An intelligent temperature control system and method for electronic cigarettes based on environmental perception

Abstract

This invention discloses an intelligent temperature control system and method for e-cigarettes based on environmental perception, relating to the field of fusion technology of intelligent temperature control and environmental perception in e-cigarettes. This intelligent temperature control system and method for e-cigarettes based on environmental perception includes: S1, a distributed environmental perception network module; S2, an environmental dynamics construction module; S3, a dynamic priority temperature control module; S4, a user behavior prediction module; S5, a multi-physics field coupling compensation module; S6, an intelligent self-healing temperature control module; and S7, an expandable environmental interface module. This invention solves the problems of limited environmental perception, single data processing, and poor system scalability in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of technology, specifically to an intelligent temperature control system and method for electronic cigarettes based on environmental perception. Background Technology

[0002] Currently, the common practice in electronic cigarette temperature control technology is to use temperature sensors to collect data. These sensors are usually installed near the heating element of the electronic cigarette to monitor the temperature of the heating element. Temperature control is achieved by using a preset fixed temperature value. When the temperature sensor detects that the temperature is lower than the preset value, the control system increases the power supply to the heating element to raise its temperature. When the temperature reaches or exceeds the preset value, the power supply is reduced or stopped to maintain a stable temperature. In terms of device-user interaction, it mainly relies on manual operation by the user to adjust device parameters, such as adjusting the power level.

[0003] However, existing technologies suffer from limitations in environmental perception, simplistic data processing, and poor system scalability. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent temperature control system and method for electronic cigarettes based on environmental perception, which solves the problems of existing intelligent temperature control systems and methods for electronic cigarettes based on environmental perception.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent temperature control system and method for electronic cigarettes based on environmental perception, comprising the following steps: S1, a distributed environmental perception network module: constructing a multi-node sensor network module to collect temperature data, humidity data, and air pressure data; S2, constructing an environmental dynamic module: achieving global data synchronization through wireless networking, combining collaborative filtering algorithms to eliminate local interference, and constructing an environmental dynamic model; S3, a dynamic priority temperature control module: dynamically adjusting control priority based on the rate of change of environmental data, prioritizing responses to high-risk data; S4, a user behavior prediction module: analyzing historical user behavior and using a deep learning module to predict short-term usage needs, and adjusting the device status in advance; S5, a multi-physics coupling compensation module: establishing an interaction and influence model of temperature data, humidity data, and air pressure data, quantifying the synergistic effect of multiple data, and dynamically optimizing the heating strategy; S6, an intelligent self-healing temperature control module: monitoring hardware status, automatically enabling redundant data or adjusting control data to ensure continuous system operation in the event of component failure, avoiding sudden shutdowns; S7, an expandable environmental interface module: supporting protocol access to third-party sensors, expanding the dimensions of environmental perception, and integrating multi-source data through a unified decision engine to enhance system adaptability.

[0006] Furthermore, the distributed environmental sensing network module adopts a high-density deployment strategy, constructing a multi-node sensor network at key locations of the e-cigarette and its surrounding environment. It utilizes temperature sensors, humidity sensors, and air pressure sensors to collect temperature, humidity, and air pressure data at sampling frequencies. It is also equipped with a data verification mechanism to transmit the basic environmental information data collected by the sensors to the basic data database through a communication protocol.

[0007] Furthermore, the environmental dynamics module utilizes wireless ad hoc networking technology to automatically identify network topology changes and achieve rapid global data synchronization. Combined with an adaptive collaborative filtering algorithm, it dynamically adjusts the filtered data based on the degree of data fluctuation, transmitting the basic environmental information data collected by the sensors to the basic data database through a communication protocol to eliminate abnormal data. Based on the acquired temperature, humidity, and air pressure data, and combined with machine learning algorithms, it constructs an environmental dynamics model, feeding back the data that has eliminated abnormal data caused by local environmental interference to the environmental dynamics model.

[0008] Furthermore, the dynamic priority temperature control module has a built-in environmental data change rate analysis engine. Based on differential calculation and threshold judgment mechanism, it obtains the rate of change of each environmental data within the corresponding time interval. It evaluates the rate of change of the collected abnormal data that has eliminated local environmental interference in real time. When the rate of change of environmental data is detected to exceed the preset threshold, it is determined to be high-risk data. The priority adjustment strategy is immediately activated to respond to and process high-risk data first. At the same time, it combines fuzzy control algorithm to regulate the temperature control equipment.

