time period through time window analysis. The analysis task first involves defining the size of the time window, which can usually be one hour or half an hour, depending on the needs of the research and the precision of the data. The smoking events in each time window are counted, and the statistical process involves calculating the number of smoking times in each window. The process is implemented by writing a data processing script. The script will traverse each time window and count the smoking events that fall within the window. After that, the smoking frequency of each time window is calculated, that is, the number of smoking times in the time window divided by the total number of smoking times. Finally, the peak smoking period of the day is marked by the frequency data, which not only helps to understand the daily pattern of smoking behavior, but also can accurately generate daily smoking peak period data, providing accurate data support for further behavior pattern analysis and health intervention.

[0096] S313: using the daily smoking peak time period data, analyzing the overall smoking frequency trend of the user, and generating the user smoking frequency change analysis result by calculating the number of smoking events in all time periods;

[0097] When analyzing the changes in users' smoking frequency, the formula is used: Quantitatively express the change in smoking frequency in a day. Assume that a day is divided into n time periods, and the number of smoking times in each time period is x. i , where i = 1, 2, ..., n. By summing Get the total number of times you smoke in a day and divide it by the number of time periods n to calculate the average smoking frequency. A day is divided into 24 hours, and the number of times you smoke per hour is: 3, 2, 2, 1, 0, 3, 4, 3, 3, 2, 1, 0, 1, 2, 3, 4, 3, 3, 2, 2, 1, 1, 0, then the change in smoking frequency is calculated as:

[0098]

[0099] The results showed that there were an average of 2 smoking events per time period, reflecting a uniform distribution of smoking behavior throughout the day.

[0100] See also Figure 5 , the steps for drawing the time distribution diagram of smoking behavior are:

[0101] S321: based on the analysis result of the user's smoking frequency change, extract the user's smoking time point, calculate the time difference between two consecutive smoking times, and obtain the interval time data of each smoking time;

[0102] Extract the timestamp of the smoking event from the database, write an SQL query script, select the timestamp field and sort the timestamps in ascending order; then, in the sorted timestamp list, take out adjacent time points in turn and calculate the difference between them. The calculation uses the datetime library in Python to process time data to ensure the accuracy of each time difference; each calculated time difference will be stored in a new list, which records the specific duration of each smoking interval, and finally generates interval duration data, which is the basis for subsequent analysis.

[0103] S322: performing classification statistics based on the interval time data of each smoking, distinguishing between weekdays and weekends, comparing the difference in smoking intervals between weekdays and weekends, and generating comparative data of smoking intervals between weekdays and weekends;

[0104] The smoking interval data on weekdays and weekends are processed separately. First, the dates in the data set are marked, and whether it is a weekday or a weekend is determined based on the date. Then, statistical processing is applied to the two groups of data to calculate the mean and standard deviation. At the same time, conditional branches are manually set to group the data, and a loop structure is used to traverse each group of data and calculate its statistical indicators. The calculation results help understand how the time interval of smoking behavior changes on different day types, providing data support for comparing behavioral patterns on weekdays and weekends.

[0105] S323: using the comparison data of smoking intervals on weekdays and weekends, perform time series analysis to determine the time distribution of smoking behavior, perform data visualization processing to reveal the time distribution of smoking behavior, and draw a time distribution graph of smoking behavior;

[0106] After obtaining the comparison results of the smoking interval data on weekdays and weekends, the data is then converted into intuitive charts. First, determine the type of chart to be used, such as a bar chart to show the average smoking interval per day; then generate the chart, including setting the axis labels, legends and titles of the chart, and adjusting the color and layout to enhance readability; the generation of the chart is completed entirely in a programming environment, using a general drawing library such as Python's Matplotlib. Every step from data to chart is controlled by code without the intervention of external tools or software. The final generated chart clearly shows the difference in smoking behavior between weekdays and weekends, providing a basis for further analysis and discussion.

