SEGMENTED HEATING TEMPERATURE CONTROL METHOD AND APPARATUS FOR ELECTRONIC VAPING SET, AND ELECTRONIC DEVICE

Information

  • Patent Application
  • 20250228301
  • Publication Number
    20250228301
  • Date Filed
    October 25, 2022
    3 years ago
  • Date Published
    July 17, 2025
    8 months ago
  • CPC
    • A24F40/57
  • International Classifications
    • A24F40/57
Abstract
A segmented heating temperature control method and apparatus for an electronic vaping set, and an electronic device. The method comprises: acquiring vaping set parameter information, and dividing into at least two e-cigarette heating segments on the basis of the vaping set parameter information (S101); determining an initial heating temperature of a vaping set, and constructing a neural convolutional network model according to the initial heating temperature of the vaping set, so as to calculate a first temperature change curve of each e-cigarette heating segment under a preset suction condition (S102); and on the basis of the first temperature change curve, separately adjusting the initial heating temperature corresponding to each e-cigarette heating segment to obtain each adjusted heating temperature, so that the temperature range of a second temperature change curve corresponding to each adjusted heating temperature is within a preset range (S103). The interior of the vaping set is divided into several e-cigarette heating segments, and by simulating and adjusting the temperature change of each e-cigarette heating segment in the vaping set vaping process under a certain vaping condition, the temperature corresponding to the finally vaped vapor being relatively balanced is ensured, and mouth-burning is prevented from occurring when a user is vaping.
Description

The present application claims priority to Chinese Patent Application No. 202111244660.0, titled “PIECEWISE HEATING TEMPERATURE CONTROL METHOD AND APPARATUS FOR VAPING SET, AND ELECTRONIC DEVICE”, filed on Oct. 26, 2021 with the China National Intellectual Property Administration, which is incorporated herein by reference in its entirety.


FIELD

The present disclosure relates to the technical field of heating control for vaping sets, and in particular to a piecewise heating temperature control method and apparatus for a vaping set, and an electronic device.


BACKGROUND

The emergence of low-temperature cigarette products has changed, through a heat-not-burn method, the traditional cigarettes which are burnt to generate visible smoke, thereby greatly reducing the release of tar and harmful components, and causing little harm to the environment and surrounding people. Currently, a heat-not-burn vaping set heats a cigarette through circumferential heating, or by inserting a central heating element into the cigarette for purpose of heating. Compared with the burning heating method for the traditional cigarettes, a cigarette is evenly hearted inside the vaping set in either of the above heating processes, and heat will continuously accumulate in the smoke inside the vaping set during the heating process due to poor air circulation. The smoke will bring the inside temperature to an outside position during vaping, which further increases the temperature at the outside position. Ultimately, the user may burn his mouth when smoking, and the smoking experience of the user is affected.


SUMMARY

In order to solve the above problem, a piecewise heating temperature control method and apparatus for a vaping set, and an electronic device are provided according to embodiments of the present disclosure.


In a first aspect, a piecewise heating temperature control method for a vaping set is provided according to embodiments of the present disclosure, and the method includes:

    • obtaining parameter information of the vaping set, and obtaining at least two cigarette heating segments based on the parameter information of the vaping set;
    • determining an initial heating temperature of the vaping set, and constructing a neural convolutional network model based on the initial heating temperature of the vaping set to calculate a first temperature change curve of each of the at least two cigarette heating segments in a preset vaping condition; and
    • regulating the initial heating temperature corresponding to each of the at least two cigarette heating segments separately based on the first temperature change curve, to obtain each regulated heating temperature, where a temperature range of a second temperature change curve corresponding to the each regulated heating temperature falls within a preset range.


Preferably, the obtaining parameter information of the vaping set, and obtaining at least two cigarette heating segments based on the parameter information of the vaping set includes:

    • obtaining the parameter information of the vaping set, where the parameter information of the vaping set includes a depth of a cigarette insertion groove and a radius of the cigarette insertion groove;
    • determining an optimal heating distance based on the depth and the radius of the cigarette insertion groove; and
    • obtaining the at least two cigarette heating segments based on the optimal heating distance.


Preferably, the regulating the initial heating temperature of each of the at least two cigarette heating segments separately based on the first temperature change curve to obtain each regulated heating temperature, where a temperature range of a second temperature change curve corresponding to the each regulated heating temperature falls within a preset range, includes:

    • regulating the initial heating temperature of each of the at least two cigarette heating segments separately based on the first temperature change curve to obtain the each regulated heating temperature;
    • inputting the each regulated heating temperature to the neural convolutional network model, to calculate the second temperature change curve of each of the at least two cigarette heating segments in the preset vaping condition;
    • regulating the each regulated heating temperature of each of the at least two cigarette heating segments separately based on the second temperature change curve, in a case that the temperature range of the second temperature change curve is not completely within the preset range; and
    • repeating the step of inputting the each regulated heating temperature to the neural convolutional network model until the temperature range of the second temperature change curve is completely within the preset range.


