Intelligent control method and system for air fryer

Abstract

The invention relates to an air fryer intelligent control method and system, and relates to the technical field of household appliance intelligent control, and the method comprises the steps: obtaining an accommodation internal image; performing feature analysis in the accommodating internal image to determine names of cooking food materials, and counting according to the names of the cooking food materials to determine the number of cooking types; when the number of the cooking types is one, the corresponding cooking operation temperature and the theoretical required duration are determined according to the names of the cooking food materials; the air fryer is controlled to work according to the cooking operation temperature, and the actual operation duration is obtained in real time in the operation process; according to the actual operation duration and the theoretical demand duration, determining a demand interval duration, and when the demand interval duration is smaller than the monitoring duration, obtaining a food material surface image in real time; analyzing according to the food material surface image to determine the food material cooking state, and controlling the air fryer to stop working when the food material cooking states of all the food materials are consistent with the reasonable cooking state. The cooking device has the characteristic of improving the cooking effect of food materials.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology for household appliances, and particularly to an intelligent control method and system for an air fryer. Background Art

[0002] Currently, compared with directly frying food materials, more and more families or stores choose to use an air fryer to cook food materials. For example, cooking chicken wings or chicken legs through an air fryer can not only retain the taste of fried food but also reduce people's intake of grease, which is beneficial to ensuring people's dietary health.

[0003] In the related art, the usage process of an air fryer is as follows: the user places the food materials to be cooked into the accommodation cavity of the air fryer, and then the user sets the cooking temperature and cooking duration of the air fryer according to experience. The air fryer cooks the food materials and sounds a prompt tone after the cooking duration to prompt the user to complete the cooking.

[0004] In the above related art, during the cooking process of food materials, whether it is the setting of the cooking temperature or the cooking duration, it needs to be determined by the user according to experience, resulting in a relatively high difficulty for beginners. Moreover, there are cases where some users set the temperature and duration inaccurately. At this time, the food materials may be undercooked or burnt, resulting in a poor cooking effect of the food materials, and there is still room for improvement. Summary of the Invention

[0005] In order to improve the cooking effect of food materials, this application provides an intelligent control method and system for an air fryer.

[0006] In a first aspect, this application provides an intelligent control method for an air fryer, adopting the following technical solution: An intelligent control method for an air fryer includes: Obtain an internal image of the accommodation; Perform feature analysis on the internal image of the accommodation to determine the name of the cooked food materials, and count according to the name of the cooked food materials to determine the number of cooking types; Judge whether the number of cooking types is one; If the number of cooking types is not one, output multiple types of prompt signals; If the number of cooking types is one, determine the cooking operation temperature and the theoretical required duration corresponding to the name of the current cooked food materials according to the preset food matching relationship; Control the air fryer to operate according to the cooking operation temperature, and obtain the actual operation duration in real time during the operation process; Calculate the difference between the actual operation duration and the theoretical required duration to determine the required interval duration, and obtain the surface image of the food materials in real time when the required interval duration is less than the preset monitoring duration; Analyze the surface image of the food ingredients to determine the cooking state of the food ingredients, and control the air fryer to stop operating when the cooking states of all food ingredients are consistent with the preset reasonable cooking states.

[0007] Optionally, the steps of analyzing the surface image of the food ingredients to determine the cooking state of the food ingredients include: Divide the surface image of the food ingredients according to the preset image division rules to determine the local images of the food ingredients; Input the local images of the food ingredients into the preset recognition model for analysis to determine the image recognition ratio, and define the local images of the food ingredients with an image recognition ratio greater than the preset effective recognition ratio as effective local images; Count according to the local images of the food ingredients to determine the local quantity of the food ingredients, and count according to the effective local images to determine the effective local quantity; Calculate based on the local quantity of the food ingredients and the effective local quantity to determine the local effective ratio; Judge whether the local effective ratio is greater than the preset required existence ratio; If the local effective ratio is greater than the required existence ratio, determine the reasonable cooking state as the cooking state of the food ingredients; If the local effective ratio is not greater than the required existence ratio, determine the preset waiting cooking state as the cooking state of the food ingredients.

