Intelligent control method and system for kitchen air purification equipment and medium

Abstract

The invention relates to the technical field of equipment control, in particular to an intelligent control method and system for kitchen air purification equipment and a medium. The method comprises the following steps: acquiring real-time air monitoring data by using an air quality monitoring module in the kitchen air purification equipment; performing harmful gas component screening on the real-time air monitoring data to obtain air harmful gas component screening data; first equipment association control is conducted on an air purification module in the kitchen air purification equipment through the air harmful gas component screening data, and an air purification association control instruction is generated; acquiring filter screen data of the air purification equipment by using an air purification module in the kitchen air purification equipment; and performing filter screen smoke monitoring on the filter screen data of the air purification equipment to generate filter screen smoke monitoring data. Through real-time monitoring, dynamic adjustment, filter screen management optimization, efficiency quantitative control and an execution feedback mechanism, the intelligent level of kitchen air purification equipment control is improved.

Description

Technical Field

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

[0002] In the early days, kitchen air purification equipment was mainly based on traditional range hoods, which mainly relied on mechanical ventilation and oil fume separation technology, and achieved basic oil fume emissions through a single air volume adjustment. However, this method has defects such as high energy consumption, low purification efficiency, and high noise, which makes it difficult to meet the requirements of modern families for cleanliness and environmental protection. Air purification equipment began to introduce multi-layer filtration technology, such as activated carbon filters 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 technology, intelligent control has gradually become a research and development focus. Based on technologies such as PM2.5 sensors and gas sensors, the equipment can sense the oil fume concentration and air quality in real time, automatically adjust the wind speed and purification mode, and improve the convenience of use. In recent years, artificial intelligence, big data, and edge computing technologies have further promoted the intelligence of kitchen air purification equipment. Through machine learning algorithms, the equipment can be personalized and optimized according to user habits and environmental characteristics. However, the current traditional equipment usually relies on manual inspection to manage the filter, and it is difficult to detect filter blockage or pollution problems in time. At the same time, the filter efficiency is usually not quantified, and the fan control is relatively extensive, which leads to a low level of intelligence in the control of kitchen air purification equipment. Summary of the invention

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

[0004] To achieve the above object, an intelligent control method for kitchen air purification equipment is provided, the method comprising the following steps: 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 filtration efficiency and generate filter filtration efficiency quantification data; performing air flow control based on the comparison result of the filter filtration efficiency quantification data and the preset standard filter filtration efficiency threshold, generating strong air flow control data and ordinary air flow control data; performing 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 generating 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.

[0005] The present invention can accurately identify potential pollutants (such as oil smoke, PM2.5, carbon monoxide, etc.) in the kitchen air through real-time air quality monitoring and screening of harmful gas components, improve the response efficiency of the air purification equipment, and the generated air purification associated control instructions enable the air purification module to automatically adjust the operation mode according to the real-time pollution situation, so as to realize dynamic and intelligent purification. This step ensures that the purification equipment can quickly perceive and respond to changes in air quality in the kitchen environment, and improves the overall sensitivity of the equipment. Through the filter smoke monitoring, the filter usage and remaining available area data can be obtained in time, the filter utilization rate can be optimized, the replacement frequency can be reduced, and the generated smoke filter path map can make the air flow more efficient, avoid invalid filtering areas, and improve the purification efficiency. Through the analysis of the available area of ​​the equipment filter, the reasonable allocation of filter resources can be achieved, and the energy consumption of equipment operation can be reduced. The quantitative data of the filter filtration efficiency provides a clear performance indicator, which is helpful to evaluate and optimize the actual purification effect of the filter. Based on the strong and ordinary air flow control data, a hierarchical response to different purification needs can be achieved to ensure that the strongly polluted areas are treated with emphasis. The second device association control optimizes the operation mode of the fan module, further enhancing the overall purification performance and energy efficiency of the air purification equipment. By sequentially executing air purification and filtration control instructions, the coordination of equipment operation is ensured and unnecessary resource consumption is reduced. The visualization of the instruction execution results can intuitively present the air purification effect and equipment operation status, which is convenient for users and maintenance personnel to monitor and adjust the equipment operation. The intelligent analysis of execution feedback can continuously optimize the equipment operation logic, making the purification process more efficient and stable. Therefore, the present invention improves the intelligence level of kitchen air purification equipment control through real-time monitoring, dynamic adjustment, filter management optimization, efficiency quantification control and execution feedback mechanism.

[0006] Preferably, step S1 comprises 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.

[0007] The present invention obtains real-time data through an air quality monitoring module, which can accurately reflect the pollutants and gas components in the kitchen air, and provides accurate basic data for subsequent analysis and optimization. The monitoring data is cleaned, filtered, processed and standardized for outliers to 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, a scientific basis is provided for the timely treatment and discharge of harmful gases, ensuring that the air quality meets the standards and avoids the harm of harmful gases. According to the screening results of harmful gas components in the air, the air purification module of the kitchen air purification equipment is intelligently controlled, the working state of the purification equipment is automatically adjusted, the automatic adjustment and purification of air quality is achieved, and the air quality of the kitchen environment is improved. 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 more fresh and comfortable living environment can be provided. Intelligent association control instruction generation can ensure that the air purification module operates efficiently when needed, avoids energy waste caused by excessive operation, and ensures that the air quality is guaranteed.

[0008] Preferably, 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.

[0009] The present invention can obtain gas concentration data in the air with high precision through gas sampling and concentration measurement, provide reliable basic data for subsequent analysis, and avoid errors caused by improper sampling or inaccurate measurement. Spectral analysis of gas concentration data using spectral technology makes gas component identification more accurate. Spectral analysis can identify multiple gas components in the air, improve recognition efficiency and accuracy, and is particularly suitable for gas component analysis in complex environments. Detailed gas component data is generated through spectral analysis of gas concentration data. This step ensures accurate identification of different gas components, avoids misidentification or missed detection, and improves the comprehensiveness of air component detection. The ratio of gas components is calculated and compared with the threshold value of a preset harmful gas database to effectively identify the harmful gas components in the air. The automation of this process enhances detection efficiency, reduces errors in manual judgment, and ensures the timeliness and accuracy of air quality monitoring. The main components of harmful gases can be screened out through harmful gas threshold comparison data and component correlation analysis. 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 can be carried out to ensure the timely removal of harmful substances in environments such as kitchens, thereby 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 intelligence level of the system, reduces the need for manual intervention, and makes air quality monitoring and optimization more efficient and reliable.

[0010] Preferably, step S2 comprises 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.

