Aerosol generating device and system

Abstract

The invention discloses an aerosol generating device and system.The aerosol generating system comprises the aerosol generating device and an aerosol generating product which is heated through the aerosol generating device to generate aerosol, and the aerosol generating device comprises a shell, a heating body, a power source, a main control circuit and a first sensor set; the first sensor group comprises a first temperature sensor and a second temperature sensor, a heating chamber for receiving the aerosol-generating substrate is defined in the housing, and the heating body is located in the heating chamber and can be in thermal contact with the received aerosol-generating substrate. A highly integrated and intelligent solution is constructed, the temperature sensor network is effectively deployed, the temperature information of multiple positions of the smoking set is captured in real time so as to obtain the real-time temperature change of tobacco, and a data foundation is laid for accurate control.

Description

Technical Field

[0001] The invention relates to an aerosol generating device and a system, belonging to the technical field of aerosol generating device control. Background Art

[0002] As countries gradually strengthen cigarette control and people pay more and more attention to health, a type of aerosol generating device for heating cigarettes has become increasingly popular in recent years. It is mainly a product that allows users to inhale after nicotine and other substances in tobacco are turned into steam through means such as atomization. In the continuous evolution of heated cigarette technology, achieving high-precision temperature control is not only a breakthrough in technology, but also a reflection of a deep understanding of consumer experience. In order to ensure that the taste, aroma and nicotine delivery of each puff of smoke remain highly consistent, various existing technologies continue to try to improve the measurement accuracy and control accuracy of temperature sensors, in order to give consumers a stable and satisfactory smoking experience. Although existing technologies have made certain progress in the temperature control of heated tobacco utensils, they usually rely on fixed heating curves and lack adaptability to different tobacco characteristics and actual use environment conditions.

[0003] However, these existing techniques ignore four fundamental issues: First, the temperature control curves of the current aerosol generating devices are all optimized heating curves that are gradually explored by a certain type of heated cigarette on a certain aerosol generating device based on manual smoking in a normal environment (room temperature of 25°C). Once an aerosol generating device is replaced with a different cigarette, or a cigarette is replaced with an aerosol generating device, a new heating curve needs to be explored again.

[0004] Second, the aerosol generating device always controls the temperature of the temperature sensor adjacent to the heater, rather than the actual temperature of the tobacco segment of the heated cigarette. For the central aerosol generating device in which the heater and the temperature sensor are directly inserted into the tobacco segment during use, the temperature sensor and the tobacco segment are already very close, but there is still a temperature difference of more than 10°C; for the circumferential aerosol generating device, since the tobacco segment is inside the cylindrical heater and the temperature sensor is often attached to the outside of the cylindrical heater, there will be a greater temperature difference, some even exceeding 30°C. Therefore, once the smoking device is taken to a special environment (winter in the north or summer in the south), the deviation between the temperature of the temperature sensor and the actual temperature of the tobacco will fluctuate greatly, making the optimal heating curve artificially explored in a conventional environment not the optimal heating curve in actual use.

[0005] Third, the currently commonly used single-loop PID control is based on the assumption that the heater temperature is equal to the temperature of the temperature sensor, which leads to large fluctuations in the temperature timing curve during the actual heating process.

[0006] Fourth, due to storage space limitations, most of the current heated tobacco devices can only store one or two heating control curves to match one or two heated cigarettes, and are not suitable for newly developed heated cigarettes; there are also proposals to obtain heating control curves through the Internet, but due to issues such as wireless module cost and stability, they cannot currently be commercialized.

[0007] Therefore, it is necessary to conduct research on the above four basic issues, find a more scientific aerosol generating device and system, and perform more precise temperature control. Summary of the invention

[0008] The purpose of the present invention is to overcome the deficiencies in the prior art and provide an aerosol generating device and system.

[0009] In order to achieve the above purpose / solve the above technical problems, the present invention is implemented by adopting the following technical solutions: In a first aspect, the present invention provides an aerosol generating device, comprising a shell, a heating body, a power supply, a main control circuit and a first sensor group, the first sensor group comprising a first temperature sensor and a second temperature sensor, a heating chamber for receiving the aerosol generating substrate is defined in the shell, the heating body is located in the heating chamber and can be in thermal contact with the received aerosol generating substrate, the main control circuit comprises a memory, a processor, a heating program stored in the memory and executable on the processor, and a model for obtaining the actual temperature of the aerosol generating substrate.

