Intelligent Control Method, System and Medium of Kitchen Air Purification Equipment

Abstract

The present invention relates to the technical field of equipment control, and particularly relates to an intelligent control method, system and medium for a kitchen air purification device. The method includes the following steps: obtaining real-time air monitoring data by using an air quality monitoring module in the kitchen air purification device; screening harmful gas components from the real-time air monitoring data to obtain air harmful gas component screening data; performing first device association control on an air purification module in the kitchen air purification device through the air harmful gas component screening data to generate an air purification association control instruction; obtaining air purification device filter data by using the air purification module in the kitchen air purification device; monitoring filter soot of the air purification device filter data to generate filter soot monitoring data. The present invention improves the intelligent level of the control of the kitchen air purification device through real-time monitoring, dynamic adjustment, filter management optimization, efficiency quantification control and an execution feedback mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment control, and particularly to an intelligent control method, system and medium for a kitchen air purification device. Background Art

[0002] In the early stage, traditional range hoods were the main kitchen air purification devices, mainly relying on mechanical ventilation and oil fume separation technologies, and achieving basic oil fume emissions through single air volume adjustment. However, this method has defects such as high energy consumption, low purification efficiency, and high noise, and it is difficult to meet the requirements of modern families for cleanliness and environmental protection. Air purification devices began to introduce multi-layer filtration technologies, such as activated carbon filter screens and electrostatic dust removal modules, which significantly improved the purification efficiency. At the same time, with the development of the Internet of Things and sensor technologies, intelligent control has gradually become the focus of research and development. Based on technologies such as PM2.5 sensors and gas sensors, the device can real-time sense the oil fume concentration and air quality, automatically adjust the wind speed and purification mode, and improve the usability. In recent years, artificial intelligence, big data, and edge computing technologies have further promoted the intelligence of kitchen air purification devices. Through machine learning algorithms, the device can perform personalized optimization according to user habits and environmental characteristics. However, currently, the management of the filter screen of traditional devices usually relies on manual inspection, and it is difficult to detect filter screen blockage or pollution problems in a timely manner. At the same time, the filter screen efficiency is usually not quantified, and the fan control is relatively rough, resulting in a low level of intelligence in the control of kitchen air purification devices. Summary of the Invention

[0003] Based on this, it is necessary to provide an intelligent control method, system and medium for a kitchen air purification device to solve at least one of the above technical problems.

[0004] To achieve the above object, an intelligent control method for a kitchen air purification device, the method includes the following steps:

[0005] Step S1: Use the air quality monitoring module in the kitchen air purification device to obtain real-time air monitoring data; screen the harmful gas components of the real-time air monitoring data to obtain air harmful gas component screening data; perform first device association control on the air purification module in the kitchen air purification device through the air harmful gas component screening data, and generate an air purification association control instruction;

[0006] Step S2: Use the air purification module in the kitchen air purification device to obtain air purification device filter screen data; monitor the filter screen soot of the air purification device filter screen data to generate filter screen soot monitoring data; perform device filter screen available area analysis on the filter screen soot monitoring data according to the air purification association control instruction to generate device filter screen available area data; construct a soot filtration path through the device filter screen available area data to generate a soot filtration path diagram;

[0007] Step S3: Transmit the soot filtration path diagram to the intelligent control module in the kitchen air purification device for quantifying the filtration efficiency of the filter screen, generating filter screen filtration efficiency quantification data; perform air flow control based on the comparison result between the filter screen filtration efficiency quantification data and the preset standard filter screen filtration efficiency threshold, generating strong air flow control data and normal air flow control data; perform second device association control on the fan module in the kitchen air purification device based on the strong air flow control data and the normal air flow control data, generating air filtration association control instructions;

[0008] Step S4: Execute the instructions of the kitchen air purification device in sequence according to the air purification association control instructions and the air filtration association control instructions, generating air purification device instruction execution data; perform execution feedback on the air purification device instruction execution data, and visualize the result of the execution feedback to perform the intelligent control operation of the kitchen air purification device.

[0009] Through real-time air quality monitoring and screening of harmful gas components, the present invention can accurately identify potential pollutants in kitchen air (such as lampblack, PM2.5, carbon monoxide, etc.), improve the response efficiency of the air purification device, and the generated air purification association control instructions enable the air purification module to automatically adjust the operation mode according to the real-time pollution situation, realizing dynamic and intelligent purification. This step ensures the rapid perception and response of the purification device to the change of air quality in the kitchen environment, and improves the overall sensitivity of the device. Through filter screen soot monitoring, the usage situation and remaining available area data of the filter screen can be obtained in a timely manner, optimizing the utilization rate of the filter screen, reducing the replacement frequency, and the generated soot filtration path diagram makes the air flow more efficient, avoiding ineffective filtration areas and improving the purification efficiency. Through the analysis of the available area of the device filter screen, the reasonable allocation of filter screen resources can be realized, reducing the energy consumption of the device operation. The filter screen filtration efficiency quantification data provides clear performance indicators, which helps to evaluate and optimize the actual purification effect of the filter screen. Based on the strong and normal air flow control data, hierarchical response to different purification requirements can be realized, ensuring that strongly polluted areas are key treated. The second device association control optimizes the operation mode of the fan module, further enhancing the overall purification performance and energy efficiency ratio of the air purification device. By sequentially executing the air purification and filtration control instructions, the coordination of device operation is ensured, reducing unnecessary resource consumption. The visualization of the instruction execution result can intuitively present the air purification effect and the device operation status, facilitating users and maintenance personnel to monitor and adjust the device operation. The intelligent analysis of the execution feedback can continuously optimize the device operation logic, making the purification process more efficient and stable. Therefore, the present invention improves the intelligent level of the control of the kitchen air purification device through real-time monitoring, dynamic adjustment, filter screen management optimization, efficiency quantification control and execution feedback mechanism.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain real-time air monitoring data by using the air quality monitoring module in the kitchen air purification device;

[0012] Step S12: Perform data preprocessing on the real-time air monitoring data to generate standard real-time air monitoring data, where data preprocessing includes data cleaning, data filtering, outlier processing, and data standardization;

[0013] Step S13: Analyze the air gas components of the standard real-time air monitoring data to generate air gas component data; use the air gas component data to screen the harmful gas components of the standard real-time air monitoring data to obtain air harmful gas component screening data;

[0014] Step S14: Perform the first device association control on the air purification module in the kitchen air purification device through the air harmful gas component screening data to generate an air purification association control instruction.

[0015] In the present invention, real-time data is obtained through the air quality monitoring module, which can accurately reflect the pollutant and gas component conditions in the kitchen air, providing accurate basic data for subsequent analysis and optimization. Cleaning, filtering, outlier processing, and standardization of the monitoring data ensure data quality and consistency, reduce errors caused by data problems, and improve the reliability and accuracy of the analysis results. By analyzing various gas components in the air and screening out harmful gases, it provides a scientific basis for the timely treatment and emission of harmful gases, ensuring that the air quality meets the standards and avoiding the harm of harmful gases. According to the screening results of air harmful gas components, the air purification module of the kitchen air purification device is intelligently controlled to automatically adjust the working state of the purification device, realizing the automatic adjustment and purification of air quality and improving the air quality of the kitchen environment. Through this system, the concentration of harmful gases in the kitchen can be effectively controlled, the impact of harmful substances on health can be reduced, and a fresher and more comfortable living environment can be provided. The generation of intelligent association control instructions can ensure the efficient operation of the air purification module when needed, avoid energy consumption waste caused by excessive operation, and ensure the air quality is guaranteed.

[0016] Preferably, step S13 includes the following steps:

[0017] Step S131: Perform gas sampling based on the standard real-time air monitoring data to generate gas sampling data; measure the gas concentration of the gas sampling data to generate air gas concentration data;

[0018] Step S132: Perform spectral analysis on the air gas concentration data using spectral technology to generate gas spectral characteristic data; perform gas component identification on the gas sampling data based on the gas spectral characteristic data to generate air gas component data;

[0019] Step S133: Calculate the content ratio of the air gas concentration data through the air gas component data to generate gas component ratio data; compare the gas component ratio data with the preset harmful gas database to generate harmful gas threshold comparison data;

[0020] Step S134: Conduct component correlation analysis on the air gas component data based on the harmful gas threshold comparison data to generate harmful gas component correlation data; screen the harmful gas components from the air gas component data through the harmful gas component correlation data to obtain air harmful gas component screening data.

[0021] Through gas sampling and concentration measurement, the present invention can accurately obtain the gas concentration data in the air, providing reliable basic data for subsequent analysis and avoiding errors caused by improper sampling or inaccurate measurement. Using spectral technology for spectral analysis of gas concentration data makes gas component identification more accurate. Spectral analysis can identify multiple gas components in the air, improving the identification efficiency and accuracy, and is particularly suitable for gas component analysis in complex environments. Through spectral analysis of gas concentration data, detailed gas component data is generated. This step ensures the accurate identification of different gas components, avoids misidentification or missed detection, and improves the comprehensiveness of air component detection. Calculating the ratio of gas components and comparing the threshold with the preset harmful gas database effectively identifies the harmful gas components in the air. The automation of this process enhances the detection efficiency and reduces the error of manual judgment, ensuring the timeliness and accuracy of air quality monitoring. Through harmful gas threshold comparison data and component correlation analysis, the main components of harmful gases can be screened out. This step helps to reveal potential hazards in the air and provides strong support for the treatment of harmful gases. By accurately identifying and screening out harmful gas components, targeted air purification treatment can be carried out to ensure the timely removal of harmful substances in environments such as kitchens, thus providing a healthier living environment and reducing the negative impact of air pollution on health. The automation of the entire process (such as gas component screening and harmful gas threshold comparison) enhances the intelligent level of the system, reduces the need for manual intervention, and makes air quality monitoring and optimization more efficient and reliable.

[0022] Preferably, step S2 includes the following steps:

[0023] Step S21: Obtain the air purification device filter data using the air purification module in the kitchen air purification device;

[0024] Step S22: Analyze the filter screen interval of the air purification equipment filter screen data to generate filter screen interval data; based on the filter screen interval data, stratify the air purification equipment filter screen data to generate air purification equipment filter screen level data;

[0025] Step S23: Divide the filter screen area of the air purification equipment filter screen level data to generate a filter screen segmentation area; use a laser particle sensor to monitor the filter screen soot in the filter screen segmentation area to generate filter screen soot monitoring data;

[0026] Step S24: Analyze the available area of the equipment filter screen based on the air purification associated control instruction for the filter screen soot monitoring data to generate equipment filter screen available area data; construct a soot filtration path map through the equipment filter screen available area data.

