Electronic cigarette service life prediction method, device, equipment and medium

Abstract

The invention relates to an electronic cigarette service life prediction method and device, equipment and a medium, and the method comprises the steps: obtaining the performance parameters of an electronic cigarette, the performance parameters of the electronic cigarette comprise the voltage, current, temperature and charging and discharging times of the electronic cigarette; curve fitting is carried out on the performance parameters of the electronic cigarette to obtain a fitting curve of the electronic cigarette, and the fitting curve of the electronic cigarette comprises a voltage curve, a current curve and a temperature curve of the electronic cigarette; inputting the fitted curve and the charging and discharging times of the electronic cigarette into a pre-trained performance model for comparative analysis to obtain a life prediction result of the electronic cigarette; and when the life prediction result of the electronic cigarette is lower than a preset life threshold value, generating an alarm signal, and transmitting the alarm signal to a user side. The method has the effect of improving the user experience.

Description

Technical Field

[0001] The present application relates to the technical field of electronic cigarettes, and in particular to a method, device, equipment and medium for predicting the life of an electronic cigarette. Background Art

[0002] At present, the life of electronic cigarettes is largely characterized by the life of lithium batteries. For example, as the number of times the lithium battery is charged and discharged increases, the amount of electricity stored in the lithium battery will gradually decrease. Correspondingly, the charging time of the lithium battery will also decrease. The voltage after full charge will also decrease as the number of times the charge and discharge increases. In addition, due to the influence of factors such as actual frequency of use and charging habits, the actual service life of electronic cigarettes often deviates from expectations. This deviation may cause users to encounter electronic cigarette performance degradation or sudden failure without preparation, affecting the user experience.

[0003] The above-mentioned existing technical solutions have the following defects: the existing prediction methods are inaccurate for monitoring the life status of electronic cigarette batteries and cannot promptly remind users to replace batteries, resulting in poor user experience. Therefore, there is room for improvement. Summary of the invention

[0004] In order to improve user experience, the present application provides an electronic cigarette life prediction method, device, equipment and medium.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A method for predicting the life span of an electronic cigarette, comprising: Acquiring performance parameters of the electronic cigarette, wherein the performance parameters of the electronic cigarette include voltage, current, temperature, and charge and discharge times of the electronic cigarette; Performing curve fitting on the performance parameters of the electronic cigarette to obtain a fitting curve of the electronic cigarette, wherein the fitting curve of the electronic cigarette includes a voltage curve, a current curve and a temperature curve of the electronic cigarette; Inputting the fitting curve and charge and discharge times of the electronic cigarette into a pre-trained performance model for comparison and analysis to obtain a life prediction result of the electronic cigarette; When the life prediction result of the electronic cigarette is lower than a preset life threshold, an alarm signal is generated and transmitted to a user terminal.

[0006] By adopting the above technical solution, by obtaining the performance parameters of the electronic cigarette, including the voltage, current, temperature and charge and discharge times of the electronic cigarette, it is possible to comprehensively monitor various key parameters of the electronic cigarette during use, thereby providing a detailed data basis for subsequent life prediction; by curve fitting the performance parameters of the electronic cigarette, the fitting curve of the electronic cigarette is obtained, and the fitting curve of the electronic cigarette includes the voltage curve, current curve and temperature curve of the electronic cigarette, the performance parameters of the electronic cigarette can be accurately modeled, and the trend of parameter changes can be captured, thereby improving the accuracy of life prediction; by inputting the fitting curve and charge and discharge times of the electronic cigarette into the pre-trained performance model for comparison and analysis, the life prediction result of the electronic cigarette is obtained, and the life of the electronic cigarette can be scientifically predicted using big data and machine learning algorithms, so as to discover potential problems in advance and take preventive measures. When the life prediction result of the electronic cigarette is lower than the preset life threshold, an alarm signal is generated and transmitted to the user end, which can promptly remind the user to replace the electronic cigarette, avoid safety hazards caused by equipment failure, and improve the user's experience and safety.

[0007] In a preferred example, the present application may be further configured as follows: the obtaining of the performance parameters of the electronic cigarette, wherein the performance parameters of the electronic cigarette include the voltage, current, temperature and charge and discharge times of the electronic cigarette, includes: The built-in sensor of the electronic cigarette monitors and records the voltage, current and temperature of the electronic cigarette in real time during use; The charging and discharging times and cycles of the electronic cigarette are recorded by a timer.

