An aerosol-generating device and a heating control method thereof and a storage medium

Abstract

The application provides an aerosol generating device and a heating control method thereof and a storage medium, the heating control method comprising: step S1: a control component obtains frequency error data, the frequency error data representing error data between a resonant frequency of a heating component and a target frequency; step S2: the control component performs fuzzy processing on the frequency error information to obtain a target frequency adjustment parameter; step S3: the control component adjusts the resonant frequency according to the target frequency adjustment parameter to obtain a target output frequency; and step S4: the control component controls the heating component to heat according to the target output frequency. The heating control method innovatively uses a frequency dynamic regulation and control strategy of a radio frequency heating system based on real-time capacitance monitoring and combining fuzzy control and a fast Fourier transform (FFT) algorithm, aiming to ensure that the heating component can always operate accurately in the best resonant frequency range, and realize efficient and stable heating performance.

Description

Technical Field

[0001] This application belongs to the field of new tobacco technology, specifically relating to an aerosol generating device, a heating control method for the aerosol generating device, and a computer-readable storage medium. Background Technology

[0002] In the field of heated tobacco products based on the principle of radio frequency induction heating, when radio frequency electromagnetic fields are used to heat materials such as tobacco matrix containing polar molecules in a non-contact manner, the dielectric constant of the heated material exhibits non-linear dynamic changes with increasing temperature, which may cause coupling drift of the equivalent capacitance and equivalent inductance parameters of the radio frequency resonant cavity, thereby causing unpredictable frequency shift of the system resonant frequency.

[0003] When the radio frequency heating system is in operation, the dielectric constant of the heated material changes dynamically due to the absorption of radio frequency energy. This time-varying characteristic of the dielectric constant directly causes electromagnetic parameter mismatch in the system's resonant cavity, specifically manifested as a shift in the system's resonant frequency.

[0004] Traditional frequency control strategies are mostly based on open-loop control or preset parameter adjustment mechanisms, making it difficult to construct a closed-loop feedback control system that adapts in real time to the dynamic changes in the resonant frequency. This results in the system being unable to achieve sub-Hertz-level precision tracking of the resonant frequency across the entire frequency band. The resulting frequency mismatch not only significantly reduces the coupling efficiency of radio frequency energy to the target material but also leads to a non-uniform phenomenon in the heating field distribution, where hot and cold spots coexist. This severely affects the overall energy efficiency and quality stability of the heating process. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide an aerosol generating apparatus, its heating control method, and a storage medium to solve the above-mentioned problems.

[0006] To solve the above-mentioned technical problems, this application adopts the following technical solution:

[0007] In a first aspect, this application provides a heating control method for an aerosol generating device. The aerosol generating device includes a heating chamber, a power supply, a heating component, and a control component. The heating component heats an aerosol forming matrix contained in the heating chamber during operation to generate aerosols. The control component generates a control signal. The heating control method includes: Step S1: The control component acquires frequency error data, which characterizes the error between the resonant frequency of the heating component and a target frequency; Step S2: The control component performs fuzzification processing on the frequency error information to obtain a target frequency adjustment parameter; Step S3: The control component adjusts the resonant frequency according to the target frequency adjustment parameter to obtain a target output frequency; Step S4: The control component controls the heating component to heat according to the target output frequency.

[0008] Furthermore, the error data includes the error value between the resonant frequency and the target frequency, and the error change rate of the error value. Step S1 includes: Step S11: The control component calculates the resonant frequency, which includes a first resonant frequency and a second resonant frequency; Step S12: The control component calculates the error value based on the resonant frequency and the target frequency, which includes the error value at a first moment corresponding to the first resonant frequency and the target frequency, and the error value at a second moment corresponding to the second resonant frequency and the target frequency; Step S13: The control component calculates the error change rate based on the error value at the first moment and the error value at the second moment.

[0009] Furthermore, the resonant frequency is calculated using the following formula: Where f0 is the resonant frequency, L is the inductance of the heating element, and C is the capacitance of the heating element.

[0010] Further, step S2 includes: step S21: the control component converts the frequency error data into fuzzy linguistic variables; step S22: the control component performs fuzzy inference processing on the fuzzy linguistic variables according to the fuzzy rule base to obtain the fuzzy output data corresponding to the frequency adjustment parameters; step S23: the control component performs defuzzification processing on the fuzzy output data to obtain the target frequency adjustment parameters.

[0011] Furthermore, the fuzzy rule base is constructed by the following steps: Step D1: Convert the frequency error data into fuzzy variables and establish a fuzzy rule table; Step D2: Define each fuzzy language in the fuzzy rule table based on the membership function and determine the membership degree corresponding to each fuzzy language.

