Intelligent smoke monitoring system and method

Abstract

The invention relates to the field of air purification, and particularly discloses an intelligent smoke monitoring system and method, and the system comprises a detection module which is used for detecting the smoke concentration and the equipment state in real time; the data processing module is used for receiving the data of the detection module, analyzing and processing the data according to a preset algorithm, and analyzing and predicting possible blood pressure and blood fat chronic disease risks of the user through lampblack components; the negative pressure suction module is used for starting and generating negative pressure when the detection module detects that the smoke concentration and the equipment state reach preset conditions; by integrating the detection module, the data processing module, the negative pressure suction module and the control module, the system can monitor the smoke concentration and the equipment state in the environment in real time and conduct intelligent analysis and processing according to a preset algorithm, and when the smoke concentration or the equipment state reaches a preset condition, the system can automatically start the negative pressure suction module, and the smoke concentration and the equipment state are controlled. And negative pressure is generated to suck and collect smoke in the air, so that the air quality is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the field of air purification, and more specifically, it is a smoke intelligent monitoring system and method. Background Art

[0002] In the household field, the power adjustment of traditional range hoods often relies on the actual observation and manual operation of users. This adjustment method is not only lagging, but also easily leads to the operation volume of the smoke extraction system of the range hood being greater than the actual demand, resulting in an increase in energy consumption and waste of energy.

[0003] In the industrial field, dust treatment also faces similar problems. Traditional dust treatment methods often rely on manual operation and empirical judgment, resulting in low treatment efficiency and high energy consumption.

[0004] Moreover, both of the above two smoke treatment methods need to rely on manual adjustment, which is prone to cause waste of energy. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a smoke intelligent monitoring system to solve the problem that most of the existing smoke treatment methods need to rely on manual adjustment, which is prone to cause waste of energy.

[0006] A smoke intelligent monitoring system includes:

[0007] A detection module for real-time detecting the smoke concentration and the device status;

[0008] A data processing module for receiving the data from the detection module, analyzing and processing it according to a preset algorithm, and predicting the risk of chronic diseases such as blood pressure and blood lipid of users through the analysis of the components of cooking fumes;

[0009] A negative pressure suction module for starting and generating negative pressure when the detection module detects that the smoke concentration and the device status reach preset conditions, so as to attract and collect the smoke in the air;

[0010] A control module for automatically adjusting the operation parameters of the negative pressure suction module according to the analysis result of the data processing module;

[0011] A medical system interface module for docking the chronic disease risk information predicted by the data processing module with an external medical system to provide preliminary guidance for pathological analysis;

[0012] A product recommendation module for pushing corresponding health products and fitness product intensity suggestions according to the eating habits of users and the predicted health risks.

[0013] Preferably, the detection module includes:

[0014] A smoke concentration detection sensor for detecting the smoke concentration in the air;

[0015] A device status detection sensor for detecting the power, current and voltage of the device;

[0016] An oil fume component detection sensor for analyzing the chemical components and trace elements in the oil fume.

[0017] Preferably, the negative pressure suction module includes a suction device and an air flow control device to ensure that the smoke can be quickly and accurately sucked into the system for processing.

[0018] Preferably, it further includes a learning module for analyzing and learning the long-term monitoring data provided by the data processing module using machine learning algorithms; the learning module predicts the future smoke concentration change trend by identifying the patterns of smoke generation and diffusion and optimizes the control strategy; the learning module automatically adjusts the operating parameters according to the user's usage habits and feedback to improve the intelligence level and user experience of the system; the learning module can also analyze the user's eating habits based on the oil fume components to provide data support for health management, and further provide more detailed life parameters for fitness and medical care.

[0019] Preferably, it further includes a network communication module for connecting to an external network and communicating with other intelligent devices and health management platforms; the network communication module supports multiple communication protocols and interfaces to ensure that the system can upload monitoring data in real time, receive remote instructions, perform remote monitoring and configuration; the network communication module supports cloud services and big data analysis to provide a more comprehensive smoke management solution for users.

[0020] Preferably, the data processing module includes a database and algorithms for storing historical data and performing intelligent analysis and prediction, and analyzing the user's eating habits and approximate dining structure through the oil fume components.