[0008] By adopting the above technical solution, the built-in sensor of the electronic cigarette can monitor and record the voltage, current and temperature of the electronic cigarette in real time during use, so as to ensure the real-time and accuracy of the obtained performance parameters, thereby providing reliable data support for life prediction; by recording the number and cycle of charge and discharge of the electronic cigarette through a timer, the frequency of use and life cycle of the electronic cigarette can be accurately counted, providing an important reference basis for life prediction.

[0009] In a preferred example, the present application may be further configured as follows: the performance parameters of the electronic cigarette are subjected to curve fitting to obtain a fitting curve of the electronic cigarette, wherein the fitting curve of the electronic cigarette includes a voltage curve, a current curve and a temperature curve of the electronic cigarette: Clustering the performance parameters of the electronic cigarette using a k-means algorithm to obtain characteristic data of the electronic cigarette; The characteristic data of the electronic cigarette is fitted using a polynomial fitting method to obtain a voltage curve, a current curve and a temperature curve of the electronic cigarette.

[0010] By adopting the above technical solution, the performance parameters of the electronic cigarette are clustered by the k-means algorithm to obtain the characteristic data of the electronic cigarette, which can effectively distinguish the performance of the electronic cigarette under different usage conditions, thereby improving the accuracy of data analysis; by fitting the characteristic data of the electronic cigarette using a polynomial fitting method, the voltage curve, current curve and temperature curve of the electronic cigarette are obtained, which can accurately describe the change law of the performance parameters of the electronic cigarette and provide an accurate model basis for subsequent life prediction.

[0011] In a preferred example, the present application may be further configured as follows: before inputting the fitting curve and the number of charge and discharge times of the electronic cigarette into a pre-trained performance model for comparison and analysis to obtain the life prediction result of the electronic cigarette, obtaining the pre-trained performance model includes: Acquire historical usage data and lifespan information of different types of electronic cigarettes, and preprocess the historical usage data and lifespan information of the different types of electronic cigarettes to obtain a training set; A performance model is constructed based on a random forest algorithm, the performance model is trained using the training set, and a genetic algorithm is used to optimize parameters of the trained performance model to obtain the pre-trained performance model.

[0012] By adopting the above technical solution, by obtaining historical usage data and life information of different types of electronic cigarettes, and pre-processing the historical usage data and life information of different types of electronic cigarettes, a training set is obtained, which can fully utilize the historical data to model the life of electronic cigarettes and improve the generalization ability and accuracy of the model; by constructing a performance model based on a random forest algorithm, using the training set to perform forward propagation and back-propagation training on the performance model, and using a genetic algorithm to optimize the parameters of the performance model after forward propagation and back-propagation training, a pre-trained performance model is obtained, which can construct a high-precision and high-robustness life prediction model, providing strong algorithmic support for electronic cigarette life prediction.

[0013] In a preferred example, the present application may be further configured as follows: the fitting curve and the number of charge and discharge times of the electronic cigarette are input into a pre-trained performance model for comparison and analysis, and the life prediction result of the electronic cigarette is obtained, including: Comparing the voltage curve of the electronic cigarette with the voltage curve in the pre-trained performance model, comparing the current curve of the electronic cigarette with the current curve in the pre-trained performance model, and comparing the temperature curve of the electronic cigarette with the temperature curve in the pre-trained performance model to obtain a curve comparison result of the electronic cigarette; The life prediction result of the electronic cigarette is obtained by analyzing the curve comparison result of the electronic cigarette and the number of charge and discharge times of the electronic cigarette.

[0014] By adopting the above technical solution, by comparing the voltage curve of the electronic cigarette with the voltage curve in the pre-trained performance model, comparing the current curve of the electronic cigarette with the current curve in the pre-trained performance model, and comparing the temperature curve of the electronic cigarette with the temperature curve in the pre-trained performance model, the curve comparison result of the electronic cigarette is obtained, and the difference between the actual performance and the expected performance of the electronic cigarette can be comprehensively analyzed, thereby improving the accuracy of life prediction; by combining the curve comparison result of the electronic cigarette and the number of charge and discharge times of the electronic cigarette for analysis, the life prediction result of the electronic cigarette is obtained, which can comprehensively consider the influence of various factors on the life of the electronic cigarette, thereby providing a more scientific and reliable life prediction result.