[0008] Optionally, after the effective local images are determined, the intelligent control method of the air fryer further includes: Define the local images of the food ingredients that are not effective local images as pending local images, and define the local images of the food ingredients connected to the pending local images as connected local images; Judge whether there is a situation where all the connected local images corresponding to a single pending local image are effective local images; If there is no situation where all the connected local images corresponding to a single pending local image are effective local images, calculate the local effective ratio according to the currently determined effective local images; If there is a situation where all the connected local images corresponding to a single pending local image are effective local images, define the pending local image as an unknown local image; Determine the unknown separation distance according to the unknown local image and the preset center point of the frying basket, and determine the effective separation distance according to each effective local image and the center point of the frying basket; Define the effective local images with an effective separation distance greater than the unknown separation distance as reference local images, and count according to the reference local images to determine the reference local quantity; Calculate based on the number of reference parts and the number of valid parts to determine the proportion of reference parts. When the proportion of reference parts is greater than the preset deviation requirement proportion, update the corresponding unclear part image to a valid part image.

[0009] Optionally, the image division rule includes: Determine the distance between points based on each point in the food surface image and the center point of the frying basket; Define the point corresponding to the smallest point-to-point distance as the nearest point, and define the point corresponding to the largest point-to-point distance as the farthest point; Construct a through-line segment based on the nearest point and the farthest point, and construct a distribution line segment perpendicular to the through-line segment on the through-line segment according to the preset distribution distance; Define the area enclosed by the distribution line segment and the contour line of the food surface image as the enclosed area, and determine the enclosed area based on each enclosed area; Calculate based on the enclosed area and the preset effective arrangement range to determine the number of enclosed divisions, and perform a subtraction calculation based on the number of enclosed divisions to determine the number of boundaries; Randomly generate non-intersecting boundary line segments in the enclosed area that form an angle with the through-line segment within the preset appropriate range based on the number of boundaries, and combine the generated boundary line segments to generate a boundary segmentation scheme; Determine the segmentation area based on the boundary line segment and the contour line of the enclosed area, and define the corresponding boundary segmentation scheme as a valid segmentation scheme when the area of each segmentation area is within the effective arrangement range; Randomly select a valid segmentation scheme as the used segmentation scheme, and determine the image corresponding to each segmentation area as the food part image according to the used segmentation scheme.

[0010] Optionally, after outputting multiple types of prompt signals, the intelligent control method of the air fryer further includes: Obtain the cooking operation temperature of each food; Determine the highest cooking operation temperature according to the preset sorting rule, and define this cooking operation temperature as the central demand temperature; Calculate the difference between the cooking operation temperatures of any two foods to determine the operation deviation temperature; Determine the permitted deviation temperature corresponding to the central demand temperature according to the preset temperature matching relationship; Judge whether all the operation deviation temperatures are less than the permitted deviation temperature; If not all the operation deviation temperatures are less than the permitted deviation temperature, output a signal indicating that cooking is not possible; If all the operation deviation temperatures are less than the permitted deviation temperature, output a demand adjustment signal.

[0011] Optionally, after the demand adjustment signal is output, the intelligent control method of the air fryer further includes: Sorting the cooking temperatures of each ingredient from high to low to determine the ingredient arrangement order; Taking the center point of the frying basket as the starting point, distributing the cooking ingredient names of each ingredient in the preset indication display screen according to the ingredient arrangement order, and determining the central separation distance between each ingredient and the center point of the frying basket according to each ingredient; Sorting each ingredient according to the central separation distance from small to large to determine the ingredient distribution order, and merging adjacent and identical cooking ingredient names in the ingredient distribution order; When the ingredient distribution order is consistent with the ingredient arrangement order, controlling the air fryer to operate at the central demand temperature.

[0012] In a second aspect, the present application provides an intelligent control system for an air fryer, adopting the following technical solution: An intelligent control system for an air fryer, comprising: An acquisition module, configured to acquire an internal image of the accommodation; A processing module, connected to the acquisition module and the judgment module, for storing and processing information; A judgment module, connected to the acquisition module and the processing module, for judging information; The processing module performs feature analysis on the internal image of the accommodation to determine the cooking ingredient name, and counts according to the cooking ingredient name to determine the number of cooking types; The judgment module judges whether the number of cooking types is one; If the judgment module judges that the number of cooking types is not one, the processing module outputs a multi-class prompt signal; If the judgment module judges that the number of cooking types is one, the processing module determines the cooking operation temperature and the theoretical required duration corresponding to the current cooking ingredient name according to the preset ingredient matching relationship; The processing module controls the air fryer to operate according to the cooking operation temperature, and obtains the actual operation duration in real time during the operation process; The processing module calculates the difference between the actual operation duration and the theoretical required duration to determine the required separation duration, and obtains the surface image of the ingredient in real time when the required separation duration is less than the preset monitoring duration; The processing module analyzes the surface image of the ingredient to determine the cooking state of the ingredient, and controls the air fryer to stop operating when the cooking states of all ingredients are consistent with the preset reasonable cooking states.