[0021] Preferably, the control module can automatically adjust the rotation speed, air volume and suction of the negative pressure suction module according to the analysis results to optimize the smoke collection efficiency.

[0022] Preferably, it further includes an AI computing power intervention module for providing strong computing power support for the learning module, and providing data support and direction guidance for the continuous optimization and performance improvement of the system by processing and analyzing a large amount of data.

[0023] Preferably, the AI computing power intervention module continuously trains and optimizes the machine learning model based on historical data and real-time data to improve the accuracy of smoke concentration prediction and optimize the control strategy of the negative pressure suction module, so as to achieve the continuous improvement and performance improvement of the system.

[0024] A smoke intelligent monitoring method includes the following steps:

[0025] a) Detect the smoke concentration, equipment status, and cooking fume components in real time through the detection module;

[0026] b) Transmit the detected data to the data processing module for analysis and processing of the smoke concentration and equipment status, as well as analysis of the cooking fume components, and predict the possible chronic disease risks of the user;

[0027] c) When the detection module detects that the smoke concentration and equipment status reach the preset conditions, trigger the start of the negative pressure suction module through the control module;

[0028] d) The negative pressure suction module generates negative pressure to attract and collect the smoke in the air;

[0029] e) The control module automatically adjusts the operating parameters of the negative pressure suction module according to the analysis results of the data processing module;

[0030] f) Connect the chronic disease risk information with the external medical system through the medical system interface module to provide preliminary guidance for pathological analysis;

[0031] g) Through the product recommendation module, push corresponding health product and fitness product intensity suggestions according to the user's eating habits and predicted health risks.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] By integrating the four major modules of detection, data processing, negative pressure suction, and control, the system can monitor the smoke concentration and equipment status in the environment in real time, and perform intelligent analysis and processing according to the preset algorithm. When the smoke concentration or equipment status reaches the preset conditions, the system can automatically start the negative pressure suction module to generate negative pressure to attract and collect the smoke in the air, thereby effectively improving the air quality;

[0034] In the industrial field, by integrating the smoke intelligent monitoring system of the present invention, metallurgical enterprises not only achieve the network data automatic matching of the dust removal air volume, significantly improve the dust removal efficiency, reduce energy consumption, effectively reduce the occurrence of environmental protection accidents, but also further improve the intelligent level of production management; the system can provide accurate monitoring of the change in the dust volume according to the actual needs of different dust removal points in industrial production; this function provides valuable data comparison support for subsequent intelligent production, enabling enterprises to more accurately master the dust emission situation in the production process, so as to carry out more scientific and reasonable production scheduling and optimization;

[0035] In addition, the system can also perform linkage adjustment according to the production parameters to realize the intelligent control and management of the production process. By collecting and analyzing the production data in real time, the system can automatically adjust the operating state of the dust removal system to ensure the perfect match between the dust removal effect and the production demand, and further improve the production efficiency and resource utilization rate of the enterprise;

[0036] In the household domain, this system can be applied to the intelligent upgrade of kitchen range hoods. By real-time monitoring parameters such as the power of induction cookers and the temperature of gas stoves, it can intelligently adjust the operating parameters of the range hood to achieve efficient collection and treatment of cooking fumes. At the same time, this system can also automatically adjust and optimize according to users' usage habits and feedback, improving the user experience and quality of life.

[0037] By integrating the comparison of data on the power usage records of induction cookers, the gas consumption during cooking, and the real-time flue gas emissions, this invention can accurately analyze users' eating habits and distinguish whether they prefer cooking methods such as steaming or frying. This function not only enriches the landscape of AI smart homes but also provides data support for personalized diet and health management.

[0038] This invention adopts a port opening mode, enabling both household range hoods and industrial dust collectors to easily access the subsequent overall intelligent system. As a part of the intelligent upgrade, this project not only focuses on flue gas analysis and intelligent control but also is responsible for data collection, laying a solid foundation for subsequent overall data integration and intelligent expansion, demonstrating high compatibility and expandability.

