CN117647497B

CN117647497B - Method for measuring smoke moisture content of electronic atomizer

Info

Publication number: CN117647497B
Application number: CN202410125764.7A
Authority: CN
Inventors: 王征宇; 加名; 黄海辉; 黄俊生; 陆机颖; 陈泓汐; 胡宗志
Original assignee: Shenzhen Borui Biotechnology Co ltd
Current assignee: Shenzhen Borui Biotechnology Co ltd
Priority date: 2024-01-30
Filing date: 2024-01-30
Publication date: 2024-03-29
Anticipated expiration: 2044-01-30
Also published as: CN117647497A

Abstract

The invention relates to the technical field of smoke measurement, in particular to a method for measuring the moisture content of smoke of an electronic atomizer. The method comprises the following steps: carrying out acquisition network construction treatment and sampling treatment on a smoke sensor in the electronic atomizer to obtain a smoke sample standard set of the electronic atomizer; detecting the humidity of a smoke sample standard set of the electronic atomizer through a humidity sensor to obtain smoke sample humidity data; the method comprises the steps of detecting the temperature of a smoke sample standard set of an electronic atomizer through a temperature sensor to obtain smoke sample temperature data; performing compensation calibration on the smoke sample humidity data according to the smoke sample temperature data to obtain smoke sample humidity compensation calibration data; and carrying out infrared spectrum analysis and differential calculation on the smoke sample standard set of the electronic atomizer based on the smoke sample humidity compensation calibration data so as to obtain a smoke sample moisture content value. The invention can improve the accuracy and efficiency of measuring the smoke moisture content of the electronic atomizer.

Description

Method for measuring smoke moisture content of electronic atomizer

Technical Field

The invention relates to the technical field of smoke measurement, in particular to a method for measuring the moisture content of smoke of an electronic atomizer.

Background

With the widespread use of electronic atomizers in the marketplace, users are increasingly concerned about the quality and composition of smoke. As a device to replace traditional cigarettes, electronic atomizers have smoke moisture content as an important parameter affecting user experience and product quality. However, the conventional method for measuring the moisture content has the problems of low measurement accuracy, long test time and the like when being applied to the smoke of the electronic atomizer.

Disclosure of Invention

Accordingly, there is a need for a method for determining the moisture content of smoke in an electronic atomizer, which solves at least one of the above-mentioned problems.

In order to achieve the purpose, the method for measuring the smoke moisture content of the electronic atomizer comprises the following steps:

step S1: performing acquisition network construction processing on a smoke sensor in the electronic atomizer to obtain a smoke acquisition sensing array network; sampling and processing smoke generated by the electronic atomizer by using a smoke acquisition sensing array network to obtain a smoke sample standard set of the electronic atomizer;

step S2: detecting the humidity of a smoke sample standard set of the electronic atomizer through a humidity sensor to obtain smoke sample humidity data; the method comprises the steps of detecting the temperature of a smoke sample standard set of an electronic atomizer through a temperature sensor to obtain smoke sample temperature data; performing compensation calibration on the smoke sample humidity data according to the smoke sample temperature data to obtain smoke sample humidity compensation calibration data;

Step S3: carrying out infrared spectrum analysis on a smoke sample standard set of the electronic atomizer based on the smoke sample humidity compensation calibration data to obtain a smoke sample humidity spectrum signal peak metric value;

step S4: drying and iterating infrared spectrum analysis on a smoke sample standard set of the electronic atomizer corresponding to the smoke sample humidity spectrum signal peak metric value to obtain a smoke sample drying spectrum signal peak metric value; and carrying out differential calculation on the peak value of the humidity spectrum signal of the smoke sample and the peak value of the drying spectrum signal of the smoke sample to obtain the moisture content value of the smoke sample.

According to the invention, firstly, the acquisition network construction processing is carried out on the smoke sensor in the electronic atomizer, so that a smoke acquisition sensing array network with wide coverage range can be established, the establishment of the network can provide comprehensive coverage for subsequent data acquisition, the global property of smoke detection is ensured, and the overall sensing capability of the electronic atomizer on smoke sensing is improved, thereby realizing comprehensive and high-density acquisition of smoke samples. Meanwhile, the established smoke collection sensing array network is used for accurately sampling smoke generated by the electronic atomizer, so that a smoke sample generated by the electronic atomizer can be efficiently captured and recorded, and pretreatment is carried out, so that the quality and consistency of a smoke sample set are further improved, the final sample set is more representative, and a comprehensive and accurate smoke sample standard set is formed. And secondly, humidity and temperature are detected by using a humidity sensor and a temperature sensor to the smoke sample standard set of the electronic atomizer, so that the measurement accuracy of temperature and humidity can be improved, and more accurate smoke sample humidity data and temperature data are obtained. And by comprehensively analyzing the temperature and humidity data, the compensation and calibration of the humidity data of the smoke sample can be realized, so that the humidity compensation and calibration data of the smoke sample can be obtained. Then, through carrying out infrared spectrum analysis on the smoke sample standard set of the electronic atomizer based on the smoke sample humidity compensation calibration data, detailed information about the molecular structure and chemical bonds of the smoke sample can be provided, so that the spectrum signal distribution condition of the smoke sample humidity is obtained, quantitative data support is provided for subsequent moisture content measurement, and meanwhile, the measurement accuracy of the moisture content is improved. Finally, drying treatment is carried out on the smoke sample standard set of the electronic atomizer corresponding to the peak value of the humidity spectrum signal of the smoke sample so as to remove moisture in the smoke sample, so that subsequent analysis is more focused, and the analysis result of the subsequent spectrum signal is more accurate and reliable. And then, through carrying out iterative infrared spectrum analysis on the dried smoke sample standard set of the electronic atomizer, the peak metric value of the spectrum signal of the smoke sample after moisture is removed can be obtained, and the information of the components and the structure of the smoke sample is further disclosed, so that high-quality input data is provided for subsequent differential calculation, and the moisture content of the smoke sample can be accurately judged. In addition, the moisture content in the smoke sample can be accurately reflected by carrying out differential calculation on the peak value of the humidity spectrum signal of the smoke sample and the peak value of the drying spectrum signal of the smoke sample, and the accurate moisture content analysis has important significance for understanding the performance and quality of the smoke product, so that accurate data support is provided for the quality control of the smoke of the electronic atomizer.

