Electronic cigarette quality testing method, device and equipment

Abstract

The present invention provides a method, device, and equipment for testing the quality of electronic cigarettes, including: testing the components of electronic cigarettes using a smoke composition analyzer to form a smoke composition parameter set; collecting the electrical parameters of the electronic cigarette to form an electrical parameter set; measuring physical characteristic parameters to construct a physical parameter set; performing dimensionality reduction analysis on the smoke composition parameter set, the electrical parameter set, and the physical parameter set based on principal component analysis to extract corresponding key feature parameters, and assigning weights to each key feature parameter based on the hierarchical analysis method; inputting each key feature parameter with assigned weights into a pre-trained quality assessment model to output a quality grade score of the electronic cigarette; and generating an encrypted QR code containing all key feature parameters and quality grade scores for qualified electronic cigarettes with quality grade scores above a threshold, and storing the code in a blockchain traceability system. In the present invention, product quality is assessed and traced from a holistic perspective.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a quality detection method, device and equipment for electronic cigarettes. Background Art

[0002] Traditional e-cigarette quality testing methods have numerous limitations. They typically rely on a single dimension, such as determining e-cigarette quality based on vapor composition or solely on electrical parameters. Without effectively correlating multiple data points, such as vapor composition, electrical properties, and physical characteristics, it's difficult to assess product quality from a holistic perspective.

[0003] Furthermore, the existing quality inspection process does not form product quality traceability, and it is impossible to accurately locate the root cause of the problem, which brings great difficulties to subsequent quality improvement and problem troubleshooting, and it is difficult to meet the requirements of traceability management for the entire life cycle of the product. Summary of the Invention

[0004] The main purpose of the present invention is to provide an electronic cigarette quality detection method, device and equipment, aiming to overcome the defects of being unable to correlate smoke composition, electrical properties and physical properties data for comprehensive quality detection and the lack of product quality traceability.

[0005] To achieve the above object, the present invention provides a method for detecting the quality of an electronic cigarette, comprising the following steps:

[0006] The smoke generated by the electronic cigarette is tested for components using a smoke composition analyzer to form a smoke composition parameter set; the electrical parameters of the electronic cigarette are collected to form an electrical parameter set; the physical characteristic parameters of the electronic cigarette are measured to construct a physical parameter set;

[0007] Based on the principal component analysis method, the dimensionality reduction analysis of the smoke component parameter set, the electrical parameter set, and the physical parameter set is performed respectively to extract the corresponding key characteristic parameters, and a weight is assigned to each key characteristic parameter based on the hierarchical analysis method;

[0008] Inputting the weighted key feature parameters into a pre-trained quality assessment model to output a quality grade score of the electronic cigarette;

[0009] For qualified e-cigarettes with a quality grade score higher than the threshold, an encrypted QR code containing all its key characteristic parameters and quality grade score is generated, and the encrypted QR code is associated with the corresponding e-cigarette product information and stored in the blockchain traceability system.

[0010] Furthermore, after outputting the quality grade score of the electronic cigarette, the method further includes:

[0011] Determine the electronic cigarettes whose quality grade score is lower than the threshold as unqualified products, and perform defect analysis on the smoke composition parameter set, electrical parameter set, and physical parameter set;

[0012] The defect analysis results are stored in the blockchain traceability system.

[0013] Furthermore, the weights assigned to the key feature parameters based on the hierarchical analysis method include:

[0014] The smoke composition parameter set, electrical parameter set, and physical property parameter set are used as the criterion layer, and the key characteristic parameters under each parameter set are used as the indicator layer;

[0015] Based on data mining, the influence of each parameter set on the quality of the electronic cigarette is analyzed to determine the relative importance between the elements of the criterion layer and the relative importance of the elements of the indicator layer relative to the criterion layer, and to generate a judgment matrix;

[0016] Solving the maximum eigenvalue of the judgment matrix and obtaining its corresponding eigenvector; performing a normalization operation on the eigenvector to obtain the initial weight vector of each indicator layer relative to the criterion layer;

[0017] Calculating the consistency ratio of the judgment matrix and judging whether the consistency ratio meets the requirement;

[0018] If the consistency ratio meets the requirement, the current initial weight vector is confirmed and weights are assigned to each key feature parameter.

[0019] Furthermore, after determining whether the consistency ratio meets the requirements, the following steps are further included:

[0020] If not, the element values ​​of the judgment matrix are automatically adjusted, and the weight vectors of each indicator layer relative to the criterion layer are recalculated until the consistency ratio meets the requirement.

[0021] Furthermore, an encrypted QR code containing all key characteristic parameters and quality ratings is generated, including:

[0022] generating an encryption key based on the smoke composition parameter set, the electrical parameter set, and the physical parameter set;

[0023] Encrypting the key feature parameters and the quality grade score based on the encryption key to obtain encrypted data;

[0024] The encrypted data is added to the QR code to generate the encrypted QR code.

[0025] Furthermore, generating an encryption key based on the smoke composition parameter set, the electrical parameter set, and the physical parameter set includes:

[0026] Extracting multiple key values ​​from the smoke component parameter set, electrical parameter set, and physical parameter set; and recombining and arranging each key value to form a data string;

[0027] Based on the length of the data string, a corresponding base point is selected on a preset elliptic curve domain;

[0028] Based on the preset elliptic curve domain and base point, performing a scalar multiplication operation on each key value in the data string as a scalar to generate a plurality of elliptic curve points;

[0029] The coordinate values ​​of each elliptic curve point are converted into a code string;

[0030] Generate a pseudo-random noise sequence, perform a bitwise exclusive OR operation on the pseudo-random noise sequence and the code string, and generate the encryption key.

[0031] Furthermore, generating an encryption key based on the smoke composition parameter set, the electrical parameter set, and the physical parameter set includes:

[0032] Encoding the smoke component parameter set, electrical parameter set, and physical parameter set to obtain encoded data;

[0033] Add the characters in the coded data to the data table one by one in sequence to generate a coded data table;

[0034] Extracting values ​​from the smoke component parameter set to generate a first curve; deforming the coded data table based on the first curve to obtain a deformed data table;

[0035] Extracting values ​​from the electrical parameter set to generate a second curve; extracting values ​​from the physical parameter set to generate a third curve;

[0036] The second curve and the third curve are superimposed on the deformation data table, and the cells in the deformation data table whose positional relationships with the second curve and the third curve meet the preset conditions are used as target cells. The characters in the target cells are combined in sequence to obtain the encryption key.

[0037] The present invention also provides an electronic cigarette quality detection device, comprising:

[0038] The acquisition unit is used to detect the composition of the smoke generated by the electronic cigarette based on the smoke composition analyzer to form a smoke composition parameter set; collect the electrical parameters of the electronic cigarette to form an electrical parameter set; measure the physical characteristic parameters of the electronic cigarette to construct a physical parameter set;

[0039] an extraction unit, configured to perform dimensionality reduction analysis on the smoke component parameter set, the electrical parameter set, and the physical parameter set based on a principal component analysis method, extract corresponding key characteristic parameters, and assign weights to the key characteristic parameters based on a hierarchical analysis method;

[0040] An evaluation unit, configured to input the weighted key feature parameters into a pre-trained quality evaluation model and output a quality grade score of the electronic cigarette;

[0041] The storage unit is used to generate an encrypted QR code containing all key characteristic parameters and quality grade scores for qualified e-cigarettes with quality grade scores higher than the threshold, and associate the encrypted QR code with the corresponding e-cigarette product information and store it in the blockchain traceability system.

