Self-cleaning control method, system and equipment for range hood and storage medium

Abstract

The invention provides a self-cleaning control method, system and equipment for a range hood and a storage medium, and the method comprises the steps: collecting oil smoke flow data in an air duct of the range hood, and recognizing key nodes with obvious solidification boundaries and oil stain characteristics in the air duct, so as to determine key targets during subsequent cleaning treatment. And the oil contamination degree of each key node is quantified by further analyzing the temperature gradient characteristics corresponding to the solidification boundary, so that the key nodes with different oil contamination degrees can be subjected to differential treatment during subsequent cleaning treatment. Moreover, for each key node, a matched cleaning demand index is determined according to the actual oil contamination degree of the key node, a nozzle cleaning priority sequence is determined on the basis of the cleaning demand index, and a corresponding control signal is generated to implement a differentiated cleaning strategy. The purpose of preferentially strengthening treatment of a severe accumulation area can be achieved, the nozzle cleaning system can focus on related areas with serious oil stains in the air duct to conduct key cleaning on the areas, and the cleaning effect is guaranteed.

Description

Technical Field

[0001] This disclosure relates to the field of range hood cleaning technology, and in particular to a self-cleaning control method, system, device and storage medium for range hoods. Background Technology

[0002] As consumers' demands for daily kitchen cleaning continue to increase, improving the cleaning effect of range hoods has gradually become an important indicator for measuring product performance.

[0003] Related technologies typically employ a nozzle cleaning system installed within the range hood's ductwork. This system sprays cleaning fluid using a uniform spray pattern to achieve the cleaning purpose. However, due to the varying states of grease in different locations within the ductwork, a uniform spray pattern makes it difficult for the nozzle cleaning system to effectively clean all grease levels. For instance, areas with higher grease hardening are more prone to incomplete cleaning compared to areas with lower grease hardening, thus affecting the equipment's lifespan. Summary of the Invention

[0004] This disclosure provides a method, apparatus, device, and storage medium for controlling the self-cleaning of a range hood, in order to at least solve the above-mentioned technical problems existing in the prior art.

[0005] A first aspect of this disclosure provides a self-cleaning control method for a range hood, the method comprising:

[0006] Collect oil fume flow data in the range hood duct, and determine the solidification boundary and key nodes based on the oil fume flow data; The oil contamination level of the key node is determined based on the temperature gradient characteristics corresponding to the solidification boundary. Based on the oil contamination level, the cleaning demand index of the key node is determined, and the corresponding nozzle cleaning priority sequence is determined by combining each cleaning demand index. A corresponding control signal is generated based on the nozzle cleaning priority sequence, and the nozzle cleaning system is driven to perform air duct cleaning according to the control signal.

[0007] In one possible implementation, the oil fume flow data includes oil fume temperature data and oil fume flow velocity data. Correspondingly, determining the solidification boundary and key nodes based on the oil fume flow data includes: The viscosity value of the oil fume at each test point in the duct is determined based on the oil fume temperature data and the oil fume flow rate data, and the viscosity change rate corresponding to adjacent test points is determined. In response to the viscosity change rate corresponding to the adjacent test point exceeding the preset change rate threshold, the corresponding viscosity abrupt point is determined, and the solidification boundary is determined by combining all viscosity abrupt points. Determine the target nodes on the contour line corresponding to the solidification boundary, and determine the fluid separation region based on the geometric parameters of the duct section and the velocity vector corresponding to each target node; The deposition density distribution data are determined based on the oil fume residence time and oil stain deposition thickness within the fluid separation area; Based on the gradient calculation results of the deposition density distribution data, density abrupt change boundary points are determined, and the key nodes are determined by spatial matching between the density abrupt change boundary points and the preset geometric structure of the air duct.

[0008] In one possible implementation, determining the oil contamination level of the key node based on the temperature gradient characteristics corresponding to the solidification boundary includes: For each target node on the solidification boundary, the temperature change rate of the target node is determined based on the temperature difference between the two adjacent nodes and the node spacing. In response to the temperature change rate satisfying a preset change condition, the corresponding target node is determined as a temperature conversion node; A temperature distribution matrix is ​​constructed to determine the temperature gradient characteristics of the solidification boundary based on the temperature time series data of the temperature conversion node within a preset range. The temperature values ​​in the temperature distribution matrix are mapped to gray values ​​to obtain a temperature grayscale image, and the contrast of the temperature conversion node is determined by combining the contrast of adjacent pixel pairs in the temperature grayscale image. The viscosity change rate is determined based on the contrast of each temperature conversion node, and the average viscosity change rate of the key node at the corresponding temperature conversion node within the target range is determined. In response to the mean viscosity change rate exceeding a preset threshold, the oil stain thickness growth rate of the key node is determined based on the mean and the deposition rate ratio coefficient. The cumulative thickness of the oil stain is determined based on the rate of increase in oil stain thickness and the duration of oil fume residence, and the degree of oil contamination at the key node is determined based on the cumulative thickness of the oil stain.

[0009] In one possible implementation, determining the cleaning demand index of the key node based on the oil contamination level, and determining the corresponding nozzle cleaning priority sequence by combining each cleaning demand index, includes: The corresponding correlation feature matrix is ​​determined based on the pollution level and cleaning intensity of the key nodes; The cleaning requirement index of the key node is determined based on the correlation feature matrix and the oil contamination level. In response to the cleaning demand index exceeding a preset cleaning threshold, the corresponding key node is identified as a priority cleaning node, and the nozzle cleaning priority sequence is determined by combining the cleaning demand indices of all priority cleaning nodes.

[0010] In one possible implementation, the method further includes: The spray coverage area of ​​the key node is determined, and the actual contact rate is determined based on the cleaning fluid flow rate of the spray coverage area, so as to optimize the nozzle cleaning priority sequence based on the actual contact rate.