Preferably, step S1 comprises the steps of:

step S11: performing measurement, adaptation and calibration treatment on a smoke sensor in the electronic atomizer to obtain a smoke calibration sensor;

step S12: performing acquisition network construction processing on the smoke calibration sensor to obtain a smoke acquisition sensor array network;

step S13: synchronous acquisition and activation processing are carried out on smoke calibration sensors in the smoke acquisition sensor array network by utilizing a data synchronization algorithm, so that a smoke acquisition synchronous array network is obtained;

step S14: sampling and processing smoke generated by the electronic atomizer by using a smoke acquisition synchronous array network to obtain an initial sample set of the smoke of the electronic atomizer;

step S15: and carrying out smoke pretreatment on the electronic atomizer smoke initial sample set to obtain an electronic atomizer smoke sample standard set.

According to the invention, firstly, the accuracy and the reliability of the smoke sensor are ensured by carrying out measurement, adaption and calibration treatment on the smoke sensor in the electronic atomizer. Through measurement adaptation calibration, errors in the measurement of the smoke sensor can be effectively eliminated, the accuracy of smoke detection is improved, the quality of subsequent data acquisition can be guaranteed, and a foundation is laid for the construction of a smoke acquisition sensing array network. Secondly, through carrying out acquisition network construction processing to the smoke calibration sensor, a smoke acquisition sensing array network with wide coverage range can be established, the establishment of the network can provide comprehensive coverage for subsequent data acquisition, the global property of smoke detection is ensured, the overall sensing capability of the electronic atomizer to smoke sensing is improved, and the acquisition of a smoke sample set is more comprehensive and accurate. Then, the synchronous acquisition and activation processing is carried out on the smoke calibration sensors in the smoke acquisition sensor array network by using a data synchronization algorithm, so that the data synchronization among the smoke calibration sensors can be realized, the consistency and accuracy of the data are improved, and the subsequent analysis is more accurate. Then, by using the established smoke collection synchronous array network to accurately sample the smoke generated by the electronic atomizer, the smoke sample generated by the electronic atomizer can be efficiently captured and recorded, so that a comprehensive and accurate smoke initial sample set is formed. Finally, the smoke pretreatment is carried out on the initial sample set of the smoke of the electronic atomizer so as to obtain a standard set of smoke samples of the electronic atomizer which is more refined and standardized, and the aim of the step is to further improve the quality and consistency of the smoke sample set, so that the final sample set is more representative and can be used for subsequent analysis, research and evaluation, thereby providing a powerful tool and data base for the related research of the moisture content of the smoke.

Preferably, step S12 comprises the steps of:

step S121: performing linear array construction treatment on the smoke calibration sensor to obtain a smoke sensor linear acquisition array;

step S122: constructing a topology network for the linear acquisition array of the smoke sensor by utilizing a network topology analysis technology to obtain an initial acquisition array network of the smoke sensor;

step S123: calculating network lacunarity of the smoke sensor initial acquisition array network by using a network lacunarity measurement calculation formula to obtain a lacunarity value of the smoke sensor array network;

step S124: and performing array arrangement optimization on the initial acquisition array network of the smoke sensor according to the gap value of the array network of the smoke sensor so as to obtain the array network of the smoke acquisition sensor.

According to the invention, the linear array construction processing is performed on the smoke calibration sensor, so that the coverage area of the smoke calibration sensor can be improved to the greatest extent through reasonable spatial arrangement, and the sensitivity and the comprehensiveness of the whole linear array to smoke signals are enhanced. By the construction mode, a compact and efficient sensor array can be obtained, and a foundation is laid for subsequent network construction. Secondly, by constructing a topology network of the smoke sensor linear acquisition array by using a network topology analysis technology, the connection relation and the communication path between the smoke sensors can be determined so as to establish an initial acquisition array network. By constructing the topology network, information transfer between smoke sensors can be effectively managed and scheduled, so that data cooperativity and consistency of the whole array are improved. Then, network clearance calculation is carried out on the initial acquisition array network of the smoke sensors by using a network clearance measurement calculation formula, the network clearance value is an important index for evaluating the stability and efficiency of the network, the calculated value reflects the relative distance and connection density between the smoke sensors, and the calculation process can provide powerful data support for subsequent network arrangement optimization so as to ensure the high efficiency and stability of the array network. Finally, the array arrangement optimization is carried out on the initial acquisition array network of the smoke sensor according to the previously calculated gap value of the array network of the smoke sensor, and the optimization process aims at adjusting the relative position of the smoke sensor so that the gap value of the network reaches the optimal state. Through the arrangement optimization, effective communication and synergistic effect between the smoke sensors are ensured, so that a highly optimized smoke acquisition sensing array network is formed, and the network structure has the advantages of improving the acquisition efficiency and accuracy of smoke signals and providing a reliable basis for the acquisition and analysis of the smoke of the electronic atomizer.

Preferably, the network lacunarity metric calculation formula in step S123 is specifically:

；

in the method, in the process of the invention,for the network lacunarity value of the smoke sensor array,/-for the network lacunarity value of the smoke sensor array,>lower limit of the time range calculated for network lacuna, < ->Top of the time frame calculated for network lacunarity, < >>Outer layer integration time variable parameter calculated for network lacuna, < ->Inner layer integration time variable parameter calculated for network lacuna, < ->For the initial acquisition of the number of smoke sensors in the array network of smoke sensors, +.>Index variable parameter for smoke sensor, +.>Initial acquisition of array network for smoke sensor +.>Individual smoke sensor at time +.>Network acquisition efficiency value at ∈>Initial acquisition of array network for smoke sensor +.>Network acquisition weight parameters of individual smoke sensors, < ->For the initial acquisition of the number of network monitoring nodes in the array network for the smoke sensor, +.>Index variable parameter for network monitoring node, +.>Initial acquisition of array network for smoke sensor +.>Degree of monitoring node by individual network,/-)>Adjusting parameters for network acquisition contribution +.>Network signal transmission efficiency value of smoke sensor in initial acquisition array network of smoke sensor, +.>Adjusting parameters for network signaling, < > >Transmitting attenuation parameters for network signals，/>Adjusting parameters for network signaling contribution, +.>And (3) correcting the gap value of the network for the smoke sensor array.

