A roasting control method, an intelligent roasting device and an intelligent control system
By acquiring data on the state and optical properties of the ingredients and adjusting the output power of the spectral thermal radiation module, the problems of over-burning on the surface and undercooking inside the ingredients in the baking equipment were solved, thus improving the accuracy and stability of baking.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NINGBO FOTILE KITCHEN WARE CO LTD
- Filing Date
- 2026-02-28
- Publication Date
- 2026-07-03
AI Technical Summary
Existing baking equipment tends to cause the surface of food to burn while the inside remains uncooked during the heating process, resulting in low baking precision and poor stability.
By acquiring the food state data and optical property data of the target food, the output power of multiple spectral thermal radiation modules is adjusted to achieve uniform heating of the food. Multiple spectral thermal radiation modules (near-infrared, mid-infrared and visible light bands) are used for dynamic adjustment, and the baking control is optimized by combining data fusion and status monitoring.
It improves the precision and stability of baking control, ensures that the ingredients are heated evenly during the baking process, reduces energy waste, and enhances the quality of baking.
Smart Images
Figure CN122331369A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of intelligent baking equipment technology, and in particular to a baking control method, intelligent baking equipment and intelligent control system. Background Technology
[0002] With the improvement of people's quality of life and the promotion and popularization of technologies such as the Internet, big data, artificial intelligence, and voice interaction, more and more traditional lifestyles are gradually changing. The use of home appliances has gradually moved towards intelligence. While bringing more convenience to users, the functions of various home appliances are also becoming more diversified. However, the heating methods used by existing baking equipment are very likely to cause the surface of the food to be over-burnt while the inside is not cooked, resulting in technical problems such as low baking precision and poor baking stability. Summary of the Invention
[0003] To address the aforementioned technical problems, this application provides a baking control method, an intelligent baking device, and an intelligent control system. Specifically, this application determines radiation energy distribution data for different wavelengths based on the food state data and the optical properties data of the food surface. This allows for the adjustment of the output power of multiple spectral thermal radiation modules based on the radiation energy distribution data. Consequently, this application can adaptively adjust the output power of multiple spectral thermal radiation modules based on the real-time state of the target food, ensuring uniform heating of the food and thus improving the accuracy and stability of baking control.
[0004] On one hand, embodiments of this application provide a baking control method applied to an intelligent baking device, the intelligent baking device including multiple spectral thermal radiation modules corresponding to different wavelength bands, the method including: Acquire the food state data of the target food and the optical property data of the food surface within the intelligent baking equipment; Data fusion processing is performed based on the food state data, the optical property data, and the radiation data corresponding to each of the multiple spectral thermal radiation modules to obtain the radiation energy distribution data corresponding to the multiple spectral thermal radiation modules. Based on the radiation energy distribution data and the food status data, the output power of each of the multiple spectral thermal radiation modules is adjusted to obtain the target output power of each of the multiple spectral thermal radiation modules. The intelligent baking equipment is controlled to bake based on the target output power.
[0005] Further, the food condition data includes food temperature data, food thickness data, and food category data; the data fusion processing based on the food condition data, the optical characteristic data, and the radiation data corresponding to each of the multiple spectral thermal radiation modules to obtain the radiation energy distribution data corresponding to the multiple spectral thermal radiation modules includes: Based on the radiation data of each spectral thermal radiation module, the food temperature data, the food thickness data, the food category data, and the optical property data, the heat absorption efficiency of the target food under the radiation of each spectral thermal radiation module is determined. Based on the heat absorption efficiency of the target food ingredient under the radiation of the multiple spectral thermal radiation modules, the radiation energy distribution data corresponding to the multiple spectral thermal radiation modules is determined.
[0006] Further, determining the radiation energy distribution data corresponding to the multiple spectral thermal radiation modules based on the respective heat absorption efficiency of the target food ingredient under the radiation of the multiple spectral thermal radiation modules includes: The heat absorption efficiency of the target food under the radiation of the multiple spectral thermal radiation modules is weighted to obtain the comprehensive heat absorption efficiency. With the goal of maximizing the overall thermal absorption efficiency, the radiation energy corresponding to each of the multiple spectral thermal radiation modules is processed to obtain radiation energy distribution data corresponding to the multiple spectral thermal radiation modules.
[0007] Further, the food condition data includes food temperature data; the step of adjusting the output power of each of the plurality of spectral thermal radiation modules based on the radiation energy distribution data and the food condition data to obtain the target output power of each of the plurality of spectral thermal radiation modules includes: Temperature deviation data is determined based on the food temperature data and preset temperature data, wherein the preset temperature data is the reference temperature data corresponding to the target food. Based on the temperature deviation data and the baking state characteristics corresponding to the target ingredient, the radiation energy distribution data is adjusted to obtain the target radiation energy distribution data; the baking state characteristics characterize the degree of maturity of the target ingredient. Based on the target radiation energy distribution data, the target output power corresponding to each of the plurality of spectral thermal radiation modules is determined.