[0010] Furthermore, when the processor executes the computer program, the following steps are implemented to heat the aerosol generating substrate: obtaining a desired heating temperature of the aerosol-generating substrate; Acquire the actual temperature of the first temperature sensor and the actual temperature of the second temperature sensor; Inputting the actual temperature of the first temperature sensor and the actual temperature of the second temperature sensor into the aerosol generation substrate actual temperature acquisition model to obtain the aerosol generation substrate actual temperature; The main control circuit controls the power supply to transmit electric energy to the heating body according to the actual temperature of the aerosol generating substrate and the ideal heating temperature.

[0011] Furthermore, the ideal heating temperature is determined by the main control circuit based on the current timing and the ideal temperature timing curve of the aerosol generating substrate; the ideal temperature timing curve is determined by the main control circuit based on the ideal heating characteristic information of the aerosol generating substrate, which includes at least the ideal preheating time, ideal preheating temperature, ideal heating time and ideal heating temperature of the aerosol generating substrate.

[0012] Furthermore, the information volume of the ideal heating characteristic information is less than or equal to 1KB, less than or equal to 800B, less than or equal to 600B, less than or equal to 400B or less than or equal to 200B.

[0013] Furthermore, the ideal heating characteristic information is pre-stored in the memory; or the main control circuit also includes a network module, and the main control circuit is connected to the Internet through the network module to obtain the ideal heating characteristic information.

[0014] Furthermore, the main control circuit further comprises a near-field transmission module, and the main control circuit obtains the ideal heating characteristic information from the aerosol generating product or the packaging of the aerosol generating product through the near-field transmission module; Or the main control circuit further includes an optical recognition device, and the main control circuit obtains the ideal heating characteristic information from the aerosol generating product or the packaging of the aerosol generating product through the optical recognition device; Or the main control circuit further includes a human-computer interaction module, and the user can input the ideal heating characteristic information or select the ideal heating characteristic information pre-stored in the memory through the human-computer interaction module.

[0015] Furthermore, the method for constructing the aerosol generating substrate actual temperature acquisition model comprises: Acquire multiple test data groups including a first temperature sensor test temperature, a second temperature sensor test temperature, and an aerosol generating substrate test temperature; Taking the test data set as training samples and the actual temperature of the aerosol generating substrate as the target, machine learning is performed to construct a model for obtaining the actual temperature of the aerosol generating substrate; The method for obtaining the test data group includes: Installing test thermocouples at two or more different positions on the aerosol generating device to form a first test thermocouple group, and installing test thermocouples at two or more different positions on the aerosol generating substrate to form a second test thermocouple group; By testing and obtaining multiple groups of test data groups including the actual measured temperature data of the first test thermocouple group and the actual measured temperature data of the second test thermocouple group during different heating processes, wherein the data obtained by the first test thermocouple group are the actual temperature of the first temperature sensor and the actual temperature of the second temperature sensor, and the data obtained by the second test thermocouple group are the actual temperature of the aerosol generation matrix.

[0016] Further, it also includes a second sensor group, wherein the second sensor group includes a first humidity sensor; The test aerosol generating device is equipped with a test humidity sensor, and the test data group also includes humidity data actually measured by the test humidity sensor.

[0017] Furthermore, after constructing the aerosol generation matrix actual temperature acquisition model, it also includes optimizing the aerosol generation matrix actual temperature acquisition model through hyperparameter tuning, model integration and model pruning; The positions of the first temperature sensor and the second temperature sensor are determined by the positions of some remaining test thermocouples in the first test thermocouple group after model pruning, wherein the first temperature sensor is closer to the heating body relative to the aerosol generating substrate, and the second temperature sensor is closer to the outer surface of the shell relative to the heating body.