The invention constructs a network gap degree measurement calculation formula which is used for carrying out network gap degree calculation on the initial acquisition array network of the smoke sensor, the network gap degree measurement calculation formula comprises a plurality of factors such as time integral, space variable, degree and weight of network nodes, and the like, through comprehensive consideration of the parameters, the calculated network gap degree value can reflect the structural characteristics and performance of the array network of the smoke sensor, and the calculation process can help to optimize the arrangement of the smoke sensor, so that the network is more efficient and stable in acquiring smoke information. The formula fully considers the gap value of the smoke sensor array networkThe lower limit of the time range of the network gap calculation +.>The upper limit of the time range for network gap calculation>Outer layer integration time variable parameter of network lacuna calculation +.>Inner layer integration time variable parameter of network lacuna calculation>The number of smoke sensors in an initial acquisition array network of smoke sensors +.>Index variable parameter of smoke sensor +.>First, in smoke sensor initial acquisition array network >Individual smoke sensor at time +.>Network acquisition efficiency value at->First, in smoke sensor initial acquisition array network>Network acquisition weight parameter of individual smoke sensor +.>Number of network monitoring nodes in an initial acquisition array network of smoke sensorsIndex variable parameter of network monitoring node>First, in smoke sensor initial acquisition array network>Degree of monitoring node in personal network->Network acquisition contribution adjustment parameter->Network signal transmission efficiency value of smoke sensor in initial acquisition array network of smoke sensor +.>Network signal transmission regulation parameter->Network signal transmission attenuation parameter->Network signal transmission contribution adjustment parameter +.>Correction value of gap value of smoke sensor array network +.>According to the network gap value of the smoke sensor array +.>The interrelationship between the parameters constitutes a functional relationship:

；

the formula can realize the network gap degree calculation process of the initial acquisition array network of the smoke sensor, and simultaneously, the correction value of the gap degree value of the array network of the smoke sensor is adoptedThe introduction of the network lacunarity measurement calculation formula can be adjusted according to error conditions in the calculation process, so that the accuracy and applicability of the network lacunarity measurement calculation formula are improved.

Preferably, step S15 comprises the steps of:

step S151: performing smoke isomerism calibration alignment on an electronic atomizer smoke initial sample set to obtain an electronic atomizer smoke isomerism alignment sample set;

step S152: carrying out suspension particle layering removal on the smoke isomerism alignment sample set of the electronic atomizer so as to obtain a smoke particle removal sample set of the electronic atomizer;

step S153: and carrying out noise filtering treatment on the sample set for removing the smoke particles of the electronic atomizer to obtain a standard set of smoke samples of the electronic atomizer.

According to the invention, firstly, the electronic atomizer smoke initial sample set is subjected to smoke isomerism calibration alignment, so that data inconsistency caused by isomerism properties of smoke samples existing in the collected electronic atomizer smoke initial sample set can be solved, and thus, the isomerism is calibrated and aligned, a more accurate and consistent electronic atomizer smoke isomerism alignment sample set is obtained, and a more reliable sample foundation is provided for subsequent processing. Then, the suspended particles in the electronic atomizer smoke are removed in a layering manner by carrying out the layered removal of the suspended particles on the isomerically aligned sample set of the electronic atomizer smoke, and the step aims to remove suspended particle components in the electronic atomizer smoke and improve the purity and the analyzability of the sample set, so that a cleaner and reliable sample set is provided for subsequent analysis and treatment. Finally, the noise filtering treatment is carried out on the sample set cleared by the smoke particles of the electronic atomizer, so that the definition and the reliability of the sample set can be improved, and noise interference caused by experiments or collection processes can be removed. Through the noise filtering, a more accurate and reliable standard set of the smoke sample of the electronic atomizer can be obtained, and the standard set is obtained through multiple processing steps, has high quality and repeatability, and is suitable for the detailed analysis and evaluation of the smoke of the electronic atomizer, so that a reliable experimental data base is provided for the subsequent moisture content measurement.

Preferably, step S152 includes the steps of:

step S1521: non-invasive detection of suspended particles is carried out on the smoke isomerism alignment sample set of the electronic atomizer through a laser scattering technology, and characteristic data of the suspended particles of the smoke sample are obtained;

step S1522: carrying out particle layering detection on the smoke sample suspended particle characteristic data by using a convolutional neural network model to obtain smoke sample suspended particle layering condition data;

step S1523: carrying out spatial distribution analysis on the layering condition data of the suspended particles of the smoke sample to obtain layering spatial distribution data of the suspended particles;

step S1524: detecting the concentration change of suspended particles in a smoke isomerism alignment sample set of the electronic atomizer according to the characteristic data of the suspended particles of the smoke sample, and obtaining the concentration change data of the suspended particles;

step S1525: establishing a self-adaptive suspended particle layering clearing strategy according to the suspended particle layering spatial distribution data and the suspended particle concentration change data; and carrying out suspension particle layering removal on the electronic atomizer smoke isomerism alignment sample set according to a self-adaptive suspension particle layering removal strategy so as to obtain an electronic atomizer smoke particle removal sample set.