[0107] See also Figure 6 , the steps to capture the relationship between battery consumption rate and atomization frequency are:

[0108] S411: extracting the initial and final data of the battery power each time the electronic cigarette is used according to the results of the smoking pattern analysis, recording the battery power change, and generating the electronic cigarette battery usage record;

[0109] Specific usage data is extracted from the results of the smoking pattern analysis. Based on the records of each startup and shutdown of the e-cigarette, the changes in battery power during each usage interval and duration are analyzed. The data includes the battery power at startup and the specific time when the atomizer starts working, as well as the power reading and timestamp at the end of use. The data can accurately track the rate of battery power loss and the activity frequency of the atomizer. The process includes continuous recording of the initial and terminal data of the battery power, and calculation of the remaining battery power after each use. The battery consumption rate is estimated by calculating the difference in power before and after each use and dividing it by the usage time. These detailed records help analyze the usage patterns and battery performance of e-cigarettes, so as to further monitor and optimize the battery usage efficiency of e-cigarettes.

[0110] S412: Based on the e-cigarette battery usage records, use the formula:

[0111]

[0112] Analyze and calculate the average battery consumption rate E avg , where E start and E end Respectively represent the battery's starting and ending power levels, and N represents the number of cigarettes smoked;

[0113] There are 5 smoking records, and the battery power percentages at the beginning and end are (100%, 95%), (95%, 90%), (90%, 85%), (85%, 80%), (80%, 75%), which translates to power consumption of 5%, 5%, 5%, 5%, 5%. The calculation process is as follows:

[0114]

[0115] The results show that on average each puff consumes 5% of the battery, which is critical for evaluating battery performance and planning battery optimization measures. Specifically, this value can help design battery capacity and atomizer power to ensure that the e-cigarette can support a sufficient number of puffs after a full charge, thereby improving user satisfaction and the practicality of the device.

[0116] S413: Monitor the frequency of use of the atomizer of the electronic cigarette f vap , the average battery consumption rate E avg With atomization frequency f vap For comparison, use the formula:

[0117]

[0118] Calculate the ratio R of battery consumption rate to atomization frequency relation , generate data on the relationship between the e-cigarette battery consumption rate and the atomization frequency;

[0119] Setting E avg is the average amount of electricity consumed per smoking, which is known to be 5%, f vap is the atomization frequency, and the average usage frequency monitored is 2 times per hour, so the formula is used to calculate:

[0120]

[0121] The results show that the ratio of power consumed per atomization to frequency is 2.5%, which means that in the current usage mode, 5% of the power is consumed every two puffs. This means that the configuration and output level of the battery are sufficient to maintain the normal operation of the atomizer without frequent charging. If the ratio is too low, it means that the battery output is excessive, and if it is too high, it means that the battery power is insufficient, affecting the user experience.

[0122] See also Figure 7 ,The steps for obtaining the electronic cigarette operation status monitoring and adjustment records are:

[0123] S421: Analyze the peak period of smoking behavior by using the data on the relationship between the electronic cigarette battery consumption rate and the atomization frequency, adjust the battery output parameters to match the energy demand during the peak period of smoking, and generate a battery output adjustment result;

[0124] The relationship between battery consumption rate and atomization frequency is extracted from the detailed monitored e-cigarette usage data. By recording the battery power at the beginning and end of each smoking session, the energy changes of the battery during each use are accurately calculated. This process involves continuous tracking of power, including the specific power values ​​of the battery at the beginning and end of each activity, so as to estimate the average consumption rate of the battery during the user's smoking period; then, by analyzing the time nodes of smoking, the peak period of power consumption is determined, that is, the period when the user uses e-cigarettes most frequently, which usually involves cluster analysis of smoking frequency to identify the period with the heaviest battery load; finally, based on the analysis results, the output parameters of the battery are adjusted to ensure that the e-cigarette can provide sufficient power during the peak smoking period, thereby optimizing the energy management of the entire device and improving the user experience.