[0018] In a second aspect, the present invention provides an aerosol generating system, comprising an aerosol generating device and an aerosol generating product as described in the first aspect, characterized in that the aerosol generating product or its packaging carries the ideal heating characteristic information carrier that can be acquired by the near-field transmission module or the optical recognition device, or can be input by the user into the human-computer interaction module.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The present invention has built a highly integrated and intelligent solution. By effectively deploying a temperature sensor network, it can capture temperature information at multiple locations of the smoking device in real time to obtain real-time temperature changes of the tobacco itself, laying a data foundation for precise control. In the future, when developing the optimal heating curve for heated cigarettes, the study of the optimal heating curve of the tobacco itself can be separated from the study of how the smoking device measures the temperature of the tobacco itself, avoiding the dilemma of having to re-explore the optimal heating curve for any combination of cigarettes and smoking devices, greatly improving the efficiency and quality of research and development. For any cigarette, it is only necessary to obtain the optimal heating curve of the tobacco itself, and the aerosol generating device can operate on the automatic control heater so that the actual heating temperature of the tobacco is heated according to the heating curve.

[0020] Compared with the prior art, the present invention can dynamically adjust the heating strategy according to the actual temperature of the tobacco, thereby improving the uniformity and efficiency of tobacco heating. At the same time, the optimal heating curve can be compressed into ideal heating characteristic information, thereby reducing the amount of information that needs to be transmitted, making it convenient for smoking devices to quickly adjust the heating method according to the information on the heated cigarette or its packaging to adapt to various types of heated cigarettes even when there is no Internet (such as outdoor adventures, etc.).

[0021] By optimizing the algorithm and streamlining the code level, redundant code is removed, and the load and power consumption of the processor and memory are greatly reduced under a certain temperature control accuracy. These comprehensive optimization measures can effectively improve the energy efficiency of the system, extend the battery life of the embedded aerosol generating device, and reduce the hardware cost of the product.

[0022] In addition, the specific positions of the temperature sensor and humidity sensor are obtained by pruning the artificial intelligence model, which can more accurately reflect the actual temperature of the tobacco, and the PID dual-loop control can make it possible to more accurately regulate the heater power, so that it can quickly approach the optimal heating temperature of the tobacco. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a cross-sectional schematic diagram of an aerosol generating system; Figure 2 It is a schematic diagram of a cigarette with a QR code; Figure 3 It is a schematic diagram of a cigarette packaging box with a QR code; Figure 4 is a schematic diagram of the longitudinal distribution of thermocouples of the aerosol generating product for testing; Figure 5 is a schematic diagram of the longitudinal distribution of thermocouples of the test aerosol generating device; Figure 6 is a schematic diagram of the circumferential distribution of thermocouples of the test aerosol generating device; Figure 7 It is a schematic diagram of the process of constructing a model for obtaining the actual temperature of the aerosol generating matrix; Figure 8 is a schematic diagram of the longitudinal distribution of temperature sensors of an aerosol generating device; Fig. 9 is a schematic flow diagram of a heating method for an aerosol generating system; Fig.10 It is a schematic diagram of the ideal temperature timing curve of aerosol-generating products; Fig.11 It is a schematic diagram of the dual-loop control process; Fig.12a It is the actual temperature curve of tobacco under different conditions of existing mass-produced models; Figure 12b It is the actual temperature curve of tobacco under different conditions of the modified sample model.

[0024] In the figure: Aerosol generating products: 100 Aerosol generating matrix: 110 Support structure: 120 Cooling structure: 130 Filter structure: 140 Aerosol generating device: 200 Battery and control components: 210 Heating chamber: 220 Heating element: 230 Thermocouples for cigarette testing: 111, 112, 113 Thermocouples for testing the inner wall of the heating element: 221, 222, 223 Thermocouples for testing the outer wall of the heating body: 224, 225, 226 Thermocouples for shell testing: 241, 242, 243, 241a, 241b, 241c Humidity sensor: 290 Multi-channel recorder: 300 Aerosol-generating product packaging: 400. DETAILED DESCRIPTION

[0025] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0026] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and the like are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", and the like may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0027] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0028] like Figure 1As shown, the aerosol generating product 100 as a heated smoking device includes a smoking section 110, a supporting structure 120, a cooling structure 130 and a filtering structure 140 from the distal end to the proximal end. The aerosol generating device 200 as an aerosol generating device includes a power supply and a main control circuit 210, a heating body 220, a heating chamber 230 for receiving an aerosol generating substrate, and a housing 240. The aerosol generating device 200 is also provided with a first temperature sensor, a second temperature sensor, and a humidity sensor, and their specific functions and positions need to be determined and optimized based on test data and machine learning results. The heating body 220 is a heating resistance wire and is arranged on the outside of a heating tube with good thermal conductivity, so that the heat is evenly distributed around the smoking section 110.