The invention firstly uses the laser scattering technology to carry out non-invasive detection on suspended particles in the smoke isomerism alignment sample set of the electronic atomizer, can sensitively detect the scattering signals of the suspended particles, and provides detailed data about the characteristics of the suspended particles. Through the step, the characteristic data of the size, the shape and the like of suspended particles of the smoke sample are obtained, so that a foundation is provided for subsequent particle layering and concentration change detection. Secondly, particle layering detection is carried out on the suspended particle characteristic data of the smoke sample by using a convolutional neural network model, the neural network can learn complex characteristics of suspended particles in the smoke sample, so that layering conditions of the suspended particles are accurately judged, the key point of the step is that the accuracy of the suspended particle layering detection can be improved, and a reliable foundation is laid for subsequent spatial distribution analysis and concentration change detection. Then, through carrying out space distribution analysis on the layering condition data of the suspended particles of the smoke sample, the distribution condition of the suspended particles in the sample set can be obtained, the analysis process is helpful for knowing layering density and distribution conditions of the suspended particles in different space positions, and key information is provided for the establishment of a subsequent self-adaptive layering removal strategy of the suspended particles. And then, detecting the concentration change of suspended particles in the smoke isomerism alignment sample set of the electronic atomizer by using the characteristic data of the suspended particles of the smoke sample so as to monitor the dynamic change condition of the concentration of the suspended particles in the smoke isomerism alignment sample set of the electronic atomizer, thereby providing real-time data support for a subsequent self-adaptive suspended particle layering removal strategy. Finally, by combining the suspended particle layered space distribution data and the suspended particle concentration change data, a self-adaptive suspended particle layered cleaning strategy is formulated, and the strategy is based on the real-time particle distribution and concentration change condition, so that the cleaning process of suspended particles can be intelligently adjusted, and the suspended particle layered cleaning system is more accurate and efficient. The smoke isomerism alignment sample set of the electronic atomizer is subjected to self-adaptive cleaning treatment by using a formulated self-adaptive suspended particle layering cleaning strategy, so that the smoke particle cleaning sample set of the electronic atomizer subjected to multiple treatments can be obtained, the smoke particle cleaning sample set of the electronic atomizer has high purity and reliability, and is suitable for precise smoke analysis and research, thereby providing a reliable experimental foundation for deep research of the smoke moisture content of the electronic atomizer.

Preferably, step S2 comprises the steps of:

step S21: detecting the humidity of a smoke sample standard set of the electronic atomizer through a humidity sensor to obtain smoke sample humidity data;

step S22: the method comprises the steps of detecting the temperature of a smoke sample standard set of an electronic atomizer through a temperature sensor to obtain smoke sample temperature data;

step S23: performing compensation calculation on the smoke sample humidity data by using a temperature compensation calculation formula based on the smoke sample temperature data to obtain a smoke sample temperature compensation measurement value;

step S24: and carrying out compensation calibration on the smoke sample humidity data according to the smoke sample temperature compensation measurement value to obtain smoke sample humidity compensation calibration data.

According to the invention, the humidity sensor is used for detecting the humidity of the smoke sample standard set of the electronic atomizer, so that the accurate measurement of the humidity of the smoke sample can be realized, and the humidity is an important parameter in the smoke of the electronic atomizer, and is directly related to the stability and user experience of the smoke. Through the step, the obtained humidity data provides a basis for subsequent temperature compensation, and the reliability and accuracy of the experiment are ensured. Secondly, temperature data of a smoke sample can be obtained by using a temperature sensor to detect the temperature of a smoke sample standard set of the electronic atomizer, and the temperature is one of key factors influencing smoke behaviors and humidity characteristics, so that accurate information about the temperature of the smoke sample can be provided, necessary data support is provided for subsequent temperature compensation, and the credibility of experimental results is ensured. Then, by performing compensation calculation on the smoke sample humidity data based on the smoke sample temperature data by using a suitable temperature compensation calculation formula, the calculation process can effectively eliminate the influence of temperature on humidity measurement, so as to obtain a smoke sample temperature compensation measurement value. Through temperature compensation, more accurate and precise correction of humidity data can be realized, and the repeatability of experiments and the stability of results are improved. And finally, compensating and calibrating the humidity data of the smoke sample by using the calculated temperature compensation measurement value of the smoke sample, wherein the calibration process can effectively eliminate humidity errors caused by temperature, improve the accuracy and reliability of the humidity data, and the obtained humidity compensation calibration data provides a more accurate basis for humidity control and analysis of the smoke sample of the electronic atomizer, thereby ensuring the authenticity of a subsequent moisture content detection result.

Preferably, the temperature compensation calculation formula in step S23 is specifically:

；

in the method, in the process of the invention,for smoke sample temperature compensation metric, +.>For the initial temperature measurement of the smoke sample, +.>To compensate the calculated temperature variable parameter, +.>Is the smoke sample temperature weight coefficient, +.>For the temperature regulation factor of the smoke sample,/->For the moisture influencing factor of the smoke sample, +.>For the humidity value of the smoke sample, +.>For the mean value of the distribution of the temperature of the smoke sample, +.>Is the standard deviation of the distribution of the temperature of the smoke sample, +.>Amplitude-regulating parameter for the distribution of the temperature of the smoke sample, +.>Frequency parameters are influenced for the humidity of the smoke sample temperature, +.>Influence of humidity for the temperature of the smoke sample on the amplitude adjustment parameter, +.>And compensating the correction coefficient of the measurement value for the temperature of the smoke sample.

The invention constructs a temperature compensation calculation formula for carrying out compensation calculation on the humidity data of the smoke sample, the temperature compensation calculation formula considers the complex relation between temperature and humidity, and carries out temperature compensation calculation on the humidity data through different coefficients and parameters, so that the calculation process can help to obtain the humidity information of the smoke sample more accurately in practical application, and the measurement precision and accuracy are improved. Therefore, the formula fully considers the smoke sample temperature compensation measurement value Initial temperature measurement value of smoke sample +.>Compensating the calculated temperature variable parameter +.>Smoke sample temperature weight coefficient +.>Smoke sample temperature Conditioning coefficient->Smoke sample humidity influence coefficient->Humidity value of smoke sample->Mean value of the distribution of the temperatures of the smoke samples>Standard deviation of the temperature distribution of the smoke samples>Distribution amplitude adjustment parameter of smoke sample temperature +.>Humidity-influencing frequency parameter of the temperature of the smoke sample>Humidity-dependent amplitude regulation parameter of the smoke sample temperature>Correction coefficient of smoke sample temperature compensation metric +.>According to smoke sample temperature compensation metric value +.>The interrelationship between the parameters constitutes a functional relationship:

；

the formula can realize the compensation calculation process of the humidity data of the smoke sample, and simultaneously, the correction coefficient of the temperature compensation measurement value of the smoke sample is adoptedThe introduction of the temperature compensation calculation formula can be adjusted according to the error condition in the calculation process, so that the accuracy and the applicability of the temperature compensation calculation formula are improved.