[0009] Furthermore, the user behavior prediction module establishes a user behavior feature database, extracts behavioral data from users' historical usage time data, frequency data, duration data, and usage scenario data, and builds a deep neural network model. The deep neural network model includes an input layer, multiple hidden layers, and an output layer. The extracted usage time data, frequency data, duration data, usage scenario data, and behavioral data are divided into training sets and validation sets. The training set is input into the deep neural network model to train the deep neural network model, and the validation set is used to validate the trained deep neural network model.

[0010] Furthermore, the multi-physics coupling compensation module, based on the finite element analysis method, establishes a three-dimensional mathematical model of the interaction and influence of temperature, humidity, and air pressure data to determine optimization objectives. These objectives include: the quality of vapor produced by the e-cigarette, maintaining a stable heating temperature, and reducing energy consumption. Based on these objectives, a multi-objective optimization method is selected, and weights and quantization ratios are calculated. Based on the importance of different optimization objectives, corresponding weights are assigned to each objective, resulting in the weight coefficients of the influence of each physical quantity on the heating process under different combinations of temperature, humidity, and air pressure data. Behavioral data, including collected temperature, humidity, and air pressure data, as well as user historical usage time, frequency, duration, and usage scenario data, are input into the established and calibrated three-dimensional mathematical model and quantization analysis module to analyze the environmental conditions and user usage scenarios of the current data. A heating strategy is generated or adjusted based on a fuzzy logic algorithm, and the optimized heating strategy is output to the e-cigarette intelligent temperature control system, allowing the heating element to operate according to the new strategy.

[0011] Furthermore, the intelligent self-healing temperature control module integrates multiple sensors, including temperature, voltage, and current sensors. The integrated sensors are initialized and calibrated, a fault diagnosis algorithm is initiated, fault judgment thresholds and rules are set, and the data acquisition frequency of each sensor is configured. The acquired temperature, voltage, and current data are transmitted to the module's data processing unit. The fault diagnosis algorithm analyzes the acquired and stored real-time data, compares the current data with preset normal operating parameter ranges, and determines whether hardware components are malfunctioning. If the data exceeds the preset range, a potential fault is identified. When a hardware component malfunction is confirmed, redundant data acquisition channels are immediately activated, a pre-set backup control algorithm is invoked, and a fuzzy logic-based backup control algorithm is switched to. Simultaneously, system resources are reallocated, and a fault warning message is sent to the user according to the selected warning method.

[0012] Furthermore, we will construct a communication protocol support and sensor compatibility system. The communication protocols include Bluetooth, Wi-Fi, and ZigBee, and the sensors include air quality sensors and wind speed sensors. We will study the data output formats, communication interface types, and electrical characteristics of operating voltage and current of these sensors, and establish a sensor compatibility database. We will also design a data interface incompatibility conversion module to convert data interfaces that are incompatible with the existing communication protocols of the module when connected third-party sensors use such protocols.

[0013] Furthermore, for sensors successfully integrated into the new environment, data acquisition initialization settings are performed, the data acquisition frequency is set, and data collected by the sensors is acquired in real time through a communication protocol according to the set parameters. A unified decision engine is constructed, including a data preprocessing module, a feature extraction module, and a decision analysis module. When conflicts occur between multi-source data, a model is established based on the Bayesian network algorithm, with sensors set as nodes to determine connection weights. Confidence is calculated based on prior probability and conditional probability. Historical data is considered when analyzing differences in sensor data. Based on the results of data analysis, corresponding environmental adaptation strategies are formulated and applied to the electronic cigarette intelligent temperature control system. Through communication and collaborative work with the state priority temperature control module and the multi-physics coupling compensation module, the system's operating data is adjusted. Simultaneously, the operating effect of the system under the new strategy is monitored in real time, and feedback data is collected, including user experience feedback and actual device performance indicators. Based on the feedback data, feedback is then fed back.