[0011] The present invention can deeply understand the working state and structural characteristics of the filter by acquiring the filter data of the air purification equipment and performing filter interval analysis, ensuring that the filter performance of the air purification equipment is accurately monitored, thereby improving the purification effect. The filter is layered through the filter interval data, which helps to identify the role and efficiency of filters at different levels, reasonably allocate resources, and optimize the working effect of the filter. This process can ensure that the air purification equipment performs efficient filtration at multiple levels and improves the purification efficiency. After the filter is segmented, the smoke concentration of each segmented area can be accurately captured by using a laser particle sensor for smoke monitoring. The monitoring method can effectively find the degree of pollution in each area of ​​the filter and provide data basis for subsequent cleaning or maintenance. According to the filter smoke monitoring data, the filter available area is analyzed, which can accurately determine which areas of the filter are effective and which areas of the filter are polluted or blocked. This intelligent analysis helps to optimize the service life of the filter and ensure the efficient operation of the purification equipment. Through the filter available area data, a smoke filtration path map is constructed to achieve the optimal working path of the air purification equipment. This path optimization can ensure that the smoke filtration process is more efficient, avoid unnecessary energy waste, and improve the speed of air quality improvement. Through real-time monitoring and optimization analysis of the filter status, it is possible to reduce overuse of the filter, 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 area segmentation, smoke monitoring and path optimization, air pollution problems in the kitchen environment can be handled more accurately, 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 that the air quality is effectively improved.

[0012] Preferably, 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.

[0013] The present invention can reflect the operation of the equipment and the ambient air quality in real time by extracting the equipment working status data and the air quality index data, which provides reliable data support for the subsequent monitoring and adjustment of the filter, and helps to timely identify the changes in air quality and the abnormal working status of the equipment. By analyzing the thickness of the smoke in the filter area, the degree of pollution in each filter area can be accurately assessed. This process helps to identify which areas are more seriously polluted and which areas can still work normally, thereby providing clear guidance for subsequent cleaning or replacement. According to the filter pollution area data, dynamic adjustments are made 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 state of the air purification equipment according to the changes in air quality, ensuring that the purification effect is always maintained in the best state. The smoke filter hole path map is generated through the filter available area data, and the filtration path of air purification is optimized. This optimized path helps to improve the air purification efficiency, so that pollutants in each area can be filtered in a timely and effective manner, reducing energy waste and improving the purification rate. By accurately identifying and adjusting the contaminated area of ​​the filter, we can ensure that the air purification equipment always maintains efficient operation and avoid the purification effect affected by excessive filter contamination. 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, we can avoid excessive use or unnecessary maintenance of the filter, reduce the frequency of filter replacement, and save costs. At the same time, the optimized filtration path improves the energy efficiency of the equipment, reduces energy consumption, and improves the overall economy of the equipment.

[0014] Preferably, constructing a smoke filter hole path using 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.

[0015] The present invention calculates the diameter of the filter holes through the available area data of the equipment filter, and can accurately evaluate the pore size of the filter, which helps to analyze the filtering capacity of the filter and its blocking effect on smoke particles of different sizes, and provides necessary data for subsequent path optimization. The orientation of the filter is confirmed based on the diameter data of the smoke blocking holes of the filter, and the flow direction and blocking area of ​​the smoke can be accurately determined. This process helps to optimize the air flow path and filtration efficiency, and ensure that the smoke particles are intercepted to the greatest extent. The initial filter smoke filtration path is constructed according to the smoke blocking orientation data, laying the foundation for subsequent path optimization and improvement. The construction of the initial path can intuitively show the flow and filtration process of pollutants in the filter, and guide equipment adjustment and optimization. The initial filtration path is analyzed for the types of smoke protection through the smoke particle database, which helps to identify the types of smoke that the filter needs to deal with, and ensure that different types of harmful gases and particles are effectively filtered. This analysis provides a targeted adjustment strategy for the optimization of the filtration path. Based on the smoke type data, the composite path marking of the filtration path can design the best filtration strategy for different types of smoke. This marking optimization process improves the accuracy of the filtration path, so that different types of smoke can be more effectively processed when passing through the filter. By analyzing the overlap of the protective composite path and the initial filtration path, a core filtration path can be generated, which has the most efficient smoke interception capability. The fine adjustment of the core path ensures the maximum effect of the air purification process and improves the working efficiency of the filter. Through the visual display of the core filtration path, the filtration process of smoke can be intuitively understood, which not only provides clear work guidance for equipment maintenance and operators, but also provides a scientific basis for optimizing the path, making air purification work more efficient and accurate. Through a series of path optimization and data analysis, it can ensure that the filter can play the best filtration effect under different smoke conditions, thereby significantly improving the air purification efficiency. These optimization strategies help to reduce the leakage in the filtration process and improve the overall air quality.

[0016] Preferably, step S3 comprises 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.

[0017] Preferably, 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.

[0018] In this specification, an intelligent control system of a kitchen air purification device is provided, which is used to execute the intelligent control method of the kitchen air purification device mentioned above. The intelligent control system of the kitchen air purification device includes: 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.

[0019] The present invention also provides a storage medium storing a computer program, which, when executed, implements any one of the intelligent control methods for kitchen air purification equipment described above.

[0020] The beneficial effect of the present invention is that the air data is obtained in real time through the air quality monitoring module, and the harmful gas components are screened out, which can respond to the changes of air pollutants in the kitchen in time and ensure that the air quality is within a safe range. The filter analysis module monitors the smoke accumulation of the filter and judges the available area of ​​the filter, thereby optimizing the smoke filtration path, improving the use efficiency of the filter, and ensuring the air purification effect. The intelligent control module performs air flow control according to the quantitative data of the filter filtration efficiency, 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 the optimized purification effect under different environments. The entire system coordinates the associated control between various devices through the intelligent control module, including air purification modules, fan modules, etc., to ensure the coordinated operation of the system. The associated control between the devices ensures that the equipment operates smoothly and efficiently. The system can visualize the feedback during the execution process, which is convenient for users to understand the effect of the air purification process and the operating status of the equipment, provide reference for subsequent operations, and help improve the intelligence and management efficiency of the system. Therefore, the present invention improves the intelligence level of kitchen air purification equipment control through real-time monitoring, dynamic adjustment, filter management optimization, efficiency quantitative control and execution feedback mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of the steps of an intelligent control method for kitchen air purification equipment; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] To achieve this, please refer to Figures 1 to 3 , an intelligent control method for kitchen air purification equipment, the method comprising the following steps: 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 filtration efficiency and generate filter filtration efficiency quantification data; performing air flow control based on the comparison result of the filter filtration efficiency quantification data and the preset standard filter filtration efficiency threshold, generating strong air flow control data and ordinary air flow control data; performing 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 generating 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.