Preferably, step S3 comprises the steps of:

step S31: performing cooperative calibration processing on the smoke sample standard set of the electronic atomizer according to the smoke sample humidity compensation calibration data to obtain a smoke humidity calibration sample set of the electronic atomizer;

Step S32: carrying out infrared spectrum scanning treatment on the smoke humidity calibration sample set of the electronic atomizer to obtain a smoke sample humidity spectrum signal distribution spectrogram;

step S33: carrying out signal peak identification on the humidity spectrum signal distribution spectrogram of the smoke sample to obtain a humidity spectrum signal distribution peak of the smoke sample;

step S34: and calculating the peak intensity of the humidity spectrum signal distribution peak of the smoke sample to obtain the peak metric value of the humidity spectrum signal of the smoke sample.

According to the method, the electronic atomizer smoke sample standard set is subjected to collaborative calibration according to the smoke sample humidity compensation calibration data, further calibration and collaborative processing of the smoke sample humidity can be achieved, and the key of the step is that the smoke sample in the standard set is subjected to accurate humidity calibration by utilizing the smoke sample humidity compensation calibration data, so that the electronic atomizer smoke humidity calibration sample set is obtained, the sample set becomes a standard of a subsequent experiment, and the experimental result is ensured to be more accurate and higher in comparability. Secondly, detailed information about the molecular structure and chemical bonds of the sample can be provided by performing an infrared spectroscopy scanning process on the electronic nebulizer smoke humidity calibration sample set using an infrared spectrometer. In this step, by means of infrared spectral scanning, a spectral signal distribution of the humidity of the smoke sample can be obtained, thus providing a basis for subsequent signal analysis. Then, by identifying the signal peak of the humidity spectrum signal distribution spectrogram of the smoke sample, in order to find the significant humidity spectrum signal peak in the humidity spectrum signal distribution spectrogram of the smoke sample, the humidity property of the smoke sample can be analyzed more accurately. Through the step, the peak information of the smoke humidity spectrum signal distribution can be obtained, so that a data basis is provided for subsequent peak intensity calculation. Finally, by calculating the peak intensity of the humidity spectrum signal distribution peak of the smoke sample, the quantitative analysis process of the humidity spectrum signal distribution peak can be realized, and the quantitative analysis process reflects the content and the concentration of relevant moisture in the smoke sample. By the step, the measurement value of the smoke humidity spectrum signal peak can be obtained, so that quantitative data support is provided for subsequent moisture content measurement.

Preferably, step S4 comprises the steps of:

step S41: drying the electronic atomizer smoke sample standard set corresponding to the peak value of the humidity spectrum signal of the smoke sample to obtain an electronic atomizer smoke dry sample set;

step S42: performing iterative infrared spectrum analysis on the smoke drying sample set of the electronic atomizer to obtain a peak metric value of a smoke sample drying spectrum signal;

step S43: and carrying out differential calculation on the peak value of the humidity spectrum signal of the smoke sample and the peak value of the drying spectrum signal of the smoke sample to obtain the moisture content value of the smoke sample.

According to the invention, the smoke sample standard set of the electronic atomizer corresponding to the peak value of the humidity spectrum signal of the smoke sample is dried, so that the moisture in the smoke sample is removed, the subsequent analysis is more focused, and the accurate and reliable analysis result of the subsequent spectrum signal is ensured. Then, through carrying out iterative infrared spectrum analysis on the smoke drying sample set of the electronic atomizer, the spectrum signal peak value of the smoke sample after moisture is removed can be obtained, and the information of the components and the structure of the smoke sample can be further revealed. This step provides high quality input data for subsequent differential calculations, helping to more accurately determine the moisture content of the smoke sample, while allowing for a more comprehensive and thorough analysis. And finally, the moisture content in the smoke sample can be accurately reflected by carrying out differential calculation on the humidity spectrum signal peak value of the smoke sample and the dry spectrum signal peak value of the smoke sample. Through the step, a more accurate and reliable moisture content value can be obtained, so that important data support is provided for moisture content measurement and analysis of the smoke of the electronic atomizer, and the accurate moisture content analysis has important significance for understanding the performance and quality of the smoke product.

Drawings

Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:

FIG. 1 is a schematic flow chart of the steps of the method for measuring the moisture content of smoke of an electronic atomizer;

FIG. 2 is a detailed step flow chart of step S1 in FIG. 1;

fig. 3 is a detailed step flow chart of step S12 in fig. 2.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

To achieve the above objective, referring to fig. 1 to 3, the present invention provides a method for determining the moisture content of smoke of an electronic atomizer, which comprises the following steps:

In the embodiment of the present invention, please refer to fig. 1, which is a schematic flow chart of steps of a method for determining the moisture content of smoke in an electronic atomizer, in this example, the method for determining the moisture content of smoke in the electronic atomizer includes:

the embodiment of the invention carries out the adaptation calibration on the smoke sensor in the electronic atomizer, comprises the steps of adjusting sensor parameters, calibrating sensitivity, detecting threshold value and the like, so as to ensure the accuracy and the reliability of the smoke sensor in actual use, and carries out proper network construction and arrangement on the calibrated smoke calibration sensor by using methods such as network topology analysis, network gap optimization and the like, so as to ensure the global performance of smoke detection, thereby obtaining the smoke acquisition sensor array network. Then, the smoke generated by the electronic atomizer is sampled and preprocessed by using the constructed smoke acquisition sensing array network, so that a smoke sample generated by the electronic atomizer is efficiently captured and recorded, and finally, a smoke sample standard set of the electronic atomizer is obtained.

according to the embodiment of the invention, the humidity sensor is used for detecting the smoke samples in the smoke sample standard set of the electronic atomizer, so that the accurate measurement of the humidity of the smoke samples is realized, and the humidity data of the smoke samples are obtained. Meanwhile, the temperature sensor is used for detecting the smoke sample in the smoke sample standard set of the electronic atomizer, so that the temperature of the smoke sample is accurately measured, and the temperature data of the smoke sample is obtained. And then, compensating and calibrating the smoke sample humidity value in the corresponding smoke sample humidity data according to the temperature condition of the smoke sample temperature data so as to effectively eliminate humidity errors caused by temperature and finally obtain smoke sample humidity compensation and calibration data.