[0011] In one possible implementation, determining the spray coverage area of ​​the key node, determining the actual contact rate based on the cleaning fluid flow rate value of the spray coverage area, and optimizing the nozzle cleaning priority sequence based on the actual contact rate includes: For each key node corresponding to the nozzle cleaning priority sequence, determine the spray distance and spray angle between the nozzle center and the key node; The spray coverage area corresponding to the key node is determined based on the spray distance and the spray angle. The cleaning fluid flow rate is determined based on the oil stain thickness and cleaning fluid penetration coefficient within the spray coverage area. The cleaning fluid coverage density of the key node is determined based on the cleaning fluid flow rate value, and the actual contact rate is determined based on the cleaning fluid coverage density. In response to the actual contact rate being greater than or equal to a preset solidified oil stain dissolution threshold, the corresponding priority cleaning node is determined as a first-level cleaning node, and all first-level cleaning nodes are combined to optimize the nozzle cleaning priority sequence.

[0012] In one possible implementation, optimizing the nozzle cleaning priority sequence based on the actual contact rate further includes: In response to the actual contact rate being less than the preset solidified oil stain dissolution threshold, the corresponding priority cleaning node is determined as a secondary cleaning node, and the cleaning intensity corresponding to the secondary cleaning node is adjusted to optimize the nozzle cleaning priority sequence.

[0013] A second aspect of this disclosure provides a self-cleaning control system for a range hood, the system comprising: The data acquisition module is used to collect oil fume flow data in the range hood duct and determine the solidification boundary and key nodes based on the oil fume flow data. An oil contamination determination module is used to determine the oil contamination level of the key node based on the temperature gradient characteristics corresponding to the solidification boundary. The cleaning requirement determination module is used to determine the cleaning requirement index of the key node based on the oil contamination level, and to determine the corresponding nozzle cleaning priority sequence by combining each cleaning requirement index. The control module is used to generate corresponding control signals according to the nozzle cleaning priority sequence, so as to drive the nozzle cleaning system to perform air duct cleaning according to the control signals.

[0014] A third aspect of this disclosure provides an electronic device comprising: At least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the range hood self-cleaning control method of this disclosure.

[0015] A fourth aspect of this disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the self-cleaning control method for a range hood as described in this disclosure.

[0016] This disclosure discloses a self-cleaning control method for range hoods. By collecting oil fume flow data within the range hood's duct, it identifies key nodes with obvious condensation boundaries and oil stain characteristics, thus determining the priority targets for subsequent cleaning. Further analysis of the temperature gradient characteristics corresponding to the condensation boundaries quantifies the oil contamination level of each key node, enabling differentiated cleaning treatment for nodes with varying oil contamination levels. Furthermore, for each key node, a matching cleaning demand index is determined based on its actual oil contamination level, and this index forms the basis for determining the nozzle cleaning priority sequence, thereby generating corresponding control signals to implement the differentiated cleaning strategy. In other words, this method allows for prioritized and intensified treatment of heavily accumulated areas, enabling the nozzle cleaning system to focus on areas with severe oil stains within the duct for intensive cleaning, thereby ensuring cleaning effectiveness and extending equipment lifespan.

[0017] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0018] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation, in which: In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

[0019] Figure 1 This illustration shows the implementation flow of a self-cleaning control method for a range hood according to an embodiment of the present disclosure. Figure 1 ; Figure 2 This illustration shows the implementation flow of a self-cleaning control method for a range hood according to an embodiment of the present disclosure. Figure 2 ; Figure 3A schematic diagram of a self-cleaning control device for a range hood according to an embodiment of the present disclosure is shown; Figure 4 A schematic diagram of the composition structure of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0020] To make the objectives, features, and advantages of this disclosure more apparent and understandable, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0021] This disclosure provides a self-cleaning control method for a range hood, applied to the control system of a range hood, such as... Figure 1 As shown, the method includes: S101. Collect the oil fume flow data in the range hood duct, and determine the solidification boundary and key nodes based on the oil fume flow data.

[0022] In this step, temperature and flow rate acquisition devices are deployed along the length of the range hood duct to collect oil fume temperature and flow rate data at various test points within the duct. The temperature acquisition devices can be infrared thermal imagers, with multiple imagers deployed at equal intervals along the duct length. For example, a test point is set every 15cm within the range hood duct, and each test point is equipped with an infrared thermal imager to collect the oil fume temperature data. Correspondingly, each test point is also equipped with a differential pressure sensor to collect the oil fume flow rate data.

[0023] For each test point, the viscosity value of the oil fume at that point is determined by multiplying the oil fume temperature data and the oil fume flow rate data. The viscosity abrupt change points are identified by determining the rate of change in oil fume viscosity between adjacent test points. Connecting all viscosity abrupt change points determines the corresponding solidification interface. It should be noted that the solidification interface in this step refers to the area where the degree of oil fume solidification changes most significantly. Correspondingly, compared to areas with lower oil fume viscosity, the cleaning requirements for areas corresponding to the solidification interface are higher. Based on the solidification interface, key nodes with obvious oil stain characteristics can be further identified, namely nodes with high oil stain thickness and high oil stain deposition density, such as a thickness exceeding 2 mm and a deposition density ≥ 0.6 g / cm³, which will serve as targets for subsequent cleaning treatment.

[0024] S102. Determine the oil contamination level of key nodes based on the temperature gradient characteristics corresponding to the solidification boundary.

[0025] In this step, the temperature gradient characteristics of each target node on the solidification boundary are determined by analyzing its temperature, thereby determining the degree of grease buildup at each key node, which in turn determines the actual grease accumulation in the duct of the range hood to be cleaned. It should be noted that the target nodes in this step refer to several nodes spaced at intervals along the contour line corresponding to the solidification boundary. Each key node can be matched with a target node based on its coordinates. By determining the temperature at each target node, the temperature transition position on the solidification boundary is identified. Then, based on the viscosity change rate corresponding to the temperature transition position, the viscosity change rate of the key node is determined. Finally, by comprehensively considering the viscosity change rate and the flow field characteristics within the duct, the grease buildup at the key node is determined, thus identifying the actual grease condition at each key node to be cleaned, allowing for targeted treatment during subsequent cleaning.