[0013] In summary, the present application includes at least one of the following beneficial technical effects: During the process of food processing, the processed food can be automatically identified and analyzed to set an appropriate temperature for cooking the food, and the food can be monitored in real time during the cooking process, thereby improving the cooking effect of the food; During the process of analyzing the food, the results of some misidentifications can be corrected, thereby improving the accuracy of food analysis; When multiple foods are processed simultaneously, it is possible to first check whether they can be processed simultaneously, and when the requirements for simultaneous processing are met, arrange and guide the foods, thereby improving the cooking effect of each food. Description of the Drawings

[0014] Figure 1 It is a flowchart of the intelligent control method for an air fryer.

[0015] Figure 2 It is a schematic diagram of the process of dividing a partial image of food.

[0016] Figure 3 It is a module flowchart of the intelligent control method for an air fryer. Detailed Embodiments

[0017] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the following combines Figures 1 - 3 with embodiments to further elaborate on the present application in detail. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0018] The following further describes the embodiments of the present application in detail with reference to the accompanying drawings of the specification.

[0019] The embodiments of the present application disclose an intelligent control method for an air fryer. Referring to Figure 1 , the method flow of the intelligent control method for an air fryer includes the following steps: Step S100: Obtain the internal image of the container.

[0020] The internal image of the container is the image in the basket accommodation cavity of the air fryer, and the image can be obtained by installing a high-temperature-resistant image capturing device at the accommodation cavity of the air fryer.

[0021] Step S101: Conduct feature analysis on the internal image of the container to determine the name of the cooked food, and count according to the name of the cooked food to determine the number of cooked food types.

[0022] The name of the cooking ingredient is the name of the ingredient that needs to be cooked in the air fryer obtained after identifying the features in the internal image. Neural network learning can be performed on various ingredients to determine it, so that each ingredient can be effectively distinguished through the image. When it is really impossible to distinguish or multiple results are distinguished, the user can determine the specific cooking ingredient by touching the display screen; the number of cooking types is the number of ingredients that need to be cooked determined in the frying basket.

[0023] Step S102: Determine whether the number of cooking types is one.

[0024] The purpose of the determination is to know that cooking treatment is only performed on one ingredient.

[0025] Step S1021: If the number of cooking types is not one, output multiple types of prompt signals.

[0026] When the number of cooking types is not one, it means that the types of ingredients to be cooked are not unique. At this time, output multiple types of prompt signals to identify this situation, so that the user can know this situation and facilitate the user to take out the ingredients to cook only one ingredient in the air fryer.

[0027] Step S1022: If the number of cooking types is one, determine the cooking operation temperature and the theoretical required duration corresponding to the current cooking ingredient name according to the preset ingredient matching relationship.

[0028] When the number of cooking types is one, it means that only one ingredient is processed. At this time, the air fryer can be controlled to cook the ingredient; the cooking operation temperature is the processing temperature when the ingredient with the cooking ingredient name can be better processed, and the theoretical required duration is the processing duration required to complete the processing at this time. The ingredient matching relationship among the three is determined by the staff through multiple experiments in advance and will not be elaborated here.

[0029] Step S103: Control the air fryer to operate according to the cooking operation temperature, and obtain the actual operation duration in real time during the operation.

[0030] The actual operation duration is the duration for which the ingredient is actually cooked.

[0031] Step S104: Calculate the difference between the actual operation duration and the theoretical required duration to determine the required interval duration, and obtain the surface image of the ingredient in real time when the required interval duration is less than the preset monitoring duration.

[0032] The demand interval duration is the difference between the actual operation duration and the theoretical demand duration, and this difference is an absolute value. The monitoring duration is the maximum demand interval duration allowed when the staff determines that the ingredients may have been processed. When the demand interval duration is less than the monitoring duration, it indicates that the ingredients have entered the time range that needs to be monitored. Therefore, obtaining the surface image of the ingredients can analyze the processing situation of the ingredients.

[0033] Step S105: Analyze according to the surface image of the ingredients to determine the cooking state of the ingredients, and control the air fryer to stop operating when the cooking states of all ingredients are consistent with the preset reasonable cooking state.

[0034] The cooking state of the ingredients is the state of whether the ingredients are processed. This state can be determined by performing neural network learning on the pictures of the ingredients when they are cooked through in advance to construct an identification database, and then inputting the current surface image of the ingredients into the identification database. The reasonable cooking state is the state when the ingredients are processed and there is no scorching. When the cooking states of all ingredients are consistent with the reasonable cooking state, it indicates that the cooking of the current ingredients is completed, and at this time, controlling the air fryer to stop operating is sufficient. Among them, a preset fixed duration can be added to the theoretical demand duration to construct an upper limit duration. When the actual operation duration reaches the upper limit duration, it indicates that there are some ingredients that cannot meet the requirements. In order to prevent the cooked ingredients from scorching, the air fryer should also be controlled to stop operating.