[0015] In a preferred example, the present application may be further configured as follows: the electronic cigarette life prediction method further includes: Acquire the user's usage habits, and adjust the pre-trained performance model according to the user's usage habits; Generate a working report of the electronic cigarette within a preset time, and transmit the working report of the electronic cigarette to the user terminal.

[0016] By adopting the above technical solution, by obtaining the user's usage habits and adjusting the pre-trained performance model according to the user's usage habits, the life prediction model can be made more personalized and accurate, adapting to the usage needs of different users, thereby improving the prediction accuracy and practicality of the model; by generating an electronic cigarette work report within a preset time and transmitting the electronic cigarette work report to the user end, the user can be fed back the electronic cigarette usage and life prediction results in a timely manner, helping the user to better understand and manage the use of electronic cigarettes, and improving user experience and satisfaction.

[0017] The second object of the invention is achieved by the following technical solutions: An electronic cigarette life prediction device, the electronic cigarette life prediction device comprising: A parameter acquisition module, used to acquire performance parameters of the electronic cigarette, wherein the performance parameters of the electronic cigarette include voltage, current, temperature and charge and discharge times of the electronic cigarette; A curve fitting module, used for performing curve fitting on the performance parameters of the electronic cigarette to obtain a fitting curve of the electronic cigarette, wherein the fitting curve of the electronic cigarette includes a voltage curve, a current curve and a temperature curve of the electronic cigarette; A prediction module, used for inputting the fitting curve and charge and discharge times of the electronic cigarette into a pre-trained performance model for comparison and analysis to obtain a life prediction result of the electronic cigarette; The alarm module is used to generate an alarm signal when the life prediction result of the electronic cigarette is lower than a preset life threshold, and transmit the alarm signal to the user end.

[0018] By adopting the above technical solution, by obtaining the performance parameters of the electronic cigarette, including the voltage, current, temperature and charge and discharge times of the electronic cigarette, it is possible to comprehensively monitor various key parameters of the electronic cigarette during use, thereby providing a detailed data basis for subsequent life prediction; by curve fitting the performance parameters of the electronic cigarette, the fitting curve of the electronic cigarette is obtained, and the fitting curve of the electronic cigarette includes the voltage curve, current curve and temperature curve of the electronic cigarette, the performance parameters of the electronic cigarette can be accurately modeled, and the trend of parameter changes can be captured, thereby improving the accuracy of life prediction; by inputting the fitting curve and charge and discharge times of the electronic cigarette into the pre-trained performance model for comparison and analysis, the life prediction result of the electronic cigarette is obtained, and the life of the electronic cigarette can be scientifically predicted using big data and machine learning algorithms, so as to discover potential problems in advance and take preventive measures. When the life prediction result of the electronic cigarette is lower than the preset life threshold, an alarm signal is generated and transmitted to the user end, which can promptly remind the user to replace the electronic cigarette, avoid safety hazards caused by equipment failure, and improve the user's experience and safety.

[0019] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for predicting the life of an electronic cigarette when executing the computer program.