[0039] By deeply analyzing the flue gas components, we have successfully constructed a database containing rich information. This database not only details users' cooking habits and dietary structures but also, as a key part of the overall data chain, is closely connected to purchase and consumption records, takeout orders, and comprehensive medical data. This data package provides complete and fundamental data support for subsequent medical and health research, enabling us to more deeply understand the complex relationship between eating habits and health conditions. At the same time, it also greatly enriches the dimension of medical data, laying a solid foundation for doctors to provide more accurate and personalized medical advice and treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the system of this invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] Next, the technical solutions in the embodiments of this invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this invention. Obviously, the described embodiments are only a part of the embodiments of this invention, rather than all of them. Based on the embodiments of this invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this invention.

[0042] As Figure 1 shown:

[0043] Embodiment 1: This invention provides a smart smoke monitoring system, including:

[0044] Detection module, used to detect smoke concentration and device status in real time;

[0045] The detection module includes a laser scattering sensor (model: PMS7003) to detect the concentration of dust in the factory building and kitchen smoke in real time (0 - 50mg / m 3 ), PM2.5 (0 - 500μg / m 3 );

[0046] The photoionization detector detects total non-methane hydrocarbons in real time (0 - 100ppm);

[0047] The detection module can connect to gas stoves, induction cookers or factory equipment via Bluetooth / Wi-Fi to obtain gas flow rate (0 - 2m 3 / h), power data (0 - 3.5kW) and factory equipment working parameters in real time. The gas flow rate can be measured by adding a flow meter;

[0048] Data processing module, used to receive the data from the detection module and analyze and process it according to a preset algorithm;

[0049] When the kitchen is working, the preset algorithm is as follows:

[0050] When the smoke concentration ≥ 5mg / m 3 , calculate the initial Hz value according to the formula ;

[0051] Using the oil fume concentration detection probe to detect the oil fume concentration generated by high temperature in cooking oil fume as 5mg / m 3 as the start benchmark, bind the Hz value of the range hood motor to it. The starting motor Hz value starts from 15. When the oil fume concentration rises by 2mg / m 3 , the Hz value of the range hood motor synchronously increases by 5, thereby improving the smoke exhaust efficiency;

[0052] Negative pressure suction module, used to start and generate negative pressure when the detection module detects that the smoke concentration and device status reach the preset conditions, so as to attract and collect the smoke in the air;

[0053] Among them, when the kitchen is working, the preset conditions include that the detection module detects the oil fume concentration of 5mg / m 3 , the induction cooker and gas stove reach the operating state of the predetermined gas flow rate or power, and the photoionization detector detects that the total non-methane hydrocarbon content exceeds the preset value;

[0054] When the factory is working, the preset conditions include that the equipment operating parameters reach the preset value and the detected dust concentration reaches the preset value;

[0055] Control module, used to automatically adjust the operating parameters of the negative pressure suction module according to the analysis result of the data processing module.

[0056] As can be seen from the above, this system integrates four major modules: detection, data processing, negative pressure suction, and control, achieving intelligent monitoring and adjustment of smoke concentration;

[0057] The detection module uses high-precision devices such as laser scattering sensors and photoionization detectors to continuously monitor the smoke concentration of soot, smoke, PM2.5, and non-methane total hydrocarbons in the environment, and at the same time obtain the equipment operation parameters;

[0058] After receiving this data, the data processing module analyzes it through a preset algorithm to provide a basis for subsequent adjustment;

[0059] When the detected smoke concentration or equipment status reaches the preset conditions, the negative pressure suction module is activated to generate negative pressure suction and collect the smoke in the air;

[0060] The control module then automatically adjusts the operation parameters of the negative pressure suction module according to the analysis results of the data processing module to achieve efficient smoke treatment;

[0061] This system can play an important role in various environments such as kitchens and factories, effectively improving air quality and protecting people's health. Its intelligent and automated features also make the operation more convenient and the maintenance cost lower.

[0062] Embodiment 2: The present invention includes a common version of the intelligent smoke monitoring system and an intelligent version of the intelligent smoke monitoring system;

[0063] The common version of the intelligent smoke monitoring system includes the following components:

[0064] Detection module:

[0065] Smoke concentration detection sensor: Responsible for continuously monitoring the smoke concentration in the air to ensure timely response to smoke conditions.