Preferably, the method further includes:

    • recording the each regulated heating temperature when a turn-off of the vaping set is detected; and
    • regulating a heating temperature inside the vaping set based on the each regulated heating temperature, in a case that the vaping set is restarted.


Preferably, the method further includes:

    • collecting user data on vaping habit, where the user data on vaping habit includes a vaping interval and a gas vaping volume each time; and
    • optimizing the neural convolutional network model based on the user data on vaping habit to recalculate the each regulated heating temperature.


Preferably, the method further includes:

    • continuously collecting the user data on vaping habit, to generate a full vaping habit curve for a user; and
    • regulating the each regulated heating temperature dynamically based on each vaping node in the full vaping habit curve for the user, in a case that the vaping set is started next time.


Preferably, the method further includes:

    • generating, after at least three full vaping habit curves for the user are generated, a standard full vaping habit curve by integrating the at least three full vaping habit curves for the user; and
    • regulating the each regulated heating temperature dynamically based on each vaping node in the standard full vaping habit curve, in a case that the vaping set is started next time.


In a second aspect, a piecewise heating temperature control apparatus for a vaping set is provided according to embodiments of the present disclosure, and the apparatus includes:

    • an obtaining module, configured to obtain parameter information of the vaping set, and obtain at least two cigarette heating segments based on the parameter information of the vaping set;
    • a determination module, configured to determine an initial heating temperature of the vaping set, and construct a neural convolutional network model based on the initial heating temperature of the vaping set to calculate a first temperature change curve of each of the at least two cigarette heating segments in a preset vaping condition; and
    • a regulating module, configured to regulate the initial heating temperature of each of the at least two cigarette heating segments separately based on the first temperature change curve, to obtain each regulated heating temperature, where a temperature range of a second temperature change curve corresponding to the each regulated heating temperature falls within a preset range.


In a third aspect, an electronic device is provided according to embodiments of the present disclosure, and the electronic device includes a memory, a processor and a computer program stored in the memory and executable by the processor, where the processor, when executing the computer program, performs the steps in the method according to the first aspect or any possible implementation in the first aspect.


In a fourth aspect, a computer-readable storage medium is provided according to embodiments of the present disclosure. The computer-readable storage medium stores a computer program thereon, where the computer program, when executed on a processor, implements steps in the method according to the first aspect or any possible implementation in the first aspect.


The beneficial effects of the present disclosure are as follows. Several cigarette heating segments are provided inside the vaping set, and by simulating and regulating the temperature change of each cigarette heating segment during the vaping process under a certain vaping condition, different cigarette heating segments have different heating temperatures, which ensures that the temperature corresponding to the smoke finally vaped is relatively uniform, protecting the user from burning his mouth when vaping. In addition, the heating processing of each cigarette heating segment is continuously regulated according to the vaping habit of the user, thereby improving the vaping experience for the user.





BRIEF DESCRIPTION OF THE DRAWINGS

For more clearly illustrating the technical solutions in the embodiments of the present disclosure, the drawings referred to for describing the embodiments are briefly described hereinafter. Apparently, the drawings in the following description are only some embodiments of the present disclosure, and for those skilled in the art, other drawings may be obtained based on the provided drawings without any creative effort.



FIG. 1 is a schematic flowchart of a piecewise heating temperature control method for a vaping set according to an embodiment of the present disclosure;



FIG. 2 is a schematic structural diagram of a piecewise heating temperature control apparatus for a vaping set according to an embodiment of the present disclosure; and



FIG. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.





DETAILED DESCRIPTION

Technical solutions of embodiments of the present disclosure are clearly and completely described hereinafter in conjunction with the drawings of the embodiments of the present disclosure.


In the introduction hereinafter, the terms “first”, “second” are merely for a purpose of description, and should not be understood as indicating or implying relative importance. The following introduction provides multiple embodiments according to the present disclosure, and different embodiments may be replaced or combined. Hence, the present disclosure may also be considered to include all possible combinations of the same and/or different embodiments described. Thus, if one embodiment contains features A, B and C, and another embodiment contains features B and D, then the present disclosure should also be considered to include all other possible combinations containing one or more of A, B, C and D, although this embodiment may not be clearly written in the following content.


The following description provides examples, and does not limit the scope, applicability or examples described in the claims. Changes may be made in the function and arrangement of described elements without departing from the scope of the present disclosure. Various examples may be omitted, substituted, or added with various procedures or components as appropriate. For example, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.