[0042] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0043] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0044] The present invention provides an electronic cigarette quality detection method, device, and equipment, including: detecting the components of the smoke generated by the electronic cigarette based on a smoke component analyzer to form a smoke component parameter set; collecting the electrical parameters of the electronic cigarette to form an electrical parameter set; measuring the physical characteristic parameters of the electronic cigarette to construct a physical parameter set; performing dimensionality reduction analysis on the smoke component parameter set, the electrical parameter set, and the physical parameter set based on the principal component analysis method to extract the corresponding key feature parameters, and assigning weights to each key feature parameter based on the hierarchical analysis method; inputting each of the weighted key feature parameters into a pre-trained quality assessment model to output the quality grade score of the electronic cigarette; for qualified electronic cigarettes with a quality grade score above a threshold, generating an encrypted QR code containing all of its key feature parameters and quality grade scores, and associating the encrypted QR code with the corresponding electronic cigarette product information and storing it in a blockchain traceability system. In the present invention, by combining the smoke component parameter set, the electrical parameter set, and the physical parameter set to perform electronic cigarette quality detection, product quality assessment is performed from the overall perspective; at the same time, an encrypted QR code containing all of its key feature parameters and quality grade scores is generated, effectively tracing the product quality after quality detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a schematic diagram of the steps of a quality detection method for an electronic cigarette according to one embodiment of the present invention;

[0046] Figure 2This is a structural block diagram of a quality detection device for an electronic cigarette according to an embodiment of the present invention;

[0047] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0048] The implementation, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] Reference Figure 1 In one embodiment of the present invention, a method for detecting the quality of an electronic cigarette is provided, comprising the following steps:

[0051] Step S1: Detecting the composition of smoke generated by the electronic cigarette using a smoke composition analyzer to form a smoke composition parameter set; collecting electrical parameters of the electronic cigarette to form an electrical parameter set; and measuring physical characteristic parameters of the electronic cigarette to construct a physical parameter set.

[0052] Step S2, performing dimensionality reduction analysis on the smoke composition parameter set, electrical parameter set, and physical parameter set based on principal component analysis, extracting corresponding key characteristic parameters, and assigning weights to each key characteristic parameter based on analytic hierarchy process;

[0053] Step S3, inputting the key feature parameters with assigned weights into a pre-trained quality assessment model, and outputting a quality grade score of the electronic cigarette;

[0054] In step S4, for qualified e-cigarettes with a quality grade score higher than the threshold, an encrypted QR code containing all its key characteristic parameters and quality grade score is generated, and the encrypted QR code is associated with the corresponding e-cigarette product information and stored in the blockchain traceability system.

[0055] In this embodiment, as described in step S1 above, the smoke composition analyzer utilizes spectral analysis, chromatographic separation, and other techniques to accurately identify and quantify various chemical components in smoke. For example, it can precisely measure the exact nicotine content, with accuracy reaching milligrams or even micrograms to ensure compliance with various regulatory standards. It can also accurately analyze the ratios of common solvents such as propylene glycol and glycerol. This not only affects the taste and volume of smoke but is also closely linked to the safety of e-cigarettes, as improper ratios can lead to excessive smoke irritation or the production of harmful decomposition products. Furthermore, it rigorously tests for a variety of potentially harmful components, such as aldehydes and ketones, such as formaldehyde, acetaldehyde, and acetone. Therefore, accurately determining their content is crucial, and all of this detected data is aggregated to form a set of smoke composition parameters.

[0056] Professional electrical measuring instruments, such as high-precision oscilloscopes and multimeters, monitor multiple electrical parameters of the e-cigarette in real time while it's operating. Voltage stability is a key indicator. Unstable voltage can cause abnormal temperature fluctuations in the heating wire, affecting vapor production while accelerating aging and even causing short circuits. Current intensity measurements can reflect the e-cigarette's power consumption, and combined with battery capacity, battery life can be estimated. If the current increases abnormally, there's a potential for leakage in the circuit. Furthermore, precise resistance measurements are also included. Resistance changes can reflect the uniformity of the heating wire's material and its operating condition. For example, localized oxidation of the heating wire can lead to increased resistance, affecting heating efficiency and vapor quality. These electrical parameters are integrated into an electrical parameter set.

[0057] Testing is carried out using high-precision three-dimensional measuring instruments, material hardness testers, and other equipment. From a dimensional perspective, the length, diameter, thickness, and other parameters of the e-cigarette shell are precisely measured to ensure compliance with ergonomic design requirements. At the same time, tolerances are kept within extremely small limits to prevent dimensional deviations from leading to poor assembly or compromising user comfort. Material property testing covers the hardness, wear resistance, and corrosion resistance of the shell material. For example, aluminum alloy shells must be tested for hardness compliance to withstand daily scratches, as well as corrosion resistance under environmental factors such as sweat and air, to prevent rust and fading that affect the shell's aesthetics and product lifespan. For internal components, the assembly clearances of each component are carefully measured. Minor assembly errors can cause loosening, friction noise, or even functional failures. For example, an unreasonable gap between the atomizer core and the battery assembly can lead to poor contact and affect the atomization effect. These physical property parameters are combined to construct a physical property parameter set.

[0058] As described in step S2 above, principal component analysis (PCA) is a mathematical tool that can project a high-dimensional data space into a low-dimensional subspace while preserving the data's key information features to the greatest extent possible. Taking the smoke component parameter set as an example, many chemical composition data points exhibit certain correlations. PCA uses linear transformations to convert these correlated variables into a small number of uncorrelated principal components, each of which is a linear combination of the original variables. For example, when testing multiple aldehydes, it was found that the content trends of some aldehydes were highly similar. PCA can extract a principal component that comprehensively reflects the overall situation of the aldehydes, thereby reducing data dimensionality and preventing redundant information from interfering with subsequent analysis. Similarly, the most representative key characteristic parameters are extracted for the electrical and physical property parameter sets. These key characteristic parameters carry the core information of each parameter set and are the key basis for judging the quality of e-cigarettes.

[0059] The aforementioned analytic hierarchy process (AHP) constructs a hierarchical structure, with comprehensive e-cigarette quality assessment as the target layer, aerosol composition, electrical properties, and physical properties parameter sets as the criterion layer, and key characteristic parameters within each parameter set as the indicator layer. Each element within the criterion and indicator layers is scored pairwise based on its relative importance to e-cigarette quality, quantified using a 1-9 scale to construct a judgment matrix. For example, based on extensive experimental data and market feedback, aerosol composition is considered to have a significant impact on human health and is more critical in quality assessment than electrical properties, thus being assigned a higher relative score. Next, the eigenvalue method is used with professional statistical software to calculate the maximum eigenvalue and corresponding eigenvector of the judgment matrix. The eigenvectors are normalized to determine the initial weight vectors for each indicator layer relative to the criterion layer. A consistency check is then performed to ensure the rationality of the weight assignment. Finally, the combined weights of each key characteristic parameter relative to the target layer are determined, ensuring that the subsequent quality assessment model accurately operates based on the true importance of each parameter.

[0060] As described in step S3 above, the pre-trained quality assessment model is constructed based on a deep learning framework, such as a multi-layer neural network structure. During the model training phase, a large amount of e-cigarette sample data covering different brands, models, and production batches is collected. These samples are accompanied by precise quality grade annotations, determined by a professional laboratory through a complex process combining human inhalation experiments and comprehensive physical and chemical performance testing. The key characteristic parameters (with weights) of the samples are used as input, and the quality grade is used as the output label. Through repeated iterative training, the model continuously adjusts the weights of the internal neuron connections and learns the characteristic patterns of e-cigarettes of different quality levels. When the key characteristic parameters of the e-cigarette to be tested are input, the model performs complex nonlinear calculations based on the learned patterns, comprehensively considering the weights and interrelationships of each parameter, and ultimately outputs a continuous quality grade score. This score intuitively reflects the comprehensive quality level of the e-cigarette in terms of composition, electrical properties, physical characteristics, and other aspects. For example, it ranges from 0 to 100, with higher scores representing better quality, providing a quantitative basis for subsequent quality assessment.