[0026] S103. Determine the cleaning demand index of key nodes based on oil contamination, and determine the corresponding nozzle cleaning priority sequence by combining each cleaning demand index.

[0027] In this step, a corresponding cleaning demand index is determined based on the oil contamination level of each key node. Different oil contamination levels correspond to different cleaning demand indices, including light, moderate, and heavy oil buildup, each with a different cleaning demand index. For example, a cleaning demand index corresponding to light buildup indicates that the corresponding key node has a relatively low demand for cleaning priority and intensity. By ranking the cleaning demand indices determined for each key node, a nozzle cleaning priority sequence can be established; that is, key nodes with high cleaning demand indices require priority cleaning with nozzles.

[0028] S104. Generate corresponding control signals according to the nozzle cleaning priority sequence, so as to drive the nozzle cleaning system to perform air duct cleaning according to the control signals.

[0029] In this step, after determining the nozzle cleaning priority sequence, a control signal is generated based on the nozzle cleaning priority sequence to control the nozzle cleaning system. Driven by the control signal, the nozzle cleaning system can perform cleaning on each key node according to the cleaning order and cleaning intensity corresponding to the nozzle cleaning priority sequence, thereby achieving the purpose of cleaning the range hood duct.

[0030] This disclosure discloses a self-cleaning control method for range hoods. By collecting oil fume flow data within the range hood's duct, it identifies key nodes with obvious condensation boundaries and oil stain characteristics, thus determining the priority targets for subsequent cleaning. Further analysis of the temperature gradient characteristics corresponding to the condensation boundaries quantifies the oil contamination level of each key node, enabling differentiated cleaning treatment for nodes with varying oil contamination levels. Furthermore, for each key node, a matching cleaning demand index is determined based on its actual oil contamination level, and this index forms the basis for determining the nozzle cleaning priority sequence, thereby generating corresponding control signals to implement the differentiated cleaning strategy. In other words, this method allows for prioritized and intensified treatment of heavily accumulated areas, enabling the nozzle cleaning system to focus on areas with severe oil stains within the duct for intensive cleaning, thereby ensuring cleaning effectiveness and extending equipment lifespan.

[0031] In one possible implementation, the oil fume flow data includes oil fume temperature data and oil fume flow velocity data. Accordingly, the solidification boundary and key nodes are determined based on the oil fume flow data, including: The viscosity value of the oil fume at each test point in the duct is determined based on the oil fume temperature data and oil fume flow rate data, and the viscosity change rate corresponding to adjacent test points is determined. When the viscosity change rate of adjacent test points exceeds the preset change rate threshold, the corresponding viscosity abrupt point is determined, and the solidification boundary is determined by combining all viscosity abrupt points. Determine the target nodes on the contour line corresponding to the solidification boundary, and determine the fluid separation region based on the geometric parameters of the duct section and the velocity vector corresponding to each target node; The deposition density distribution data are determined based on the oil fume residence time and oil stain deposition thickness within the fluid separation area; Based on the gradient calculation results of the sediment density distribution data, density abrupt change boundary points are determined, and key nodes are determined by spatial matching between density abrupt change boundary points and the preset geometric structure of the air duct.

[0032] In this embodiment, for multiple test points spaced apart within the duct, the viscosity value of the oil fume is determined by multiplying the corresponding oil fume temperature data and oil fume flow velocity data at each test point. For two adjacent test points, if the corresponding viscosity value change rate exceeds a preset change rate threshold, such as 30%, the test point furthest from the air inlet of the duct can be identified as the viscosity abrupt change point. This process is repeated to identify each viscosity abrupt change point from all test points. Subsequently, all viscosity abrupt change points are connected using cubic spline interpolation, and the resulting smooth contour line is the solidification boundary.

[0033] Furthermore, several nodes on the solidification boundary contour line are extracted as target nodes, and the corresponding duct cross-sectional geometric parameters are obtained based on the coordinate position of each target node. It should be noted that the duct cross-sectional geometric parameters in this embodiment are obtained based on a pre-established three-dimensional digital model, which includes the width, height, and radius of curvature information of each position of the duct, with a storage accuracy of up to the millimeter level.

[0034] The fluid separation region within the duct is determined by analyzing the duct cross-sectional geometry and flow velocity vector. It's important to note that oil deposits are most pronounced at these separation regions, requiring focused cleaning, such as duct corners, abrupt changes in cross-section, and vortex centers. Duct corners can be identified based on the rate of curvature change; a curvature change exceeding 15 degrees at three consecutive measuring points indicates a duct corner. Abrupt changes in cross-section can be identified by the ratio of adjacent cross-sectional areas; a ratio less than 0.8 or greater than 1.2 indicates an abrupt change in duct cross-section. The cross-sectional area is determined based on the height and width parameters of the duct cross-section. Vortex centers can be identified by the angle between the flow velocity vector direction and the duct wall; an angle exceeding 60 degrees indicates a vortex region. Furthermore, the Reynolds number within the duct can be considered to increase the accuracy of vortex identification; a Reynolds number exceeding 2300 and an angle greater than 60 degrees indicates a vortex region.

[0035] Furthermore, for different fluid separation zones, the corresponding deposition density distribution data can be determined by comprehensively considering the regional characteristics and the oil fume residence time and oil deposit thickness within the zone. Different fluid separation zones have different flow field characteristics, resulting in different oil fume residence times and deposition thicknesses, and consequently, different deposition density data. For example, the oil fume residence time at a duct corner is typically 2-3 times that of a straight section, while the residence time at abrupt changes in cross-section can be 3-5 times that of a straight section. This embodiment determines the deposition density data based on an experimentally calibrated power function correlation model of oil fume residence time, oil deposit thickness, and deposition density. Moreover, by performing gradient calculations on the deposition density data, density abrupt change boundary points are determined. The gradient calculation results of the deposition density data reflect the degree and direction of change in oil deposit density; locations with larger gradient amplitudes show more significant oil deposit characteristics. When the gradient amplitude exceeds a set amplitude threshold, the corresponding location is extracted as a density abrupt change boundary point. The amplitude threshold can be dynamically adjusted based on the standard deviation of the average deposition density within the duct, typically set to twice the standard deviation. By spatially matching the identified boundary points with the preset geometry of the duct, key nodes with obvious oil stain deposition characteristics and their corresponding coordinates are identified within the duct, thus determining the key targets for subsequent cleaning. The key nodes identified using deposition density distribution data closely match the actual oil stain accumulation within the range hood duct, providing reliable data support for subsequent precise cleaning.