[0066] Equipment status detection sensor: Monitors the power, current, and voltage of the equipment to ensure stable operation of the equipment.

[0067] Oil fume component detection sensor (basic version): Although it has the function of detecting oil fume components, its analysis ability is relatively basic compared to the intelligent version, mainly used for simple oil fume component identification.

[0068] Negative pressure suction module:

[0069] It includes a gas extraction device and an air flow control device to ensure that the smoke can be quickly and accurately sucked into the system for processing. This module is reasonably designed and can efficiently remove the smoke in the kitchen.

[0070] Data processing module:

[0071] It includes a database and basic algorithms for storing historical data and performing simple data analysis. Although its processing power is limited, it is sufficient to meet the basic smoke monitoring requirements.

[0072] Control module:

[0073] According to the analysis results of the data processing module, it automatically adjusts the rotation speed, air volume, and suction of the negative pressure suction module to optimize the smoke collection efficiency. The control strategy is relatively fixed but can meet daily use.

[0074] Network communication module:

[0075] It supports basic communication protocols and interfaces, can upload monitoring data in real time, receive remote instructions, and perform remote monitoring and configuration. Although its functions are relatively simple, it provides users with basic network connection capabilities.

[0076] The intelligent version adds the following upgraded functions on the basis of the ordinary version:

[0077] Upgrade of the detection module:

[0078] Oil fume component detection sensor (intelligent version): The upgraded sensor has more powerful analysis capabilities and can deeply analyze the chemical components and trace elements in oil fume, providing data support for users' eating habits and health management.

[0079] Introduction of the learning module:

[0080] It uses machine learning algorithms to analyze and learn long-term monitoring data, predict the future change trend of smoke concentration, and optimize the control strategy.

[0081] It automatically adjusts the operating parameters according to the user's usage habits and feedback, improving the intelligent level and user experience of the system.

[0082] By analyzing the user's historical cooking data, it establishes an association model between time periods, cooking types, and smoke volume, and automatically optimizes the adjustment parameters.

[0083] Upgrade of the network communication module:

[0084] It supports more types of communication protocols and interfaces, ensuring that the system can communicate with other intelligent devices and health management platforms more flexibly.

[0085] Relying on cloud services and big data analysis, it provides users with a more comprehensive smoke management solution and health advice.

[0086] AI computing power intervention module:

[0087] It provides powerful computing power support for the learning module. By processing and analyzing a large amount of data, it provides data support and direction guidance for the continuous optimization and performance improvement of the system.

[0088] Continuously train and optimize the machine learning model based on historical data and real-time data to improve the accuracy of smoke concentration prediction and optimize the control strategy of the negative pressure suction module.

[0089] The intelligent version (fully equipped) has all the upgraded functions, providing users with a comprehensive intelligent experience.

[0090] The economy version (low configuration) is simplified based on the basic version and does not have learning ability, but it can achieve basic functions through data sharing with the intelligent version to meet the needs of different consumer groups.

[0091] Embodiment 3:

[0092] The present invention can also be applied to the industrial field:

[0093] The following is the application method of the basic version:

[0094] System composition:

[0095] Detection module:

[0096] Dust removal port air volume detection sensor: Used to monitor the air volume of the dust removal port in real time.

[0097] Ambient dust concentration detection sensor: Used to detect the dust concentration in the production environment.

[0098] Equipment status monitoring sensor: Monitors the operating status of the dust collector, such as motor current, voltage, etc.

[0099] Manual control module:

[0100] Air volume regulating valve: Used to manually adjust the air volume entering the dust collector.

[0101] Control panel: Displays the working status and detection data of the dust collector, and provides an interface for manually adjusting the air volume.

[0102] Data processing module (basic version):

[0103] Data acquisition unit: Collects data from the detection module.

[0104] Data analysis software: Performs simple analysis on the data, such as calculating the average air volume, the number of times of dust concentration exceeding the standard, etc.

[0105] Working principle:

[0106] The detection module collects the dust removal port air volume, ambient dust concentration and equipment status data in real time.

[0107] The operator views the data through the control panel and manually adjusts the air volume regulating valve according to experience to maintain the dust removal effect and avoid excessive air volume.

[0108] The data processing module conducts a simple analysis of the data to provide decision-making support for the operators.