[0035] The steps of analyzing according to the surface image of the ingredients to determine the cooking state of the ingredients include: Step S200: Divide the surface image of the ingredients according to the preset image division rule to determine the local images of the ingredients.

[0036] The image division rule is a rule that can divide the surface image of the ingredients into several images that are convenient for analyzing the processing state of the ingredients. The local image of the ingredients is the image obtained from the surface image of the ingredients through the image division rule. Among them, the image division rule can be the single-threshold division method, the region growing method, etc., or the method described in steps S400 - S407.

[0037] Step S201: Input the local images of the ingredients into the preset recognition model for analysis to determine the image recognition ratio, and define the local images of the ingredients with an image recognition ratio greater than the preset effective recognition ratio as effective local images.

[0038] The recognition model is a model that can analyze the specific situation of food processing by training and learning through each processed food ingredient sample. The image recognition ratio is the ratio of the area of the successfully recognized area. The effective recognition ratio is the minimum image recognition ratio that needs to be met when the staff determines that the area corresponding to the partial image of the food ingredient has been processed. Therefore, the effective partial image is defined to distinguish the processed part, facilitating subsequent analysis.

[0039] Step S202: Count according to the partial images of the food ingredient to determine the number of partial images of the food ingredient, and count according to the effective partial images to determine the number of effective partial images.

[0040] The number of partial images of the food ingredient is the total number of the determined partial images of the food ingredient, and the number of effective partial images is the total number of the determined effective partial images.

[0041] Step S203: Calculate based on the number of partial images of the food ingredient and the number of effective partial images to determine the local effective ratio.

[0042] The local effective ratio is the ratio of the effective partial images to all the partial images of the food ingredient, which is determined by dividing the number of effective partial images by the number of partial images of the food ingredient.

[0043] Step S204: Determine whether the local effective ratio is greater than the preset required existence ratio.

[0044] The required existence ratio is the minimum local effective ratio that needs to be achieved when the staff determines that the whole food ingredient has been processed. The purpose of the determination is to know whether the current food ingredient has been cooked.

[0045] Step S2041: If the local effective ratio is greater than the required existence ratio, determine the reasonable cooking state as the cooking state of the food ingredient.

[0046] When the local effective ratio is greater than the required existence ratio, it indicates that the food ingredient has been cooked. At this time, just determine the reasonable cooking state as the cooking state of the food ingredient.

[0047] Step S2042: If the local effective ratio is not greater than the required existence ratio, determine the preset waiting-to-cook state as the cooking state of the food ingredient.

[0048] When the local effective ratio is not greater than the required existence ratio, it indicates that the food ingredient has not been cooked yet. Therefore, just determine the waiting-to-cook state as the cooking state of the food ingredient.

[0049] After the effective partial images are determined, the intelligent control method of the air fryer further includes: Step S300: Define the partial images of the food ingredient that are not effective partial images as undetermined partial images, and define the partial images of the food ingredient connected to the undetermined partial images as connected partial images.

[0050] Defining the to-be-determined local image and the connected local images can distinguish different local images of food ingredients, facilitating subsequent analysis.

[0051] Step S301: Determine whether there is a situation where all the connected local images corresponding to a single to-be-determined local image are valid local images.

[0052] The purpose of the determination is to find out whether there is a situation where the surrounding areas of a single to-be-determined local image are all fully cooked. At this time, there may be cases of misdetection and further analysis is required.

[0053] Step S3011: If there is no situation where all the connected local images corresponding to a single to-be-determined local image are valid local images, calculate the local effective occupancy ratio based on the currently determined valid local images.

[0054] When there is no situation where all the connected local images corresponding to a single to-be-determined local image are valid local images, it indicates that there is basically no misdetection of the to-be-determined local image. At this time, normal analysis can be carried out.

[0055] Step S3012: If there is a situation where all the connected local images corresponding to a single to-be-determined local image are valid local images, define this to-be-determined local image as an unclear local image.

[0056] When there is a situation where all the connected local images corresponding to a single to-be-determined local image are valid local images, it indicates the possibility of misdetection of the to-be-determined local image and further analysis is required. Therefore, an unclear local image is defined to distinguish different to-be-determined local images, facilitating subsequent analysis.