[0014] Furthermore, a multi-node sensor network module is constructed to collect temperature, humidity, and air pressure data; global data synchronization is achieved through wireless networking, and local interference is eliminated by combining collaborative filtering algorithms to build a dynamic environmental model; control priorities are dynamically adjusted based on the rate of change of environmental data, prioritizing responses to high-risk data; historical user behavior is analyzed and short-term usage needs are predicted by a deep learning module to adjust equipment status in advance; an interaction and influence model of temperature, humidity, and air pressure data is established to quantify the synergistic effect of multiple data and dynamically optimize the heating strategy; hardware status is monitored, and redundant data is automatically enabled or control data is adjusted to ensure continuous system operation in the event of component failure and avoid sudden shutdowns; third-party sensor access is supported to expand the dimensions of environmental perception, and multi-source data is integrated through a unified decision engine to enhance system adaptability.

[0015] The present invention has the following beneficial effects:

[0016] This invention relates to an intelligent temperature control system and method for e-cigarettes based on environmental perception. It utilizes multi-node sensors to collect temperature, humidity, and air pressure data, which are then processed to construct a dynamic environmental model. The system prioritizes high-risk data based on the rate of change in environmental data, precisely controlling the temperature. By analyzing historical user behavior, it predicts user needs and enables personalized experiences. Furthermore, it establishes a multi-data interaction model to optimize heating strategies, improving smoke quality, stabilizing temperature, and reducing energy consumption. The system also possesses intelligent self-healing capabilities, maintaining operation even in the event of hardware failure. Simultaneously, it supports the integration of third-party sensors, consolidating multi-source data to enhance adaptability and comprehensively improve the user experience, performance, and reliability of e-cigarettes.

[0017] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0018] Figure 1 This is a block diagram of an intelligent temperature control system for electronic cigarettes based on environmental perception, according to the present invention.

[0019] Figure 2 This diagram illustrates an intelligent temperature control method for electronic cigarettes based on environmental perception, as described in this invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 The present invention provides a technical solution: an intelligent temperature control system and method for electronic cigarettes based on environmental perception, comprising the following steps: S1, distributed environmental perception network module: constructing a multi-node sensor network module to collect temperature data, humidity data, and air pressure data;

[0022] The distributed environmental sensing network module adopts a high-density deployment strategy, constructing a multi-node sensor network at key locations of e-cigarettes and in the surrounding environment. It utilizes temperature sensors, humidity sensors, and air pressure sensors to collect temperature, humidity, and air pressure data at sampling frequencies. It is also equipped with a data verification mechanism to transmit the basic environmental information data collected by the sensors to the basic data database through a communication protocol.

[0023] In this implementation scheme, the distributed environmental sensing network module operates by constructing a multi-node sensor network, employing a high-density deployment strategy to widely deploy it in key areas of the e-cigarette and its surrounding environment. Utilizing temperature, humidity, and air pressure sensors, it accurately collects data on temperature, humidity, and air pressure at specific sampling frequencies. Simultaneously, it is equipped with a data verification mechanism to filter and correct the collected basic environmental information data, ensuring data accuracy. Finally, the processed data is transmitted to the basic data database according to a communication protocol.

[0024] The multi-node and reasonable sampling frequency ensure the accuracy and timeliness of data collection, providing a reliable basis for in-depth analysis of the effects of the environment on e-cigarettes. On the other hand, the high-density deployment enables all-round environmental monitoring without blind spots, resulting in more comprehensive environmental information. Furthermore, the data verification mechanism effectively improves data quality, making subsequent analyses and applications based on this data more reliable, laying a solid foundation for the stable operation and effective decision-making of the entire system.

[0025] S2. Constructing an environmental dynamic module: Achieving global data synchronization through wireless networking, eliminating local interference by combining collaborative filtering algorithms, and constructing an environmental dynamic model;

[0026] The environmental dynamics module utilizes wireless ad hoc networking technology to automatically identify network topology changes and achieve rapid global data synchronization. Combined with an adaptive collaborative filtering algorithm, it dynamically adjusts the filtered data based on the degree of data fluctuation, transmitting basic environmental information data collected by sensors to the basic data database through a communication protocol to eliminate abnormal data. Based on the acquired temperature, humidity, and air pressure data, and combined with machine learning algorithms, it constructs an environmental dynamics model, feeding back the data that has eliminated abnormal data caused by local environmental interference to the environmental dynamics model.