[0012] Furthermore, step S23 includes: step S231: the control component performs transformation processing on the fuzzy output data based on the center of gravity algorithm to obtain the target frequency adjustment parameters.

[0013] Furthermore, the heating control method also includes: step S5: the control component acquires heating temperature data; step S6: the control component corrects and calculates the capacitance value of the heating component based on the linear regression model and the heating temperature data.

[0014] Secondly, this application provides an aerosol generating apparatus, comprising: a heating chamber, a power supply, a heating component, and a control component. The heating component heats an aerosol forming matrix contained within the heating chamber during operation to generate aerosols. The control component generates a control signal and includes: an acquisition module, a fuzzy module, an adjustment module, and a control module. The acquisition module acquires frequency error data, which characterizes the error between the resonant frequency of the heating component and a target frequency. The fuzzy module performs fuzzification processing on the frequency error information to obtain a target frequency adjustment parameter. The adjustment module adjusts the resonant frequency according to the target frequency adjustment parameter to obtain a target output frequency. The control module controls the heating component to heat according to the target output frequency.

[0015] Furthermore, the aerosol generating device also includes a capacitive sensor, which is disposed in the middle and / or edge of the heating component electrode plate.

[0016] Thirdly, a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the heating control method described above.

[0017] Among them, aerosol-generating products are smoking products, including aerosol-forming matrix, which generates aerosols through heating that can be directly inhaled into the lungs of the user through the user's mouth.

[0018] Preferably, the aerosol forming matrix is ​​a solid aerosol forming matrix. The aerosol forming matrix may include both solid and liquid components.

[0019] Preferably, the aerosol-forming matrix includes nicotine. In some preferred embodiments, the aerosol-forming matrix includes tobacco.

[0020] An aerosol generating device is used to describe an apparatus that interacts with an aerosol-forming matrix of an aerosol-generating article to generate an aerosol. Preferably, the aerosol generating device is a smoking device that interacts with the aerosol-generating matrix of the aerosol-generating article to generate an aerosol that can be directly inhaled into the user's lungs through the user's mouth. The aerosol generating device may be a fixator for a smoking article.

[0021] A sensor is a material that can convert electromagnetic energy into heat. When placed in a undulating electromagnetic field, the eddy currents induced in the sensor cause it to heat up. When an elongated sensor is positioned in thermal contact with an aerosol-forming matrix, the aerosol-forming matrix is ​​heated by the sensor.

[0022] The aerosol generating article is designed to engage with an electrically operated aerosol generating device, including an induction heating source. The induction heating source or sensor generates a fluctuating electromagnetic field to heat a sensor located within the fluctuating electromagnetic field. In use, the aerosol generating article engages with the aerosol generating device such that the sensor is located within the fluctuating electromagnetic field generated by the sensor.

[0023] The length of the receptor is greater than its width or thickness, for example, more than twice its width or thickness. Therefore, the receptor can be described as an elongated receptor. The receptor can be arranged generally longitudinally within the aerosol-generating matrix. This means that the length of the elongated receptor is arranged approximately parallel to the longitudinal direction of the aerosol-generating matrix, for example, within plus or minus 10 degrees. In a preferred embodiment, the elongated receptor can be located at a radial center position within the aerosol-generating matrix and extend along the longitudinal axis of the aerosol-generating matrix.

[0024] The sensor can be made of any material capable of being heated inductively to a temperature sufficient to generate an aerosol matrix. Preferred sensors include metals or carbon. Preferred sensors may include ferromagnetic materials, such as ferrite, ferromagnetic steel, or stainless steel. Suitable sensors may be aluminum or may include aluminum. Preferred sensors may be made of 400 series stainless steel, such as grade 410, 420, or 430 stainless steel. Different materials will consume different amounts of energy when placed in an electromagnetic field with similar frequency and field strength. Therefore, parameters of the sensor, such as material type, length, width, and thickness, can be varied within a known electromagnetic field to provide the desired energy consumption.

[0025] The receptors are arranged in thermal contact with the aerosol-forming matrix. Therefore, when the receptors are heated, the aerosol-forming matrix is ​​heated and forms an aerosol. In one embodiment, a heating element including the receptors is inserted into the aerosol-forming matrix, and the aerosol-generating apparatus may include one or more elongated heating elements. In another embodiment, the aerosol-generating matrix may include the receptors; alternatively, the aerosol-generating matrix may include multiple receptors, and the receptors may be elongated, granular, mesh-like, radial, tubular, hourglass-shaped, spiral, etc.

[0026] The aerosol generating device can generate a fluctuating electromagnetic field between approximately 1 MHz and 30 MHz, for example, between 2 MHz and 10 MHz, or for example, between 5 MHz and 7 MHz, through the induction coil of the induction emitter.