[0057] Step S302: Determine the unclear separation distance based on the unclear local image and the preset center point of the frying basket, and determine the effective separation distance based on each valid local image and the center point of the frying basket.

[0058] The center point of the frying basket is the central position point of the frying basket of the air fryer, that is, the position point with the highest temperature in the frying basket. The unclear separation distance is the distance value between the unclear local image and the center point of the frying basket, and this distance value can be determined by calculating the average value of the distances between all pixel points in this image and the center point of the frying basket; the effective separation distance is the distance value between the valid local image and the center point of the frying basket.

[0059] Step S303: Define the valid local images with effective separation distances greater than the unclear separation distance as reference local images, and count based on the reference local images to determine the number of reference local images.

[0060] When the effective separation distance is greater than the unknown separation distance, it indicates that the corresponding effective local image is farther from the center point of the frying basket than the current unknown local image. At this time, a reference local image is defined to distinguish the effective local image for subsequent analysis; the number of reference locals is the total number of the determined reference local images.

[0061] Step S304: Calculate based on the number of reference locals and the number of effective locals to determine the reference local ratio. When the reference local ratio is greater than the preset deviation requirement ratio, update the corresponding unknown local image to an effective local image.

[0062] The reference local ratio is the ratio of the determined reference local images to all effective local images, which is determined by dividing the number of reference locals by the number of effective locals; the deviation requirement ratio is the minimum reference local ratio that needs to be satisfied when the staff sets that the areas corresponding to most of the relatively far images are fully cooked and this area is also basically fully cooked. When the reference local ratio is greater than the deviation requirement ratio, it indicates that most of the areas farther from the center point of the frying basket than the area of the current unknown local image are already fully cooked, indicating that the area corresponding to this unknown local image has a high possibility of being fully cooked. Therefore, it is updated to an effective local image.

[0063] The image division rules include: Step S400: Determine the point separation distance based on each point in the food surface image and the center point of the frying basket.

[0064] The point separation distance is the straight-line distance value between the area point of the actual area corresponding to the food surface image and the center point of the frying basket.

[0065] Step S401: Define the point corresponding to the minimum point separation distance as the nearest point, and define the point corresponding to the maximum point separation distance as the farthest point.

[0066] Defining the nearest point and the farthest point can distinguish different points for subsequent analysis.

[0067] Step S402: Construct a through line segment based on the nearest point and the farthest point, and construct a distribution line segment perpendicular to the through line segment according to the preset distribution distance on the through line segment.

[0068] The through line segment is a line segment formed by using the farthest point and the nearest point as two endpoints. The distribution distance is a fixed distance set by the staff. The distribution line segment is a line segment perpendicular to the through line segment and with endpoints being the image contour points of the food surface image. Refer to Figure 2 , and the distribution line segments are arranged at intervals of the distribution distance along the length direction of the through line segment.

[0069] Step S403: Define the enclosed area as the area enclosed by the distribution line segment and the contour line of the food material surface image, and determine the enclosed area according to each enclosed area.

[0070] Defining the enclosed area is to distinguish different areas, and the enclosed area is the area corresponding to the enclosed area.

[0071] Step S404: Calculate according to the enclosed area and the preset effective arrangement range to determine the number of enclosed divisions, and perform a subtraction calculation based on the number of enclosed divisions to determine the number of boundaries.

[0072] The effective arrangement range is the area range corresponding to the image when the staff sets that it can better recognize and analyze the image. The number of enclosed divisions is the number of areas into which the enclosed area needs to be divided. For example, if the enclosed area of the enclosed area is 10 units and the effective arrangement range is 1.5 units - 2.5 units, then the number of enclosed divisions can be 4, 5, or 6. At this time, randomly select one as the number of enclosed divisions to be used; the number of boundaries is the number of boundary lines to be delimited. For example, in a cuboid, if two areas need to be divided, only one boundary line needs to be added. Therefore, the number of boundaries can be determined by subtracting one from the number of enclosed divisions.

[0073] Step S405: Randomly generate non-intersecting boundary line segments within the enclosed area and with an included angle with the penetration line segment within the preset appropriate range, and combine the generated boundary line segments to generate a boundary segmentation scheme.

[0074] The boundary line segment is a line segment formed with the contour line of the enclosed area as the endpoints. The setting of the appropriate range can ensure that the delimited area will not be slender. The specific value is set by the staff according to the actual situation, such as 0 - 45° and 135° - 180°; the boundary segmentation scheme is the combination scheme formed by all boundary line segments.