[0008] Furthermore, the wavelengths corresponding to the spectral thermal radiation module include the near-infrared band, the mid-infrared band, and the visible light band; the adjustment and processing of the radiation energy distribution data based on the temperature deviation data and the baking state characteristics corresponding to the target food ingredient to obtain the target radiation energy distribution data includes: When the baking state characteristics indicate that the target food is not fully cooked, and the temperature deviation data is greater than a first preset deviation, the ratio of the near-infrared band radiation energy in the total radiation energy is increased to a first preset value, where the total radiation energy is the sum of the near-infrared band radiation energy, the mid-infrared band radiation energy, and the visible light band radiation energy. When the baking state characteristic indicates that the target food is in a mature state, and the temperature deviation data is less than the second preset deviation, the ratio of the visible light band radiation energy in the total radiation energy is increased to the second preset value, where the first preset deviation is greater than the second preset deviation.
[0009] Furthermore, the optical characteristic data includes scattering data and absorption data; after the step of controlling the baking of the intelligent baking device based on the target output power, the method further includes: Monitor the core temperature data of the target ingredient; If the center temperature data is less than a preset temperature threshold, the scattering data is corrected to obtain target scattering data; Based on the target scattering data, the food state data, the absorption data, and the radiation data corresponding to each of the multiple spectral thermal radiation modules, data fusion processing is performed to obtain updated radiation energy distribution data; Based on the updated radiation energy distribution data, the output power of each of the multiple spectral thermal radiation modules is adjusted to obtain the adjusted output power.
[0010] Furthermore, acquiring the food status data of the target food within the intelligent baking equipment includes: Acquire the multispectral image stack data of the target food ingredient; Data extraction and processing are performed based on the multispectral image stack data to obtain the food status data corresponding to the target food ingredient.
[0011] Further, the food ingredient state data includes food ingredient moisture content data, food ingredient temperature data, and food ingredient category data; before the step of adjusting the radiation energy distribution data based on the temperature deviation data and the baking state characteristics corresponding to the target food ingredient to obtain the target radiation energy distribution data, the method further includes: The moisture content data, temperature data, and category data of the ingredients are input into a state extraction model to extract the baking state features corresponding to the target ingredients. The state extraction model is trained on a preset model based on the sample state data.
[0012] On the other hand, embodiments of this application provide an intelligent baking device, which includes a controller for executing the baking control method described above.
[0013] On the other hand, embodiments of this application provide an intelligent control system, which includes a server and an intelligent baking device. The server is communicatively connected to the intelligent baking device and is capable of data interaction with the intelligent baking device. The intelligent baking device is used to execute the baking control method described above.
[0014] Implementing this application will have the following beneficial effects: This application determines the radiation energy distribution data of different wavelengths based on the food state data and the optical property data of the food surface. Based on the radiation energy distribution data, the output power of each of the multiple spectral thermal radiation modules can be adjusted. Thus, based on the real-time state of the target food, this application can adaptively adjust the output power of each of the multiple spectral thermal radiation modules to ensure that the food is heated evenly, thereby improving the accuracy and stability of baking control. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 A schematic diagram of an implementation environment provided for an embodiment of this application; Figure 2 A schematic flowchart of a baking control method provided in an embodiment of this application; Figure 3 A schematic flowchart illustrating the target output power determination method provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of a baking control device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a server provided in an embodiment of this application. Detailed Implementation
[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application 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 this application 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] Please see Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application, such as... Figure 1 The implementation environment shown may include a smart baking device 01 and a control terminal 02 for controlling the smart baking device 01 to bake. In practical applications, the smart baking device 01 and the control terminal 02 can be connected wirelessly or via wired communication to realize the interaction between the smart baking device 01 and the control terminal 02.
[0020] In this embodiment, the intelligent baking device 01 can be an intelligent oven, etc. The intelligent baking device 01 is equipped with multiple spectral thermal radiation modules corresponding to different wavelengths, so as to bake the food inside the intelligent baking device 01 using the multiple spectral thermal radiation modules corresponding to different wavelengths. Specifically, the different wavelengths include near-infrared, mid-infrared and visible light. Among them, the near-infrared band can achieve a heating method with rapid heating and fast heating response, the mid-infrared band can achieve a heating method that balances heating speed and penetration depth, and the visible light band can achieve heating control for food surface heating and color control. In addition, by dynamically adjusting the heating time and output power of the above-mentioned multiple spectral thermal radiation modules in real time, it is ensured that the food is heated evenly during the baking process to achieve the best taste and baking effect.