[0027] The induction coil material should be a material with good conductivity, such as metal; in addition, in this patent, the induction coil material should also have good elastic deformation ability, and can be spring steel, gold, silver or other metals.

[0028] The power source can be any suitable power source, such as a DC voltage source, like a battery. In one embodiment, the power source is a lithium-ion battery. Alternatively, the power source can be a nickel-metal hydride battery, a nickel-cadmium battery, or a lithium-based battery, such as a lithium cobalt, lithium iron phosphate, lithium titanate, or lithium polymer battery.

[0029] The control element can be a simple switch. Alternatively, the control element can be a circuit and may include one or more microprocessors or microcontrollers.

[0030] An aerosol generation system may include an aerosol generation device and one or more aerosol generation articles, wherein the aerosol generation device is configured with a corresponding number of heating chambers to contain the aerosol generation articles.

[0031] As can be seen from the above technical solutions, the advantages and positive effects of the aerosol generating device, its heating control method, and the storage medium proposed in this application are as follows:

[0032] The heating control method for the aerosol generation device proposed in this application innovatively employs a frequency dynamic adjustment strategy for the radio frequency heating system based on real-time capacitance monitoring and integrating fuzzy control and fast Fourier transform (FFT) algorithms. This aims to ensure that the heating components can always operate precisely within the optimal resonant frequency range, thereby achieving efficient and stable heating performance.

[0033] (1) High-precision real-time capacitance monitoring mechanism: Based on a variable dielectric capacitance sensor, a high-sensitivity real-time capacitance monitoring system was constructed. This system can accurately capture subtle fluctuations in the system's resonant frequency, ensuring that any frequency change can be detected in a timely and accurate manner, providing reliable data support for subsequent frequency regulation.

[0034] (2) Frequency regulation technology that deeply integrates fuzzy control and FFT algorithm: By organically combining the fuzzy control algorithm and the FFT algorithm, this method realizes intelligent and dynamic adjustment of the signal source frequency. With its powerful nonlinear processing capability, the fuzzy control algorithm can quickly generate a preliminary frequency adjustment strategy based on the real-time monitored changes in capacitance value; while the FFT algorithm further performs spectrum analysis on the signal to accurately locate the resonant frequency point, ensuring the accuracy and timeliness of frequency adjustment, thereby ensuring that the system is always in the optimal resonant state and maximizing heating efficiency.

[0035] (3) Miniaturization Design and Electromagnetic Shielding Enhancement Strategy: To improve the overall performance of the sensor, this method adopts advanced miniaturization design technology, effectively reducing the sensor's size and weight, while optimizing its internal structural layout and improving space utilization. In addition, comprehensive electromagnetic shielding measures are introduced. By rationally arranging the shielding layer and selecting highly conductive materials, the impact of external electromagnetic interference on the sensor is effectively reduced, significantly improving the sensor's stability and anti-interference ability, and ensuring the accuracy and reliability of the monitoring data.

[0036] (4) Hardware and software co-operated temperature compensation technology: To address the measurement deviation that may occur in sensors under different temperature environments, this method innovatively adopts a temperature compensation technology that combines hardware and software. At the hardware level, by selecting components with small temperature coefficients and high stability, and designing a reasonable temperature compensation circuit, the impact of temperature on sensor performance is effectively reduced. At the software level, advanced algorithms are used to perform real-time correction and processing of measurement data, further eliminating temperature-induced errors and ensuring that the sensor can provide accurate and reliable measurement data under various temperature conditions. Attached Figure Description

[0037] The above description of this application and the following detailed embodiments will be better understood when read in conjunction with the accompanying drawings. It should be noted that the drawings are merely examples of the claimed technical solutions.

[0038] Figure 1 This is a structural diagram of the aerosol generating apparatus provided in this application;

[0039] Figure 2 This is a flowchart of the heating control method for the aerosol generating apparatus provided in this application;

[0040] Figure 3 This is a schematic diagram of the membership function provided in this application;

[0041] Figure 4 This is a schematic diagram of the location of the capacitive sensor provided in this application;

[0042] Figure 5 This is a schematic diagram of the error-membership function provided in this application;

[0043] Figure 6 This is a schematic diagram of the error change rate-membership function provided in this application;

[0044] Figure 7 This is a schematic diagram of the frequency adjustment amount-membership function provided in this application.

[0045] The reference numerals in the attached figures are explained as follows:

[0046] Aerosol generating device: 10;

[0047] Heating components: 11;

[0048] Power supply: 12;

[0049] Control components: 13;

[0050] Heating chamber: 14;

[0051] Aerosol forming matrix: 20;

[0052] Capacitive sensor: 30. Detailed Implementation

[0053] The following detailed description of the features and advantages of this application is sufficient to enable any person skilled in the art to understand the technical content of this application and implement it accordingly. Furthermore, based on the specification, claims and drawings disclosed in this specification, those skilled in the art can easily understand the related objectives and advantages of this application.