[0036] In one possible implementation, the oil contamination level of key nodes is determined based on the temperature gradient characteristics corresponding to the solidification boundary, including: For each target node on the solidification boundary, the temperature change rate of the target node is determined based on the temperature difference between the two adjacent nodes and the node spacing. In response to the temperature change rate meeting the preset change condition, the corresponding target node is determined as the temperature conversion node; A temperature distribution matrix is ​​constructed based on the temperature time series data of the temperature conversion node within a preset range to determine the temperature gradient characteristics of the solidification boundary. The temperature values ​​in the temperature distribution matrix are mapped to gray values ​​to obtain a temperature grayscale image, and the contrast of the temperature conversion node is determined by combining the contrast of adjacent pixel pairs in the temperature grayscale image. The viscosity change rate is determined based on the contrast of each temperature transition node, and the average viscosity change rate of the critical node at the corresponding temperature transition node within the target range is determined. In response to the mean viscosity change rate exceeding a preset threshold, the oil stain thickness growth rate at key nodes is determined based on the mean and deposition rate ratio coefficient. The cumulative thickness of the oil stain is determined based on the rate of increase in oil stain thickness and the duration of oil fume residence, and the degree of oil contamination at key nodes is determined based on the cumulative thickness of the oil stain.

[0037] In this embodiment, for each target node on the solidification boundary, the temperature difference between two adjacent nodes is calculated, and the temperature difference is divided by the distance between the two adjacent nodes to determine the temperature change rate of the target node. In response to the temperature change rate meeting a preset change condition, such as the temperature change rate changing from increasing to decreasing, or falling below a preset percentage of the peak value, the corresponding target node is marked as a temperature transition node.

[0038] For example, for the m-th target node, its temperature change rate is equal to the temperature difference between the (m+1)-th and (m-1)-th nodes divided by the node spacing between the (m+1)-th and (m-1)-th nodes. When the temperature change rate of three consecutive target nodes changes from positive to negative, or drops below 30% of the peak value, the intermediate node is marked as a temperature transition node, and so on, to determine all temperature transition nodes.

[0039] Furthermore, for each temperature conversion node, temperature time-series data within a preset range is acquired to construct a temperature distribution matrix that determines the characteristics of the solidification boundary temperature gradient. It should be noted that the construction of the temperature distribution matrix in this embodiment involves mapping multi-dimensional data. For each temperature conversion node, temperature data from N sampling points at the corresponding location of that node are acquired to form N... A temperature vector of 1 is collected, and the temperature vectors of n time slices are continuously collected using a sliding time window method to construct N. The temperature distribution matrix of n.

[0040] Furthermore, to map the temperature values ​​in the temperature distribution matrix to grayscale values, a linear normalization method can be used to map the temperature range [Tmin, Tmax] to the grayscale range [0, 255]. The mapping formula is as follows:

[0041] in, Grayscale value For each temperature value, a corresponding grayscale image of temperature is obtained. It should be noted that each pixel in the grayscale image corresponds to a temperature sampling point. The horizontal axis represents spatial location, and the vertical axis represents the time series, forming a two-dimensional image reflecting the spatiotemporal distribution characteristics of temperature. It should also be noted that the contrast of each temperature conversion node is determined by combining the grayscale contrast of adjacent pixel pairs in the grayscale image.

[0042] For example, selecting four directions—0 degrees, 45 degrees, 90 degrees, and 135 degrees—with a step size of 1 pixel, and counting the frequency of gray-level pairs in each direction, the corresponding contrast formula is: In the formula This represents the probability that grayscale values ​​i and j coexist. When the contrast exceeds the contrast threshold, it indicates that the temperature at the corresponding location changes drastically, and consequently, the viscosity of the oil fume changes significantly.

[0043] For each temperature transition node, the viscosity change rate can be determined based on the contrast value and a pre-calibrated viscosity-contrast relationship curve. This relationship curve can be obtained through temperature image analysis of oil fumes with different viscosities under laboratory conditions. For each critical node, a matching temperature transition node can be determined based on its coordinates and the coordinates of the temperature transition nodes. The average viscosity change rate is then determined based on the viscosity change rates of all temperature transition nodes within the target range corresponding to the critical node. In this embodiment, the target range is preferably 10% of the duct cross-sectional dimension. If the average viscosity change rate exceeds a threshold, the oil stain thickness growth rate of the corresponding critical node is further calculated based on the ratio of the average viscosity change rate to the deposition rate. The deposition rate ratio can be obtained based on duct simulation experiments, such as 0.8 mm / h or 1.2 mm / h. In the experiment, the oil stain deposition rate is measured under different flow conditions to establish a database of the correspondence between viscosity change rate and deposition rate.

[0044] Furthermore, the cumulative oil stain thickness at each critical node is determined based on the rate of increase in oil stain thickness and the residence time of oil fumes at the corresponding critical nodes. The cumulative oil stain thickness is determined through integration; that is, the cumulative oil stain thickness at each critical node is obtained by integrating the rate of increase in oil stain thickness over time. The upper limit of integration is the cumulative operating time of the equipment. The oil contamination level at each critical node is determined by the ratio of the cumulative oil stain thickness to the average oil stain thickness within the air duct. The average oil stain thickness within the air duct can be obtained by randomly selecting several target nodes within the air duct and calculating the arithmetic mean of the oil stain thickness at the corresponding target nodes. If the ratio of the cumulative thickness of the oil stain to the average thickness is less than 1.5, the corresponding oil stain degree is light; if the ratio is between 1.5 and 2.5, the corresponding oil stain degree is moderate; if the ratio is greater than 2.5, the corresponding oil stain degree is heavy. This quantifies the actual oil stain situation at each key point, allowing for targeted treatment during subsequent cleaning to ensure that oil stains of different solidification levels can be effectively cleaned and to prevent incomplete cleaning.