Reference is made to FIG. 1, which is a schematic flowchart of a piecewise heating temperature control method for a vaping set according to an embodiment of the present disclosure. In this embodiment of the present disclosure, the method includes step S101 to S103.


In step S101, parameter information of the vaping set is obtained, and at least two cigarette heating segments are obtained based on the parameter information of the vaping set.


The execution subject in the present disclosure may be a controller in a heat-not-burn vaping set.


In the embodiment of the present disclosure, during use of the heat-not-burn vaping set, a heat-not-burn cigarette is inserted into the corresponding heat-not-burn vaping set, and the heat-not-burn vaping set is heated to thereby heat the cigarette, to generate smoke for the user to vape. Different vaping sets with different models correspond to different vaping set parameter information. That is to say, a cigarette insertion depth and a corresponding size of an insertable cigarette vary in vaping sets with different models, resulting in different heating lengths of the cigarette when the cigarette is heated by the vaping set. In the present disclosure, to ensure that the smoke finally vaped out from each part of the vaping set does not burn the mouth, the inserted part of the cigarette is segmented for piecewise heating. Therefore, firstly, the parameter information of the vaping set is obtained, and the number of cigarette heating segments is determined based on the parameter information of the vaping set.


In an embodiment, the obtaining parameter information of the vaping set, and obtaining at least two cigarette heating segments based on the parameter information of the vaping set includes:

    • obtaining the parameter information of the vaping set, where the parameter information of the vaping set includes a depth of a cigarette insertion groove and a radius of the cigarette insertion groove;
    • determining an optimal heating distance based on the depth and the radius of the cigarette insertion groove; and
    • obtaining the at least two cigarette heating segments based on the optimal heating distance.


In this embodiment of the present disclosure, the controller can obtain the depth and the radius of the cigarette insertion groove from the parameter information of the vaping set. In order to ensure the effect of the piecewise heating, the segment length may not be too long, and the temperature for heating the inside of the cigarette increases when the radius of the cigarette insertion groove increases, which also affects the temperature at the intersection of adjacent heating segments. Therefore, a suitable optimal heating distance is determined according to the depth and the radius of the cigarette insertion groove, based on which the cigarette heating segments are obtained.


In S102, an initial heating temperature of the vaping set is determined, and a neural convolutional network model is constructed according to the initial heating temperature of the vaping set to calculate a first temperature change curve of each of the at least two cigarette heating segments in a preset vaping condition.


The vaping condition in the embodiment of the present disclosure may be understood as a simulated environmental condition when the vaping set is vaped, such as vaping intensity, vaping interval, and the like.


In the embodiment of the present disclosure, the heating temperature of each part of the conventional vaping set is the same. The controller determines the initial heating temperature which is the default heating temperature of the vaping set, obtains a training model to perform a simulated heating training based on the initial heating temperature of the vaping set, to thereby construct a neural convolutional network model suitable for the vaping set. The specific construction and generation process is common knowledge that can be easily conceived by those skilled in the art, and the model construction process is not the focus of the present disclosure, so it is not described in detail here. Through the constructed neural convolutional network model, a first temperature change curve of each of the cigarette heating segments is determined during the heating process in which the vaping set is uniformly heated internally from the initial heating temperature of the vaping set in the preset vaping condition (i.e., with the determined vaping time interval and vaping intensity).


In step S103, the initial heating temperature corresponding to each of the at least two cigarette heating segments is regulated separately based on the first temperature change curve to obtain each regulated heating temperature, where a temperature range of a second temperature change curve corresponding to the each regulated heating temperature falls within a preset range.


In the embodiment of the present disclosure, the temperature threshold range of each of the cigarette heating segments is obtained from the first temperature change curve, in a case that the vaping set is heated evenly from the initial heating temperature. In order not to burn the mouth and ensure the vaping experience, the temperature of the vaped smoke cannot be too high or too low, that is, during the whole vaping process, the temperature change of each of the cigarette heating segments should be within a preset range. Therefore, the heating temperature of each cigarette heating segment is regulated separately according to the first temperature change curve corresponding to each of the cigarette heating segments, and finally a regulated heating temperature corresponding to the second temperature change curve whose temperature range is completely within the preset range is obtained. By maintaining the regulated heating temperature of each of the cigarette heating segments, the user's vaping experience throughout the vaping process is improved without burning the mouth. And such a regulation process is relatively simple, and can be used in different vaping sets.