[0054] It should be noted that in this specification, similar reference numerals and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0055] In the description of this embodiment, it should be noted that the terms "upper", "lower", "inner", "bottom", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product is usually placed during use. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0057] Please refer to Figure 1 This application provides a heating control method that can be applied to an aerosol generating device 10, which may include a heating chamber 14, a power supply 12, a heating component 11, and a control component 13.

[0058] The heating component 11 is used to heat the aerosol forming matrix 20 contained in the heating chamber 14 during operation to generate aerosols, and the control component 13 is used to generate control signals.

[0059] Heating chamber 14 is the heating space for aerosol forming matrix 20. Its internal environment has been calculated to ensure that the matrix can be uniformly and efficiently converted into aerosols during heating. Power module 12 acts as the power source for aerosol generating device 10, continuously providing electrical energy to the entire system. Its stability and efficiency directly affect the heating effect.

[0060] The heating component 11 is responsible for converting electrical energy into heat energy to heat the aerosol forming matrix 20. It adopts advanced heating technology and materials, which can accurately control the temperature while rapidly heating up, avoiding overheating and damage to the matrix 20, thereby preserving the flavor and texture of the matrix 20. The heating component 11 can be an internal heating component, an external heating component, or a combination of internal and external heating components, and this application is not limited to this.

[0061] Internal heating refers to the heating element in the heating assembly 11 being at least partially positioned inside the aerosol forming matrix 20, directly heating the aerosol forming matrix 20. Internal heating is achieved through the design of specific heating tubes or heating elements. For example, a heating cavity is formed inside the heating tube to accommodate the aerosol forming matrix 20, and a heating layer is provided on the outer or inner side of the heating tube. Heat is generated by passing electricity to heat the matrix 20. Additionally, auxiliary structures such as a heat spreader layer or a dielectric layer can be added as needed to improve heating uniformity and efficiency. Because the heating element is in closer contact with the matrix, the required heating temperature can be reached more quickly. Internal heating allows for more direct heating of the aerosol forming matrix, improving heating efficiency.

[0062] External heating refers to the placement of the heating element outside the aerosol-forming matrix 20, heating the matrix 20 through heat conduction or radiation. External heating typically involves designing a specific heating cavity or tubular structure to contain the aerosol-generated product. The heating element (such as a heating element, planar spiral coil, etc.) is positioned outside the heating cavity or tubular structure. External heating methods also incorporate structures such as heat insulation pipes and support frames to improve heating uniformity and stability. External heating avoids direct contact between the heating element and the aerosol-forming matrix, reducing contamination and damage to the heating element from the matrix. Through proper design of the heating cavity and heat insulation structure, uniform heating of the matrix can be achieved, improving the quality of aerosol generation.

[0063] The control component 13 is the intelligent control center of the entire aerosol generating device 10. It can generate and adjust the heating operating parameters of the aerosol generating device 10, such as target temperature and heating time, and regulate the amount of electrical energy delivered by the power supply 12 to the heating component 11 to achieve precise temperature control.

[0064] This application provides a computer-readable storage medium that can be combined with a control component 13. The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps in the heating control method.

[0065] Exemplary examples show that the memory in this application embodiment can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which serves as an external cache.

[0066] By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0067] The control component 13 may include a computer-readable storage medium, and the control component 13 may execute the steps of the following heating control method through a program stored in the computer-readable storage medium itself.

[0068] Please refer to Figure 2 The specific steps of the heating control method provided in this application are as follows:

[0069] Step S1: The control component acquires frequency error data.

[0070] Among them, the frequency error data represents the error between the resonant frequency and the target frequency of the heating component.

[0071] Specifically, the error data includes the error value between the resonant frequency and the target frequency, and the rate of change of the error value.

[0072] Step S1 includes:

[0073] Step S11: The control component calculates the resonant frequency.

[0074] In the radio frequency heating system of the aerosol generating device 10, the dielectric constant of the material changes due to heating. In particular, heating cigarettes causes complex physicochemical changes, and the change in dielectric constant is more obvious, which leads to a change in the capacitance of the system and further to a change in the resonant frequency of the system.

[0075] The resonant frequency can be calculated using the following formula (1):

[0076]

[0077] Where f0 is the resonant frequency, L is the inductance of the heating element, and C is the capacitance of the heating element.

[0078] The heating component 11 consists of two conductive electrode plates close to each other, with a dielectric (e.g., for heating cigarettes) sandwiched in between, forming a capacitor.