[0027] Through obtaining the air purification equipment filter screen data and conducting filter screen interval analysis, the present invention can deeply understand the working state and structural characteristics of the filter screen, ensure that the filter screen performance of the air purification equipment is accurately monitored, thereby improving the purification effect. Stratifying the filter screen through the filter screen interval data helps to identify the functions and efficiencies of different levels of filter screens, rationally allocate resources, and optimize the working effect of the filter screen. This process can ensure that the air purification equipment conducts efficient filtration at multiple levels and improves the purification efficiency. After dividing the filter screen area, using a laser particle sensor for soot monitoring can accurately capture the soot concentration in each segmented area. This monitoring method can effectively detect the pollution degree of each area of the filter screen and provide data basis for subsequent cleaning or maintenance. According to the filter screen soot monitoring data, analyzing the available area of the filter screen can accurately determine which areas of the filter screen are effective and which areas are polluted or blocked. This intelligent analysis helps to optimize the service life of the filter screen and ensure the efficient operation of the purification equipment. Through the equipment filter screen available area data, constructing a soot filtration path map to achieve the optimal working path of the air purification equipment. This path optimization can ensure that the soot filtration process is more efficient, avoid unnecessary energy waste, and at the same time improve the speed of air quality improvement. Through real-time monitoring and optimization analysis of the filter screen state, it is possible to reduce the overuse of the filter screen, detect problems in advance and perform repairs or replacements, thereby extending the service life of the equipment and reducing the cost and time of frequent repairs. Through filter screen area segmentation, soot monitoring and path optimization, the air pollution problem in the kitchen environment can be more precisely addressed, ensuring that the air in each part can be efficiently filtered, thereby improving the performance and energy efficiency of the entire air purification equipment and ensuring effective improvement of air quality.

[0028] Preferably, step S24 includes the following steps:

[0029] Step S241: Extract device working status data according to the air purification control instruction to obtain the device working status data; extract air quality index data according to the device working status data to obtain the air quality index data;

[0030] Step S242: Analyze the soot thickness in the filter area based on the air quality index data to obtain the soot thickness data in the filter area; analyze the pollution degree of the filter area based on the soot thickness data in the filter area to obtain the polluted area data of the filter;

[0031] Step S243: Dynamically adjust the polluted area data of the filter according to the air purification associated control instruction to obtain the available area data of the device filter; construct the soot filtration hole path through the available area data of the device filter to generate the soot filtration path diagram.

[0032] By extracting the device working status data and the air quality index data, the present invention can reflect the operation status of the device and the ambient air quality in real time, which provides reliable data support for subsequent filter monitoring and adjustment, and helps to timely identify the changes in air quality and the abnormalities in the device working status. By analyzing the soot thickness in the filter area, the pollution degree of each filter area can be accurately evaluated. This process helps to identify which areas are more severely polluted and which areas can still work normally, thus providing clear guidance for subsequent cleaning or replacement. According to the polluted area data of the filter, dynamic adjustment is carried out to optimize the available area of the filter. This adjustment not only improves the utilization efficiency of the filter, but also can dynamically adjust the working status of the air purification device according to the change of air quality to ensure that the purification effect always remains in the best state. By generating the soot filtration hole path diagram through the available area data of the filter, the filtration path of air purification is optimized. This optimized path helps to improve the air purification efficiency, enabling the pollutants in each area to be filtered in a timely and effective manner, reducing energy waste and increasing the purification rate. Through the accurate identification and adjustment of the polluted area of the filter, it is ensured that the air purification device always operates efficiently, avoiding the influence of excessive filter pollution on the purification effect. This not only effectively improves the air purification effect, but also improves the overall air quality and ensures a healthier indoor environment. By dynamically adjusting the available area of the filter in real time, overuse or unnecessary maintenance of the filter can be avoided, the filter replacement frequency can be reduced, and costs can be saved. At the same time, the optimized filtration path improves the energy efficiency of the device, reduces energy consumption, and improves the overall economy of the device.

[0033] Preferably, constructing the soot filtration hole path through the available area data of the device filter includes:

[0034] Calculate the filter hole diameter of the filter layer data of the air purification equipment through the available area data of the equipment filter screen to obtain the filter screen soot blocking hole diameter data; confirm the soot blocking direction of the filter layer data of the air purification equipment based on the filter screen soot blocking hole diameter data to obtain the filter screen soot blocking direction data;

[0035] Construct the initial filter screen soot filtration path for the available area data of the equipment filter screen according to the filter screen soot blocking direction data to generate the initial filter screen soot filtration path; analyze the types of protected soot of the initial filter screen soot filtration path through a preset soot particle database to generate the filter screen protected soot type data;

[0036] Use the filter screen protected soot type data to mark the soot protection composite path of the initial filter screen soot filtration path to generate the filter screen soot protection composite path; generate the filter screen soot core filtration path based on the coincidence of the filter screen soot protection composite path and the initial filter screen soot filtration path;

[0037] Visualize the filter screen soot core filtration path to generate a soot filtration path diagram.

[0038] The present invention calculates the diameter of the filter holes through the available area data of the equipment filter screen, which can accurately evaluate the aperture size of the filter screen. This helps to analyze the filtering ability of the filter screen and its blocking effect on soot of different particle sizes, providing necessary data for subsequent path optimization. Based on the diameter data of the soot-blocking holes in the filter screen, the orientation of the filter screen can be confirmed, and the flow direction and blocking area of the soot can be accurately determined. This process helps to optimize the air flow path and filtration efficiency, ensuring that soot particles are intercepted to the greatest extent. An initial filter path for soot filtration by the filter screen is constructed based on the soot-blocking orientation data, laying a foundation for subsequent path optimization and improvement. The construction of the initial path can visually display the flow and filtration process of pollutants in the filter screen, guiding the adjustment and optimization of the equipment. Analyzing the types of soot protected by the initial filter path through the soot particle database helps to identify the types of soot that the filter screen needs to handle, ensuring that different types of harmful gases and particles are effectively filtered. This analysis provides a targeted adjustment strategy for the optimization of the filter path. Marking the composite path of the filter path based on the soot type data can design the best filtration strategy for different types of soot. This marking optimization process improves the accuracy of the filter path, enabling different types of soot to be processed more effectively when passing through the filter screen. By performing a coincidence analysis on the protected composite path and the initial filter path, a core filter path can be generated, which has the most efficient soot interception ability. The fine adjustment of the core path ensures the maximum effect of the air purification process and improves the working efficiency of the filter screen. Through the visual display of the core filter path, the filtration process of the soot can be intuitively understood. This not only provides clear working guidance for equipment maintenance and operators but also provides a scientific basis for optimizing the path, making the air purification work more efficient and accurate. Through a series of path optimizations and data analyses, it can be ensured that the filter screen can achieve the best filtration effect under different soot conditions, thereby significantly improving the air purification efficiency. These optimization strategies help to reduce the missed fish during the filtration process and improve the overall air quality.

[0039] Preferably, step S3 includes the following steps:

[0040] Step S31: Transmit the soot filtration path diagram to the intelligent control module in the kitchen air purification equipment for dynamic monitoring of the filter screen state, thereby generating dynamic monitoring data of the filter screen state; quantify the filtration efficiency of the filter screen for the dynamic monitoring data of the filter screen state to generate quantified filtration efficiency data of the filter screen;

[0041] Step S32: Compare the quantified filtration efficiency data of the filter screen with a preset standard filtration efficiency threshold of the filter screen. When the quantified filtration efficiency data of the filter screen is less than the preset standard filtration efficiency threshold of the filter screen, then based on the fan module in the kitchen air purification equipment, perform a strong air flow adjustment on the soot filtration path diagram to generate strong air flow control data;

[0042] Step S33: When the quantified filter efficiency data of the filter screen is greater than or equal to the preset standard filter efficiency threshold of the filter screen, intermittent air flow control is performed on the soot filtration path diagram based on the fan module in the kitchen air purification device to generate ordinary air flow control data;

[0043] Step S34: Based on the strong air flow control data and the ordinary air flow control data, the filter screen filtration mode is switched for the filter screen status dynamic monitoring data to generate filter screen filtration mode switching data; according to the filter screen filtration mode switching data, the second device association control is performed on the fan module in the kitchen air purification device to generate an air filtration association control instruction.

[0044] Preferably, step S32 includes the following steps:

[0045] Step S321: Compare the quantified filter efficiency data of the filter screen with the preset standard filter efficiency threshold of the filter screen. When the quantified filter efficiency data of the filter screen is less than the preset standard filter efficiency threshold of the filter screen, initial fan data acquisition is performed based on the fan module in the kitchen air purification device to obtain initial fan operation data;

[0046] Step S322: Extract the flow trajectory of the soot on the soot filtration path diagram according to the initial fan operation data to obtain soot flow trajectory data, where the extraction of the flow trajectory of the soot includes the flow velocity and the flow direction; perform a hydrodynamic simulation on the soot flow trajectory data to generate soot filtration flow simulation data;

[0047] Step S323: Extract the key filtration nodes from the soot flow trajectory data through the soot filtration flow simulation data to obtain the soot flow rate concentration nodes on the filtration path; based on the soot flow rate concentration nodes on the filtration path, mark the soot filtration flow rate regions on the soot filtration path diagram to generate a high soot filtration flow rate region and a low soot filtration flow rate region;

[0048] Step S324: Use the high soot filtration flow rate region to perform air valve introduction and shunt control on the initial fan operation data to generate high flow rate region fan regulation data; perform air redistribution control on the initial fan operation data according to the low soot filtration flow rate region to generate low flow rate region fan regulation data;

[0049] Step S325: Integrate the high flow rate region fan regulation data and the low flow rate region fan regulation data to generate strong air flow control data.

[0050] In this specification, an intelligent control system for a kitchen air purification device is provided, which is used to execute the intelligent control method of the kitchen air purification device. The intelligent control system of the kitchen air purification device includes:

[0051] A gas recognition module, which is used to obtain real-time air monitoring data by using the air quality monitoring module in the kitchen air purification equipment; screen the harmful gas components from the real-time air monitoring data to obtain air harmful gas component screening data; perform the first device association control on the air purification module in the kitchen air purification equipment through the air harmful gas component screening data, and generate an air purification association control instruction;

[0052] A filter screen analysis module, which is used to obtain the filter screen data of the air purification equipment by using the air purification module in the kitchen air purification equipment; monitor the filter screen soot of the air purification equipment filter screen data to generate filter screen soot monitoring data; analyze the available area of the equipment filter screen according to the air purification association control instruction for the filter screen soot monitoring data, and generate equipment filter screen available area data; construct a soot filtration path through the equipment filter screen available area data to generate a soot filtration path diagram;

[0053] An air purification module, which is used to transmit the soot filtration path diagram to the intelligent control module in the kitchen air purification equipment for quantifying the filter screen filtration efficiency, and generate filter screen filtration efficiency quantification data; perform air flow control based on the comparison result between the filter screen filtration efficiency quantification data and the preset standard filter screen filtration efficiency threshold, and generate strong air flow control data and normal air flow control data; perform the second device association control on the fan module in the kitchen air purification equipment based on the strong air flow control data and the normal air flow control data, and generate an air filtration association control instruction;

[0054] An intelligent control module, which is used to execute the instructions of the kitchen air purification equipment in sequence according to the air purification association control instruction and the air filtration association control instruction, and generate air purification equipment instruction execution data; perform execution feedback on the air purification equipment instruction execution data, and visualize the result of the execution feedback to perform the intelligent control operation of the kitchen air purification equipment.

[0055] The present invention also provides a storage medium storing a computer program, and when the computer program is executed, it implements the intelligent control method of the kitchen air purification equipment as described in any one of the above.