[0109] Advantages:

[0110] It has a simple structure and is easy to maintain and operate.

[0111] It has a relatively low cost and is suitable for metallurgical enterprises with limited budgets;

[0112] The following is the application method of the intelligent version:

[0113] System composition:

[0114] Intelligent detection module:

[0115] Dust extraction port air volume detection sensor: Real-time monitoring of the air volume at the dust extraction port.

[0116] Ambient dust concentration detection sensor: Detects the dust concentration and supports multi-point detection to obtain more comprehensive data.

[0117] Equipment status monitoring sensor: Monitors the operating status of the dust collector and its related equipment.

[0118] Humidity and weather data interface: Obtains real-time weather and humidity data through the network to optimize the dust removal strategy.

[0119] Intelligent control module:

[0120] Electric air volume regulating valve: Automatically adjusts the air volume according to the control signal.

[0121] Intelligent controller: Receives the detection data, analyzes it through a preset algorithm, and issues a control signal.

[0122] Data processing and analysis module:

[0123] Data acquisition and storage unit: Collects and stores the detection data.

[0124] Advanced data analysis software: Analyzes the data using machine learning algorithms, predicts the changing trend of the dust concentration, and optimizes the air volume regulation strategy.

[0125] Networked data management system:

[0126] Data sharing interface: Shares data with other production systems or cloud platforms to achieve remote monitoring and intelligent early warning.

[0127] Intelligent cloud system: Analyzes abnormal data and provides predictive equipment failure alarms and maintenance suggestions.

[0128] Example 4: The detection module includes:

[0129] A smoke concentration detection sensor is used to detect the smoke concentration in the air;

[0130] A device status detection sensor is used to detect the power, current, and voltage of the device;

[0131] An oil fume component detection sensor is used to analyze the chemical components and trace elements in the oil fume.

[0132] Specifically, the negative pressure suction module includes an air extraction device and an air flow control device to ensure that the smoke can be quickly and accurately sucked into the system for processing.

[0133] Specifically, it further includes a learning module, which is used to analyze and learn the long-term monitoring data provided by the data processing module using machine learning algorithms; the learning module predicts the future smoke concentration change trend by identifying the patterns of smoke generation and diffusion, and optimizes the control strategy; the learning module automatically adjusts the operating parameters according to the user's usage habits and feedback to improve the intelligent level and user experience of the system; the learning module can also analyze the user's eating habits based on the oil fume components to provide data support for health management, and further provide more detailed life parameters for sports fitness and medical care;

[0134] Among them, the learning module can establish an association model between time period, cooking type, and smoke volume by analyzing the user's historical cooking data, and automatically optimize the adjustment parameters;

[0135] Intelligent analysis of the flue gas volume in the same time period of the year according to the main living habits of the range hood for advanced data management;

[0136] Specifically as follows:

[0137] 11 am is the main meal time. According to eating habits, for boiling or frying, an intelligent adjustment amount of +5% Hz is given according to different flue gas volumes;

[0138] 10 pm is analyzed as the late-night snack time according to habits. If mainly cooking noodles, an intelligent auxiliary adjustment of -5% Hz can be made;

[0139] For customers whose diet mainly consists of steaming and boiling according to the diet list and structure, the method of reverse deduction based on the oxygen content in the air can be adopted. The theoretical oxygen content in the atmosphere is 21%. For every 0.5% decrease, the range hood Hz is increased by 5%. Synchronously, the oxygen content can be calibrated with the theoretical value according to the user's altitude during installation;

[0140] After the range hood is installed, data statistics and comparison are carried out on the eating habits, flue gas generation time, and gas flow (or induction cooker power). According to the comparison data for three months, the range hood Hz is adjusted by about 5% to ensure the experience during operation.

[0141] Specifically as follows:

[0142] During regular meal times, based on eating habits, it runs at a normal Hz and is increased by 5% to ensure oil fume absorption. After 10 pm, through data analysis, the habit of having a midnight snack like cooking instant noodles doesn't require additional activation of the auxiliary Hz. Synchronously, according to data statistics at 3 pm, the habit of having afternoon tea like making desserts can also be analyzed.