[0079] The parallel plate capacitor can be calculated using the following formula (2):

[0080]

[0081] Where C is the capacitance of the capacitor, and ε0 is the vacuum permittivity (≈8.854 × 10⁻⁶). -12 F / m); S is the area of ​​the plates facing each other, d is the distance between the plates, ε r is the relative permittivity of the medium, representing the capacitance enhancement factor of the medium relative to vacuum.

[0082] For example, 1-System initialization:

[0083] 1) Set the initial frequency of the signal source to ensure that the system can work normally in the initial state.

[0084] 2) Initialize the parameters of the fuzzy controller.

[0085] 2-Frequency Detection and Feedback:

[0086] 1) Use the FFT algorithm to perform spectrum analysis on the radio frequency signal and extract the current resonant frequency of the system.

[0087] ① Sampling: The radio frequency signal is sampled at a frequency of fs and the number of sampling points is N.

[0088] ② Window function: Apply Hanning window to reduce spectral leakage.

[0089] ③FFT Transform: Perform a Fast Fourier Transform on the sampled signal to obtain the spectrum.

[0090] ④ Frequency extraction: Find the frequency corresponding to the maximum amplitude in the spectrum and use it as the current resonant frequency.

[0091] The resonant frequency includes the first resonant frequency and the second resonant frequency.

[0092] Step S12: The control component calculates the error value based on the resonant frequency and the target frequency.

[0093] The error values ​​include the first time error value corresponding to the first resonant frequency and the target frequency, and the second time error value corresponding to the second resonant frequency and the target frequency.

[0094] The error value can be calculated using the following formula (3):

[0095] e = f target -f current (3)

[0096] Where e is the error value, f target For the target frequency, f current It is the resonant frequency.

[0097] Step S13: The control component calculates the error change rate based on the error value at the first time step and the error value at the second time step.

[0098] The rate of change of error can be calculated using the following formula (4):

[0099] Δe=e current -e previous (4)

[0100] Where Δe is the rate of change of error, e current e represents the error value at the first moment. previous This is the error value at the second time step.

[0101] Step S2: The control component performs fuzzification processing on the frequency error information to obtain the target frequency adjustment parameters.

[0102] Specifically, step S2 includes:

[0103] Step S21: The control component converts the frequency error data into fuzzy linguistic variables.

[0104] Step S22: The control component performs fuzzy inference processing on the fuzzy linguistic variables according to the fuzzy rule base to obtain the fuzzy output data corresponding to the frequency adjustment parameters.

[0105] A fuzzy rule base can be constructed using the following steps:

[0106] Step D1: Convert the frequency error data into fuzzy variables and establish a fuzzy rule table.

[0107] Step D2: Based on the membership function, define each fuzzy language in the fuzzy rule table and determine the membership degree corresponding to each fuzzy language.

[0108] Step S23: The control component performs defuzzification processing on the fuzzy output data to obtain the target frequency adjustment parameters.

[0109] Please refer to the following examples for details:

[0110] (1) Establish a fuzzy rule base:

[0111] 1. Convert the error e and the rate of change of error Δe into fuzzy variables (such as "positive large (PB)", "zero (Z)", "negative large (NB)") and establish a fuzzy rule table (as shown in Table 1).

[0112] Table 1: Fuzzy Rule Table

[0113] NB NB NB Z Z NB Z PB PB Z PB PB

[0114] 2. Membership function: The triangular membership function is used to define the membership degree of each fuzzy linguistic variable.

[0115] For example, for x∈[-5,5], the parameters of each membership function are defined as follows:

[0116] Table 2: Parameter Definition Table of Membership Functions

[0117] NB -5 -2.5 0 Z -2.5 0 2.5 PB 0 2.5 5

[0118] NB: Covers the negative interval, from -5 to 0, with a vertex at -2.5.

[0119] Z: Covers the middle region, from -2.5 to 2.5, with vertices at 0.

[0120] PB: Covers the positive interval from 0 to 5, with the vertex at 2.5.

[0121] like Figure 3 As shown, the mathematical expression for each membership function is as follows:

[0122]

[0123] (2) Fuzzy reasoning: Based on the fuzzy rule table, fuzzy reasoning is performed on the error and the rate of change of error to obtain the fuzzy output of the frequency adjustment amount.

[0124] (3) Defuzzification: The fuzzy output is converted into a precise frequency adjustment amount using the centroid method, calculated by formula (8).

[0125]

[0126] Where Δf is the frequency adjustment amount, f i It is the resonant frequency at time i, u i It is its degree of membership.

[0127] Step S3: The control component adjusts the parameters according to the target frequency to adjust the resonant frequency and obtain the target output frequency.

