Trigger method and trigger device for intervention reminder based on user smoking behavior record

Abstract

The present disclosure provides a method for triggering an intervention reminder based on a user's smoking behavior record, including: receiving smoking behavior record data associated with the user, where the smoking behavior record data includes multiple smoking behavior records; analyzing the smoking behavior record data to determine smoking behavior location data associated with each smoking behavior record; calculating the distance between the locations represented by every two smoking behavior location data; recording the first number of smoking behavior records with the distance lower than the first distance as a set of smoking behavior record data; in the case where the first number is higher than the first threshold, determining a reminder range based on the location data associated with the set of smoking behavior record data, where the reminder range is an uninterrupted and connected area or space and includes the locations represented by each location data associated with the set of smoking behavior record data; and when the user enters the reminder range from the outside, triggering the push of the intervention reminder.

Description

Technical Field

[0001] The present disclosure relates to a triggering mechanism for smoking cessation intervention reminders. Specifically, the present disclosure relates to a method for triggering reminders for smoking cessation intervention based on the theory of CBT (Cognitive Behavioral Therapy), and more specifically to a method for triggering intervention reminders based on a user's smoking behavior record, a triggering device for intervention reminders based on a user's smoking behavior record for performing the above triggering method, and a corresponding computer-readable storage medium. Background Art

[0002] The smoking cessation reminder functions of existing smoking cessation software mainly have several modes:

[0003] The first is that the software pushes reminders regularly, such as sending a reminder when getting up at 7:00 every morning, or pushing a reminder regularly at the time of arriving at the office at 9:00 every morning;

[0004] The second is a reminder for a specific smoking location set or located by the user. For example, the user inputs the location information of the company pantry or smoking area where they often smoke into the software, and then the software compares its own located position with the input position information to perform a reminder for the specific smoking location;

[0005] The third is a reminder for a specific smoking time set by the user. For example, if the user inputs that the time period when they often smoke is within one hour after lunch (for example, lunch ends at 12:00), then the software, based on the time obtained by its own time unit, pushes a smoking cessation reminder to the user regularly at 11:30 every day.

[0006] From the above three types of smoking cessation message reminders, it can be seen that the reminder methods of the current existing technologies are all in a fixed manner, that is, either a fixed time reminder is performed, or a specific fixed location reminder is performed, and the goal of combining the user's smoking habits for precise and intelligent intervention cannot be achieved. Summary of the Invention

[0007] As described above, the following technical problems exist in the prior art, that is, traditional smoking cessation methods either perform fixed time reminders or specific fixed location reminders, and the goal of combining the user's smoking habits for precise and intelligent intervention cannot be achieved. Moreover, when performing a specific fixed location reminder, due to the inaccuracy of positioning methods such as GPS and the problems of the traditional reminder mechanism itself, and because only a single specific smoking point location is considered, when walking between smoking points that are relatively close, reminders will be received continuously, thereby reducing the actual effect of the reminder.

[0008] The objective aimed to be achieved by the present disclosure is to statistically analyze the smoking habits of a user based on the user's personal smoking behavior records, predict the high-risk times and locations where the user may smoke, and through message push reminders, conduct cognitive behavioral therapy smoking cessation interventions to avoid the occurrence of smoking behavior.

[0009] To achieve the above objective, the inventors of the present disclosure have realized that the traditional specific smoking location reminder mode has the following defects:

[0010] Firstly, its positioning accuracy is not accurate enough. The accuracy of current civilian GPS positioning is generally about 10 meters, and such accuracy will make the specific location reminder inaccurate.

[0011] Secondly, the specific location reminder does not distinguish the smoking frequency, that is, it does not consider the user's smoking craving degree and gives reminders without discrimination. In this way, the reminder is too frequent and the reminder effect is greatly reduced.

[0012] Furthermore, the existing smoking reminder function does not adjust dynamically, that is, it does not correct with the change of external data, so that the fixed position reminder cannot be dynamically matched with the actual situation, which will inevitably also reduce the effect of the smoking cessation reminder.

[0013] In view of the above technical problems, based on the above considerations, the inventors of the present disclosure have proposed the following smoking cessation reminder trigger mechanism based on the CBT theory. Specifically, the present disclosure relates to a trigger method for intervention reminders based on user smoking behavior records, and the trigger method includes:

[0014] Receiving smoking behavior record data associated with the user, wherein the smoking behavior record data includes multiple smoking behavior records;

[0015] Analyzing the smoking behavior record data to determine the smoking behavior location data associated with each smoking behavior record;

[0016] Calculating the distance between the positions represented by every two smoking behavior location data;

[0017] Recording the first number of smoking behavior records with the distance lower than the first distance as a set of smoking behavior record data;

[0018] In the case where the first number is higher than the first threshold, determining a reminder range based on the position data associated with the set of smoking behavior record data, the reminder range being an uninterrupted and connected area or space and containing the positions represented by each position data associated with the set of smoking behavior record data; and

[0019] When the user enters the reminder range from the outside, triggering the push of the intervention reminder.

[0020] In the method for triggering intervention reminders based on user smoking behavior records provided according to the present disclosure, a set of smoking behavior record data is aggregated according to the distance between smoking locations, and then a reminder range including these locations is calculated based on the aggregated set of smoking behavior record data, and reminders are made within a certain reminder range. On the one hand, it solves the problem of overly frequent reminders, enabling the technical effect of more accurate single reminders to be achieved. On the other hand, it makes up for the defect that the positioning reminder range is inaccurate due to the too low accuracy of traditional positioning means. In addition, the influence of smoking frequency is fully considered, and reminders are only made for aggregated smoking points with a frequency higher than the first threshold, thereby improving the pertinence of intervention reminders, which will inevitably lead to an increase in user attention and ultimately achieve the purpose of improving the smoking cessation effect.

[0021] In one embodiment according to the present disclosure, determining the reminder range based on the location data associated with the set of smoking behavior record data further includes:

[0022] Calculating the physical center point of the location data associated with the set of smoking behavior record data, wherein the reminder range includes the range of the second distance around the physical center point, and the second distance is not less than the distance between the physical center point and the smoking behavior location farthest from the physical center point represented by the set of smoking behavior record data.

[0023] In this way, by calculating the physical center point including these locations based on the aggregated set of smoking behavior record data, the calculation of the reminder range is further simplified, making the method for triggering intervention reminders based on user smoking behavior records proposed according to the present disclosure easier to implement.

[0024] In one embodiment according to the present disclosure, determining the reminder range based on the location data associated with the set of smoking behavior record data further includes:

[0025] Determining a first reminder range based on the location represented by each location data associated with the set of smoking behavior record data, wherein the reminder range is composed of a plurality of the first reminder ranges.

[0026] In this way, the influence of each location data on the final reminder range can be further accurately determined, making the calculated reminder range more targeted, and thus improving the pertinence of location-based intervention reminders.

