Intelligent control method and system for air fryer

Abstract

The invention relates to an intelligent control method and system for an air fryer, and relates to the technical field of household appliances, and the method comprises the steps: obtaining an internal image of a frying basket; determining a food material placement area and a food material placement type according to the internal image of the frying basket; constructing a random blowing operation direction and a heating operation temperature as a heating treatment scheme, and determining a theoretical holding temperature of each position point according to the heating treatment scheme and the food material placement area; determining a food material demand temperature corresponding to the food material placement type according to a preset temperature matching relationship, and performing difference calculation according to the food material demand temperature and the theoretical holding temperature to determine a food material deviation temperature; analyzing according to the deviation temperatures of all the food materials to determine appropriate scheme values, defining the heating treatment scheme corresponding to the maximum appropriate scheme value as an optimal treatment scheme, and controlling the air fryer to automatically work according to the optimal treatment scheme. The air fryer has the function of improving the overall using effect of the air fryer.

Description

Technical Field

[0001] This application relates to the field of household appliance technology, and in particular to an intelligent control method and system for an air fryer. Background Technology

[0002] Air fryers, as a type of household appliance that uses high-speed circulating hot air technology for cooking, are widely popular because they reduce oil consumption and are easy to operate. Their core working principle is that the built-in heating element, together with a high-speed fan, creates high-temperature turbulence in the cavity, and heats the food through forced convection heat transfer, thereby achieving a crispy texture similar to deep-frying.

[0003] Currently, the heat field inside an air fryer is formed by a fan blowing heat from the heating point. Temperature sensors are installed at various locations in the air fryer to monitor the temperature. When the temperature at a location deviates from the allowable range, the operating direction of the fan is adjusted to adjust the temperature. For example, if the temperature at a certain location is too low, the operating direction of the fan will be adjusted to blow towards that location, thereby raising the temperature.

[0004] In the aforementioned technologies, when temperature deviations occur in some parts of the air fryer, adjusting the fan position can easily cause temperature changes in other parts, resulting in the temperature in those other parts not meeting the requirements. In this case, the fan needs to be frequently adjusted, leading to poor overall performance. Summary of the Invention

[0005] To improve the overall performance of air fryers, this application provides an intelligent control method and system for air fryers.

[0006] Firstly, this application provides an intelligent control method for an air fryer, employing the following technical solution: A smart control method for an air fryer, comprising: Obtain an image of the inside of the exploding basket; Feature recognition analysis is performed based on images inside the frying basket to determine the food placement area and food type. A random blowing direction and heating temperature are constructed as a heating treatment scheme, and the theoretical holding temperature at each location point is determined by analysis based on the heating treatment scheme and the food placement area. The required temperature of the ingredients is determined according to the preset temperature matching relationship, and the temperature deviation of the ingredients is determined by calculating the difference between the required temperature of the ingredients and the theoretical holding temperature. The appropriate value for the treatment plan is determined by analyzing the temperature deviation of all ingredients. The heating treatment plan corresponding to the largest appropriate value is defined as the optimal treatment plan, and the air fryer is controlled to operate automatically with the optimal treatment plan.

[0007] Optionally, the step of determining the food placement area by performing feature recognition analysis based on the image inside the frying basket includes: Feature recognition analysis is performed on the internal image of the frying basket to determine the feature points of the ingredients, and the distance between the features is determined based on the feature points of each ingredient; Two food feature points whose feature distance is less than a preset similar distance are grouped into the same preset initially empty feature collection set; Connect the feature points of each ingredient in the feature collection to construct the outer enclosure contour, and determine the outer enclosure line segment based on the outer enclosure contour; Based on the food feature points that are not on the outer enclosure contour, a perpendicular point is drawn on the outer enclosure line segment, and the food feature points that are on the outer enclosure line segment are defined as the accompanying points of the outer enclosure line segment, and the accompanying distance is determined based on the accompanying points. The point corresponding to the smallest adjacent distance is defined as the extension point of the outer enclosure line segment. The outer enclosure line segment is then modified based on the extension point, and the outer enclosure outline is redefined based on the modified outer enclosure line segment until no extension point exists. The area enclosed by the outer enclosure outline is then defined as the food placement area.

