Personalized service method based on user behavior time series data, computer readable storage medium and computer program product

Abstract

The invention relates to the technical field of electronic cigarettes, and discloses a personalized service method based on user behavior time sequence data, a computer readable storage medium and a computer program product. The method comprises the steps of anchoring a plurality of smoke cartridges stably used by a user in recent M days according to behavior time sequence data of electronic cigarette smoking of the user, and analyzing a current nicotine harm reduction process of the user; if the user is in the harm reduction stable period at present, marking a'harm reduction stable period 'label for the user, and when it is monitored that the nicotine concentration of the smoke cartridge stably used by the user reaches a preset minimum threshold value, pushing an exclusive coupon of the smoke cartridge without the nicotine version to the user. According to the electronic cigarette smoking behavior time sequence data of the user, the current nicotine harm reduction process of the user is automatically quantified, and after the user is in the harm reduction stable period, the user is induced to zero nicotine in a mode of pushing coupons to the user, so that the user is scientifically assisted to achieve the harm reduction target.

Description

Technical Field

[0001] This invention relates to the field of electronic cigarette technology, and in particular to a personalized service method, computer-readable storage medium, and computer program product based on user behavior time-series data. Background Technology

[0002] In recent years, e-cigarettes have seen a year-on-year increase in usage as an alternative to traditional tobacco. However, current e-cigarette products on the market suffer from the following significant drawbacks:

[0003] (1) Single function: Traditional e-cigarettes only provide nicotine intake function and lack intelligent and personalized support for users' harm reduction goals; users have difficulty quantifying their harm reduction process and cannot obtain scientific behavioral guidance.

[0004] (2) Insufficient user experience: The purchase and replacement of e-cigarette cartridges mainly rely on users' subjective memory and offline search. When the device battery is depleted or the e-cigarette cartridges are about to run out, timely replenishment guidance cannot be provided, resulting in interruption of user experience.

[0005] (3) Extensive marketing methods: Existing marketing strategies are mostly broad-based, lacking in-depth insights into individual user behavior preferences (such as taste exploration and concentration reduction trends), resulting in low relevance of promotional content, poor conversion effect, and may even interfere with users.

[0006] Therefore, there is an urgent need in this field for a solution that can accurately identify user needs or potential needs, provide proactive services, and effectively assist users in achieving their harm reduction goals. Summary of the Invention

[0007] The purpose of this invention is to provide a personalized service method, a computer-readable storage medium, and a computer program product based on user behavior time-series data, so as to solve or at least partially solve the technical problems mentioned in the background art.

[0008] To achieve this objective, the present invention employs the following technical formulation:

[0009] In a first aspect, the present invention provides a personalized service method based on user behavior time-series data, comprising:

[0010] The AI ​​analysis cloud platform identifies several e-cigarette cartridges that a user has consistently used over the past M days based on the time-series data of the user's e-cigarette vaping behavior.

[0011] Based on the nicotine concentration of several anchored cartridges and the number of puffs per day, analyze the user's current nicotine reduction progress;

[0012] If a user is currently in a harm reduction and stabilization period, label the user as "harm reduction and stabilization period";

[0013] Determine whether the minimum nicotine concentration in the anchored cartridges is less than or equal to a predetermined minimum threshold;

[0014] If so, push exclusive coupons for nicotine-free e-cigarette cartridges to users;

[0015] The user behavior time-series data includes the nicotine concentration of each cartridge used by the user, the number of puffs per session, and the duration of each puff; M is a preset natural number greater than or equal to 7.

[0016] Optionally, the step of anchoring a number of e-cigarette cartridges that the user has consistently used over the past M days based on the time-series data of the user's e-cigarette vaping behavior includes:

[0017] Within the last M days, e-cigarette cartridges that have been used continuously by the user for N consecutive days are considered to be in stable use.

[0018] Where N is a preset natural number greater than or equal to 3, and M is greater than or equal to N.

[0019] Optionally, the step of analyzing the user's current nicotine reduction progress based on the nicotine concentration of the anchored cartridges and the number of puffs per day specifically includes:

[0020] Based on the nicotine concentration and inhalation time of the various cartridges being anchored, the system analyzes whether the user is currently in the nicotine concentration decreasing phase or in a plateau phase following the nicotine concentration decreasing phase.

