Cooking temperature data correction method, system, storage medium and electronic device
By acquiring cookware images and infrared temperature data during the cooking process, mapping and performing outlier detection and dynamic weighted interpolation processing on the food outline region, the problem of slow infrared temperature data correction speed or large error in the prior art is solved, achieving fast and accurate temperature correction, and improving cooking effect and user experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NINGBO FOTILE KITCHEN WARE CO LTD
- Filing Date
- 2026-01-07
- Publication Date
- 2026-06-05
AI Technical Summary
In existing technologies, methods for correcting infrared temperature data during cooking suffer from slow response speeds or large errors, affecting cooking results and user experience.
By acquiring cookware images and infrared temperature data, mapping them onto the cookware images, determining the outline area of the ingredients, randomly selecting temperature sampling points, performing outlier detection and dynamic weighted interpolation processing, and obtaining corrected temperature data for the ingredients, which is then used to adjust cooking parameters.
It enables rapid and accurate correction of outlier temperature data, improving the accuracy and response speed of temperature data, avoiding the impact of erroneous temperature data on cooking results, and enhancing the user experience.
Smart Images
Figure CN122156665A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of infrared temperature measurement technology, and in particular to a method, system, storage medium, and electronic device for correcting cooking temperature data. Background Technology
[0002] To visualize the cooking process from a temperature field perspective, infrared sensors are integrated into appliances such as range hoods. These sensors acquire infrared temperature data in real time during cooking and interpolate outlier temperature data. However, existing interpolation methods primarily include simple interpolation algorithms and high-precision interpolation algorithms.
[0003] While the simple interpolation algorithm offers a fast response time, it suffers from low contrast and somewhat blurry temperature data processing, leading to significant errors in the corrected temperature data and inaccurate temperature information observed by the user, thus affecting the overall cooking results. Conversely, the high-precision interpolation algorithm achieves higher accuracy and smaller errors in temperature data correction, but its computational complexity and slow response time make it difficult to provide timely and accurate temperature feedback to the user during cooking, impacting the user experience. Summary of the Invention
[0004] This application provides a method, system, storage medium, and electronic device for correcting cooking temperature data, to at least solve the problem in related technologies of how to correct outlier food temperature data in a timely and accurate manner. The technical solution of this application is as follows: According to a first aspect of the embodiments of this application, a method for correcting cooking temperature data is provided, comprising: Acquire images of the cookware during cooking and corresponding infrared temperature data of the cookware; By mapping infrared temperature data onto the cookware image, target mapping information corresponding to different coordinates in the cookware image and infrared temperature data can be obtained. Based on target mapping information and infrared temperature data, determine the food outline region and the corresponding food temperature data; the infrared temperature data includes the food temperature data. Randomly select multiple food temperature sampling points from the food outline area; Outlier detection is performed on the food temperature data corresponding to multiple food temperature sampling points, and food temperature sampling points whose food temperature data are within the preset food outlier temperature range are marked as food outlier interpolation points. Dynamic weighted interpolation is performed on the outlier interpolation points of the ingredients to obtain the ingredient correction coefficients corresponding to the outlier interpolation points; Based on the ingredient correction coefficient, the temperature data of the ingredients corresponding to the outlier interpolation points are processed to obtain the ingredient corrected temperature data; the ingredient temperature corrected data is used to adjust the cooking parameters of the ingredients.
[0005] According to a second aspect of the embodiments of this application, a cooking temperature data correction system is provided, comprising: The acquisition module is used to acquire images of the cookware in the cooking state and the corresponding infrared temperature data of the cookware; The mapping module is used to map infrared temperature data onto the cookware image to obtain target mapping information corresponding to different coordinates in the cookware image and infrared temperature data. The contour region determination module is used to determine the food contour region and the corresponding food temperature data based on target mapping information and infrared temperature data; the infrared temperature data includes food temperature data. The random selection module is used to randomly select multiple food temperature sampling points from the food outline region; The outlier detection module is used to detect outliers in the food temperature data corresponding to multiple food temperature sampling points, and to mark the food temperature sampling points whose food temperature data is within the preset outlier temperature range as outlier interpolation points. The ingredient correction coefficient determination module is used to perform dynamic weighted interpolation on the outlier interpolation points of ingredients to obtain the ingredient correction coefficients corresponding to the outlier interpolation points. The ingredient temperature correction data determination module is used to perform temperature correction processing on the ingredient temperature data corresponding to the outlier interpolation points of the ingredients based on the ingredient correction coefficient, so as to obtain the ingredient temperature correction data; the ingredient temperature correction data is used to adjust the cooking parameters of the ingredients.