[0061] As described in step S4 above, all key characteristic parameters of the e-cigarette (including the final data of smoke composition, electrical properties, physical properties, etc. after dimensionality reduction and weighting) and the quality grade score are first integrated. This data is hashed using a hash function and mapped into a fixed-length hash value to ensure data uniqueness and integrity and prevent data tampering. Next, an encryption algorithm is used to encrypt the hashed data to generate encrypted ciphertext. Based on this ciphertext and combined with the QR code generation rules, an encrypted QR code with a complex pattern is generated. This QR code not only carries rich quality information about the e-cigarette, but also, due to the encryption process, it cannot be easily accessed by outsiders, effectively protecting the privacy of product quality data and providing a reliable data carrier for subsequent traceability and verification.

[0062] The aforementioned blockchain traceability system utilizes its distributed ledger technology, with each node storing a complete or partial copy of the ledger to ensure data immutability and high reliability. The encrypted QR code is associated with product information such as the e-cigarette's production batch number, production date, production equipment number, and raw material supplier information, packaged in a specific data structure, and uploaded to the blockchain network. When tracing the source of product quality is necessary, consumers and businesses alike simply scan the encrypted QR code on the e-cigarette and trigger a data query command through a smart contract on the blockchain. This allows for quick and accurate access to detailed information on the entire process, from raw material procurement and production to quality testing. This enables full lifecycle traceability of product quality information, greatly improving the transparency and efficiency of quality control.

[0063] In one embodiment, after outputting the quality grade score of the electronic cigarette, the method further includes:

[0064] Determine the electronic cigarettes whose quality grade score is lower than the threshold as unqualified products, and perform defect analysis on the smoke composition parameter set, electrical parameter set, and physical parameter set;

[0065] The defect analysis results are stored in the blockchain traceability system.

[0066] In this embodiment, when the quality grade score of the electronic cigarette is lower than a preset threshold, it is directly classified as an unqualified product based on clear and quantitative standards.

[0067] Regarding smoke composition, we thoroughly compare the standard composition range of qualified products with the actual test data of unqualified products. We focus on whether nicotine content deviates from the acceptable range; analyze whether the propylene glycol:glycerine ratio is imbalanced; and closely monitor whether harmful components such as aldehydes and ketones exceed standards. Using chemical data analysis software combined with big data statistical models, we pinpoint the factors that lead to unqualified smoke composition, such as excessive raw material impurities, poor temperature control during the atomization process, or improper use of flavoring additives. This provides key information for subsequent improvements to the production process.

[0068] Check the voltage stability of the e-cigarette. Unstable voltage performance indicates defects in the circuit design, such as capacitor leakage and poor performance of the voltage stabilizing chip. This not only affects the atomization effect, but may also cause battery overheating due to excessively high voltage, or low voltage resulting in a thin vapor volume. By monitoring the current fluctuation curve, determine whether there are abnormal current spikes or troughs. Abnormal current may mean that there are short circuits or open circuit hazards in the circuit, or that the load capacity of the electronic components does not match. Analyze the gap between the battery life and the nominal value. If the battery life is significantly lower than expected, it may be that the battery capacity is falsely marked, the battery is aging too quickly, or the power management chip consumes too much energy. These problems require accurate diagnosis to optimize electrical performance and ensure product safety and reliability.

[0069] For the appearance dimensions, compare the design drawings with the standard tolerances to check for dimensional deviations caused by mold wear or rough processing. Excessive or small dimensions may affect the tightness of the product assembly, causing parts to loosen or fall off, and even affecting the user's grip. Evaluate the material properties of the shell. For example, if the hardness does not meet the standard, the product will easily be scratched and worn during daily use, affecting the appearance and product life. If the material has poor corrosion resistance, the shell will easily rust and fade after contact with sweat, humid air and other environments. For internal assembly gaps, use high-precision image measurement equipment to review. Excessive gaps may cause friction and collisions between parts, generating noise, and even affecting the normal operation of key functional components such as atomizer cores and heating wires. Through detailed analysis, find out the potential root causes of problems in the physical structure.

[0070] Finally, through the consensus mechanism between blockchain nodes, the data block is uploaded and stored in the blockchain network, ensuring that each node has a complete or partial copy, ensuring the data is tamper-proof and traceable.

[0071] In one embodiment, allocating weights to the key feature parameters based on the analytic hierarchy process includes:

[0072] The smoke composition parameter set, electrical parameter set, and physical property parameter set are used as the criterion layer, and the key characteristic parameters under each parameter set are used as the indicator layer;

[0073] Based on data mining, the influence of each parameter set on the quality of the electronic cigarette is analyzed to determine the relative importance between the elements of the criterion layer and the relative importance of the elements of the indicator layer relative to the criterion layer, and to generate a judgment matrix;

[0074] Solving the maximum eigenvalue of the judgment matrix and obtaining its corresponding eigenvector; performing a normalization operation on the eigenvector to obtain the initial weight vector of each indicator layer relative to the criterion layer;

[0075] Calculating the consistency ratio of the judgment matrix and judging whether the consistency ratio meets the requirement;

[0076] If the consistency ratio meets the requirements, the current initial weight vector is confirmed and weights are assigned to each key feature parameter. If not, the element values ​​of the judgment matrix are automatically adjusted and the weight vectors of each indicator layer relative to the criterion layer are recalculated until the consistency ratio meets the requirements.

[0077] In this embodiment, the smoke composition parameter set, electrical parameter set, and physical property parameter set are used as the criterion layer, and the key characteristic parameters under each parameter set are used as the indicator layer. This step forms the foundational framework for constructing the AHP model. This layering clearly outlines the logical structure of e-cigarette quality assessment. The criterion layer categorizes the key areas that influence e-cigarette quality, and the numerous key characteristic parameters encompassed by each area are further refined in the indicator layer. For example, the smoke composition parameter set includes key characteristic parameters such as nicotine content, harmful aldehyde content, and atomizer ratio; key characteristic parameters under the electrical parameter set, such as voltage stability, battery life, and charging efficiency, determine the power supply performance of the e-cigarette; and key characteristic parameters under the physical property parameter set, such as housing material hardness, component assembly accuracy, and product dimensional tolerance, influence product durability and assembly quality. This hierarchical structure provides a structured framework for subsequent precise quantification of the importance of each parameter.

[0078] Using data mining, we analyze the impact of various parameter sets on e-cigarette quality, thereby determining the relative importance of elements at the criterion level, as well as the relative importance of elements at the indicator level relative to the criterion level, and generating a judgment matrix. Data mining techniques utilize algorithms such as association analysis and cluster analysis to deeply explore the patterns underlying the data. At the criterion level, we consider the relative importance of the three criterion-level elements—smoke composition, electrical properties, and physical properties—to overall quality, by analyzing a large number of health complaints related to smoke composition, safety incidents caused by electrical failures, and negative user reviews due to poor physical properties. Combined with product recall data and after-sales repair frequency, we quantify the relative importance of smoke composition, electrical properties, and physical properties—the three key characteristic parameters—in overall quality. At the indicator level, we also leverage data mining to compare the market acceptance of products with different nicotine content levels and the impact of different levels of harmful aldehydes on long-term user health indicators. This accurately determines the importance of each key characteristic parameter relative to its corresponding criterion level. Based on these in-depth analysis results, we construct a judgment matrix using a 1-9 scaling method, numerically quantifying the relative importance of each element, providing the raw data foundation for subsequent weight calculations.