[0008] Optionally, after the expansion point is determined, the intelligent control method for the air fryer also includes: Count the number of accompanying extensions when a single accompanying point is defined as an extension point; Determine if the number of accompanying expansions is greater than one; If the number of expansions is no more than one, then the current relationship between the expansion point and the external enclosing line segment will be maintained. If the number of expansions is greater than one, the length of the outer segment is determined based on the outer enclosing segment, and the inner area is calculated based on the length of the outer segment and the corresponding adjacent distance. All corresponding relationships except those corresponding to the expansion point of the largest inward contraction area and the corresponding relationships of the outer enclosing line segment are eliminated.

[0009] Optionally, the step of determining the food placement type by performing feature recognition analysis based on the image inside the frying basket includes: The area of ​​the food storage area is determined based on the storage area, and the required quantity is calculated based on the area of ​​the storage area. Based on the required number of representatives, random detection points are generated within the food placement area, and the detection area is defined based on these detection points. The number of internal features is determined by counting all food feature points, and the internal coverage ratio is determined by calculating the number of food feature points within the detection area and the number of internal features. Feature identification is performed on the detection area determined by the largest internal coverage ratio to determine the local type of food, and analysis is performed on each local type of food to determine the food placement type.

[0010] Optionally, after the internal coverage ratio is determined, the intelligent control method for the air fryer also includes: Within a single detection area, the number of individual features is determined by counting the feature points of the food ingredients. The single-unit correction coefficient corresponding to the number of single-unit features is determined based on the preset correction matching relationship; The overall correction factor is determined by calculating all individual correction factors, and the internal coverage percentage is updated based on the overall correction factor.

[0011] Optionally, the steps to determine the appropriate values ​​for the solution by analyzing the temperature deviations of all ingredients include: The upper limit deviation temperature and temperature sensitivity feedback corresponding to the food placement type are determined based on the preset food matching relationship. Determine whether the temperature deviation of the food ingredients exceeds the corresponding upper limit temperature deviation. If the temperature deviation of the food ingredient is greater than the corresponding upper limit temperature deviation, then the first suitable value is output, where the first suitable value is a negative value. If the temperature deviation of the food is not greater than the corresponding upper limit temperature deviation, the second suitable value is determined by calculation based on the temperature sensitivity feedback and the temperature deviation of the food. The optimal value for the scheme is determined by calculation based on all the first and second optimal values.

[0012] Optionally, after the appropriate values ​​for the scheme are determined, the intelligent control method for air fryers may also include: Determine whether the maximum suitable value of the proposed solution is greater than the preset benchmark suitable value; If the maximum suitable value of the proposed solution is greater than the baseline suitable value, then the optimal processing solution is determined for automatic operation. If the maximum suitable value of the solution is not greater than the baseline suitable value, an adjustment signal is output, and the food placement type corresponding to the suitable value being less than the preset demand suitable value is defined as the demand change type, and the food placement area corresponding to the demand change type is defined as the change simulation area. The simulation area of ​​the change is randomly placed within the preset available placement area to construct an adjustment processing scheme. The appropriate value of the scheme is determined based on the adjustment processing scheme, and the adjustment processing scheme corresponding to the largest appropriate value is defined as the effective processing scheme and output.