[0006] According to a third aspect of the embodiments of this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the method as described in any one of the first aspects above.
[0007] According to a fourth aspect of the present application, a computer-readable storage medium is provided, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform any of the methods described in the first aspect of the present application. According to a fifth aspect of the embodiments of this application, a computer program product is provided, including computer instructions that, when executed by a processor, cause a computer to perform the method described in any one of the first aspects of the embodiments of this application.
[0008] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this application.
[0009] The technical solutions provided by the embodiments of this application have at least the following beneficial effects: By mapping infrared temperature onto the cookware image, target mapping information is obtained. Based on the target mapping information and infrared temperature data, the food outline area and the corresponding food temperature data are determined. Multiple food temperature sampling points are randomly selected from the food outline area, which can quickly and accurately locate the food outline area, avoid interference from infrared temperature data in non-food areas, and improve the targeting and reliability of temperature acquisition. Outlier detection is performed on the food temperature data corresponding to multiple food temperature sampling points, and the previous temperature sampling point whose food temperature data is within the preset outlier temperature range is marked as the outlier interpolation point, which can effectively identify abnormal temperature data. Dynamic weighted interpolation is performed on outlier interpolation points of ingredients to obtain ingredient correction coefficients corresponding to these outlier interpolation points. Based on these correction coefficients, temperature correction processing is performed on the temperature data of the ingredients corresponding to these outlier interpolation points to obtain corrected temperature data. This allows for timely and accurate correction of outlier ingredient temperature data, improving the accuracy and response speed of temperature data correction, preventing erroneous temperature data from affecting the overall cooking effect, and enhancing the accuracy and reliability of ingredient temperature data.
[0010] Other features and aspects of this application will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description
[0011] To more clearly illustrate the technical solutions and advantages in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0012] Figure 1 This is a flowchart illustrating a cooking temperature data correction method according to an exemplary embodiment.
[0013] Figure 2 This is a schematic diagram of a cookware according to an exemplary embodiment.
[0014] Figure 3 This is a block diagram of a cooking temperature data correction system according to an exemplary embodiment.
[0015] Figure 4 This is a block diagram of an electronic device for correcting cooking temperature data, according to an exemplary embodiment. Figure 1 .
[0016] Figure 5This is a block diagram of an electronic device for correcting cooking temperature data, according to an exemplary embodiment. Figure 2 . Detailed Implementation
[0017] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments in the specification, and not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.
[0019] Various exemplary embodiments, features, and aspects of this application will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.
[0020] The term "exemplary" as used herein means "serving as an example, embodiment, or illustration." Any embodiment illustrated herein as "exemplary" is not necessarily to be construed as superior to or better than other embodiments. The term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships may exist, for example, A and / or B, which can represent: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more of a plurality, for example, including at least one of A, B, and C, which can represent including any one or more elements selected from the set consisting of A, B, and C.
[0021] Unless otherwise specified, the directions in this article should be understood as follows: the direction closer to the user is forward, and the direction farther from the user is backward.
[0022] Furthermore, to better illustrate this application, numerous specific details are provided in the following detailed description. Those skilled in the art should understand that this application can be implemented without certain specific details. In some instances, methods, means, components, and circuits well-known to those skilled in the art have not been described in detail in order to highlight the main points of this application.
[0023] Figure 1 This is a flowchart illustrating a cooking temperature data correction method according to an exemplary embodiment. Figure 1 As shown, the steps may include the following.
[0024] In step S101, the image of the cookware in the cooking state and the corresponding infrared temperature data of the cookware are acquired.
[0025] In the embodiments of this specification, the cookware image can refer to a visual sensor installed in the kitchen, such as a visual sensor installed on a range hood. The visual sensor can be a camera used to capture images of the cookware. Infrared temperature data can refer to temperature data measured on the surface of the cookware using an infrared temperature sensor or thermal imaging device. In this application, infrared temperature data characterizes the temperature data at different locations on the surface of the cookware and the food inside the cookware.
[0026] For example, when a user starts cooking, the cooking temperature data correction system is activated. At this time, a visual sensor in the kitchen captures an image of the cookware, and at the same time, an infrared temperature sensor or thermal imaging device acquires temperature data at different locations on the surface of the cookware and food in real time, i.e., the infrared temperature data corresponding to the cookware.
[0027] In step S103, the infrared temperature data is mapped onto the cookware image to obtain target mapping information corresponding to different coordinates in the cookware image and the infrared temperature data.
[0028] In the embodiments of this specification, target mapping information can refer to the information obtained by corresponding infrared temperature data with pixel coordinates in the cookware image, that is, each different coordinate in the cookware image has a corresponding infrared temperature data.