According to the embodiment of the invention, the humidity of the corresponding smoke sample in the smoke sample standard set of the electronic atomizer is further calibrated and cooperatively processed by using the compensation calibration condition in the smoke sample humidity compensation calibration data so as to accurately reflect the accurate humidity change, and the calibrated smoke sample standard set of the electronic atomizer is scanned by using an infrared spectrometer so as to provide detailed information about the molecular structure and chemical bonds of the smoke sample and the spectrum signal distribution condition of the humidity of the smoke sample, and meanwhile, the amplitude or area of the spectrum signal peak is measured to represent the intensity of the peak, so that the spectrum signal peak value of the humidity of the smoke sample is finally obtained.

According to the embodiment of the invention, the smoke sample corresponding to the humidity spectrum signal peak metric value of the smoke sample is obtained from the smoke sample standard set of the electronic atomizer, the selected smoke sample is dried by using modes such as ventilation and heating, so that the moisture in the smoke sample is fully evaporated, and simultaneously, the infrared spectrum analysis is iteratively carried out on the evaporated smoke sample standard set of the electronic atomizer by using an infrared spectrum instrument, so that the spectrum signal peak metric value of the smoke sample in the smoke sample standard set of the electronic atomizer after the moisture is removed is obtained, and the smoke sample drying spectrum signal peak metric value is obtained. Then, calculating the peak value of the humidity spectrum signal of the smoke sample and the peak value of the drying spectrum signal of the smoke sample by using simple difference calculation or a more complex difference mathematical model, and finally obtaining the calculated value which is the moisture content value of the smoke sample.

Preferably, step S1 comprises the steps of:

As an embodiment of the present invention, referring to fig. 2, a detailed step flow chart of step S1 in fig. 1 is shown, in which step S1 includes the following steps:

according to the embodiment of the invention, the smoke sensor in the electronic atomizer is subjected to adaptive calibration, and the method comprises the steps of adjusting sensor parameters, calibrating sensitivity, detecting threshold value and the like, so that the accuracy and the reliability of the smoke sensor in actual use are ensured, and finally the smoke calibrating sensor is obtained.

according to the embodiment of the invention, the calibrated smoke calibration sensor is subjected to proper network construction and arrangement by using methods such as network topology analysis, network gap optimization and the like, so that the global performance of smoke detection is ensured, and the smoke acquisition sensor array network is finally obtained.

according to the embodiment of the invention, the smoke calibration sensors in the smoke acquisition sensor array network are synchronously activated by using a proper data synchronization algorithm, so that the smoke calibration sensors in the smoke acquisition sensor array network can acquire data at the same time, the data synchronization among the smoke calibration sensors is realized, and finally the smoke acquisition synchronous array network is obtained.

according to the embodiment of the invention, the activated smoke collection synchronous array network is used for synchronously sampling the smoke generated by the electronic atomizer, so that the smoke sample generated by the electronic atomizer is efficiently captured and recorded, and finally, the initial sample set of the smoke of the electronic atomizer is obtained.

According to the embodiment of the invention, the smoke sample in the smoke initial sample set of the electronic atomizer is preprocessed, including noise, suspended particles and the like are removed, so that the accuracy and usability of the smoke sample are improved, and finally the smoke sample standard set of the electronic atomizer is obtained.

Preferably, step S12 comprises the steps of:

As an embodiment of the present invention, referring to fig. 3, a detailed step flow chart of step S12 in fig. 2 is shown, in which step S12 includes the following steps:

according to the embodiment of the invention, the plurality of calibrated smoke calibration sensors are arranged and constructed in the form of the linear array, so that the distance and the relative position between the sensors are ensured, the coverage area of the smoke calibration sensors can be improved to the greatest extent, and the linear acquisition array of the smoke sensors is finally obtained.

the embodiment of the invention constructs the network of the constructed smoke sensor linear acquisition array by using a proper network topology analysis technology (such as graph theory or other related methods) so as to construct a compact and efficient sensor array network, and finally obtains the initial acquisition array network of the smoke sensor.

according to the embodiment of the invention, a proper network clearance measurement calculation formula is formed by combining the time variable parameters calculated by the network clearance, the number of smoke sensors, the network acquisition efficiency value, the network acquisition weight parameter, the number of network monitoring nodes, the degree of the network monitoring nodes, the network acquisition contribution adjustment parameter, the network signal transmission efficiency value, the network signal transmission adjustment parameter, the network signal transmission attenuation parameter, the network signal transmission contribution adjustment parameter and related parameters, so that the network clearance calculation is performed on the initial acquisition array network of the smoke sensors, the network clearance between the sensors in the initial acquisition array network of the smoke sensors is quantized, and finally the network clearance value of the array network of the smoke sensors is obtained. In addition, the network lacunarity metric calculation formula can also use any network lacunarity algorithm in the field to replace the process of network lacunarity calculation, and is not limited to the network lacunarity metric calculation formula.

According to the embodiment of the invention, the calculated gap value of the smoke sensor array network is used for rearranging and optimizing the smoke sensor initial acquisition array network, including adjusting the distance between the sensors, changing the relative positions of the sensors and the like, so that the network gap value reaches the optimal state, effective communication and synergy among the smoke sensors are ensured, and finally the smoke acquisition sensor array network is obtained.

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in the method, in the process of the invention,for the network lacunarity value of the smoke sensor array,/-for the network lacunarity value of the smoke sensor array,>lower limit of the time range calculated for network lacuna, < ->Top of the time frame calculated for network lacunarity, < >>Outer layer integration time variable parameter calculated for network lacuna, < ->Inner layer integration time variable parameter calculated for network lacuna, < ->For the initial acquisition of the number of smoke sensors in the array network of smoke sensors, +.>Index variable parameter for smoke sensor, +.>Initial acquisition of array network for smoke sensor +.>Individual smoke sensor at time +.>Network acquisition efficiency value at ∈>Initial acquisition of array network for smoke sensor +.>Network acquisition weight parameters of individual smoke sensors, < ->For the initial acquisition of the number of network monitoring nodes in the array network for the smoke sensor, +.>Index variable parameter for network monitoring node, +.>Initial acquisition of array network for smoke sensor +.>Degree of monitoring node by individual network,/-)>Adjusting parameters for network acquisition contribution +.>Initial acquisition of network signal transmission efficiency values for smoke sensors in an array network for smoke sensors,/->Adjusting parameters for network signaling, < > >Attenuation parameters for network signaling, < >>Adjusting parameters for network signaling contribution, +.>And (3) correcting the gap value of the network for the smoke sensor array.