[0027] In this implementation scheme, the environmental dynamics module utilizes wireless ad hoc networking technology to automatically identify network topology changes and achieve rapid global data synchronization, laying the foundation for effective data integration and analysis. Combined with an adaptive collaborative filtering algorithm, it dynamically adjusts the filtered data based on data fluctuations, eliminating abnormal data from sensor-collected data, effectively removing local interference, and improving data quality. Furthermore, based on acquired data such as temperature, humidity, and air pressure, a dynamic environmental model is constructed using machine learning algorithms. The processed, anomaly-free data is then fed back to the model, ensuring data accuracy and stability, providing reliable support for subsequent analysis, and enabling more precise simulation of dynamic environmental changes. This makes the model more closely resemble the actual environment, enhancing the system's ability to perceive and respond to environmental changes, and contributing to the reliability and effectiveness of the entire invention in electronic cigarette environmental monitoring and related applications.

[0028] S3, Dynamic Priority Temperature Control Module: Dynamically adjusts control priority based on the rate of change of environmental data, prioritizing responses to high-risk data;

[0029] The dynamic priority temperature control module has a built-in environmental data change rate analysis engine. Based on differential calculation and threshold judgment mechanism, it obtains the rate of change of each environmental data within the corresponding time interval. It evaluates the rate of change of the collected abnormal data that has eliminated local environmental interference in real time. When the rate of change of environmental data is detected to exceed the preset threshold, it is determined to be high-risk data. The priority adjustment strategy is immediately activated to respond to and process high-risk data first. At the same time, it combines fuzzy control algorithm to regulate the temperature control equipment.

[0030] In this implementation scheme, the dynamic priority temperature control module has a built-in environmental data change rate analysis engine. Using differential calculation and threshold judgment mechanisms, it assesses in real time the rate of change of collected environmental data (after eliminating local interference anomalies) within a corresponding time interval. When the rate of change of environmental data exceeds a preset threshold and is determined to be high-risk data, a priority adjustment strategy is quickly activated to prioritize and process the high-risk data. Simultaneously, a fuzzy control algorithm is used to regulate the temperature control device. This facilitates the keen detection of abnormal changes in environmental data, timely response to high-risk situations, and effective prevention of potential risks. Furthermore, by dynamically adjusting the control priority and precisely regulating the temperature control device, the timeliness and accuracy of temperature control are improved, ensuring the e-cigarette operates under suitable environmental conditions. This enhances the system's ability to cope with environmental changes and further improves the reliability and stability of the entire invention in e-cigarette environmental monitoring and related applications.

[0031] S4, User Behavior Prediction Module: Analyzes historical user behavior and uses deep learning to predict short-term usage needs, adjusting device status in advance;

[0032] The user behavior prediction module establishes a user behavior feature database, extracts behavioral data from users' historical usage time data, frequency data, duration data, and usage scenario data, and builds a deep neural network model.

[0033] A deep neural network model includes an input layer, multiple hidden layers, and an output layer. The extracted usage time data, frequency data, duration data, usage scenario data, and behavior data are divided into a training set and a validation set. The training set is input into the deep neural network model to train the model, and the validation set is used to validate the trained deep neural network model.

[0034] In this implementation plan, the user behavior prediction module first establishes a user behavior feature database, extracts behavioral data such as users' historical usage time, frequency, duration, and usage scenarios, and builds a deep neural network model containing an input layer, multiple hidden layers, and an output layer. Next, the extracted data is divided into a training set and a validation set. The model is trained using the training set and then validated using the validation set. This approach allows for the prediction of users' short-term usage needs by analyzing their historical behavior and leveraging deep learning. The advantages include the ability to adjust device status in advance, achieving intelligent and personalized device adaptation, improving the user experience, making the device more aligned with user habits, enhancing product usability and user stickiness, optimizing resource allocation, and improving device operating efficiency, demonstrating unique advantages in e-cigarette-related applications.

[0035] S5. Multi-physics field coupling compensation module: Establishes an interactive influence model of temperature data, humidity data, and air pressure data, quantifies the synergistic effect of multiple data, and dynamically optimizes the heating strategy;

[0036] The multiphysics coupling compensation module is based on the finite element analysis method to establish a three-dimensional mathematical model of the interaction between temperature data, humidity data, and air pressure data, and to determine the optimization objective.