[0023] The present invention can accurately identify potential pollutants (such as oil smoke, PM2.5, carbon monoxide, etc.) in the kitchen air through real-time air quality monitoring and screening of harmful gas components, improve the response efficiency of the air purification equipment, and the generated air purification associated control instructions enable the air purification module to automatically adjust the operation mode according to the real-time pollution situation, so as to realize dynamic and intelligent purification. This step ensures that the purification equipment can quickly perceive and respond to changes in air quality in the kitchen environment, and improves the overall sensitivity of the equipment. Through the filter smoke monitoring, the filter usage and remaining available area data can be obtained in time, the filter utilization rate can be optimized, the replacement frequency can be reduced, and the generated smoke filter path map can make the air flow more efficient, avoid invalid filtering areas, and improve the purification efficiency. Through the analysis of the available area of ​​the equipment filter, the reasonable allocation of filter resources can be achieved, and the energy consumption of equipment operation can be reduced. The quantitative data of the filter filtration efficiency provides a clear performance indicator, which is helpful to evaluate and optimize the actual purification effect of the filter. Based on the strong and ordinary air flow control data, a hierarchical response to different purification needs can be achieved to ensure that the strongly polluted areas are treated with emphasis. The second device association control optimizes the operation mode of the fan module, further enhancing the overall purification performance and energy efficiency of the air purification equipment. By sequentially executing air purification and filtration control instructions, the coordination of equipment operation is ensured and unnecessary resource consumption is reduced. The visualization of the instruction execution results can intuitively present the air purification effect and equipment operation status, which is convenient for users and maintenance personnel to monitor and adjust the equipment operation. The intelligent analysis of execution feedback can continuously optimize the equipment operation logic, making the purification process more efficient and stable. Therefore, the present invention improves the intelligence level of kitchen air purification equipment control through real-time monitoring, dynamic adjustment, filter management optimization, efficiency quantification control and execution feedback mechanism.

[0024] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a step flow chart of an intelligent control method for kitchen air purification equipment of the present invention. In this example, the intelligent control method for kitchen air purification equipment includes the following steps: 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; In an embodiment of the present invention, by enabling the air quality monitoring module in the kitchen air purification device, ensure that its sensor is in working condition. 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. The 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 via wireless transmission (such as Wi-Fi or Zigbee). The control unit parses the original data and extracts key parameters (such as pollutant concentration, gas type). Compare the regional air quality standards to filter out the harmful gas component data that exceeds the standard. The type, concentration and degree of exceeding the standard of the gas that exceeds 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, select a suitable purification strategy. For example: for PM2.5 exceeding the standard, activate the high-efficiency filtration module. For VOC exceeding the standard, start the activated carbon adsorption module. For CO exceeding the standard, start the oxidation catalytic module. The purification strategy is converted into executable equipment control instructions, including starting the module, adjusting the wind speed, changing the purification mode, etc. The generated instructions are as follows: Instruction 1: Start the high-efficiency filtration module and set the wind speed to level 3. Instruction 2: Start the activated carbon adsorption module and set the running time to 10 minutes. The control unit sends the generated air purification-related control instructions to the air purification module. The module executes the operation immediately after receiving the instruction. The purification effect is monitored in real time, and the new air quality data is fed back to the control unit.

[0025] 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; In an embodiment of the present invention, by enabling a filter sensor (such as a resistance sensor, a pressure sensor, or an optical particle detection sensor) in an air purification module, the filter status is monitored in real time. Collect filter-related data, including filter blockage rate, particle deposition concentration, filter ventilation, etc. The filter sensor transmits the collected data to the device control unit in the form of a digital signal. The data is recorded by timestamp to form a filter monitoring data stream. The control unit analyzes the filter data to evaluate the smoke accumulation of the filter: calculate the filter load degree according to the particle concentration. Compare the filter design standard to determine whether the smoke accumulation exceeds the limit, and generate structured filter smoke monitoring data, including the following: smoke deposition (unit: g / m²). Blockage rate (unit: %). Filter warning status (normal / warning / overlimit). Divide the filter into multiple logical grid units (such as 10×10 partitions), and analyze the availability of each unit one by one. According to the smoke deposition and blockage rate, mark the status of each grid: Available area: deposition is lower than the threshold and the blockage rate is less than 70%. Unavailable area: deposition exceeds the standard or the blockage rate exceeds 90%. Output the available area data of the device filter, and record the location and status of the available grid cells. Use a path planning algorithm (such as dynamic planning) to analyze the available area data and construct the optimal filtration path: give priority to the path with the lowest resistance. Avoid passing through unavailable areas. Construct a smoke filtration path diagram to show the optimal filtration flow direction of smoke in the filter. Optimize the path to improve air purification efficiency and reduce equipment energy consumption. Convert the filtration path data into a visual graphic, including: filter area division (color distinguishes available and unavailable areas). Smoke filtration path (arrows indicate flow direction). Output the smoke filtration path diagram for monitoring or debugging.

[0026] Step S3: transmitting the smoke filtration path map to the intelligent control module in the kitchen air purification device to quantify the filter filtration efficiency and generate filter filtration efficiency quantification data; performing air flow control based on the comparison result of the filter filtration efficiency quantification data and the preset standard filter filtration efficiency threshold, generating strong air flow control data and ordinary air flow control data; performing 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 generating air filtration association control instructions; In an embodiment of the present invention, smoke concentration data in the kitchen is collected by using a smoke detection system, and a smoke filtration path map is generated according to the data. The path map is designed based on the air flow direction and pollution source distribution in the kitchen to guide the work of the air purification device. The smoke filtration path map is transmitted to the intelligent control module in the kitchen air purification device through a communication module (such as Wi-Fi, Bluetooth or wired connection). After receiving the path map, the control module starts to quantify the filter efficiency. The intelligent control module analyzes the filtration path map and identifies key areas in the filtered air (such as smoke sources, areas with high oil smoke concentration, etc.). Then, based on this information, the filtration efficiency of the filter 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, and the particle concentration (such as PM2.5, PM10), odor concentration and other indicators are usually used to evaluate the filtration efficiency. The quantitative data will include the filtration efficiency value (such as percentage) and the filter operation effect in a specific time period. The filter efficiency quantitative data includes the current filtering effect of the filter, such as "80% effective filtering", "90% effective filtering", etc. These data will be transmitted back to the central control system or directly stored in the internal storage of the kitchen air purification device for subsequent analysis. The intelligent control module will compare the actual filter efficiency quantitative data according to 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 means that the filtering effect is good; if it is lower than the threshold, it means 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 air flow, increase the speed and flow of air through the filter, and thus improve the overall air purification efficiency. Strong mode usually involves increasing the fan speed, adjusting the fan direction, or adjusting 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 normal mode, the fan speed is low and the air flow is maintained within the normal range. Based on the filter efficiency quantitative data and comparison results, the system generates two categories of air flow control data: strong air flow control data includes information such as fan speed, air flow rate and fan start / stop. Ordinary air flow control data includes information such as the normal operation status, flow rate and load of the fan. According to the generated strong and ordinary 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; in the ordinary air flow mode, the fan is maintained in the standard operating state. The working state of the fan is achieved through power regulation, wind speed setting and other methods to ensure that the air flow matches the air purification effect. The system will generate control instructions according to the strong and ordinary air flow control modes, including the adjustment parameters of the fan (such as speed, air flow, etc.).Air filtration-related control instructions will be sent to the fan module and other related equipment (such as filter cleaning mechanism, sensors, etc.) of the kitchen air purification equipment to ensure the efficiency and stability of the air purification process.