[0013] Secondly, this application provides an intelligent control system for an air fryer, employing the following technical solution: A smart control system for an air fryer, comprising: The acquisition module is used to acquire images of the inside of the exploding basket; The processing module, connected to the acquisition module, is used for information storage and processing; The processing module performs feature recognition analysis based on the image inside the frying basket to determine the food placement area and food placement type; The processing module constructs a random blowing direction and heating temperature as a heating treatment scheme, and analyzes the heating treatment scheme and the food placement area to determine the theoretical temperature to be maintained at each location. The processing module determines the required temperature of the food corresponding to the food placement type based on the preset temperature matching relationship, and calculates the difference between the required temperature of the food and the theoretical holding temperature to determine the food deviation temperature. The processing module analyzes the temperature deviations of all ingredients to determine the appropriate value for the solution, and defines the heating treatment solution corresponding to the largest appropriate value as the optimal treatment solution, and controls the air fryer to operate automatically with the optimal treatment solution.

[0014] In summary, this application includes at least one of the following beneficial technical effects: Before the air fryer is started, the heating status of the food currently placed in the frying basket can be simulated and analyzed. This allows the fan to be controlled at the appropriate angle and temperature during actual food processing, ensuring that there is a heat field inside the air fryer that meets the requirements of the food for food processing, thereby improving the overall performance of the air fryer. When analyzing food ingredients, identifying the location and type of ingredients can improve the accuracy of the simulation analysis. Attached Figure Description

[0015] Figure 1 This is a flowchart of an intelligent control method for air fryers.

[0016] Figure 2 This is a flowchart of the module for the intelligent control method used in air fryers. Detailed Implementation

[0017] To make the purpose, technical solution, and advantages of this application clearer, the following is combined with Figures 1-2 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0018] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0019] This application discloses an intelligent control method for an air fryer, referring to... Figure 1 The method flow for intelligent control of air fryers includes the following steps: Step S100: Obtain an image of the inside of the exploding basket.

[0020] The image inside the frying basket is an image of the inside of the frying basket when it is installed in the heating chamber of the air fryer. It can be obtained by an image capturing device installed inside the air fryer.

[0021] Step S101: Perform feature recognition analysis based on the image inside the frying basket to determine the food placement area and food placement type.

[0022] The food placement area refers to the location where the food is placed, and the food placement type refers to the type of food being placed. For specific determination methods, please refer to steps S200-S403.

[0023] Step S102: Construct a random blowing direction and heating temperature as a heating treatment scheme, and analyze the heating treatment scheme and the food placement area to determine the theoretical holding temperature at each location point.

[0024] The blowing direction is the direction in which the fan operates, and the heating temperature is the operating temperature of the heating rod. By constructing a random heating treatment scheme, different heating schemes are simulated and analyzed, which facilitates subsequent simulation and analysis of the specific thermal field under each heating scheme. The theoretical holding temperature is the temperature value that each point will reach when the heating treatment scheme is executed under the current food placement conditions. It can be determined by thermodynamic simulation analysis based on the heating treatment scheme and the food placement conditions.

[0025] Step S103: Determine the required temperature of the ingredients corresponding to the type of ingredients to be placed according to the preset temperature matching relationship, and calculate the difference between the required temperature of the ingredients and the theoretical holding temperature to determine the temperature deviation of the ingredients.

[0026] The required temperature for ingredients is the operating temperature at which the ingredients are cooked. The temperature matching relationship between the two is determined in advance by the staff based on different set operating times. The temperature deviation of ingredients is the difference between the required temperature of the ingredients and the theoretical holding temperature. This difference is an absolute value.

[0027] Step S104: Analyze the temperature deviations of all ingredients to determine the appropriate value for the solution, define the heating treatment solution corresponding to the largest appropriate value as the optimal treatment solution, and control the air fryer to operate automatically with the optimal treatment solution.

[0028] The optimal solution value reflects whether the current heating treatment solution meets the current food processing requirements. The larger the value, the better it meets the processing requirements. The specific determination method is referred to steps S600-S602. At this time, the heating treatment solution corresponding to the largest optimal solution value is the most conducive to food processing. Therefore, it is defined as the optimal treatment solution and the operation is carried out accordingly to make the overall use effect of the air fryer better.

[0029] The steps for determining the food placement area based on feature recognition analysis of images inside the frying basket include: Step S200: Perform feature recognition analysis based on the image inside the frying basket to determine the feature points of the ingredients, and determine the distance between the features based on the feature points of each ingredient.