[0021] If so, calculate the average nicotine intake of the user over the most recent predetermined period based on the nicotine concentration of the anchored cartridges and the number of puffs per day;

[0022] If not, return to step: anchor several e-cigarette cartridges that the user has consistently used within a recent predetermined period based on the time sequence data of the user's e-cigarette vaping behavior;

[0023] After calculating the average nicotine intake of the user over the most recent predetermined period based on the nicotine concentration of several anchored cartridges and the number of puffs per day, the method further includes:

[0024] Determine whether the average nicotine intake is less than or equal to the predetermined nicotine intake threshold;

[0025] If so, determine that the user is currently in a harm reduction and stabilization period, and label the user as being in a "harm reduction and stabilization period";

[0026] If not, the user is currently in a period of unstable harm reduction.

[0027] Optionally, after determining whether the minimum nicotine concentration in the anchored cartridges is less than or equal to a predetermined minimum threshold, the method further includes:

[0028] If not, do not push exclusive coupons, or push exclusive coupons to users for e-cigarette cartridges with a lower nicotine concentration than the minimum among the anchored cartridges.

[0029] Optionally, N equals 7.

[0030] Optionally, the personalized service method further includes: analyzing the frequency with which a user changes the flavor of their e-cigarette cartridge within a predetermined time window based on the time-series data of the user's e-cigarette vaping behavior;

[0031] If the frequency with which a user changes the flavor of the e-liquid cartridge is greater than or equal to the predetermined number of times, calculate the standard deviation of the usage time of each flavor of e-liquid cartridge used by the user.

[0032] Determine whether the standard deviation of usage time is greater than or equal to a predetermined standard deviation threshold;

[0033] If so, label the user as an "active flavor explorer"; determine if the user's current e-cigarette cartridge has less than the predetermined minimum lifespan;

[0034] If not, return to step: Analyze the frequency with which users change e-cigarette flavors within a predetermined time window based on the time sequence data of users' e-cigarette vaping behavior;

[0035] After determining whether the remaining lifespan of the user's current e-cigarette cartridge is less than the predetermined minimum lifespan, the process further includes:

[0036] If so, push newly launched or niche flavored e-cigarette cartridges to users;

[0037] If not, return to step: Determine if the remaining lifespan of the user's current e-cigarette cartridge is less than the predetermined minimum lifespan.

[0038] Optionally, the predetermined time window is a continuous 30 days.

[0039] Optionally, the personalized service method further includes: real-time monitoring of the electronic cigarette's device battery level and the remaining lifespan of the cartridge;

[0040] Determine if the device's battery level is lower than the predetermined minimum or if the remaining lifespan of the cartridge is lower than the predetermined minimum.

[0041] If so, push the location, real-time distance, and real-time inventory information of several nearby authorized retail stores to the user;

[0042] If not, continue to monitor the device battery level and remaining cartridge life in real time;

[0043] The remaining lifespan of the cartridge is calculated as (remaining puffs of the cartridge ÷ total number of puffs of the cartridge) * 100%.

[0044] Secondly, the present invention provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement a personalized service method based on user behavior time-series data as described above.

[0045] Thirdly, the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements a personalized service method based on user behavior time-series data as described above.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] This invention proposes a personalized service method based on user behavior time-series data. It automatically quantifies the user's current nicotine reduction process based on the user's e-cigarette vaping behavior time-series data, and after the user is in a stable period of harm reduction, it induces the user to reduce nicotine to zero by pushing coupons to the user, thus scientifically assisting the user in achieving the harm reduction goal. Attached Figure Description

[0048] To more clearly illustrate the technical formulations in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0049] Figure 1 A method flow for a personalized service method based on user behavior time-series data provided in this embodiment of the invention. Figure 1 .

[0050] Figure 2 Method flow Figure 1 The flowchart shows the implementation method of some steps in the process.

[0051] Figure 3 A method flow for a personalized service method based on user behavior time-series data provided in this embodiment of the invention. Figure 2 .

[0052] Figure 4 A method flow for a personalized service method based on user behavior time-series data provided in this embodiment of the invention. Figure 3 .

[0053] Figure 5 This is an architecture diagram of a personalized service system based on user behavior time-series data, provided as an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical formulations of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0055] Example 1:

[0056] Please refer to Figure 1 and Figure 2 , Figure 1 A method flow for a personalized service method based on user behavior time-series data provided in this embodiment of the invention. Figure 1 , Figure 2 Method flow Figure 1 The flowchart illustrates the implementation method of some steps in the process; this personalized service method specifically includes:

[0057] Step 110: Based on the time-series data of the user's e-cigarette vaping behavior, identify several e-cigarette cartridges that the user has consistently used over the past M days.