[0056] The beneficial effects of the present invention are as follows: By using the air quality monitoring module to obtain real-time air data and screening out harmful gas components, it can respond in a timely manner to changes in air pollutants in the kitchen and ensure that the air quality is within a safe range. The filter analysis module monitors the soot accumulation on the filter, determines the available area of the filter, thereby optimizing the soot filtration path, improving the usage efficiency of the filter, and ensuring the air purification effect. The intelligent control module quantifies the data based on the filtration efficiency of the filter, controls the air flow, and adjusts the working state of the fan module to improve the air purification efficiency. It can adjust the air flow mode according to actual needs to achieve an optimized purification effect in different environments. The entire system coordinates the associated control among various devices through the intelligent control module, including the air purification module, the fan module, etc., to ensure the coordinated operation of the system. The associated control among devices ensures smooth and efficient device operation. The system can visually display the feedback during the execution process, facilitating users to understand the effect of the air purification process and the operating state of the devices, providing a reference for subsequent operations, and helping to improve the intelligence and management efficiency of the system. Therefore, the present invention improves the intelligent level of the control of kitchen air purification equipment through real-time monitoring, dynamic adjustment, filter management optimization, efficiency quantification control, and execution feedback mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the step flow of an intelligent control method for a kitchen air purification device;

[0058] Figure 2 For Figure 1 it is a schematic diagram of the detailed implementation step flow of step S2 in

[0059] Figure 3 For Figure 1 it is a schematic diagram of the detailed implementation step flow of step S3 in

[0060] The realization, functional characteristics, and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] To achieve the above object, please refer to Figures 1 to 3 , an intelligent control method for a kitchen air purification device, the method includes the following steps:

[0062] Step S1: Use the air quality monitoring module in the kitchen air purification device to obtain real-time air monitoring data; screen the harmful gas components from the real-time air monitoring data to obtain air harmful gas component screening data; perform the first device associated control on the air purification module in the kitchen air purification device through the air harmful gas component screening data to generate an air purification associated control instruction;

[0063] Step S2: Obtain the filter data of the air purification device using the air purification module in the kitchen air purification device; monitor the filter soot of the air purification device filter data to generate filter soot monitoring data; analyze the available area of the device filter based on the air purification association control instruction for the filter soot monitoring data to generate device filter available area data; construct a soot filtration path through the device filter available area data to generate a soot filtration path diagram;

[0064] Step S3: Transmit the soot filtration path diagram to the intelligent control module in the kitchen air purification device for quantifying the filter filtration efficiency to generate filter filtration efficiency quantification data; perform air flow control based on the comparison result between the filter filtration efficiency quantification data and the preset standard filter filtration efficiency threshold to generate strong air flow control data and normal air flow control data; perform secondary device association control on the fan module in the kitchen air purification device based on the strong air flow control data and the normal air flow control data to generate an air filtration association control instruction;

[0065] Step S4: Execute the instructions of the kitchen air purification device in sequence according to the air purification association control instruction and the air filtration association control instruction to generate air purification device instruction execution data; perform execution feedback on the air purification device instruction execution data and visualize the result of the execution feedback to execute the intelligent control operation of the kitchen air purification device.

[0066] Through real-time air quality monitoring and screening of harmful gas components, the present invention can accurately identify potential pollutants in kitchen air (such as lampblack, PM2.5, carbon monoxide, etc.), improve the response efficiency of air purification equipment, and generate air purification-related control instructions that enable the air purification module to automatically adjust the operating mode according to the real-time pollution situation, realizing dynamic and intelligent purification. This step ensures the rapid perception and response of the purification equipment to changes in air quality in the kitchen environment, and improves the overall sensitivity of the equipment. Through filter soot monitoring, the usage situation and remaining available area data of the filter can be obtained in a timely manner, optimizing the filter utilization rate, reducing the replacement frequency, and generating a soot filtration path map that makes air flow more efficient, avoiding ineffective filtration areas, and improving the purification efficiency. Through the analysis of the available area of the equipment filter, the reasonable allocation of filter resources can be achieved, reducing the energy consumption of equipment operation. The quantitative data of the filter filtration efficiency provides clear performance indicators, which helps to evaluate and optimize the actual purification effect of the filter. Based on the strong and ordinary air flow control data, hierarchical response to different purification requirements can be achieved, ensuring that strongly polluted areas are key treated. The second equipment association control optimizes the operation mode of the fan module, further enhancing the overall purification performance and energy efficiency ratio of the air purification equipment. By sequentially executing the air purification and filtration control instructions, the coordination of equipment operations is ensured, and unnecessary resource consumption is reduced. The visualization of the instruction execution results can intuitively present the air purification effect and the equipment operation status, facilitating users and maintenance personnel to monitor and adjust the equipment operation. The intelligent analysis of the execution feedback can continuously optimize the equipment operation logic, making the purification process more efficient and stable. Therefore, the present invention improves the intelligent level of the control of kitchen air purification equipment through real-time monitoring, dynamic adjustment, filter management optimization, efficiency quantification control, and execution feedback mechanism.

[0067] In an embodiment of the present invention, with reference to Figure 1 As shown, it is a schematic diagram of the step flow of an intelligent control method for a kitchen air purification equipment of the present invention. In this example, the intelligent control method for a kitchen air purification equipment includes the following steps:

[0068] Step S1: Use the air quality monitoring module in the kitchen air purification equipment to obtain real-time air monitoring data; screen the harmful gas components from the real-time air monitoring data to obtain air harmful gas component screening data; perform the first equipment association control on the air purification module in the kitchen air purification equipment through the air harmful gas component screening data, and generate air purification-related control instructions;

[0069] In an embodiment of the present invention, by enabling the air quality monitoring module in the kitchen air purification device, it is ensured that its sensors are in a working state. The monitoring module includes multiple high-sensitivity sensors (such as PM2.5, formaldehyde, CO, NO2, volatile organic compound (VOC) sensors, etc.) to capture various pollutant data in the kitchen air. Air monitoring data is collected in real time at a preset time interval (such as every 1 second) to generate an original air quality data stream. The data is sent to the control unit of the device through wireless transmission (such as Wi-Fi or Zigbee). The control unit analyzes the original data and extracts key parameters (such as pollutant concentration, gas type). By comparing with the regional air quality standard, the data of the harmful gas components that exceed the standard is screened out. The type, concentration, and degree of exceeding the standard of the gas exceeding the standard are marked to generate structured air harmful gas component screening data (for example, formaldehyde: 0.12mg / m³, exceeding the standard by 20%). According to the air harmful gas component screening data, a suitable purification strategy is selected. For example: when PM2.5 exceeds the standard, activate the high-efficiency filtration module. When VOC exceeds the standard, start the activated carbon adsorption module. When CO exceeds the standard, start the oxidation catalytic module. The purification strategy is converted into executable device control instructions, including starting the module, adjusting the wind speed, changing the purification mode, etc. The generated instruction examples are: Instruction 1: Start the high-efficiency filtration module, and set the wind speed to gear 3. Instruction 2: Start the activated carbon adsorption module, and set the operation time to 10 minutes. The control unit sends the generated air purification associated control instructions to the air purification module. After receiving the instructions, the module immediately executes the operation. The purification effect is monitored in real time, and the new air quality data is fed back to the control unit.

[0070] Step S2: Obtain the filter data of the air purification device using the air purification module in the kitchen air purification device; conduct filter soot monitoring on the filter data of the air purification device to generate filter soot monitoring data; perform device filter available area analysis on the filter soot monitoring data according to the air purification associated control instructions to generate device filter available area data; construct a soot filtration path through the device filter available area data to generate a soot filtration path diagram;

[0071] In the embodiments of the present invention, by enabling the filter sensors (such as resistance sensors, pressure sensors or optical particle detection sensors) in the air purification module, the status of the filter is monitored in real time. Filter-related data is collected, including the filter blockage rate, particle deposition concentration, filter ventilation volume, etc. The filter sensors transmit the collected data to the device control unit in the form of digital signals. The data is recorded according to the time stamp to form a filter monitoring data stream. The control unit analyzes the filter data to evaluate the soot accumulation of the filter: calculates the filter load level according to the particle concentration. Compares with the filter design standard to determine whether the soot accumulation exceeds the limit, and generates structured filter soot monitoring data, including the following content: soot deposition amount (unit: g / m²). Blockage rate (unit: %). Filter warning status (normal / warning / exceeding the limit). The filter is divided into multiple logical grid units (such as 10×10 partitions), and the availability of each unit is analyzed one by one. According to the soot deposition amount and the blockage rate, mark the status of each grid: Available area: deposition amount is lower than the threshold, and the blockage rate is less than 70%. Unavailable area: deposition amount exceeds the standard or the blockage rate exceeds 90%. Output the available area data of the device filter, and record the position and status of the available grid units. Use a path planning algorithm (such as dynamic programming) to analyze the available area data, construct an optimal filtration path: give priority to the path with the lowest resistance. Avoid passing through unavailable areas. Construct a soot filtration path diagram to show the best filtration flow direction of soot in the filter. Optimize the path to improve the air purification efficiency and reduce the energy consumption of the device. Convert the filtration path data into a visual graph, including: filter area division (using colors to distinguish available and unavailable areas). Soot filtration path (arrows indicate the flow direction). Output the soot filtration path diagram for monitoring or debugging purposes.

[0072] Step S3: Transmit the soot filtration path diagram to the intelligent control module in the kitchen air purification device for quantifying the filter filtration efficiency, and generate filter filtration efficiency quantification data; perform air flow control based on the comparison result between the filter filtration efficiency quantification data and the preset standard filter filtration efficiency threshold, and generate strong air flow control data and normal air flow control data; perform second device association control on the fan module in the kitchen air purification device based on the strong air flow control data and the normal air flow control data, and generate an air filtration association control instruction;

[0073] In the embodiments of the present invention, by using a soot detection system to collect the soot concentration data in the kitchen, a soot filtration path map is generated based on the data. This path map is designed based on the air flow direction and pollution source distribution in the kitchen to guide the operation of the air purification equipment. The soot filtration path map is transmitted to the intelligent control module in the kitchen air purification equipment through a communication module (such as Wi-Fi, Bluetooth or wired connection). After receiving the path map, the control module starts to perform quantitative calculation of the filter efficiency. The intelligent control module analyzes the filtration path map to identify the key areas in the filtered air (such as the smoke source, the area with higher oil fume concentration, etc.). Then, based on this information, the filtration efficiency of the filter screen in different areas is evaluated. The quantification of the filter efficiency is carried out by detecting the difference in air quality before and after the filter screen. Usually, indicators such as particulate matter concentration (such as PM2.5, PM10), odor concentration, etc. are used to evaluate the filtration efficiency. The quantification data will include the filtration efficiency value (such as percentage), and the operation effect of the filter screen within a specific time period. The filter efficiency quantification data of the filter screen contains the current filtration effect of the filter screen, such as "80% effective filtration", "90% effective filtration", etc. These data will be transmitted back to the central control system, or directly stored in the internal storage of the kitchen air purification equipment for subsequent analysis. The intelligent control module will compare the actual filter efficiency quantification data with the preset standard filter efficiency threshold (such as 85% as the minimum standard). If the actual efficiency is higher than or equal to the threshold, it indicates that the filtration effect is good; if it is lower than the threshold, it indicates that the filter efficiency is poor and needs to be processed or replaced. When the filter efficiency is low (lower than the preset threshold), the intelligent control module starts the strong air flow control mode to enhance the air flow, increase the speed and flow rate of the air passing through the filter screen, thereby improving the overall air purification efficiency. The strong mode usually involves increasing the fan speed, adjusting the fan direction or regulating the air flow direction. When the filter efficiency is good (higher than the preset threshold), the system maintains the normal air flow control mode to maintain a stable air flow and reduce energy consumption. In the normal mode, the fan speed is low and the air flow is maintained within the normal range. Based on the filter efficiency quantification data and the comparison result, the system generates two categories of air flow control data: The strong air flow control data includes information such as fan speed, air flow rate, and fan start / stop. The normal air flow control data includes information such as the normal operation state, flow rate, and load of the fan. According to the generated strong and normal air flow control data, the intelligent control module performs associated control on the fan module in the kitchen air purification equipment. In the strong air flow control mode, the fan module will adjust the speed according to the control data to increase the air flow volume; in the normal air flow mode, the fan will maintain the standard operation state. The working state of the fan is achieved through power regulation, wind speed setting, etc., to ensure that the air flow matches the air purification effect. The system will generate control instructions according to the strong and normal air flow control modes, specifically including the adjustment parameters of the fan (such as speed, air flow rate, etc.).The air filtration related control instructions will be sent to the fan module and other related devices (such as the filter cleaning mechanism, sensors, etc.) of the kitchen air purification device to ensure the efficiency and stability of the air purification process.