[0030] The food feature points are obtained by feature recognition analysis of the images inside the frying basket, and the distance between the features is the straight-line distance between two food feature points.

[0031] Step S201: Group two food feature points whose feature distance is less than the preset similar distance into the same preset initially empty feature collection set.

[0032] The "proximity distance" is the maximum allowable distance between two food feature points, as defined by staff, when they are considered to be close enough to be features of the same food. By categorizing food feature points, feature points corresponding to the same type of food can be identified, facilitating subsequent analysis. The feature categorization set is a pre-set empty set for placing food feature points. The categorization method for each food feature point is as follows: For example, there are three food feature points A, B, and C. The feature distance between A and B is less than the proximity distance, the feature distance between B and C is less than the proximity distance, and the feature distance between A and C is not less than the proximity distance. Since B needs to be categorized into the same feature categorization set as A and C, points A, B, and C are categorized into one feature categorization set.

[0033] Step S202: Connect the food feature points in each feature collection set to construct the outer enclosure contour, and determine the outer enclosure line segment based on the outer enclosure contour.

[0034] The outer enclosure contour is the outermost line segment contour formed by connecting the feature points of the ingredients in the feature collection set. The outer enclosure line segment is the line segment that constructs the outer enclosure contour.

[0035] Step S203: Based on the food feature points that are not on the outer enclosure contour, make a perpendicular point on the outer enclosure line segment, and define the food feature points that are on the outer enclosure line segment as the accompanying points of the outer enclosure line segment, and determine the accompanying distance based on the accompanying points.

[0036] By constructing a perpendicular point, it can be determined whether the area can be shrunk. When the perpendicular point is on the outer enclosing line segment, it means that the food feature point can be used to reconstruct the outer enclosing line segment with the two endpoints of the outer enclosing line segment, thereby achieving the shrunk area and thus ensuring the accuracy of the food placement area determination. Therefore, an accompanying point is defined to identify and distinguish the food feature points that can meet the shrunk requirements. The distance between the accompanying points is the perpendicular distance from the accompanying point to the corresponding outer enclosing line segment.

[0037] Step S204: Define the accompanying point corresponding to the smallest accompanying distance as the extension point of the outer enclosure line segment, and correct the outer enclosure line segment according to the extension point. Then, redetermine the outer enclosure outline according to the corrected outer enclosure line segment until there is no extension point. Finally, determine the area enclosed by the outer enclosure outline as the food placement area.

[0038] By defining expansion points, we can determine the situation where all food feature points are still within the enclosed area after the inward contraction is completed. At this point, we use the expansion points to reconstruct the outer enclosed line segment with the two endpoints of the outer enclosed line segment. By continuously contracting inward, we can determine the food placement area that reflects the actual location of the food, which is convenient for subsequent data analysis.

[0039] Once the expansion point is determined, the intelligent control methods for air fryers also include: Step S300: Count the number of accompanying expansions when a single accompanying point is defined as an expansion point.

[0040] The number of times an accompanying point is extended is defined as the number of times a single accompanying point is defined as an extension point by the externally enclosed line segment.

[0041] Step S301: Determine whether the number of accompanying expansions is greater than one.

[0042] The purpose of this judgment is to determine whether the relationship between the expansion point and the outer enclosing line segment is unique, that is, whether an expansion point has multiple inward contraction schemes, which facilitates subsequent analysis.

[0043] Step S3011: If the number of expansions is no more than one, then maintain the current relationship between the expansion point and the corresponding external enclosing line segment.

[0044] When the number of expansions is no more than one, it indicates that the relationship between the expansion point and the outer enclosing line segment is unique, and the subsequent steps can be processed normally.

[0045] Step S3012: If the number of expansions is greater than one, the length of the outer segment is determined based on the outer enclosing segment, and the inner shrinkage area is determined based on the length of the outer segment and the corresponding adjacent distance.