[0029] For example, the infrared temperature data of different coordinates in the pot image are mapped one by one onto the pot, thereby forming a target mapping relationship, that is, each coordinate in the pot corresponds to an infrared temperature data.
[0030] In step S105, based on the target mapping information and infrared temperature data, the food outline region and the corresponding food temperature data are determined.
[0031] In the embodiments of this specification, the food ingredient outline region can refer to the boundary range of the region corresponding to the food ingredient extracted from the cookware image. Food ingredient temperature data can refer to the temperature data corresponding to each sampling point within the food ingredient outline region; infrared temperature data includes food ingredient temperature data, meaning the food ingredient temperature data is obtained from detection by an infrared temperature sensor.
[0032] For example, based on temperature gradient changes or image edge detection algorithms, the outline region of the food can be divided. For instance, when pan-frying steak, the temperature of the steak may be room temperature when it is first put into the pan. At this time, the temperature data of the steak is significantly different from the background temperature data of the pan surface. Therefore, the outline region of the steak can be extracted based on the target mapping information, and the steak temperature data corresponding to the outline region can also be extracted.
[0033] In one possible implementation, infrared temperature data is predicted based on target mapping information to determine the background temperature data corresponding to the non-food outline region and the non-food outline region.
[0034] In the embodiments of this specification, the non-food outline area can refer to the area outside the food outline area in the cookware. Background temperature data can refer to the temperature data corresponding to each coordinate position in the non-food outline area.
[0035] For example, Figure 2 This is a schematic diagram illustrating a cookware according to an exemplary embodiment. Figure 2 As shown, the shaded areas represent the outline of the food ingredients, while the blank areas represent the non-outlined areas. While defining the outline of the food ingredients, the areas outside this outline can be defined as the non-outlined areas. These two areas together constitute the entire cookware. Furthermore, based on target mapping information and infrared temperature data, the background temperature data corresponding to the non-outlined areas is determined.
[0036] In step S107, multiple food temperature sampling points are randomly selected from the food outline region.
[0037] In the embodiments of this specification, the food temperature sampling point can refer to multiple points randomly selected within the outline area of the food for temperature analysis, that is, points used for food outlier detection and difference processing.
[0038] For example, the ingredient is steak, and five steak temperature sampling points are randomly selected from the steak outline area. This application does not limit the type of ingredient or the number of sampling points.
[0039] In one possible implementation, multiple background temperature sampling points are randomly collected from non-food outline regions.
[0040] In the embodiments of this specification, the background temperature sampling points can refer to multiple coordinate points selected within the non-food outline area for analyzing the background temperature.
[0041] For example, from Figure 2 In the blank areas, i.e., the non-food outline areas, multiple background temperature sampling points are randomly selected.
[0042] In step S109, outlier detection is performed on the food temperature data corresponding to each of the multiple food temperature sampling points, and the food temperature sampling points whose food temperature data are within the preset food outlier temperature range are marked as food outlier interpolation points.
[0043] In the embodiments of this specification, the preset outlier temperature range for food ingredients can refer to the outlier temperature range obtained according to the outlier detection algorithm, that is, it is used to determine whether a certain food ingredient temperature sampling point is an outlier interpolation point. An outlier interpolation point for food ingredients can refer to a food ingredient temperature sampling point with an abnormal temperature.
[0044] In one possible implementation, the temperature data corresponding to multiple food temperature sampling points are sorted to determine the median temperature and multiple temperature differences between the food temperature data and the median temperature for each sampling point; the multiple temperature differences are sorted to determine the median temperature difference; based on the median temperature and the median temperature difference, a preset minimum outlier temperature threshold and a preset maximum outlier temperature threshold are determined; food temperature sampling points whose food temperature data is less than the preset minimum outlier temperature threshold or greater than the maximum outlier temperature threshold are marked as outlier interpolation points.
[0045] In the embodiments of this specification, the median temperature can refer to the temperature data of the food at the middle position among multiple food temperature sampling points. If the number of data points is odd, the median temperature is the temperature data in the exact middle; if the number of data points is even, the median temperature is the average of the two middle values.
[0046] Temperature difference can refer to the interpolation between the temperature data of each ingredient and the median temperature. The median temperature difference can refer to the value in the middle among all temperature differences. The preset minimum ingredient temperature threshold can refer to a lower limit temperature value for outliers. The preset maximum ingredient temperature threshold can refer to an upper limit temperature value for outliers.