[0027] In one embodiment according to the present disclosure, the triggering method further includes:

[0028] Analyzing the smoking behavior record data and determining the smoking behavior time data associated with each smoking behavior record data;

[0029] Calculate the number of smoking times within a predetermined time period based on the smoking behavior time data;

[0030] Record the predetermined time period during which the number of smoking times is higher than the second threshold; and

[0031] Trigger the push of the intervention reminder before the start time point of the predetermined time period.

[0032] Based on the above location-based reminder, the present disclosure also proposes a time-based reminder. By sending a reminder again before the start of the time period with a frequency exceeding the second threshold, the pertinence of the smoking reminder can be further improved, which will inevitably lead to an increase in user attention and ultimately achieve the purpose of improving the smoking cessation effect.

[0033] In one embodiment according to the present disclosure, the triggering method further includes:

[0034] Analyze the smoking behavior record data and determine the smoking behavior time data associated with each smoking behavior record data;

[0035] Calculate the number of smoking times within a predetermined time period based on the smoking behavior time data;

[0036] Sort the predetermined time periods in descending order according to the number of smoking times; and

[0037] Trigger the push of the intervention reminder before the start time points of the top predetermined number of predetermined time periods.

[0038] Based on the above location-based reminder, the present disclosure also proposes a time-based reminder. By sending a reminder again before the start of the top predetermined number of time periods, the pertinence of the smoking reminder can be further improved, which will inevitably lead to an increase in user attention and ultimately achieve the purpose of improving the smoking cessation effect.

[0039] In one embodiment according to the present disclosure, the form of the intervention reminder includes voice reminder, video reminder, picture reminder, and text reminder, and among them, the form of the intervention reminder is related to the specific time represented by the predetermined time period.

[0040] Since humans, as a kind of organism, have different feedbacks or attentions to various reminder forms at different time periods. For example, in the morning, they tend not to carefully read text reminders and are more willing to accept voice or picture type reminders; while before going to bed at night, they are more willing to accept video or text type reminders. Based on this, the inventors of the present disclosure innovatively thought of designing the form of the intervention reminder to be related to the specific time represented by the predetermined time period to further improve user attention and ultimately achieve the purpose of improving the smoking cessation effect.

[0041] In one embodiment according to the present disclosure, the triggering method further includes:

[0042] Receiving user data associated with the user;

[0043] Determining the nicotine dependence degree of the user based on the user data; and

[0044] Determining the type of the intervention reminder based on the nicotine dependence degree of the user, wherein the type of the intervention reminder includes positive messages, neutral messages, and negative messages.

[0045] Since humans, as a kind of organism, have different attentions to different types of reminders. For example, users with a high nicotine dependence degree are more in need of being informed of the severe consequences of smoking, so the proportion of negative messages is correspondingly higher than that of users with a low or medium nicotine dependence degree; while for users with a low nicotine dependence degree, reminders with positive messages of an encouraging nature have a better effect, so for users with a low nicotine dependence degree, the proportion of positive messages will be correspondingly higher than that of users with a medium or high nicotine dependence degree.

[0046] The second aspect of the present disclosure provides a triggering device for an intervention reminder based on a user's smoking behavior record, and the triggering device includes:

[0047] A data receiving module configured to receive smoking behavior record data associated with a user, wherein the smoking behavior record data includes a plurality of smoking behavior records;

[0048] An analysis module configured to analyze the smoking behavior record data to determine smoking behavior position data associated with each smoking behavior record;

[0049] A first calculation module configured to calculate the distance between the positions represented by every two smoking behavior position data;

[0050] A grouping module configured to record the first number of smoking behavior records with a distance lower than a first distance as a set of smoking behavior record data;

[0051] A second calculation module configured to, when the first number is higher than a first threshold, determine a reminder range based on the position data associated with the set of smoking behavior record data, and the reminder range is an uninterrupted and connected area or space and includes the positions represented by each position data associated with the set of smoking behavior record data; and

[0052] A trigger module, which is configured to trigger the push of the intervention reminder when the user enters the reminder range from the outside.

[0053] In an embodiment according to the present disclosure, determining the reminder range based on the location data associated with the set of smoking behavior record data further includes:

[0054] Calculating the physical center point of the location data associated with the set of smoking behavior record data, wherein the reminder range includes a range of a second distance around the physical center point, and the second distance is not less than the distance between the physical center point and the smoking behavior location that is the farthest from the physical center point represented by the set of smoking behavior record data.

[0055] In an embodiment according to the present disclosure, determining the reminder range based on the location data associated with the set of smoking behavior record data further includes:

[0056] Determining a first reminder range based on the location represented by each location data associated with the set of smoking behavior record data, wherein the reminder range is composed of a plurality of the first reminder ranges.

[0057] In an embodiment according to the present disclosure, the analysis module is further configured to analyze the smoking behavior record data and determine the smoking behavior time data associated with each smoking behavior record data, the first calculation module is further configured to calculate the number of smoking times within a predetermined time period based on the smoking behavior time data, and the trigger device further includes:

[0058] A recording module, which is configured to record the predetermined time period in which the number of smoking times is higher than a second threshold; and

[0059] A first time trigger module, which is configured to trigger the push of the intervention reminder before the start time point of the predetermined time period.

[0060] In an embodiment according to the present disclosure, the analysis module is further configured to analyze the smoking behavior record data and determine the smoking behavior time data associated with each smoking behavior record data, the first calculation module is further configured to calculate the number of smoking times within a predetermined time period based on the smoking behavior time data, and the trigger device further includes:

[0061] A sorting module, which is configured to sort the predetermined time periods in descending order according to the number of smoking times; and

[0062] A second time trigger module, which is configured to trigger the push of the intervention reminder before the start time points of a predetermined number of predetermined time periods with a higher ranking.

[0063] In an embodiment according to the present disclosure, the form of the intervention reminder includes a voice reminder, a video reminder, a picture reminder, and a text reminder, and among them, the form of the intervention reminder is related to the specific time represented by the predetermined time period.

[0064] In an embodiment according to the present disclosure, the data receiving module is further configured to receive user data associated with the user, and the triggering device further includes:

[0065] A third calculation module, which is configured to determine the nicotine dependence degree of the user based on the user data; and

[0066] An intervention reminder type determination module, which is configured to determine the type of the intervention reminder based on the nicotine dependence degree of the user, where the type of the intervention reminder includes a positive message, a neutral message, and a negative message.

[0067] A third aspect of the present disclosure provides a tangible computer-readable storage medium, the storage medium includes instructions for executing a method for triggering an intervention reminder based on a user's smoking behavior record, and when the instructions are executed, the processor of the computer is at least used for:

[0068] Receiving smoking behavior record data associated with a user, where the smoking behavior record data includes multiple smoking behavior records;

[0069] Analyzing the smoking behavior record data to determine smoking behavior location data associated with each smoking behavior record;

[0070] Calculating the distance between the positions represented by every two smoking behavior location data;

[0071] Recording the first number of smoking behavior records with the distance lower than the first distance as a group of smoking behavior record data;

[0072] In the case where the first number is higher than the first threshold, determining a reminder range based on the location data associated with the group of smoking behavior record data, the reminder range is an uninterrupted and connected area or space and includes the positions represented by each location data associated with the group of smoking behavior record data; and

[0073] When the user enters the reminder range from the outside, triggering the push of the intervention reminder.