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Preferably, step S15 comprises the steps of:

according to the embodiment of the invention, firstly, the electronic atomizer smoke initial sample sets are collected, and the sample sets comprise data inconsistency caused by smoke samples generated under different equipment or conditions, so that the consistency and comparability of the smoke samples are ensured by calibrating and aligning the isomerism of the electronic atomizer smoke initial sample sets, the data inconsistency caused by the isomerism of the collected smoke samples in the electronic atomizer smoke initial sample sets is solved, and finally the electronic atomizer smoke isomerism alignment sample sets are obtained.

according to the embodiment of the invention, the suspension particles existing in the smoke isomerism alignment sample set of the electronic atomizer are removed in a layered manner by using the corresponding suspension particle removal method, so that the suspension particle components in the smoke sample of the electronic atomizer are removed in a layered manner, the purity and the analyzability of the sample set are improved, and finally the smoke particle removal sample set of the electronic atomizer is obtained.

According to the embodiment of the invention, the type and the level of noise are determined by firstly carrying out noise analysis on the sample set for removing the smoke particles of the electronic atomizer, and then the sample set for removing the smoke particles of the electronic atomizer is processed by using a proper noise filtering algorithm (such as an average filtering algorithm, a median filtering algorithm and the like) so as to remove noise interference introduced in an experiment or a collection process, improve the definition and the reliability of the sample set and finally obtain the standard set of the smoke sample of the electronic atomizer.

Preferably, step S152 includes the steps of:

according to the embodiment of the invention, the laser scattering technology is used for non-invasive detection of suspended particles existing in the smoke isomerism alignment sample set of the electronic atomizer, so that scattering signals of the suspended particles are sensitively detected to obtain the characteristic data of the size, shape and the like of the suspended particles, and finally the characteristic data of the suspended particles of the smoke sample are obtained.

according to the embodiment of the invention, the layering detection is carried out by designing and training the convolutional neural network model to learn the size, shape and other characteristics of the suspended particles in the characteristic data of the suspended particles of the smoke sample, so that the layering condition of the suspended particles is accurately judged, and finally the layering condition data of the suspended particles of the smoke sample is obtained.

According to the embodiment of the invention, the layering situation data of the suspended particles in the smoke sample is analyzed by using a spatial distribution technology, so that the layering distribution situation of the suspended particles in the smoke sample is analyzed and known, and finally the layering spatial distribution data of the suspended particles is obtained.

according to the embodiment of the invention, the corresponding suspended particles in the smoke isomerism alignment sample set of the electronic atomizer are detected according to the characteristics of the size, the shape and the like of the suspended particles in the smoke sample suspended particle characteristic data, so that the dynamic change condition of the suspended particle concentration in the smoke isomerism alignment sample set of the electronic atomizer is detected, and finally the suspended particle concentration change data is obtained.

According to the embodiment of the invention, the corresponding suspended particle cleaning strategy is formulated according to the layering distribution condition of the corresponding suspended particles in the layering spatial distribution data of the suspended particles and the corresponding concentration change condition in the concentration change data of the suspended particles, so that the cleaning process of the suspended particles is intelligently adjusted, and the process is more accurate and efficient, and the self-adaptive layering suspended particle cleaning strategy is obtained. And then, carrying out layered cleaning on the suspended particles corresponding to the smoke isomerism alignment sample set of the electronic atomizer according to a formulated self-adaptive suspended particle layered cleaning strategy, and finally obtaining a smoke particle cleaning sample set of the electronic atomizer.

Preferably, step S2 comprises the steps of:

according to the embodiment of the invention, the humidity sensor is used for detecting the smoke samples in the smoke sample standard set of the electronic atomizer, so that the accurate measurement of the humidity of the smoke samples is realized, and finally the humidity data of the smoke samples are obtained.

according to the embodiment of the invention, the temperature sensor is used for detecting the smoke sample in the smoke sample standard set of the electronic atomizer, so that the accurate measurement of the temperature of the smoke sample is realized, and finally the temperature data of the smoke sample is obtained.

according to the embodiment of the invention, under the temperature condition based on the temperature data of the smoke sample, a proper temperature compensation calculation formula is formed by combining an initial temperature measurement value, a temperature variable parameter, a smoke sample temperature weight coefficient, a smoke sample temperature adjustment coefficient, a smoke sample humidity influence coefficient, a smoke sample humidity value, a smoke sample temperature distribution mean value, a distribution standard deviation, a distribution amplitude adjustment parameter, a humidity influence frequency parameter, a humidity influence amplitude adjustment parameter and related parameters of the smoke sample temperature data, so as to carry out compensation calculation on the smoke sample humidity data, quantify a compensation measurement value of temperature to humidity measurement, and finally obtain the smoke sample temperature compensation measurement value. In addition, the temperature compensation calculation formula can also use any temperature compensation detection algorithm in the field to replace the compensation calculation process, and is not limited to the temperature compensation calculation formula.

According to the embodiment of the invention, the calculated smoke sample temperature compensation measurement value is used for compensating and calibrating the smoke sample humidity value in the corresponding smoke sample humidity data, so that humidity errors caused by temperature are effectively eliminated, and finally the smoke sample humidity compensation calibration data are obtained.

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Preferably, step S3 comprises the steps of:

According to the embodiment of the invention, the humidity of the corresponding smoke sample in the smoke sample standard set of the electronic atomizer is further calibrated and cooperatively processed by using the compensation calibration condition in the smoke sample humidity compensation calibration data so as to accurately reflect the accurate humidity change, and finally the smoke humidity calibration sample set of the electronic atomizer is obtained.

according to the embodiment of the invention, the calibrated smoke humidity calibration sample set of the electronic atomizer is scanned by using an infrared spectrometer to provide detailed information about the molecular structure and chemical bonds of the smoke sample, and the spectrum signal distribution condition of the smoke sample humidity is obtained, so that a spectrum signal distribution spectrogram of the smoke sample humidity is finally obtained.

according to the embodiment of the invention, the peak identification algorithm is used for analyzing the humidity spectrum signal distribution spectrogram of the smoke sample, and the signal peak position with significance of the corresponding water molecules is found in an automatic or semi-automatic mode, so that the humidity spectrum signal distribution peak of the smoke sample is finally obtained.