[0029] like Figure 2 and Figure 3 As shown, the aerosol generating product 100 or its package 400 as a cigarette carries a two-dimensional code that can be recognized by the optical recognition device on the heating smoking device. After decoding, the ideal heating characteristic information such as the ideal preheating time, the ideal preheating temperature, the ideal heating time and the ideal heating temperature can be obtained. Figure 8 As shown in FIG. 1 , a typical ideal temperature timing curve can be determined according to the ideal preheating time t1 (e.g., 22s), the ideal preheating temperature TEMP1 (e.g., 320°C), the ideal heating time t2 (300s), and the ideal heating temperature TEMP2 (300°C). Therefore, after decoding the QR code, as long as a code with a length of less than 30B such as "TA022S-TPA300C-TB300S-TPB300C" is given, it can be used to restore the ideal temperature timing curve. Even if the verification and fault tolerance functions are added, the 21×21 size QR code can meet the use requirements. In another specific embodiment, the ideal heating characteristic information is directly printed on the cigarette or its packaging for the user to more conveniently input into the heating device through the human-computer interaction module. In another specific embodiment, a non-contact card label is attached to the cigarette or its packaging, and the ideal heating characteristic information can be decoded by the near-field transmission module of the heating device. In another specific embodiment, as shown in Table 1, various types of ideal heating characteristic information and their codes are stored in the smoking device, and the user enters a code (e.g., 4133) to determine the corresponding ideal heating characteristic information.

[0030] Table 1: Ideal heating characteristic information and its code

[0031] Test equipment preparation (step a) like Figure 2As shown, thermocouples for testing are installed at two or more different positions on the test aerosol generating matrix to form a second test thermocouple. Specifically, the cigarette used as the test aerosol generating product 100 is inserted into three test thermocouples 111, 112, and 113 at the center position and at the upper, middle, and lower longitudinal positions respectively. The thermocouples are connected to the multi-channel recorder 300 (GL240 thermometer + Japanese graphtec GL240 insulated multi-channel data recorder) signal, so as to realize the temperature of different positions inside the cigarette during the heating process. The number of thermocouples can be increased or decreased when necessary. A larger number of thermocouples can improve the accuracy of machine learning results, but it will also bring the trouble of building an overly complex model structure and processing massive data during the use of the smoking device.

[0032] like Figure 3 As shown, test thermocouples are installed at two or more different positions on the test aerosol generating device to form a first test thermocouple group. Specifically, the aerosol generating device as the test aerosol generating device 200 is first inserted with a plurality of cigarette test thermocouples, wherein three thermocouples 221, 222, 223 are located on the inner wall of the heating body (heating tube) and are located at the upper, middle, and lower longitudinal positions, respectively; three thermocouples 224, 225, 226 are located on the outer wall of the heating body (heating tube) and are located at the upper, middle, and lower longitudinal positions, respectively; three thermocouples 241, 242, 243 are located on the shell test thermocouples and are located at the upper, middle, and lower longitudinal positions, respectively. In addition, as Figure 4 As shown, for a non-circumferentially symmetrical structure, the number of thermocouples can be increased to further obtain temperature information related to the shape of the aerosol generating device, for example, thermocouples 241a, 241b, and 241c are respectively arranged at asymmetrical positions of the shell. Similarly, the number of thermocouples can be increased or decreased when necessary. Thermocouples 221, 222, and 223 are closer to the aerosol generating substrate 110 relative to the heating body 220, thermocouples 224, 225, and 226 are closer to the heating body 220 relative to the aerosol generating substrate 110, and thermocouples 241, 242, and 243 are closer to the outer surface of the shell relative to the heating body. A temperature field gradient is formed between these thermocouple groups. The temperature field gradient is related to the structure, material properties, heating temperature, etc. of the aerosol generating device itself, but it is not a simple linear relationship. Therefore, it is necessary to build an artificial intelligence model and use machine learning to discover the law of the temperature field gradient, so that the thermocouples on the smoking device can be used to more accurately obtain the temperature of the aerosol generating substrate, rather than regarding the thermocouple temperature on the smoking device as the temperature of the aerosol generating substrate as in the prior art.