[0046] When the number of accompanying expansions is greater than one, it indicates that there are multiple matching relationships. Therefore, it is necessary to further analyze which outer enclosing line segment the expansion point is matched with in order to perform the shrinkage operation. The length of the outer line segment is the length of the outer enclosing line segment, and the shrinkage area is the area value obtained by multiplying the length of the outer line segment by the corresponding accompanying distance and dividing by 2. That is, the area value of the region to be shrunk.

[0047] Step S302: Eliminate all corresponding relationships except for the expansion point corresponding to the largest inward shrinkage area and the corresponding relationship of the outer enclosing line segment.

[0048] The largest shrinkage area indicates the best shrinkage effect, as it best reflects the actual position of the ingredients. Therefore, other matching relationships that do not meet the requirements can be eliminated to improve the accuracy of data analysis.

[0049] The steps for determining the food placement type based on feature recognition analysis of images inside the frying basket include: Step S400: Determine the area of ​​the food placement area based on the food placement area, and calculate the required representative quantity based on the area of ​​the placement area.

[0050] The placement area is the area where the food is placed. The required number of representative points is the number of representative points that need to be determined when conducting detection and analysis on this area. It can be determined by dividing the placement area by the preset individual area and rounding the result up.

[0051] Step S401: Randomly generate detection points in the food placement area according to the required number of representatives, and delineate the detection area based on the detection points.

[0052] The detection point is a randomly constructed location point, and the detection area is the area defined by the detection point as the center and the preset unit distance as the radius.

[0053] Step S402: Count all food feature points to determine the number of internal features, and calculate the internal coverage ratio based on the food feature points within the detection area and the number of internal features.

[0054] The number of internal features is the total number of all food feature points within a feature set, and the internal coverage ratio is the proportion of food feature points within the detection area to all food feature points.

[0055] Step S403: Based on the detection area determined by the largest internal coverage ratio, feature recognition is performed to determine the local type of food, and analysis is performed based on each local type of food to determine the food placement type.

[0056] The highest internal coverage ratio indicates that the current detection area scheme can effectively analyze the food situation. Therefore, this detection area can be used for food analysis. The local food type is the most likely food type obtained by identifying and analyzing the detection area. At this time, the food types in each detection area are analyzed in a unified manner to determine the local food type that appears most frequently as the food placement type. This achieves orderly identification from the local to the overall, improving the accuracy of data analysis.

[0057] Once the internal coverage ratio is determined, the intelligent control methods for air fryers also include: Step S500: Count the number of individual features based on the feature points of the food within a single detection area.

[0058] The number of individual features refers to the number of food feature points within a single detection area.

[0059] Step S501: Determine the single-unit correction coefficient corresponding to the number of single-unit features based on the preset correction matching relationship.

[0060] The single-unit correction coefficient is a value that reflects the effectiveness of type determination for a single detection area. The larger the value, the better the effect. Different numbers of single-unit features indicate different numbers of food feature points contained in the detection area, and the detection and analysis effects will also be different. The correction and matching relationship between the two is determined in advance by the staff. It is necessary to ensure that the larger the number of single-unit features, the larger the corresponding single-unit correction coefficient.

[0061] Step S502: Calculate the overall correction factor based on all individual correction factors, and update the internal coverage percentage based on the overall correction factor.

[0062] The overall correction coefficient can be obtained by averaging all the individual correction coefficients. Then, the data can be updated by adding the internal coverage ratio to the overall correction coefficient, so as to determine the optimal solution for food identification.

[0063] The steps to determine the appropriate values ​​for the solution based on the temperature deviations of all ingredients include: Step S600: Determine the upper limit deviation temperature and temperature sensitivity feedback degree corresponding to the food placement type based on the preset food matching relationship.