In an embodiment, the regulating the initial heating temperature of each of the at least two cigarette heating segments based on the first temperature change curve to obtain each regulated heating temperature, where a temperature range of a second temperature change curve corresponding to the each regulated heating temperature falls within a preset range, includes:

    • regulating the initial heating temperature of each of the at least two cigarette heating segments separately based on the first temperature change curve to obtain the each regulated heating temperature;
    • inputting the each regulated heating temperature to the neural convolutional network model, to calculate the second temperature change curve of each of the at least two cigarette heating segments in the preset vaping condition;
    • regulating the each regulated heating temperature of each of the at least two cigarette heating segments separately based on the second temperature change curve, in a case that the temperature range of the second temperature change curve is not completely within the preset range; and
    • repeating the step of inputting the each regulated heating temperature to the neural convolutional network model until the temperature range of the second temperature change curve is completely within the preset range.


In the embodiment of the present disclosure, the initial heating temperature is regulated according to the first temperature change curve, to obtain the regulated heating temperature, and simulation calculation is performed in the neural convolutional network model based on the regulated heating temperature, so as to determine whether the generated second temperature change curve is completely within the preset range. When the generated second temperature change curve is not completely within the preset range, the regulated heating temperature is regulated again based on the second temperature change curve. The process of simulating in the model is repeated until a regulated heating temperature meeting the condition is obtained.


In an embodiment, the method further includes:

    • recording the each regulated heating temperature when a turn-off of the vaping set is detected; and
    • regulating a heating temperature inside the vaping set based on the each regulated heating temperature, in a case that the vaping set is restarted.


In the embodiment of the present disclosure, after the regulated heating temperature suitable for the vaping set is calculated, the regulated heating temperature is recorded when the vaping set is turned off, so that the heating temperature of the vaping set can be directly regulated based on each regulated heating temperature when the vaping set is started again. In this way, it is not necessary to calculate the regulated heating temperature with the neural convolutional network model every time, which ensures the vaping efficiency of the vaping set.


In an embodiment, the method further includes:

    • collecting user data on vaping habit, where the user data on vaping habit includes a vaping interval and a gas vaping volume each time; and
    • optimizing the neural convolutional network model based on the user data on vaping habit to recalculate the each regulated heating temperature.


In the embodiment of the present disclosure, in the above-described process, the regulated heating temperature is obtained in the preset vaping condition, that is, based on the average vaping interval and the average vaping volume determined according to a large amount of user data. In practice, for individual users, their vaping habits are different, and different vaping habits affect the air flow inside the vaping set and the heat accumulation time, which leads to inaccurate calculation of the regulated heating temperature and thereby occurrence of mouth burn. Therefore, when the user is actually vaping, the user data on vaping habit is collected, and the simulation process of the neural convolutional network model is optimized based on the user data on vaping habit, so as to recalculate the regulated heating temperature. In this way, it is ensured that the finally obtained regulated heating temperature can best suit the user.


In an embodiment, the method further includes:

    • continuously collecting the user data on vaping habit, to generate a full vaping habit curve for a user; and
    • regulating the each regulated heating temperature dynamically based on each vaping node in the full vaping habit curve for the user, in a case that the vaping set is started next time.


In the embodiment of the present disclosure, during the actual vaping process of the user, he is unlikely to completely vape at constant interval and intensity, but may have his personal habit, such as vaping twice and tasting the smoke before continuing, so it may still be unlikely to obtain the most suitable regulated heating temperature only according to the user data on vaping habit. Therefore, the user data on vaping habit is continuously collected and the habit curve of the full vaping process of the user is generated accordingly. The dynamic change of the user's vaping habit during the full vaping process can be accurately characterized through the habit curve. Thereby, when the vaping set is started next time, data such as the time interval and change in vaping intensity between vaping nodes can be determined based on the vaping nodes in the vaping habit curve. The regulated heating temperature is dynamically regulated accordingly, better adapting to vaping of the user.


In an embodiment, the method further includes:

    • generating, after at least three full vaping habit curves for the user are generated, a standard full vaping habit curve by integrating the at least three full vaping habit curves for the user; and
    • regulating the each regulated heating temperature dynamically based on each vaping node in the standard full vaping habit curve, in a case that the vaping set is started next time.


In the embodiment of the present disclosure, every time the user completes a full vaping process, a full vaping habit curve for the user is generated. The controller integrates and regulates the at least three vaping habit curves and exclude errors, to obtain the standard full vaping habit curve. Subsequently, the regulated heating temperature is regulated based on the standard full vaping habit curve in the following process.


A piecewise heating temperature control apparatus for a vaping set according to the embodiments of the present disclosure is described in detail below with reference to FIG. 2. It should be noted that the piecewise heating temperature control apparatus for a vaping set shown in FIG. 2 is configured to implement the method according to the embodiment shown in FIG. 1 of the present disclosure, and for the sake of illustration, only the parts thereof relevant to the embodiment of the present disclosure are shown. For the specific technical details not disclosed, reference is made to the embodiment shown in FIG. 1 according to the present disclosure.