[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned electronic cigarette life prediction method are implemented.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. By obtaining the performance parameters of the electronic cigarette, including the voltage, current, temperature and charge and discharge times of the electronic cigarette, it is possible to comprehensively monitor various key parameters of the electronic cigarette during use, thereby providing a detailed data basis for subsequent life prediction; by curve fitting the performance parameters of the electronic cigarette, the fitting curve of the electronic cigarette is obtained, and the fitting curve of the electronic cigarette includes the voltage curve, current curve and temperature curve of the electronic cigarette, the performance parameters of the electronic cigarette can be accurately modeled and the trend of parameter changes can be captured, thereby improving the accuracy of life prediction; 2. By inputting the fitting curve and charge and discharge times of the electronic cigarette into the pre-trained performance model for comparison and analysis, the life prediction result of the electronic cigarette is obtained. Big data and machine learning algorithms can be used to scientifically predict the life of the electronic cigarette, so as to discover potential problems in advance and take preventive measures. When the life prediction result of the electronic cigarette is lower than the preset life threshold, an alarm signal is generated and transmitted to the user end, which can promptly remind the user to replace the electronic cigarette, avoid safety hazards caused by equipment failure, and improve the user experience and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a method for predicting the life span of an electronic cigarette in one embodiment of the present application; Figure 2 This is a flowchart for implementing step S10 in a method for predicting the life of an electronic cigarette in an embodiment of the present application; Figure 3 This is a flowchart for implementing step S20 in a method for predicting the life of an electronic cigarette in an embodiment of the present application; Figure 4 This is another implementation flow chart of step S30 in a method for predicting the life of an electronic cigarette in an embodiment of the present application; Figure 5 This is a flowchart for implementing step S30 in a method for predicting the life of an electronic cigarette in an embodiment of the present application; Figure 6 This is a flowchart of an implementation method of an electronic cigarette life prediction method in an embodiment of the present application; Figure 7 This is a principle block diagram of an electronic cigarette life prediction device in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1 As shown, the present application discloses a method for predicting the life span of an electronic cigarette, which specifically includes the following steps: S10: Obtaining performance parameters of the electronic cigarette, the performance parameters of the electronic cigarette including voltage, current, temperature, and charge and discharge times of the electronic cigarette.

[0025] Specifically, the voltage level of the electronic cigarette battery affects its output power and operating status. The current flowing through the electronic cigarette is related to the charge and discharge rate of the battery and the working status of the atomizer. Too high a temperature of the electronic cigarette when it is working may indicate an overheating risk. Therefore, by obtaining the performance parameters of the electronic cigarette, including the voltage, current, temperature and charge and discharge times of the electronic cigarette, it is helpful to provide real-time feedback on whether the working status of the electronic cigarette is normal.

[0026] S20: performing curve fitting on the performance parameters of the electronic cigarette to obtain a fitting curve of the electronic cigarette, where the fitting curve of the electronic cigarette includes a voltage curve, a current curve, and a temperature curve of the electronic cigarette.

[0027] Specifically, the collected performance parameter data will be used to generate fitting curves. These curves can describe the performance changes of the electronic cigarette at different stages of use, and finally obtain the fitting curve of the electronic cigarette. The fitting curve of the electronic cigarette includes the voltage curve, current curve and temperature curve of the electronic cigarette. The voltage curve can indicate the changing trend of voltage over time or number of uses, the current curve can reflect the changing situation of current over time or number of uses, and the temperature curve can show the temperature changes of the electronic cigarette at different stages of use.

[0028] S30: Inputting the fitting curve and charge and discharge times of the electronic cigarette into a pre-trained performance model for comparison and analysis to obtain a life prediction result of the electronic cigarette.

[0029] Specifically, the fitted curve and charge and discharge times data are input into a pre-trained performance model. This model is trained using a machine learning algorithm based on historical data and known life information. The model can analyze the input performance curve and charge and discharge times, and compare them with the patterns in the training data to predict the remaining service life of the e-cigarette. The final performance model will output a life prediction result, which is based on a comprehensive evaluation of current performance parameters and historical data.

[0030] S40: When the life prediction result of the electronic cigarette is lower than a preset life threshold, an alarm signal is generated and transmitted to the user end.

[0031] Specifically, if the predicted life result is lower than the system preset life threshold, indicating that the e-cigarette may be about to reach the end of its service life. In this case, the system will automatically generate an alarm signal. The purpose of the alarm signal is to remind users to pay attention to the remaining life of the e-cigarette so as to replace or maintain it in time to avoid sudden failure. The generated alarm signal will be transmitted to the user end, which can be the display screen of the e-cigarette, a paired mobile device or other notification method.

[0032] By adopting the above technical solution, by obtaining the performance parameters of the electronic cigarette, including the voltage, current, temperature and charge and discharge times of the electronic cigarette, it is possible to comprehensively monitor various key parameters of the electronic cigarette during use, thereby providing a detailed data basis for subsequent life prediction; by curve fitting the performance parameters of the electronic cigarette, the fitting curve of the electronic cigarette is obtained, and the fitting curve of the electronic cigarette includes the voltage curve, current curve and temperature curve of the electronic cigarette, the performance parameters of the electronic cigarette can be accurately modeled, and the trend of parameter changes can be captured, thereby improving the accuracy of life prediction; by inputting the fitting curve and charge and discharge times of the electronic cigarette into the pre-trained performance model for comparison and analysis, the life prediction result of the electronic cigarette is obtained, and the life of the electronic cigarette can be scientifically predicted using big data and machine learning algorithms, so as to discover potential problems in advance and take preventive measures. When the life prediction result of the electronic cigarette is lower than the preset life threshold, an alarm signal is generated and transmitted to the user end, which can promptly remind the user to replace the electronic cigarette, avoid safety hazards caused by equipment failure, and improve the user's experience and safety.