[0128] Specifically, the output frequency of the signal source is dynamically adjusted according to the frequency adjustment amount Δf, and the calculation formula (9) is as follows:

[0129] f new =f current +Δf (9)

[0130] Among them, f new For the target output frequency, f current Δf represents the current output frequency, and Δf represents the frequency adjustment amount.

[0131] Step S4: The control component controls the heating component to heat according to the target output frequency.

[0132] Specifically, after obtaining the target output frequency, the control component can also compare the target output frequency with a preset threshold to determine whether the error between the adjusted target output frequency and the threshold is within the preset range. If yes, the control component controls the heating component to heat according to the target output frequency; if no, the control component re-executes the heating control method to obtain the target output frequency.

[0133] Heating control methods may also include:

[0134] Step S5: The control component acquires heating temperature data.

[0135] For example, such as Figure 4 As shown, the aerosol generating device 10 may also include a capacitive sensor 30.

[0136] Preferably, the capacitance sensor 30 is a variable dielectric type capacitance sensor, which monitors the change in capacitance value in real time to ensure that the change in the resonant frequency of the system can be detected in a timely manner.

[0137] Option 1: The capacitive sensor 30 is located in the middle of the electrode plate of the heating assembly 11.

[0138] Option 2: The capacitive sensor 30 is set at the edge of the electrode plate of the heating assembly 11.

[0139] It is understandable that: ① Measurement accuracy: Scheme 1 has higher measurement accuracy due to direct contact with the electric field; Scheme 2 has strong anti-interference ability, but its measurement accuracy may be slightly lower than that of Scheme 1.

[0140] ② Response speed: Option 1 has a faster response speed and is suitable for real-time monitoring; Option 2 has a slower response speed and is suitable for scenarios where response speed requirements are not high.

[0141] ③ Anti-interference capability: Since Option 2 is far from the center of the electromagnetic field, it has a stronger anti-interference capability and is suitable for use in environments with strong electromagnetic interference.

[0142] ④ Installation complexity: Option 1 has a higher installation complexity and requires space to be reserved between the plates; Option 2 is easy to install and is suitable for use in environments with limited space.

[0143] Step S6: The control component corrects and calculates the capacitance value of the heating component based on the linear regression model and heating temperature data.

[0144] Hardware temperature compensation: Thermistors are placed around the sensor to measure the temperature in real time.

[0145] Software temperature compensation: A linear regression model is used to compensate for the temperature of the sensor output. The calculation formula (10) is as follows:

[0146] C comp =C measured +k(T-T0) (10)

[0147] Among them, C comp This is the compensated capacitance value, C. measured Here, k is the measured capacitance value, k is the temperature coefficient, T is the current temperature, and T0 is the reference temperature.

[0148] It is understandable that by conducting multiple experiments and adjustments, the fuzzy rule base or PID parameters can be optimized to improve the system's response speed and stability.

[0149] To facilitate understanding of the inventive concept of this application, please refer to the following specific embodiments.

[0150] 1. Define the error e∈[-5,5]; define the rate of change of error Δe∈[-3,3]; define the frequency adjustment Δf∈[-2,2]. Their triangular membership functions are as follows: Figure 5-7 As shown:

[0151] 2. Assuming the target frequency is 27MHz, the current resonant frequency is 25MHz, and the resonant frequency calculated at the previous sampling point is 24.5MHz, then the current error e = 2MHz, and the current error change rate Δe = -0.5MHz.

[0152] ①The current error e is 2MHz:

[0153] NB: Membership degree is 0; Z: Membership degree is 0.2; PB: Membership degree is 0.8.

[0154] ②The current error change rate Δe is -0.5MHz:

[0155] NB: Membership degree is 0.33; Z: Membership degree is 0.67; PB: Membership degree is 0.

[0156] 3. Calculate the activation strength of each rule based on the fuzzy rule table:

[0157] ① Rule 1: If e is Z and Δe is NB, then Δf is NB. Activation intensity = min(0.2, 0.33) = 0.2.

[0158] ② Rule 2: If e is Z and Δe is Z, then Δf is Z. Activation intensity = min(0.2, 0.67) = 0.2.

[0159] ③ Rule 3: If e is PB and Δe is NB, then Δf is Z. Activation intensity = min(0.8, 0.33) = 0.33.

[0160] ④ Rule 4: If e is PB and Δe is Z, then Δf is PB. Activation intensity = min(0.8, 0.67) = 0.67.

[0161] 4. Aggregation: Merge the output membership degrees of all rules to obtain a fuzzy output of the overall frequency adjustment amount.

[0162] NB: 0.2; Z: max(0.2,0.33)=0.33; PB: 0.67.