[0047] For example, using steak as the ingredient, five temperature sampling points are taken within the steak's outline region: sampling point A, sampling point B, sampling point C, sampling point D, and sampling point E. The temperature data corresponding to sampling point A and E are 180℃, 182℃, 181℃, 300℃, and 179℃, respectively. The temperature data corresponding to these five sampling points are then sorted to obtain [179℃, 180℃, 181℃, 182℃, 300℃]. After sorting, the median temperature M1 is 181. Correspondingly, multiple temperature differences are calculated as [1, 1, 0, 119, 2]. These temperature differences are then sorted to obtain [0, 1, 1, 2, 119]. Based on the sorting structure, the median temperature difference M2 is determined to be 1. Based on the median temperature difference M2, the median temperature M1, and the preset outlier temperature formula M1±3M2, the preset minimum outlier temperature threshold is calculated to be 181-3=178, and the preset maximum outlier temperature threshold is calculated to be 181+3=184. Accordingly, the preset outlier temperature range is less than 178 or greater than 184. Further, sampling point D within the preset outlier temperature range is marked as the outlier interpolation point. Simultaneously, the temperature data of sampling points A, B, C, and E outside the preset outlier temperature range (i.e., [178, 184]) are all determined as normal temperature data.
[0048] In step S111, dynamic weighted interpolation is performed on the outlier interpolation points of the ingredients to obtain the ingredient correction coefficients corresponding to the outlier interpolation points.
[0049] In the embodiments of this specification, the ingredient correction factor may refer to the coefficient used to correct the temperature data of the ingredients.
[0050] In one possible implementation, multiple food correction sampling points within a preset range of the outlier interpolation point and the corresponding food temperature data for each of the multiple food sampling points are obtained, and multiple sampling distance information between the multiple food correction sampling points and the outlier interpolation point is determined; based on the multiple sampling distance information, the weight value corresponding to each of the multiple food correction sampling points is determined; and the multiple temperature sampling points, the corresponding food temperature data for each of the multiple food correction sampling points, and the corresponding weight values for each of the multiple food correction sampling points are processed by weighted least squares to obtain the temperature correction coefficient.
[0051] In the embodiments of this specification, the food ingredient correction sampling point can refer to the food ingredient correction sampling point within a preset range of the food ingredient outlier interpolation point, that is, the coordinate point of the accurate temperature within the preset range. The sampling distance information can refer to the Euclidean distance between each food ingredient correction sampling point and the food ingredient outlier interpolation point.
[0052] For example, the pot is a round pot, the center point of the pot is set as the origin, and a coordinate system is established, such as... Figure 2 As shown, the coordinates of the outlier interpolation point q can be set to (0.5, 0.5). Four food correction sampling points are dynamically selected within a radius of two centimeters from the outlier interpolation point; this application does not limit this range. To simplify the calculation, four food correction sampling points are used in the following example; preferably, this application dynamically selects eight or more food correction sampling points within a preset range, and this application does not limit this. The coordinates, temperature, and weight values of the four food correction sampling points are shown in Table 1 below.
[0053] Table 1
[0054] The coordinates of the four food ingredient correction sampling points are (0, 0), (1, 0), (0, 1), and (1, 1); the temperatures of sampling points 1-4 are 179℃, 180℃, 181℃, and 182℃, respectively. Then, the weight values are calculated using the inverse distance weighting method, which is based on the Euclidean distance between the food ingredient correction sampling points and the outlier interpolation points. The weighting formula is ; in, This represents the weight value of the i-th sampling point; p represents the power parameter, usually p=2; Let represent the Euclidean distance from the i-th sampling point to the outlier interpolation point q. Then, the weights of sampling points 1-4 are determined to be 0.25, 0.2, 0, 2, and 0.15, respectively.
[0055] The model corresponding to the corrected temperature data for food ingredients is constructed as T(x,y)=a+bx+cy+d +e +fxy. Simultaneously, construct and minimize the weighted residual sum of squares (WRSS): WRSS= ; To minimize WRSS, solve the normal equations. = The corresponding ingredient correction coefficients a, b, c, d, e, and f are determined.
[0056] Where X is the design matrix, containing x, y, , The terms are x and y, W is the weight matrix, and T is the observed sampled temperature vector. =[a, b, c, d, e, f , are the coefficients to be determined. The design matrix x is:
[0057] Weight matrix W:
[0058] Sampling temperature vector T:
[0059] Calculated :
[0060] Calculated :
[0061] calculate =[a, b, c, d, e, f :
[0062] Finally, the ingredient correction coefficients were obtained as follows: a=179, b=1, c=2, d=0, e=0, f=0.
[0063] In step S113, based on the ingredient correction coefficient, the temperature data of the ingredients corresponding to the outlier interpolation points are subjected to temperature correction processing to obtain the corrected temperature data of the ingredients.