[0021] It should be noted that the multiple spectral thermal radiation modules corresponding to different wavebands are all independently controlled heating sources.
[0022] In one specific embodiment, the intelligent baking device 01 is also equipped with a multispectral camera, wherein the shooting angle and lighting conditions of the multispectral camera are adjustable, thereby ensuring that the food can be covered.
[0023] In practical applications, the control terminal 02 can be a controller, which can be located on the intelligent baking equipment 01 or exist independently. It is used to acquire the food state data and optical property data of the target food surface within the intelligent baking equipment 01, and to perform data fusion processing based on the food state data, optical property data, and radiation data corresponding to each of the multiple spectral thermal radiation modules to obtain radiation energy distribution data corresponding to the multiple spectral thermal radiation modules. Based on the radiation energy distribution data and food state data, it adjusts the output power of each of the multiple spectral thermal radiation modules to obtain the target output power of each of the multiple spectral thermal radiation modules. This allows the intelligent baking equipment 01 to be baked based on the target output power, thereby achieving uniform heating of the food.
[0024] In addition, it should be noted that, Figure 1 The diagram shown is merely a schematic representation of an implementation environment, which may include more or fewer nodes; this application makes no limitation on this.
[0025] Figure 2 This is a schematic flowchart of a baking control method provided in an embodiment of this application. The following is in conjunction with... Figure 2 The technical solution of this application is described in detail below. It should be noted that this specification provides the operational steps of the method as described in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many steps and does not represent the only execution order. Specifically, this baking control method is applied to an intelligent baking device, which includes multiple spectral thermal radiation modules corresponding to different wavebands. Therefore, the baking control method specifically includes the following steps: S101: Acquire the food state data and optical property data of the target food surface inside the intelligent baking equipment.
[0026] In this application embodiment, the food status data represents the real-time status data of the target food. For example, the food status data includes, but is not limited to, food moisture content data, food temperature data, food thickness data, and food category data. The optical property data is the optical property exhibited by the surface of the target food. For example, the optical property data includes, but is not limited to, scattering data and absorption data. The intelligent baking device can be an intelligent baking oven, and the target food can be pasta, meat, or potatoes, etc.
[0027] In an optional implementation, step S101 may include: S1011: Acquire multispectral image stack data of the target ingredient; S1012: Data extraction and processing based on multispectral image stack data to obtain the food status data corresponding to the target food.
[0028] In this embodiment, the acquisition of multispectral image stack data is achieved through a multispectral camera integrated into a smart baking device. This multispectral camera can select multiple specific bands within the ultraviolet to infrared range of the electromagnetic spectrum to simultaneously or sequentially image the same target food ingredient. Subsequently, the images captured under these different bands are strictly stacked and aligned according to the wavelength order, ultimately constructing a three-dimensional data cube containing two-dimensional spatial information and one-dimensional spectral information, i.e., multispectral image stack data. Specifically, the food ingredient state data includes food ingredient moisture content data, food ingredient temperature data, food ingredient thickness data, and food ingredient category data, etc. Furthermore, by extracting data from the multispectral image stack data, non-contact and non-destructive detection of the food ingredient state data corresponding to the target food ingredient can be achieved, thereby enabling simultaneous detection of multiple parameters and improving the efficiency, accuracy, and reliability of the detection.
[0029] S102: Based on the food condition data, optical property data, and radiation data corresponding to each of the multiple spectral thermal radiation modules, data fusion processing is performed to obtain radiation energy distribution data corresponding to the multiple spectral thermal radiation modules.
[0030] In this embodiment, the radiation energy distribution data represents the proportion of radiation energy in different bands in the total radiation energy. Specifically, the different bands include the near-infrared band, the mid-infrared band, and the visible light band. Thus, the total radiation energy is equal to the sum of the radiation energy in the near-infrared band, the mid-infrared band, and the visible light band. Based on the obtained radiation energy distribution data corresponding to multiple spectral thermal radiation modules, a theoretical basis can be provided for real-time adjustment of the output power of each of the multiple spectral thermal radiation modules.
[0031] In an optional implementation, the food status data includes food temperature data, food thickness data, and food category data; step S102 may include: S1021: Based on the radiation data, food temperature data, food thickness data, food category data, and optical property data of each spectral thermal radiation module, determine the heat absorption efficiency of the target food under the radiation of each spectral thermal radiation module. S1022: Based on the heat absorption efficiency of the target food under the radiation of multiple spectral thermal radiation modules, determine the radiation energy distribution data corresponding to the multiple spectral thermal radiation modules.