[0045] Therefore, this embodiment uses the temperature change rate to screen temperature conversion nodes, constructs a temperature distribution matrix based on time-series data, and converts it into a grayscale image. Contrast is used to quantify the relationship between temperature and viscosity, and finally, the corresponding oil stain degree is calculated using the average viscosity change rate and the rate of oil stain thickness increase. Compared to using a single indicator to assess oil stain degree, this embodiment employs a multi-factor collaborative assessment, which better reflects the actual oil stain accumulation state and stubbornness at each key node. This prevents insufficient or excessive cleaning due to incorrect oil stain degree assessment, ensuring that the cleaning strategy determined based on this approach can adapt to different oil stain characteristics and guaranteeing the reliability of the range hood's self-cleaning function.

[0046] In one implementation, a cleaning demand index for key nodes is determined based on the degree of oil contamination, and a corresponding nozzle cleaning priority sequence is determined by combining each cleaning demand index, including: The corresponding correlation feature matrix is ​​determined based on the pollution level and cleaning intensity of the key nodes; The cleaning demand index of key nodes is determined based on the correlation feature matrix and oil contamination level. When the cleaning demand index exceeds the preset cleaning threshold, the corresponding key node is identified as the priority cleaning node, and the nozzle cleaning priority sequence is determined by combining the cleaning demand indices of all priority cleaning nodes.

[0047] In this embodiment, the association feature matrix of key nodes is constructed based on the contamination level and the required cleaning intensity. The contamination level is divided into 10 levels according to the oil stain thickness. The cleaning intensity is determined comprehensively based on the oil stain thickness, solidification degree, and coverage difficulty. The coverage difficulty is determined by comprehensively considering the duct geometry and corresponding flow field characteristics of the key node. It should be noted that determining the contamination level and cleaning intensity requires normalization to unify the dimensions to form the final association feature matrix.

[0048] Furthermore, for each key node, a corresponding cleaning demand index is determined based on the contamination level, cleaning intensity, and oiliness corresponding to the node's associated feature matrix. It should be noted that the cleaning threshold in this embodiment is determined based on the mean and standard deviation of the cleaning demand indices of all nodes, preferably the sum of the mean and 1.5 times the standard deviation. In response to a cleaning demand index exceeding the cleaning threshold, the corresponding key node can be determined as a preferred cleaning node, meaning its cleaning priority is higher than that of other key nodes that have not exceeded the cleaning threshold.

[0049] It should also be noted that in this embodiment, when determining the nozzle cleaning priority sequence, an arbitrary priority cleaning node is used as the starting point, and the unvisited node closest to that node (the current node) is selected as the next access target. If the cleaning demand index of the next access target exceeds that of the current node, the node position corresponding to the next access target is moved forward, and so on, forming the final nozzle cleaning priority sequence. The obtained nozzle cleaning priority sequence represents the cleaning priority and cleaning intensity of each key node. Based on this, corresponding control signals are generated to drive the nozzle cleaning system to perform duct cleaning according to the cleaning priority, realizing the quantitative assessment and differentiated ranking of cleaning needs. Compared with related technologies that adopt a uniform cleaning mode, i.e., sequential cleaning along the air outlet direction of the duct, this method can target the cleaning of oil stains with different degrees of solidification in the duct, taking into account both cleaning effect and cleaning resource utilization, and significantly improving cleaning efficiency.

[0050] In one possible implementation, the method further includes: Step S105: Determine the spray coverage area of ​​key nodes, determine the actual contact rate based on the cleaning fluid flow rate of the spray coverage area, and optimize the nozzle cleaning priority sequence based on the actual contact rate.

[0051] In this embodiment, for each key node (i.e., the priority cleaning node) corresponding to the nozzle cleaning priority sequence, the corresponding spray coverage area is determined by calculating the spray distance and spray angle from the nozzle center to that node. It should be noted that the spray coverage area in this embodiment is the elliptical coverage area of ​​the nozzle spraying cleaning fluid on the target plane. The coverage density of the cleaning fluid reaching the key node is determined by combining the predicted cleaning fluid flow rate value corresponding to the spray coverage area with the spray distance. Based on the coverage density, the actual contact rate of the cleaning fluid corresponding to each key node is determined. The key nodes corresponding to the nozzle cleaning priority sequence are then screened based on the actual contact rate. First-level and second-level cleaning nodes are further determined based on the original priority cleaning nodes. The nozzle cleaning priority sequence is then readjusted based on the first-level and second-level cleaning nodes to optimize the cleaning order and intensity, ensuring the cleaning effect. This embodiment uses the actual contact rate of each key node (priority cleaning node) as the optimization basis, quantifying the effective coverage capability of the cleaning fluid for each node, and readjusting the cleaning order and intensity based on this. This avoids ineffective cleaning actions while ensuring the actual cleaning effect.

[0052] In one possible implementation, the spray coverage area of ​​key nodes is determined, and the actual contact rate is determined based on the cleaning fluid flow rate value of the spray coverage area. The nozzle cleaning priority sequence is then optimized based on the actual contact rate, including: For each critical node corresponding to the nozzle cleaning priority sequence, determine the spray distance and spray angle between the nozzle center and the critical node; Determine the spray coverage area corresponding to the key nodes based on the spray distance and spray angle; The cleaning fluid flow rate is determined based on the oil stain thickness and cleaning fluid penetration coefficient within the spray coverage area. The cleaning fluid coverage density at key nodes is determined based on the cleaning fluid flow rate, and the actual contact rate is determined based on the cleaning fluid coverage density. In response to the actual contact rate being greater than or equal to the preset solidified oil stain dissolution threshold, the corresponding priority cleaning node is determined as the first-level cleaning node, and all first-level cleaning nodes are combined to optimize the nozzle cleaning priority sequence.