Reference is made to FIG. 2, which is a schematic structural diagram of a piecewise heating temperature control apparatus for a vaping set according to an embodiment of the present disclosure. As shown in FIG. 2, the apparatus includes:

    • an obtaining module 201, configured to obtain parameter information of the vaping set, and obtain at least two cigarette heating segments based on the parameter information of the vaping set;
    • a determination module 202, configured to determine an initial heating temperature of the vaping set, and construct a neural convolutional network model based on the initial heating temperature of the vaping set to calculate a first temperature change curve of each of the at least two cigarette heating segments in a preset vaping condition; and
    • a regulating module 203, configured to regulate the initial heating temperature of each of the at least two cigarette heating segments separately based on the first temperature change curve to obtain each regulated heating temperature, where a temperature range of a second temperature change curve corresponding to the each regulated heating temperature falls within a preset range.


In an embodiment, the obtaining module 201 includes:

    • an obtaining unit, configured to obtain the parameter information of the vaping set, where the parameter information of the vaping set includes a depth of a cigarette insertion groove and a radius of the cigarette insertion groove;
    • an optimal heating distance determination unit, configured to determine an optimal heating distance based on the depth and the radius of the cigarette insertion groove;
    • a heating segment obtaining unit, configured to obtain the at least two cigarette heating segments based on the optimal heating distance.


In an embodiment, the regulating module 203 includes:

    • a first regulating unit, configured to regulate the initial heating temperature of each of the at least two cigarette heating segments separately based on the first temperature change curve to obtain the each regulated heating temperature;
    • an inputting unit, configured to input the each regulated heating temperature to the neural convolutional network model and calculate the second temperature change curve of each of the at least two cigarette heating segments in the preset vaping condition;
    • a second regulating unit, configured to regulate the each regulated heating temperature of each of the at least two cigarette heating segments separately based on the second temperature change curve, in a case that the temperature range of the second temperature change curve is not completely within the preset range; and repeat the step of inputting the each regulated heating temperature to the neural convolutional network model until the temperature range of the second temperature change curve is completely within the preset range.


In an embodiment, the apparatus further includes:

    • a recording module, configured to record the each regulated heating temperature when a turn-off of the vaping set is detected; and
    • a starting module, configured to regulate a heating temperature inside the vaping set based on the each regulated heating temperature, in a case that the vaping set is restarted.


In an embodiment, the apparatus further includes:

    • a collecting module, configured to collect user data on vaping habit, where the user data on vaping habit includes a vaping interval and a gas vaping volume each time; and
    • an optimizing module, configured to optimize the neural convolutional network model based on the user data on vaping habit to recalculate the each regulated heating temperature.


In an embodiment, the apparatus further includes:

    • a continuous collecting module, configured to continuously collect the user data on vaping habit, to generate a full vaping habit curve for a user; and
    • a first dynamic regulating module, configured to regulate the each regulated heating temperature dynamically based on each vaping node in the full vaping habit curve for the user, in a case that the vaping set is started next time.


In an embodiment, the apparatus further includes:

    • an integrating module, configured to generate, after at least three full vaping habit curves for the user are generated, a standard full vaping habit curve by integrating the at least three full vaping habit curves for the user; and
    • a second dynamic regulating module, configured to regulate the each regulated heating temperature dynamically based on each vaping node in the standard full vaping habit curve, in a case that the vaping set is started next time.


Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present disclosure may be implemented by means of software and/or hardware. “Unit” and “module” in this specification refer to software and/or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware may be, for example, a field-programmable gate array (FPGA), integrated circuit (IC), etc.


Each processing unit and/or module in the embodiment of the present disclosure may be implemented by an analog circuit for realizing the functions described in the embodiments of the present disclosure, or may be realized by software for performing the functions described in the embodiments of the present disclosure.


Reference is made to FIG. 3, which is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. The device can be used to implement the method in the embodiment shown in FIG. 1. As shown in FIG. 3, the electronic device 300 may include: at least one central processing unit 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.


The communication bus 302 is used to implement connection and communication between the modules.


The user interface 303 may include a display screen (Display) and a camera (Camera), and optionally, the user interface 303 may also include a standard wired interface and a wireless interface.


Optionally, the network interface 304 may include a standard wired interface and a wireless interface (such as a WI-FI interface).