[0032] In this embodiment, the radiation data refers to the radiation energy data of the spectral thermal radiation module. Specifically, the radiation data includes the radiation wavelength and radiation intensity. The food temperature data refers to the real-time temperature of the target food, specifically including the surface temperature of the target food. The optical property data refers to the optical properties exhibited by the surface of the target food, specifically including scattering data and absorption data. The scattering data is the scattering coefficient, and the absorption data is the absorption coefficient. The thermal absorption efficiency is the absorption efficiency of the target food for thermal radiation energy. Therefore, after determining the thermal absorption efficiency of the target food under the radiation of each spectral thermal radiation module, the radiation energy of each spectral thermal radiation module can be allocated based on the corresponding thermal absorption efficiency of the target food under the radiation of multiple spectral thermal radiation modules to optimize the allocation ratio of radiation energy in each band and obtain the optimal energy allocation of different bands of thermal radiation on the target food to ensure the efficient absorption of thermal radiation energy by the target food.
[0033] In practical applications, let the heat absorption efficiency be... ,but
[0034] In the formula, The wavelength of radiation. The surface temperature of the food. Let be Planck's constant. At the speed of light, To be at the radiation wavelength and food surface temperature The heat absorption efficiency is as follows. The absorption coefficient is the data corresponding to the food category. Radiation intensity, For the thickness data of the ingredients, This represents the scattering coefficient corresponding to the food category data. It should be noted that different food categories have different optical property data.
[0035] In one specific implementation, step S1022 may include: S10221: Weight the heat absorption efficiency of the target food under the radiation of multiple spectral thermal radiation modules to obtain the comprehensive heat absorption efficiency. S10222: With the goal of maximizing the overall thermal absorption efficiency, the radiation energy corresponding to each of the multiple spectral thermal radiation modules is processed to obtain the radiation energy distribution data corresponding to the multiple spectral thermal radiation modules.
[0036] Specifically, the overall thermal absorption efficiency is the sum of the thermal absorption efficiencies corresponding to different wavelengths. Therefore, maximizing the overall thermal absorption efficiency can be the optimization goal. The radiation energy corresponding to each of the multiple spectral thermal radiation modules is processed to obtain radiation energy distribution data for the multiple spectral thermal radiation modules. This optimization method takes into account the differences in the absorption characteristics of the target food to thermal radiation energy of different wavelengths, thereby achieving a reasonable distribution of radiation energy and ensuring the efficient absorption of thermal radiation energy by the target food, thus improving the heating efficiency.
[0037] In practical applications, let the overall heat absorption efficiency be... ,but
[0038] In the formula, Data is allocated to the radiant energy corresponding to each band, and , These represent the radiation wavelengths corresponding to different wavebands. The surface temperature of the food. For heat absorption efficiency, To be at each radiation wavelength and food surface temperature The heat absorption efficiency is reduced.
[0039] In an optional implementation, the food condition data includes food moisture content data, food temperature data, and food category data; the method further includes: S1013: Input the moisture content data, temperature data, and category data of the ingredients into the state extraction model to extract the state and obtain the baking state features corresponding to the target ingredients. The state extraction model is trained on the preset model based on the sample state data.
[0040] In this embodiment, the moisture content data, temperature data, and category data of the ingredients are used as input data for the state extraction model, and the baking state features are used as output data for the state extraction model. The baking state features characterize the maturity of the target ingredients. In a specific embodiment, the baking state features may include immature and mature states. Specifically, the immature state includes medium-rare, medium-rare, medium-rare, and medium-rare states. The sample state data are the moisture content data, temperature data, and category data of the target ingredients at different maturity levels, collected through a large number of experiments. The state extraction model can then be obtained by training a preset model based on the sample state data. The preset model is a neural network model.
[0041] Specifically, the collected data on food moisture content, food temperature, and food category can be directly input into the state extraction model for state extraction to obtain the baking state characteristics corresponding to the target food. This enables accurate identification of the baking state characteristics corresponding to the target food, thereby improving the efficiency and accuracy of obtaining the baking state characteristics corresponding to the target food.
[0042] S103: Based on radiation energy distribution data and food status data, adjust the output power of each of the multiple spectral thermal radiation modules to obtain the target output power of each of the multiple spectral thermal radiation modules.
[0043] S104: Control the baking of the intelligent baking equipment based on the target output power.
[0044] In this embodiment, based on radiation energy distribution data and food condition data, the output power of each of the multiple spectral thermal radiation modules is adjusted so that baking control can be performed based on the adjusted output power. In this way, the output power of each of the multiple spectral thermal radiation modules can be adaptively adjusted based on the real-time condition of the target food, so that the food is heated evenly, thereby improving the accuracy and stability of baking control.