[0037] The optimization objectives include: the quality of the vapor produced by the e-cigarette, maintaining a stable heating temperature, and reducing energy consumption. Based on the optimization objectives, a multi-objective optimization method is selected, the weights and quantization ratios are calculated, and based on the importance of different optimization objectives, corresponding weights are assigned to each objective to obtain the weight coefficients of the influence of each physical quantity on the heating process under different combinations of temperature data, humidity data, and air pressure data.

[0038] The collected temperature, humidity, air pressure, and user history data, frequency, duration, and usage scenario data are input into the established and calibrated three-dimensional mathematical model and quantitative analysis module to analyze the environmental conditions and user usage scenarios of the current data. The heating strategy is generated or adjusted based on fuzzy logic algorithms, and the optimized heating strategy is output to the electronic cigarette intelligent temperature control system, and the heating element works according to the new strategy.

[0039] In this implementation plan, the multi-physics coupling compensation module, based on the finite element analysis method, constructs a three-dimensional mathematical model of the interaction and influence of temperature, humidity, and air pressure data. It identifies optimization objectives such as e-cigarette vapor quality, stable heating temperature, and reduced energy consumption. Through a multi-objective optimization method, weights and quantification ratios are calculated, and weights are allocated according to the importance of the objectives, yielding the weight coefficients of each physical quantity's influence on the heating process under different data combinations. Collected environmental and user behavior data are input into the model and quantification analysis module to analyze environmental conditions and usage scenarios. Then, based on fuzzy logic algorithms, a heating strategy is generated or adjusted and output to the temperature control system. This facilitates accurate quantification of the synergistic effects of multiple data points, dynamic optimization of the heating strategy, improved e-cigarette vapor quality, stable heating temperature, and reduced energy consumption. This enables the e-cigarette to operate efficiently and stably under different environments and usage scenarios, improving user experience and enhancing the product's overall performance and market competitiveness.

[0040] S6 Intelligent self-healing temperature control module: monitors hardware status, automatically activates redundant data or adjusts control data to ensure continuous operation of the system in the event of component failure and avoids sudden shutdown;

[0041] The intelligent self-healing temperature control module integrates multiple sensors, including temperature, voltage, and current sensors. It initializes and calibrates the integrated sensors, starts the fault diagnosis algorithm program, sets the threshold and rules for fault judgment, sets the data acquisition frequency of each sensor, and transmits the acquired temperature, voltage, and current data to the module's data processing unit. The fault diagnosis algorithm analyzes the acquired and stored real-time data, compares the current data with the preset normal operating parameter range, and determines whether the hardware components are abnormal. If the data exceeds the preset range, a fault may exist.

[0042] When a hardware component is confirmed to have malfunctioned, the redundant data acquisition channel is immediately activated, the pre-set backup control algorithm is invoked, and the backup control algorithm based on fuzzy logic is switched to. At the same time, system resources are reallocated, and fault warning information is sent to the user according to the selected warning method.

[0043] In this implementation plan, the intelligent self-healing temperature control module plays a crucial role in ensuring the stable operation of the system. This module integrates multiple sensors, including temperature, voltage, and current sensors. After initialization and calibration, it initiates a fault diagnosis algorithm, sets fault judgment thresholds and rules, and determines the data acquisition frequency. The acquired data is then transmitted to the processing unit for analysis, comparing the current data with normal operating parameters to determine if the hardware is malfunctioning. When a hardware component failure is confirmed, redundant data acquisition channels are quickly activated, a backup control algorithm is invoked, and a fuzzy logic-based control method is switched. Simultaneously, system resources are reallocated, and a fault warning message is sent to the user. This approach allows for real-time monitoring of hardware status, timely fault detection, and automatic countermeasures, ensuring the system continues to operate even with component failures. It avoids sudden shutdowns, improves system reliability, stability, and fault tolerance, reduces the impact of equipment failures on user experience, and guarantees the normal operation of the electronic cigarette temperature control system and the user experience.