The central processing unit 301 may include one or more processing cores. The central processing unit 301 uses various interfaces and lines to connect various parts in the electronic device 300, and executes various functions of the terminal 300 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305 and calling data stored in the memory 305. Optionally, the central processing unit 301 may be implemented in at least one hardware manner selected from a digital signal processing (Digital Signal Processing, DSP), a field-programmable gate array (Field-Programmable Gate Array, FPGA), and a programmable logic array (Programmable Logic Array, PLA). The central processing unit 301 may integrate one or a combination of a central processing unit (Central Processing Unit, CPU), graphics central processing unit (Graphics Processing Unit, GPU), a modem, and the like. The CPU mainly handles the operating system, user interface and application programs, and the like; the GPU is used to render and draw the content to be displayed on the display screen; the modem is used to handle wireless communication. It can be understood that, the above-mentioned modem may not be integrated into the central processing unit 301, and may be implemented by a single chip.


The memory 305 may include a random access memory (Random Access Memory, RAM), or may include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium (non-transitory computer-readable storage medium). The memory 305 may be used to store an instruction, program, code, code set, or instruction set. The memory 305 may include a program storage area and a data storage area, where the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), and for implementing instructions in the method of the embodiments mentioned above; the data storage area may store the data and the like involved in the method of the embodiments described above. Optionally, the memory 305 may alternatively be at least one storage device arranged away from the aforementioned central processing unit 301. As shown in FIG. 3, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.


In the electronic device 300 shown in FIG. 3, the user interface 303 is mainly used to provide a user with an input interface to obtain data input by the user; and the central processing unit 301 may be used to invoke a piecewise heating temperature control application program for a vaping set which is stored in the memory 305, to perform the following operations:

    • obtaining parameter information of the vaping set, and obtaining at least two cigarette heating segments based on the parameter information of the vaping set;
    • determining an initial heating temperature of the vaping set, and constructing a neural convolutional network model based on the initial heating temperature of the vaping set to calculate a first temperature change curve of each of the at least two cigarette heating segments in a preset vaping condition; and
    • regulating the initial heating temperature corresponding to each of the at least cigarette heating segments separately based on the first temperature change curve to obtain each regulated heating temperature, where a temperature range of a second temperature change curve corresponding to the each regulated heating temperature falls within a preset range.


A computer-readable storage medium is also provided according to the present disclosure. The computer-readable storage medium stores a computer program thereon, where the computer program, when executed on a processor, implements steps in the method described above. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, optical disk, DVD, CD-ROM, microdrive, magneto-optical disk, ROM, RAM, EPROM, EEPROM, DRAM, VRAM, flash memory device, magnetic or optical card, nanosystem (including molecular memory IC), or any type of medium or device suitable for storing instructions and/or data.


It should be noted that for the foregoing method of the embodiments, for the sake of simple description, they are expressed as a series of action combinations, but those skilled in the art should know that the present disclosure is not limited by the described action sequence. Depending on the present disclosure, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are some preferred embodiments, and the actions and modules involved are not necessarily required by the present disclosure.


In the foregoing embodiments, the descriptions of each embodiment have their own emphases, and for parts not described in detail in a certain embodiment, reference may be made to relevant descriptions of other embodiments.


In the several embodiments provided in the present disclosure, it should be understood that the disclosed device may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or may be integrated into another system, or some features may be ignored, or not implemented. Additionally, the mutual coupling or direct coupling or communication connection shown or discussed may be through some service interfaces, and the indirect coupling or communication connection of devices or units, and may be in electrical or other forms.


The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiments.


In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software functional units.


If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, in the present disclosure, the core or the contributing part to the conventional technology of the technical solution, or all or part of the technical solution may be embodied in the form of a software product, and the computer software product is stored in a memory. Several instructions are included in the computer software product to enable a computer device (may be a personal computer, server or network device, etc.) to execute all or some of the steps of the methods described in the various embodiments of the present disclosure. The above-mentioned memory includes: various media capable of storing program codes such as a U disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), mobile hard disk, magnetic disk or optical disk.


Those skilled in the art can understand that all or some of the steps in the various methods of the above-described embodiments can be completed by a program instructing the related hardware. The program may be stored in a computer-readable memory, and the memory may include: a flash memory disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk, and the like.