[0045] In one alternative implementation, such as Figure 3 As shown, this is a flowchart illustrating the target output power determination method provided in this application embodiment. Specifically, the food ingredient status data includes food ingredient temperature data, so step S103 may include: S1031: Determine temperature deviation data based on food temperature data and preset temperature data, where the preset temperature data is the reference temperature data corresponding to the target food. S1032: Based on temperature deviation data and the baking state characteristics of the target ingredients, the radiation energy distribution data is adjusted to obtain the target radiation energy distribution data; the baking state characteristics characterize the maturity of the target ingredients. S1033: Based on the target radiation energy distribution data, determine the target output power corresponding to each of the multiple spectral thermal radiation modules.
[0046] In this embodiment, the food temperature data includes the internal temperature of the food, and the temperature deviation data is used to characterize the deviation between the current temperature value of the target food and the reference temperature value. That is, the temperature stage of the target food can be determined by the temperature deviation between the food temperature data and the preset temperature data. The temperature stage includes the initial heating stage, the temperature rise stage, and the stage approaching the target temperature. Then, the corresponding distribution of radiation energy in each band can be carried out at different stages to ensure accurate temperature tracking of the food, thereby reducing energy consumption. Furthermore, the baking state characteristics of the target food characterize the degree of maturity of the target food. Specifically, the baking state characteristics can characterize the target food to be in a medium-rare, medium-well, medium-rare, medium-well, and mature state. When the target food is in different baking states, its corresponding food temperature data and food moisture content data are different. Therefore, the radiation energy distribution data can be adjusted by combining the current temperature stage of the target food and the corresponding baking state characteristics of the target food to obtain the target radiation energy distribution data at the current moment, thereby optimizing energy efficiency, reducing energy waste, improving baking quality, ensuring that the target food is heated evenly during the baking process, and achieving the best taste and appearance.
[0047] In practical applications, the spectral thermal radiation modules correspond to the near-infrared, mid-infrared, and visible light bands. The radiation energy distribution data is then calculated as the ratio of near-infrared:mid-infrared:visible light bands. Specifically, if the target radiation energy distribution data is 60%:25%:15%, then the near-infrared band accounts for 60%, the mid-infrared band accounts for 25%, and the visible light band accounts for 15%. Consequently, the target output powers of the multiple spectral thermal radiation modules are 600W, 250W, and 150W, respectively. In other words, this application can control multiple spectral thermal radiation modules to heat the intelligent baking equipment based on the current target output power.
[0048] In an optional implementation, the wavelength bands corresponding to the spectral thermal radiation module include the near-infrared band, the mid-infrared band, and the visible light band; step S1032 may include: S10321: When the target food ingredient is in an immature state as characterized by the baking state feature and the temperature deviation data is greater than the first preset deviation, the ratio of the radiation energy in the near-infrared band to the total radiation energy is increased to the first preset value. The total radiation energy is the sum of the radiation energy in the near-infrared band, the radiation energy in the mid-infrared band, and the radiation energy in the visible light band. S10322: When the baking state characteristics indicate that the target food is in a mature state and the temperature deviation data is less than the second preset deviation, the ratio of the radiation energy in the visible light band to the total radiation energy is increased to the second preset value, and the first preset deviation is greater than the second preset deviation.
[0049] Specifically, the near-infrared band corresponds to wavelengths of 700nm to 1400nm. The near-infrared band enables rapid heating of target ingredients and has a fast heating response. The mid-infrared band corresponds to wavelengths of 1400nm to 3000nm. The mid-infrared band enables heating that balances heating speed and penetration depth. The visible light band corresponds to wavelengths of 400nm to 700nm. The visible light band enables heating control that allows for surface heating and color control of food.
[0050] Furthermore, if the baking state characteristics indicate that the target ingredient is immature and the temperature deviation is greater than the first preset deviation, it indicates that the target ingredient needs accelerated internal heating. Therefore, the proportion of near-infrared radiation energy, which enables rapid heating and has a fast response, in the total radiation energy can be increased to the first preset value. This allows for rapid heating of the target ingredient, reducing redundant heating time, minimizing energy waste, and improving baking efficiency. Conversely, if the baking state characteristics indicate that the target ingredient is mature and the temperature deviation is less than the second preset deviation, it indicates that the target ingredient does not require the more penetrating near-infrared and mid-infrared bands. Therefore, the proportion of near-infrared and mid-infrared bands can be reduced, and the proportion of visible light radiation energy, which enables surface heating and color control, in the total radiation energy can be increased to the second preset value. This ensures uniform heating of the target ingredient during baking while also improving the taste and baking effect.