[0143] Specifically, it also includes a network communication module for connecting to an external network and communicating with other intelligent devices and health management platforms. The network communication module supports multiple communication protocols and interfaces to ensure that the system can upload monitoring data in real time, receive remote instructions, conduct remote monitoring and configuration. The network communication module supports cloud services and big data analysis to provide users with a more comprehensive smoke management solution.

[0144] Specifically, the data processing module includes a database and algorithms for storing historical data and conducting intelligent analysis and prediction, and analyzing users' eating habits and approximate dining structures through oil fume components.

[0145] Specifically, the control module can automatically adjust the rotation speed, air volume, and suction of the negative pressure suction module according to the analysis results to optimize the smoke collection efficiency.

[0146] Specifically, it also includes an AI computing power intervention module for providing strong computing power support for the learning module. By processing and analyzing a large amount of data, it provides data support and direction guidance for the continuous optimization and performance improvement of the system.

[0147] Specifically, the AI computing power intervention module continuously trains and optimizes the machine learning model based on historical data and real-time data to improve the accuracy of smoke concentration prediction and optimize the control strategy of the negative pressure suction module, thereby achieving the continuous improvement and performance enhancement of the system.

[0148] As can be seen from the above, in this embodiment, the negative pressure suction module ensures the rapid and accurate inhalation of smoke through the air extraction device and the air flow control device;

[0149] The introduction of the learning module uses machine learning algorithms to analyze long-term monitoring data, predict the trend of smoke concentration, and optimize the control strategy. At the same time, it automatically adjusts parameters according to user habits and feedback, improving the intelligence of the system and the user experience;

[0150] The network communication module supports multiple communication protocols to realize functions such as data upload and remote instruction reception, and relies on cloud services and big data analysis to provide a more comprehensive smoke management solution;

[0151] The AI computing power intervention module provides strong computing power support for the learning module, continuously optimizing the machine learning model and improving the prediction accuracy and system performance.

[0152] Example 5: An intelligent smoke monitoring method, comprising the following steps:

[0153] a) Detect the smoke concentration, equipment status, and oil fume components in real time through a detection module;

[0154] b) Transmit the detection data to a data processing module for analysis and processing of the smoke concentration and equipment status, as well as analysis of the oil fume components to predict the possible chronic disease risks of the user;

[0155] c) When the detection module detects that the smoke concentration and equipment status reach preset conditions, trigger the start of a negative pressure suction module through a control module;

[0156] d) The negative pressure suction module generates negative pressure to attract and collect the smoke in the air;

[0157] e) The control module automatically adjusts the operating parameters of the negative pressure suction module according to the analysis results of the data processing module;

[0158] f) Connect the chronic disease risk information with an external medical system through a medical system interface module to provide preliminary guidance for pathological analysis;

[0159] g) Push corresponding health product and fitness product intensity suggestions according to the user's eating habits and predicted health risks through a product recommendation module.

[0160] The standard parts used in the present invention can all be purchased from the market. The special-shaped parts can be customized according to the description in the specification and the drawings. The specific connection methods of each part all adopt conventional means such as bolts, rivets, and welding that are mature in the prior art. The machines, parts, and equipment all adopt conventional models in the prior art. In addition, the circuit connection adopts a conventional connection method in the prior art, which will not be elaborated here. The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0161] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The meaning of "plurality" is two or more unless otherwise specifically defined.

[0162] In the present invention, unless otherwise clearly defined or limited, terms such as "install", "connect", "link", "fix", etc. shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0163] In the present invention, unless otherwise clearly defined or limited, the first feature being "on" or "under" the second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on top of" the second feature may be that the first feature is directly above or obliquely above the second feature, or merely indicates that the first feature has a higher horizontal height than the second feature. The first feature being "under", "beneath" and "underneath" the second feature may be that the first feature is directly below or obliquely below the second feature, or merely indicates that the first feature has a lower horizontal height than the second feature.