[0075] Step S406: Determine the divided areas according to the boundary line segments and the contour line of the enclosed area, and define the corresponding boundary segmentation scheme as an effective segmentation scheme when the areas of all divided areas are within the effective arrangement range.

[0076] The divided area is the area divided by the boundary line segments. When the areas of all divided areas are within the effective arrangement range, it means that the current area division meets the requirements of subsequent recognition and analysis. Therefore, it can be defined as an effective segmentation scheme for distinction.

[0077] Step S407: Randomly select an effective segmentation scheme as the used segmentation scheme, and determine the images corresponding to each divided area as local food material images according to the used segmentation scheme.

[0078] By randomly selecting a segmentation scheme, the image can be divided, so that local images of ingredients convenient for recognition and analysis can be obtained.

[0079] After multiple types of prompt signals are output, the intelligent control method of the air fryer further includes: Step S500: Obtain the cooking operation temperature of each ingredient.

[0080] By obtaining the cooking operation temperature of various ingredients, it is convenient to analyze the subsequent cooking situation.

[0081] Step S501: Determine the cooking operation temperature with the largest value according to a preset sorting rule, and define this cooking operation temperature as the central demand temperature.

[0082] The sorting rule is a method set by the staff to sort the numerical values. For example, the bubble sort method. Through the sorting rule, the cooking operation temperature with the largest value can be determined and defined as the central demand temperature for subsequent analysis.

[0083] Step S502: Calculate the difference between the cooking operation temperatures of any two ingredients to determine the operation deviation temperature.

[0084] The operation deviation temperature is the difference between two cooking operation temperatures, and this difference is an absolute value.

[0085] Step S503: Determine the permitted deviation temperature corresponding to the central demand temperature according to a preset temperature matching relationship.

[0086] The permitted deviation temperature is the maximum temperature difference that will occur between the center point of the frying basket and the edge of the frying basket when the center point temperature of the air fryer reaches the central demand temperature. Different central demand temperatures correspond to different permitted deviation temperatures, and the temperature matching relationship between the two is determined by the staff through multiple tests in advance and will not be elaborated here.

[0087] Step S504: Determine whether all the operation deviation temperatures are less than the permitted deviation temperature.

[0088] The purpose of the determination is to find out whether multiple ingredients can be cooked simultaneously.

[0089] Step S5041: If not all the operation deviation temperatures are less than the permitted deviation temperature, output a signal indicating that cooking is not possible.

[0090] When not all the operation deviation temperatures are less than the permitted deviation temperature, it means that there is a large deviation in the processing temperatures of each ingredient, and the requirement of simultaneous cooking cannot be met. Therefore, output a signal indicating that cooking is not possible to identify this situation, so that the user can timely take out some of the ingredients to avoid affecting the food processing.

[0091] Step S5042: If all the operating deviation temperatures are less than the permitted deviation temperature, then output a demand adjustment signal.

[0092] When all the operating deviation temperatures are less than the permitted deviation temperature, it indicates that the temperature deviations of each food ingredient meet the requirements. At this time, only the positions of each food ingredient need to be adjusted to achieve effective processing of each food ingredient. Therefore, it is only necessary to output a demand adjustment signal to identify this situation.

[0093] After the demand adjustment signal is output, the intelligent control method of the air fryer further includes: Step S600: Sort the cooking operation temperatures of each food ingredient from high to low to determine the food ingredient arrangement order.

[0094] The food ingredient arrangement order is the order obtained by sorting the food ingredients according to the cooking operation temperature from high to low.

[0095] Step S601: Starting from the center point of the frying basket, distribute the cooking food ingredient names of each food ingredient in a preset indication display screen according to the food ingredient arrangement order, and determine the central separation distance between each food ingredient and the center point of the frying basket according to each food ingredient.

[0096] By distributing each food ingredient in the indication display screen according to the food ingredient arrangement order, it is possible to guide the user to adjust the positions of the food ingredients, so that the food ingredients with lower required temperatures are far from the center point of the frying basket, and the food ingredients with higher required temperatures are close to the center point of the frying basket; the central separation distance is the distance value between the food ingredient placed in the frying basket and the center point of the frying basket.

[0097] Step S602: Sort each food ingredient from small to large according to the central separation distance of each food ingredient to determine the food ingredient distribution order, and merge adjacent and identical cooking food ingredient names in the food ingredient distribution order.

[0098] The food ingredient distribution order is the arrangement order obtained from the nearest to the farthest of each food ingredient and the center point of the frying basket. By merging adjacent cooking food ingredient names, the specific distribution situation of the food ingredients can be determined.