[0051] In an optional implementation, the optical characteristic data includes scattering data and absorption data; after step S104, the method further includes: S1051: Monitors the core temperature data of the target ingredient; S1052: When the center temperature data is less than the preset temperature threshold, the scattering data is corrected to obtain the target scattering data; S1053: Based on the target scattering data, food state data, absorption data and radiation data corresponding to multiple spectral thermal radiation modules, data fusion processing is performed to obtain updated radiation energy distribution data; S1054: Based on the updated radiation energy distribution data, adjust the output power of each of the multiple spectral thermal radiation modules to obtain the adjusted output power.
[0052] Specifically, the food temperature data includes the internal temperature of the food, and the center temperature data is the temperature data of the middle part of the internal temperature of the food. If the center temperature data is lower than the preset temperature threshold, it indicates that the heat is not evenly transferred to the innermost part of the target food. Therefore, the scattering data can be corrected. Based on the corrected scattering data, food state data, absorption data, and radiation data corresponding to each of the multiple spectral thermal radiation modules, the radiation energy distribution data can be updated. The method for determining the radiation energy distribution data can refer to the above solution method, which will not be elaborated here.
[0053] Furthermore, by adjusting the output power of each of the multiple spectral thermal radiation modules through the updated radiation energy distribution data, feedback control of the target food can be achieved. This ensures that the target food is heated evenly during the baking process while avoiding energy waste caused by overheating, thus improving baking efficiency.
[0054] As can be seen from the above technical solutions of the embodiments of this application, the following technical effects are achieved: This application determines the radiation energy distribution data of different wavelengths based on the food state data and the optical property data of the food surface. Based on the radiation energy distribution data, the output power of each of the multiple spectral thermal radiation modules can be adjusted. Furthermore, based on the real-time state of the target food, the output power of each of the multiple spectral thermal radiation modules can be adaptively adjusted to ensure that the food is heated evenly, thereby improving the accuracy and stability of baking control.
[0055] This application also provides a baking control device, such as... Figure 4 The diagram shown is a structural schematic of a baking control device provided in an embodiment of this application. This baking control device is applied to an intelligent baking equipment, which includes multiple spectral thermal radiation modules corresponding to different wavelength bands, including: The acquisition module 10 is used to acquire the food state data and the optical property data of the food surface of the target food in the intelligent baking equipment; The fusion processing module 20 is used to perform data fusion processing based on food status data, optical characteristic data and radiation data corresponding to multiple spectral thermal radiation modules to obtain radiation energy distribution data corresponding to multiple spectral thermal radiation modules. The power output module 30 is used to adjust the output power of each of the multiple spectral thermal radiation modules based on radiation energy distribution data and food status data, so as to obtain the target output power of each of the multiple spectral thermal radiation modules. Baking control module 40 is used to control the baking of the intelligent baking equipment based on the target output power.
[0056] Furthermore, the food ingredient status data includes food ingredient temperature data, food ingredient thickness data, and food ingredient category data; therefore, the fusion processing module 20 includes: The heat absorption efficiency determination unit 201 is used to determine the heat absorption efficiency of the target food under the radiation of each spectral thermal radiation module based on the radiation data of each spectral thermal radiation module, the food temperature data, the food thickness data, the food category data and the optical property data. The energy distribution determination unit 202 is used to determine the radiation energy distribution data corresponding to multiple spectral thermal radiation modules based on the heat absorption efficiency of the target food under the radiation of multiple spectral thermal radiation modules.
[0057] Furthermore, the energy allocation determination unit 202 includes: The comprehensive absorption efficiency determination subunit 2021 is used to weight the heat absorption efficiency of the target food under the radiation of multiple spectral heat radiation modules to obtain the comprehensive heat absorption efficiency. The optimization subunit 2022 is used to process the radiation energy corresponding to each of the multiple spectral thermal radiation modules with the goal of maximizing the overall thermal absorption efficiency, and obtain the radiation energy distribution data corresponding to the multiple spectral thermal radiation modules.
[0058] Furthermore, the food status data includes food temperature data; the power output module 30 includes: Temperature deviation determination unit 301 is used to determine temperature deviation data based on food temperature data and preset temperature data, wherein the preset temperature data is the reference temperature data corresponding to the target food. The adjustment unit 302 is used to adjust the radiation energy distribution data based on the temperature deviation data and the baking state characteristics of the target food to obtain the target radiation energy distribution data; the baking state characteristics characterize the degree of maturity of the target food. The target output power determination unit 303 is used to determine the target output power of each of the multiple spectral thermal radiation modules based on the target radiation energy distribution data.