[0064] In the embodiments of this specification, the corrected temperature data for ingredients can refer to the corrected temperature data, that is, the temperature that is closer to the actual temperature during the cooking process, which is used to adjust the cooking parameters of the ingredients.
[0065] For example, substituting the calculated ingredient correction coefficients into the model corresponding to the ingredient correction temperature data, we obtain the ingredient correction temperature data T(0.5,0.5)=179+1x+2y+0 +0 +0xy=179+0.5+1=180.5. Furthermore, the abnormal temperature data corresponding to the outlier interpolation point of the food ingredient is replaced with the corrected temperature data of the food ingredient, i.e., 300℃, to avoid interference from abnormal temperature data in the food ingredient cooking process and to improve the accuracy and stability of the corrected temperature data.
[0066] In one possible implementation, bilinear interpolation is performed on the background outlier interpolation points to obtain the background corrected temperature data corresponding to the background outlier interpolation points.
[0067] Bilinear interpolation is used to consider multiple background temperature correction points in non-food outline regions. This application does not limit the number of background temperature correction points. A weighted average is calculated based on their distances to determine the background correction temperature data corresponding to outlier interpolation points. This helps eliminate interference from abnormal background temperature data, improving the overall accuracy of the system's temperature data. Simultaneously, it allows for the display of the entire cookware's color change, enhancing detection completeness and optimizing intelligent cooking control.
[0068] For example, the coordinates of the background outlier to be interpolated are (x, y); four background correction sampling points are collected, with coordinates (x1, y1), (x1, y2), (x2, y1), and (x2, y2), respectively, and the corresponding background temperature data are T11, T12, T21, and T22, respectively. This application does not limit the number of background correction sampling points. Furthermore, based on the bilinear interpolation method, the horizontal interpolation is calculated as follows: the upper temperature is T_top = (x2-x) / (x2-x1)*T11 + (x-x1) / (x2-x1)*T21; the lower temperature is T_bottom = (x2-x) / (x2-x1)*T12 + (x-x1) / (x2-x1)*T22. Then, the vertical interpolation yields the final background corrected temperature data = (y2-y) / (y2-y1)*T_top + (y-y1) / (y2-y1)*T_bottom. This improves the robustness of temperature detection and stable correction in the cooking temperature data correction system, eliminates abnormal interference, and allows users to accurately understand the infrared temperature data corresponding to the cookware, thereby improving the user experience.
[0069] In one possible implementation, the food-corrected temperature data is mapped to a temperature-color view with a preset grayscale range; cooking parameters are adjusted based on the color changes corresponding to the food-corrected temperature data in the temperature-color view.
[0070] In the embodiments of this specification, the preset grayscale range can refer to a pre-defined grayscale value interval, such as 0-255, used to map temperature values to grayscale images of different shades of color. This application does not limit this. A temperature-color visualization can refer to an image that visually presents the corrected temperature data of food in color or grayscale form. Cooking parameters can include heat level, such as gas stove heat, induction cooker power, etc.; heating time; this application does not limit the type of cooking parameters.
[0071] In one possible implementation, the maximum and minimum food temperature data within a preset food distance range of the outlier interpolation point are determined; based on the corrected food temperature data, the maximum food temperature data, and the minimum food temperature data, the target grayscale mapping value corresponding to the outlier interpolation point is determined; based on the target grayscale mapping value, the corrected food temperature data is mapped to a temperature-color visualization.
[0072] In the embodiments of this specification, the preset food distance range can refer to a pre-defined neighborhood range centered on the outlier interpolation point of the food, used to filter temperature data points participating in the grayscale mapping calculation. For example, the preset food range distance can refer to the food range with a radius of 3 cm centered on the outlier interpolation point of the food. The maximum food temperature data can refer to the maximum temperature value of all valid temperature sampling points within the preset food range. The minimum food temperature data can refer to the minimum temperature value of all valid temperature sampling points within the preset food range. The target grayscale mapping value can refer to the specific value mapped from the corrected food temperature data to the preset grayscale range, used to generate a pseudo-color image.
[0073] For example, the maximum temperature data of the food within 3 cm of the outlier interpolation point is 200℃ and the minimum temperature data of the food is 180℃, and the corrected temperature data of the food is set to 182℃.
[0074] The mapping formula is: Target grayscale mapping value X = (food temperature correction data - food temperature minimum data) / (food temperature maximum data - food temperature minimum data) * 255 = (182 - 180) / (200 - 180) * 255 = 25.5.