The above-described are merely some exemplary embodiments of the present disclosure, and should not limit the scope of the present disclosure. That is, all equivalent changes and modifications made according to the teachings of the present disclosure still fall within the scope of the present disclosure. Embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any modification, use or adaptation of the present disclosure, and these modifications, uses or adaptations follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not described in the present disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims
  • 1. A piecewise heating temperature control method for a vaping set, comprising: obtaining parameter information of the vaping set, and obtaining at least two cigarette heating segments based on the parameter information of the vaping set;determining an initial heating temperature of the vaping set, and constructing a neural convolutional network model based on the initial heating temperature of the vaping set to calculate a first temperature change curve of each of the at least two cigarette heating segments in a preset vaping condition; andregulating the initial heating temperature corresponding to each of the at least two cigarette heating segments separately based on the first temperature change curve, to obtain each regulated heating temperature, wherein a temperature range of a second temperature change curve corresponding to the each regulated heating temperature falls within a preset range.
  • 2. The method according to claim 1, wherein the obtaining parameter information of the vaping set, and obtaining at least two cigarette heating segments based on the parameter information of the vaping set comprises: obtaining the parameter information of the vaping set, wherein the parameter information of the vaping set comprises a depth of a cigarette insertion groove and a radius of the cigarette insertion groove;determining an optimal heating distance based on the depth and the radius of the cigarette insertion groove; andobtaining the at least two cigarette heating segments based on the optimal heating distance.
  • 3. The method according to claim 1, wherein the regulating the initial heating temperature of each of the at least two cigarette heating segments separately based on the first temperature change curve to obtain each regulated heating temperature, wherein a temperature range of a second temperature change curve corresponding to the each regulated heating temperature falls within a preset range, comprises: regulating the initial heating temperature of each of the at least two cigarette heating segments separately based on the first temperature change curve to obtain the each regulated heating temperature;inputting the each regulated heating temperature to the neural convolutional network model, to calculate the second temperature change curve of each of the at least two cigarette heating segments in the preset vaping condition;regulating the each regulated heating temperature of each of the at least two cigarette heating segments separately based on the second temperature change curve, in a case that the temperature range of the second temperature change curve is not completely within the preset range; andrepeating the step of inputting the each regulated heating temperature to the neural convolutional network model until the temperature range of the second temperature change curve is completely within the preset range.
  • 4. The method according to claim 1, further comprising: recording the each regulated heating temperature when a turn-off of the vaping set is detected; andregulating a heating temperature inside the vaping set based on the each regulated heating temperature, in a case that the vaping set is restarted.
  • 5. The method according to claim 1, further comprising: collecting user data on vaping habit, wherein the user data on vaping habit comprises a vaping interval and a gas vaping volume each time; andoptimizing the neural convolutional network model based on the user data on vaping habit to recalculate the each regulated heating temperature.
  • 6. The method according to claim 5, further comprising: continuously collecting the user data on vaping habit, to generate a full vaping habit curve for a user; andregulating the each regulated heating temperature dynamically based on each vaping node in the full vaping habit curve for the user, in a case that the vaping set is started next time.
  • 7. The method according to claim 6, further comprising: generating, after at least three full vaping habit curves for the user are generated, a standard full vaping habit curve by integrating the at least three full vaping habit curves for the user; andregulating the each regulated heating temperature dynamically based on each vaping node in the standard full vaping habit curve, in a case that the vaping set is started next time.
  • 8. (canceled)
  • 9. An electronic device, comprising: a memory;a processor; anda computer program stored in the memory and executed in the processor;wherein the processor, when executing the computer program, performs the steps of:obtaining parameter information of the vaping set, and obtaining at least two cigarette heating segments based on the parameter information of the vaping set;determining an initial heating temperature of the vaping set, and constructing a neural convolutional network model based on the initial heating temperature of the vaping set to calculate a first temperature change curve of each of the at least two cigarette heating segments in a preset vaping condition; andregulating the initial heating temperature corresponding to each of the at least two cigarette heating segments separately based on the first temperature change curve, to obtain each regulated heating temperature, wherein a temperature range of a second temperature change curve corresponding to the each regulated heating temperature falls within a preset range.
  • 10. A computer-readable storage medium, storing a computer program thereon, wherein the computer program, when executed on a processor, implements the steps of: obtaining parameter information of the vaping set, and obtaining at least two cigarette heating segments based on the parameter information of the vaping set;determining an initial heating temperature of the vaping set, and constructing a neural convolutional network model based on the initial heating temperature of the vaping set to calculate a first temperature change curve of each of the at least two cigarette heating segments in a preset vaping condition; andregulating the initial heating temperature corresponding to each of the at least two cigarette heating segments separately based on the first temperature change curve, to obtain each regulated heating temperature, wherein a temperature range of a second temperature change curve corresponding to the each regulated heating temperature falls within a preset range.