[0027] 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.

[0028] In an embodiment of the present invention, the intelligent control system of the kitchen air purification device receives the "air purification associated control instructions" and "air filtration associated control instructions" generated by the intelligent control module. The system schedules these control instructions according to priority and dependency. According to the real-time status of the equipment operation (such as the current air quality, filter status, etc.), the instructions will be executed in a reasonable order. First, adjust the fan and air flow direction to ensure that the air flow operates in the required mode. After ensuring smooth air flow, perform operations such as filter adjustment, cleaning or replacement to maintain the high efficiency of air filtration. The system sends instructions to various related hardware (such as fan module, filter control module, sensor module, etc.) and starts various operations. Specifically, it includes: adjusting the fan speed, wind speed and working status according to the air flow control data. According to the air filtration control data, perform filter cleaning, adjustment or replacement operations. Real-time monitoring of air quality, smoke concentration, etc. to ensure the normal operation of the equipment. During the execution of the instruction, the system will record the execution status of each step in real time, including: equipment status: such as fan speed, filter working status, etc. Air quality data: such as PM2.5 concentration, smoke concentration, etc. Operation time: working time of each module, filter cleaning cycle, etc. Equipment consumption data: such as power consumption, fan operating power, etc. These data will be integrated into "air purification equipment command execution data" for subsequent feedback analysis. During the execution of the command, the system monitors the feedback equipment status in real time to determine whether it is executed according to the predetermined plan. For example: whether the fan has reached the predetermined speed, whether the filter is cleaned or replaced according to the plan, and whether the air quality meets the set standards. The system generates execution feedback data based on the equipment response during the execution process.

[0029] Preferably, step S1 comprises 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.

[0030] In the embodiment of the present invention, a variety of sensors are built into the air quality monitoring module, for example, the laser scattering principle is used to detect the concentration of particles with a diameter of less than or equal to 2.5 microns in the air, in 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 30μg / m³. Also based on a specific sensing technology, the content of particles with a diameter of less than or equal to 10 microns in the air is measured, and the unit is also μg / m³. Assume that the data obtained is 50μg / m³. Using the infrared absorption principle, the concentration of CO2 in the air is monitored in ppm (parts per million), and the real-time data is assumed to be 500ppm. Using metal oxide semiconductor and other technologies, the content of VOC in the air is detected in milligrams per cubic meter (mg / m³), for example, the data obtained is 0.1mg / m³. Thermistors and capacitive humidity sensors are used to measure the temperature (unit: ℃) and relative humidity (unit: %) in the kitchen. Assuming that the temperature data is 25℃ and the relative humidity is 50%, these sensors collect data in real time and summarize them to form real-time air monitoring data. Check the rationality of the data and remove obviously erroneous data. For example, if the PM2.5 sensor returns a negative number (assuming that the PM2.5 concentration cannot be negative under normal conditions), correct it to 0μg / m³. For numerical data, use the mean filtering method. Taking PM2.5 data as an example, set a time window, such as collecting data every 10 seconds in the past 1 minute, for a total of 6 data points. Average these 6 data points to obtain the filtered PM2.5 data to smooth data fluctuations. Set thresholds based on experience and relevant standards to handle outliers. For example, for PM2.5, when the concentration exceeds 100μg / m³, it is considered to be an abnormally high value and is corrected to 100μg / m³; for CO2, if the concentration exceeds 1000ppm, it is corrected to 1000ppm. Use the Min-Max normalization method to map each data indicator to the interval of 0-1. Taking PM2.5 as an example, assuming that its minimum value under normal conditions is 0μg / m³ and its maximum value is 100μg / m³, for the PM2.5 concentration value X (unit: μg / m³) monitored in real time, the standardized value = (X-0) / (100-0). For example, if the real-time PM2.5 concentration is 30μg / m³, the standardized value is 0.3. The same method is applied to other data indicators, such as PM10 (assuming the normal range is 0-150μg / m³), CO2 (assuming the normal range is 0-1000ppm), VOC (assuming the normal range is 0-1mg / m³), etc., to generate standard real-time air monitoring data. Extract data related to air gas composition from standard real-time air monitoring data, such as standardized data of CO2 and VOC.Assuming that the standardized CO2 data is 0.5 (corresponding to the standardized value of 500ppm), and the VOC data is 0.1 (corresponding to the standardized value of 0.1mg / m³), the air gas composition data is generated. 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 greater than 500ppm, assuming this is the harmful critical value), CO2 is considered to be a harmful gas component and is included in the air harmful gas component screening data; for VOC, when the standardized value is greater than 0.5 (corresponding to the actual value greater than 0.5mg / m³, assuming this is the harmful critical value), it is included in the screening data. Assuming that the current CO2 standardized value is 0.5, which does not exceed the critical value, and the VOC standardized value is 0.1, which does not exceed the critical value, the air harmful gas component screening data is empty at this time. Control the air purification module according to the situation of the air harmful gas component screening data. If the air harmful gas component screening data is not empty, it indicates that harmful gases exist, and the command "turn on the air purification module and strengthen purification" is generated to make the air purification module run at high power to accelerate the filtering and decomposition of harmful gases; if the air harmful gas component screening data is empty, it means that the current air quality is relatively good, and the command "air purification module maintains the current state" is generated to maintain the current operating power to save energy and continuously monitor the air conditions.

[0031] Preferably, 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.