[0079] The judgment matrix is ​​solved for its maximum eigenvalue and its corresponding eigenvector is obtained. The eigenvector is normalized to determine the initial weight vectors for each indicator layer relative to the criterion layer. Solving for the maximum eigenvalue and corresponding eigenvector is one of the core mathematical operations of the AHP. Using mathematical software or an algorithm library, the constructed judgment matrix is ​​input and its maximum eigenvalue and corresponding eigenvector are iteratively calculated. Each component of the eigenvector represents the relative weight of the corresponding indicator layer element within the corresponding criterion layer. However, the values ​​of the components are not normalized at this point, and their sum does not necessarily equal 1. Normalization is performed, dividing each component of the eigenvector by the sum of all components to rescale the components into a percentage. This yields the initial weight vectors for each indicator layer relative to the criterion layer. This provides a preliminary understanding of the weight distribution of each key characteristic parameter within the corresponding criterion, paving the way for subsequent consistency testing and final weight determination.

[0080] Calculate the consistency ratio of the judgment matrix to determine whether it meets the requirements. This consistency ratio is a key indicator for evaluating the rationality of the judgment matrix. During the construction of the judgment matrix, due to the use of a 1-9 scale for pairwise comparisons based on experience and data analysis, there is a certain degree of subjectivity or accumulated data errors, which may lead to logical inconsistencies within the judgment matrix. The consistency ratio is calculated using a specific formula. This formula generally involves calculating the consistency index (CI): CI = (λmax - n) / (n - 1), where λmax is the maximum eigenvalue and n is the order of the judgment matrix. The corresponding RI value is obtained from the average random consistency index (RI) table, and the consistency ratio (CR) is calculated as CI / RI. If CR < 0.1, the judgment matrix passes the consistency test, indicating that the constructed judgment matrix is ​​logically sound and the relative importance of each element is reliable. The weight confirmation process can be continued. If CR ≥ 0.1, the judgment matrix has significant inconsistencies and requires readjustment.

[0081] If the consistency ratio meets the requirements, the current initial weight vector is confirmed and weights are assigned to each key characteristic parameter. If not, the judgment matrix elements are automatically adjusted, and the weight vectors of each indicator layer relative to the criterion layer are recalculated until the consistency ratio meets the requirements. If the judgment matrix fails the consistency test, it requires retroactive correction. Various automatic adjustment strategies are commonly used. For example, based on data sensitivity analysis, elements with the greatest impact on consistency are identified and re-evaluated using a combination of data mining and expert experience to adjust their relative importance scores. Alternatively, optimization algorithms are used to iteratively adjust the judgment matrix elements in small increments with the goal of minimizing the consistency ratio. After each adjustment, the eigenvalues, eigenvectors, and consistency ratio are recalculated until the CR < 0.1 requirement is met. Once the requirements are met, the weight vectors for each key characteristic parameter are scientifically sound and accurately reflect the true importance of each parameter in e-cigarette quality assessment. This facilitates subsequent analysis using weight-based quality assessment models and ensures the accuracy and reliability of e-cigarette quality testing.

[0082] In one embodiment, an encrypted QR code containing all key characteristic parameters and quality ratings is generated, including:

[0083] generating an encryption key based on the smoke composition parameter set, the electrical parameter set, and the physical parameter set;

[0084] Encrypting the key feature parameters and the quality grade score based on the encryption key to obtain encrypted data;

[0085] The encrypted data is added to the QR code to generate the encrypted QR code.

[0086] In this embodiment, encryption keys are generated based on the aerosol composition parameter set, electrical parameter set, and physical parameter set, leveraging the comprehensive quality characteristics of the e-cigarette to ensure data security. These parameter sets are processed using a specific algorithm. For example, a hash function is used to hash the data in each parameter set, extracting hash values ​​of a fixed length that represent the data characteristics. These hash values ​​are then concatenated or further processed according to predetermined rules. For example, a key generation algorithm (such as a key derivation function based on elliptic curve cryptography) is used to generate a high-strength encryption key using the concatenated data as input. The generated encryption key is tightly bound to the core quality attributes of the e-cigarette, providing a foundation for subsequent encryption and significantly increasing the difficulty of cracking the key. This is because an attacker seeking to obtain the key requires precise information about the e-cigarette's composition, electrical properties, and physical properties, making decryption more challenging.

[0087] After obtaining the encryption key, critical information is protected using either symmetric or asymmetric encryption algorithms. If a symmetric encryption algorithm (such as the Advanced Encryption Standard (AES)) is used, the encryption key is used for both encryption and decryption. Key characteristic parameters (the most representative data after prior dimensionality reduction and weighting, such as the key hazard comprehensive index extracted from the smoke composition parameter set, the core power stability parameter from the electrical parameter set, and the key assembly precision parameter from the physical property parameter set) and the quality grade score are used as plaintext input. The encryption algorithm, in conjunction with the encryption key, transforms the plaintext bit by bit to generate the encrypted data in ciphertext form. This encryption method offers high computational speed and is suitable for rapidly processing large amounts of e-cigarette product data. If an asymmetric encryption algorithm (such as RSA) is used, the public key in the encryption key is used to encrypt the data, and only the corresponding private key can decrypt it. This further enhances security, especially when data transmission and storage involve external, insecure networks. This effectively prevents theft and tampering of critical data, ensuring that even if the encrypted QR code is accidentally leaked, the e-cigarette quality information contained therein cannot be easily deciphered, thus maintaining the confidentiality and integrity of product quality data.

[0088] The encrypted data is added to a QR code to generate the encrypted QR code. QR codes are a convenient and widely used information carrier. First, based on the length and type of the encrypted data and the intended application scenario (such as consumer scanning and querying), an appropriate QR code standard (such as QR code, Data Matrix code, etc.) and version are selected to ensure the QR code has sufficient capacity to carry the encrypted data. Next, specialized QR code generation software or an algorithm module converts the encrypted data into a QR code graphic pattern according to specific encoding rules. This involves integrating the encrypted information into the QR code's black and white module arrangement. The generated encrypted QR code appears identical to a regular QR code, but contains encrypted key characteristic parameters and quality ratings of the e-cigarette. This information can only be accurately restored using a supporting decryption program (a decryption terminal with the corresponding encryption key, such as a manufacturer's quality traceability system or a management department's dedicated verification software). This not only facilitates product identification and management during distribution, but also provides a reliable and secure technical means for e-cigarette quality traceability and authenticity verification.

[0089] In one embodiment, generating an encryption key based on the smoke composition parameter set, the electrical parameter set, and the physical parameter set includes:

[0090] Extracting multiple key values ​​from the smoke component parameter set, electrical parameter set, and physical parameter set; and recombining and arranging each key value to form a data string;

[0091] Based on the length of the data string, a corresponding base point is selected on a preset elliptic curve domain;

[0092] Based on the preset elliptic curve domain and base point, performing a scalar multiplication operation on each key value in the data string as a scalar to generate a plurality of elliptic curve points;

[0093] The coordinate values ​​of each elliptic curve point are converted into a code string;

[0094] Generate a pseudo-random noise sequence, perform a bitwise exclusive OR operation on the pseudo-random noise sequence and the code string, and generate the encryption key.

[0095] In this embodiment, multiple key values ​​are extracted from the smoke composition parameter set, the electrical parameter set, and the physical parameter set. This is the fundamental step in generating the encryption key. For the smoke composition parameter set, core data such as the specific nicotine content, the concentration limit of specific harmful aldehydes, and the precise ratio of key aerosol components are selected. These values ​​directly reflect key quality attributes such as the potential impact of e-cigarette smoke on human health and smoke generation performance. For the electrical parameter set, key values ​​such as the rated battery voltage, the standard current value under stable operating conditions, and the extreme values ​​of the power fluctuation range are extracted. These values ​​are related to the stability of the e-cigarette's power supply and its safety. For the physical parameter set, values ​​such as the standard thickness of key parts of the casing, the reasonable range of assembly clearances for core components, and specific indicators of material hardness are selected. These values ​​reflect the reliability and durability of the e-cigarette's physical structure. The key values ​​extracted from the different parameter sets are recombined and arranged according to predetermined rules to form a complete data string. This rule can be based on the specific quality assessment logic of the e-cigarette, industry standards, or an effective sequence verified by extensive experiments. For example, the key values ​​of each parameter set are first arranged in the order of smoke composition, electrical properties, and physical characteristics. Within each parameter set, the order is further refined based on the degree of component hazard, the importance of electrical performance, the criticality of physical structure, etc. This makes the formed data string unique and holistic, and integrates the key quality information scattered in different parameter sets, making it the basic input for subsequent encryption operations such as elliptic curve operations. At the same time, the generated encryption key is deeply bound to the comprehensive quality characteristics of the e-cigarette, enhancing the key's pertinence and security.