[0064] The upper limit deviation temperature is the maximum allowable temperature deviation for a single type of ingredient. Temperature sensitivity feedback is the degree to which a single type of ingredient responds to temperature changes. Ingredients that are sensitive to temperature changes have a higher temperature sensitivity feedback. The ingredient matching relationship between the three is determined in advance by the staff and recorded and stored.

[0065] Step S601: Determine whether the temperature deviation of the food ingredient is greater than the corresponding upper limit temperature deviation.

[0066] The purpose of this judgment is to determine whether the current ingredients can be processed effectively.

[0067] Step S6011: If the temperature deviation of the food ingredient is greater than the corresponding upper limit temperature deviation, then output the first suitable value, where the first suitable value is a negative value.

[0068] When the temperature deviation of the food ingredient exceeds the corresponding upper limit temperature deviation, it indicates that the food ingredient cannot be processed properly. Therefore, a first suitable value is output to identify and confirm this situation. The first suitable value is negative and the negative value is large to better reflect the situation where the food ingredient cannot be processed properly.

[0069] Step S6012: If the temperature deviation of the food ingredient is not greater than the corresponding upper limit temperature deviation, then the second suitable value is determined by calculation based on the temperature sensitivity feedback and the temperature deviation of the food ingredient.

[0070] When the temperature deviation of the ingredients is not greater than the corresponding upper limit temperature deviation, it means that the ingredients can be processed well. Therefore, it is necessary to determine the impact of the current temperature deviation on the ingredients. The second suitable value is determined by multiplying the temperature sensitivity feedback by the temperature deviation and taking the reciprocal.

[0071] Step S602: Calculate and determine the appropriate value of the scheme based on all the first and second appropriate values.

[0072] At this point, adding all the first suitable values ​​and the second suitable values ​​together will give the suitable value of the solution. If there is a first suitable value, it means that the current solution does not meet the requirements.

[0073] Once the appropriate values ​​for the scheme are determined, the intelligent control methods for air fryers also include: Step S700: Determine whether the maximum suitable value of the scheme is greater than the preset benchmark suitable value.

[0074] The baseline suitable value is the minimum suitable value that the staff set when all the identified ingredients can be handled well. The purpose of the judgment is to determine whether the current optimal solution can meet the cooking requirements of the ingredients.

[0075] Step S7001: If the maximum suitable value of the solution is greater than the baseline suitable value, then the optimal processing solution is determined for automatic operation.

[0076] When the maximum suitable value of the solution is greater than the baseline suitable value, it means that the optimal solution meets the cooking requirements of the ingredients. Therefore, it can be determined as the optimal processing solution for air fryer control.

[0077] Step S7002: If the maximum suitable value of the scheme is not greater than the baseline suitable value, then output an adjustment signal, and define the food placement type corresponding to when the suitable value is less than the preset demand suitable value as the demand change type, and define the food placement area corresponding to the demand change type as the change simulation area.

[0078] When the maximum suitable value of the solution is not greater than the baseline suitable value, it means that even the optimal solution cannot meet the cooking requirements of the ingredients. Therefore, there is an inaccurate placement of the ingredients. An adjustment signal is output to identify and distinguish this situation for further analysis and processing. The demand suitable value is the minimum suitable value that the staff sets for a single ingredient to be processed well. When the suitable value is less than the demand suitable value, it means that there is still a lot of room for optimization in the position of the ingredient. Therefore, demand change types and change simulation areas are defined to identify and distinguish different data for subsequent analysis.

[0079] Step S701: Randomly place the change simulation area within the preset available placement area to construct an adjustment processing scheme, determine the appropriate value of the scheme based on the adjustment processing scheme, and define the adjustment processing scheme corresponding to the largest appropriate value as the valid processing scheme and output it.

[0080] The available placement area refers to the area inside the frying basket where ingredients can be placed, excluding the area where ingredients are already placed in their fixed positions. By randomly placing the simulated change area, various situations are simulated and analyzed to determine the adjustment scheme for each ingredient. At this time, the ingredient situation can be re-analyzed based on the adjustment scheme. The largest suitable value of the scheme indicates that the ingredient position is optimal, so it is defined as the output of the effective processing scheme for users to refer to, thereby facilitating the adjustment of ingredient positions.