[0099] Step S603: When the food ingredient distribution order is consistent with the food ingredient arrangement order, control the air fryer to operate at the central required temperature.

[0100] When the food ingredient distribution order is consistent with the food ingredient arrangement order, it indicates that the position adjustment of the food ingredients has been completed. At this time, each food ingredient can be cooked better. Therefore, it is only necessary to control the air fryer to operate.

[0101] Refer to Figure 3 , based on the same inventive concept, an embodiment of the present invention provides an intelligent control system for an air fryer, including: An acquisition module, configured to acquire the internal image of the accommodation; A processing module, connected to the acquisition module and the judgment module, for storing and processing information; A judgment module, connected to the acquisition module and the processing module, for judging information; The processing module performs feature analysis on the internal image in the accommodation to determine the name of the cooked food ingredients, and counts according to the name of the cooked food ingredients to determine the number of cooking types; The judgment module judges whether the number of cooking types is one; If the judgment module judges that the number of cooking types is not one, the processing module outputs a multi-category prompt signal; If the judgment module judges that the number of cooking types is one, the processing module determines the cooking operation temperature and the theoretical required duration corresponding to the current cooked food ingredient name according to the preset ingredient matching relationship; The processing module controls the air fryer to operate according to the cooking operation temperature, and obtains the actual operation duration in real time during the operation process; The processing module calculates the difference between the actual operation duration and the theoretical required duration to determine the required interval duration, and obtains the surface image of the food ingredient in real time when the required interval duration is less than the preset monitoring duration; The processing module analyzes the surface image of the food ingredient to determine the cooking state of the food ingredient, and controls the air fryer to stop operating when the cooking states of all food ingredients are consistent with the preset reasonable cooking states; A food ingredient cooking state determination module, for determining the cooking states of each food ingredient; A to-be-determined local image analysis module, for further precisely analyzing the to-be-determined local image; An image division rule determination module, for determining a suitable image division rule for use; A multi-category food ingredient analysis module, for analyzing the situation where multiple types of food ingredients need to be processed synchronously; A food ingredient adjustment analysis module, for analyzing the specific adjustment situation of each food ingredient, and setting a suitable temperature to process each food ingredient when the cooking requirements are met.

[0102] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above division of each functional module is used as an example. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working processes of the system, device, and unit described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

Claims

1. An intelligent control method for an air fryer, characterized in that: include: Get the internal image of the container; Performing feature analysis in the image inside the container to determine the names of the cooked ingredients, and counting the names of the cooked ingredients to determine the number of cooked types; Determine whether the number of cooking types is one; If the number of cooking types is not one, multiple types of prompt signals are output; If the number of cooking types is one, the cooking operation temperature and theoretical required time corresponding to the current cooking ingredient name are determined according to the preset ingredient matching relationship; Control the air fryer to operate according to the cooking operation temperature, and obtain the actual operation time in real time during the operation; The difference between the actual operation time and the theoretical required time is calculated to determine the required time interval, and the surface image of the food is obtained in real time when the required time interval is less than the preset monitoring time; The cooking status of the ingredients is determined by analyzing the surface image of the ingredients, and the air fryer is controlled to stop working when the cooking status of all the ingredients is consistent with the preset reasonable cooking status.

2. The air fryer intelligent control method according to claim 1, characterized in that: The steps of analyzing the food surface image to determine the cooking state of the food include: Dividing the surface image of the food material according to a preset image division rule to determine a local image of the food material; Inputting the partial image of the food into a preset recognition model for analysis to determine the image recognition ratio, and defining the partial image of the food whose image recognition ratio is greater than the preset effective recognition ratio as an effective partial image; Counting the food partial images to determine the quantity of the food partials, and counting the effective partial images to determine the effective partial quantity; Calculate the effective proportion of the ingredients based on the quantity of the ingredients and the effective quantity of the ingredients; Determine whether the local effective ratio is greater than the preset demand existence ratio; If the local effective proportion is greater than the demand existence proportion, the reasonable cooking state is determined as the food cooking state; If the local effective proportion is not greater than the demand existence proportion, the preset waiting for cooking state is determined as the food cooking state.