[0125] S422: According to the battery output adjustment result, the temperature setting of the atomizer is adjusted synchronously, using the formula:

[0126]

[0127] Calculate and set the new atomizer temperature T new , generate the adjusted atomizer temperature setting result, where T set Represents the set target temperature, T max and T min Represent the maximum and minimum temperature limits, T obs is the currently observed atomizer temperature;

[0128] Set the target temperature T set The maximum safe temperature is 200℃. max The minimum safe temperature is 250℃. min is 150°C, and the currently observed atomizer temperature T obs is 210℃.

[0129] Compute extreme value differences:

[0130] T max -T min =250℃-150℃=100℃

[0131] Calculate the current temperature deviation:

[0132] T max -T obs =250℃-210℃=40℃

[0133] Calculate the deviation ratio:

[0134]

[0135] Final adjustment temperature:

[0136] T new =200℃-0.4=199.6℃

[0137] The results show that the new atomizer temperature should be adjusted to 199.6℃ to avoid exceeding the set heat range. This temperature adjustment is implemented based on the difference ratio between the current observed temperature and the temperature safety limit, ensuring that the atomizer will not overheat when the user uses it continuously, thereby ensuring the safe use of the device and maintaining the performance of the device. In this way, the e-cigarette can maintain a constant output during peak user usage hours while avoiding any safety risks or performance degradation caused by overheating.

[0138] S423: integrating the battery output adjustment result and the adjusted atomizer temperature setting result, summarizing the battery output and atomizer temperature settings, and generating an electronic cigarette operation status monitoring adjustment record;

[0139] All adjusted battery output and atomizer temperature parameters are recorded, and this information is systematically integrated into a detailed e-cigarette operating status monitoring adjustment record; the process first involves collecting all relevant parameters before and after the adjustment, including but not limited to battery output voltage, current, and atomizer temperature settings, and then using these data for trend analysis to determine which adjustment measures are most effective and which require further optimization; next, the data is combined with user feedback to evaluate the specific impact of the improvement in device performance on the user experience, including user satisfaction surveys when using the device and statistical analysis of device failure rates; finally, this record is not only used as a basis for daily maintenance by the technical support team, but is also provided to the R&D department as a reference for future improvements and new product development, thereby ensuring that e-cigarette products can continue to adapt to market demand and user expectations.

[0140] A Bluetooth-based electronic cigarette status and user behavior monitoring system, comprising:

[0141] The connection and synchronization module establishes a connection between the electronic cigarette and the mobile phone through BLE communication, evaluates the connection status, synchronously activates the signal collection of the electronic cigarette, records the signal timestamps of the electronic cigarette startup and shutdown, calculates the duration of a single smoking event, and transmits it to the mobile phone APP in real time, and timestamps each data to generate a record of the electronic cigarette usage time;

[0142] The behavior analysis module uses the e-cigarette usage time record to extract the record from each start-up to shutdown, calculates the daily smoking frequency, analyzes the total duration of smoking in different time periods, and accumulates the smoking duration of all time periods. By comparing the duration of different time periods, the smoking behavior in different time periods is classified to obtain the smoking pattern analysis results;

[0143] The trend identification module uses the results of smoking pattern analysis to organize the user's smoking records in a day in chronological order, identify the smoking frequency in each time period, and mark the peak smoking period of each day. It analyzes the overall smoking frequency trend of the user, calculates the time difference between two consecutive smokings, compares the difference in smoking intervals between weekdays and weekends, determines the time distribution of smoking behavior, and performs data visualization processing to draw a time distribution diagram of smoking behavior;

[0144] The battery monitoring module records the battery charge changes based on the smoking pattern analysis results, analyzes and calculates the average battery consumption rate, analyzes the relationship between the battery consumption rate and the atomization frequency, adjusts the battery output parameters to match the energy demand during the smoking peak period, and synchronously adjusts the atomizer temperature setting. The battery output and atomizer temperature settings are summarized and processed to generate an electronic cigarette operation status monitoring adjustment record.