[0044] S7, Scalable Environment Interface Module: Supports protocol access to third-party sensors, expands the dimensions of environmental perception, and integrates multi-source data through a unified decision engine to enhance system adaptability;

[0045] The project aims to build a communication protocol that supports and is compatible with various sensors, including Bluetooth, Wi-Fi, and ZigBee. Sensors include air quality sensors and wind speed sensors. The project investigates the data output formats, communication interface types, and electrical characteristics of the operating voltage and current of these sensors, and establishes a sensor compatibility database. A data interface incompatibility conversion module is designed to convert data interfaces that are incompatible with the existing communication protocols of the module when connected third-party sensors use such protocols.

[0046] For sensors successfully integrated into a new environment, initial data acquisition settings are performed, including setting the data acquisition frequency and acquiring sensor data in real time via a communication protocol according to the set parameters. A unified decision engine is constructed, including a data preprocessing module, a feature extraction module, and a decision analysis module. When conflicts occur between multi-source data, a model is built based on a Bayesian network algorithm, with sensors designated as nodes and connection weights determined. Confidence is calculated based on prior probability and conditional probability. Historical data is considered when analyzing sensor data differences. Based on the data analysis results, corresponding environmental adaptation strategies are formulated and applied to the electronic cigarette intelligent temperature control system. Through communication and collaborative work with the state priority temperature control module and the multi-physics coupling compensation module, the system's operating data is adjusted. Simultaneously, the system's operating effect under the new strategy is monitored in real time, and feedback data is collected, including user experience feedback and actual device performance indicators. Based on the feedback data, the data is fed back to the Bayesian network algorithm.

[0047] In this implementation scheme, an extensible environmental interface module is constructed to support communication protocols and sensor compatibility mechanisms, covering communication protocols such as Bluetooth, Wi-Fi, and ZigBee. The characteristics of various sensors (such as air quality and wind speed sensors) are studied, and a compatibility database is established. A conversion module is designed to handle incompatible protocols, ensuring smooth access for third-party sensors. Successfully connected sensors are configured and data is acquired in real time. Simultaneously, a unified decision engine is built, integrating multi-source data. When data conflicts occur, an environmental adaptation strategy is formulated based on Bayesian network algorithms, collaborating with other modules and optimizing based on feedback. This approach is beneficial for supporting third-party sensor access, expanding environmental perception dimensions, enhancing system adaptability, integrating multi-source data to better cope with complex environments, optimizing operational performance, improving user experience and device performance, and promoting the intelligent and diversified development of electronic cigarette intelligent temperature control systems.

[0048] An intelligent temperature control method for electronic cigarettes based on environmental perception, specifically including:

[0049] A multi-node sensor network module is constructed to collect temperature, humidity, and air pressure data. Global data synchronization is achieved through wireless networking, and local interference is eliminated using a collaborative filtering algorithm to build a dynamic environmental model. Control priorities are dynamically adjusted based on the rate of change of environmental data, prioritizing responses to high-risk data. User historical behavior is analyzed, and a deep learning module predicts short-term usage needs, allowing for proactive adjustments to equipment status. An interaction model of temperature, humidity, and air pressure data is established to quantify the synergistic effects of multiple data sources and dynamically optimize heating strategies. Hardware status is monitored, and redundant data is automatically activated or control data is adjusted to ensure continuous system operation even in the event of component failure, preventing sudden shutdowns. Protocol access to third-party sensors is supported to expand environmental perception dimensions, and a unified decision engine integrates multi-source data to enhance system adaptability.

[0050] In this implementation scheme, an intelligent temperature control method for e-cigarettes based on environmental perception collects key environmental data by constructing a multi-node sensor network to provide basic information for temperature control; it utilizes wireless networking and collaborative filtering to build a dynamic environmental model, ensuring data accuracy and reflecting environmental changes; it dynamically adjusts priorities based on the rate of change of environmental data, prioritizing high-risk data to improve risk response capabilities; it predicts usage needs by analyzing historical user behavior, pre-optimizing device status and enhancing user experience; it establishes a multi-physics data interaction model to quantify synergistic effects to optimize heating strategies and improve heating efficiency; it monitors hardware status and automatically handles faults to ensure stable system operation; it supports the access of third-party sensors and integrates multi-source data to expand the sensing dimensions and system adaptability; and it is conducive to comprehensively improving the intelligence level of the e-cigarette temperature control system, accurately perceiving the environment and user needs, effectively responding to various situations, ensuring stable and efficient device operation, and significantly enhancing product reliability, comfort, and market competitiveness.