[0163] 5. Defuzzification:

[0164] 6. Update the signal source frequency: f new =25 + 0.39 = 25.39MHz

[0165] Fuzzy inference adjusts PID parameters, thereby indirectly affecting the frequency adjustment Δf. Example illustration:

[0166] (1) Initialization:

[0167] Set target frequency f target =1MHz,

[0168] Initial detection frequency f measured =0.95MHz

[0169] Initial PID parameters: K p =0.8, K i =0.1, Kd =0.05. (Adjustment limit to 0)

[0170] Steady-state error e∈[-2kHz, 2kHz]

[0171] (2) Real-time control:

[0172] 1. Read the FFT detection frequency fmeasured every 10ms and calculate e and Δe.

[0173] 2. Adjust the PID parameters using fuzzy inference and output the control quantity. The calculation formula (11) is as follows:

[0174] Δf=K p e+K i ·∑e+K d ·Δe (11)

[0175] 3. Update signal source: f new =f current +Δf.

[0176] 4. PID fuzzy rule table (as shown in Table 3):

[0177] Table 3: PID Fuzzy Rule Table

[0178]

[0179] Among them, ↑↑ / ↓↓: significantly increase / decrease (e.g., ±0.3); ↑ / ↓: slightly increase / decrease (e.g., ±0.1); →: remain unchanged.

[0180] 5. Summary table of parameter adjustment process (as shown in Table 4):

[0181] Table 4: Summary of Parameter Adjustment Process

[0182]

[0183] 6. Fuzzy inference adjusts PID parameters, thereby indirectly affecting the frequency adjustment amount Δf.

[0184] This application provides two methods for fuzzy processing to obtain the frequency adjustment amount Δf:

[0185] Method 1: Directly obtain the fuzzy output of the frequency adjustment Δf through fuzzy inference, and then obtain the precise Δf value through defuzzification. This method is suitable for scenarios where the control quantity is directly output, especially when the system response speed requirement is high and PID parameter adjustment is not the primary concern.

[0186] Method 2: Dynamically adjust the parameters (K) of the PID controller through fuzzy inference. p K i Kd This indirectly affects the frequency adjustment value Δf.

[0187] This method is suitable for scenarios that require dynamic adjustment of PID parameters, especially when the system characteristics change significantly or the PID parameters are difficult to determine in advance.

[0188] The control components of the aerosol generating device 10 of this application include: an acquisition module, a fuzzy module, an adjustment module, and a control module.

[0189] The acquisition module is used to acquire frequency error data, which represents the error between the resonant frequency of the heating component and the target frequency.

[0190] The fuzzy module is used to fuzzify the frequency error information to obtain the target frequency adjustment parameters.

[0191] The adjustment module is used to adjust parameters according to the target frequency, thereby adjusting the resonant frequency to obtain the target output frequency.

[0192] The control module is used to control the heating components to heat according to the target output frequency.

[0193] It is understood that the acquisition module is also used to calculate the resonant frequency, which includes the first resonant frequency and the second resonant frequency; based on the resonant frequency and the target frequency, the error value is calculated, which includes the error value at the first moment corresponding to the first resonant frequency and the target frequency and the error value at the second moment corresponding to the second resonant frequency and the target frequency; based on the error value at the first moment and the error value at the second moment, the error change rate is calculated.

[0194] The fuzzy module is also used to convert frequency error data into fuzzy linguistic variables; based on the fuzzy rule base, it performs fuzzy inference processing on the fuzzy linguistic variables to obtain the fuzzy output data corresponding to the frequency adjustment parameters; and it performs defuzzification processing on the fuzzy output data to obtain the target frequency adjustment parameters.

[0195] The fuzzy module is also used to transform fuzzy output data based on the centroid algorithm to obtain target frequency adjustment parameters.

[0196] The adjustment module is also used to acquire heating temperature data; based on the linear regression model and the heating temperature data, it corrects and calculates the capacitance value of the heating component.

[0197] It is understood that the control components provided in this application correspond to the heating control method provided in this application. In order to keep the specification concise, the same or similar parts can be referred to the content of the heating control method section, and will not be repeated here.

[0198] It should be noted that if a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0199] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0200] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0201] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0202] The terminology and expressions used herein are for descriptive purposes only and should not be construed as limiting of this application. The use of these terms and expressions does not preclude any illustrative and descriptive equivalents (or parts thereof), and it should be recognized that various modifications that may exist should also be included within the scope of the claims. Other modifications, variations, and substitutions may also exist. Accordingly, the claims should be considered to cover all such equivalents.

[0203] Similarly, it should be noted that although this application has been described with reference to specific embodiments, those skilled in the art should recognize that the above embodiments are only used to illustrate this application, and various equivalent changes or substitutions can be made without departing from the spirit of the invention. Therefore, any changes or modifications to the above embodiments within the scope of the essential spirit of this application will fall within the scope of the claims of this application.