[0081] Reference Figure 2 Based on the same inventive concept, embodiments of the present invention provide an intelligent control system for an air fryer, comprising: The acquisition module is used to acquire images of the inside of the exploding basket; The processing module, connected to the acquisition module, is used for information storage and processing; The processing module performs feature recognition analysis based on the image inside the frying basket to determine the food placement area and food placement type; The processing module constructs a random blowing direction and heating temperature as a heating treatment scheme, and analyzes the heating treatment scheme and the food placement area to determine the theoretical temperature to be maintained at each location. The processing module determines the required temperature of the food corresponding to the food placement type based on the preset temperature matching relationship, and calculates the difference between the required temperature of the food and the theoretical holding temperature to determine the food deviation temperature. The processing module analyzes the temperature deviation of all ingredients to determine the appropriate value for the solution, and defines the heating treatment solution corresponding to the largest appropriate value as the optimal treatment solution, and controls the air fryer to operate automatically with the optimal treatment solution. The food placement area determination module is used to determine the food placement area; The extension point relationship determination module is used to determine the situation where an extension point corresponds to multiple external enclosing line segments; The food placement type determination module is used to determine the food placement type; The internal coverage percentage update module is used to correct and update the internal coverage percentage. The suitable value determination module is used to determine the suitable value of each heating treatment scheme. The ingredient position adjustment reference module is used to provide reference for situations where ingredient positions need to be adjusted.

[0082] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

Claims

1. A smart control method for an air fryer, characterized in that, include: Obtain an image of the inside of the exploding basket; Feature recognition analysis is performed based on images inside the frying basket to determine the food placement area and food type. A random blowing direction and heating temperature are constructed as a heating treatment scheme, and the theoretical holding temperature at each location is determined by analysis based on the heating treatment scheme and the food placement area. The required temperature of the ingredients is determined according to the preset temperature matching relationship, and the temperature deviation of the ingredients is determined by calculating the difference between the required temperature of the ingredients and the theoretical holding temperature. The appropriate value for the treatment plan is determined by analyzing the temperature deviation of all ingredients. The heating treatment plan corresponding to the largest appropriate value is defined as the optimal treatment plan, and the air fryer is controlled to operate automatically with the optimal treatment plan.

2. The intelligent control method for an air fryer according to claim 1, characterized in that, The steps for determining the food placement area based on feature recognition analysis of images inside the frying basket include: Feature recognition analysis is performed on the internal image of the frying basket to determine the feature points of the ingredients, and the distance between the features is determined based on the feature points of each ingredient; Two food feature points whose feature distance is less than a preset similar distance are grouped into the same preset initially empty feature collection set; Connect the feature points of each ingredient in the feature collection to construct the outer enclosure contour, and determine the outer enclosure line segment based on the outer enclosure contour; Based on the food feature points that are not on the outer enclosure contour, a perpendicular point is drawn on the outer enclosure line segment, and the food feature points that are on the outer enclosure line segment are defined as the accompanying points of the outer enclosure line segment, and the accompanying distance is determined based on the accompanying points. The point corresponding to the smallest adjacent distance is defined as the extension point of the outer enclosure line segment. The outer enclosure line segment is then corrected based on the extension point, and the outer enclosure outline is redefined based on the corrected outer enclosure line segment until no extension point exists. The area enclosed by the outer enclosure outline is then defined as the food placement area.

3. The intelligent control method for an air fryer according to claim 2, characterized in that, Once the expansion point is determined, the intelligent control methods for air fryers also include: Count the number of accompanying extensions when a single accompanying point is defined as an extension point; Determine if the number of accompanying expansions is greater than one; If the number of expansions is no more than one, then the current relationship between the expansion point and the external enclosing line segment will be maintained. If the number of expansions is greater than one, the length of the outer segment is determined based on the outer enclosing segment, and the inner area is calculated based on the length of the outer segment and the corresponding adjacent distance. All corresponding relationships except those corresponding to the expansion point of the largest inward contraction area and the corresponding relationships of the outer enclosing line segment are eliminated.