[0051] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.

[0052] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0054] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0055] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0056] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0057] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An environment-sensing-based electronic cigarette intelligent temperature control system, characterized in that, The system comprises a distributed environment sensing network module, a dynamic environment construction module, a dynamic priority temperature control module, a user behavior prediction module, a multi-physical field coupling compensation module, an intelligent self-healing temperature control module, and an extensible environment interface module. The distributed environment sensing network module comprises a multi-node sensor network module for collecting temperature data, humidity data, and air pressure data. The dynamic environment construction module synchronizes global data through wireless networking and eliminates local interference by using a collaborative filtering algorithm to construct an environment dynamic model. The dynamic priority temperature control module dynamically adjusts the control priority based on the rate of change of environmental data and responds to high-risk data first. The user behavior prediction module analyzes historical user behavior and short-term usage demand predicted by a deep learning module to adjust the device state in advance. The multi-physical field coupling compensation module establishes an interactive influence model of temperature data, humidity data, and air pressure data, quantifies the collaborative effect of multiple data, and dynamically optimizes the heating strategy. The intelligent self-healing temperature control module monitors hardware status, automatically enables redundant data or adjusts control data to ensure continuous operation of the system in the event of component failure and avoid sudden shutdown. The extensible environment interface module supports protocol access to third-party sensors, expands the environmental sensing dimension, integrates multi-source data through a unified decision engine, and enhances system adaptability. The multi-physical field coupling compensation module comprises: Based on finite element analysis, the multi-physical field coupling compensation module establishes a three-dimensional mathematical model of the interactive influence of temperature data, humidity data, and air pressure data to determine the optimization target. The optimization target includes the quality of the smoke generated by the electronic cigarette, the maintenance of stable heating temperature, and the reduction of energy consumption. Based on the optimization target, a multi-objective optimization method is selected to calculate the weight and quantization ratio.

2. The electronic cigarette intelligent temperature control system based on environmental perception according to claim 1, characterized in that, Based on the importance of different optimization targets, each target is assigned a corresponding weight to obtain the influence weight coefficient of each physical quantity on the heating process under different combinations of temperature data, humidity data, and air pressure data. The collected temperature data, humidity data, air pressure data, and user historical usage time data, frequency data, duration data, and usage scenario data are input into the established and calibrated three-dimensional mathematical model and quantization analysis module to analyze the environmental conditions and user usage scenarios of the current data.

3. The electronic cigarette intelligent temperature control system based on environmental perception according to claim 1, characterized in that, Based on the fuzzy logic algorithm, the heating strategy is generated or adjusted, and the optimized heating strategy is output to the electronic cigarette intelligent temperature control system. The distributed environment sensing network module comprises: The distributed environment sensing network module adopts a high-density deployment strategy to construct a multi-node sensor network in key locations of the electronic cigarette and the surrounding environment. The dynamic environment construction module comprises: The environmental dynamic module uses wireless ad hoc network technology, can automatically identify network topology changes, realizes global data rapid synchronization, combines adaptive collaborative filtering algorithm, dynamically adjusts the filtered data based on the data fluctuation degree, transmits the basic environmental information data collected by the sensor to the basic data database through the communication protocol, and eliminates abnormal data; based on the obtained temperature data, humidity data and air pressure data, combined with machine learning algorithm, an environmental dynamic model is constructed, and the data eliminated by the environmental dynamic model is fed back to the environmental dynamic model.

4. The environmental perception based electronic cigarette intelligent temperature control system according to claim 1, characterized in that, The dynamic priority temperature control module specifically includes: The dynamic priority temperature control module is built-in environment data change rate analysis engine, based on differential calculation and threshold judgment mechanism, the change speed of each environmental data in the corresponding time interval is obtained, the change rate of the collected data eliminated by the local environmental interference is evaluated in real time, when the change rate of the environmental data is detected to exceed the preset threshold, it is determined as high risk data, the priority adjustment strategy is started immediately, the high risk data is responded and processed preferentially, and the fuzzy control algorithm is combined to control the temperature control equipment.