[0145] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for monitoring electronic cigarette status and user behavior based on Bluetooth, characterized in that: The following steps are involved: S1: Establish a stable connection between the electronic cigarette and the mobile phone through BLE communication, collect the start and shutdown signals of the electronic cigarette, record the duration of each smoking, transmit the data to the mobile phone APP in real time, and simultaneously timestamp the collected data to form a record of the electronic cigarette usage time; S2: Analyze the smoking frequency through the electronic cigarette usage time record, count the total daily smoking time, calculate the average duration of smoking activities in different time periods, and classify the smoking behaviors in different time periods to obtain the smoking pattern analysis results; S3: using the smoking pattern analysis results, performing data sorting, identifying the daily smoking peak period through time tags, analyzing the change in the user's smoking frequency from morning to night, calculating the interval between each smoking according to the analysis results, and simultaneously comparing the smoking behavior on weekdays and weekends to determine the time distribution of the smoking behavior, and drawing a smoking behavior time distribution graph; S4: According to the smoking pattern analysis results, the battery power and atomizer status of the electronic cigarette are monitored, the relationship between the electronic cigarette battery consumption rate and the atomization frequency is captured, and the electronic cigarette battery output is adjusted to match the peak period of the smoking behavior according to the captured results. The temperature of the atomizer is simultaneously adjusted to ensure that it does not exceed the set heat range during continuous use, and the device adjustment parameters are recorded to obtain the electronic cigarette operation status monitoring adjustment record.

2. The method for monitoring the status and user behavior of an electronic cigarette based on Bluetooth according to claim 1, characterized in that: The steps for obtaining the electronic cigarette usage time record are as follows: S111: Establish a connection between the electronic cigarette and the mobile phone through BLE communication, record the number of successful connection attempts and the success rate, perform status evaluation simultaneously, and generate a preliminary connection status evaluation result; S112: According to the preliminary connection status evaluation result, activate the signal collection of the electronic cigarette, record the signal timestamps of starting and shutting down the electronic cigarette, and use the formula: T s =S off -S on ; Calculate the duration T of a single smoking event s , generate smoking duration records, where S on Represents the start signal timestamp, S off Represents the closing signal timestamp; S113: Based on the smoking duration record, extract the data of each smoking event, transmit it to the mobile phone APP in real time, and timestamp each data to reveal the user's smoking habits and generate an electronic cigarette usage time record.

3. The method for monitoring the status and user behavior of an electronic cigarette based on Bluetooth according to claim 2, characterized in that: The steps for obtaining the smoking pattern analysis result are: S211: Using the electronic cigarette usage time record, extract the record from each startup to shutdown, using the formula: Calculate the daily smoking frequency frequency and generate the smoking frequency and total duration statistics, where N represents the number of times you smoke in a day, T represents the total number of days counted, D represents the number of effective monitoring hours in a day, and H represents the average number of minutes each smoking session lasts; S212: Analyze the total duration of smoking in different time periods according to the statistical results of smoking frequency and total duration, and generate statistical data of smoking duration in each time period by accumulating the smoking duration in all time periods; S213: Analyze the smoking patterns of different time periods according to the smoking duration statistics of the time periods, and classify the smoking behaviors of the different time periods by comparing the durations of the different time periods to obtain a smoking pattern analysis result.

4. The method for monitoring the status and user behavior of an electronic cigarette based on Bluetooth according to claim 3, characterized in that: The analysis steps of the smoking frequency change are: S311: using the smoking pattern analysis result, performing data sorting, organizing the user's smoking records in a day in chronological order, and generating time-sorted smoking records; S312: Analyzing the smoking records sorted by time, performing time window analysis, identifying the smoking frequency in each time period, marking the peak smoking period of each day, and generating the peak smoking period data of each day; S313: Analyze the overall smoking frequency trend of the user using the daily smoking peak time period data, and generate a user smoking frequency change analysis result by calculating the number of smoking events in all time periods.