[0058] The AI ​​analytics cloud platform identifies several e-cigarette cartridges that a user has consistently used over the past M days based on time-series data of their e-cigarette vaping behavior; specifically:

[0059] Within the last M days, e-cigarette cartridges that have been used continuously by the user for N consecutive days are denoted as stable-use cartridges; where N is a preset natural number greater than or equal to 3, and M is greater than or equal to N.

[0060] For example, if N is set to 7 and M to 30, a cartridge that a user has used stably for 7 consecutive days within the last 30 days is considered a stably used cartridge. Whether a cartridge is stably used can be determined by setting the daily usage time or the number of puffs per day; for example, if the daily usage time of the cartridge exceeds a predetermined duration (e.g., 1 hour), or the daily number of puffs exceeds a predetermined number (e.g., a high-concentration cartridge accounts for 20% of the total daily puffs), then the cartridge is considered a stably used cartridge for that day.

[0061] It should be noted that if the current test fails to identify a stable e-cigarette cartridge, the process will continue until the next testing cycle, and then proceed to step 110. For example, a specific day of the week (e.g., Sunday) can be set as the testing time, making one week a testing cycle.

[0062] Step 120: Analyze the user's current nicotine reduction progress based on the nicotine concentration of the anchored cartridges and the number of puffs per day.

[0063] This step primarily aims to monitor the downward trend of the user's nicotine concentration, changes in total daily intake, and indicators of usage stability; specifically, it includes:

[0064] Step 121: Based on the nicotine concentration and inhalation time of the anchored cartridges, analyze whether the user is currently in the nicotine concentration decreasing phase or the plateau phase after the nicotine concentration decreasing phase; if yes, proceed to step 122; if no, return to step 110.

[0065] Understandably, once the AI ​​analytics cloud platform obtains the time-series data of users' e-cigarette vaping behavior, it will clean, denoise, normalize, and structure the data to form a behavior time-series database.

[0066] By analyzing the behavioral time series database, the nicotine concentration and inhalation time of the anchored e-cigarette cartridges can be obtained, allowing for the creation of trend charts or curves of nicotine concentration to analyze whether the user is currently in a nicotine concentration decline phase or a plateau phase following the decline.

[0067] Step 122: Calculate the average nicotine intake of the user over the most recent predetermined period based on the nicotine concentration of the anchored cartridges and the number of puffs per day.

[0068] In this embodiment, the user's nicotine dependence is quantified by the user's nicotine intake in order to determine whether the user is currently in a harm reduction and stability period.

[0069] Step 123: Determine whether the average nicotine intake is less than or equal to the predetermined nicotine intake threshold; if yes, proceed to step 131; if no, proceed to step 132.

[0070] Step 131: Determine that the user is currently in a harm reduction and stability period, and label the user as being in a "harm reduction and stability period";

[0071] Step 132: Determine that the user is currently in a period of unstable harm reduction.

[0072] Following step 131, the following also includes:

[0073] Step 140: Determine whether the minimum nicotine concentration in the anchored cartridges is less than or equal to a predetermined minimum threshold; if yes, proceed to step 150; if no, proceed to step 160.

[0074] Step 150: Send users exclusive coupons for nicotine-free e-cigarette cartridges;

[0075] The user behavior time-series data includes the nicotine concentration of each cartridge used by the user, the number of puffs per session, and the duration of each puff; M is a preset natural number greater than or equal to 7.

[0076] Step 160: Do not push exclusive coupons, or push exclusive coupons to users for e-cigarette cartridges with a lower nicotine concentration than the minimum among the anchored cartridges.

[0077] In a preferred implementation, step 160 can be configured to push exclusive coupons to users for e-cigarette cartridges with a lower nicotine concentration than the minimum among the anchored cartridges.

[0078] Through methods and processes Figure 1 The above steps systematically guide users to gradually reduce nicotine intake, providing a quantifiable technical path for e-cigarettes as a harm reduction tool, which has positive public health significance.

[0079] Please continue to refer to this. Figure 3 , Figure 3 A method flow for a personalized service method based on user behavior time-series data provided in this embodiment of the invention. Figure 2 .

[0080] This personalized service approach also includes:

[0081] Step 210: Analyze the frequency with which users change e-cigarette flavors within a predetermined time window based on the time sequence data of their e-cigarette vaping behavior.