[0033] In one embodiment, if Figure 2 As shown, in step S10, the performance parameters of the electronic cigarette are obtained. The performance parameters of the electronic cigarette include the voltage, current, temperature and charge and discharge times of the electronic cigarette, specifically including: S11: The built-in sensor of the electronic cigarette is used to monitor and record the voltage, current and temperature of the electronic cigarette in real time during use.

[0034] Specifically, the key performance indicators of the electronic cigarette during use are monitored in real time through the built-in sensors in the electronic cigarette, the voltage level of the battery is monitored to ensure that it is within a safe and effective working range, the current flowing through the electronic cigarette is measured, the charge and discharge rate of the battery and the working status of the atomizer are understood, and the temperature changes of the electronic cigarette under different usage conditions are recorded to prevent overheating and ensure safe use.

[0035] S12: Record the number and cycle of charging and discharging of the electronic cigarette through a timer.

[0036] Specifically, counting the number of charge and discharge cycles completed by the battery and recording the length of each charge and discharge cycle helps analyze the battery usage pattern.

[0037] In one embodiment, if Figure 3 As shown, in step S20, the performance parameters of the electronic cigarette are curve-fitted to obtain a fitting curve of the electronic cigarette. The fitting curve of the electronic cigarette includes a voltage curve, a current curve and a temperature curve of the electronic cigarette, specifically including: S21: clustering the performance parameters of the electronic cigarette using a k-means algorithm to obtain characteristic data of the electronic cigarette.

[0038] Specifically, the K-means algorithm is used to cluster the performance parameters of electronic cigarettes to divide the data into several groups or "clusters" with similar characteristics. The performance parameters of electronic cigarettes may include voltage, current, temperature, and charge and discharge times. The obtained characteristic data represents the performance characteristics of electronic cigarettes under different usage conditions. Each cluster may represent a specific usage mode or performance state, and finally the characteristic data of the electronic cigarette is obtained.

[0039] S22: Fitting characteristic data of the electronic cigarette using a polynomial fitting method to obtain a voltage curve, a current curve, and a temperature curve of the electronic cigarette.

[0040] Specifically, a polynomial fitting method is used to fit the electronic cigarette characteristic data obtained from K-means clustering, and a set of data points is approximated by a polynomial equation to describe the change trend of the data, so as to obtain the voltage curve, current curve and temperature curve of the electronic cigarette.

[0041] In one embodiment, if Figure 4 As shown, before step S30, that is, before inputting the fitting curve and the number of charge and discharge times of the electronic cigarette into the pre-trained performance model for comparison and analysis to obtain the life prediction result of the electronic cigarette, obtaining the pre-trained performance model specifically includes: S301: Obtain historical usage data and lifespan information of different types of electronic cigarettes, and pre-process the historical usage data and lifespan information of different types of electronic cigarettes to obtain a training set.

[0042] Specifically, historical usage data of different types of e-cigarettes are collected, which may include performance parameters such as voltage, current, temperature, number of charge and discharge times, and their lifespan information. The collected data are cleaned and processed in preparation for model training, which may include handling missing values, outliers, data standardization or normalization, and feature selection. The preprocessed data is organized into a format suitable for model training to form a training set.

[0043] S302: constructing a performance model based on a random forest algorithm, training the performance model using a training set, and optimizing parameters of the trained performance model using a genetic algorithm to obtain a pre-trained performance model.

[0044] Specifically, the performance model is constructed based on the random forest algorithm. Random forest is an integrated learning method that improves the accuracy and robustness of the model by constructing multiple decision trees and combining their prediction results. Multiple decision trees are constructed and their prediction results are voted or averaged to obtain the final prediction. The parameters of the random forest model are optimized using a genetic algorithm. The genetic algorithm is a heuristic search algorithm that imitates the process of natural selection and optimizes the model's hyperparameters, such as the number of trees and the depth of the trees, through selection, crossover and mutation operations in an iterative process. After training and parameter optimization, a pre-trained performance model is obtained. This model can predict the life of an e-cigarette based on its performance parameters.