[0074] In one embodiment according to the present disclosure, determining the reminder range based on the location data associated with the set of smoking behavior record data further includes:

[0075] Calculating a physical center point of the location data associated with the set of smoking behavior record data, wherein the reminder range includes a range of a second distance around the physical center point, and wherein the second distance is not less than the distance between the physical center point and the smoking behavior location that is the farthest from the physical center point among the smoking behavior record data represented by the set of smoking behavior record data.

[0076] In one embodiment according to the present disclosure, determining the reminder range based on the location data associated with the set of smoking behavior record data further includes:

[0077] Determining a first reminder range based on the location represented by each location data associated with the set of smoking behavior record data, wherein the reminder range is composed of a plurality of the first reminder ranges.

[0078] In one embodiment according to the present disclosure, when the instruction is executed, it further causes the processor of the computer to at least be used for:

[0079] Analyzing the smoking behavior record data and determining the smoking behavior time data associated with each smoking behavior record data;

[0080] Calculating the number of smoking times within a predetermined time period based on the smoking behavior time data;

[0081] Recording the predetermined time periods in which the number of smoking times is higher than a second threshold; and

[0082] Triggering the push of the intervention reminder before the start time point of the predetermined time period.

[0083] In one embodiment according to the present disclosure, when the instruction is executed, it further causes the processor of the computer to at least be used for:

[0084] Analyzing the smoking behavior record data and determining the smoking behavior time data associated with each smoking behavior record data;

[0085] Calculating the number of smoking times within a predetermined time period based on the smoking behavior time data;

[0086] Sorting the predetermined time periods in descending order according to the number of smoking times; and

[0087] Triggering the push of the intervention reminder before the start time points of the top predetermined number of predetermined time periods in the sorting.

[0088] In one embodiment according to the present disclosure, the forms of the intervention reminder include voice reminder, video reminder, picture reminder, and text reminder, and among them, the form of the intervention reminder is related to the specific time indicated by the predetermined time period.

[0089] In one embodiment according to the present disclosure, when the instruction is executed, it also causes the processor of the computer to at least be used for:

[0090] Receive user data associated with the user;

[0091] Determine the nicotine dependence degree of the user based on the user data; and

[0092] Determine the type of the intervention reminder based on the nicotine dependence degree of the user, where the types of the intervention reminder include positive messages, neutral messages, and negative messages.

[0093] In summary, through the method for triggering an intervention reminder based on user smoking behavior records, the device for triggering an intervention reminder based on user smoking behavior records for executing the above triggering method, and a corresponding computer-readable storage medium provided according to three aspects of the present disclosure, a set of smoking behavior record data is aggregated according to the distance between smoking locations, and then a reminder range including these locations is calculated based on the aggregated set of smoking behavior record data, and a reminder within a certain reminder range is performed. On the one hand, it solves the problem of overly frequent reminders, enabling a more accurate single reminder to be achieved. On the other hand, it makes up for the defect that the positioning reminder range is inaccurate due to the too low accuracy of traditional positioning means; in addition, the influence of smoking frequency is fully considered, and only the aggregated smoking points with a frequency higher than the first threshold are reminded, thereby improving the pertinence of the intervention reminder, which will inevitably lead to an increase in user attention and ultimately achieve the purpose of improving the smoking cessation effect. Other advantages of the present disclosure will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] In combination with the accompanying drawings and with reference to the following detailed description, the features, advantages, and other aspects of the embodiments of the present disclosure will become more obvious. Several embodiments of the present disclosure are shown herein in an exemplary rather than restrictive manner. In the drawings:

[0095] Figure 1 The flowchart of a method 100 for triggering an intervention reminder based on user smoking behavior records according to an embodiment of the present disclosure is shown;

[0096] Figure 2 The flowchart of a method 200 for triggering an intervention reminder based on user smoking behavior records according to another embodiment of the present disclosure is shown;

[0097] Figure 3 FIG. 300 is a schematic block diagram of a triggering device for intervention reminder based on user smoking behavior records according to an embodiment of the present disclosure; and

[0098] Figure 4 FIG. 400 is a schematic block diagram of a triggering device for intervention reminder based on user smoking behavior records according to another embodiment of the present disclosure. DETAILED DESCRIPTION

[0099] The following describes in detail various exemplary embodiments of the present disclosure with reference to the accompanying drawings. Although the exemplary methods and devices described below include software and / or firmware executed on hardware among other components, it should be noted that these examples are merely illustrative and should not be considered restrictive. For example, it is contemplated that any or all of the hardware, software, and firmware components may be implemented exclusively in hardware, exclusively in software, or in any combination of hardware and software. Thus, although exemplary methods and devices have been described below, those skilled in the art will readily appreciate that the examples provided are not intended to limit the manner in which these methods and devices are implemented.

[0100] In addition, the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the methods and systems according to various embodiments of the present disclosure. It should be noted that the functions labeled in the blocks may occur in a different order than that labeled in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the flowchart and / or block diagram, and the combination of blocks in the flowchart and / or block diagram, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or may be implemented using a combination of dedicated hardware and computer instructions.

[0101] Before introducing in detail the specific embodiments according to the present disclosure, some terms used in the present disclosure will be described first.

[0102] In the present disclosure, the term "physical center point" refers to the center point of the position represented by a set of smoking behavior record data. For example, in a two-dimensional plane, the average longitude of all a set of points is assigned as the longitude of the physical center point, and the average latitude of all a set of points is assigned as the latitude of the physical center point. Similarly, in a three-dimensional space, the average longitude of all a set of points is assigned as the longitude of the physical center point, the average latitude of all a set of points is assigned as the latitude of the physical center point, and the average height of all a set of points is assigned as the height of the physical center point.

[0103] In the present disclosure, the term "reminder range" refers to a two-dimensional planar region or a three-dimensional spatial region. When a user enters the reminder range from the outside, the push of the intervention reminder will be triggered.

[0104] In the present disclosure, the term "smoking behavior record data" refers to the smoking behavior records associated with a specific user, which can be either the static data of the smoking behavior records input by the user in the preparation stage or the dynamic data of the smoking behavior records generated during the entire smoking cessation process.

[0105] Before introducing the reminder mechanism proposed in the present disclosure, the applicant of the present disclosure hopes to first introduce several models that will be mentioned next, specifically involving the user model, the intervention model, and the message model. Other models with low relevance will be briefly mentioned without being introduced in detail.