[0033] In addition, a temperature sensor 290 may be provided at a location such as inside the heating chamber 230 to increase the dimension of the machine learning model parameters.

[0034] Reference Figure 5 Continue testing and training: Testing Process The test experiment adopts a three-factor mixed experimental design: (ambient temperature: 0-40℃, according to actual weather conditions) × (body temperature: 70-250℃, thermal engine data is recorded every 30℃) × (ambient humidity: 10%-80%, according to actual weather conditions). In order to make the ambient temperature and ambient humidity have more possibilities, the present invention carried out test experiments in three cities (Haikou, Chengdu, and Harbin) with large differences in ambient temperature and humidity, in different time periods of early spring weather, including early morning, morning, noon, afternoon, and evening.

[0035] Prepare the test smoking device and the thermocouples on the test cigarettes according to Table 1, insert the test cigarettes into the test smoking device, connect the recording equipment (GL240 thermometer + Japan Graphtec GL240 insulated multi-channel data recorder), start the aerosol generating device and perform artificial inhalation (heat each cigarette for 210 seconds and take 7 puffs), and the recording equipment will automatically record the timing, the real-time temperature and humidity of each thermocouple, and the real-time voltage and power of the smoking device.

[0036] Under the same ambient temperature and humidity, the experiment was conducted with one room temperature startup test and two continuous tests after the machine was warmed up. A fully charged test smoking device can undergo three rounds of experiments.

[0037] The test data can be recorded according to Table 2 (automatically recorded by computer).

[0038] Table 2: Test data record table

[0039] Machine Learning with Test Data The multiple test data groups obtained through the test are preprocessed (step b), including: Derivative processing b21: Supplementary calculation of the change rate (derivative) of temperature and humidity at each time series based on the real-time temperature value; Outlier processing b22: Use the interquartile range (IQR) method to detect and remove outliers to remove data noise; Data normalization b23: Apply Z-score normalization to temperature and voltage data to eliminate dimensional differences and accelerate model convergence; Time series feature extraction 24: Introducing time lag features, such as the temperature change rate and humidity change rate, enhances the model’s ability to capture time dependencies. This step ensures that the model can fully understand the dynamic behavior of temperature and humidity over time.

[0040] In addition, in order to verify the effectiveness of the model, it is necessary to use unseen data sets to evaluate the prediction performance and stability of the model. The specific settings include: Dataset partitioning b31: Divide the dataset into training set, validation set, and test set to ensure the generalization ability of the model on different data; Evaluation indicators establishment b32: Mean square error (MSE) and root mean square error (RMSE) are used as evaluation indicators to measure the prediction accuracy of the model.

[0041] Taking the test data set as training samples and the actual temperature of the aerosol generating matrix as the target, machine learning is performed to build a model for obtaining the actual temperature of the aerosol generating matrix (step c). The model is based on a multi-layer deep neural network (DNN) and can be implemented in PyTorch. Its structure is as follows: Input layer M1: Design the input layer according to the number of preprocessed data features, such as temperature, humidity, duration and its derivatives; Hidden layer M2: Three hidden layers are used, each layer contains 128 neurons, and uses ReLU (Rectified Linear Unit) activation function. This design can provide sufficient model complexity to help capture the complex nonlinear relationship in the temperature field; Output layer M3: A single output node used to predict the actual temperature of the aerosol generating matrix. The output layer uses a linear activation function to adapt to the prediction task of continuous values.