[0074] Step S4: Execute the instructions of the kitchen air purification device in sequence according to the air purification related control instructions and the air filtration related control instructions to generate the instruction execution data of the air purification device; perform execution feedback on the instruction execution data of the air purification device, and visualize the result of the execution feedback to perform the intelligent control operation of the kitchen air purification device.

[0075] In the embodiment of the present invention, the intelligent control system of the kitchen air purification device receives the "air purification related control instructions" and "air filtration related control instructions" generated by the intelligent control module. The system schedules these control instructions according to the priority and dependency relationship. According to the real-time state of the device operation (such as the current air quality, filter state, etc.), the instructions will be executed in a reasonable order. First, adjust the fan and the air flow direction to ensure that the air flow operates in the required mode. After ensuring the smooth air flow, perform operations such as filter adjustment, cleaning or replacement to maintain the high efficiency of air filtration. The system sends the instructions to each relevant hardware (such as the fan module, filter control module, sensor module, etc.) and starts various operations. Specifically, it includes: adjusting the rotation speed, wind speed and working state of the fan according to the air flow control data. Performing the cleaning, adjustment or replacement operation of the filter according to the air filtration control data. Real-time monitoring of air quality, smoke concentration, etc. to ensure the normal operation of the device. During the execution of the instructions, the system will record the execution status of each step in real time, including: Device status: such as fan rotation speed, filter working state, etc. Air quality data: such as PM2.5 concentration, smoke concentration, etc. Operation duration: the working duration of each module, filter cleaning cycle, etc. Device consumption data: such as power consumption, fan operating power, etc. These data will be integrated into the "instruction execution data of the air purification device" for subsequent feedback analysis. During the execution of the instructions, the system judges whether it is executed according to the predetermined plan by real-time monitoring and feedback of the device status. For example: whether the fan reaches the predetermined rotation speed, whether the filter is cleaned or replaced according to the plan, and whether the air quality reaches the set standard. The system generates execution feedback data according to the device response situation during the execution process.

[0076] Preferably, step S1 includes the following steps:

[0077] Step S11: Use the air quality monitoring module in the kitchen air purification device to obtain real-time air monitoring data;

[0078] Step S12: Perform data preprocessing on the real-time air monitoring data to generate standard real-time air monitoring data, where the data preprocessing includes data cleaning, data filtering, outlier processing, and data standardization;

[0079] Step S13: Conduct air gas component analysis on the standard real-time air monitoring data to generate air gas component data; use the air gas component data to screen for harmful gas components in the standard real-time air monitoring data to obtain air harmful gas component screening data;

[0080] Step S14: Perform first device associated control on the air purification module in the kitchen air purification device through the air harmful gas component screening data to generate an air purification associated control instruction.

[0081] In the embodiments of the present invention, by integrating multiple sensors in the air quality monitoring module, for example: based on the principle of laser scattering, the concentration of particulate matter with a diameter less than or equal to 2.5 micrometers in the air is detected, with the unit of micrograms per cubic meter (μg / m³), and the concentration data of PM2.5 in the current kitchen air is obtained in real time, such as 30 μg / m³. Similarly, based on a specific sensing technology, the content of particulate matter with a diameter less than or equal to 10 micrometers in the air is measured, and the unit is also μg / m³. Suppose the obtained data is 50 μg / m³. Using the principle of infrared absorption, the concentration of CO2 in the air is monitored, with the unit of ppm (parts per million). Suppose the real-time data is 500 ppm. Using technologies such as metal oxide semiconductors, the content of VOC in the air is detected, with the unit of milligrams per cubic meter (mg / m³). For example, the obtained data is 0.1 mg / m³. Technologies such as thermistors and capacitive humidity sensors are respectively used to measure the temperature (unit: °C) and relative humidity (unit: %) in the kitchen. Suppose the obtained temperature data is 25 °C and the relative humidity is 50%. These sensors collect data in real time and summarize it to form real-time air monitoring data. Check the rationality of the data and remove the obviously incorrect data. For example, if the PM2.5 sensor returns a negative number (assuming that the PM2.5 concentration cannot be negative in a normal environment), it is corrected to 0 μg / m³. For numerical data, the mean filtering method is adopted. Taking the PM2.5 data as an example, set a time window. For example, data is collected every 10 seconds within the past 1 minute, and a total of 6 data points are collected. Calculate the average of these 6 data points to obtain the filtered PM2.5 data to smooth the data fluctuations. Set thresholds according to experience and relevant standards to handle outliers. For example, for PM2.5, when the concentration exceeds 100 μg / m³, it is considered an abnormally high value and is corrected to 100 μg / m³; for CO2, if the concentration exceeds 1000 ppm, it is corrected to 1000 ppm. The Min-Max normalization method is adopted to map each data index to the interval of 0-1. Taking PM2.5 as an example, assume that its minimum value in a normal environment is 0 μg / m³ and the maximum value is 100 μg / m³. For the real-time monitored PM2.5 concentration value X (unit: μg / m³), the normalized value = (X - 0) / (100 - 0). For example, if the real-time PM2.5 concentration is 30 μg / m³, the normalized value is 0.3. The same method is applied to other data indexes, such as PM10 (assuming the normal range is 0-150 μg / m³), CO2 (assuming the normal range is 0-1000 ppm), VOC (assuming the normal range is 0-1 mg / m³), etc., to generate standard real-time air monitoring data. Extract the data related to air gas components from the standard real-time air monitoring data, such as the normalized data of CO2 and VOC.Assume that the standardized CO2 data is 0.5 (corresponding to the standardized value of 500 ppm), and the VOC data is 0.1 (corresponding to the standardized value of 0.1 mg / m³), and generate air gas composition data. Set the judgment criteria for harmful gases. For example, for CO2, when the standardized value is greater than 0.5 (corresponding to the actual value being greater than 500 ppm, assuming this is the harmful critical value), it is considered that CO2 belongs to the harmful gas composition and is included in the air harmful gas composition screening data; for VOC, when the standardized value is greater than 0.5 (corresponding to the actual value being greater than 0.5 mg / m³, assuming this is the harmful critical value), it is included in the screening data. Assume that the current standardized value of CO2 is 0.5, which does not exceed the critical value, and the standardized value of VOC is 0.1, which also does not exceed the critical value. At this time, the air harmful gas composition screening data is empty. Control the air purification module according to the situation of the air harmful gas composition screening data. If the air harmful gas composition screening data is not empty, it indicates the existence of harmful gases, and generate the instruction "Turn on the air purification module and strengthen purification", so that the air purification module operates at a high power to accelerate the filtration and decomposition of harmful gases; if the air harmful gas composition screening data is empty, it means that the current air quality is relatively good, and generate the instruction "The air purification module maintains the current state", maintaining the current operating power to save energy and continuously monitor the air condition.

[0082] Preferably, step S13 includes the following steps:

[0083] Step S131: Perform gas sampling based on the standard real-time air monitoring data to generate gas sampling data; measure the gas concentration of the gas sampling data to generate air gas concentration data;

[0084] Step S132: Use spectroscopic technology to perform spectral analysis on the air gas concentration data to generate gas spectral characteristic data; identify the gas components of the gas sampling data based on the gas spectral characteristic data to generate air gas composition data;

[0085] Step S133: Calculate the content ratio of the air gas concentration data through the air gas composition data to generate gas component ratio data; compare the gas component ratio data with the harmful gas threshold based on the preset harmful gas database to generate harmful gas threshold comparison data;

[0086] Step S134: Perform component correlation analysis on the air gas composition data according to the harmful gas threshold comparison data to generate harmful gas component correlation data; screen the harmful gas components of the air gas composition data through the harmful gas component correlation data to obtain the air harmful gas composition screening data.

[0087] In the embodiments of the present invention, a certain volume of air sample is extracted from the kitchen environment by using a sampling pump. The sampling pump can extract air according to a set flow rate (for example, at a flow rate of 1 liter per minute) to ensure that the collected gas sample can represent the air conditions in the kitchen. These gas samples are the gas sampling data. For different gas components, different sampling methods and equipment are required. For example, for volatile organic compounds (VOCs), an adsorption tube can be used for sampling, while for common gaseous pollutants such as sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), etc., gas bags or specific absorbent solutions can be used for sampling. For some common gases, such as sulfur dioxide (SO2), the chemical reagent method can be used. The collected gas is passed through an absorption flask containing an absorbent solution (such as potassium tetrachloromercurate solution) to cause a chemical reaction with the absorbent solution to form a stable compound. Then, the amount of the compound formed in the absorbent solution is determined by colorimetry or titration, and further the concentration of SO2 in the original gas is calculated. Suppose the SO2 concentration in the kitchen is measured to be 0.05 ppm (parts per million) by this method. For nitrogen oxides (NOx), the chemiluminescence method can be used. The gas sample is introduced into the reaction chamber and reacts with ozone under certain conditions to produce excited NO2, which emits light when returning to the ground state. The light intensity is detected by a photomultiplier tube, and the concentration of NOx is calculated according to the quantitative relationship between the light intensity and the NOx concentration. Suppose the measured NOx concentration is 0.1 ppm. For carbon monoxide (CO), an electrochemical sensor is used. When the gas diffuses into the sensor, an electrochemical reaction occurs on the electrode, generating a current signal proportional to the gas concentration, and the concentration of CO is calculated according to the magnitude of the current. Suppose the measured CO concentration is 5 ppm. For oxygen (O2), an oxygen sensor is used. The concentration of oxygen is determined according to the potential difference generated by the reduction reaction of oxygen on the electrode. For example, the O2 concentration in the kitchen is measured to be 20.9% (volume fraction). The gas sample is introduced into the sample cell of an infrared spectrometer. When infrared light passes through the gas sample, different gas molecules will absorb infrared light at specific wavelengths. According to the position and intensity of the absorption peaks, gas spectral characteristic data are generated. For example, carbon dioxide (CO2) will have a characteristic absorption peak at a specific wavenumber (such as 2349 cm⁻¹), and the intensity of the absorption peak is related to the concentration of CO2. By analyzing these absorption peaks, gas spectral characteristic data can be obtained. For some hydrocarbon compounds, such as methane (CH4), absorption peaks will be generated at different characteristic wavenumbers. By analyzing the infrared spectrum, the spectral characteristics of different gas components can be determined. When a laser beam irradiates the gas sample, Raman scattering will occur, and different gas molecules will generate Raman scattered light at different frequencies. According to the frequency shift and intensity of the Raman scattered light, gas spectral characteristic data can be obtained. For example, water vapor (H2O) will have its unique Raman scattering spectral characteristics. By analyzing these characteristics, the water vapor component in the gas sample can be identified.Based on the spectral feature database, compare the measured gas spectral feature data with it to determine the type of gas. For example, if a characteristic peak corresponding to formaldehyde (HCHO) appears in the spectrogram, and the intensity of the peak is proportional to the concentration, combined with the previously measured concentration data, it can be determined that the gas component data contains formaldehyde, and according to the spectral intensity and the pre-established quantitative relationship, the concentration of formaldehyde is obtained as 0.02 ppm. The concentration data of various gas components are obtained, such as the CO2 concentration is 400 ppm, the O2 concentration is 20.9%, and the CO concentration is 5 ppm, etc. Calculate the proportion of various gas components in the total gas. For CO2, assuming the total gas volume is 1 cubic meter, then the content proportion of CO2 = 400 ppm / (1×10. 6 ) = 0.04%. Similarly, similar calculations are performed for other gas components to obtain the content proportions of various gas components in the total gas, and generate gas component proportion data. The preset harmful gas database contains threshold information for various harmful gases. For example, for CO, the maximum allowable concentration is 10 ppm, and for SO2, the maximum allowable concentration is 0.5 ppm, etc. Convert the calculated gas component proportion data into actual concentrations and compare them with the thresholds in the harmful gas database. If the concentration of CO is 5 ppm and the threshold is 10 ppm, record the comparison result as not exceeding the threshold; if the concentration of SO2 is 0.05 ppm and the threshold is 0.5 ppm, it is also recorded as not exceeding the threshold, and generate harmful gas threshold comparison data. There are correlations between some gas components. For example, high concentrations of carbon monoxide (CO) are related to incomplete combustion. When high concentrations of CO are detected, other combustion products such as sulfur dioxide (SO2) and nitrogen oxides (NOx) will be simultaneously concerned. If the harmful gas threshold comparison data of CO shows that it is close to or exceeds the threshold, analyze its correlation with other gas components, generate harmful gas component correlation data, and point out the existing combustion source or other pollution sources. For some volatile organic compounds (VOCs), analyze their correlations with other chemical substances. For example, some specific VOCs are related to the cleaners used in the kitchen or the volatiles of food ingredients. Analyze their correlations to provide information for subsequent processing. According to the harmful gas threshold comparison data and the results of component correlation analysis, screen out gas components that exceed the threshold or have potential hazards. For example, if in the harmful gas threshold comparison data, it is found that the concentration of formaldehyde exceeds its threshold (assuming the threshold of formaldehyde is 0.01 ppm and the measured concentration is 0.02 ppm), include formaldehyde in the air harmful gas component screening data; if CO is close to its threshold, also include it. For gases with concentrations below the threshold and no special correlations, such as normal concentration of O2, do not include them in the screening data.