[0106] User model

[0107] The user classifies the user portrait through questionnaire tests in the App (gender, smoking cessation experience, BMI index, nicotine dependence degree, etc.), and records the smoking behavior through the smoking record function (smoking time, GPS longitude, GPS latitude, number of cigarettes smoked, degree of craving, etc.). Some of these parameters will also be used for decision-making in the subsequent triggering method of the intervention reminder based on the user's smoking behavior record, so as to perform targeted intervention reminders for specific users.

[0108] Intervention model

[0109] Intervention model based on smoking location: According to the longitude and latitude of the GPS data collected from the user's smoking behavior records, the smoking dense points are statistically analyzed and defined as within a matrix within a range of 200 meters (the first distance), and the number of smoking record points is greater than or equal to 5. A center point is calculated based on these smoking record position points for subsequent location intervention. If the distance is within 200 meters (the second distance) from the center point and enters from the outside, the push condition is formed. According to this algorithm rule, we may calculate multiple smoking dense center points of the user for precise intervention. Setting the 200-meter range statistical mode can minimize the positioning error and make the intervention reminder more accurate, reasonable, and effective compared with a single fixed site.

[0110] Smoking dense point algorithm, that is, the algorithm for calculating which points are classified as a set of smoking behavior record data:

[0111] Enumerate the matrix through permutations and combinations. First, calculate the matching of the first point with all other points and find all points within a distance of 200 meters. For example: 1, 2, 4, 5, 7, 8, 9, 12, 22, 23, 24, 25, 27, 29, 33, 34, 35, 37........ these points; then calculate the matching of the second point with all other points and find all points within a distance of 200 meters. For example: 2, 1, 4, 5, 12, 22, 23........; next, calculate the matching of the third point with all other points and find all points within a distance of 200 meters. For example: 4, 1, 2, 5, 12, 22, 23...... Then calculate the matching of the fourth point with all other points and find all points within a distance of 200 meters. 5, 1, 2, 4, 12, 33, 34, 35........; then calculate the matching of the fifth point with all other points and find all points within a distance of 200 meters. For example 12, 1, 2, 4, 5, 36, 37, 41.......; and so on. Integrate these matrices and take the intersection. If the intersection has more than 5 points, these points are the dense points we are looking for. For example: 1, 2, 4, 5.......

[0112] Generally speaking, as Figure 1 shown, Figure 1 shows a flowchart of a method 100 for triggering an intervention reminder based on a user's smoking behavior record according to an embodiment of the present disclosure. As can be seen from the figure, the method 100 for triggering an intervention reminder based on a user's smoking behavior record according to the present disclosure includes at least the following six steps:

[0113] First, in method step 110, smoking behavior record data associated with the user will be received, where the smoking behavior record data includes multiple smoking behavior records; to provide a basis for subsequent judgments. For example, the smoking behavior record data includes 20 smoking behavior records. Next, in method step 120, these smoking behavior record data will be analyzed to determine the smoking behavior location data associated with each smoking behavior record; since the smoking behavior record data may contain not only location data, but also other information such as time data and information about who smokes with, etc., it is necessary to analyze these smoking behavior record data to extract the smoking behavior location data associated with each smoking behavior record. Then, in method step 130, the distance between the positions represented by every two smoking behavior location data is calculated. Here, as shown in the detailed example introduced above, each smoking behavior record is traversed to find the distance between every two smoking positions, some of which may be 50 meters, 80 meters, 170 meters, 185 meters, 5 kilometers, etc. Then, in method step 140, the first number of smoking behavior records with the distance lower than the first distance is recorded as a group of smoking behavior record data. For example, here the first distance can be exemplarily selected as 200 meters. At this time, if there is an associated smoking location point and the distance from one of the location points is less than 200 meters, it will be classified as a clustering point. For example, among these 20 records, 7 are around the residence, 8 are around the office, 2 are at places such as subway stations on the way to work, and 3 are in the mall. Then, in method step 150, when the first number is higher than the first threshold, a reminder range is determined based on the location data associated with the group of smoking behavior record data. The reminder range is an uninterrupted and connected area or space and includes the positions represented by each location data associated with the group of smoking behavior record data. Here, the first threshold can be exemplarily selected as 5 times. Then, as shown in the above example, two smoking clustering points will be obtained, namely the residence with 7 smoking times and the office with 8 smoking times. Then, for these two places, a reminder range will be formed respectively. This reminder range is an uninterrupted and connected area or space and includes the positions represented by each location data associated with the 7 smoking behavior record data around the residence or includes the positions represented by each location data associated with the 8 smoking behavior record data around the office. Correspondingly, since the selected first threshold is 5 times, no reminder range will be formed for places such as subway stations on the way to work with 2 smoking records or for the mall with 3 smoking records. Finally, in method step 160, when the user enters the reminder range from the outside, the push of the intervention reminder is triggered.In the method for triggering intervention reminders based on user smoking behavior records provided according to the present disclosure, a set of smoking behavior record data is aggregated according to the distance between smoking locations, and then a reminder range including these locations is calculated based on the aggregated set of smoking behavior record data, and reminders are made within a certain reminder range. On the one hand, it solves the problem of overly frequent reminders, achieving the technical effect of making each reminder more accurate. On the other hand, it makes up for the defect that the positioning reminder range is inaccurate due to the too low accuracy of traditional positioning means; in addition, the influence of smoking frequency is fully considered, and reminders are only made for aggregated smoking points with a frequency higher than the first threshold, thereby improving the pertinence of the intervention reminder, which will inevitably lead to an increase in user attention and ultimately achieve the purpose of improving the smoking cessation effect.

[0114] In one embodiment according to the present disclosure, determining the reminder range based on the location data associated with the set of smoking behavior record data in method step 150 further includes: calculating the physical center point of the location data associated with the set of smoking behavior record data, wherein the reminder range includes the range of a second distance around the physical center point, and the second distance is not less than the distance between the physical center point and the smoking behavior location farthest from the physical center point represented by the set of smoking behavior record data. For example, when the locations represented by these location data are on a plane, such as in a two-dimensional plane, the average longitude of all a set of points is assigned to the longitude of the physical center point, and the average latitude of all a set of points is assigned to the latitude of the physical center point. Similarly, in a three-dimensional space such as an office building, that is, in a three-dimensional space, the average longitude of all a set of points is assigned to the longitude of the physical center point, the average latitude of all a set of points is assigned to the latitude of the physical center point, and the average height of all a set of points is assigned to the height of the physical center point. In this way, by calculating the physical center point including these locations based on the aggregated set of smoking behavior record data, the calculation of the reminder range is further simplified, making the method for triggering intervention reminders based on user smoking behavior records proposed according to the present disclosure easier to implement.