[0045] In one embodiment, if Figure 5 As shown, in step S30, the fitting curve and the number of charge and discharge times of the electronic cigarette are input into the pre-trained performance model for comparison and analysis to obtain the life prediction result of the electronic cigarette, which specifically includes: S31: comparing the voltage curve of the electronic cigarette with the voltage curve in the pre-trained performance model, comparing the current curve of the electronic cigarette with the current curve in the pre-trained performance model, and comparing the temperature curve of the electronic cigarette with the temperature curve in the pre-trained performance model to obtain a curve comparison result of the electronic cigarette.

[0046] Specifically, the voltage curve actually measured by the electronic cigarette is compared with the standard or expected voltage curve in the performance model. The voltage curve can usually reflect the charging and discharging performance and status of the battery. The current curve of the electronic cigarette is compared with the current curve in the performance model. The current curve can reflect the charging and discharging rate and load characteristics of the battery. The temperature curve of the electronic cigarette is compared with the temperature curve in the performance model. The temperature curve helps to identify overheating problems and thermal management efficiency. Through comparison, the deviation, trend difference, performance degradation and other information of each curve can be obtained. For example, if the actual voltage curve shows a faster downward trend than the model curve, this may indicate that the battery is degrading faster.

[0047] S32: Analyze the curve comparison result of the electronic cigarette and the number of times the electronic cigarette is charged and discharged to obtain a life prediction result of the electronic cigarette.

[0048] Specifically, combining the curve comparison results with the number of charge and discharge times of the e-cigarette can more comprehensively evaluate the battery status. Using the curve comparison results and the number of charge and discharge times, the relationship between the current performance status and the expected life of the e-cigarette can be evaluated through an analytical model. For example, if the battery performance drops to a certain threshold, or the number of charge and discharge times is close to the battery's designed cycle number, it may indicate that the battery life is about to end.

[0049] In one embodiment, if Figure 6As shown, the electronic cigarette life prediction method also includes: S51: Obtain the user's usage habits, and adjust the pre-trained performance model according to the user's usage habits.

[0050] Specifically, by monitoring the user's usage behavior, such as frequency of use, duration of use, charging and discharging patterns, etc., we can understand the user's usage habits, and use the acquired user usage habit data to adjust the pre-trained performance model, such as updating the model's parameters to reflect the user's specific usage patterns; retraining the model to include the impact of user usage habits; optimizing the model's prediction algorithm to make it more accurately predict the life of e-cigarettes with specific usage habits.

[0051] S52: Generate a working report of the electronic cigarette within a preset time, and transmit the working report of the electronic cigarette to the user end.

[0052] Specifically, within a preset time, such as weekly, monthly or after a specific usage cycle, the system will automatically generate an e-cigarette usage work report. The report may include: historical data and trend analysis of e-cigarette performance parameters, battery health status and estimated remaining life, assessment of the impact of usage habits on e-cigarette performance and life, maintenance and usage recommendations to extend the service life of the e-cigarette. The generated e-cigarette work report will be transmitted to the user end via wireless connection, email or other communication methods. The user end can be the user's smartphone, tablet or other device.

[0053] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0054] In one embodiment, an electronic cigarette life prediction device is provided, and the electronic cigarette life prediction device corresponds one-to-one to an electronic cigarette life prediction method in the above embodiment. Figure 7 As shown, the electronic cigarette life prediction device includes a parameter acquisition module, a curve fitting module, a prediction module and an alarm module. The detailed description of each functional module is as follows: A parameter acquisition module is used to acquire the performance parameters of the electronic cigarette, including the voltage, current, temperature and charge and discharge times of the electronic cigarette; A curve fitting module is used to perform curve fitting on the performance parameters of the electronic cigarette to obtain a fitting curve of the electronic cigarette. The fitting curve of the electronic cigarette includes a voltage curve, a current curve and a temperature curve of the electronic cigarette. A prediction module is used to input the fitting curve and charge and discharge times of the electronic cigarette into a pre-trained performance model for comparison and analysis to obtain a prediction result of the life of the electronic cigarette; The alarm module is used to generate an alarm signal when the life prediction result of the electronic cigarette is lower than a preset life threshold, and transmit the alarm signal to the user end.