[0042] The training process includes selecting a suitable loss function, optimization algorithm, regularization technique, and setting training parameters. The specific steps are as follows: Loss function T1: The mean square error (MSE) is used as the loss function, which directly reflects the difference between the predicted temperature and the actual temperature; Optimizer selection T2: Use the Adam optimizer, which has the advantage of adaptive learning rate and is suitable for processing large-scale parameter optimization tasks; Regularization technology T3: Introducing L2 regularization to reduce the risk of overfitting of the model and maintain the generalization ability of the model; Batch size and number of iterations T4: Set the batch size to 32 and the number of iterations to 1000. This configuration strikes a balance between memory usage and training speed and ensures stable convergence of the model.

[0043] The experimental results section will record the performance of the model on the training set and the validation set in detail, including the error reduction curve, the prediction accuracy under different operating conditions, and the final performance on the test set.

[0044] In order to further optimize the above-mentioned aerosol generation matrix actual temperature acquisition model (step d), the following optimization method is adopted: Hyperparameter tuning d1: Use grid search or Bayesian optimization to tune hyperparameters and find the optimal learning rate, batch size, regularization coefficient and other parameters; Model ensemble d2: Use ensemble learning methods, such as random forests, combined with neural networks to improve overall prediction accuracy; Model pruning d3: Reduce redundant neurons and connections through model pruning technology, improve the operation efficiency and deployment convenience of the model.

[0045] Through the above optimization method, the optimal number and location of temperature and humidity sensors can be selected, so as to achieve the simplest model structure and data processing consumption while meeting the accuracy requirements (the difference between the predicted temperature and the actual temperature does not exceed 1°C).

[0046] like Figure 6 As shown, through the above-mentioned machine learning and optimization processes, it is found that it is necessary to set the first temperature sensor at position 221, the second temperature sensor at position 243, and the first humidity sensor at position 290, and adopt a neural network model with three hidden layers and 128 neurons in each layer that can be run on the main control circuit of the handheld aerosol generating device to obtain the actual heating temperature of the tobacco (±3°C) with sufficient accuracy.

[0047] The model can be deployed to an actual aerosol generating device and applied by following the steps below: Aerosol generating device preparation e1: an aerosol generating device is manufactured in a conventional manner, including a housing, a heating body, a power supply, a main control circuit, a first temperature sensor, a second temperature sensor and a first humidity sensor; Model export e2: Export the trained model to a deployable format; Device integration e3: Integrate the exported model into the main control circuit; Equipment operation e4 (see Figure 7 ): Step A: After inserting the cigarette, start the aerosol generating device, select and read the ideal temperature time series curve of the cigarette according to the type of cigarette. Figure 8As shown, a typical ideal temperature timing curve can be determined by the main control circuit according to the ideal heating characteristic information including at least the ideal preheating time t1, the ideal preheating temperature TEMP1, the ideal heating time t2 and the ideal heating temperature TEMP2 of the aerosol generating matrix. The ideal heating characteristic information can be stored in the main control circuit and automatically executed, for example, the main control circuit pre-stores the relevant information of the cigarettes matched with the heating smoking device; can be input by the user, for example, manually input several ideal heating characteristic information; can be obtained from the Internet through the network by the main control circuit, for example, after the user enters the cigarette code on the smoking device, the active circuit of the smoking device obtains the relevant information of the cigarette through the network; can be obtained from near field transmission by the main control circuit, for example, the main control circuit recognizes the contactless card information on the cigarette through NFC or other methods; or obtained from the cigarette or cigarette packaging by the main control circuit, and the QR code information on the cigarette is recognized by scanning the QR code or other methods; it can also be a combination of the above methods, for example, the smoking device scans the QR code of the cigarette and obtains the relevant information of the cigarette through the network.

[0048] Step B: The main control circuit control system obtains the temperature of the first temperature sensor, the temperature of the second temperature sensor, and the humidity of the first humidity sensor of the device in real time; Step C: input into the model to obtain the actual temperature of the tobacco in the cigarette; Step D: Increase or decrease the heating power of the heating body so that the actual temperature of the tobacco meets the ideal temperature time series curve of the cigarette. Fig. 9 A dual-loop control is adopted. The outer loop of the dual-loop control is the actual temperature loop of the aerosol generation matrix, and the inner loop is the actual temperature change rate loop of the aerosol generation matrix. The inner loop adopts PI control, and the feedback of the inner loop adopts an ordinary differentiator or a tracking differentiator. The specific strategies are as follows: Step 1: A series of reference coordinate values ​​are set , , the interval corresponding to the reference coordinate value is set to ,in is a design parameter.