[0115] In one embodiment according to the present disclosure, determining the reminder range based on the location data associated with the set of smoking behavior record data in method step 150 further includes: determining a first reminder range based on the location represented by each location data associated with the set of smoking behavior record data, wherein the reminder range is composed of a plurality of the first reminder ranges. For example, since it is required that the distance between two adjacent smoking locations is less than 200 meters when previously selecting the gathering points, then when drawing a circle or a sphere with each location in the set of smoking behavior record data as the center and a radius greater than 100 meters, the entire interconnected reminder range can be formed. If it is a two-dimensional plane, drawing a circle to form a connected planar area can form such a reminder range. Correspondingly, if it is a spatial area, then drawing a sphere will surely be able to form an interconnected reminder range. In another implementation form, any two points can also be connected to form a closed area or space, and this area or space can be formed as the reminder range here. Preferably, based on this area or space, it can also be extended outward by, for example, 5 meters to improve the tolerance of such a reminder range and further improve the accuracy of the intervention reminder. In this way, the influence of each location data on the final reminder range can be further refined, making the calculated reminder range more targeted, and thus improving the pertinence of the location reminder.

[0116] Figure 2 FIG. shows a flowchart of a method 200 for triggering an intervention reminder based on a user's smoking behavior record according to another embodiment of the present disclosure. As can be seen from Figure 2 it, in addition to Figure 1 the six steps included therein, the method 200 for triggering an intervention reminder based on a user's smoking behavior record according to another embodiment of the present disclosure further includes an additional four steps, which are used to implement additional time reminders.

[0117] Since method steps 210 to 260 are the same as Figure 1The method steps 110 to 160 are corresponding to those described above, so they will not be elaborated here again. Only the last four steps will be introduced here for the sake of brevity. That is, in method step 270, the triggering method will also analyze the smoking behavior record data and determine the smoking behavior time data associated with each piece of smoking behavior record data. As mentioned above, since the smoking behavior record data may contain not only location data but also other information such as time data and information about who smokes with, it is necessary to analyze these smoking behavior record data to determine the smoking behavior time data associated with each piece of smoking behavior record data. Next, in method step 275, the number of smoking times within a predetermined time period is calculated based on the smoking behavior time data. Then, in method step 280, the predetermined time periods with the number of smoking times higher than the second threshold are recorded. Here, for example, the number of smoking times is set to 3, that is, the predetermined time periods with the number of smoking times higher than the second threshold, which is 3, are counted and recorded. Alternatively, the predetermined time periods can be sorted in descending order according to the number of smoking times here. For example, the top three or top five predetermined time periods are selected, that is, the predetermined time periods that need to be reminded are selected in method step 280. Finally, in method step 285, the push of the intervention reminder is triggered before the start time point of the predetermined time period or the push of the intervention reminder is triggered before the start time points of the top predetermined number of predetermined time periods sorted in the front. Based on the above location-based reminder, the present disclosure also proposes a time-based reminder. By sending a reminder again before the start of a time period with a frequency exceeding the second threshold or by sending a reminder again before the start time points of the top predetermined number of time periods sorted in the front, the pertinence of the smoking reminder can be further improved, which will inevitably lead to an increase in user attention and ultimately achieve the purpose of improving the smoking cessation effect.

[0118] Specifically, the intervention model based on smoking time: According to the smoking time data collected from the user's smoking behavior records, calculate the number of smoking times within the whole-hour data segments. For example: 1:00 - 2:00, 2:00 - 3:00,... Count and sort the smoking frequencies in different whole-hour time periods within a day. For the time periods with high smoking frequencies, an intervention reminder is sent 30 minutes before the whole hour, and the number of reminders is gradually reduced according to the extension of the smoking cessation time. The specific reminder settings are shown in Table 1 below:

[0119]

[0120]

[0121] Table 1: Relationship between the number of reminders and the length of smoking cessation time

[0122] For example, if a person is currently in the stage of quitting smoking for 0 days to 1 week, and it is analyzed from the data collected in the past that smoking is most frequent between 8:00 - 9:00, 11:00 - 12:00, and 15:00 - 16:00 in a day, then push interventions are carried out at 7:30, 10:30, and 14:30 respectively.

[0123] This time - intervention push mode can dynamically analyze and adjust the push time according to the input of the user's smoking behavior, and use statistical data to more representatively reflect the user's habits for accurate and reasonable prediction.

[0124] In addition, the forms of intervention reminders are also diverse. Since humans, as a biological species, have different feedback or attentions to various reminder forms at different time periods. For example, in the morning, they are more inclined to not carefully read text reminders and are more willing to accept voice or picture - type reminders; while before going to bed at night, they are more willing to accept video or text - type reminders. Based on this, the inventors of the present disclosure innovatively thought of designing the form of the intervention reminder to be related to the specific time represented by the predetermined time period, further to improve user attention and ultimately achieve the purpose of improving the smoking cessation effect. With such considerations, in an embodiment according to the present disclosure, the form of the intervention reminder includes voice reminder, video reminder, picture reminder, and text reminder, and among them, the form of the intervention reminder is related to the specific time represented by the predetermined time period.

[0125] Furthermore, since humans, as a biological species, have different attentions to different types of reminders. For example, users with a high nicotine dependence need to be informed of the severe consequences of smoking more, so the proportion of negative news is correspondingly higher than that of users with low or medium nicotine dependence; while for users with low nicotine dependence, reminders of positive news with an encouraging nature have a better effect, so for users with low nicotine dependence, the proportion of positive news will be correspondingly higher than that of users with medium or high nicotine dependence. Based on this, in an embodiment according to the present disclosure, the triggering method further includes: receiving user data associated with the user; determining the nicotine dependence of the user based on the user data; and determining the type of the intervention reminder based on the nicotine dependence of the user, where the type of the intervention reminder includes positive news, neutral news, and negative news.

[0126] For example, it is found from a certain research questionnaire that he is mildly nicotine-dependent and smokes frequently in the morning, so he needs to receive a push intervention at a certain time in the morning. It can be analyzed from the above two tables that it is necessary to randomly select messages of only voice type and picture type from all push messages, and the number of voice messages: the number of picture messages = 80%:20% = 4:1, and the composition of voice-type messages is positive: neutral: negative = 60%:20%:20% = 3:1:1, and the composition of picture-type messages is also positive: neutral: negative = 60%:20%:20% = 3:1:1. Finally, randomly select one message from the screened messages for pushing. Based on the above considerations, the reminder messages pushed according to the intervention push model are classified into positive content, neutral content, and negative content according to the content, and each type of content is divided into four types: voice, video, text, and picture according to the presentation form. Message pushing is classified and screened according to the user model classification and the pushing time period, and the specific rules are shown in Table 2 below:

[0127]

[0128] Table 2: Relationship between the type of user's intervention reminder and nicotine dependence

[0129] In addition, in the morning, there is a tendency to not look closely at text reminders and be more willing to accept voice or picture type reminders; while before going to bed at night, be more willing to accept video or text type reminders. Based on this, the inventor of the present disclosure innovatively thought of designing the form of the intervention reminder to be related to the specific time represented by the predetermined time period, so as to further improve user attention and ultimately achieve the purpose of improving the smoking cessation effect.