[0032] In an embodiment 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 samples can represent the air conditions in the kitchen. These gas samples are gas sampling data. Different sampling methods and equipment are required for different gas components. For example, for volatile organic compounds (VOCs), adsorption tubes can be used for sampling, while for common gaseous pollutants such as sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), etc., air bags or specific absorption liquids can be used for sampling. For some common gases, such as sulfur dioxide (SO2), a chemical reagent method can be used to pass the collected gas through an absorption bottle filled with an absorption liquid (such as potassium tetrachloromercuric solution) to cause a chemical reaction with the absorption liquid to generate a stable compound, and then the amount of the generated compound in the absorption liquid is determined by colorimetry or titration, and then the concentration of SO2 in the original gas is calculated. Assume that the SO2 concentration in the kitchen measured by this method is 0.05ppm (one part per million). For nitrogen oxides (NOx), the chemiluminescence method can be used to pass the gas sample into the reaction chamber. Under certain conditions, it reacts with ozone to produce excited NO2, which emits light when it returns to the ground state. The light intensity is detected by a photomultiplier tube, and the NOx concentration is calculated based on the quantitative relationship between the light intensity and the NOx concentration. Assume that the measured NOx concentration is 0.1ppm. 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. The CO concentration is calculated based on the current. Assume that the measured CO concentration is 5ppm. For oxygen (O2), an oxygen sensor is used to determine the oxygen concentration based on 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 the infrared spectrometer. When infrared light passes through the gas sample, different gas molecules will absorb infrared light of specific wavelengths. According to the position and intensity of the absorption peak, the gas spectrum characteristic data is generated. For example, carbon dioxide (CO2) will be at a specific wave number (such as 2349cm -¹) has a characteristic absorption peak, and the intensity of the absorption peak is related to the concentration of CO2. By analyzing these absorption peaks, gas spectrum 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 is irradiated on a gas sample, Raman scattering will occur, and different gas molecules will produce Raman scattered light of different frequencies. According to the frequency shift and intensity of the Raman scattered light, the gas spectrum characteristic data can be obtained. For example, water vapor (H2O) has 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 characteristic database, the measured gas spectral characteristic data is compared with it to determine the type of gas. For example, if a characteristic peak corresponding to formaldehyde (HCHO) appears in the spectrum, and the peak intensity is proportional to the concentration, combined with the previously measured concentration data, it can be determined that the gas composition data contains formaldehyde, and based on the spectral intensity and the pre-established quantitative relationship, the formaldehyde concentration is 0.02ppm, and the concentration data of various gas components are obtained, such as CO2 concentration of 400ppm, O2 concentration of 20.9%, CO concentration of 5ppm, etc. Calculate the proportion of various gas components in the total gas. For CO2, assuming that the total gas volume is 1 cubic meter, then the CO2 content ratio = 400ppm / (1×10 6)=0.04%. Similarly, similar calculations are performed for other gas components to obtain the content ratio of various gas components in the total gas, and generate gas component ratio data. The preset harmful gas database contains threshold information for various harmful gases. For example, for CO, the maximum allowed concentration is 10ppm, for SO2, the maximum allowed concentration is 0.5ppm, etc. The calculated gas component ratio data is converted into actual concentrations and compared with the thresholds in the harmful gas database. If the concentration of CO is 5ppm and the threshold is 10ppm, the comparison result is recorded as not exceeding the threshold; if the concentration of SO2 is 0.05ppm and the threshold is 0.5ppm, it is also recorded as not exceeding the threshold, and harmful gas threshold comparison data is generated. There is a correlation between certain 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) are also paid attention to. If the harmful gas threshold comparison data of CO shows that it is close to or exceeds the threshold, its correlation with other gas components is analyzed to generate harmful gas component correlation data, indicating the existence of combustion sources or other pollution sources. For some volatile organic compounds (VOCs), their correlation with other chemical substances will be analyzed. For example, some specific VOCs are related to the volatilization of cleaning agents or food ingredients used in the kitchen. The correlation between them is analyzed to provide information for subsequent processing. According to the harmful gas threshold comparison data and component correlation analysis results, gas components that exceed the threshold or have potential hazards are screened out. For example, if the concentration of formaldehyde is found to exceed its threshold in the harmful gas threshold comparison data (assuming that the threshold of formaldehyde is 0.01ppm, and the measured concentration is 0.02ppm), formaldehyde will be included in the air harmful gas component screening data; if CO is close to its threshold, it will also be included. For gases that are below the threshold and have no special correlation, such as normal concentration of O2, they are not included in the screening data.

[0033] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: 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.

[0034] In an embodiment of the present invention, the life of the filter is evaluated by recording the usage time of the filter. For example, the time from the time when the built-in clock of the device records the start of use of the filter to the current time is used as the filter usage time information, assuming that the current filter has been used for 100 hours. Pressure sensors are installed before and after the filter to measure the pressure difference before and after the air passes through the filter 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 absorbs more dust and impurities, the pressure difference will increase. Assuming that the initial pressure difference is 10Pa, it will rise to 50Pa with use. Some advanced air purification equipment will have built-in sensors to monitor the concentration of pollutants before and after the filter, so as to calculate the filtration efficiency of the filter. For example, for PM2.5, assuming that the PM2.5 concentration before the filter is 100μg / m³ and after the filter is 20μg / m³, it can be calculated that the filtration efficiency of the filter for PM2.5 is 80%. According to the physical structure of the filter, the intervals at different levels are analyzed. For example, for a multi-layer composite filter, the spacing between different filter layers is determined by physical measurement or by consulting the equipment specification. Assume that a composite filter has three layers, the spacing between the first and second layers is 5 mm, and the spacing between the second and third layers is 8 mm. These spacing information is used as filter spacing data. According to the function of the filter, the filter is divided into different levels. For example, the first layer is a primary filter, which mainly intercepts large particles of dust and hair; the second layer is a medium-efficiency filter, which is used to filter medium-sized particles such as pollen; and the third layer is a high-efficiency filter, which mainly filters tiny 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 spacing data and the filter level data are associated, for example, the spacing of the primary filter is marked as 5 mm, the spacing of the medium-efficiency filter is marked as 8 mm, and the high-efficiency filter is the last layer of the filter, forming a complete filter level information, including the spacing and function of each layer. According to the shape of the filter (such as rectangle or circle), the surface of the filter is divided into multiple areas. For a rectangular filter, it can be divided into multiple small rectangular areas. For example, a 30cm×20cm filter can be divided into 6 small areas of 10cm×10cm. For a circular filter, it can be divided into several sector-shaped areas according to the radius and angle. Combined with the filter level data, the filter areas with different functions are further subdivided. For example, in the high-efficiency filter layer, according to the airflow direction and the importance of the filter, it is divided into a central area and an edge area. The central area undertakes a higher filtering task and bears a larger amount of smoke. Laser particle sensors are arranged in each filter segmentation area. These sensors can emit laser beams and receive scattered light. When smoke particles pass through the laser beam, the laser is scattered. The sensor measures the number and size of smoke particles in the area according to the intensity and angle changes 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.

[0035] Preferably, 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.