[0049] Step 2: Kinetic Equations:

[0050] in is the gain parameter, is the input to be designed later. is the output.

[0051] Step 3: Phase 1:

[0052]

[0053] Phase 2:

[0054]

[0055] in as well as is a design parameter.

[0056] Tobacco actual temperature curve test The existing mass-produced model F5216 and the prototype model of the invention modified based on F5216 were tested for the actual temperature curve of tobacco during the actual heating process. First, a thermocouple was inserted at the 111 position of the cigarette, and then the two models were heated at different locations (with different ambient temperatures and humidity) according to the same ideal heating curve, and the actual temperature curves of the tobacco were recorded respectively. Fig.12a and Figure 12b As shown (the horizontal axis is time / s, the vertical axis is temperature / ℃). It can be found that the temperature control of the existing mass-produced models is very imprecise and fluctuates greatly; while the sample model of the present invention can quickly reach the target ideal temperature and achieve stable temperature control.

[0057] The present invention has built a highly integrated and intelligent solution. By effectively deploying a temperature sensor network, it can capture temperature information at multiple locations of the smoking device in real time to obtain real-time temperature changes of the tobacco itself, laying a data foundation for precise control. In the future, when developing the optimal heating curve for heated cigarettes, the study of the optimal heating curve of the tobacco itself can be separated from the study of how the smoking device measures the temperature of the tobacco itself, avoiding the dilemma of having to re-explore the optimal heating curve for any combination of cigarettes and smoking devices, greatly improving the efficiency and quality of research and development. For any cigarette, it is only necessary to obtain the optimal heating curve of the tobacco itself, and the aerosol generating device can operate on the automatic control heater so that the actual heating temperature of the tobacco is heated according to the heating curve.

[0058] The present invention realizes a temperature control deviation of the heating element within ±3°C, a prediction accuracy within 5°C, a minimum autonomous adjustment accuracy of 1°C, and a temperature adjustment time not exceeding 1000ms.

[0059] In addition, by optimizing the algorithm and streamlining the code level, redundant code is removed, and the load and power consumption of the processor and memory are greatly reduced under a certain temperature control accuracy. These comprehensive optimization measures can effectively improve the energy efficiency of the system, extend the battery life of the embedded aerosol generating device, and reduce the hardware cost of the product.

[0060] In a typical configuration of the present invention, the main control circuit includes one or more processors (CPU), input / output interface, network interface and memory. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium. Computer-readable media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be a computer-readable instruction, a data structure, a module of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk-read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0061] The terms and expressions used herein are for descriptive purposes only, and the present invention should not be limited to these terms and expressions. The use of these terms and expressions does not mean to exclude any equivalent features of the illustrations and descriptions (or parts thereof), and it should be recognized that various modifications that may exist should also be included in the scope of the claims. Other modifications, variations and substitutions may also exist. Accordingly, the claims should be deemed to cover all such equivalents.

[0062] Similarly, it should be pointed out that although the present invention has been described with reference to the current specific embodiments, ordinary technicians in this technical field should realize that the above embodiments are only used to illustrate the present invention, and various equivalent changes or substitutions may be made without departing from the spirit of the present invention. Therefore, as long as the changes and modifications to the above embodiments are within the scope of the essential spirit of the present invention, they will fall within the scope of the claims of the present invention.

Claims

1. An aerosol generating device, comprising a housing, a heating body, a power supply, a main control circuit and a first sensor group, wherein the first sensor group comprises a first temperature sensor and a second temperature sensor, wherein a heating chamber for receiving the aerosol generating substrate is defined in the housing, and the heating body is located in the heating chamber and can be in thermal contact with the received aerosol generating substrate, wherein: The main control circuit includes a memory, a processor, a heating program stored in the memory and executable on the processor, and a model for obtaining the actual temperature of an aerosol generating substrate.