[0059] Furthermore, the spectral thermal radiation module corresponds to the near-infrared band, mid-infrared band, and visible light band; the adjustment unit 302 includes: The first adjustment subunit 3021 is used to increase the ratio of near-infrared radiation energy in the total radiation energy to a first preset value when the baking state characteristics characterize the target food as immature and the temperature deviation data is greater than the first preset deviation. The total radiation energy is the sum of the radiation energy of the near-infrared band, the radiation energy of the mid-infrared band and the radiation energy of the visible light band. The second adjustment subunit 3022 is used to increase the ratio of visible light band radiation energy in total radiation energy to a second preset value when the baking state characteristics characterize the target food as mature and the temperature deviation data is less than the second preset deviation. The first preset deviation is greater than the second preset deviation.
[0060] Furthermore, the optical characteristic data includes scattering data and absorption data; the device also includes: Monitoring module 50 is used to monitor the core temperature data of the target food ingredient; The correction module 60 is used to correct the scattering data when the center temperature data is less than a preset temperature threshold, so as to obtain the target scattering data. The update module 70 is used to perform data fusion processing based on target scattering data, food status data, absorption data and radiation data corresponding to multiple spectral thermal radiation modules to obtain updated radiation energy distribution data. The power adjustment module 80 is used to adjust the output power of each of the multiple spectral thermal radiation modules based on the updated radiation energy distribution data, so as to obtain the adjusted output power.
[0061] Furthermore, the acquisition module 10 includes: Acquisition unit 101 is used to acquire multispectral image stack data of the target food ingredient; The state determination unit 102 is used to perform data extraction processing based on multispectral image stack data to obtain the food state data corresponding to the target food.
[0062] Furthermore, the food ingredient status data includes food ingredient moisture content data, food ingredient temperature data, and food ingredient category data; the device also includes: The data processing module 90 is used to input the moisture content data, temperature data and category data of the ingredients into the state extraction model to extract the state and obtain the baking state features corresponding to the target ingredients. The state extraction model is trained on the preset model based on the sample state data.
[0063] 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.
[0064] This application embodiment also provides an intelligent baking device, which includes a controller and multiple spectral thermal radiation modules corresponding to different wavelengths disposed on the intelligent baking device, as well as a multispectral camera. The multiple spectral thermal radiation modules corresponding to different wavelengths are used to bake the target food in the intelligent baking device. Specifically, the different wavelengths include the near-infrared band, the mid-infrared band, and the visible light band. The multispectral camera is used to acquire multispectral image stack data of the target food. The controller is used to execute the baking control method provided in the above method embodiment.
[0065] For example, the controller is used to acquire the food state data and optical property data of the target food surface in the intelligent baking equipment, and to perform data fusion processing based on the food state data, optical property data and radiation data corresponding to each of the multiple spectral thermal radiation modules to obtain radiation energy distribution data corresponding to the multiple spectral thermal radiation modules. Based on the radiation energy distribution data and food state data, the controller adjusts the output power of each of the multiple spectral thermal radiation modules to obtain the target output power of each of the multiple spectral thermal radiation modules; so as to perform baking control of the intelligent baking equipment based on the target output power.
[0066] This application embodiment also provides an intelligent control system, which includes a server 500 and an intelligent baking device. The server 500 is communicatively connected to the intelligent baking device and can interact with the intelligent baking device for data exchange. Specifically, the server 500 can provide remote services to the intelligent baking device. For example, the server 500 can provide services such as baking mode selection and online upgrades of the baking program.
[0067] In one specific embodiment, such as Figure 5The diagram illustrates the structure of a server according to an embodiment of this application. The server 500 can vary significantly depending on its configuration or performance, and may include one or more processors 510 (e.g., one or more processors) and storage 530, and one or more storage media 520 (e.g., one or more mass storage devices) for storing applications 523 or data 522. The memory 530 and storage media 520 can be temporary or persistent storage. The programs stored in the storage media 520 may include one or more modules, each module including a series of instruction operations on the server. Furthermore, the processor 510 may be configured to communicate with the storage media 520 and execute the series of instruction operations in the storage media 520 on the server 500. The server 500 may also include one or more power supplies 560, one or more wired or wireless network interfaces 550, one or more input / output interfaces 550, and / or one or more operating systems 521, such as Windows Server™, Mac OSX™, Unix™, Linux™, FreeBSD™, etc.
[0068] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0069] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system and server embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.