[0130]

[0131] Table 3: Relationship between the form of user's intervention reminder and the specific time period

[0132] The learning algorithm of the above intervention model: Through buried point analysis and user feedback data collection, which type of smoking cessation attribute population is more suitable for which type of smoking cessation plan, which messages and content are more interested in, and the subsequent success factors for smoking cessation are added together to form a machine learning algorithm to dynamically adjust the algorithm threshold of the intervention model.

[0133] The above triggering method for intervention reminder based on user smoking behavior records can also be implemented by a general computer device (such as a smart phone, a tablet computer, a notebook computer or a desktop computer, etc.) or a dedicated computer device (such as a smoking cessation smart bracelet or a dedicated smoking cessation device), and such a computer device must include a triggering device for intervention reminder based on user smoking behavior records. Figure 3FIG. 0 shows a schematic block diagram of a trigger device 300 for intervention reminder based on user smoking behavior records according to an embodiment of the present disclosure. As can be seen from Figure 3 it, the trigger device includes: a data receiving module 310 configured to receive smoking behavior record data associated with a user, wherein the smoking behavior record data includes a plurality of smoking behavior records; an analysis module 320 configured to analyze the smoking behavior record data to determine smoking behavior location data associated with each smoking behavior record; a first calculation module 330 configured to calculate the distance between the positions represented by every two smoking behavior location data; a grouping module 340 configured to record the first number of smoking behavior records with a distance lower than a first distance as a set of smoking behavior record data; a second calculation module 350 configured to, when the first number is higher than a first threshold, determine a reminder range based on the location data associated with the set of smoking behavior record data, the reminder range being an uninterrupted and connected area or space and including the positions represented by each location data associated with the set of smoking behavior record data; and a trigger module 360 configured to trigger the push of the intervention reminder when the user enters the reminder range from the outside. Optionally, in an embodiment according to the present disclosure, determining the reminder range based on the location data associated with the set of smoking behavior record data further includes: calculating a physical center point of the location data associated with the set of smoking behavior record data, wherein the reminder range includes a range of a second distance around the physical center point, and the second distance is not less than the distance between the physical center point and the smoking behavior location farthest from the physical center point among the set of smoking behavior record data. Optionally or alternatively, in an embodiment according to the present disclosure, determining the reminder range based on the location data associated with the set of smoking behavior record data further includes: determining a first reminder range based on the positions represented by each location data associated with the set of smoking behavior record data, wherein the reminder range is composed of a plurality of the first reminder ranges.

[0134] In addition, in an embodiment according to the present disclosure, the analysis module is further configured to analyze the smoking behavior record data and determine smoking behavior time data associated with each piece of smoking behavior record data. The first calculation module is further configured to calculate the number of smoking times within a predetermined time period based on the smoking behavior time data. And the triggering device further includes: a recording module configured to record the predetermined time period in which the number of smoking times is higher than a second threshold; and a first time triggering module configured to trigger the push of the intervention reminder before the start time point of the predetermined time period. Optionally or alternatively, in an embodiment according to the present disclosure, the analysis module is further configured to analyze the smoking behavior record data and determine smoking behavior time data associated with each piece of smoking behavior record data. The first calculation module is further configured to calculate the number of smoking times within a predetermined time period based on the smoking behavior time data. And the triggering device further includes: a sorting module configured to sort the predetermined time periods in descending order according to the number of smoking times; and a second time triggering module configured to trigger the push of the intervention reminder before the start time points of a predetermined number of the predetermined time periods with higher rankings.

[0135] In addition, in an embodiment according to the present disclosure, the form of the intervention reminder includes voice reminder, video reminder, picture reminder, and text reminder. And among them, the form of the intervention reminder is related to the specific time represented by the predetermined time period. In an embodiment according to the present disclosure, the data receiving module is further configured to receive user data associated with the user. The triggering device further includes: a third calculation module configured to determine the nicotine dependence degree of the user based on the user data; and an intervention reminder type determination module configured to determine the type of the intervention reminder based on the nicotine dependence degree of the user. Wherein, the type of the intervention reminder includes positive news, neutral news, and negative news.

[0136] In addition, alternatively, the above method can be implemented by a computer program product, i.e., a computer-readable storage medium. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for performing various aspects of the present disclosure. The computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. The computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punched card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium used herein is not construed as being a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0137] Figure 4 FIG. 400 is a schematic block diagram of a trigger device 400 for an intervention reminder based on a user's smoking behavior record according to an embodiment of the present disclosure. It should be understood that the trigger device 400 can be implemented to implement Figure 1 the trigger method 100 for the intervention reminder based on the user's smoking behavior record in Figure 2 or the function of the trigger method 200 for the intervention reminder based on the user's smoking behavior record in Figure 4 It can be seen from that the device 400 includes a central processing unit (CPU) 401 (e.g., a processor) that can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 402 or computer program instructions loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the trigger device 400 can also be stored. The CPU 401, ROM 402, and RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0138] Multiple components in the triggering device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disc, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0139] Generally speaking, a third aspect of the present disclosure provides a tangible computer-readable storage medium. The storage medium includes instructions for executing a triggering method for an intervention reminder based on a user's smoking behavior record. When the instructions are executed, the processor of the computer is at least configured to: receive smoking behavior record data associated with the user, where the smoking behavior record data includes multiple smoking behavior records; analyze the smoking behavior record data to determine smoking behavior location data associated with each smoking behavior record; calculate the distance between the positions represented by every two smoking behavior location data; record the first number of smoking behavior records with the distance lower than the first distance as a set of smoking behavior record data; in the case where the first number is higher than the first threshold, determine a reminder range based on the location data associated with the set of smoking behavior record data, the reminder range being an uninterrupted and connected area or space and containing the positions represented by each location data associated with the set of smoking behavior record data; and trigger the push of the intervention reminder when the user enters the reminder range from the outside.

[0140] In an embodiment according to the present disclosure, determining the reminder range based on the location data associated with the set of smoking behavior record data further includes: calculating the physical center point of the location data associated with the set of smoking behavior record data, where the reminder range includes a range of a second distance around the physical center point, and the second distance is not less than the distance between the physical center point and the smoking behavior position farthest from the physical center point among the smoking behavior positions represented by the set of smoking behavior record data.

[0141] In an embodiment according to the present disclosure, determining the reminder range based on the location data associated with the set of smoking behavior record data further includes: determining a first reminder range based on the position represented by each location data associated with the set of smoking behavior record data, where the reminder range is composed of multiple first reminder ranges.

[0142] In one embodiment according to the present disclosure, when the instruction is executed, it further causes the processor of the computer to at least: analyze the smoking behavior record data and determine the smoking behavior time data associated with each piece of smoking behavior record data; calculate the number of smoking times within a predetermined time period based on the smoking behavior time data; record the predetermined time periods in which the number of smoking times is higher than a second threshold; and trigger the push of the intervention reminder before the start time point of the predetermined time period.