[0096] In cryptographic applications, an elliptic curve domain is a predefined mathematical environment, typically defined based on elliptic curve equations over a finite field. For example, the finite field and equation form corresponding to the common secp256k1 curve. This predefined elliptic curve domain possesses excellent mathematical properties, and its discrete logarithm problem is computationally difficult, making it difficult for an attacker to reverse engineer the parameters associated with a known elliptic curve point, thus providing a high level of security for encryption. It forms the foundational framework for elliptic curve cryptography. Different elliptic curve domain standards are used for different application scenarios and security requirements. In this cryptographic application related to e-cigarette quality testing, the elliptic curve domain selected was carefully considered to meet the security requirements of data encryption while also being compatible with subsequent encryption steps and the overall quality testing system.

[0097] The corresponding base point is selected based on the length of the previously generated data string. This step further deepens the correlation between encryption key generation and e-cigarette quality data. The length of the data string serves as a unique identifying feature, guiding the selection of an appropriate base point. Different base point selections correspond to different data strings of different lengths, making the selection of base points non-fixed and predictable, increasing the complexity and randomness of the entire encryption process. The base point plays a key starting point role in elliptic curve cryptography. Subsequent scalar multiplication operations, such as the base point, are all based on this selected base point. Dynamically selecting the base point based on the length of the data string allows the generated encryption key to better incorporate data characteristics related to e-cigarette quality. Furthermore, each e-cigarette product, due to its own unique set of quality parameters, will ultimately select a different base point, thereby generating a differentiated and personalized encryption key, effectively resisting cracking attempts against universal encryption modes. The above base point selection method can be customized in advance.

[0098] Based on a pre-set elliptic curve domain and a selected base point, scalar multiplication is performed on each key value in the data string as a scalar, generating multiple elliptic curve points. Elliptic curve scalar multiplication is not simply a numerical multiplication, but rather follows the mathematical rules of the elliptic curve. Specifically, addition and point doubling operations are used to simulate the effect of multiplication over the finite field defined by the elliptic curve. For example, for a selected base point G and a key value k (scalar), when calculating kG, k is first converted to binary form, and each bit is processed sequentially from high to low. If the current bit is 1, the current point is doubled (adding the point to itself using the elliptic curve's addition rules) and added to the previous result. If the current bit is 0, only the corresponding point shift operation is performed, without any addition. Through this bit-by-bit processing, a new elliptic curve point P = kG is ultimately obtained. By repeating this process and treating each key value in the data string as a scalar for operation, multiple different elliptic curve points will be generated. These elliptic curve points carry key information based on the quality parameters of the e-cigarette. Due to the complexity and irreversibility of elliptic curve operations, the security of encryption is greatly improved.

[0099] The coordinate values ​​of the elliptic curve points generated above contain encrypted information that has been integrated with the key values ​​of the e-cigarette's quality after complex calculations. They become the core elements for the subsequent construction of encryption keys. Each elliptic curve point is generated based on a unique quality parameter (key value) on a specific elliptic curve domain. Its coordinate values ​​are highly random and unpredictable, making it difficult to restore the original key value through external observation or simple analysis. This further ensures the confidentiality and security of the encryption key. At the same time, it also makes the encryption key closely linked to the specific quality status of the e-cigarette, providing a reliable encryption foundation for subsequent quality traceability, data verification, and other links. It ensures that only authorized parties who master the correct decryption method and the corresponding relationship between relevant quality parameters can interpret the encrypted information.

[0100] The coordinate values ​​of each elliptic curve point are converted to a coded string. This step is primarily intended to represent the coordinate values ​​of the elliptic curve points in a form more suitable for subsequent encryption operations, data storage, and transmission. The coordinates of elliptic curve points are typically numerical values ​​over a specific finite field, with complex mathematical representations and large data lengths. Direct use hinders subsequent integration with other cryptographic elements (such as pseudorandom noise sequences) and is also hindered by storage and transmission in media such as QR codes. Through encoding conversion, such as using common encoding methods like hexadecimal and Base64, the coordinate values ​​are converted into a unified, more compact, and easier-to-process binary data sequence, forming a coded string.

[0101] The encoded string carries the encryption elements containing key information about the quality of e-cigarettes after elliptic curve calculations. It is an important intermediate product for constructing the final encryption key. As the basic data for subsequent XOR operations with pseudo-random noise sequences, its accuracy and integrity are crucial for generating high-quality and high-security encryption keys.

[0102] Generating a pseudo-random noise sequence is intended to further increase the complexity and unpredictability of encryption keys. This pseudo-random noise sequence is typically generated using a specific algorithm, such as a cryptographically secure pseudo-random number generator (CSPRNG). Its seed value can be linked to other e-cigarette identification information (such as product serial number, production batch number, etc.), ensuring that the pseudo-random noise sequence generated by each e-cigarette product is unique. While the pseudo-random noise sequence appears to be random, similar to a true random number, it is actually generated using a specific algorithm. However, the complexity of the generation algorithm and its sensitivity to the seed value make it difficult to predict its specific value. This introduces an additional element of randomness into the encryption process, strengthening the security of the encryption key.

[0103] The generated pseudo-random noise sequence is subjected to a bitwise XOR operation with the code string to generate an encryption key. The XOR operation is a simple but effective encryption method. It is reversible, meaning that the same operation can be decrypted by performing the XOR operation again. However, without knowing the pseudo-random noise sequence, it is extremely difficult to reverse engineer the code string from the encryption key (and thus obtain the original key information on e-cigarette quality). Through this bitwise XOR operation, the quality encryption information contained in the code string based on elliptic curve operations is combined with the randomness of the pseudo-random noise sequence. The resulting encryption key combines the dual characteristics of e-cigarette quality characteristics and random noise, with extremely high confidentiality and anti-cracking capabilities, and can effectively protect the key data involved in the e-cigarette quality testing process.

[0104] In one embodiment, generating an encryption key based on the smoke composition parameter set, the electrical parameter set, and the physical parameter set includes:

[0105] Encoding the smoke component parameter set, electrical parameter set, and physical parameter set to obtain encoded data;

[0106] Add the characters in the coded data to the data table one by one in sequence to generate a coded data table;

[0107] Extracting values ​​from the smoke component parameter set to generate a first curve; deforming the coded data table based on the first curve to obtain a deformed data table;

[0108] Extracting values ​​from the electrical parameter set to generate a second curve; extracting values ​​from the physical parameter set to generate a third curve;

[0109] The second curve and the third curve are superimposed on the deformation data table, and the cells in the deformation data table whose positional relationships with the second curve and the third curve meet the preset conditions are used as target cells. The characters in the target cells are combined in sequence to obtain the encryption key.

[0110] In this embodiment, the purpose of encoding the smoke composition parameter set, electrical parameter set, and physical parameter set is to convert these different types of parameter information, each with its own complex format and meaning, into a unified form that is more convenient for subsequent processing and operation. For example, the smoke composition parameter set may contain data such as the names and contents of various chemical components, some presented as numerical values, while others are specific identifiers for the chemical substances; the electrical parameter set includes values ​​for voltage, current, resistance, and other parameters with different units and precision requirements; and the physical parameter set covers various types of data, such as size and hardness. Through encoding, using unified encoding rules, this data is converted into standardized coded data, allowing subsequent integration, calculations, and other processing within a common logical framework.