4. The intelligent control method for an air fryer according to claim 3, characterized in that, The steps for determining the food placement type based on feature recognition analysis of images inside the frying basket include: The area of ​​the food storage area is determined based on the storage area, and the required quantity is calculated based on the area of ​​the storage area. Based on the required number of representatives, random detection points are generated within the food placement area, and the detection area is defined based on these detection points. The number of internal features is determined by counting all food feature points, and the internal coverage ratio is determined by calculating the number of food feature points within the detection area and the number of internal features. Feature identification is performed on the detection area determined by the largest internal coverage ratio to determine the local type of food, and analysis is performed on each local type of food to determine the food placement type.

5. The intelligent control method for an air fryer according to claim 4, characterized in that, Once the internal coverage ratio is determined, the intelligent control methods for air fryers also include: Within a single detection area, the number of individual features is determined by counting the feature points of the food ingredients. The single-unit correction coefficient corresponding to the number of single-unit features is determined based on the preset correction matching relationship; The overall correction factor is determined by calculating all individual correction factors, and the internal coverage percentage is updated based on the overall correction factor.

6. The intelligent control method for an air fryer according to claim 1, characterized in that, The steps to determine the appropriate values ​​for the solution based on the temperature deviations of all ingredients include: The upper limit deviation temperature and temperature sensitivity feedback corresponding to the food placement type are determined based on the preset food matching relationship. Determine whether the temperature deviation of the food ingredients exceeds the corresponding upper limit temperature deviation. If the temperature deviation of the food ingredient is greater than the corresponding upper limit temperature deviation, then the first suitable value is output, where the first suitable value is a negative value. If the temperature deviation of the food is not greater than the corresponding upper limit temperature deviation, the second suitable value is determined by calculation based on the temperature sensitivity feedback and the temperature deviation of the food. The optimal value for the scheme is determined by calculation based on all the first and second optimal values.

7. The intelligent control method for an air fryer according to claim 6, characterized in that, Once the appropriate values ​​for the scheme are determined, the intelligent control methods for air fryers also include: Determine whether the maximum suitable value of the proposed solution is greater than the preset benchmark suitable value; If the maximum suitable value of the proposed solution is greater than the baseline suitable value, then the optimal processing solution is determined for automatic operation. If the maximum suitable value of the solution is not greater than the baseline suitable value, an adjustment signal is output, and the food placement type corresponding to the suitable value being less than the preset demand suitable value is defined as the demand change type, and the food placement area corresponding to the demand change type is defined as the change simulation area. The simulation area of ​​the change is randomly placed within the preset available placement area to construct an adjustment processing scheme. The appropriate value of the scheme is determined based on the adjustment processing scheme, and the adjustment processing scheme corresponding to the largest appropriate value is defined as the effective processing scheme and output.

8. An intelligent control system for an air fryer, used to implement the intelligent control method for an air fryer as described in any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire images of the inside of the exploding basket; The processing module, connected to the acquisition module, is used for information storage and processing; The processing module performs feature recognition analysis based on the image inside the frying basket to determine the food placement area and food placement type; The processing module constructs a random blowing direction and heating temperature as a heating treatment scheme, and analyzes the heating treatment scheme and the food placement area to determine the theoretical temperature to be maintained at each location. The processing module determines the required temperature of the food corresponding to the food placement type based on the preset temperature matching relationship, and calculates the difference between the required temperature of the food and the theoretical holding temperature to determine the food deviation temperature. The processing module analyzes the temperature deviations of all ingredients to determine the appropriate value for the solution, and defines the heating treatment solution corresponding to the largest appropriate value as the optimal treatment solution, and controls the air fryer to operate automatically with the optimal treatment solution.

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