According to the embodiment of the invention, the distribution peak of the humidity spectrum signal of the smoke sample is calculated by using a mathematical statistical method, so that the amplitude or the area of the distribution peak of the humidity spectrum signal of the smoke sample is measured to represent the intensity of the peak, the quantitative analysis process of the distribution peak of the humidity spectrum signal of the smoke sample is realized, and finally the peak metric value of the humidity spectrum signal of the smoke sample is obtained.

Preferably, step S4 comprises the steps of:

according to the embodiment of the invention, the smoke sample corresponding to the peak value of the humidity spectrum signal of the smoke sample is obtained from the smoke sample standard set of the electronic atomizer, and the selected smoke sample is dried by using ventilation, heating and other modes, so that the moisture in the smoke sample is fully evaporated, and finally the smoke dry sample set of the electronic atomizer is obtained.

according to the embodiment of the invention, infrared spectrum analysis is iteratively carried out on the evaporated smoke drying sample set of the electronic atomizer by using an infrared spectrum instrument, so that the spectrum signal peak metric value of the smoke sample in the smoke drying sample set of the electronic atomizer after moisture removal is obtained, and finally the smoke sample drying spectrum signal peak metric value is obtained.

According to the embodiment of the invention, the smoke sample humidity spectrum signal peak value and the smoke sample drying spectrum signal peak value are calculated by using a simple difference calculation or a more complex difference mathematical model, and the finally calculated value is the smoke sample moisture content value.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The method for measuring the moisture content of the smoke of the electronic atomizer is characterized by comprising the following steps of:

step S1: performing acquisition network construction processing on a smoke sensor in the electronic atomizer to obtain a smoke acquisition sensing array network; sampling and processing smoke generated by the electronic atomizer by using a smoke acquisition sensing array network to obtain a smoke sample standard set of the electronic atomizer; wherein, step S1 comprises the following steps:

step S12: performing acquisition network construction processing on the smoke calibration sensor to obtain a smoke acquisition sensor array network; wherein, step S12 includes the following steps:

step S123: calculating network lacunarity of the smoke sensor initial acquisition array network by using a network lacunarity measurement calculation formula to obtain a lacunarity value of the smoke sensor array network; the network lacunarity measurement calculation formula specifically comprises:

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in the method, in the process of the invention,for the network lacunarity value of the smoke sensor array,/-for the network lacunarity value of the smoke sensor array,>lower limit of the time range calculated for network lacuna, < ->Top of the time frame calculated for network lacunarity, < >>Outer layer integration time variable parameter calculated for network lacuna, < ->Inner layer integration time variable parameter calculated for network lacuna, < ->For smoke transmission Sensor initial acquisition number of smoke sensors in array network, +.>Index variable parameter for smoke sensor, +.>Initial acquisition of array network for smoke sensor +.>Individual smoke sensor at time +.>Network acquisition efficiency value at ∈>Initial acquisition of array network for smoke sensor +.>Network acquisition weight parameters of individual smoke sensors, < ->For the initial acquisition of the number of network monitoring nodes in the array network for the smoke sensor, +.>Index variable parameter for network monitoring node, +.>Initial acquisition of array network for smoke sensor +.>Degree of monitoring node by individual network,/-)>Adjusting parameters for network acquisition contribution +.>Initial acquisition of network signal transmission efficiency values for smoke sensors in an array network for smoke sensors,/->Adjusting parameters for network signaling, < >>Attenuation parameters for network signaling, < >>Adjusting parameters for network signaling contribution, +.>A correction value for the network gap value of the smoke sensor array;

step S124: according to the gap value of the smoke sensor array network, performing array arrangement optimization on the smoke sensor initial acquisition array network to obtain a smoke acquisition sensing array network;

step S15: performing smoke pretreatment on an electronic atomizer smoke initial sample set to obtain an electronic atomizer smoke sample standard set;

step S2: detecting the humidity of a smoke sample standard set of the electronic atomizer through a humidity sensor to obtain smoke sample humidity data; the method comprises the steps of detecting the temperature of a smoke sample standard set of an electronic atomizer through a temperature sensor to obtain smoke sample temperature data; performing compensation calibration on the smoke sample humidity data according to the smoke sample temperature data to obtain smoke sample humidity compensation calibration data; wherein, step S2 includes the following steps:

step S23: performing compensation calculation on the smoke sample humidity data by using a temperature compensation calculation formula based on the smoke sample temperature data to obtain a smoke sample temperature compensation measurement value; the temperature compensation calculation formula is as follows:

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In the method, in the process of the invention,for smoke sample temperature compensation metric, +.>For the initial temperature measurement of the smoke sample, +.>To compensate the calculated temperature variable parameter, +.>Is the smoke sample temperature weight coefficient, +.>For the temperature regulation factor of the smoke sample,/->For the moisture influencing factor of the smoke sample, +.>For the humidity value of the smoke sample, +.>For the mean value of the distribution of the temperature of the smoke sample, +.>Is the standard deviation of the distribution of the temperature of the smoke sample, +.>Amplitude-regulating parameter for the distribution of the temperature of the smoke sample, +.>Frequency parameters are influenced for the humidity of the smoke sample temperature, +.>Influence of humidity for the temperature of the smoke sample on the amplitude adjustment parameter, +.>A correction coefficient for the smoke sample temperature compensation metric;

step S24: performing compensation calibration on the smoke sample humidity data according to the smoke sample temperature compensation measurement value to obtain smoke sample humidity compensation calibration data;

2. The method of determining the moisture content of a smoke of an electronic atomizer according to claim 1, wherein step S15 comprises the steps of:

3. The method of determining the moisture content of a mist of an electronic atomizer according to claim 2, characterized in that step S152 comprises the steps of:

4. The method of determining the moisture content of a smoke of an electronic atomizer according to claim 1, wherein step S3 comprises the steps of:

5. The method of determining the moisture content of a smoke of an electronic atomizer according to claim 1, wherein step S4 comprises the steps of: