Electronic cigarette intelligent temperature control system and method based on environmental perception

Abstract

The invention discloses an electronic cigarette intelligent temperature control system and method based on environmental perception, and relates to the technical field of fusion of electronic cigarette intelligent temperature control and environmental perception. According to the electronic cigarette intelligent temperature control system and method based on environment perception, S1, a distributed environment perception network module; s2, constructing an environment dynamic 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 extensible environment interface module. The problems that in the prior art, environment perception is limited, data processing is single, and system expansibility is poor are solved.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to an electronic cigarette intelligent temperature control system and method based on environmental perception. Background Art

[0002] At present, 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 elements of electronic cigarettes to monitor the temperature of the heating elements. Temperature control is performed by using a preset fixed temperature value. When the temperature sensor detects that the temperature is lower than the preset value, the control system will increase the power supply to the heating element to make it heat up. When the temperature reaches or exceeds the preset value, the power supply is reduced or stopped to maintain temperature stability. In terms of interaction between the device and the user, it mainly relies on manual operation of the user to adjust the device parameters, such as adjusting the power size.

[0003] However, existing technologies have problems such as limited environmental perception, single data processing, and poor system scalability. Summary of the Invention

[0004] In view of the deficiencies of the existing technology, the present invention provides an electronic cigarette intelligent temperature control system and method based on environmental perception, which solves the problems of the existing electronic cigarette intelligent temperature control system and method based on environmental perception.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an electronic cigarette intelligent temperature control system and method 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; S2, constructing an environmental dynamic module: realizing global data synchronization through wireless networking, combining collaborative filtering algorithm to eliminate local interference, and constructing an environmental dynamic model; S3, dynamic priority temperature control module: dynamically adjusting control priority based on the rate of change of environmental data, giving priority to responding to high-risk data; S4, user behavior prediction module: analyzing user historical behavior and deep learning module to predict short-term usage needs, and adjusting device status in advance; S5, multi-physical field coupling compensation module: establishing an interactive 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, intelligent self-healing temperature control module: monitoring hardware status, automatically enabling redundant data or adjusting control data to ensure that the system continues to run when a component fails and avoid sudden shutdown; S7, extensible environmental interface module: supporting protocol access to third-party sensors, expanding the environmental perception dimension, and integrating multi-source data through a unified decision engine to enhance system adaptability.

[0006] Furthermore, the distributed environmental perception network module adopts a high-density deployment strategy to build a multi-node sensor network at key locations of the e-cigarette and its surrounding environment. It uses temperature sensors, humidity sensors, and air pressure sensors to collect temperature data, humidity data, and air pressure data at a sampling frequency. At the same time, it is 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 uses wireless ad hoc network technology to automatically identify changes in network topology and achieve rapid synchronization of global data. Combined with the adaptive collaborative filtering algorithm, it dynamically adjusts the filtered data based on the degree of data fluctuation, and transmits the basic environmental information data collected by the sensor to the basic data database through the communication protocol to eliminate abnormal data; based on the acquired temperature data, humidity data, and air pressure data, combined with the machine learning algorithm, it constructs an environmental dynamics model, and feeds back the data that has eliminated the 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 speed of change of each environmental data in the corresponding time interval, and conducts real-time evaluation of the data change rate of the collected abnormal data that has eliminated the local environmental interference. When it is detected that the environmental data change rate exceeds the preset threshold, that is, it is judged to be high-risk data, the priority adjustment strategy is immediately activated to respond to and process the high-risk data first, and at the same time, the fuzzy control algorithm is combined to regulate the temperature control equipment.

[0009] Furthermore, the user behavior prediction module establishes a user behavior feature database, extracts the user's 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, and 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, the deep neural network model is trained, and the trained deep neural network model is verified using the validation set.

[0010] Furthermore, the multi-physical field coupling compensation module establishes a three-dimensional mathematical model of the interaction between temperature data, humidity data, and air pressure data based on the finite element analysis method to determine the optimization target; the optimization targets include: the quality of the smoke generated by the electronic cigarette, maintaining a stable heating temperature, and reducing energy consumption; based on the optimization target, a multi-objective optimization method is selected, the weight and quantization ratio are calculated, and based on the importance of different optimization targets, corresponding weights are assigned to each target to obtain the weight coefficient of the influence of each physical quantity on the heating process under different combinations of temperature data, humidity data, and air pressure data; based on the collected temperature data, humidity data, air pressure data and the user's historical usage time data, frequency data, duration data, and usage scenario data, the behavioral data is 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; 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, and the heating element works according to the new strategy.

[0011] Furthermore, 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 fault judgment thresholds and rules are set, and the data acquisition frequency of each sensor is set. The collected temperature data, voltage data, and current data are transmitted to the module's data processing unit. The fault diagnosis algorithm analyzes the collected and stored real-time data, compares the current data with the preset normal operating parameter range, and determines whether the hardware component has an abnormality. If it exceeds the preset range, it is determined that a fault may exist; when it is determined that a hardware component has a fault, the redundant data acquisition channel is immediately enabled, the pre-set backup control algorithm is called, and it is switched to a backup control algorithm based on fuzzy logic. At the same time, the system resources are reallocated; at the same time, according to the selected warning method, a fault warning information is sent to the user.

[0012] Furthermore, communication protocol support and sensor compatibility are built. The communication protocols include: Bluetooth, Wi-Fi, ZigBee, and the sensors include: air quality sensor, wind speed sensor. The data output format, communication interface type, and electrical characteristics of working voltage and current of these sensors are studied, and a sensor compatibility database is established; a data interface incompatibility conversion module is designed. When the connected third-party sensor uses a protocol that is incompatible with the module's existing communication protocol, the data interface incompatibility conversion module is used for conversion.

[0013] Furthermore, for sensors that are successfully connected to a new environment, data collection initialization settings are performed, the frequency of data collection is set, and the data collected by the sensors is obtained in real time through the 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 in multi-source data, a model is established based on the Bayesian network algorithm to set the sensor as a node to determine the connection weight, and the credibility is calculated based on the prior probability and conditional probability. Historical data is considered when analyzing the difference in sensor data. According to the results of the data analysis, a corresponding environmental adaptation strategy is formulated, and the formulated environmental adaptation strategy is applied to the electronic cigarette intelligent temperature control system. By communicating and working together with the state priority temperature control module and the multi-physical field coupling compensation module, the system's operating data is adjusted; at the same time, the system's operating effect under the new strategy is monitored in real time, and feedback data is collected, including: user experience feedback, actual working performance indicators of the equipment, and feedback is given back based on the feedback data.

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

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

[0016] This electronic cigarette intelligent temperature control system and method based on environmental perception uses multi-node sensors to collect temperature data, humidity data, and air pressure data, and constructs an environmental dynamic model after processing. It can prioritize high-risk data based on the rate of change of environmental data and accurately control the temperature. It can predict needs by analyzing user historical behavior to achieve a personalized experience, and can also establish a multi-data interaction model to optimize heating strategies, improve smoke quality, stabilize temperature and reduce energy consumption. Moreover, the system has intelligent self-healing capabilities and can maintain operation in the event of hardware failure. It also supports the connection of third-party sensors, integrates multi-source data, enhances adaptability, and comprehensively improves the user experience, performance and reliability of electronic cigarettes.

[0017] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a module diagram of an electronic cigarette intelligent temperature control system based on environmental perception in the present invention.

[0019] Figure 2 This is a diagram of an electronic cigarette intelligent temperature control method based on environmental perception in the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] See also Figure 1 , an embodiment of the present invention provides a technical solution: an electronic cigarette intelligent temperature control system and method 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 perception network module adopts a high-density deployment strategy to build a multi-node sensor network at key locations of the e-cigarette and its surrounding environment. It uses temperature sensors, humidity sensors, and air pressure sensors to collect temperature data, humidity data, and air pressure data at a sampling frequency. At the same time, it is 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, the distributed environmental perception network module operates by constructing a multi-node sensor network, employing a high-density deployment strategy and extensively distributed throughout key locations within the e-cigarette and its surroundings. Using temperature, humidity, and pressure sensors, these sensors accurately collect temperature, humidity, and pressure data at specific sampling frequencies. Furthermore, a data verification mechanism is implemented to filter and correct the collected basic environmental information to ensure accuracy. Finally, the processed data is transmitted to a basic data database based on 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 impact of the environment on e-cigarettes; on the other hand, high-density deployment realizes all-round environmental monitoring, with no blind spots, and the environmental information obtained is more comprehensive; furthermore, the data verification mechanism effectively improves data quality, making subsequent analyses and applications based on these data more reliable, laying a solid foundation for the stable operation of the entire system and effective decision-making.

[0025] S2. Build an environmental dynamics module: Use wireless networking to synchronize global data, combine collaborative filtering algorithms to eliminate local interference, and build an environmental dynamics model.

[0026] The environmental dynamics module uses wireless ad hoc network technology to automatically identify changes in network topology and achieve rapid synchronization of global data. Combined with the adaptive collaborative filtering algorithm, it dynamically adjusts the filtered data based on the degree of data fluctuation, and transmits the basic environmental information data collected by the sensor to the basic data database through the communication protocol to eliminate abnormal data; based on the acquired temperature data, humidity data, and air pressure data, combined with machine learning algorithms, it constructs an environmental dynamics model, and feeds 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 uses wireless ad hoc network technology to automatically identify changes in network topology and achieve rapid synchronization of global data, laying the foundation for effective data integration and analysis. Combined with the adaptive collaborative filtering algorithm, the filtered data is dynamically adjusted according to the degree of data fluctuation, which can eliminate abnormal data in the sensor collected data, effectively eliminate local interference, and improve data quality. In addition, based on the acquired temperature, humidity, air pressure and other data, an environmental dynamics model is constructed with the help of a machine learning algorithm, and the processed data with abnormalities eliminated is fed back to the model, which is beneficial to ensure the accuracy and stability of the data, provide reliable support for subsequent analysis, and more accurately simulate environmental dynamic changes, making the model more in line with the actual environment, enhancing the system's perception and response capabilities to environmental changes, and helping to improve 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, giving priority to responding 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 speed of change of each environmental data within the corresponding time interval, and conducts real-time evaluation of the data change rate of the collected data after eliminating abnormal data caused by local environmental interference. When it is detected that the environmental data change rate exceeds the preset threshold, that is, it is judged as high-risk data, the priority adjustment strategy is immediately activated to respond to and process high-risk data first, and at the same time, the fuzzy control algorithm is combined to regulate the temperature control equipment.

[0030] In this embodiment, the dynamic priority temperature control module has a built-in environmental data change rate analysis engine, which uses differential calculation and threshold judgment mechanism to evaluate in real time the speed of change of the collected environmental data that has eliminated local interference abnormal data within the corresponding time interval. When the environmental data change rate exceeds the preset threshold and is judged to be high-risk data, the priority adjustment strategy is quickly started to respond to and process the high-risk data first. At the same time, the fuzzy control algorithm is used to regulate the temperature control equipment, which is conducive to keenly capturing abnormal changes in environmental data, responding to high-risk situations in a timely manner, and effectively preventing potential risks. On the other hand, by dynamically adjusting the control priority and precisely regulating the temperature control equipment, the timeliness and accuracy of temperature control are improved, ensuring that the electronic cigarette operates under suitable environmental conditions, enhancing the system's ability to respond to environmental changes, and further improving the reliability and stability of the entire invention in electronic cigarette environmental monitoring and related applications.

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

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

[0033] 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 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 deep neural network model, and the validation set is used to verify the trained deep neural network model.

[0034] In this implementation scheme, the user behavior prediction module first establishes a user behavior feature database, extracts behavioral data such as the user's historical usage time, frequency, duration, and usage scenarios, and builds a deep neural network model consisting of an input layer, multiple hidden layers, and an output layer; then, the extracted data is divided into a training set and a validation set, the model is trained with the training set, and then verified with the validation set; it is beneficial to predict the user's short-term usage needs through deep learning by analyzing the user's historical behavior. The advantage is that it can adjust the device status in advance, realize the intelligent and personalized adaptation of the device, improve the user experience, make the device more in line with the user's usage habits, enhance the product's usability and user stickiness, and optimize resource allocation, improve equipment operation efficiency, and show unique advantages in e-cigarette related applications.

[0035] S5, Multi-physics coupling compensation module: establishes a model for the interaction of temperature data, humidity data, and air pressure data, quantifies the synergistic effect of multiple data, and dynamically optimizes the heating strategy;

[0036] The multi-physics field coupling compensation module uses the finite element analysis method to establish a three-dimensional mathematical model of the interaction between temperature data, humidity data, and air pressure data to determine the optimization target;

[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, and the weights and quantization ratios are calculated. Based on the importance of different optimization objectives, corresponding weights are assigned to each objective, and the weight coefficients of the influence of various physical quantities on the heating process under different combinations of temperature data, humidity data, and air pressure data are obtained.

[0038] The behavioral data based on 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 quantitative analysis module to analyze the environmental conditions and user usage scenarios of the current data; 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, and the heating element works according to the new strategy.

[0039] In this implementation, the multi-physics coupling compensation module, based on the finite element analysis method, constructs a three-dimensional mathematical model of the interactive influence of temperature, humidity, and air pressure data. It determines the optimization objectives of e-cigarette smoke quality, stable heating temperature, and reduced energy consumption. The weights and quantization ratios are calculated using a multi-objective optimization method, and weights are assigned based on the importance of the objectives to obtain the weight coefficients of the influence of each physical quantity on the heating process under different data combinations. The collected environmental data and user behavior data are input into the model and quantitative analysis module to analyze the environmental conditions and usage scenarios. The heating strategy is then generated or adjusted based on the fuzzy logic algorithm and output to the temperature control system. This is beneficial for accurately quantifying the synergistic effects of multiple data, dynamically optimizing the heating strategy, improving the quality of e-cigarette smoke, stabilizing the heating temperature, and reducing energy consumption. This allows e-cigarettes to operate efficiently and stably in different environments and usage scenarios, improving user experience, and enhancing the overall product performance and market competitiveness.

[0040] S6, Intelligent Self-Healing Temperature Control Module: Monitors hardware status and automatically enables redundant data or adjusts control data to ensure continuous system operation in the event of component failure, avoiding sudden downtime.

[0041] The intelligent self-healing temperature control module integrates multiple sensors, including temperature, voltage, and current sensors. The integrated sensors are initialized and calibrated, the fault diagnosis algorithm is started, the fault judgment thresholds and rules are set, and the data collection frequency of each sensor is set. The collected temperature, voltage, and current data are transmitted to the module's data processing unit. The fault diagnosis algorithm analyzes the collected and stored real-time data, compares the current data with the preset normal operating parameter range, and determines whether there is any abnormality in the hardware component. If it exceeds the preset range, it is determined that a fault may exist.

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

[0043] In this implementation scheme, the intelligent self-healing temperature control module plays a key role in ensuring the stable operation of the system. The intelligent self-healing temperature control module integrates multiple sensors such as temperature, voltage, and current. After initialization and calibration, it starts the fault diagnosis algorithm program, sets the fault judgment threshold and rules as well as the data acquisition frequency, and transmits the collected data to the processing unit for analysis. The current data is compared with the normal operating parameter range to determine whether the hardware is abnormal. When it is determined that the hardware component has failed, the redundant data acquisition channel is quickly enabled, the backup control algorithm is called, and the control method based on fuzzy logic is switched. At the same time, the system resources are reallocated and a fault warning message is sent to the user. This is beneficial for real-time monitoring of the hardware status, timely detection of faults and automatic response measures to ensure that the system can continue to operate when a component fails, avoid sudden shutdowns, improve the reliability, stability and fault tolerance of the system, reduce the impact of equipment failures on user use, and ensure the normal operation of the electronic cigarette temperature control system and user experience.

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

[0045] Build communication protocol support and sensor compatibility. Communication protocols include: Bluetooth, Wi-Fi, ZigBee. Sensors include: air quality sensor, wind speed sensor. Study the data output format, communication interface type, and electrical characteristics of working voltage and current of these sensors, and establish a sensor compatibility database; design a data interface incompatible conversion module. When the connected third-party sensor uses a protocol incompatible with the module's existing communication protocol, use the data interface incompatible conversion module for conversion.

[0046] For sensors that are successfully connected to a new environment, data collection initialization settings are performed, the frequency of data collection is set, and the data collected by the sensors is obtained in real time through the communication protocol according to the set parameters; a unified decision engine is constructed, including: data preprocessing module, feature extraction module, and decision analysis module. When conflicts occur in multi-source data, a model is established based on the Bayesian network algorithm to set the sensor as a node to determine the connection weight, calculate the credibility based on the prior probability and conditional probability, consider historical data when analyzing the difference in sensor data, and formulate corresponding environmental adaptation strategies based on the results of the data analysis. The formulated environmental adaptation strategies are applied to the electronic cigarette intelligent temperature control system, and the system's operating data is adjusted by communicating and working in collaboration with the state priority temperature control module and the multi-physical field coupling compensation module; at the same time, 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 working performance indicators of the equipment. Based on the feedback data, the feedback is fed back to the Bayesian network algorithm.

[0047] In this implementation plan, the extensible environmental interface module builds a communication protocol support and sensor compatibility mechanism, covering communication protocols such as Bluetooth, Wi-Fi, and ZigBee, studies the characteristics of various sensors (such as air quality and wind speed sensors) and establishes a compatibility database, designs a conversion module to handle incompatible protocols, ensures the smooth access of third-party sensors, and performs collection settings for successfully connected sensors and obtains data in real time; at the same time, a unified decision-making engine is built to integrate multi-source data. When data conflicts, an environmental adaptation strategy is formulated based on Bayesian network algorithm analysis, and it collaborates with other modules and optimizes based on feedback; it is beneficial to support the access of third-party sensors, expand the dimension of environmental perception, enhance system adaptability, integrate multi-source data to enable the system to better cope with complex environments, optimize operating results, improve user experience and equipment performance, and promote the intelligent and diversified development of electronic cigarette smart temperature control systems.

[0048] An electronic cigarette intelligent temperature control method based on environmental perception, specifically comprising:

[0049] Build a multi-node sensor network module to collect temperature data, humidity data, and air pressure data; achieve global data synchronization through wireless networking, combine collaborative filtering algorithms to eliminate local interference, and build an environmental dynamic model; dynamically adjust control priorities based on the rate of change of environmental data, and give priority to responding to high-risk data; analyze user historical behavior and deep learning modules to predict short-term usage needs and adjust equipment status in advance; establish an interactive impact model for temperature data, humidity data, and air pressure data, quantify the synergistic effect of multiple data, and dynamically optimize heating strategies; monitor hardware status, automatically enable redundant data or adjust control data to ensure that the system continues to run when components fail, and avoid sudden downtime; support protocol access to third-party sensors, expand the dimension of environmental perception, and integrate multi-source data through a unified decision engine to enhance system adaptability.

[0050] In this implementation scheme, an intelligent temperature control method for electronic cigarettes based on environmental perception collects key environmental data by constructing a multi-node sensor network to provide basic information for temperature control; uses wireless networking and collaborative filtering to build an environmental dynamic model to ensure data accuracy and reflect environmental changes; dynamically adjusts priorities based on the rate of change of environmental data, prioritizes high-risk data, and improves the ability to cope with risks; predicts usage needs by analyzing user historical behavior, optimizes device status in advance, and enhances user experience; establishes a multi-physical field data interaction model, quantifies synergistic effects to optimize heating strategies and improve heating efficiency; monitors hardware status and automatically handles faults to ensure stable system operation; supports third-party sensor access and integration of multi-source data, expands perception dimensions and system adaptability; is conducive to comprehensively improving the intelligence level of electronic cigarette temperature control systems, accurately perceives the environment and user needs, effectively responds to various conditions, ensures stable and efficient operation of equipment, and significantly enhances product reliability, comfort and market competitiveness.

[0051] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should fall within the scope of protection of the present invention.

[0052] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0054] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0055] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0056] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0057] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An electronic cigarette intelligent temperature control system based on environmental perception, characterized in that: The following steps are involved: S1, Distributed environment perception network module: Build a multi-node sensor network module to collect temperature data, humidity data, and air pressure data; S2. Build an environmental dynamics module: Use wireless networking to synchronize global data, combine collaborative filtering algorithms to eliminate local interference, and build an environmental dynamics model. S3, dynamic priority temperature control module: dynamically adjusts control priority based on the rate of change of environmental data, giving priority to responding to high-risk data; S4, User Behavior Prediction Module: Analyzes historical user behavior and deep learning modules to predict short-term usage needs and adjust device status in advance; S5, Multi-physics coupling compensation module: establishes a model for the interaction of temperature data, humidity data, and air pressure data, quantifies the synergistic effect of multiple data, and dynamically optimizes the heating strategy; S6, Intelligent Self-Healing Temperature Control Module: Monitors hardware status and automatically enables redundant data or adjusts control data to ensure continuous system operation in the event of component failure, avoiding sudden downtime. S7, Scalable Environmental Interface Module: Supports protocol access to third-party sensors, expands environmental perception dimensions, and integrates multi-source data through a unified decision engine to enhance system adaptability.

2. The electronic cigarette intelligent temperature control system based on environmental perception according to claim 1, characterized in that: S1, distributed environment perception network module, specifically including: The distributed environmental perception network module adopts a high-density deployment strategy to build a multi-node sensor network at key locations of the e-cigarette and its surrounding environment. It uses temperature sensors, humidity sensors, and air pressure sensors to collect temperature data, humidity data, and air pressure data at a sampling frequency. At the same time, it is 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.

3. The electronic cigarette intelligent temperature control system based on environmental perception according to claim 1, characterized in that: S2. Build the dynamic environment module, including: The environmental dynamics module uses wireless ad hoc network technology to automatically identify changes in network topology and achieve rapid synchronization of global data. Combined with the adaptive collaborative filtering algorithm, it dynamically adjusts the filtered data based on the degree of data fluctuation, and transmits the basic environmental information data collected by the sensor to the basic data database through the communication protocol to eliminate abnormal data; based on the acquired temperature data, humidity data, and air pressure data, combined with machine learning algorithms, it constructs an environmental dynamics model, and feeds back the data that has eliminated abnormal data caused by local environmental interference to the environmental dynamics model.

4. The electronic cigarette intelligent temperature control system based on environmental perception according to claim 1, characterized in that: S3, dynamic priority temperature control module, specifically including: 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 speed of change of each environmental data within the corresponding time interval, and conducts real-time evaluation of the data change rate of the collected data after eliminating abnormal data caused by local environmental interference. When it is detected that the environmental data change rate exceeds the preset threshold, that is, it is judged as high-risk data, the priority adjustment strategy is immediately activated to respond to and process high-risk data first, and at the same time, the fuzzy control algorithm is combined to regulate the temperature control equipment.

5. The electronic cigarette intelligent temperature control system based on environmental perception according to claim 1, characterized in that: S4, user behavior prediction module, specifically including: The user behavior prediction module establishes a user behavior feature database, extracts user 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 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 deep neural network model, and the validation set is used to verify the trained deep neural network model.