[0143] In one embodiment according to the present disclosure, when the instruction is executed, it further causes the processor of the computer to at least: analyze the smoking behavior record data and determine the smoking behavior time data associated with each piece of smoking behavior record data; calculate the number of smoking times within a predetermined time period based on the smoking behavior time data; sort the predetermined time periods in descending order according to the number of smoking times; and trigger the push of the intervention reminder before the start time points of a predetermined number of the predetermined time periods ranked higher.

[0144] In one embodiment according to the present disclosure, the form of the intervention reminder includes a voice reminder, a video reminder, a picture reminder, and a text reminder, and among them, the form of the intervention reminder is related to the specific time represented by the predetermined time period.

[0145] In one embodiment according to the present disclosure, when the instruction is executed, it further causes the processor of the computer to at least: receive user data associated with the user; determine the nicotine dependence degree of the user based on the user data; and determine the type of the intervention reminder based on the nicotine dependence degree of the user, where the type of the intervention reminder includes a positive message, a neutral message, and a negative message.

[0146] In summary, through the method for triggering an intervention reminder based on a user's smoking behavior record, the device for triggering an intervention reminder based on a user's smoking behavior record for executing the above triggering method, and a corresponding computer-readable storage medium provided in three aspects according to the present disclosure, a set of smoking behavior record data is aggregated according to the distance between smoking locations, and then a reminder range including these locations is calculated based on the aggregated set of smoking behavior record data, and a reminder within a certain reminder range is performed. On the one hand, it solves the problem of overly frequent reminders, and realizes the technical effect of making each reminder more accurate. On the other hand, it makes up for the defect that the positioning reminder range is inaccurate due to the too low accuracy of traditional positioning means. In addition, the influence of the smoking frequency is fully considered, and only the aggregated smoking points with a frequency higher than the first threshold are reminded, thereby improving the pertinence of the intervention reminder, which will inevitably lead to an increase in user attention and ultimately achieve the purpose of improving the smoking cessation effect.

[0147] The various methods described above, such as the triggering method 100 for intervention reminder based on user smoking behavior records or the triggering method 200 for intervention reminder based on user smoking behavior records, can be executed by the processing unit 401. For example, in some embodiments, the triggering method 100 for intervention reminder based on user smoking behavior records or the triggering method 200 for intervention reminder based on user smoking behavior records can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed onto the triggering device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the processor CPU 401, one or more actions or steps of the triggering method 100 for intervention reminder based on user smoking behavior records or the triggering method 200 for intervention reminder based on user smoking behavior records described above can be performed.

[0148] Generally speaking, the various example embodiments of the present disclosure can be implemented in hardware or dedicated circuits, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while other aspects can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of the embodiments of the present disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, devices, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuits or logic, general hardware or controllers or other computing devices, or some combination thereof.

[0149] Although the various exemplary embodiments of the present disclosure can be implemented in hardware or dedicated circuits as described above, the data processing device for the blockchain described above can be implemented either in the form of hardware or in the form of software, because: in the 1990s, it was easy to determine whether a technical improvement belonged to an improvement in hardware (e.g., an improvement in the circuit structure of a diode, transistor, switch, etc.) or an improvement in software (e.g., an improvement in a method flow). However, with the continuous development of technology, many improvements in method flows today can almost be achieved by programming the improved method flow into a hardware circuit. In other words, by programming different programs for the hardware circuit, the corresponding hardware circuit structure can be obtained, that is, the change in the hardware circuit structure is realized. Therefore, it cannot be said that an improvement in a method flow cannot be implemented with a hardware entity module. For example, a Programmable Logic Device (PLD) (e.g., a Field Programmable Gate Array (FPGA)) is such an integrated circuit, and its logical function is determined by the user programming the device. The designer can program by himself to "integrate" a digital system on a programmable logic device, without having to ask a chip manufacturer to design and manufacture a dedicated integrated circuit chip.Moreover, instead of fabricating integrated circuit chips manually, such programming is mostly implemented using "logic compiler" software nowadays. It is similar to the software compilers used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called Hardware Description Language (HDL). There is not just one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. Currently, the most commonly used ones are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that by simply performing a little logical programming on the method flow with the above-mentioned several hardware description languages and programming it into the integrated circuit, it is easy to obtain the hardware circuit that implements the logical method flow.

[0150] The computer-readable program instructions or computer program products for implementing various aspects of the present disclosure can also be stored in the cloud. When needed, users can access the computer-readable program instructions stored in the cloud for implementing one aspect of the present disclosure through the mobile Internet, fixed network, or other networks, so as to implement the technical solutions disclosed according to various aspects of the present disclosure.

[0151] The above are only optional embodiments of the embodiments of the present disclosure and are not used to limit the embodiments of the present disclosure. For those skilled in the art, there can be various changes and modifications to the embodiments of the present disclosure. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present disclosure shall be included within the protection scope of the embodiments of the present disclosure.

[0152] Although embodiments of the present disclosure have been described with reference to several specific embodiments, it should be understood that the embodiments of the present disclosure are not limited to the specific embodiments disclosed. The embodiments of the present disclosure are intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. The scope of the claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.

Claims

1. A triggering method for intervention reminder based on user smoking behavior records, the triggering method comprising: Receiving smoking behavior record data associated with a user, wherein the smoking behavior record data includes multiple smoking behavior records; Analyzing the smoking behavior record data to determine smoking behavior location data associated with each smoking behavior record; Calculating the distance between the positions represented by every two smoking behavior location data; Recording the first quantity of smoking behavior records with the distance lower than a first distance as a set of smoking behavior record data; In the case where the first quantity is higher than a first threshold, determining a reminder range based on the location data associated with the set of smoking behavior record data, the reminder range being an uninterrupted and connected area or space and including the positions represented by each location data associated with the set of smoking behavior record data; and When the user enters the reminder range from the outside, triggering the push of the intervention reminder.

2. The triggering method according to claim 1, wherein, Determining the reminder range based on the location data associated with the set of smoking behavior record data further includes: Calculating the physical center point of the location data associated with the set of smoking behavior record data, wherein the reminder range includes the range of a second distance around the physical center point, and wherein the second distance is not less than the distance between the physical center point and the smoking behavior location farthest from the physical center point represented by the set of smoking behavior record data.

3. The triggering method according to claim 1, wherein, Determining the reminder range based on the location data associated with the set of smoking behavior record data further includes: Determining a first reminder range based on the position represented by each location data associated with the set of smoking behavior record data, wherein the reminder range is composed of multiple first reminder ranges.

4. The triggering method according to any one of claims 1 to 3, wherein, The triggering method further includes: Analyzing the smoking behavior record data and determining smoking behavior time data associated with each smoking behavior record data; Calculating the number of smoking times within a predetermined time period based on the smoking behavior time data; Recording the predetermined time periods with the number of smoking times higher than a second threshold; and Triggering the push of the intervention reminder before the start time point of the predetermined time period.