[0053] In this embodiment, when the nozzle's spray axis is not perpendicular to the target plane, the projection of the spray coverage area onto the target plane is elliptical, and the corresponding major axis of the ellipse... minor axis of the ellipse In the formula For the spray distance, The spray cone angle, This is the angle between the spray axis and the normal to the target plane. The spray cone angle is the diffusion angle formed after the cleaning fluid leaves the nozzle, typically between 15-45 degrees. Elliptical coverage area. That is, the area of ​​the sprayed coverage area.

[0054] Furthermore, the required volume of cleaning fluid is determined by multiplying the area of ​​the spray coverage region, the average thickness of the oil stains within the spray coverage region, and the cleaning fluid penetration parameters. The corresponding formula is as follows:

[0055] In the formula, These are the penetration parameters of the cleaning fluid. The average thickness of the oil stain.

[0056] It should be noted that the cleaning fluid penetration parameter characterizes the ability of the cleaning fluid to penetrate the oil stain layer, and is related to the porosity and viscosity of the oil stain, and can be determined based on Darcy's law. The calculated cleaning fluid volume is then used to determine the corresponding cleaning fluid mass, i.e., the cleaning fluid flow rate, and thus the cleaning fluid coverage density. The cleaning fluid coverage density is the mass of cleaning fluid per unit area.

[0057] Furthermore, the actual contact rate is determined by multiplying the cleaning fluid coverage density by a preset oil stain surface roughness coefficient. The range is 0.1-1, and the actual contact rate is... ,in This is a reference density value.

[0058] It should be noted that when the actual contact rate exceeds the preset solidified oil stain dissolution threshold, the corresponding priority cleaning node becomes the first-level cleaning node. The initial nozzle coverage path is determined by combining all identified first-level cleaning nodes to optimize the nozzle cleaning priority sequence. For example, a solidified oil stain dissolution threshold of 0.7 means that when the actual contact rate of the cleaning fluid at the corresponding priority cleaning node exceeds 70%, the cleaning fluid can effectively penetrate and dissolve the oil stain. This determines the corresponding first-level cleaning node. The priority cleaning path is then determined by combining all first-level cleaning nodes to optimize the nozzle cleaning priority sequence. Based on the optimized nozzle cleaning priority sequence, corresponding control signals are generated to determine whether the nozzle cleaning system should perform duct cleaning. Based on the optimized nozzle cleaning priority sequence determined by the first-level cleaning nodes, the system can further focus on nodes that can achieve the expected cleaning effect, achieving initial cleaning results. This allows the nozzles to perform cleaning actions according to the rules of efficient coverage and priority achievement, improving cleaning efficiency and fluid consumption utilization.

[0059] In one possible implementation, optimizing the nozzle cleaning priority sequence based on the actual contact rate further includes: In response to the actual contact rate being less than the preset solidified oil stain dissolution threshold, the corresponding priority cleaning node is determined as the secondary cleaning node, and the cleaning intensity of the secondary cleaning node is adjusted to optimize the nozzle cleaning priority sequence.

[0060] In this embodiment, nodes with an actual contact rate less than a preset threshold for dissolving solidified grease are identified as secondary cleaning nodes, i.e., nodes that are relatively difficult to clean, where the original cleaning intensity may not achieve the desired cleaning effect. Based on this, the cleaning intensity of the secondary cleaning nodes is readjusted, and a secondary nozzle coverage path is generated accordingly. In other words, for the more difficult-to-clean key nodes, the cleaning intensity of the nozzles is increased, i.e., the nozzle pressure value is enhanced, thereby ensuring that each key node can be effectively cleaned, thus guaranteeing the cleaning effect. This avoids blindly cleaning nodes with insufficient contact rate; by strengthening the cleaning intensity, such as increasing the cleaning fluid flow rate or pressure, it ensures that even difficult-to-clean nodes can meet the cleaning standards, balancing comprehensiveness and efficiency of cleaning, and further improving the self-cleaning adaptive capability of the range hood duct.

[0061] In addition, this embodiment also monitors the oil residue images after each cleaning in real time. The images are acquired using an industrial camera and a ring light source. Each image is acquired 5 seconds after the end of each cleaning cycle to ensure that the cleaning fluid is fully utilized and discharged.

[0062] It should be noted that the cleaning cycle in this embodiment refers to one cycle consisting of cleaning both the primary and secondary cleaning nodes once. The collected residual oil stain image is compared with the initial oil stain image to determine the cleaning completion rate. The cleaning completion rate is calculated as: (Original oil stain area - Residual oil stain area) / Original oil stain area × 100%. The original oil stain area is obtained from the initial oil stain image before cleaning, and the residual oil stain area is measured from the current image. For each cleaning node, the corresponding original and residual oil stain areas are determined by setting a preset radius around that point, thereby determining the cleaning completion rate of each cleaning node after one cleaning cycle. If the cleaning completion rate is lower than the cleaning threshold, the cleaning intensity and priority are increased accordingly, the nozzle cleaning priority sequence is readjusted, and the cleaning action is executed. For example, if the cleaning completion rate is lower than 70%, the cleaning intensity is increased by 20%, and the cleaning priority is increased; if the cleaning completion rate is higher than or equal to 70%, the cleaning intensity remains unchanged, and the cleaning action is executed again.

[0063] It should also be noted that in this embodiment, each cleaning node contains multi-dimensional information, including cleaning intensity, cleaning priority, spatial coordinates, and node number. The magnitude of the control signal driving the nozzle cleaning system is determined based on the cleaning intensity mapped to voltage, and the corresponding formula is as follows: ,in The cleaning intensity value is used to determine the cleaning sequence of each cleaning node based on the cleaning priority. This generates a corresponding timing control signal to ensure that each node to be cleaned in the air duct can be fully cleaned under the action of the nozzle cleaning system determined by the timing control signal, thus ensuring the cleaning effect of the air duct and protecting the service life of the equipment.