[0075] Furthermore, based on the target grayscale mapping value, a thermal imaging pseudo-color encoding algorithm is used to convert the food correction temperature data into a pseudo-color image. That is, R=a*|sin(b*x)|; G=a*|sin(b*x+c)|; B=a*|sin(b*x+2c)|; where a=255, b=2π / 255, c=π / 5. Correspondingly, the target grayscale mapping value is input into the RGB color model, which accurately presents the color changes of the food during cooking from the temperature-color visualization. Users can see the food correction temperature data on the range hood's display screen, and thus accurately adjust cooking parameters, such as increasing the range hood's airflow (this application does not limit this), or the range hood control system can automatically adjust the cooking parameters based on color changes. For example, a deep red color indicates that the temperature inside the pot is too high, at which point the system can reduce the heat and increase the range hood's airflow.
[0076] In one possible implementation, food temperature sampling points where the food temperature data does not meet the preset outlier temperature range are marked as normal temperature sampling points; the food temperature data corresponding to the normal temperature sampling points are mapped to a temperature-color view; and the cooking parameters are adjusted according to the color change corresponding to the food temperature data in the temperature-color view.
[0077] In the embodiments of this specification, a normal temperature sampling point can refer to a food temperature sampling point where the food temperature data does not meet the preset food outlier temperature range, that is, the food temperature data is greater than or equal to the preset minimum food outlier temperature threshold and less than or equal to the maximum food outlier temperature threshold.
[0078] For example, multiple food temperature data corresponding to normal temperature sampling points in the food are mapped one by one to a temperature color view. In this way, the complete color change of the food can be displayed on the screen. For example, the edge of the steak is red and the center of the steak is light red. At this time, the user can cook the steak a little longer according to the display screen, or the system can turn off the stove fire after five seconds according to the color change on the display screen to achieve intelligent linkage between the range hood and the stove.
[0079] Figure 3 This is a block diagram of a cooking temperature data correction system according to an exemplary embodiment. (Refer to...) Figure 3 The system may include: The acquisition module 301 is used to acquire the image of the cookware in the cooking state and the corresponding infrared temperature data of the cookware; The mapping module 303 is used to map infrared temperature data onto the cookware image to obtain target mapping information corresponding to different coordinates in the cookware image and infrared temperature data. The contour region determination module 305 is used to determine the food contour region and the corresponding food temperature data based on the target mapping information and infrared temperature data; the infrared temperature data includes the food temperature data. The random selection module 307 is used to randomly select multiple food temperature sampling points from the food outline region; The outlier detection module 309 is used to detect outliers in the food temperature data corresponding to multiple food temperature sampling points, and to mark the food temperature sampling points whose food temperature data is within the preset food outlier temperature range as food outlier interpolation points.
[0080] In one possible implementation, the outlier detection module 309 includes: The temperature difference determination module is used to sort the temperature data of each of the multiple food temperature sampling points, determine the median temperature, and determine multiple temperature differences between the food temperature data of each of the multiple food temperature sampling points and the median temperature. The temperature difference median determination module is used to sort multiple temperature differences and determine the median of the temperature difference.
[0081] The threshold determination module is used to determine the preset minimum outlier temperature threshold and the preset maximum outlier temperature threshold based on the median temperature and the median temperature difference; the preset outlier temperature range for food ingredients is less than the preset minimum outlier temperature threshold for food ingredients, or greater than the preset maximum outlier temperature threshold for food ingredients. The marking module is used to mark food temperature sampling points whose food temperature data is less than the preset minimum food outlier temperature threshold, or whose food temperature data is greater than the maximum food outlier temperature threshold, as food outlier interpolation points.
[0082] The ingredient correction coefficient determination module 311 is used to perform dynamic weighted interpolation processing on the outlier interpolation points of the ingredients to obtain the ingredient correction coefficients corresponding to the outlier interpolation points of the ingredients.
[0083] In one possible implementation, the ingredient correction coefficient determination module 311 includes: The sampling point acquisition module is used to acquire multiple food correction sampling points within a preset range of the food outlier interpolation point and the food temperature data corresponding to each of the multiple food correction sampling points, and to determine multiple sampling distance information between the multiple food correction sampling points and the food outlier interpolation point. The weight value determination module is used to determine the weight value corresponding to each of the multiple food ingredient correction sampling points based on multiple sampling distance information. The weighted processing module is used to perform weighted least squares processing on the food temperature data corresponding to multiple temperature sampling points, multiple food correction sampling points, and the weight values corresponding to multiple food correction sampling points to obtain the temperature correction coefficient.
[0084] The ingredient temperature correction data determination module 313 is used to perform temperature correction processing on the ingredient temperature data corresponding to the outlier interpolation point of the ingredient based on the ingredient correction coefficient, so as to obtain ingredient temperature correction data; the ingredient temperature correction data is used to adjust the ingredient cooking parameters.