[0082] Within a predetermined time window, the AI ​​analytics cloud platform analyzes the frequency with which users change e-cigarette flavors based on the time-series data of their e-cigarette vaping behavior.

[0083] Step 220: Determine whether the frequency at which the user changes the flavor of the e-cigarette cartridge is greater than or equal to the predetermined number of times; if yes, proceed to step 230; if no, return to step 210.

[0084] For example, in this embodiment, the predetermined time window is 30 days and the predetermined number of times is 3; that is, this step is: the AI ​​analysis cloud platform determines whether the user changes the flavor of the e-cigarette cartridge 3 times or more within 30 days.

[0085] Step 230: Calculate the standard deviation of the usage time of each flavor of e-cigarette cartridge used by the user.

[0086] Step 240: Determine whether the standard deviation of the usage time is greater than or equal to the predetermined standard deviation threshold; if yes, proceed to step 250; if no, return to step 210.

[0087] Step 250: Tag users as "Active Taste Explorers".

[0088] After step 250, it also includes:

[0089] Step 260: Determine whether the remaining lifespan of the user's current e-cigarette cartridge is less than the predetermined minimum lifespan; if yes, proceed to step 270; if no, return to step 260.

[0090] The remaining lifespan of the cartridge is calculated as (remaining puffs of the cartridge ÷ total number of puffs of the cartridge) * 100%.

[0091] Step 260: Push newly launched or niche flavors of other e-cigarette cartridges to users.

[0092] Through methods and processes Figure 2 The above steps analyze the frequency, duration dispersion, and leaps between flavor families of users changing different flavor cartridges within a specific time window. When a user changes cartridge flavors ≥ 3 times / month and the duration of use for each flavor varies significantly (high standard deviation), they are labeled as an "active flavor explorer," and "new flavor" cartridges that they have not purchased before are recommended to the user. This push method, based on a deep insight into individual user behavior preferences, results in highly relevant promotional content and better conversion rates.

[0093] Please continue to refer to this. Figure 4 , Figure 4 A method flow for a personalized service method based on user behavior time-series data provided in this embodiment of the invention. Figure 3 .

[0094] Step 310: Monitor the device battery level and remaining lifespan of the e-cigarette cartridges in real time.

[0095] Step 320: Determine whether the device battery level is less than the predetermined minimum battery level or whether the remaining lifespan of the cartridge is less than the predetermined minimum lifespan; if yes, proceed to step 330; if no, return to step 310.

[0096] Step 330: Push the location, real-time distance, and real-time inventory information of several nearby authorized retail stores to the user.

[0097] For example, the predetermined minimum battery level is 20% and the remaining lifespan of the e-cigarette cartridge is 10%. When the user's e-cigarette device battery level is below 20% and / or the remaining lifespan of the e-cigarette cartridge is less than 10%, the system will push the location, real-time distance, real-time inventory information in the POS system of the three nearest authorized retail stores to the user, along with a one-click navigation link.

[0098] Through methods and processes Figure 2 The above steps can provide users with timely replenishment guidance and improve user experience.

[0099] In summary, the personalized service method based on user behavior time-series data provided in this embodiment seamlessly addresses user needs and creates an excellent user experience through accurate personalized recommendations and timely LBS (Location Based Services), thereby enhancing brand loyalty.

[0100] Systematically guiding users to gradually reduce nicotine intake provides a quantifiable technical path for e-cigarettes as a harm reduction tool, which has positive public health significance.

[0101] It provides operators with unprecedented user insights, maximizing the ROI of marketing campaigns; at the same time, the collected anonymized group behavior data can be used to guide business decisions such as new flavor development and inventory planning.

[0102] Example 2:

[0103] Please refer to Figure 2 , Figure 2 This is a schematic diagram of the architecture of a personalized service system based on user behavior time-series data, provided for an embodiment of the present invention.

[0104] The system includes a mobile terminal 20 and an AI analysis cloud platform 30; the AI ​​analysis cloud platform 30 establishes a communication connection with the mobile terminal 20, and the mobile terminal 20 establishes a communication connection with the electronic cigarette 10;

[0105] For example, the mobile terminal 20 and the electronic cigarette 10 can establish a communication connection via Bluetooth, Wi-Fi or the Internet, and the mobile terminal 20 and the AI ​​analysis cloud platform 30 can establish a communication connection via the Internet.