Claims

1. A heating control method for an aerosol generating device, the aerosol generating device comprising: The system includes a heating chamber, a power supply, a heating assembly, and a control assembly. The heating assembly heats an aerosol-forming matrix housed within the heating chamber during operation to generate an aerosol. The control assembly generates a control signal. The heating control method comprises: Step S1: The control component acquires frequency error data, which represents the error between the resonant frequency and the target frequency of the heating component; Step S2: The control component performs fuzzification processing on the frequency error data to obtain the target frequency adjustment parameters; Step S3: The control component adjusts the resonant frequency according to the target frequency adjustment parameters to obtain the target output frequency; Step S4: The control component controls the heating component to heat according to the target output frequency; The frequency error data includes the error value between the resonant frequency and the target frequency and the error change rate of the error value. Step S1 includes: Step S11: The control component calculates the resonant frequency, which includes a first resonant frequency and a second resonant frequency; Step S12: The control component calculates the error value based on the resonant frequency and the target frequency, which includes a first-time error value corresponding to the first resonant frequency and the target frequency and a second-time error value corresponding to the second resonant frequency and the target frequency; Step S13: The control component calculates the error change rate based on the first-time error value and the second-time error value. The resonant frequency is calculated using the following formula: ， in, Where L is the resonant frequency, L is the inductance of the heating component, and C is the capacitance of the heating component; The heating control method further includes: Step S5: The control component acquires heating temperature data; Step S6: The control component, based on a linear regression model and the heating temperature data, corrects and calculates the capacitance value of the heating component. The calculation formula for the linear regression model is as follows: in, This is the compensated capacitance value. Here, k is the measured capacitance value, k is the temperature coefficient, T is the current temperature, and T0 is the reference temperature.

2. The heating control method according to claim 1, characterized in that, Step S2 includes: Step S21: The control component converts the frequency error data into fuzzy linguistic variables; Step S22: The control component performs fuzzy inference processing on the fuzzy linguistic variables according to the fuzzy rule base to obtain fuzzy output data corresponding to the frequency adjustment parameters; Step S23: The control component performs defuzzification processing on the fuzzy output data to obtain the target frequency adjustment parameters.

3. The heating control method according to claim 2, characterized in that, The fuzzy rule base is constructed using the following steps: Step D1: Convert the frequency error data into fuzzy variables and establish a fuzzy rule table; Step D2: Based on the membership function, define each fuzzy language in the fuzzy rule table and determine the membership degree corresponding to each fuzzy language.

4. The heating control method according to claim 2, characterized in that, Step S23 includes: Step S231: The control component performs transformation processing on the fuzzy output data based on the center of gravity algorithm to obtain the target frequency adjustment parameters.

5. An aerosol generating apparatus, the aerosol generating apparatus comprising: The system comprises a heating chamber, a power supply, a heating component, a control component, and a resonant component. The heating component heats an aerosol-forming matrix contained within the heating chamber during operation to generate an aerosol. The control component generates a control signal. The control component includes an acquisition module, a fuzzy logic module, an adjustment module, and a control module. The acquisition module is used to acquire frequency error data, which represents the error between the resonant frequency and the target frequency of the heating component. The fuzzy module is used to fuzzify the frequency error data to obtain the target frequency adjustment parameters. The adjustment module is used to adjust the resonant frequency according to the target frequency adjustment parameters to obtain the target output frequency; The control module is used to control the heating component to heat according to the target output frequency; The frequency error data includes the error value between the resonant frequency and the target frequency, and the rate of change of the error value. The acquisition module is further configured to calculate the resonant frequency, which includes a first resonant frequency and a second resonant frequency; calculate the error value based on the resonant frequency and the target frequency, which includes a first-time error value corresponding to the first resonant frequency and the target frequency and a second-time error value corresponding to the second resonant frequency and the target frequency; and calculate the error change rate based on the first-time error value and the second-time error value. The resonant frequency is calculated using the following formula: ， in, Where L is the resonant frequency, L is the inductance of the heating component, and C is the capacitance of the heating component; The adjustment module is also used to acquire heating temperature data; and based on the linear regression model and the heating temperature data, to correct and calculate the capacitance value of the heating component. The calculation formula for the linear regression model is as follows: in, This is the compensated capacitance value. Here, k is the measured capacitance value, k is the temperature coefficient, T is the current temperature, and T0 is the reference temperature.

6. The aerosol generating apparatus according to claim 5, characterized in that, The aerosol generating device also includes a capacitive sensor, which is disposed in the middle and / or edge of the heating component electrode plate.

7. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the heating control method of the aerosol generating apparatus according to any one of claims 1-4.

Images