[0085] In one possible implementation, the cooking temperature data control system also includes: The background temperature data determination module is used to determine the background temperature data corresponding to the non-food outline area and the non-food outline area based on the target mapping information and infrared temperature data. The background temperature sampling point acquisition module is used to randomly acquire multiple background temperature sampling points from non-food outline areas; The background outlier interpolation point marking module is used to detect outliers in the background temperature data corresponding to multiple background temperature sampling points, and mark the background temperature sampling points whose background temperature data meets the preset background outlier temperature range as background outlier interpolation points. The background correction temperature data determination module is used to perform bilinear interpolation on the background outlier interpolation points to obtain the background correction temperature data corresponding to the background outlier interpolation points.
[0086] In one possible implementation, the cooking temperature data control system also includes: The color mapping module is used to map the corrected temperature data of ingredients to a temperature-color visual chart with a preset grayscale range. The first cooking parameter adjustment module is used to adjust cooking parameters based on the color change corresponding to the temperature data of the ingredients in the temperature-color view.
[0087] In one possible implementation, the color mapping module includes: The food temperature data extreme value determination module determines the maximum and minimum food temperature data within a preset food distance range of the outlier interpolation point of the food. The target grayscale mapping value determination module is used to determine the target grayscale mapping value corresponding to the outlier interpolation point of the food based on the food temperature correction data, the maximum temperature data of the food, and the minimum temperature data of the food. The temperature data mapping module is used to map the corrected temperature data of the ingredients to a temperature-color visualization based on the target grayscale mapping value.
[0088] In one possible implementation, the cooking temperature data correction factor also includes: The normal temperature sampling point marking module is used to mark the food temperature sampling points where the food temperature data does not meet the preset food outlier temperature range as normal temperature sampling points. The normal temperature mapping module is used to map the food temperature data corresponding to the normal temperature sampling point to the temperature-color view. The second cooking parameter adjustment module is used to adjust the cooking parameters based on the color changes corresponding to the food temperature data in the temperature-color visualization.
[0089] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0090] Figure 4 This is a block diagram illustrating an electronic device for correcting cooking temperature data according to an exemplary embodiment. The electronic device may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown, the electronic device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a cooking temperature data correction method. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse. Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.
[0091] Figure 5 This is a block diagram illustrating an electronic device for correcting cooking temperature data according to an exemplary embodiment. The electronic device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, the electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When executed by the processor, the computer program implements a cooking temperature data correction method. Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. In an exemplary embodiment, an electronic device is also provided, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the cooking temperature data correction method as described in the embodiments of this application.
[0092] In an exemplary embodiment, a computer-readable storage medium is also provided, which, when executed by a processor of an electronic device, enables the electronic device to perform the cooking temperature data correction method of the present application embodiments. The computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc.
[0093] In an exemplary embodiment, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the cooking temperature data correction method in the embodiments of this application.
[0094] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.
[0095] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.
[0096] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.
Claims
1. A method for correcting cooking temperature data, characterized in that, include: Acquire images of the cookware during cooking and corresponding infrared temperature data of the cookware; The infrared temperature data is mapped onto the cookware image to obtain target mapping information corresponding to different coordinates in the cookware image and the infrared temperature data. Based on the target mapping information and the infrared temperature data, the food outline region and the corresponding food temperature data are determined; the infrared temperature data includes the food temperature data. Randomly select multiple food temperature sampling points from the food outline region; Outlier detection is performed on the food temperature data corresponding to each of the multiple food temperature sampling points, and the food temperature sampling points whose food temperature data is within the preset food outlier temperature range are marked as food outlier interpolation points. Dynamic weighted interpolation is performed on the outlier interpolation points of the ingredients to obtain the ingredient correction coefficients corresponding to the outlier interpolation points of the ingredients; Based on the ingredient correction coefficient, the temperature data of the ingredients corresponding to the outlier interpolation points of the ingredients are subjected to temperature correction processing to obtain the ingredient corrected temperature data; the ingredient corrected temperature data is used to adjust the cooking parameters of the ingredients.