[0111] Adding the encoded characters one by one to the data table is the process of presenting the linear encoded data in a two-dimensional structure. A data table can be viewed as a storage structure in the form of a matrix, for example, organizing data in rows and columns. By filling the encoded characters into it in sequence, a coded data table is formed. By converting the one-dimensional encoded data into a two-dimensional data table, the positional relationship between the data can be better utilized to explore and incorporate more encryption logic, so that the encryption key generation process can fully integrate the information of each parameter set of the e-cigarette. It also increases the complexity and unpredictability of the encryption process. Because each character position in the data table is associated with the original parameter set information, it is difficult for an attacker to directly infer the layout of the original data in this two-dimensional structure from the final encryption key.

[0112] Extracting values ​​from the smoke component parameter set to generate the first curve is a step that represents the smoke component-related data in a visual, curve-like form that can be used for subsequent calculations. For example, the vertical coordinate can be the content of different chemical components, and the horizontal coordinate can be the order in which these components are detected or ranked according to their importance in the e-cigarette smoke. A suitable curve fitting algorithm (such as polynomial fitting, spline interpolation, etc.) can be used to draw a continuous curve. This curve contains the key numerical characteristics and interrelationships of the smoke component parameter set. Due to the differences in the smoke composition of different e-cigarette products, the shape and trend of the generated first curve will be different, making this curve a unique identifier representing the characteristics of the e-cigarette smoke composition.

[0113] Using the generated first curve to deform the coded data table is a key operation to integrate the characteristic information of the smoke components into the entire data table structure. There are many specific deformation methods. For example, the degree of deformation such as stretching, compression, and torsion of each row or column in the data table can be determined based on the slope change and curvature of the curve. For example, if the slope of the curve is large in a certain interval, it means that the corresponding part of the smoke composition has changed dramatically. Then, the rows or columns at the corresponding position in the data table can be stretched significantly to change the relative position and arrangement order of the characters in the data table. Through such deformation operations, the originally regular coded data table carries the changing characteristics of the smoke composition. The generated deformed data table is no longer a simple sequential arrangement structure, but a special data organization form that integrates the smoke composition information. This further increases the difficulty of extracting the encryption key from the deformed data table. At the same time, it also makes the encryption key closely related to the smoke composition, an important e-cigarette quality parameter.

[0114] Similarly, extracting values ​​from the electrical parameter set to generate the second curve represents the e-cigarette's electrical performance data in a graphical form. For example, plotting a curve with voltage as the ordinate and different operating states or time series as the abscissa can intuitively reflect the e-cigarette's electrical characteristics, such as voltage stability and fluctuation. A similar approach is used to generate the third curve from the physical parameter set. For example, plotting the dimensions of different parts of the housing as the ordinate and the order of these parts as the abscissa reveals the characteristics of the product's physical structure. These two curves, respectively, carry key information about the e-cigarette's electrical and physical properties. Like the first curve, they each serve as unique identifiers reflecting the e-cigarette's characteristics across different quality dimensions and provide the foundational data for subsequent operations such as overlaying on the deformation data table. This allows information about electrical and physical properties to be incorporated into the encryption key generation process, enabling the encryption key to comprehensively reflect the e-cigarette's multifaceted quality characteristics.

[0115] Overlaying the second and third curves onto the deformation data table is a key step in further integrating the electronic cigarette's electrical and physical properties into the deformation data table, which already incorporates smoke composition information. This overlay can be performed according to pre-set rules. For example, the numerical changes represented by the second and third curves are applied to the corresponding cells in the deformation data table using a certain operation (such as addition, multiplication, or more complex custom operations) based on their coordinates. After the overlay is completed, target cells are filtered based on pre-set conditions. These conditions can be based on whether the numerical change in the cell after the curve overlay meets a specific threshold or the position of the intersection with the curves. For example, a cell can be identified as a target only if the sum of the distances between its location and the corresponding second and third curves exceeds a set value and the cell falls within a specific row and column range after the deformation operation. Through such strict screening conditions, which are correlated with the parameter curves, cells that comprehensively reflect the key information on the composition, electrical, and physical properties of the electronic cigarette smoke are precisely located, allowing the resulting encryption key to be closely linked to the overall product quality.

[0116] Finally, the characters in the target cells are combined in sequence to obtain the encryption key. The above characters carry the encrypted information of various quality parameters of the e-cigarette after multiple rounds of complex operations such as fusion, deformation, and superposition. The order and combination are unique and depend on the processing of the parameter set in the previous steps and the corresponding rule settings. Since the encryption key generation process deeply combines the multiple quality characteristics of the e-cigarette's smoke composition, electrical properties, physical properties, etc., and has undergone complex deformation and screening operations, it is difficult for external attackers to crack this encryption key through conventional means to obtain the original e-cigarette quality parameter information, thereby providing high-intensity security for subsequent data encryption using the encryption key, ensuring the confidentiality and integrity of e-cigarette quality data in storage, transmission, and traceability.

[0117] In one embodiment, generating an encryption key based on the smoke composition parameter set, the electrical parameter set, and the physical parameter set includes:

[0118] For the smoke component parameter set, the frequency and proportion of each component are counted and converted into a frequency distribution vector. For the electrical parameter set, the root mean square value and power spectrum density of voltage and current are calculated to form an electrical characteristic sequence. For the physical parameter set, the standard deviation of the shell size and the quantitative grade of the material hardness are extracted to form a physical characteristic index.

[0119] Perform weighted summation on the frequency distribution vector, electrical characteristic sequence, and physical characteristic index to obtain a comprehensive characteristic vector;

[0120] The three coordinate axes are set to correspond to the three major characteristics of smoke, electricity, and physics respectively. The element values ​​in the comprehensive feature vector are mapped to the three-dimensional space, and a parameter-related surface is generated through the surface fitting algorithm.

[0121] On the parametrically related surface, differential geometry methods are used to calculate the principal curvature lines of the surface, and one of them is selected as the densified contour line;

[0122] The encrypted contour line is discretized to obtain a sequence of coordinate points. The coordinate value of each coordinate point is encoded and converted, and the encoded coordinate point sequence is embedded in the grid space. Multiple rounds of encryption operations are performed to generate the final encryption key.

[0123] In this embodiment, the frequency and proportion of each component are calculated to extract representative and general information from complex smoke composition data. The e-cigarette smoke composition parameter set includes a variety of chemical components, such as nicotine, propylene glycol, glycerol, various flavorings, and potentially harmful aldehydes. By calculating their respective frequencies—that is, the proportion of each component detected in multiple tests relative to the total number of tests—and their proportion (i.e., the mass or volume percentage of each component in the overall smoke composition), the general composition characteristics of the smoke can be intuitively displayed. By organizing these frequency and proportion data and converting them into a frequency distribution vector, the smoke composition information is quantified in the form of a mathematical vector, where each dimension of the vector corresponds to the relevant frequency and proportion information of a component.

[0124] Calculating the root mean square (RMS) value of voltage and current, along with the power spectrum density, to form an electrical characteristic sequence is a process that deeply quantifies the performance of e-cigarettes from an electrical perspective. The root mean square (RMS) value is often used to measure the effective value of electrical quantities such as voltage and current. It reflects the average energy level of these quantities over a period of time and is crucial for assessing the stable power supply of e-cigarettes during operation. The calculated characteristic quantities are sequentially organized into an electrical characteristic sequence, allowing the key information about the electrical performance of the e-cigarette, contained in the electrical parameter set, to be presented in an orderly and quantitative manner.

[0125] Extracting the standard deviation of shell dimensions and the quantitative grade of material hardness to form physical characteristic indicators provides a quantitative description of e-cigarettes from the perspective of physical structure and material properties. By forming such physical characteristic indicators, key information from the physical parameter set is extracted, providing indispensable physical characteristic data support for the subsequent construction of encryption keys that can comprehensively reflect the overall quality of the product. Due to the different material selection and manufacturing processes, the physical characteristic indicators of different products are also unique.