[0164] In the description of this specification, the descriptions with reference to terms such as "an embodiment", "some embodiments", "example", "specific example" or "some examples", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not have to be directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0165] In the attached drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved, and other structures can refer to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0166] Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A smoke intelligent monitoring system, characterized in that: include: Detection module, used to detect smoke concentration and equipment status in real time; The data processing module is used to receive the data from the detection module, analyze and process it according to the preset algorithm, and predict the risk of blood pressure and blood lipid chronic diseases that the user may have through the analysis of the fume components; The negative pressure suction module is used to start and generate negative pressure to attract and collect smoke in the air when the detection module detects that the smoke concentration and the device status reach the preset conditions; A control module, used for automatically adjusting the operating parameters of the negative pressure suction module according to the analysis results of the data processing module; The medical system interface module is used to connect the chronic disease risk information predicted by the data processing module with the external medical system and provide preliminary guidance for pathological analysis; The product recommendation module pushes corresponding health care products and fitness product strength recommendations based on the user's eating habits and predicted health risks.

2. A smoke intelligent monitoring system as claimed in claim 1, characterized in that: The detection module comprises: Smoke concentration detection sensor, used to detect the smoke concentration in the air; Equipment status detection sensor, used to detect the power, current and voltage of the equipment; Oil fume component detection sensor is used to analyze the chemical composition and trace elements in oil fume.

3. A smoke intelligent monitoring system as claimed in claim 2, characterized in that: The negative pressure suction module includes an exhaust device and an airflow control device to ensure that smoke can be quickly and accurately sucked into the system for processing.

4. A smoke intelligent monitoring system as claimed in claim 3, characterized in that: It also includes a learning module for analyzing and learning the long-term monitoring data provided by the data processing module using a machine learning algorithm; The learning module predicts the future trend of smoke concentration changes and optimizes the control strategy by identifying the pattern of smoke generation and diffusion; The learning module automatically adjusts the operating parameters according to the user's usage habits and feedback to improve the system's intelligence level and user experience; The learning module can also analyze the user's eating habits based on the composition of oil smoke, provide data support for health management, and further provide more detailed life parameters for sports fitness and medical care.

5. A smoke intelligent monitoring system as claimed in claim 4, characterized in that: It also includes a network communication module for connecting to an external network and communicating with other smart devices and health management platforms; the network communication module supports multiple communication protocols and interfaces to ensure that the system can upload monitoring data in real time, receive remote instructions, and perform remote monitoring and configuration; the network communication module supports cloud services and big data analysis to provide users with a more comprehensive smoke management solution.

6. The intelligent smoke monitoring system according to claim 1, characterized in that: The data processing module includes a database and an algorithm for storing historical data and performing intelligent analysis and prediction, and analyzing the user's eating habits and general dining structure through the components of oil smoke.

7. The intelligent smoke monitoring system according to claim 1, characterized in that: The control module can automatically adjust the rotation speed, air volume and suction force of the negative pressure suction module according to the analysis results to optimize the smoke collection efficiency.

8. The intelligent smoke monitoring system according to claim 3, characterized in that: It also includes an AI computing power intervention module, which is used to provide powerful computing power support for the learning module. By processing and analyzing large amounts of data, it provides data support and directional guidance for the continuous optimization and performance improvement of the system.

9. The intelligent smoke monitoring system according to claim 8, characterized in that: The AI ​​computing intervention module continuously trains and optimizes the machine learning model based on historical data and real-time data to improve the accuracy of smoke concentration prediction and optimize the control strategy of the negative pressure suction module, thereby achieving continuous improvement and performance enhancement of the system.

10. A smoke intelligent monitoring method, characterized in that: The following steps are involved: a) Real-time detection of smoke concentration, equipment status and oil smoke composition through the detection module; b) Transmit the detection data to the data processing module for analysis and processing of smoke concentration, equipment status, and fume composition, and predict the risk of chronic diseases that users may have; c) When the detection module detects that the smoke concentration and the equipment status reach the preset conditions, the control module triggers the start of the negative pressure suction module; d) The negative pressure suction module generates negative pressure to attract and collect smoke in the air; e) The control module automatically adjusts the operating parameters of the negative pressure suction module according to the analysis results of the data processing module; f) Connect chronic disease risk information with external medical systems through the medical system interface module to provide preliminary guidance for pathological analysis; g) The product recommendation module pushes corresponding health care products and fitness product strength recommendations based on the user's eating habits and predicted health risks.

Images