3. The air fryer intelligent control method according to claim 2, characterized in that: After the effective local image is determined, the air fryer intelligent control method further includes: defining the food partial image that is not a valid partial image as a pending partial image, and defining the food partial image connected to the pending partial image as a connected partial image; Determine whether there is a situation where all the connected local images corresponding to a single pending local image are valid local images; If there is no situation where all the connected local images corresponding to a single pending local image are valid local images, the local effective ratio is calculated based on the currently determined valid local image; If there is a situation where all the connected local images corresponding to a single pending local image are valid local images, the pending local image is defined as an unknown local image; Determine the unknown separation distance according to the unknown partial image and the preset frying basket center point, and determine the effective separation distance according to each effective partial image and the frying basket center point; A valid local image whose valid separation distance is greater than an unknown separation distance is defined as a reference local image, and the reference local image is counted to determine the number of reference locals; The reference local ratio is determined by calculation based on the reference local number and the valid local number, and when the reference local ratio is greater than the preset deviation requirement ratio, the corresponding unknown local image is updated to a valid local image.

4. The air fryer intelligent control method according to claim 3, characterized in that: Image segmentation rules include: Determine the distance between points based on each point in the food surface image and the center point of the frying basket; The point with the smallest value is defined as the closest point, and the point with the largest value is defined as the farthest point. A through line segment is constructed according to the nearest point and the farthest point, and a distribution line segment perpendicular to the through line segment is constructed on the through line segment according to a preset distribution distance; The area enclosed by the distribution line segments and the contour line of the food surface image is defined as an enclosed area, and the enclosed area is determined according to each enclosed area; Calculate the number of enclosure divisions based on the enclosed area and the preset effective arrangement range, and subtract one from the number of enclosure divisions to determine the number of boundaries; Randomly generate boundary line segments in the enclosed area according to the number of boundaries, which do not intersect each other and whose angles with the through line segments are within a preset appropriate range, and combine the generated boundary line segments to generate a boundary segmentation scheme; Determine the segmentation area according to the boundary line segments and the contour lines of the enclosed area, and define the corresponding boundary segmentation scheme as a valid segmentation scheme when the area of ​​each segmentation area is within the valid arrangement range; An effective segmentation scheme is randomly selected as the used segmentation scheme, and the image corresponding to each segmentation area is determined as the food local image according to the used segmentation scheme.

5. The air fryer intelligent control method according to claim 1, characterized in that: After the multiple prompt signals are output, the air fryer intelligent control method further includes: Obtain the cooking temperature of each ingredient; Determine the cooking operation temperature with the largest value according to a preset sorting rule, and define the cooking operation temperature as the core required temperature; Calculate the difference between the cooking temperatures of any two ingredients to determine the operating deviation temperature; Determine the allowable deviation temperature corresponding to the center demand temperature according to the preset temperature matching relationship; Determine whether all operating deviation temperatures are less than the allowable deviation temperature; If all the operation deviation temperatures are not less than the allowable deviation temperature, a cooking failure signal will be output; If all operating deviation temperatures are less than the allowable deviation temperature, a demand adjustment signal is output.

6. The air fryer intelligent control method according to claim 5, characterized in that: After the demand adjustment signal is output, the air fryer intelligent control method further includes: Sort the ingredients by their cooking temperatures from high to low to determine the order in which they should be arranged; Taking the center point of the frying basket as the starting point, the names of the ingredients to be cooked are distributed on a preset indicator display screen according to the order in which the ingredients are arranged, and determining the center distance between the ingredients and the center point of the frying basket according to the ingredients; Sort the ingredients according to the distance between the centers of the ingredients from small to large to determine the order of ingredient distribution, and merge adjacent and identical names of cooking ingredients in the order of ingredient distribution; When the order of food distribution is consistent with the order of food arrangement, the air fryer is controlled to operate at the required center temperature.

7. An intelligent control system for an air fryer, characterized in that: include: An acquisition module, used for acquiring an internal image; A processing module, connected to the acquisition module and the judgment module, for storing and processing information; A judgment module, connected with the acquisition module and the processing module, for judging the information; The processing module performs feature analysis in the internal image to determine the names of the ingredients to be cooked, and counts the names of the ingredients to determine the number of the types of ingredients to be cooked; The judging module judges whether the number of cooking types is one; If the determination module determines that the number of cooking types is not one, the processing module outputs multiple types of prompt signals; If the judging module determines that the number of cooking types is one, the processing module determines the cooking operation temperature and theoretical required time corresponding to the current cooking ingredient name according to the preset ingredient matching relationship; The processing module controls the air fryer to operate according to the cooking operation temperature, and obtains the actual operation time in real time during the operation; The processing module calculates the difference between the actual operation time and the theoretical required time to determine the required time interval, and obtains the surface image of the food in real time when the required time interval is less than the preset monitoring time; The processing module analyzes the surface image of the food to determine the cooking status of the food, and controls the air fryer to stop working when the cooking status of all the food is consistent with the preset reasonable cooking status.

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