[0088] As an example of the present invention, refer to Figure 2 shown. In this example, the step S2 includes:

[0089] Step S21: Obtain the filter data of the air purification device by using the air purification module in the kitchen air purification device;

[0090] Step S22: Conduct filter interval analysis on the filter data of the air purification device to generate filter interval data; based on the filter interval data, conduct filter layer division on the filter data of the air purification device to generate the filter layer data of the air purification device;

[0091] Step S23: Conduct filter area division on the filter layer data of the air purification device to generate the filter divided area; conduct filter soot monitoring on the filter divided area through a laser particle sensor to generate filter soot monitoring data;

[0092] Step S24: Conduct analysis on the available area of the device filter on the filter soot monitoring data according to the air purification associated control instruction to generate the available area data of the device filter; construct the soot filtration path through the available area data of the device filter to generate the soot filtration path diagram.

[0093] In the embodiments of the present invention, the lifespan of the filter is evaluated by recording the usage time of the filter. For example, the time duration from the time point when the filter starts to be used recorded by the built-in clock of the device to the current time is used as the filter usage duration information. Suppose the current filter has been used for 100 hours. Pressure sensors are installed before and after the filter to measure the pressure difference of the air before and after passing through the filter, so as to reflect the degree of blockage of the filter. When the air passes through a clean filter, the pressure difference is small. As the filter adsorbs more dust and impurities, the pressure difference will increase. Suppose the initial pressure difference is 10 Pa and it will rise to 50 Pa with use. Some advanced air purification devices are built-in with sensors to monitor the pollutant concentration before and after passing through the filter, so as to calculate the filtration efficiency of the filter. For example, for PM2.5, suppose the PM2.5 concentration before the filter is 100 μg / m³ and it is 20 μg / m³ after passing through the filter, and the filtration efficiency of the filter for PM2.5 can be calculated as 80%. According to the physical structure of the filter, the intervals of its different layers are analyzed. For example, for a multi-layer composite filter, the spacing between different filter layers is determined by physical measurement or by referring to the device specification. Suppose a composite filter has three layers, the interval between the first layer and the second layer is 5 mm, and the interval between the second layer and the third layer is 8 mm. These spacing information are used as the filter interval data. According to the function of the filter, the filters are divided into different levels. For example, the first layer is a primary filter, mainly intercepting large particles of dust and hair; the second layer is an intermediate filter, used to filter medium-sized particles such as pollen; the third layer is a high-efficiency filter, mainly filtering fine particles such as PM2.5 and harmful gases. According to the functional characteristics of the filter and the analysis of the filter data, different filter levels are marked to generate the filter level data of the air purification device. The filter interval data and the filter level data are associated. For example, the interval of the primary filter is marked as 5 mm, the interval of the intermediate filter is 8 mm, and the high-efficiency filter is the last layer of the filter, forming a complete filter level information, including the intervals and functions of each layer. According to the shape of the filter (such as rectangular or circular), the surface of the filter is divided into multiple regions. For a rectangular filter, it can be divided into multiple small rectangular regions. For example, a filter of 30 cm × 20 cm can be divided into 6 small regions of 10 cm × 10 cm. For a circular filter, it can be divided into several fan-shaped regions according to the radius and angle. Combining the filter level data, the filter regions with different functions are further subdivided. For example, in the high-efficiency filter layer, according to the air flow direction and the importance of the filter, it is divided into a central region and an edge region. The central region undertakes a higher filtration task and bears a larger amount of soot. Laser particle sensors are arranged in each filter segmentation region. These sensors can emit laser beams and receive scattered light. When soot particles pass through the laser beam, the laser will be scattered. The sensors measure the number and size of the soot particles in this region according to the intensity and angle change of the scattered light.Each sensor will continuously monitor and feed back the data. For example, in a certain filter segmentation area, the monitored PM2.5 particle concentration is 30μg / m³, and the PM10 particle concentration is 50μg / m³, forming the filter smoke monitoring data of the area. By summarizing the monitoring data of different areas, the smoke distribution information of the entire filter can be obtained. If the air purification associated control instruction is "turn on the air purification module and strengthen purification", it means that there are more pollutants at present, and it is necessary to focus on areas with high smoke concentration. Combined with the filter smoke monitoring data, find out the areas where the smoke concentration exceeds a certain threshold (such as PM2.5 concentration exceeds 40μg / m³), and mark them as high-load areas; for areas where the smoke concentration is lower than the threshold, mark them as low-load areas. Assuming that the PM2.5 concentration exceeds 40μg / m³ in some areas and is lower than this value in other areas, the device filter available area data is generated based on this information. If the filter has been used for a long time, some areas of it will fail locally, and these failed areas will also be marked in the device filter available area data. According to the structure of the filter and the airflow organization method in the equipment, the filtration path of the smoke is constructed. For the normal operating area, according to the position of the air inlet and outlet of the equipment, combined with the available area data of the filter, the flow path of the smoke is simulated. For example, in the filter area near the air inlet, the smoke concentration will first increase, and then as the air passes through the filter, the smoke is filtered and the concentration decreases. Use software or drawing tools to graphically represent the various areas of the filter and their corresponding smoke concentration information, airflow direction, and filtration conditions. Different colors can be used to represent different smoke concentrations, arrows can be used to represent the airflow direction, and a smoke filtration path diagram can be generated to intuitively observe the working status of the filter and the filtration process of the smoke. For example, red is used to represent high smoke concentration areas, blue is used to represent low smoke concentration areas, and arrows are used to point from the air inlet to the air outlet to show the filtration direction of the smoke.

[0094] Preferably, step S24 includes the following steps:

[0095] Step S241: extracting equipment working status data according to the air purification control instruction, thereby obtaining equipment working status data; extracting air quality index data according to the equipment working status data, thereby obtaining air quality index data;

[0096] Step S242: performing a filter area smoke thickness analysis on the filter area smoke monitoring data according to the air quality index data, thereby obtaining filter area smoke thickness data; performing a filter area pollution degree analysis on the filter area smoke thickness data, thereby obtaining filter area pollution data;

[0097] Step S243: Dynamically adjust the data of the filter screen pollution area according to the air purification associated control instruction, so as to obtain the available area data of the equipment filter screen; construct the soot filtering hole path through the available area data of the equipment filter screen, and generate a soot filtering path diagram.