5. The environmental perception based electronic cigarette intelligent temperature control system according to claim 1, characterized in that: The user behavior prediction module specifically includes: The user behavior prediction module establishes a user behavior feature database, extracts the behavior data of the user historical use time data, frequency data, duration data and use scene data, and builds a deep neural network model; The deep neural network model includes: an input layer, a plurality of hidden layers and an output layer, the extracted use time data, frequency data, duration data, use scene data and behavior data are divided into a training set and a verification set, the training set is input into the deep neural network model, the deep neural network model is trained, and the verification set is used to verify the trained deep neural network model.

6. The environmental perception based electronic cigarette intelligent temperature control system according to claim 1, wherein, The intelligent self-healing temperature control module specifically includes: The intelligent self-healing temperature control module integrates a variety of sensors, including temperature sensors, voltage sensors and current sensors, the integrated sensors are initialized and calibrated, the fault diagnosis algorithm program is started, the threshold and rules for fault judgment are set, the data acquisition frequency of each sensor is set, the collected temperature data, voltage data and current data are transmitted to the data processing unit of the module, the fault diagnosis algorithm analyzes the real-time data collected and stored, compares the current data with the preset normal working parameter range, judges whether the hardware component is abnormal, if it exceeds the preset range, it is judged that there may be a fault; When it is determined that the hardware component has a fault, the redundant data acquisition channel is immediately enabled, the pre-set standby control algorithm is called, the standby control algorithm based on fuzzy logic is switched, and the system resources are reallocated; at the same time, according to the selected early warning mode, the fault early warning information is sent to the user.

7. The environmental perception based electronic cigarette intelligent temperature control system according to claim 1, wherein, The expandable environmental interface module specifically includes: Build communication protocol support and sensor compatibility, communication protocols include: Bluetooth, Wi-Fi, ZigBee, sensors include: air quality sensor, wind speed sensor, research the data output format of these sensors, communication interface type and electrical characteristics of working voltage and current, establish a sensor compatibility database; Design data interface incompatible conversion module, when the accessed third-party sensor uses a protocol that is incompatible with the existing communication protocol of the module, use the data interface incompatible conversion module for conversion.

8. The environment-aware based electronic cigarette intelligent temperature control system according to claim 7, characterized in that: Access to new environmental sensors, including the following steps: For successfully accessed new environmental sensors, perform data acquisition initialization settings, set the data acquisition frequency, and real-time acquire sensor collected data according to the set parameters through the communication protocol; Build a unified decision engine, including: data preprocessing module, feature extraction module, decision analysis module When multiple source data conflicts occur, based on the Bayesian network algorithm, establish a model to determine the connection weight by setting the sensor as a node, calculate the credibility based on the prior probability and conditional probability, consider historical data when analyzing sensor data differences, according to the results of the number analysis, formulate the corresponding environmental adaptation strategy, apply the formulated environmental adaptation strategy to the electronic cigarette intelligent temperature control system, adjust the system's running data through communication and collaborative work with the state priority temperature control module and multi-physical field coupling compensation module; At the same time, monitor the running effect of the system under the new strategy in real time, collect feedback data, including: user experience feedback, actual working performance indicators of the equipment, according to the feedback data, feedback to the Bayesian network algorithm again.

9. An environmental perception-based intelligent temperature control method for an electronic cigarette, according to the environmental perception-based intelligent temperature control system for an electronic cigarette in any one of claims 1-8, characterized in that, Specifically includes: Build a multi-node sensor network module to collect temperature data, humidity data, and air pressure data; Realize global data synchronization through wireless networking, eliminate local interference by combining collaborative filtering algorithm, and build environmental dynamic model; Based on the rate of change of environmental data, dynamically adjust the control priority and respond to high-risk data first; Analyze user historical behavior and deep learning module to predict short-term usage demand, and adjust device state in advance; Establish temperature data, humidity data, and air pressure data interaction model, quantify multi-data collaborative effect, and dynamically optimize heating strategy; Monitor hardware status, automatically enable redundant data or adjust control data to ensure system continuous operation when components fail, and avoid sudden shutdown; Support protocol access to third-party sensors, expand environmental perception dimensions, and integrate multi-source data through a unified decision engine to enhance system adaptability.

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