2. The cooking temperature data correction method according to claim 1, characterized in that, The step of detecting outliers in the food temperature data corresponding to each of the multiple food temperature sampling points, and marking the food temperature sampling points whose food temperature data falls within a preset outlier temperature range as outlier interpolation points, includes: The temperature data corresponding to each of the multiple food temperature sampling points are sorted by temperature to determine the median temperature and multiple temperature differences between the food temperature data corresponding to each of the multiple food temperature sampling points and the median temperature. The multiple temperature differences are sorted to determine the median of the temperature differences. Based on the median temperature and the median temperature difference, a preset minimum outlier temperature threshold and a preset maximum outlier temperature threshold are determined; the preset outlier temperature range for food ingredients is less than the preset minimum outlier temperature threshold for food ingredients, or greater than the preset maximum outlier temperature threshold for food ingredients. The food temperature sampling points whose food temperature data is less than the preset minimum food outlier temperature threshold, or whose food temperature data is greater than the maximum food outlier temperature threshold, are marked as food outlier interpolation points.
3. The cooking temperature data correction method according to claim 1, characterized in that, The step of performing dynamic weighted interpolation on the outlier interpolation points of the food ingredients to obtain the food ingredient correction coefficients corresponding to the outlier interpolation points includes: Acquire multiple food correction sampling points within a preset range of the outlier interpolation point of the food and the food temperature data corresponding to each of the multiple food correction sampling points, and determine multiple sampling distance information between the multiple food correction sampling points and the outlier interpolation point of the food. Based on the multiple sampling distance information, determine the weight value corresponding to each of the multiple food ingredient correction sampling points; The temperature correction coefficient is obtained by performing weighted least squares processing on the temperature data corresponding to each of the multiple temperature sampling points and the multiple food correction sampling points, as well as the weight values corresponding to each of the multiple food correction sampling points.
4. The cooking temperature data correction method according to claim 1, characterized in that, The method further includes: Based on the target mapping information and the infrared temperature data, determine the non-food outline region and the background temperature data corresponding to the non-food outline region; Multiple background temperature sampling points were randomly collected from the non-food outline region; Outlier detection is performed on the background temperature data corresponding to each of the multiple background temperature sampling points, and the background temperature sampling points whose background temperature data meets the preset background outlier temperature range are marked as background outlier interpolation points. The background outlier interpolation points are subjected to bilinear interpolation to obtain the background corrected temperature data corresponding to the background outlier interpolation points.
5. The cooking temperature data correction method according to claim 1, characterized in that, The method further includes: Map the corrected temperature data of the ingredients to a temperature-color visualization within a preset grayscale range; The cooking parameters are adjusted based on the color change corresponding to the temperature data of the ingredients in the temperature-color visualization, which is then corrected.
6. The cooking temperature data correction method according to claim 5, characterized in that, The step of mapping the corrected temperature data of the food ingredients to a temperature-color visual chart with a preset grayscale range includes: Determine the maximum and minimum food temperature data within the preset food distance range of the food outlier interpolation point; Based on the food ingredient corrected temperature data, the maximum food ingredient temperature data, and the minimum food ingredient temperature data, determine the target grayscale mapping value corresponding to the outlier interpolation point of the food ingredient. Based on the target grayscale mapping value, the food ingredient corrected temperature data is mapped to the temperature-color visualization.
7. The cooking temperature data correction method according to claim 5, characterized in that, The method further includes: The food temperature sampling points whose food temperature data do not meet the preset food outlier temperature range are marked as normal temperature sampling points. Map the food temperature data corresponding to the normal temperature sampling points to the temperature-color visual chart; The cooking parameters are adjusted based on the color changes corresponding to the food temperature data in the temperature-color visualization.
8. A cooking temperature data correction system, characterized in that, include: The acquisition module is used to acquire images of the cookware in the cooking state and the corresponding infrared temperature data of the cookware; The mapping module is used to map the infrared temperature data onto the cookware image to obtain target mapping information corresponding to different coordinates in the cookware image and the infrared temperature data. The contour region determination module is used to determine the food contour region and the corresponding food temperature data based on the target mapping information and the infrared temperature data; the infrared temperature data includes the food temperature data. The random selection module is used to randomly select multiple food temperature sampling points from the food outline region; The outlier detection module is used to detect outliers in the food temperature data corresponding to each of the multiple food temperature sampling points, and to mark the food temperature sampling points whose food temperature data is within a preset food outlier temperature range as food outlier interpolation points. The ingredient correction coefficient determination module is used to perform dynamic weighted interpolation processing on the outlier interpolation points of the ingredients to obtain the ingredient correction coefficients corresponding to the outlier interpolation points of the ingredients. The ingredient temperature correction data determination module is used to perform temperature correction processing on the ingredient temperature data corresponding to the outlier interpolation point of the ingredient based on the ingredient correction coefficient, so as to obtain ingredient temperature correction data; the ingredient temperature correction data is used to adjust the ingredient cooking parameters.
9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the cooking data method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is enabled to perform the cooking temperature data correction method as described in any one of claims 1 to 7.