[0098] In the embodiments of the present invention, an air purification device is usually equipped with an internal control system, which records and stores various operating parameters of the device. By establishing a communication connection with the device control system (for example, through wired or wireless communication protocols such as RS485, Wi-Fi, etc.), it is possible to extract the device operating status data related to the air purification control instructions. Determine what operating mode the device is currently in, such as automatic mode, manual mode, sleep mode, etc. For example, if the air purification associated control instruction is "turn on the air purification module and enhance purification", the device switches to the high-power operation mode, and this mode information is part of the device operating status data. Obtain the current operating power value of the device, in watts (W). For example, in the enhanced purification mode, the device operating power increases from 30W usually to 60W. The fan is an important part of the air purification device, and its rotational speed directly affects the air circulation volume. Through the information recorded by the sensors or control system inside the device, obtain the real-time rotational speed of the fan, in revolutions per minute (RPM). For example, the fan rotational speed is 1500 RPM in the normal mode and increases to 2500 RPM during enhanced purification. There is a certain correlation between the device operating status data and the air quality index data. A variety of air quality sensors are usually installed inside the device, such as PM2.5 sensors, PM10 sensors, formaldehyde sensors, TVOC (total volatile organic compounds) sensors, etc. According to the operating mode and the current air treatment situation reflected by the device operating status data, obtain the air quality index data from the corresponding sensors. Obtain the current PM2.5 concentration value in the air from the PM2.5 sensor, in micrograms per cubic meter (μg / m³). If the device is in the enhanced purification mode, it means that the current PM2.5 concentration is relatively high. Suppose the obtained PM2.5 concentration is 80 μg / m³. Similarly, obtain the PM10 concentration data, such as 120 μg / m³. Obtain the formaldehyde concentration from the formaldehyde sensor, in milligrams per cubic meter (mg / m³). Suppose the measured formaldehyde concentration is 0.15 mg / m³; obtain the TVOC concentration from the TVOC sensor, for example, 0.8 mg / m³. Organize and summarize these data to form the air quality index data. Establish an association model between the air quality index data and the filter soot monitoring data. This model can be constructed based on experimental data or empirical formulas. For example, through a large number of experiments, it is found that within a certain period of time, there is a linear relationship between the PM2.5 concentration in the air and the soot thickness growth in a specific area of the filter. Let this relationship be: filter area soot thickness (mm) = k × PM2.5 concentration (μg / m³) + b, where k and b are coefficients determined through experiments. According to the above model, combined with the obtained air quality index data (such as PM2.5 concentration) and the filter soot monitoring data (soot-related information for each filter segmentation area), calculate the soot thickness of each filter area.For example, for a certain filter screen area, it is known that the PM2.5 concentration corresponding to this area is 60 μg / m³, and the soot thickness of this area is calculated to be 0.3 mm according to the model. Similar calculations are performed for all filter screen areas to obtain the soot thickness data of the filter screen areas. According to the material, design life, and air purification requirements of the filter screen, different pollution degree standards for the filter screen areas are set. For example, areas with a soot thickness less than 0.2 mm in the filter screen area are defined as lightly polluted areas; areas with a soot thickness between 0.2 - 0.5 mm are defined as moderately polluted areas; areas with a soot thickness greater than 0.5 mm are defined as severely polluted areas. Based on the set standards, the soot thickness data of each filter screen area is judged to determine the pollution degree of each area. The areas are classified and sorted according to the pollution degree, and the positions and ranges of areas with different pollution degrees are marked to generate filter screen pollution area data. For example, information such as marking the upper left part of the filter screen as a severely polluted area and the lower right part as a lightly polluted area is generated. Analyze the specific content of the air purification associated control instruction. If the instruction is "turn on the air purification module and strengthen purification", it indicates that a higher requirement is placed on the filtering effect of the filter screen, and the available areas of the filter screen need to be evaluated more strictly. At this time, the ranges of the lightly and moderately polluted areas recognized as available areas will be reduced, or special treatment measures will be taken for the severely polluted areas, such as marking them as unavailable but requiring key attention for cleaning. According to the instruction requirements, the filter screen pollution area data is adjusted accordingly. For example, an area originally defined as lightly polluted, under the strengthening purification instruction, if the soot thickness exceeds 0.15 mm, it will be adjusted to a moderately polluted area, and its eligibility as an available area of the equipment filter screen will be re-evaluated. Through this dynamic adjustment, equipment filter screen available area data is generated to clarify which filter screen areas can still effectively participate in the air purification work under the current instruction. Based on the equipment filter screen available area data, combined with the physical structure of the filter screen (such as the material and pore distribution of the filter screen) and the air flow principle in the air purification equipment, using computer simulation technology or algorithms based on physical models, simulate the filtration process of soot in the filter screen. Determine the hole paths through which the soot passes within the available areas of the filter screen. For example, according to the arrangement of the filter screen fibers, pore size, and air flow direction, determine how the soot enters the filter screen from the air inlet, passes through the available areas with different pollution degrees, and finally reaches the air outlet. Display these paths in a graphical manner to generate a soot filtration path diagram. Professional drawing software or the built-in visualization interface of the device can be used to represent the flow direction and filtration path of the soot with elements such as lines and arrows, and different colors or line thicknesses can represent the path conditions of areas with different pollution degrees, so as to intuitively understand the filtration process of the soot in the filter screen and the actual working state of the filter screen.

[0099] Preferably, constructing the soot filtration hole paths through the equipment filter screen available area data includes:

[0100] Calculate the filter hole diameter of the filter layer data of the air purification equipment through the available area data of the equipment filter screen to obtain the filter hole diameter data for blocking soot; confirm the soot blocking direction of the filter layer data of the air purification equipment based on the filter hole diameter data for blocking soot to obtain the filter soot blocking direction data;

[0101] Construct an initial filter soot filtration path for the available area data of the equipment filter screen according to the filter soot blocking direction data to generate an initial filter soot filtration path; analyze the types of protective soot for the initial filter soot filtration path through a preset soot particle database to generate filter protective soot type data;

[0102] Use the filter protective soot type data to mark the soot protection composite path for the initial filter soot filtration path to generate a filter soot protection composite path; generate a filter soot core filtration path based on the coincidence of the filter soot protection composite path and the initial filter soot filtration path;

[0103] Visualize the filter soot core filtration path to generate a soot filtration path diagram.

[0104] In the embodiments of the present invention, the available area data of the device filter and the filter layer data of the air purification device are integrated. The available area data of the device filter contains the availability information of different areas of the filter, while the filter layer data contains the structural information of different layers of the filter, such as the filter types of different layers, the initial designed filter hole diameter range, etc. For multi-layer filters, different layers of filters have different functions and different initial hole diameters. For example, the primary filter has a relatively large hole diameter, ranging from 100 to 500 microns, while the high-efficiency filter has a smaller hole diameter, between 0.1 and 10 microns. According to the filter usage conditions in the available area data of the device filter, such as the usage duration of the filter and the pollution degree of this area (inferred from the filter soot monitoring data), the initial filter hole diameter is adjusted. Suppose a filter area has been used for a long time and has a high pollution degree, its filter holes will become smaller due to the blockage of dust and soot. Empirical formulas or physical models can be used to calculate the blocked filter hole diameter. For example, assume that the initial average hole diameter of the high-efficiency filter is 5 microns. According to the soot accumulation amount (known from the filter soot monitoring data) and the usage time of this area, the calculated current filter soot blocking hole diameter of this area is 3 microns, and so on, the filter soot blocking hole diameter data for different layers and different areas of the filter are calculated. The filter soot blocking hole diameter data of different filter layers will affect the blocking direction of soot. In the filter, the air flow usually flows from the intake side to the outlet side. However, due to the structure and hole diameter distribution of the filter, the blocking effect of soot in different directions is different. For filter areas with different hole diameters, soot is more likely to pass through the area with a larger hole diameter. For example, in a layered filter, if a certain area of the upper filter has a reduced filter soot blocking hole diameter due to local blockage, while the diameter of the adjacent area is relatively large, then soot is more likely to pass through from the direction of the adjacent area. Combining the air flow organization mode of the filter (which can be known from the design specifications of the device or air flow simulation), according to the filter soot blocking hole diameter data, determine in which directions different areas and layers of the filter will more effectively block soot. For different filter layers, by analyzing the distribution and change of the hole diameter, mark the directions where soot is more easily blocked. For example, in the central area of the high-efficiency filter, since the filter soot blocking hole diameter of this area is relatively small at a certain moment, it will become the main soot blocking direction, record this information, and generate the filter soot blocking direction data. Based on the filter soot blocking direction data, determine the approximate path of soot when passing through the filter. Starting from the intake side, depict the flow trajectory of soot according to the blocking directions of different areas and layers.For example, in a filter screen in the shape of a cuboid, assuming the air inlet is on the left side and the air outlet is on the right side, according to the filter screen soot blocking orientation data, the soot will first pass through the upper area of the primary filter screen (because the hole diameter in this area is relatively large and not severely blocked), and then more of it will pass through the central area at the high-efficiency filter screen (because this area is the main soot blocking orientation). Use professional modeling software or algorithms to digitally represent the above path information. Divide the physical space of the filter screen into multiple small units, and based on the filter screen soot blocking orientation data and the available area data of the equipment filter screen, determine the flow order of the soot in these units to form an initial filter screen soot filtration path, which can be represented as a sequence of coordinate points or vectors describing the flow route of the soot from the air inlet to the air outlet. The preset soot particle database contains the physical and chemical properties of various soot particles, such as different particle size ranges, densities, shapes, adsorption properties, etc., as well as the behaviors of these soot particles in different environments. For different parts of the initial filter screen soot filtration path, according to the particle size range of the soot particles passing through this path (which can be inferred from the filtration characteristics of the filter screen and the existing air quality index data), look up the corresponding information in the soot particle database. Suppose the hole diameter range of a certain section of the initial filter screen soot filtration path is suitable for blocking particulate matter with a size of 0.5 - 2 microns. Look up the information of soot particles in this particle size range in the soot particle database, including common bacteria, certain fine dust, etc., and organize this information to form the filter screen protected soot type data, clarifying the types of soot that will be blocked and filtered on this path. Based on the filter screen protected soot type data, refine the marking of the initial filter screen soot filtration path. For different parts of the initial filter screen soot filtration path, make special markings according to the types of soot it can protect. For example, if a section of the initial filter screen soot filtration path mainly protects bacterial soot, it can be marked as "bacterial protection path"; another section mainly protects fine dust, then it is marked as "dust protection path", forming a composite path with multiple soot protection functions. Add these markings to the initial filter screen soot filtration path so that each part of the path contains the information of the types of soot it can protect, thus generating a filter screen soot protection composite path. Such a composite path not only contains the flow direction of the soot but also the soot protection functions possessed by this path at different positions. Compare the filter screen soot protection composite path with the initial filter screen soot filtration path and find the common parts between the two. These common parts are the key paths for truly realizing the soot filtration function. Since the filter screen soot protection composite path contains the information of the protected soot types, while the initial filter screen soot filtration path focuses more on the flow trajectory of the soot, the overlapping part of the two will comprehensively consider the filtration and flow information of the soot. For example, in some areas, both the flow requirements of the soot are met and there are clear soot protection functions. Extract these overlapping parts.Sort out and optimize the overlapping parts, remove some existing redundant information, and generate the core filtration path of the filter screen for soot. This core path more accurately reflects the process of how soot passes through the filter screen and is effectively filtered during actual use, including the actual flow trajectory of the soot and the information on the protection function. Professional drawing software such as AutoCAD, SketchUp, etc. can be used, or specialized data visualization tools such as Matplotlib in Python or D3.js in JavaScript can also be used. Input the information in the core filtration path of the filter screen for soot into the visualization tool. According to the coordinate points and marking information in the path, draw the physical shape of the filter screen (such as representing the filter screen with a cuboid or a cylinder), and use lines or arrows to represent the flow path of the soot. Different soot protection functions can be represented by different colors or line styles. For example, the "bacterial protection path" is represented by a green line, and the "dust protection path" is represented by a blue line, presenting the core filtration path in an intuitive graph to form a soot filtration path diagram. At the same time, a legend can be added to explain the meaning of different colors and lines to facilitate users' understanding of the soot filtration mechanism and path situation of the filter screen.

[0105] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0106] Step S31: Transmit the soot filtration path diagram to the intelligent control module in the kitchen air purification device for dynamic monitoring of the filter screen state, thereby generating dynamic monitoring data of the filter screen state; quantify the filter efficiency of the filter screen for the dynamic monitoring data of the filter screen state to generate quantified filter efficiency data of the filter screen;

[0107] Step S32: Compare the quantified filter efficiency data of the filter screen with the preset standard filter efficiency threshold of the filter screen. When the quantified filter efficiency data of the filter screen is less than the preset standard filter efficiency threshold of the filter screen, then based on the fan module in the kitchen air purification device, perform a strong air flow adjustment on the soot filtration path diagram to generate strong air flow control data;

[0108] Step S33: When the quantified filter efficiency data of the filter screen is greater than or equal to the preset standard filter efficiency threshold of the filter screen, then perform intermittent air flow control on the soot filtration path diagram based on the fan module in the kitchen air purification device to generate normal air flow control data;

[0109] Step S34: Based on the strong air flow control data and the normal air flow control data, perform a filter screen filtration mode switch on the dynamic monitoring data of the filter screen state to generate filter screen filtration mode switch data; perform a second device association control on the fan module in the kitchen air purification device according to the filter screen filtration mode switch data to generate an air filtration association control instruction.