[0036] In an embodiment of the present invention, the 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 a wired or wireless communication protocol, such as RS485, Wi-Fi, etc.), the device working status data related to the air purification control instruction can be extracted from it. 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 a high-power operating mode, and the mode information is part of the device working status data. Get the current operating power value of the device in watts (W). For example, in the enhanced purification mode, the operating power of the device is increased from 30W to 60W. The fan is an important part of the air purification device, and its speed directly affects the air circulation. The real-time speed of the fan is obtained through the information recorded by the sensor or control system inside the device, in revolutions per minute (RPM). For example, the fan speed is 1500RPM in normal mode and is increased to 2500RPM when enhanced purification. There is a certain correlation between the device working status data and the air quality index data. There are usually a variety of air quality sensors installed in the equipment, such as PM2.5 sensors, PM10 sensors, formaldehyde sensors, TVOC (total volatile organic compound) sensors, etc. According to the operation mode and current air treatment conditions reflected by the equipment working status data, the air quality index data is obtained from the corresponding sensor. The concentration value of PM2.5 in the current air is obtained from the PM2.5 sensor in micrograms per cubic meter (μg / m³). If the equipment is in enhanced purification mode, it means that the current PM2.5 concentration is high. Assume that the PM2.5 concentration obtained is 80μg / m³. Similarly, the concentration data of PM10 is obtained, such as 120μg / m³. The formaldehyde concentration is obtained from the formaldehyde sensor in milligrams per cubic meter (mg / m³). Assume that the measured formaldehyde concentration is 0.15mg / m³; the TVOC concentration is obtained from the TVOC sensor, such as 0.8mg / m³. These data are sorted and summarized to form air quality index data. Establish a correlation model between air quality index data and filter smoke 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 growth of smoke thickness in a specific area of ​​the filter. Assume that the relationship is: filter area smoke thickness (mm) = k × PM2.5 concentration (μg / m³) + b, where k and b are coefficients determined by experiments. According to the above model, combined with the obtained air quality index data (such as PM2.5 concentration) and filter smoke monitoring data (smoke-related information in each filter segmentation area), calculate the smoke thickness of each filter area.For example, for a certain filter area, it is known that the PM2.5 concentration corresponding to the area is 60μg / m³, and the smoke thickness of the area is calculated to be 0.3mm according to the model. Similar calculations are performed for all filter areas to obtain the smoke thickness data of the filter area. Different standards for the degree of pollution of the filter area are set according to the material, design life and air purification requirements of the filter. For example, the area where the smoke thickness of the filter area is less than 0.2mm is defined as a lightly polluted area; the area where the smoke thickness is between 0.2-0.5mm is defined as a moderately polluted area; and the area where the smoke thickness is greater than 0.5mm is defined as a heavily polluted area. According to the set standards, the smoke thickness data of each filter area is judged to determine the degree of pollution of each area. Each area is classified and sorted according to the degree of pollution, and the location and range of the area with different degrees of pollution are marked to generate the filter pollution area data. For example, the upper left corner of the filter is marked as a heavily polluted area, and the lower right corner is marked as a lightly polluted area. Analyze the specific content of the air purification associated control instructions. If the instruction is "turn on the air purification module and strengthen purification", it means that the filtering effect of the filter is required to be higher, and the available area of ​​the filter needs to be evaluated more strictly. At this time, the scope of the lightly and moderately polluted areas identified as available areas will be narrowed, or special treatment measures will be taken for the heavily polluted areas, such as marking them as unavailable but requiring special attention to cleaning. According to the requirements of the instruction, the filter pollution area data is adjusted accordingly. For example, in an area originally defined as lightly polluted, if the smoke thickness exceeds 0.15mm under the enhanced purification instruction, it will be adjusted to a moderately polluted area, and re-evaluated whether it belongs to the available area of ​​the device filter. Through this dynamic adjustment, the available area data of the device filter is generated to clarify which filter areas can still effectively participate in air purification under the current instruction. Based on the available area data of the device filter, combined with the physical structure of the filter (such as the material of the filter, pore distribution, etc.) and the airflow principle in the air purification equipment, the filtering process of smoke in the filter is simulated using computer simulation technology or an algorithm based on a physical model. Determine the hole path through which the smoke passes in the available area of ​​the filter. For example, based on the arrangement and pore size of the filter fibers and the direction of the airflow, determine how the smoke enters the filter from the air inlet, passes through the available areas with different pollution levels, and finally reaches the air outlet. These paths are displayed in a graphical way to generate a smoke filtration path map. You can use professional drawing software or the device's built-in visualization interface to use lines, arrows and other elements to represent the flow direction and filtration path of the smoke. Different colors or line thicknesses can represent the path conditions of areas with different pollution levels, so as to intuitively understand the filtration process of the smoke in the filter and the actual working status of the filter.

[0037] Preferably, constructing a smoke filter hole path using 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.