5. The triggering method according to any one of claims 1 to 3, wherein, The triggering method further includes: Analyzing the smoking behavior record data and determining smoking behavior time data associated with each smoking behavior record data; Calculating the number of smoking times within a predetermined time period based on the smoking behavior time data; Sorting the predetermined time periods in descending order according to the number of smoking times; and Triggering the push of the intervention reminder before the start time points of the top predetermined number of predetermined time periods.

6. The triggering method according to any one of claims 1 to 3, wherein, The form of the intervention reminder includes voice reminder, video reminder, picture reminder, and text reminder, and wherein the form of the intervention reminder is related to the specific time represented by the predetermined time period.

7. The triggering method according to any one of claims 1 to 3, the triggering method further includes: Receiving user data associated with the user; Determining the nicotine dependence degree of the user based on the user data; And Determine the type of the intervention reminder based on the user's nicotine dependence degree, wherein the type of the intervention reminder includes positive messages, neutral messages, and negative messages.

8. A triggering device for an intervention reminder based on a user's smoking behavior record, the triggering device comprising: A data receiving module configured to receive smoking behavior record data associated with a user, wherein the smoking behavior record data includes multiple smoking behavior records; An analysis module configured to analyze the smoking behavior record data to determine smoking behavior location data associated with each smoking behavior record; A first calculation module configured to calculate the distance between the positions represented by every two smoking behavior location data; A grouping module configured to record the first number of smoking behavior records with a distance lower than a first distance as a set of smoking behavior record data; A second calculation module configured to, when the first number is higher than a first threshold, determine a reminder range based on the location data associated with the set of smoking behavior record data, the reminder range being an uninterrupted and connected area or space and including the positions represented by each location data associated with the set of smoking behavior record data; and A triggering module configured to trigger the push of the intervention reminder when the user enters the reminder range from the outside.

9. The triggering device according to claim 8, wherein, Determining the reminder range based on the location data associated with the set of smoking behavior record data further includes: Calculating the physical center point of the location data associated with the set of smoking behavior record data, wherein the reminder range includes the range of a second distance around the physical center point, and the second distance is not less than the distance between the physical center point and the smoking behavior position farthest from the physical center point represented by the set of smoking behavior record data.

10. The triggering device according to claim 8, wherein, Determining the reminder range based on the location data associated with the set of smoking behavior record data further includes: Determining a first reminder range based on the position represented by each location data associated with the set of smoking behavior record data, wherein the reminder range is composed of multiple first reminder ranges.

11. The triggering device according to any one of claims 8 to 10, wherein, The analysis module is further configured to analyze the smoking behavior record data and determine smoking behavior time data associated with each smoking behavior record data. The first calculation module is further configured to calculate the number of smoking times within a predetermined time period based on the smoking behavior time data, and the triggering device further includes: A recording module configured to record the predetermined time period during which the number of smoking times is higher than a second threshold; and A first time triggering module configured to trigger the push of the intervention reminder before the start time point of the predetermined time period.

12. The triggering device according to any one of claims 8 to 10, wherein, The analysis module is further configured to analyze the smoking behavior record data and determine smoking behavior time data associated with each piece of smoking behavior record data. The first calculation module is further configured to calculate the number of smoking times within a predetermined time period based on the smoking behavior time data. And the triggering device further includes: a sorting module configured to sort the predetermined time periods in descending order according to the number of smoking times; and a second time triggering module configured to trigger the push of the intervention reminder before the start time points of a predetermined number of the predetermined time periods with higher rankings.

13. The trigger device according to any one of claims 8 to 10, wherein, The form of the intervention reminder includes a voice reminder, a video reminder, a picture reminder, and a text reminder. And among them, the form of the intervention reminder is related to the specific time represented by the predetermined time period.

14. The triggering device according to any one of claims 8 to 10, wherein the data receiving module is further configured to receive user data associated with the user, and the triggering device further includes: a third calculation module configured to determine the nicotine dependence degree of the user based on the user data; and an intervention reminder type determination module configured to determine the type of the intervention reminder based on the nicotine dependence degree of the user, wherein the type of the intervention reminder includes a positive message, a neutral message, and a negative message.

15. A tangible computer-readable storage medium, the storage medium including instructions for executing a method for triggering an intervention reminder based on a user's smoking behavior record. When the instructions are executed, the processor of the computer is at least used for: Receive smoking behavior record data associated with a user, where The smoking behavior record data includes multiple pieces of smoking behavior records; analyze the smoking behavior record data to determine smoking behavior location data associated with each piece of smoking behavior record; calculate the distance between the locations represented by every two pieces of smoking behavior location data; record the first number of smoking behavior records with the distance lower than a first distance as a set of smoking behavior record data; in the case where the first number is higher than a first threshold, determine a reminder range based on the location data associated with the set of smoking behavior record data. The reminder range is an uninterrupted and connected area or space and includes the locations represented by each piece of location data associated with the set of smoking behavior record data; and when the user enters the reminder range from the outside, trigger the push of the intervention reminder.

16. The computer-readable storage medium according to claim 15, wherein, Determining the reminder range based on the location data associated with the set of smoking behavior record data further includes: calculating the physical center point of the location data associated with the set of smoking behavior record data, wherein the reminder range includes the range of a second distance around the physical center point, and the second distance is not less than the distance between the physical center point and the smoking behavior location farthest from the physical center point among the set of smoking behavior record data.

17. The computer-readable storage medium according to claim 15, wherein, Determining the reminder range based on the location data associated with the set of smoking behavior record data further includes: A first reminder range is determined based on the position represented by each position data associated with the set of smoking behavior record data, wherein the reminder range is composed of a plurality of the first reminder ranges.

18. The computer-readable storage medium according to any one of claims 15 to 17, when the instructions are executed, further causes the processor of the computer to at least: Analyzing the smoking behavior record data and determining smoking behavior time data associated with each smoking behavior record data; Calculating the number of times of smoking within a predetermined time period based on the smoking behavior time data; Recording a predetermined time period during which the number of puffs is higher than a second threshold; and The push of the intervention reminder is triggered before the start time point of the predetermined time period.

19. The computer-readable storage medium according to any one of claims 15 to 17, when the instructions are executed, further causes the processor of the computer to at least: Analyzing the smoking behavior record data and determining smoking behavior time data associated with each smoking behavior record data; Calculating the number of times of smoking within a predetermined time period based on the smoking behavior time data; sorting the predetermined time periods from high to low according to the number of times of smoking; as well as The push of the intervention reminder is triggered before the start time of a predetermined number of predetermined time periods ranked first.

20. The computer-readable storage medium according to any one of claims 15 to 17, wherein The form of the intervention reminder includes voice reminder, video reminder, picture reminder and text reminder, and wherein the form of the intervention reminder is related to the specific time represented by the predetermined time period.

21. The computer-readable storage medium according to any one of claims 15 to 17, when the instructions are executed, further causes the processor of the computer to at least: receiving user data associated with the user; determining the user's nicotine dependence based on the user data; and Determine the type of the intervention reminder based on the nicotine dependence degree of the user, wherein, The types of intervention reminders include positive messages, neutral messages, and negative messages.

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