[0110] In the embodiments of the present invention, by using the communication interface of the kitchen air purification device, such as through wired communication (such as RS485, CAN bus, etc.) or wireless communication (such as Wi-Fi, Bluetooth, etc.), the generated soot filtration path map is transmitted to the intelligent control module. The soot filtration path map exists in the form of image data or structured data format (such as a data table containing information such as path coordinates, path attributes, etc.). After receiving the soot filtration path map, the intelligent control module uses its built-in sensors and monitoring algorithms to dynamically monitor the state of the filter screen. For example, pressure sensors are arranged in different areas of the filter screen, and the state of the filter screen is reflected by monitoring the pressure difference before and after the filter screen; at the same time, combined with the soot monitoring data of the filter screen (data generated in the previous steps), the soot distribution and blockage conditions in different areas of the filter screen are grasped in real time, and the data collected by these sensors constitute the dynamic monitoring data of the filter screen state. The intelligent control module samples the state of the filter screen at regular time intervals (for example, every 5 minutes), and records information such as the pressure difference in different areas of the filter screen and the distribution of soot particles. Suppose at a certain moment, the pressure on the intake side of the filter screen is measured to be 100 Pa, the pressure on the outlet side is 80 Pa, and the soot particle concentrations in different areas are: the PM2.5 concentration in area A is 50 μg / m³, and the PM2.5 concentration in area B is 30 μg / m³, etc. Based on the dynamic monitoring data of the filter screen state, the filtration efficiency of the filter screen is calculated. For the filtration efficiency of particulate matter, the following formula can be used: Filtration efficiency = (Particulate matter concentration on the intake side of the filter screen - Particulate matter concentration on the outlet side of the filter screen) / Particulate matter concentration on the intake side of the filter screen × 100%. Suppose the average PM2.5 concentration on the intake side is 100 μg / m³ and on the outlet side is 20 μg / m³, then the filtration efficiency of PM2.5 is (100 - 20) / 100 × 100% = 80%. For different pollutants, their filtration efficiencies can be calculated separately, such as formaldehyde, TVOC, etc. Considering these data comprehensively, the quantified data of the filter screen filtration efficiency is generated. For formaldehyde, suppose the concentration on the intake side is 0.2 mg / m³ and on the outlet side is 0.05 mg / m³, then the filtration efficiency of formaldehyde is (0.2 - 0.05) / 0.2 × 100% = 75%. The filtration efficiency data of these different pollutants are sorted together as the quantified data of the filter screen filtration efficiency. For different pollutants and different filter screen levels, standard filter screen filtration efficiency thresholds are preset. For example, for the filtration of PM2.5, the set standard filter screen filtration efficiency threshold is 85%, and for the filtration of formaldehyde, the threshold is 80%. The indicators in the quantified data of the filter screen filtration efficiency are compared with the corresponding thresholds. If the calculated PM2.5 filtration efficiency is 80% (less than 85%) and the formaldehyde filtration efficiency is 75% (less than 80%), it indicates that the filtration efficiency of the filter screen is lower than the standard. When the filtration efficiency of the filter screen is lower than the standard, it is necessary to adjust the strong air flow of the soot filtration path map through the fan module. The rotation speed of the fan can be increased to enhance the air flow, thereby improving the filtration efficiency of the filter screen.The intelligent control module calculates the required adjustment amount of the fan speed according to the current filter screen state and the filtration path diagram. For example, if the current fan speed is 1500 RPM, according to the clogging situation and the degree of efficiency reduction of the filter screen, the fan speed is increased to 2000 RPM. At the same time, the working mode of the fan is adjusted, such as switching to the strong mode to increase the air flow. The generated strong air flow control data includes information such as the new fan speed (2000 RPM), the fan working mode (strong mode), and the duration (such as 10 minutes). These data will be sent to the fan module to perform corresponding operations. If all the indicators in the filter screen filtration efficiency quantization data are greater than or equal to the preset standard filter screen filtration efficiency threshold, it means that the filtration effect of the filter screen is good and there is no need for strong air flow. To save energy and extend the life of the filter screen, an intermittent operation mode is adopted. The intelligent control module adjusts the working mode of the fan according to the filter screen state and the filtration path diagram. For example, the fan is set to operate intermittently, running for 5 minutes and stopping for 2 minutes, and so on in a cycle. The ordinary air flow control data includes information such as the intermittent operation time of the fan (running for 5 minutes and stopping for 2 minutes) and the fan speed (maintaining the normal speed, such as 1500 RPM). According to the strong air flow control data and the ordinary air flow control data, combined with the dynamic monitoring data of the filter screen state, the filtration mode of the filter screen is determined. If the current strong air flow control data is adopted, it is marked as the "strong filtration mode"; if the ordinary air flow control data is adopted, it is marked as the "ordinary filtration mode". The filter screen filtration mode switching data also includes information such as the switching time and the switching condition. For example, it is recorded that at a certain time point, the mode is switched from the ordinary filtration mode to the strong filtration mode, or at another time point, it is switched back from the strong filtration mode to the ordinary filtration mode. According to the filter screen filtration mode switching data, corresponding air filtration associated control instructions are generated and sent to the fan module in the kitchen air purification equipment. When in the "strong filtration mode", the air filtration associated control instruction is "adjust the fan speed to 2000 RPM, turn on the strong mode, and last for 10 minutes"; when in the "ordinary filtration mode", the instruction is "let the fan operate intermittently at a speed of 1500 RPM, run for 5 minutes, and stop for 2 minutes". These instructions are sent to the fan module through the communication interface, and the fan module performs corresponding operations according to the received instructions to achieve precise control of the fan, so as to optimize the performance of the air purification equipment and extend the life of the filter screen.

[0111] Preferably, step S32 includes the following steps:

[0112] Step S321: Compare the filter screen filtration efficiency quantization data with the preset standard filter screen filtration efficiency threshold. When the filter screen filtration efficiency quantization data is less than the preset standard filter screen filtration efficiency threshold, initial fan data collection is performed based on the fan module in the kitchen air purification equipment to obtain initial fan operation data;

[0113] Step S322: Extract the flow trajectory of the soot from the initial fan operation data for the soot filtration path diagram to obtain the soot flow trajectory data, where the extraction of the soot flow trajectory includes the flow velocity and the flow direction; perform a hydrodynamic simulation on the soot flow trajectory data to generate the soot filtration flow simulation data;

[0114] Step S323: Extract the filtration key nodes from the soot flow trajectory data through the soot filtration flow simulation data to obtain the soot flow rate concentration nodes on the filtration path; based on the soot flow rate concentration nodes on the filtration path, mark the soot filtration flow rate regions of the soot filtration path diagram to generate the high soot filtration flow rate region and the low soot filtration flow rate region;

[0115] Step S324: Use the high soot filtration flow rate region to perform the air valve introduction and shunt control on the initial fan operation data to generate the fan regulation data for the high flow rate region; perform the air re-distribution control on the initial fan operation data according to the low soot filtration flow rate region to generate the fan regulation data for the low flow rate region;

[0116] Step S325: Integrate the fan regulation data for the high flow rate region and the fan regulation data for the low flow rate region to generate the strong air flow control data.

[0117] In the embodiments of the present invention, various indicators are extracted from the filter efficiency quantification data obtained in the previous steps, such as the filter efficiency of PM2.5, the filter efficiency of formaldehyde, etc. These indicators are compared one by one with the preset standard filter efficiency thresholds of the filter. For example, assume that the preset standard filter efficiency threshold of PM2.5 is 85%, and the current quantified filter efficiency data of the PM2.5 filter is 78%, the standard filter efficiency threshold of formaldehyde is 80%, and the actual filter efficiency of the formaldehyde filter is 72%. When both are less than the corresponding thresholds, subsequent operations are triggered. By communicating with the fan module in the kitchen air purification device, the initial operation data of the fan is obtained. These data include the rotational speed of the fan (expressed in revolutions per minute, RPM), the power of the fan (expressed in watts, W), the blade angle of the fan (expressed in degrees), and the operation mode of the fan (such as normal mode, sleep mode, strong mode, etc.). Assume that the initially collected fan operation data is: the fan rotational speed is 1500 RPM, the fan power is 30 W, the blade angle is 30°, and the operation mode is normal mode. According to the initial fan operation data and the soot filtration path diagram, using the principles of fluid mechanics, the flow velocities of soot at different positions are calculated. For example, based on the rotational speed and power of the fan, the flow velocity of air in the device can be estimated through empirical formulas or pre-established models. Assume that according to the 1500 RPM rotational speed and 30 W power of the fan, and the internal structure of the device (such as the sizes of the air inlet and outlet, the position of the filter, etc.), the flow velocity near the air inlet is calculated to be 2 m / s, the flow velocity in front of the filter is 1.5 m / s, and the flow velocity near the air outlet is 1 m / s. According to the structure of the device and the installation position of the fan, the general flow direction of the soot is determined. Usually, the soot enters from the air inlet, passes through the filter, and finally exits from the air outlet, forming a main flow direction from the air inlet to the air outlet. However, inside the device, the flow direction of the soot will change due to the blockage of the filter and the influence of the internal structure of the device. By analyzing the internal layout of the device, the flow direction of the soot at each position is clarified. For example, when passing through different regions of the filter, part of the soot will have local flow direction changes due to the different resistances of the filter. The flow velocity and flow direction information are organized into soot flow trajectory data, which represents the flow situation of the soot in the device in the form of coordinates or vectors. For example, the flow velocity at the coordinate (x1, y1, z1) is 2 m / s, and the flow direction is (1, 0, 0), indicating that the soot at this position flows along the positive x-axis at a speed of 2 m / s, and so on, to construct the soot flow trajectory data inside the entire device. Professional CFD software (such as ANSYS Fluent, OpenFOAM, etc.) or simulations are performed based on the CFD algorithm. The geometric structure of the device, the initial fan operation data (including rotational speed, power, blade angle, etc.), the soot flow trajectory data, etc. are used as inputs. These software or algorithms will solve the Navier-Stokes equations to simulate the detailed flow of the soot in the device.Through simulation, more accurate information on the soot flow can be obtained, such as the flow velocity distribution, pressure distribution, turbulence intensity, etc. at different positions. The generated soot filtration flow simulation data contains more detailed soot flow characteristics, such as the flow velocity change curves at different times and positions, and the flow separation and reattachment phenomena in complex structure areas. According to the soot filtration flow simulation data, find the positions where the soot flow is concentrated. These positions are usually areas with higher flow velocities or larger flow velocity gradients, which are affected by the internal structure of the equipment or the state of the filter mesh. For example, the soot flow will be concentrated near the air inlet, in areas where the filter mesh is partially unblocked, or in narrow channels inside the equipment. By analyzing the flow velocity distribution and flow rate data in the simulation data, extract these key nodes where the flow is concentrated, and use them as the soot flow concentration nodes on the filtration path. These nodes can be represented by coordinates or area identifiers within the equipment. For example, a flow concentration node is located at (x2, y2, z2). According to the soot flow concentration nodes on the filtration path, divide the soot filtration path map into regions. Mark the regions with concentrated flow as high-flow regions for soot filtration, and mark the regions with relatively low flow as low-flow regions for soot filtration. The division can be made according to a set flow rate threshold. For example, if the average flow velocity in a certain region exceeds 1.8 m / s, it is marked as a high-flow region for soot filtration, and the region with a flow velocity below 1.2 m / s is marked as a low-flow region for soot filtration. By analyzing the entire soot filtration path map, different flow regions are divided, and the corresponding marking information is generated. For the high-flow regions for soot filtration, consider introducing a damper for flow diversion to reduce the flow rate in this region and avoid a decrease in the filtration effect or equipment damage caused by excessive local flow velocity. Determine the opening degree and position of the damper according to the initial fan operation data and the specific conditions of the high-flow region. For example, install a damper near the high-flow region and adjust the damper opening degree to 50% according to the flow velocity and flow rate in this region to divert part of the air flow. The generated fan control data for the high-flow region includes information such as the position and opening degree of the damper (e.g., 50%) and the adjustment of the fan operation state (e.g., maintaining the fan speed at 1500 RPM). For the low-flow regions for soot filtration, it is necessary to increase the air flow rate in this region to improve the filtration effect. This can be achieved by adjusting the blade angle or speed of the fan. For example, adjust the blade angle of the fan to 35° to make more air flow to the low-flow region, or appropriately increase the fan speed to 1600 RPM to enhance the air flow in the low-flow region. The generated fan control data for the low-flow region includes information such as the adjusted blade angle (35°) and speed (1600 RPM) of the fan. Combine the fan control data for the high-flow region and the fan control data for the low-flow region together.For example, the strong air flow control data includes the following information: the damper position and opening degree in the high flow area (such as the damper at position (x3, y3, z3) with an opening degree of 50%); the fan adjustment information in the low flow area (such as the blade angle adjusted to 35° and the rotational speed of 1600 RPM); the operation mode of the overall fan (such as still maintaining the normal mode but making local adjustments).