[0055] Optionally, the electronic cigarette life prediction device further includes: The model adjustment module is used to obtain the user's usage habits and adjust the pre-trained performance model according to the user's usage habits; The report generation module is used to generate a work report of the electronic cigarette within a preset time and transmit the work report of the electronic cigarette to the user end.

[0056] A training set acquisition module is used to acquire historical usage data and life information of different types of electronic cigarettes, and pre-process the historical usage data and life information of different types of electronic cigarettes to obtain a training set; The model training module is used to build a performance model based on the random forest algorithm, train the performance model using the training set, and use the genetic algorithm to optimize the parameters of the trained performance model to obtain a pre-trained performance model.

[0057] Optionally, the parameter acquisition module includes: The sensor monitoring submodule is used to monitor and record the voltage, current and temperature of the electronic cigarette in real time during use through the built-in sensors of the electronic cigarette; The timer monitoring submodule is used to record the number and cycle of charge and discharge of the electronic cigarette through the timer.

[0058] Optionally, the curve fitting module includes: The clustering submodule is used to cluster the performance parameters of the electronic cigarette using the k-means algorithm to obtain the characteristic data of the electronic cigarette; The fitting submodule is used to fit the characteristic data of the electronic cigarette using a polynomial fitting method to obtain the voltage curve, current curve and temperature curve of the electronic cigarette.

[0059] Optionally, the prediction module includes: A curve comparison submodule is used to compare the voltage curve of the electronic cigarette with the voltage curve in the pre-trained performance model, compare the current curve of the electronic cigarette with the current curve in the pre-trained performance model, and compare the temperature curve of the electronic cigarette with the temperature curve in the pre-trained performance model to obtain the curve comparison result of the electronic cigarette; The analysis submodule is used to analyze the electronic cigarette curve comparison result and the number of charge and discharge times of the electronic cigarette to obtain the life prediction result of the electronic cigarette.

[0060] For the specific definition of an electronic cigarette life prediction device, please refer to the definition of an electronic cigarette life prediction method above, which will not be repeated here. Each module in the above-mentioned electronic cigarette life prediction device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0061] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device 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 the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for predicting the life of an electronic cigarette is implemented.

[0062] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Obtaining performance parameters of the electronic cigarette, including voltage, current, temperature, and charge and discharge times of the electronic cigarette; Performing curve fitting on the performance parameters of the electronic cigarette to obtain a fitting curve of the electronic cigarette, wherein the fitting curve of the electronic cigarette includes a voltage curve, a current curve and a temperature curve of the electronic cigarette; The fitting curve and charge and discharge times of the electronic cigarette are input into the pre-trained performance model for comparison and analysis to obtain the life prediction result of the electronic cigarette; When the life prediction result of the electronic cigarette is lower than the preset life threshold, an alarm signal is generated and transmitted to the user end.

[0063] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Obtaining performance parameters of the electronic cigarette, including voltage, current, temperature, and charge and discharge times of the electronic cigarette; Performing curve fitting on the performance parameters of the electronic cigarette to obtain a fitting curve of the electronic cigarette, wherein the fitting curve of the electronic cigarette includes a voltage curve, a current curve and a temperature curve of the electronic cigarette; The fitting curve and charge and discharge times of the electronic cigarette are input into the pre-trained performance model for comparison and analysis to obtain the life prediction result of the electronic cigarette; When the life prediction result of the electronic cigarette is lower than the preset life threshold, an alarm signal is generated and transmitted to the user end.

[0064] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application 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. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0065] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0066] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for predicting the life of an electronic cigarette, characterized in that: The electronic cigarette life prediction method comprises: Acquiring performance parameters of the electronic cigarette, wherein the performance parameters of the electronic cigarette include voltage, current, temperature, and charge and discharge times of the electronic cigarette; Performing curve fitting on the performance parameters of the electronic cigarette to obtain a fitting curve of the electronic cigarette, wherein the fitting curve of the electronic cigarette includes a voltage curve, a current curve and a temperature curve of the electronic cigarette; Inputting the fitting curve and charge and discharge times of the electronic cigarette into a pre-trained performance model for comparison and analysis to obtain a life prediction result of the electronic cigarette; When the life prediction result of the electronic cigarette is lower than a preset life threshold, an alarm signal is generated and transmitted to a user terminal.