[0106] The mobile terminal 20 has an e-cigarette APP installed. After the e-cigarette APP is activated, the mobile terminal 20 obtains the attribute information of the e-cigarette cartridge, the device battery level and remaining lifespan in real time, and uploads it to the AI ​​analysis cloud platform 30.

[0107] Specifically, the e-cigarette 10 can be equipped with various flavors and concentrations of e-liquid cartridges, which include a cartridge ID recognition module; the cartridge has a built-in chip or NFC tag that identifies information such as the cartridge flavor, nicotine concentration, production batch, and initial expected number of puffs, and the cartridge ID recognition module is used to read this information;

[0108] The electronic cigarette 10 also includes a usage behavior data collection module, which is used to collect the number of puffs of each flavor cartridge used each day, the distribution of usage time periods, the duration and frequency of each inhalation, and to estimate the remaining lifespan of the cartridge by calculation (based on the initial number of puffs minus the number of puffs already used).

[0109] The e-cigarette 10 also includes a device data acquisition module, which is used to collect real-time battery level and geographical location information (GPS / LBS) of the device: after authorization by the e-cigarette APP, the e-cigarette APP obtains and associates the device data.

[0110] For example, the e-cigarette app obtains the aforementioned data from the e-cigarette 10 via Bluetooth and uploads it to the AI ​​analysis cloud platform 30 via Wi-Fi encryption.

[0111] The AI ​​analysis cloud platform 30 is used to execute a personalized service method based on user behavior time-series data as described in Embodiment 1; since the method has been described in detail in Embodiment 1, it will not be repeated in this embodiment.

[0112] The AI ​​analytics cloud platform 30 includes a user stage labeling model. This model can use existing AI models with machine learning capabilities. The logic is to use time series analysis for trend prediction and clustering algorithms for user segmentation.

[0113] There are many existing AI models capable of trend prediction and user segmentation. Since these are existing technologies, their principles and architecture will not be described in detail in this embodiment.

[0114] For example, the user phase tagging model continuously monitors the decreasing trend of nicotine concentration in the e-cigarette cartridges used by users, changes in total daily intake, and indicators of usage stability.

[0115] If the model detects that a user has been using low-concentration e-cigarette cartridges for ≥7 consecutive days and the total number of puffs per day has decreased by ≥20% compared to the period when using high-concentration e-cigarette cartridges, then the user's harm reduction behavior is considered stable, and the user is automatically labeled as having a "stable harm reduction period".

[0116] For example, the user stage tagging model also analyzes the frequency, duration dispersion, and jumps between flavor families (such as switching from tobacco to fruit) of users changing different flavor cartridges within a specific time window (such as 30 days).

[0117] For example, if a user changes their e-cigarette flavor ≥ 3 times per month, and the duration of use for each flavor varies significantly (high standard deviation), they will be labeled as an "active flavor explorer".

[0118] Based on the same concept, embodiments of the present invention also provide a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement a personalized service method based on user behavior time-series data provided in Embodiment 1.

[0119] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0120] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0121] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0122] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming 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).

[0123] Based on the same concept, embodiments of the present invention also provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements a personalized service method based on user behavior time-series data provided in Embodiment 1.

[0124] Computer program products may be loaded onto computer devices, and the components of computer devices may include, but are not limited to: one or more processors or processing units, system memory, and buses connecting different system components (including system memory and processing units).

[0125] Computer devices typically include a variety of computer system-readable media. These media can be any available media that can be accessed by a computer device, including volatile and non-volatile media, and removable and non-removable media.

[0126] System memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The computer device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system may be used to read and write non-removable, non-volatile magnetic media. The computer program product has a set (e.g., at least one) of program modules configured to perform the functions of the various embodiments of the present invention.

[0127] A program / utility having a set (at least one) of program modules can be stored, for example, in memory. Such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules typically perform the functions and / or methods described in the embodiments of this invention.

[0128] Computer devices can also communicate with one or more external devices (e.g., keyboards, pointing devices, monitors, etc.), one or more devices that enable user interaction with the computer device, and / or any device that enables the computer device to communicate with one or more other computing devices (e.g., network interface cards, modems, etc.). This communication can be achieved through input / output (I / O) interfaces. Furthermore, computer devices can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapters. As shown in the figure, the network adapter communicates with other modules of the computer device via a bus. It should be understood that other hardware and / or software modules can be used in conjunction with the computer device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0129] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware, or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk. Based on this understanding, the technical formula of the present invention, or the part that contributes to the prior art, or all or part of the technical formula, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0130] The above-described embodiments are only used to illustrate the technical formulation of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical formulations described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical formulations to deviate from the spirit and scope of the technical formulations of the embodiments of the present invention.