[0126] The weighted summation of the frequency distribution vector, electrical characteristic sequence, and physical characteristic indexes obtained above is intended to integrate information from different dimensions (aerosol composition, electrical properties, and physical characteristics) that are closely related to e-cigarette quality, forming a feature vector that comprehensively reflects the overall quality of the product. The weighting coefficient here is determined based on the importance of each parameter to e-cigarette quality. This importance is usually determined based on a large amount of experimental data, market feedback, industry quality standards, and professional experience.

[0127] Three coordinate axes are set to correspond to the three major categories of characteristics: smoke, electrical properties, and physical properties. The element values ​​in the comprehensive feature vector are mapped to three-dimensional space. This step presents the abstract comprehensive feature vector in a visual and geometric way, facilitating intuitive observation and exploration of the intrinsic connections between different parameters. Through this mapping, the comprehensive feature vector of each e-cigarette product corresponds to a point set in three-dimensional space. These point sets, due to the integration of quality characteristics from different dimensions, exhibit certain distribution patterns and morphological characteristics. Next, a surface fitting algorithm (such as radial basis function fitting) is used to generate a parameter-correlated surface that can smoothly pass through or approximate these point sets, thereby integrating the scattered quality characteristics represented by vectors into a continuous geometric surface. The surface's geometric properties, such as shape, undulation, and curvature, contain a wealth of information. For example, the surface's steepness along the smoke characteristic axis may reflect the sensitivity of smoke composition changes to overall quality. Concave and convex areas of the surface may correspond to areas of superior and inferior product quality under different parameter combinations. This surface becomes the key to subsequent, more complex encryption operations. Furthermore, because different products have different comprehensive feature vectors, the resulting parameter-associated surface is unique.

[0128] On parametrically linked surfaces, differential geometry methods are used to calculate the principal curvature lines of the surface. This approach leverages in-depth research on the geometric properties of surfaces in advanced mathematics to uncover the most representative and characteristic geometric elements on the surface. The principal curvature lines reflect the curvature of the surface in different directions and are an important manifestation of the surface's inherent geometric structure. Each principal curvature line contains both local and global geometric information about the surface in that direction. One of these lines was selected as the encrypted contour line because it inherits the multifaceted quality characteristics of the e-cigarette carried by the parametrically linked surface, presenting it in a relatively concise yet highly condensed curve. Its direction in three-dimensional space, curvature changes, and other characteristics are closely related to the product's smoke, electrical properties, and physical characteristics. For example, a sudden change in curvature in a certain section corresponds to a special correlation between the proportion of key components in the smoke composition and the voltage stability of the electrical parameters. This unique geometric information makes the encrypted contour line a core element for subsequent encryption key generation. Furthermore, the parameter-related surfaces of different products are different, and the selected encrypted contour lines will also vary greatly, greatly increasing the unpredictability and security of the encryption key, providing basic data rich in product quality codes for subsequent encryption operations.

[0129] Discretizing the encrypted contour to obtain a sequence of coordinate points is a necessary step in converting a continuous geometric curve into a digital form that can be easily processed and calculated by computers. By sampling points on the encrypted contour at regular intervals or patterns, the points are represented as a series of discrete coordinate points. Each coordinate point contains the coordinate value of the corresponding position in three-dimensional space. These coordinate values ​​carry the e-cigarette quality characteristics previously integrated into the encrypted contour. Encoding the coordinate values ​​of each point (e.g., using Gray code) enhances the data's robustness against interference. Gray code, with its characteristic of only one bit varying between adjacent code values, minimizes the impact of individual bit errors on data interpretation during data transmission or storage. Furthermore, the encoded coordinate point sequence is more suitable for subsequent embedding in a specific mathematical space (e.g., a grid space) for encryption operations, ensuring compliance with the rules and requirements of the corresponding encryption algorithm and facilitating the generation of secure and reliable encryption keys.

[0130] Embedding the encoded coordinate point sequence into a grid space is a key step in encryption using lattice cryptography, a highly secure modern cryptographic technique. Lattice cryptography is a unique mathematical structure with excellent mathematical properties. For example, difficult problems on lattices (such as the shortest vector problem and the nearest vector problem) are computationally difficult, providing strong security for encryption. After embedding the encoded coordinate point sequence into the grid space, multiple rounds of lattice cryptography-based encryption operations, such as linear transformations and modular arithmetic, repeatedly obfuscate and diffuse the information in the coordinate point sequence. This highly encrypts the e-cigarette quality characteristics contained in the original coordinate point sequence, making it difficult for external attackers to decrypt the original information from the encrypted result. After these multiple rounds of complex encryption, the resulting encryption key fully incorporates comprehensive e-cigarette parameter information. Lattice cryptography's high-strength encryption provides a solid foundation for the subsequent encryption and protection of e-cigarette quality data (such as key characteristic parameters and quality ratings), ensuring that this data is protected against potential security threats during storage, transmission, and quality traceability, ensuring the confidentiality and integrity of the information.

[0131] Reference Figure 2 In one embodiment of the present invention, a quality detection device for an electronic cigarette is provided, comprising:

[0132] The acquisition unit is used to detect the composition of the smoke generated by the electronic cigarette based on the smoke composition analyzer to form a smoke composition parameter set; collect the electrical parameters of the electronic cigarette to form an electrical parameter set; measure the physical characteristic parameters of the electronic cigarette to construct a physical parameter set;

[0133] an extraction unit, configured to perform dimensionality reduction analysis on the smoke component parameter set, the electrical parameter set, and the physical parameter set based on a principal component analysis method, extract corresponding key characteristic parameters, and assign weights to the key characteristic parameters based on a hierarchical analysis method;

[0134] An evaluation unit, configured to input the weighted key feature parameters into a pre-trained quality evaluation model and output a quality grade score of the electronic cigarette;

[0135] The storage unit is used to generate an encrypted QR code containing all key characteristic parameters and quality grade scores for qualified e-cigarettes with quality grade scores higher than the threshold, and associate the encrypted QR code with the corresponding e-cigarette product information and store it in the blockchain traceability system.

[0136] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0137] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, memory, display screen, input device, network interface and database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0138] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0139] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-described method when executed by a processor. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0140] In summary, the quality inspection method, device, and apparatus provided in the embodiments of the present invention include: detecting the components of the smoke generated by the electronic cigarette based on a smoke component analyzer to form a smoke component parameter set; collecting the electrical parameters of the electronic cigarette to form an electrical parameter set; measuring the physical characteristic parameters of the electronic cigarette to construct a physical parameter set; performing dimensionality reduction analysis on the smoke component parameter set, the electrical parameter set, and the physical parameter set based on the principal component analysis method to extract the corresponding key feature parameters, and assigning weights to each key feature parameter based on the hierarchical analysis method; inputting each of the weighted key feature parameters into a pre-trained quality assessment model to output the quality grade score of the electronic cigarette; for qualified electronic cigarettes with a quality grade score higher than a threshold, generating an encrypted QR code containing all of its key feature parameters and quality grade scores, and associating the encrypted QR code with the corresponding electronic cigarette product information and storing it in a blockchain traceability system. In the present invention, by combining the smoke component parameter set, the electrical parameter set, and the physical parameter set to perform quality inspection of the electronic cigarette, the product quality assessment is performed from the overall perspective; at the same time, an encrypted QR code containing all of its key feature parameters and quality grade scores is generated, effectively tracing the product quality after quality inspection.

[0141] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM.