2. The method for predicting the life of an electronic cigarette according to claim 1, characterized in that: The obtaining of the performance parameters of the electronic cigarette, wherein the performance parameters of the electronic cigarette include the voltage, current, temperature and charge and discharge times of the electronic cigarette, includes: The built-in sensor of the electronic cigarette monitors and records the voltage, current and temperature of the electronic cigarette in real time during use; The charging and discharging times and cycles of the electronic cigarette are recorded by a timer.

3. The method for predicting the life of an electronic cigarette according to claim 1, characterized in that: The curve fitting of the performance parameters of the electronic cigarette to obtain the fitting curve of the electronic cigarette includes the voltage curve, the current curve and the temperature curve of the electronic cigarette: Clustering the performance parameters of the electronic cigarette using a k-means algorithm to obtain characteristic data of the electronic cigarette; The characteristic data of the electronic cigarette is fitted using a polynomial fitting method to obtain a voltage curve, a current curve and a temperature curve of the electronic cigarette.

4. The method for predicting the life of an electronic cigarette according to claim 1, characterized in that: Before inputting the fitting curve and the charge and discharge times of the electronic cigarette into a pre-trained performance model for comparison and analysis to obtain the life prediction result of the electronic cigarette, obtaining the pre-trained performance model includes: Acquire historical usage data and lifespan information of different types of electronic cigarettes, and preprocess the historical usage data and lifespan information of the different types of electronic cigarettes to obtain a training set; A performance model is constructed based on a random forest algorithm, the performance model is trained using the training set, and a genetic algorithm is used to optimize parameters of the trained performance model to obtain the pre-trained performance model.

5. The method for predicting the life of an electronic cigarette according to claim 1, characterized in that: The fitting curve and the number of charge and discharge times of the electronic cigarette are input into a pre-trained performance model for comparison and analysis to obtain the life prediction result of the electronic cigarette, including: Comparing the voltage curve of the electronic cigarette with the voltage curve in the pre-trained performance model, comparing the current curve of the electronic cigarette with the current curve in the pre-trained performance model, and comparing the temperature curve of the electronic cigarette with the temperature curve in the pre-trained performance model to obtain a curve comparison result of the electronic cigarette; The life prediction result of the electronic cigarette is obtained by analyzing the curve comparison result of the electronic cigarette and the number of charge and discharge times of the electronic cigarette.

6. The method for predicting the life of an electronic cigarette according to claim 1, characterized in that: The electronic cigarette life prediction method further includes: Acquire the user's usage habits, and adjust the pre-trained performance model according to the user's usage habits; Generate a working report of the electronic cigarette within a preset time, and transmit the working report of the electronic cigarette to the user terminal.

7. An electronic cigarette life prediction device, characterized in that: The electronic cigarette life prediction device comprises: A parameter acquisition module, used to acquire performance parameters of the electronic cigarette, wherein the performance parameters of the electronic cigarette include voltage, current, temperature and charge and discharge times of the electronic cigarette; A curve fitting module, used for performing curve fitting on the performance parameters of the electronic cigarette to obtain a fitting curve of the electronic cigarette, wherein the fitting curve of the electronic cigarette includes a voltage curve, a current curve and a temperature curve of the electronic cigarette; A prediction module, used for inputting the fitting curve and charge and discharge times of the electronic cigarette into a pre-trained performance model for comparison and analysis to obtain a life prediction result of the electronic cigarette; The alarm module is used to generate an alarm signal when the life prediction result of the electronic cigarette is lower than a preset life threshold, and transmit the alarm signal to the user end.

8. The electronic cigarette life prediction device according to claim 7, characterized in that: The electronic cigarette life prediction device further includes: A model adjustment module, used to obtain the user's usage habits and adjust the pre-trained performance model according to the user's usage habits; The report generating module is used to generate a working report of the electronic cigarette within a preset time and transmit the working report of the electronic cigarette to the user terminal.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for predicting the life of an electronic cigarette as claimed in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting the life of an electronic cigarette as claimed in any one of claims 1 to 6 are implemented.

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