[0038] In an embodiment of the present invention, the device filter available area data and the air purification device filter level data are integrated. The device filter available area data contains the availability information of different areas of the filter, and the filter level data contains the structural information of different levels of the filter, such as the filter types of different levels, the range of the filter hole diameters of the initial design, etc. For multi-layer filters, filters of different levels have different functions and different initial hole diameters. For example, the primary filter has a larger hole diameter, ranging from 100 to 500 microns, while the high-efficiency filter has a smaller hole diameter, ranging from 0.1 to 10 microns. According to the filter usage in the device filter available area data, such as the filter usage time and the pollution level of the area (inferred from the filter smoke monitoring data), the initial filter hole diameter is adjusted. Assuming that a filter area has been used for a long time and has a high pollution level, its filter holes will become smaller due to the blockage of dust and smoke. An empirical formula or a physical model can be used to calculate the filter hole diameter after blockage. For example, assuming that the initial average hole diameter of the high-efficiency filter is 5 microns, based on the amount of smoke accumulated in the area (learned from the filter smoke monitoring data) and the usage time, it is calculated that the current filter smoke blocking hole diameter in the area is 3 microns. Similarly, the filter smoke blocking hole diameter data for filters of different levels and different areas are calculated. The filter smoke blocking hole diameter data of different filter levels will affect the blocking direction of smoke. In the filter, the airflow usually flows from the air inlet side to the air outlet side, but due to the structure of the filter and the distribution of hole diameters, the blocking effect of smoke in different directions is different. For filter areas with different hole diameters, smoke tends to pass through areas with larger hole diameters. For example, in a layered filter, if a certain area of ​​the upper filter is partially blocked, resulting in a smaller diameter of the filter smoke blocking hole in that area, while the diameter of the adjacent area is relatively large, then smoke is more likely to pass from the direction of the adjacent area. Combined with the airflow organization method of the filter (which can be learned from the design specifications of the equipment or airflow simulation), according to the diameter data of the smoke blocking holes of the filter, determine in which directions the filters in different areas and levels will more effectively block the smoke. For different filter levels, by analyzing the distribution and changes in the hole diameter, mark the directions where the smoke is more easily blocked. For example, in the central area of ​​the high-efficiency filter, because the diameter of the smoke blocking holes of the filter in this area is smaller at a certain moment, it will become the main smoke blocking direction. This information is recorded to generate the filter smoke blocking direction data. Based on the filter smoke blocking direction data, determine the approximate path of the smoke when passing through the filter. Starting from the air intake side, depict the flow trajectory of the smoke according to the blocking directions of different areas and levels.For example, in a rectangular filter, assuming that the air inlet is on the left and the air outlet is on the right, according to the filter smoke blocking position data, the smoke will first pass through the upper area of ​​the primary filter (because the hole diameter in this area is relatively large and not seriously blocked), and then pass more through the central area at the high-efficiency filter (because this area is the main smoke blocking position). Use professional modeling software or algorithms to digitally represent the above path information. Divide the physical space of the filter into multiple small units, and determine the flow order of smoke in these units based on the filter smoke blocking position data and the equipment filter available area data to form the initial filter smoke filtration path. This path can be represented as a series of coordinate point sequences or vectors to describe the flow route of smoke from the air inlet to the air outlet. The preset smoke particle database contains the physical and chemical properties of various smoke particles, such as different particle size ranges, densities, shapes, adsorption, etc., as well as the behavior of these smoke particles in different environments. For different parts of the initial filter smoke filtration path, according to the particle size range of the smoke particles passing through the path (which can be inferred from the filter's filtering characteristics and the existing air quality index data), the corresponding information is searched in the smoke particle database. Assuming that the filter hole diameter range of a certain section of the initial filter smoke filtration path is suitable for blocking particles of 0.5-2 microns, the smoke particle information of the particle size range is searched in the smoke particle database, including common bacteria, some fine dust, etc., and this information is sorted out to form the filter protection smoke type data, and the smoke types that will be blocked and filtered on this path are clearly defined. Based on the filter protection smoke type data, the initial filter smoke filtration path is refined and marked. For different parts of the initial filter smoke filtration path, special markings are made according to the types of smoke they can protect. For example, if a section of the initial filter smoke filtration path mainly protects bacterial smoke, it can be marked as a "bacteria protection path"; another section mainly protects tiny dust, it is marked as a "dust protection path", forming a composite path with multiple smoke protection functions. Add these marks to the initial filter smoke filtration path so that each part of the path contains information about the types of smoke it can protect, thereby generating a filter smoke protection composite path. Such a composite path not only contains the flow direction of the smoke, but also contains the smoke protection function of the path at different positions. Compare the filter smoke protection composite path with the initial filter smoke filtration path to find out the common parts of the two. These common parts are the key paths to truly realize the smoke filtration function. Since the filter smoke protection composite path contains information about the types of smoke to be protected, and the initial filter smoke filtration path focuses more on the flow trajectory of the smoke, the overlapping parts of the two will comprehensively consider the filtering and flow information of the smoke. For example, in some areas, these overlapping parts are extracted to meet the smoke flow requirements and have clear smoke protection functions.The overlapping parts are sorted and optimized, some redundant information is removed, and the core filtering path of the filter smoke is generated. This core path more accurately reflects how the smoke passes through the filter and is effectively filtered in actual use, and contains the actual flow trajectory and protection function information of the smoke. You can use professional drawing software such as AutoCAD, SketchUp, etc., or you can use specialized data visualization tools such as Python's Matplotlib or JavaScript's D3.js. Input the information in the core filtering path of the filter smoke into the visualization tool. According to the coordinate points and marking information in the path, draw the physical shape of the filter (such as a cuboid or cylinder to represent the filter), and use lines or arrows to represent the flow path of the smoke. Different smoke protection functions can be represented by different colors or line styles. For example, "bacteria protection path" is represented by green lines, and "dust protection path" is represented by blue lines. The core filtering path is displayed in an intuitive graphic to form a smoke filtration path diagram. At the same time, legends can be added to explain the meaning of different colors and lines to facilitate users to understand the smoke filtration mechanism and path of the filter.

[0039] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: 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.

[0040] In an embodiment of the present invention, the generated smoke filter path map is transmitted to the intelligent control module by utilizing 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 smoke filter path map exists in the form of image data or structured data format (such as a data table containing information such as path coordinates and path attributes). After receiving the smoke filter path map, the intelligent control module uses its built-in sensors and monitoring algorithms to dynamically monitor the filter state. For example, pressure sensors are arranged in different areas of the filter, and the filter state is reflected by monitoring the pressure difference before and after the filter; at the same time, combined with the filter smoke monitoring data (data generated in the previous step), the smoke distribution and blockage in different areas of the filter are grasped in real time, and the data collected by these sensors constitute the dynamic monitoring data of the filter state. The intelligent control module samples the filter state once at a certain time interval (for example, every 5 minutes), and records the pressure difference in different areas of the filter, the distribution of smoke particles, and other information. Assume that at a certain moment, the pressure on the inlet side of the filter is monitored to be 100Pa, the pressure on the outlet side is 80Pa, and the concentration of smoke particles in different areas is: the PM2.5 concentration in area A is 50μg / m³, the PM2.5 concentration in area B is 30μg / m³, etc. Calculate the filtration efficiency of the filter based on the dynamic monitoring data of the filter status. For the filtration efficiency of particulate matter, the following formula can be used: filtration efficiency = (particle concentration on the inlet side of the filter-particle concentration on the outlet side of the filter) / particle concentration on the inlet side of the filter × 100%. Assuming that the average concentration of PM2.5 on the inlet side is 100μg / m³ and the outlet side is 20μg / m³, the filtration efficiency of PM2.5 is (100-20) / 100×100%=80%. For different pollutants, their filtration efficiency can be calculated separately, such as formaldehyde, TVOC, etc., and these data are comprehensively considered to generate quantitative data on the filtration efficiency of the filter. For formaldehyde, assuming that the concentration on the inlet side is 0.2mg / m³ and on the outlet side is 0.05mg / m³, the formaldehyde filtration efficiency is (0.2-0.05) / 0.2×100%=75%. The filtration efficiency data of these different pollutants are sorted together as the filter filtration efficiency quantitative data. For different pollutants and different filter levels, the standard filter filtration efficiency threshold is pre-set. For example, for PM2.5 filtration, the standard filter filtration efficiency threshold is set to 85%, and for formaldehyde filtration, the threshold is 80%. Compare the various indicators in the filter filtration efficiency quantitative data 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 filter filtration efficiency is lower than the standard. When the filter filtration efficiency is lower than the standard, the fan module needs to be used to adjust the smoke filtration path map for strong air flow. The fan speed can be increased to enhance air flow, thereby improving the filter filtration efficiency.The intelligent control module calculates the required fan speed adjustment amount according to the current filter status and the filter path map. For example, if the current fan speed is 1500RPM, the fan speed is increased to 2000RPM according to the filter blockage and the degree of efficiency reduction. At the same time, the fan working mode is adjusted, such as switching to the strong mode to increase the air flow. The generated strong air flow control data includes the new fan speed (2000RPM), fan working mode (strong mode), duration (such as 10 minutes) and other information. These data will be sent to the fan module to perform the corresponding operation. If all indicators in the filter filtration efficiency quantitative data are greater than or equal to the preset standard filter filtration efficiency threshold, it means that the filter has a good filtration effect and no strong air flow is required. In order to save energy and extend the life of the filter, the intermittent operation mode is adopted. The intelligent control module adjusts the fan working mode according to the filter status and the filter path map. For example, the fan is set to intermittent operation, running for 5 minutes, stopping for 2 minutes, and so on. The normal air flow control data includes the intermittent operation time of the fan (run for 5 minutes and stop for 2 minutes), the fan speed (maintain normal speed, such as 1500RPM) and other information. According to the strong air flow control data and the normal air flow control data, combined with the dynamic monitoring data of the filter status, the filter mode is determined. If the strong air flow control data is currently used, it is marked as "strong filtration mode"; if the normal air flow control data is used, it is marked as "normal filtration mode". The filter mode switching data also includes switching time, switching conditions and other information. For example, it is recorded that the switch from normal filtration mode to strong filtration mode is made at a certain time point, or the switch from strong filtration mode back to normal filtration mode is made at another time point. According to the filter mode switching data, the corresponding air filtration associated control instruction is generated and sent to the fan module in the kitchen air purification equipment. When in "strong filtration mode", the air filtration associated control instruction is "adjust the fan speed to 2000RPM, turn on the strong mode, and continue for 10 minutes"; when in "normal filtration mode", the instruction is "run the fan intermittently at 1500RPM, run for 5 minutes, and stop for 2 minutes". These instructions are sent to the fan module through the communication interface. 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.