[0142] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0143] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for detecting the quality of an electronic cigarette, characterized in that: The following steps are involved: The smoke generated by the electronic cigarette is tested for components using a smoke composition analyzer to form a smoke composition parameter set; the electrical parameters of the electronic cigarette are collected to form an electrical parameter set; the physical characteristic parameters of the electronic cigarette are measured to construct a physical parameter set; Based on the principal component analysis method, the dimensionality reduction analysis of the smoke component parameter set, the electrical parameter set, and the physical parameter set is performed respectively to extract the corresponding key characteristic parameters, and a weight is assigned to each key characteristic parameter based on the hierarchical analysis method; Inputting the weighted key feature parameters into a pre-trained quality assessment model to output a quality grade score of the electronic cigarette; For qualified e-cigarettes with a quality grade score higher than the threshold, an encrypted QR code containing all its key characteristic parameters and quality grade score is generated, and the encrypted QR code is associated with the corresponding e-cigarette product information and stored in the blockchain traceability system; Generating an encrypted QR code containing all key characteristic parameters and quality grade scores, including: generating an encryption key based on the smoke composition parameter set, the electrical parameter set, and the physical parameter set; encrypting the key characteristic parameters and the quality grade score based on the encryption key to obtain encrypted data; and adding the encrypted data to the QR code to generate the encrypted QR code; Generating an encryption key based on the smoke component parameter set, the electrical parameter set, and the physical parameter set, including: encoding the smoke component parameter set, the electrical parameter set, and the physical parameter set to obtain encoded data; sequentially adding characters in the encoded data to a data table to generate an encoded data table; extracting values ​​from the smoke component parameter set to generate a first curve; deforming the encoded data table based on the first curve to obtain a deformed data table; extracting values ​​from the electrical parameter set to generate a second curve; extracting values ​​from the physical parameter set to generate a third curve; superimposing the second and third curves on the deformed data table, using cells in the deformed data table whose positional relationships with the second and third curves simultaneously meet preset conditions as target cells, and sequentially combining the characters in the target cells to obtain the encryption key; Alternatively, an encryption key is generated based on the smoke component parameter set, electrical parameter set, and physical parameter set, including: for the smoke component parameter set, counting the frequency and proportion of each component, and converting it into a frequency distribution vector; for the electrical parameter set, calculating the root mean square value and power spectrum density of the voltage and current to form an electrical characteristic sequence; for the physical parameter set, extracting the standard deviation of the shell size and the quantitative level of the material hardness to form a physical characteristic index; performing weighted summation on the frequency distribution vector, the electrical characteristic sequence, and the physical characteristic index to obtain a comprehensive characteristic vector; setting three coordinate axes to correspond to the three major categories of smoke, electrical, and physical characteristics, respectively, mapping the element values ​​in the comprehensive characteristic vector to three-dimensional space, and generating a parameter-associated surface through a surface fitting algorithm; on the parameter-associated surface, using differential geometry methods to calculate the main curvature lines of the surface, and selecting one of them as the encrypted contour line; discretizing the encrypted contour line to obtain a coordinate point sequence, encoding the coordinate value of each coordinate point, and embedding the encoded coordinate point sequence into a grid space, performing multiple rounds of encryption operations, and generating a final encryption key.

2. The electronic cigarette quality detection method according to claim 1, characterized in that: After outputting the quality rating of the electronic cigarette, the method further includes: Determine the electronic cigarettes whose quality grade score is lower than the threshold as unqualified products, and perform defect analysis on the smoke composition parameter set, electrical parameter set, and physical parameter set; The defect analysis results are stored in the blockchain traceability system.

3. The electronic cigarette quality detection method according to claim 1, characterized in that: The method of assigning weights to each key feature parameter based on the hierarchical analysis method includes: The smoke composition parameter set, electrical parameter set, and physical property parameter set are used as the criterion layer, and the key characteristic parameters under each parameter set are used as the indicator layer; Based on data mining, the influence of each parameter set on the quality of the electronic cigarette is analyzed to determine the relative importance between the elements of the criterion layer and the relative importance of the elements of the indicator layer relative to the criterion layer, and to generate a judgment matrix; Solving the maximum eigenvalue of the judgment matrix and obtaining its corresponding eigenvector; performing a normalization operation on the eigenvector to obtain the initial weight vector of each indicator layer relative to the criterion layer; Calculating the consistency ratio of the judgment matrix and judging whether the consistency ratio meets the requirement; If the consistency ratio meets the requirement, the current initial weight vector is confirmed and weights are assigned to each key feature parameter.

4. The electronic cigarette quality detection method according to claim 3, characterized in that: After determining whether the consistency ratio meets the requirements, the following steps are further included: If not, the element values ​​of the judgment matrix are automatically adjusted, and the weight vectors of each indicator layer relative to the criterion layer are recalculated until the consistency ratio meets the requirement.

5. A quality detection device for electronic cigarettes, characterized in that: include: The collection unit is used to detect the components of the smoke generated by the electronic cigarette based on the smoke composition analyzer to form a smoke composition parameter set; collect the electrical parameters of the electronic cigarette to form an electrical parameter set; Measure the physical characteristic parameters of the electronic cigarette and construct a physical parameter set; an extraction unit, configured to perform dimensionality reduction analysis on the smoke component parameter set, the electrical parameter set, and the physical parameter set based on a principal component analysis method, extract corresponding key characteristic parameters, and assign weights to the key characteristic parameters based on a hierarchical analysis method; An evaluation unit, configured to input the weighted key feature parameters into a pre-trained quality evaluation model and output a quality grade score of the electronic cigarette; A storage unit is used to generate an encrypted QR code containing all key characteristic parameters and quality grade scores of qualified e-cigarettes with quality grade scores higher than a threshold, and associate the encrypted QR code with the corresponding e-cigarette product information and store it in the blockchain traceability system; Generating an encrypted QR code containing all key characteristic parameters and quality grade scores, including: generating an encryption key based on the smoke composition parameter set, the electrical parameter set, and the physical parameter set; encrypting the key characteristic parameters and the quality grade score based on the encryption key to obtain encrypted data; and adding the encrypted data to the QR code to generate the encrypted QR code; Generating an encryption key based on the smoke component parameter set, the electrical parameter set, and the physical parameter set, including: encoding the smoke component parameter set, the electrical parameter set, and the physical parameter set to obtain encoded data; sequentially adding characters in the encoded data to a data table to generate an encoded data table; extracting values ​​from the smoke component parameter set to generate a first curve; deforming the encoded data table based on the first curve to obtain a deformed data table; extracting values ​​from the electrical parameter set to generate a second curve; extracting values ​​from the physical parameter set to generate a third curve; superimposing the second and third curves on the deformed data table, using cells in the deformed data table whose positional relationships with the second and third curves simultaneously meet preset conditions as target cells, and sequentially combining the characters in the target cells to obtain the encryption key; Alternatively, an encryption key is generated based on the smoke component parameter set, electrical parameter set, and physical parameter set, including: for the smoke component parameter set, counting the frequency and proportion of each component, and converting it into a frequency distribution vector; for the electrical parameter set, calculating the root mean square value and power spectrum density of the voltage and current to form an electrical characteristic sequence; for the physical parameter set, extracting the standard deviation of the shell size and the quantitative level of the material hardness to form a physical characteristic index; performing weighted summation on the frequency distribution vector, the electrical characteristic sequence, and the physical characteristic index to obtain a comprehensive characteristic vector; setting three coordinate axes to correspond to the three major categories of smoke, electrical, and physical characteristics, respectively, mapping the element values ​​in the comprehensive characteristic vector to three-dimensional space, and generating a parameter-associated surface through a surface fitting algorithm; on the parameter-associated surface, using differential geometry methods to calculate the main curvature lines of the surface, and selecting one of them as the encrypted contour line; discretizing the encrypted contour line to obtain a coordinate point sequence, encoding the coordinate value of each coordinate point, and embedding the encoded coordinate point sequence into a grid space, performing multiple rounds of encryption operations, and generating a final encryption key.

6. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

Images