[0064] To implement the above method, this application also provides an example of a range hood self-cleaning control system 300, such as... Figure 3 As shown, the system includes: The data acquisition module 301 is used to collect the oil fume flow data in the range hood duct and determine the solidification boundary and key nodes based on the oil fume flow data. Oil contamination determination module 302 is used to determine the oil contamination of key nodes based on the temperature gradient characteristics corresponding to the solidification boundary. The cleaning requirement determination module 303 is used to determine the cleaning requirement index of key nodes based on the degree of oil contamination, and to determine the corresponding nozzle cleaning priority sequence by combining each cleaning requirement index. The control module 304 is used to generate corresponding control signals according to the nozzle cleaning priority sequence, so as to drive the nozzle cleaning system to perform air duct cleaning according to the control signals.

[0065] In one embodiment, the oil fume flow data includes oil fume temperature data and oil fume flow velocity data. Accordingly, the data acquisition module 301 is used to determine the oil fume viscosity value at each test point in the duct based on the oil fume temperature data and oil fume flow velocity data, and to determine the viscosity change rate corresponding to adjacent test points. When the viscosity change rate of adjacent test points exceeds the preset change rate threshold, the corresponding viscosity abrupt point is determined, and the solidification boundary is determined by combining all viscosity abrupt points. Determine the target nodes on the contour line corresponding to the solidification boundary, and determine the fluid separation region based on the geometric parameters of the duct section and the velocity vector corresponding to each target node; The deposition density distribution data are determined based on the oil fume residence time and oil stain deposition thickness within the fluid separation area; Based on the gradient calculation results of the sediment density distribution data, density abrupt change boundary points are determined, and key nodes are determined by spatial matching between density abrupt change boundary points and the preset geometric structure of the air duct.

[0066] In one embodiment, the oil contamination determination module 302 is used to determine the temperature change rate of each target node on the solidification boundary based on the temperature difference between two adjacent nodes and the node spacing. In response to the temperature change rate meeting the preset change condition, the corresponding target node is determined as the temperature conversion node; A temperature distribution matrix is ​​constructed based on the temperature time series data of the temperature conversion node within a preset range to determine the temperature gradient characteristics of the solidification boundary. The temperature values ​​in the temperature distribution matrix are mapped to gray values ​​to obtain a temperature grayscale image, and the contrast of the temperature conversion node is determined by combining the contrast of adjacent pixel pairs in the temperature grayscale image. The viscosity change rate is determined based on the contrast of each temperature transition node, and the average viscosity change rate of the critical node at the corresponding temperature transition node within the target range is determined. In response to the mean viscosity change rate exceeding a preset threshold, the oil stain thickness growth rate at key nodes is determined based on the mean and deposition rate ratio coefficient. The cumulative thickness of the oil stain is determined based on the rate of increase in oil stain thickness and the duration of oil fume residence, and the degree of oil contamination at key nodes is determined based on the cumulative thickness of the oil stain.

[0067] In one embodiment, the cleaning requirement determination module 303 is used to determine the corresponding correlation feature matrix based on the contamination level and cleaning intensity of the key nodes; The cleaning demand index of key nodes is determined based on the correlation feature matrix and oil contamination level. When the cleaning demand index exceeds the preset cleaning threshold, the corresponding key node is identified as the priority cleaning node, and the nozzle cleaning priority sequence is determined by combining the cleaning demand indices of all priority cleaning nodes.

[0068] In one embodiment, the device further includes an optimization module for determining the spray coverage area of ​​key nodes, determining the actual contact rate based on the cleaning fluid flow rate value of the spray coverage area, and optimizing the nozzle cleaning priority sequence based on the actual contact rate.

[0069] In one embodiment, the optimization module is further configured to determine the spray coverage area of ​​key nodes, determine the actual contact rate based on the cleaning fluid flow rate value of the spray coverage area, and optimize the nozzle cleaning priority sequence based on the actual contact rate, including: For each critical node corresponding to the nozzle cleaning priority sequence, determine the spray distance and spray angle between the nozzle center and the critical node; Determine the spray coverage area corresponding to the key nodes based on the spray distance and spray angle; The cleaning fluid flow rate is determined based on the oil stain thickness and cleaning fluid penetration coefficient within the spray coverage area. The cleaning fluid coverage density at key nodes is determined based on the cleaning fluid flow rate, and the actual contact rate is determined based on the cleaning fluid coverage density. In response to the actual contact rate being greater than or equal to the preset solidified oil stain dissolution threshold, the corresponding priority cleaning node is determined as the first-level cleaning node, and all first-level cleaning nodes are combined to optimize the nozzle cleaning priority sequence.

[0070] In one embodiment, the optimization module is further configured to, in response to the actual contact rate being less than a preset solidified oil stain dissolution threshold, determine the corresponding priority cleaning node as a secondary cleaning node, and adjust the cleaning intensity corresponding to the secondary cleaning node to optimize the nozzle cleaning priority sequence.

[0071] For example, this application also provides an electronic device 400, including: processor; Memory used to store processor-executable instructions; The processor is used to read executable instructions from memory and execute the instructions to implement the above-mentioned self-cleaning control method for range hoods.

[0072] By way of example, this application also provides a computer-readable storage medium storing a computer program for performing the above-described self-cleaning control method for a range hood.

[0073] Figure 4 A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0074] like Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. The RAM 403 may also store various programs and data required for the operation of the device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0075] Multiple components in electronic device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of displays, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0076] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as a range hood self-cleaning control method. For example, in some embodiments, a range hood self-cleaning control method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of a context processing method described above can be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform a range hood self-cleaning control method by any other suitable means (e.g., by means of firmware).