2. The aerosol generating device according to claim 1, characterized in that: When the processor executes the computer program, the following steps are performed to heat the aerosol generating substrate: obtaining a desired heating temperature of the aerosol-generating substrate; Acquire the actual temperature of the first temperature sensor and the actual temperature of the second temperature sensor; Inputting the actual temperature of the first temperature sensor and the actual temperature of the second temperature sensor into the aerosol generation substrate actual temperature acquisition model to obtain the aerosol generation substrate actual temperature; The main control circuit controls the power supply to transmit electric energy to the heating body according to the actual temperature of the aerosol generating substrate and the ideal heating temperature.

3. The aerosol generating device according to claim 2, characterized in that: The ideal heating temperature is determined by the main control circuit based on the current timing and the ideal temperature timing curve of the aerosol generating substrate; the ideal temperature timing curve is determined by the main control circuit based on the ideal heating characteristic information of the aerosol generating substrate including at least the ideal preheating time, ideal preheating temperature, ideal heating time and ideal heating temperature of the aerosol generating substrate.

4. The aerosol generating device according to claim 3, characterized in that: The information volume of the ideal heating characteristic information is less than or equal to 1KB, less than or equal to 800B, less than or equal to 600B, less than or equal to 400B or less than or equal to 200B.

5. The aerosol generating device according to claim 4, characterized in that: The ideal heating characteristic information is pre-stored in the memory; or the main control circuit further includes a network module, and the main control circuit is connected to the Internet through the network module to obtain the ideal heating characteristic information.

6. The aerosol generating device according to claim 1, characterized in that: The main control circuit further comprises a near field transmission module, and the main control circuit obtains the ideal heating characteristic information from the aerosol generating article or the packaging of the aerosol generating article through the near field transmission module; Or the main control circuit further includes an optical recognition device, and the main control circuit obtains the ideal heating characteristic information from the aerosol generating product or the packaging of the aerosol generating product through the optical recognition device; Or the main control circuit further includes a human-computer interaction module, and the user can input the ideal heating characteristic information or select the ideal heating characteristic information pre-stored in the memory through the human-computer interaction module.

7. The aerosol generating device according to claim 6, characterized in that: The method for constructing the actual temperature acquisition model of the aerosol generating substrate comprises: Acquire multiple test data groups including a first temperature sensor test temperature, a second temperature sensor test temperature, and an aerosol generating substrate test temperature; Taking the test data set as training samples and the actual temperature of the aerosol generating substrate as the target, machine learning is performed to construct a model for obtaining the actual temperature of the aerosol generating substrate; The method for obtaining the test data group includes: Installing test thermocouples at two or more different positions on the aerosol generating device to form a first test thermocouple group, and installing test thermocouples at two or more different positions on the aerosol generating substrate to form a second test thermocouple group; By testing and obtaining multiple groups of test data groups including the actual measured temperature data of the first test thermocouple group and the actual measured temperature data of the second test thermocouple group during different heating processes, wherein the data obtained by the first test thermocouple group are the actual temperature of the first temperature sensor and the actual temperature of the second temperature sensor, and the data obtained by the second test thermocouple group are the actual temperature of the aerosol generation matrix.

8. The aerosol generating device according to claim 6, characterized in that: Also included is a second sensor group including a first humidity sensor; The test aerosol generating device is equipped with a test humidity sensor, and the test data group also includes humidity data actually measured by the test humidity sensor.

9. The aerosol generating device according to claim 7, characterized in that: After constructing the aerosol generation matrix actual temperature acquisition model, the method further includes optimizing the aerosol generation matrix actual temperature acquisition model through hyperparameter tuning, model integration and model pruning; The positions of the first temperature sensor and the second temperature sensor are determined by the positions of some remaining test thermocouples in the first test thermocouple group after model pruning, wherein the first temperature sensor is closer to the heating body relative to the aerosol generating substrate, and the second temperature sensor is closer to the outer surface of the shell relative to the heating body.

10. An aerosol generating system, comprising the aerosol generating device and the aerosol generating article according to claim 6, characterized in that: The aerosol generating product or its packaging carries the ideal heating characteristic information carrier which can be acquired by the near-field transmission module or the optical recognition device, or can be input by the user to the human-computer interaction module.

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