Segmented heating temperature control method and apparatus for electronic vaping set, and electronic device

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.

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; 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, 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; and   obtaining 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; 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.   
     
     
         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; 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.   
     
     
         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; and   optimizing 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; 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.   
     
     
         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; 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.   
     
     
         8 . (canceled) 
     
     
         9 . An electronic device, comprising:
 a memory;   a processor; and   a 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; 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, 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; 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, 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; and   obtaining 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; 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.   
     
     
         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; 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.   
     
     
         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; and   optimizing 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; 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.   
     
     
         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; 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.   
     
     
         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; and   obtaining 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; 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.   
     
     
         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; 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.   
     
     
         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; and   optimizing 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; 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.