Claims

1. An intelligent control method for kitchen air purification equipment, characterized in that: The following steps are involved: Step S1: using the air quality monitoring module in the kitchen air purification device to obtain real-time air monitoring data; performing harmful gas component screening on the real-time air monitoring data to obtain air harmful gas component screening data; performing first device association control on the air purification module in the kitchen air purification device according to the air harmful gas component screening data to generate an air purification association control instruction; Step S2: using the air purification module in the kitchen air purification device to obtain the filter data of the air purification device; performing filter smoke monitoring on the filter data of the air purification device to generate filter smoke monitoring data; performing device filter available area analysis on the filter smoke monitoring data according to the air purification associated control instruction to generate device filter available area data; constructing a smoke filtration path through the device filter available area data to generate a smoke filtration path map; Step S3: transmitting the smoke filtration path map to the intelligent control module in the kitchen air purification device to quantify the filter efficiency and generate filter efficiency quantification data; Perform air flow control based on the comparison result between the quantified data of the filter efficiency and the preset threshold value of the standard filter efficiency, and generate strong air flow control data and ordinary air flow control data; perform second device association control on the fan module in the kitchen air purification device based on the strong air flow control data and the ordinary air flow control data, and generate air filtration association control instructions; Step S4: Execute the instructions of the kitchen air purification equipment in sequence according to the air purification associated control instructions and the air filtration associated control instructions to generate air purification equipment instruction execution data; perform execution feedback on the air purification equipment instruction execution data, and visualize the results of the execution feedback to perform intelligent control operations on the kitchen air purification equipment.

2. The intelligent control method of kitchen air purification equipment according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: using the air quality monitoring module in the kitchen air purification device to obtain real-time air monitoring data; Step S12: performing data preprocessing on the real-time air monitoring data to generate standard real-time air monitoring data, wherein the data preprocessing includes data cleaning, data filtering, outlier processing and data standardization; Step S13: performing air gas composition analysis on the standard real-time air monitoring data to generate air gas composition data; using the air gas composition data to screen the standard real-time air monitoring data for harmful gas components to obtain air harmful gas component screening data; Step S14: performing first device association control on the air purification module in the kitchen air purification device through the screening data of harmful gas components in the air, and generating an air purification association control instruction.

3. The intelligent control method of kitchen air purification equipment according to claim 2 is characterized in that: Step S13 includes the following steps: Step S131: performing gas sampling based on standard real-time air monitoring data to generate gas sampling data; performing gas concentration measurement on the gas sampling data to generate air gas concentration data; Step S132: using spectral technology to perform spectral analysis on the air gas concentration data to generate gas spectral characteristic data; based on the gas spectral characteristic data, performing gas component identification on the gas sampling data to generate air gas composition data; Step S133: calculating the content ratio of the air gas concentration data through the air gas composition data to generate gas composition ratio data; performing a harmful gas threshold comparison on the gas composition ratio data based on a preset harmful gas database to generate harmful gas threshold comparison data; Step S134: performing component correlation analysis on the air gas composition data according to the harmful gas threshold comparison data to generate harmful gas component correlation data; performing harmful gas component screening on the air gas composition data using the harmful gas component correlation data to obtain air harmful gas component screening data.

4. The intelligent control method of kitchen air purification equipment according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: using the air purification module in the kitchen air purification device to obtain the filter data of the air purification device; Step S22: performing filter interval analysis on the filter data of the air purification device to generate filter interval data; performing filter stratification on the filter data of the air purification device based on the filter interval data to generate filter level data of the air purification device; Step S23: performing filter area segmentation on the filter level data of the air purification device to generate filter segmentation areas; performing filter smoke monitoring on the filter segmentation areas through a laser particle sensor to generate filter smoke monitoring data; Step S24: Analyze the available area of ​​the equipment filter on the filter smoke monitoring data according to the air purification associated control instruction to generate the available area data of the equipment filter; construct the smoke filtration path through the available area data of the equipment filter to generate the smoke filtration path map.

5. The intelligent control method of kitchen air purification equipment according to claim 4 is characterized in that: Step S24 includes the following steps: Step S241: extracting equipment working status data according to the air purification control instruction, thereby obtaining equipment working status data; extracting air quality index data according to the equipment working status data, thereby obtaining air quality index data; Step S242: performing a filter area smoke thickness analysis on the filter area smoke monitoring data according to the air quality index data, thereby obtaining filter area smoke thickness data; performing a filter area pollution degree analysis on the filter area smoke thickness data, thereby obtaining filter area pollution data; Step S243: dynamically adjust the filter contamination area data according to the air purification associated control instruction, so as to obtain the equipment filter available area data; construct the smoke filter hole path through the equipment filter available area data to generate a smoke filter path map.

6. The intelligent control method of kitchen air purification equipment according to claim 5, characterized in that: The smoke filter hole path construction based on the available area data of the equipment filter includes: Calculate the diameter of the filter holes of the air purification equipment filter level data through the available area data of the equipment filter, and obtain the diameter data of the smoke and dust blocking holes of the filter; confirm the smoke and dust blocking position of the air purification equipment filter level data based on the smoke and dust blocking hole diameter data of the filter, and obtain the smoke and dust blocking position data of the filter; According to the smoke blocking position data of the filter, the initial smoke filtering path of the filter is constructed for the available area data of the equipment filter, and the initial smoke filtering path of the filter is generated; the smoke type protection of the initial smoke filtering path of the filter is analyzed through the preset smoke particle database, and the smoke type protection data of the filter is generated; Using the filter protection smoke type data, the initial filter smoke filtration path is marked with a smoke protection composite path to generate a filter smoke protection composite path; based on the filter smoke protection composite path and the initial filter smoke filtration path, the filter paths are overlapped to generate a filter smoke core filtration path; The filter core filtration path of the soot is visualized to generate a soot filtration path map.

7. The intelligent control method of kitchen air purification equipment according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: transmitting the smoke filtration path map to the intelligent control module in the kitchen air purification device to dynamically monitor the filter status, thereby generating dynamic monitoring data of the filter status; quantifying the filter efficiency of the dynamic monitoring data of the filter status, and generating quantitative data of the filter efficiency; Step S32: comparing the quantified data of the filter efficiency with a preset threshold value of the standard filter efficiency. When the quantified data of the filter efficiency is less than the preset threshold value of the standard filter efficiency, a strong air flow adjustment is performed on the smoke filter path diagram based on the fan module in the kitchen air purification device to generate strong air flow control data. Step S33: when the quantified data of the filter efficiency is greater than or equal to the preset threshold value of the standard filter efficiency, intermittent air flow control is performed on the smoke filter path map based on the fan module in the kitchen air purification device to generate normal air flow control data; Step S34: Based on the strong air flow control data and the ordinary air flow control data, the filter status dynamic monitoring data is used to switch the filter filtering mode, and the filter filtering mode switching data is generated; according to the filter filtering mode switching data, the fan module in the kitchen air purification equipment is subjected to the second device association control, and the air filtration association control instruction is generated.

8. The intelligent control method of kitchen air purification equipment according to claim 7, characterized in that: Step S32 includes the following steps: Step S321: comparing the quantified data of the filter efficiency with a preset threshold value of the standard filter efficiency. When the quantified data of the filter efficiency is less than the preset threshold value of the standard filter efficiency, initial fan data collection is performed based on the fan module in the kitchen air purification device to obtain initial fan operation data. Step S322: extracting the flow trajectory of smoke from the smoke filtration path diagram according to the initial fan operation data to obtain smoke flow trajectory data, wherein the smoke flow trajectory extraction includes flow velocity and flow direction; performing fluid dynamics simulation on the smoke flow trajectory data to generate smoke filtration flow simulation data; Step S323: extracting key nodes from the smoke flow trajectory data through the smoke filtration flow simulation data to obtain smoke flow concentration nodes of the filtration path; marking the smoke filtration flow area of ​​the smoke filtration path diagram based on the smoke flow concentration nodes of the filtration path to generate a smoke filtration high flow area and a smoke filtration low flow area; Step S324: using the smoke filtration high flow area to perform air valve introduction diversion control on the initial fan operation data, and generating the fan control data of the high flow area; performing air redistribution control on the initial fan operation data according to the smoke filtration low flow area, and generating the fan control data of the low flow area; Step S325: Integrate the fan control data in the high-flow area and the fan control data in the low-flow area to generate high-efficiency air flow control data.

9. An intelligent control system for kitchen air purification equipment, characterized in that: For executing the intelligent control method of the kitchen air purification device according to claim 1, the intelligent control system of the kitchen air purification device comprises: The gas identification module is used to obtain real-time air monitoring data using the air quality monitoring module in the kitchen air purification device; screen the real-time air monitoring data for harmful gas components to obtain air harmful gas component screening data; perform first device association control on the air purification module in the kitchen air purification device through the air harmful gas component screening data to generate air purification association control instructions; The filter analysis module is used to obtain the filter data of the air purification equipment by using the air purification module in the kitchen air purification equipment; perform filter smoke monitoring on the filter data of the air purification equipment to generate filter smoke monitoring data; perform equipment filter available area analysis on the filter smoke monitoring data according to the air purification associated control instructions to generate equipment filter available area data; construct a smoke filtration path through the equipment filter available area data to generate a smoke filtration path map; The air purification module is used to transmit the smoke and dust filtering path map to the intelligent control module in the kitchen air purification device to quantify the filter filtration efficiency and generate the filter filtration efficiency quantification data; perform air flow control based on the comparison result of the filter filtration efficiency quantification data and the preset standard filter filtration efficiency threshold value, and generate strong air flow control data and ordinary air flow control data; perform second device association control on the fan module in the kitchen air purification device based on the strong air flow control data and the ordinary air flow control data, and generate air filtration association control instructions; The intelligent control module is used to execute the instructions of the kitchen air purification equipment in sequence according to the air purification related control instructions and the air filtration related control instructions, and generate the air purification equipment instruction execution data; perform execution feedback on the air purification equipment instruction execution data, and visualize the results of the execution feedback to perform the intelligent control operation of the kitchen air purification equipment.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed, the intelligent control method for the kitchen air purification device according to any one of claims 1 to 8 is implemented.

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