[0041] Preferably, 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.

[0042] In the embodiment of the present invention, various indicators such as PM2.5 filtration efficiency and formaldehyde filtration efficiency are extracted from the quantitative data of the filter filtration efficiency obtained in the previous step. These indicators are compared one by one with the preset threshold values ​​of the standard filter filtration efficiency. For example, assuming that the preset threshold value of PM2.5 standard filter filtration efficiency is 85%, and the current quantitative data of PM2.5 filter filtration efficiency is 78%, the threshold value of the standard filter filtration efficiency of formaldehyde is 80%, and the actual formaldehyde filter filtration efficiency is 72%. When both are less than the corresponding thresholds, the subsequent operation is triggered. By communicating with the fan module in the kitchen air purification device, the initial operation data of the fan is obtained, which includes the 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 initial fan operation data collected is: the fan speed is 1500RPM, the fan power is 30W, the blade angle is 30°, and the operation mode is normal mode. According to the initial fan operation data and the smoke filter path diagram, the flow rate of smoke at different positions is calculated using the principles of fluid mechanics. For example, based on the speed and power of the fan, the flow rate of air in the equipment can be estimated by an empirical formula or a pre-established model. Assuming that the fan has a speed of 1500RPM and a power of 30W, as well as the internal structure of the equipment (such as the size of the air inlet and outlet, the location of the filter, etc.), the flow rate near the air inlet is calculated to be 2m / s, the flow rate in front of the filter is 1.5m / s, and the flow rate near the air outlet is 1m / s. Determine the approximate flow direction of smoke based on the structure of the equipment and the installation position of the fan. Normally, smoke enters from the air inlet, passes through the filter, and is finally discharged from the air outlet, forming a mainstream direction from the air inlet to the air outlet. However, inside the equipment, the flow direction of smoke will change due to the obstruction of the filter and the influence of the internal structure of the equipment. By analyzing the internal layout of the equipment, the flow direction of the smoke at each position is clarified. For example, when passing through different areas of the filter, some smoke will have local flow changes due to the different resistance of the filter. The flow velocity and flow direction information are sorted into smoke flow trajectory data, and the flow of smoke in the equipment is represented in the form of coordinates or vectors. For example, the flow velocity at the coordinate (x1, y1, z1) is 2m / s, and the flow direction is (1, 0, 0), which means that the smoke at this position flows along the positive direction of the x-axis at a speed of 2m / s. By analogy, the smoke flow trajectory data in the entire equipment is constructed. Use professional CFD software (such as ANSYS Fluent, OpenFOAM, etc.) or CFD algorithm-based simulation. Take the geometric structure of the equipment, the initial fan operation data (including speed, power, blade angle, etc.), the smoke flow trajectory data, etc. as input, these software or algorithms will solve the Navier-Stokes equations to simulate the flow details of the smoke in the equipment.Through simulation, more accurate smoke flow information can be obtained, such as velocity distribution, pressure distribution, turbulence intensity, etc. at different positions. The generated smoke filtration flow simulation data contains more detailed smoke flow characteristics, such as velocity change curves at different times and positions, and flow separation and reattachment phenomena in complex structural areas. According to the smoke filtration flow simulation data, find out the locations where the smoke flow is concentrated. These locations are usually areas with high flow velocity or large flow velocity gradient, which are affected by the internal structure of the equipment or the state of the filter. For example, the smoke flow will be concentrated near the air inlet, the local unblocked area of ​​the filter, or the narrow channel inside the equipment. By analyzing the velocity distribution and flow data in the simulation data, the key nodes of these flow concentrations are extracted and used as the smoke flow concentration nodes of the filtration path. These nodes can be represented by coordinates or regional identifiers within the device, such as a flow concentration node at (x2, y2, z2). According to the smoke flow concentration nodes of the filtration path, the smoke filtration path diagram is divided into regions. The area with concentrated flow is marked as the smoke filtration high flow area, and the area with relatively small flow is marked as the smoke filtration low flow area. It can be divided according to the set flow threshold. For example, if the average flow rate of a certain area exceeds 1.8m / s, it is marked as a high flow area for smoke filtration, and the area below 1.2m / s is marked as a low flow area for smoke filtration. By analyzing the entire smoke filtration path map, different flow areas are divided and corresponding marking information is generated. For the high flow area of ​​smoke filtration, consider introducing a wind valve for diversion to reduce the flow in the area to avoid the reduction of filtration effect or equipment damage caused by excessive local flow rate. According to the initial fan operation data and the specific situation of the high flow area, determine the opening and position of the wind valve. For example, install a wind valve near the high flow area, adjust the opening of the wind valve to 50% according to the flow rate and flow rate of the area to divert part of the air flow, and the generated high flow area fan control data includes the position of the wind valve, the opening (such as 50%) and the adjustment of the fan operation status (such as keeping the fan speed at 1500RPM) and other information. For the low flow area of ​​smoke filtration, it is necessary to increase the air flow in the area to improve the filtration effect. This can be achieved by adjusting the blade angle or speed of the fan. For example, the fan blade angle is adjusted to 35° to make more air flow to the low-flow area, or the fan speed is appropriately increased to 1600RPM to enhance the air flow in the low-flow area. The generated low-flow area fan control data includes the adjusted fan blade angle (35°), speed (1600RPM) and other information. The high-flow area fan control data and the low-flow area fan control data are merged together.For example, the high-efficiency air flow control data includes the following information: the position and opening of the air valve in the high-flow area (such as the air valve at the position (x3, y3, z3) with an opening of 50%); the fan adjustment information in the low-flow area (such as the blade angle is adjusted to 35° and the speed is 1600RPM); the overall fan operation mode (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 4, 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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