[0077] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0078] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0079] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0080] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0081] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0082] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0083] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0084] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0085] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A self-cleaning control method for a range hood, characterized in that, The method includes: Collect oil fume flow data in the range hood duct, and determine the solidification boundary and key nodes based on the oil fume flow data; The oil contamination level of the key node is determined based on the temperature gradient characteristics corresponding to the solidification boundary. Based on the oil contamination level, the cleaning demand index of the key node is determined, and the corresponding nozzle cleaning priority sequence is determined by combining each cleaning demand index. A corresponding control signal is generated based on the nozzle cleaning priority sequence, and the nozzle cleaning system is driven to perform air duct cleaning according to the control signal.

2. The self-cleaning control method for a range hood according to claim 1, characterized in that, The oil fume flow data includes oil fume temperature data and oil fume flow velocity data. Correspondingly, determining the solidification boundary and key nodes based on the oil fume flow data includes: The viscosity value of the oil fume at each test point in the duct is determined based on the oil fume temperature data and the oil fume flow rate data, and the viscosity change rate corresponding to adjacent test points is determined. In response to the viscosity change rate corresponding to the adjacent test point exceeding the preset change rate threshold, the corresponding viscosity abrupt point is determined, and the solidification boundary is determined by combining all viscosity abrupt points. Determine the target nodes on the contour line corresponding to the solidification boundary, and determine the fluid separation region based on the geometric parameters of the duct section and the velocity vector corresponding to each target node; The deposition density distribution data are determined based on the oil fume residence time and oil stain deposition thickness within the fluid separation area; Based on the gradient calculation results of the deposition density distribution data, density abrupt change boundary points are determined, and the key nodes are determined by spatial matching between the density abrupt change boundary points and the preset geometric structure of the air duct.

3. The self-cleaning control method for a range hood according to claim 1, characterized in that, The step of determining the oil contamination level of the key node based on the temperature gradient characteristics corresponding to the solidification boundary includes: For each target node on the solidification boundary, the temperature change rate of the target node is determined based on the temperature difference between the two adjacent nodes and the node spacing. In response to the temperature change rate satisfying a preset change condition, the corresponding target node is determined as a temperature conversion node; A temperature distribution matrix is ​​constructed to determine the temperature gradient characteristics of the solidification boundary based on the temperature time series data of the temperature conversion node within a preset range. The temperature values ​​in the temperature distribution matrix are mapped to gray values ​​to obtain a temperature grayscale image, and the contrast of the temperature conversion node is determined by combining the contrast of adjacent pixel pairs in the temperature grayscale image. The viscosity change rate is determined based on the contrast of each temperature conversion node, and the average viscosity change rate of the key node at the corresponding temperature conversion node within the target range is determined. In response to the mean viscosity change rate exceeding a preset threshold, the oil stain thickness growth rate of the key node is determined based on the mean and the deposition rate ratio coefficient. The cumulative thickness of the oil stain is determined based on the rate of increase in oil stain thickness and the duration of oil fume residence, and the degree of oil contamination at the key node is determined based on the cumulative thickness of the oil stain.

4. The self-cleaning control method for a range hood according to claim 1, characterized in that, The process of determining the cleaning demand index of the key node based on the oil contamination level, and determining the corresponding nozzle cleaning priority sequence by combining each cleaning demand index, includes: The corresponding correlation feature matrix is ​​determined based on the pollution level and cleaning intensity of the key nodes; The cleaning requirement index of the key node is determined based on the correlation feature matrix and the oil contamination level. In response to the cleaning demand index exceeding a preset cleaning threshold, the corresponding key node is identified as a priority cleaning node, and the nozzle cleaning priority sequence is determined by combining the cleaning demand indices of all priority cleaning nodes.

5. The self-cleaning control method for a range hood according to any one of claims 1-4, characterized in that, The method further includes: The spray coverage area of ​​the key node is determined, and the actual contact rate is determined based on the cleaning fluid flow rate of the spray coverage area, so as to optimize the nozzle cleaning priority sequence based on the actual contact rate.

6. The self-cleaning control method for a range hood according to claim 5, characterized in that, The process of determining the spray coverage area of ​​the key node, determining the actual contact rate based on the cleaning fluid flow rate of the spray coverage area, and optimizing the nozzle cleaning priority sequence based on the actual contact rate includes: For each key node corresponding to the nozzle cleaning priority sequence, determine the spray distance and spray angle between the nozzle center and the key node; The spray coverage area corresponding to the key node is determined based on the spray distance and the spray angle. The cleaning fluid flow rate is determined based on the oil stain thickness and cleaning fluid penetration coefficient within the spray coverage area. The cleaning fluid coverage density of the key node is determined based on the cleaning fluid flow rate value, and the actual contact rate is determined based on the cleaning fluid coverage density. In response to the actual contact rate being greater than or equal to a preset solidified oil stain dissolution threshold, the corresponding priority cleaning node is determined as a first-level cleaning node, and all first-level cleaning nodes are combined to optimize the nozzle cleaning priority sequence.

7. The self-cleaning control method for a range hood according to claim 6, characterized in that, The optimization of the nozzle cleaning priority sequence based on the actual contact rate further includes: In response to the actual contact rate being less than the preset solidified oil stain dissolution threshold, the corresponding priority cleaning node is determined as a secondary cleaning node, and the cleaning intensity corresponding to the secondary cleaning node is adjusted to optimize the nozzle cleaning priority sequence.

8. A self-cleaning control system for a range hood, characterized in that, The system includes: The data acquisition module is used to collect oil fume flow data in the range hood duct and determine the solidification boundary and key nodes based on the oil fume flow data. An oil contamination determination module is used to determine the oil contamination level of the key node based on the temperature gradient characteristics corresponding to the solidification boundary. The cleaning requirement determination module is used to determine the cleaning requirement index of the key node based on the oil contamination level, and to determine the corresponding nozzle cleaning priority sequence by combining each cleaning requirement index. The control module is used to generate corresponding control signals according to the nozzle cleaning priority sequence, so as to drive the nozzle cleaning system to perform air duct cleaning according to the control signals.

9. An electronic device, characterized in that, include: At least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform claim 1. The self-cleaning control method for range hoods as described in any one of the 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the self-cleaning control method for a range hood according to any one of claims 1-7.

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