  • 11. The electronic device according to claim 9, wherein the obtaining parameter information of the vaping set, and obtaining at least two cigarette heating segments based on the parameter information of the vaping set comprises: obtaining the parameter information of the vaping set, wherein the parameter information of the vaping set comprises a depth of a cigarette insertion groove and a radius of the cigarette insertion groove;determining an optimal heating distance based on the depth and the radius of the cigarette insertion groove; andobtaining the at least two cigarette heating segments based on the optimal heating distance.
  • 12. The electronic device according to claim 9, wherein the regulating the initial heating temperature of each of the at least two cigarette heating segments separately based on the first temperature change curve to obtain each regulated heating temperature, wherein a temperature range of a second temperature change curve corresponding to the each regulated heating temperature falls within a preset range, comprises: regulating the initial heating temperature of each of the at least two cigarette heating segments separately based on the first temperature change curve to obtain the each regulated heating temperature;inputting the each regulated heating temperature to the neural convolutional network model, to calculate the second temperature change curve of each of the at least two cigarette heating segments in the preset vaping condition;regulating the each regulated heating temperature of each of the at least two cigarette heating segments separately based on the second temperature change curve, in a case that the temperature range of the second temperature change curve is not completely within the preset range; andrepeating the step of inputting the each regulated heating temperature to the neural convolutional network model until the temperature range of the second temperature change curve is completely within the preset range.
  • 13. The electronic device according to claim 9, wherein the processor further performs the step of: recording the each regulated heating temperature when a turn-off of the vaping set is detected; andregulating a heating temperature inside the vaping set based on the each regulated heating temperature, in a case that the vaping set is restarted.
  • 14. The electronic device according to claim 9, wherein the processor further performs the step of: collecting user data on vaping habit, wherein the user data on vaping habit comprises a vaping interval and a gas vaping volume each time; andoptimizing the neural convolutional network model based on the user data on vaping habit to recalculate the each regulated heating temperature.
  • 15. The electronic device according to claim 14, wherein the processor further performs the step of: continuously collecting the user data on vaping habit, to generate a full vaping habit curve for a user; andregulating the each regulated heating temperature dynamically based on each vaping node in the full vaping habit curve for the user, in a case that the vaping set is started next time.
  • 16. The electronic device according to claim 15, wherein the processor further performs the step of: generating, after at least three full vaping habit curves for the user are generated, a standard full vaping habit curve by integrating the at least three full vaping habit curves for the user; andregulating the each regulated heating temperature dynamically based on each vaping node in the standard full vaping habit curve, in a case that the vaping set is started next time.
  • 17. The computer-readable storage medium according to claim 10, wherein the obtaining parameter information of the vaping set, and obtaining at least two cigarette heating segments based on the parameter information of the vaping set comprises: obtaining the parameter information of the vaping set, wherein the parameter information of the vaping set comprises a depth of a cigarette insertion groove and a radius of the cigarette insertion groove;determining an optimal heating distance based on the depth and the radius of the cigarette insertion groove; andobtaining the at least two cigarette heating segments based on the optimal heating distance.
  • 18. The computer-readable storage medium according to claim 10, wherein the regulating the initial heating temperature of each of the at least two cigarette heating segments separately based on the first temperature change curve to obtain each regulated heating temperature, wherein a temperature range of a second temperature change curve corresponding to the each regulated heating temperature falls within a preset range, comprises: regulating the initial heating temperature of each of the at least two cigarette heating segments separately based on the first temperature change curve to obtain the each regulated heating temperature;inputting the each regulated heating temperature to the neural convolutional network model, to calculate the second temperature change curve of each of the at least two cigarette heating segments in the preset vaping condition;regulating the each regulated heating temperature of each of the at least two cigarette heating segments separately based on the second temperature change curve, in a case that the temperature range of the second temperature change curve is not completely within the preset range; andrepeating the step of inputting the each regulated heating temperature to the neural convolutional network model until the temperature range of the second temperature change curve is completely within the preset range.
  • 19. The computer-readable storage medium according to claim 10, wherein the computer program further implements the steps of: recording the each regulated heating temperature when a turn-off of the vaping set is detected; andregulating a heating temperature inside the vaping set based on the each regulated heating temperature, in a case that the vaping set is restarted.
  • 20. The computer-readable storage medium according to claim 10, wherein the computer program further implements the steps of: collecting user data on vaping habit, wherein the user data on vaping habit comprises a vaping interval and a gas vaping volume each time; andoptimizing the neural convolutional network model based on the user data on vaping habit to recalculate the each regulated heating temperature.
  • 21. The computer-readable storage medium according to claim 20, wherein the computer program further implements the steps of: continuously collecting the user data on vaping habit, to generate a full vaping habit curve for a user; andregulating the each regulated heating temperature dynamically based on each vaping node in the full vaping habit curve for the user, in a case that the vaping set is started next time.
Priority Claims (1)
Number Date Country Kind
202111244660.0 Oct 2021 CN national
PCT Information
Filing Document Filing Date Country Kind
PCT/CN2022/127257 10/25/2022 WO