6. The electronic cigarette intelligent temperature control system based on environmental perception according to claim 1, characterized in that: S5, multi-physics field coupling compensation module, specifically including: The multi-physics field coupling compensation module uses the finite element analysis method to establish a three-dimensional mathematical model of the interaction between temperature data, humidity data, and air pressure data to determine the optimization target; 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, and the weights and quantization ratios are calculated. Based on the importance of different optimization objectives, corresponding weights are assigned to each objective, and the weight coefficients of the influence of various physical quantities on the heating process under different combinations of temperature data, humidity data, and air pressure data are obtained. The behavioral data based on 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 quantitative analysis module to analyze the environmental conditions and user usage scenarios of the current data; 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, and the heating element works according to the new strategy.

7. The electronic cigarette intelligent temperature control system based on environmental perception according to claim 1, characterized in that: S6, intelligent self-healing temperature control module, specifically including: The intelligent self-healing temperature control module integrates multiple sensors, including temperature, voltage, and current sensors. The integrated sensors are initialized and calibrated, the fault diagnosis algorithm is started, the fault judgment thresholds and rules are set, and the data collection frequency of each sensor is set. The collected temperature, voltage, and current data are transmitted to the module's data processing unit. The fault diagnosis algorithm analyzes the collected and stored real-time data, compares the current data with the preset normal operating parameter range, and determines whether there is any abnormality in the hardware component. If it exceeds the preset range, it is determined that a fault may exist. When a hardware component failure is determined, the redundant data acquisition channel is immediately enabled, the pre-set backup control algorithm is called, and the backup control algorithm based on fuzzy logic is switched. At the same time, the system resources are reallocated; at the same time, fault warning information is sent to the user according to the selected warning method.

8. The electronic cigarette intelligent temperature control system based on environmental perception according to claim 1, characterized in that: S7, scalable environment interface module, specifically including: Build communication protocol support and sensor compatibility. Communication protocols include: Bluetooth, Wi-Fi, ZigBee. Sensors include: air quality sensor, wind speed sensor. Study the data output format, communication interface type, and electrical characteristics of working voltage and current of these sensors, and establish a sensor compatibility database; design a data interface incompatible conversion module. When the connected third-party sensor uses a protocol incompatible with the module's existing communication protocol, use the data interface incompatible conversion module for conversion.

9. The electronic cigarette intelligent temperature control system based on environmental perception according to claim 8, characterized in that: To connect sensors to a new environment, the following steps are included: For sensors that are successfully connected to a new environment, data collection initialization settings are performed, the frequency of data collection is set, and the data collected by the sensors is obtained in real time through the communication protocol according to the set parameters; a unified decision engine is constructed, including: data preprocessing module, feature extraction module, and decision analysis module. When conflicts occur in multi-source data, a model is established based on the Bayesian network algorithm to set the sensor as a node to determine the connection weight, calculate the credibility based on the prior probability and conditional probability, consider historical data when analyzing the difference in sensor data, and formulate corresponding environmental adaptation strategies based on the results of the data analysis. The formulated environmental adaptation strategies are applied to the electronic cigarette intelligent temperature control system, and the system's operating data is adjusted by communicating and working in collaboration with the state priority temperature control module and the multi-physical field coupling compensation module; at the same time, 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 working performance indicators of the equipment. Based on the feedback data, the feedback is fed back to the Bayesian network algorithm.

10. An electronic cigarette intelligent temperature control method based on environmental perception according to any one of claims 1 to 9, characterized in that: Specifically include: Build a multi-node sensor network module to collect temperature data, humidity data, and air pressure data; Global data synchronization is achieved through wireless networking, and collaborative filtering algorithms are used to eliminate local interference and build a dynamic model of the environment. Dynamically adjust control priorities based on the rate of change of environmental data, giving priority to responding to high-risk data; Analyze user historical behavior and deep learning modules to predict short-term usage needs and adjust device status in advance; Establish a model for the interaction of temperature, humidity, and air pressure data, quantify the synergistic effect of multiple data, and dynamically optimize heating strategies; Monitor hardware status and automatically enable redundant data or adjust control data to ensure continuous system operation in the event of component failure and avoid sudden downtime; It supports protocol access to third-party sensors, expands the dimension of environmental perception, and integrates multi-source data through a unified decision-making engine to enhance system adaptability.

Images