Claims

1. A personalized service method based on user behavior time series data, characterized by, The method comprises the following steps: An AI analysis cloud platform anchors a plurality of cartridges used by a user stably in the last M days according to user behavior time series data of the user vaping an electronic cigarette; The AI analysis cloud platform analyzes the current nicotine harm reduction process of the user according to the nicotine concentration and the number of puffs per day of the anchored plurality of cartridges; If the user is currently in a stable harm reduction period, the user is labeled as "in a stable harm reduction period"; It is judged whether the minimum nicotine concentration in the anchored plurality of cartridges is less than or equal to a predetermined minimum threshold; If yes, a special coupon for a nicotine-free cartridge is pushed to the user; The user behavior time series data includes the nicotine concentration of each cartridge used by the user, the number of puffs per puff, and the puffing time; M is a natural number greater than or equal to 7. 2.The method of claim 1, wherein, The method comprises the following steps: In the last M days, the cartridges used by the user continuously for N consecutive days are anchored as the stably used cartridges; N is a natural number greater than or equal to 3, and M is greater than or equal to N. 3.The method of claim 2, wherein, The method comprises the following steps: According to the nicotine concentration and the number of puffs per day of the anchored plurality of cartridges, it is analyzed whether the user is currently in a nicotine concentration decreasing stage or a flat stage after the nicotine concentration decreasing stage; If yes, the average value of the nicotine intake of the user in the last predetermined period of time is calculated according to the nicotine concentration and the number of puffs per day of the anchored plurality of cartridges; After the average value of the nicotine intake is calculated, the method further comprises the following steps: If yes, it is determined that the user is currently in a stable harm reduction period, and the user is labeled as "in a stable harm reduction period"; If no, it is determined that the user is currently in an unstable harm reduction period. After it is determined whether the minimum nicotine concentration in the anchored plurality of cartridges is less than or equal to a predetermined minimum threshold, the method further comprises the following steps: 4.The method of claim 3, wherein, If no, no special coupon is pushed, or a special coupon for a cartridge with a lower nicotine concentration than the minimum nicotine concentration in the anchored plurality of cartridges is pushed to the user. N is equal to 7. 5.The method of claim 2, wherein, The method further comprises the following steps: 6.The method of claim 1, wherein, According to the user behavior time series data, the frequency of the user changing the taste of the cartridge in a predetermined time window is analyzed; If the frequency of the user changing the taste of the cartridge is greater than or equal to a predetermined number of times, the standard deviation of the use time of each taste of the cartridge used by the user is calculated; If yes, the user is labeled as "active taste explorer"; it is judged whether the remaining life of the cartridge currently used by the user is less than a predetermined minimum life; After it is judged whether the remaining life of the cartridge currently used by the user is less than a predetermined minimum life, the method further comprises the following steps: If yes, other cartridges with new or niche tastes are pushed to the user; If no, return to the step of judging whether the remaining life of the cartridge currently used by the user is less than a predetermined minimum life; ​ ​ The remaining life of the cartridge is (the remaining number of puffs of the cartridge ÷ the total number of puffs of the cartridge) * 100%. 7.The method of claim 6, wherein, The predetermined time window is 30 consecutive days. 8.The method of claim 1, wherein, Further comprising: Real-time monitoring of the device power and the remaining life of the cartridge of the electronic cigarette; Determining whether the device power is less than a predetermined minimum power or the remaining life of the cartridge is less than a predetermined minimum life; If yes, pushing the location, real-time distance and real-time inventory information of a number of authorized retail stores nearby to the user; If no, continuing real-time monitoring of the device power and the remaining life of the cartridge of the electronic cigarette; The remaining life of the cartridge is (the remaining number of puffs of the cartridge ÷ the total number of puffs of the cartridge) * 100%.

9. A computer-readable storage medium, having stored therein at least one instruction, the medium comprising: The instructions are loaded and executed by the processor to implement the personalized service method based on user behavior time series data as claimed in any one of claims 1-8.

10. Computer program product comprising computer programs / instructions, characterized in that, When the computer program / instructions are executed by the processor, the personalized service method based on user behavior time series data as claimed in any one of claims 1-8 is implemented.

Images