5. The method for monitoring the status and user behavior of an electronic cigarette based on Bluetooth according to claim 4, characterized in that: The steps of drawing the smoking behavior time distribution diagram are as follows: S321: based on the analysis result of the user's smoking frequency change, extract the user's smoking time point, calculate the time difference between two consecutive smoking times, and obtain the interval time length data of each smoking time; S322: performing classification statistics based on the interval time data of each smoking, distinguishing between weekdays and weekends, comparing the difference in smoking intervals between weekdays and weekends, and generating comparison data of smoking intervals between weekdays and weekends; S323: Using the comparison data of the smoking intervals on weekdays and weekends, perform time series analysis to determine the time distribution of smoking behavior, perform data visualization processing to reveal the time distribution of smoking behavior, and draw a time distribution graph of smoking behavior.

6. The method for monitoring the status and user behavior of an electronic cigarette based on Bluetooth according to claim 5, characterized in that: The steps for capturing the relationship between the battery consumption rate and the atomization frequency are as follows: S411: extracting the initial and final data of the battery power each time the electronic cigarette is used according to the smoking pattern analysis result, recording the battery power change, and generating the electronic cigarette battery usage record; S412: Based on the electronic cigarette battery usage record, the formula is used: Analyze and calculate the average battery consumption rate E avg , where E start and E end Respectively represent the battery's starting and ending power levels, and N represents the number of cigarettes smoked; S413: Monitor the frequency of use of the atomizer of the electronic cigarette f vap , the average battery consumption rate E avg With atomization frequency f vap For comparison, use the formula: Calculate the ratio R of battery consumption rate to atomization frequency relation , generate data on the relationship between the e-cigarette battery consumption rate and the atomization frequency.

7. The method for monitoring the status and user behavior of an electronic cigarette based on Bluetooth according to claim 6, characterized in that: The steps for obtaining the electronic cigarette operation status monitoring and adjustment record are as follows: S421: Analyze the peak period of smoking behavior by using the data on the relationship between the consumption rate of the electronic cigarette battery and the atomization frequency, adjust the battery output parameters to match the energy demand during the peak period of smoking, and generate a battery output adjustment result; S422: According to the battery output adjustment result, synchronously adjust the temperature setting of the atomizer, using the formula: Calculate and set the new atomizer temperature T new , generate the adjusted atomizer temperature setting result, where T set Represents the set target temperature, T max and T min Represent the maximum and minimum temperature limits, T obs is the currently observed atomizer temperature; S423: The battery output adjustment result and the adjusted atomizer temperature setting result are integrated to summarize the battery output and atomizer temperature settings, and generate an electronic cigarette operation status monitoring adjustment record.

8. A Bluetooth-based electronic cigarette status and user behavior monitoring system, characterized in that: The system is used to execute the Bluetooth-based electronic cigarette status and user behavior monitoring method according to any one of claims 1 to 7, comprising: The connection and synchronization module establishes a connection between the electronic cigarette and the mobile phone through BLE communication, evaluates the connection status, synchronously activates the signal collection of the electronic cigarette, records the signal timestamps of the electronic cigarette startup and shutdown, calculates the duration of a single smoking event, and transmits it to the mobile phone APP in real time, and timestamps each data to generate a record of the electronic cigarette usage time; The behavior analysis module uses the electronic cigarette usage time record to extract the record from each startup to shutdown, calculates the daily smoking frequency, analyzes the total duration of smoking in different time periods, and accumulates the smoking duration of all time periods. By comparing the duration of different time periods, the smoking behavior in different time periods is classified to obtain the smoking pattern analysis result; The trend recognition module uses the smoking pattern analysis results to organize the user's smoking records in a day in chronological order, identify the smoking frequency in each time period, and mark the peak smoking period of each day, analyze the user's overall smoking frequency trend, calculate the time difference between two consecutive smokings, compare the difference in smoking intervals between weekdays and weekends, determine the time distribution of smoking behavior and perform data visualization processing to draw a smoking behavior time distribution diagram; The battery monitoring module records the battery power changes according to the smoking pattern analysis results, analyzes and calculates the average battery consumption rate, analyzes the relationship between the battery consumption rate and the atomization frequency, adjusts the battery output parameters to match the energy demand during the smoking peak period, synchronously adjusts the temperature setting of the atomizer, summarizes the battery output and the atomizer temperature setting, and generates an electronic cigarette operation status monitoring adjustment record.

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