[0070] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A roasting control method, characterized by, The method, applied to intelligent baking equipment, which includes multiple spectral thermal radiation modules corresponding to different wavelength bands, comprises: Acquire the food state data of the target food and the optical property data of the food surface within the intelligent baking equipment; Data fusion processing is performed based on the food state data, the optical property data, and the radiation data corresponding to each of the multiple spectral thermal radiation modules to obtain the radiation energy distribution data corresponding to the multiple spectral thermal radiation modules. Based on the radiation energy distribution data and the food status data, the output power of each of the multiple spectral thermal radiation modules is adjusted to obtain the target output power of each of the multiple spectral thermal radiation modules. The intelligent baking equipment is controlled to bake based on the target output power.
2. The method of claim 1, wherein, The food ingredient status data includes food ingredient temperature data, food ingredient thickness data, and food ingredient category data; the data fusion processing based on the food ingredient status data, the optical characteristic data, and the radiation data corresponding to each of the multiple spectral thermal radiation modules to obtain the radiation energy distribution data corresponding to the multiple spectral thermal radiation modules includes: Based on the radiation data of each spectral thermal radiation module, the food temperature data, the food thickness data, the food category data, and the optical property data, the heat absorption efficiency of the target food under the radiation of each spectral thermal radiation module is determined. Based on the heat absorption efficiency of the target food ingredient under the radiation of the multiple spectral thermal radiation modules, the radiation energy distribution data corresponding to the multiple spectral thermal radiation modules is determined.
3. The method of claim 2, wherein, The step of determining the radiation energy distribution data corresponding to the multiple spectral thermal radiation modules based on the respective heat absorption efficiency of the target food ingredient under the radiation of the multiple spectral thermal radiation modules includes: The heat absorption efficiency of the target food under the radiation of the multiple spectral thermal radiation modules is weighted to obtain the comprehensive heat absorption efficiency. With the goal of maximizing the overall thermal absorption efficiency, the radiation energy corresponding to each of the multiple spectral thermal radiation modules is processed to obtain radiation energy distribution data corresponding to the multiple spectral thermal radiation modules.
4. The method of claim 1, wherein, The food ingredient status data includes food ingredient temperature data; the process of adjusting the output power of each of the plurality of spectral thermal radiation modules based on the radiation energy distribution data and the food ingredient status data to obtain the target output power of each of the plurality of spectral thermal radiation modules includes: Temperature deviation data is determined based on the food temperature data and preset temperature data, wherein the preset temperature data is the reference temperature data corresponding to the target food. Based on the temperature deviation data and the baking state characteristics corresponding to the target ingredient, the radiation energy distribution data is adjusted to obtain the target radiation energy distribution data; the baking state characteristics characterize the degree of maturity of the target ingredient. Based on the target radiation energy distribution data, the target output power corresponding to each of the plurality of spectral thermal radiation modules is determined.
5. The method of claim 4, wherein, The spectral thermal radiation module corresponds to the near-infrared band, mid-infrared band, and visible light band; the adjustment and processing of the radiation energy distribution data based on the temperature deviation data and the baking state characteristics of the target food ingredient to obtain target radiation energy distribution data includes: When the baking state characteristics indicate that the target food is not fully cooked, and the temperature deviation data is greater than a first preset deviation, the ratio of the near-infrared band radiation energy in the total radiation energy is increased to a first preset value, where the total radiation energy is the sum of the near-infrared band radiation energy, the mid-infrared band radiation energy, and the visible light band radiation energy. When the baking state characteristic indicates that the target food is in a mature state, and the temperature deviation data is less than the second preset deviation, the ratio of the visible light band radiation energy in the total radiation energy is increased to the second preset value, where the first preset deviation is greater than the second preset deviation.
6. The method of claim 1, wherein, The optical characteristic data includes scattering data and absorption data; after the step of controlling the baking of the intelligent baking device based on the target output power, the method further includes: Monitor the core temperature data of the target ingredient; If the center temperature data is less than a preset temperature threshold, the scattering data is corrected to obtain target scattering data; Based on the target scattering data, the food state data, the absorption data, and the radiation data corresponding to each of the multiple spectral thermal radiation modules, data fusion processing is performed to obtain updated radiation energy distribution data; Based on the updated radiation energy distribution data, the output power of each of the multiple spectral thermal radiation modules is adjusted to obtain the adjusted output power.
7. The method of claim 1, wherein, The step of acquiring the food status data of the target food within the intelligent baking equipment includes: Acquire the multispectral image stack data of the target food ingredient; Data extraction and processing are performed based on the multispectral image stack data to obtain the food status data corresponding to the target food ingredient.
8. The method of claim 4, wherein, The food ingredient state data includes food ingredient moisture content data, food ingredient temperature data, and food ingredient category data; before the step of adjusting the radiation energy distribution data based on the temperature deviation data and the baking state characteristics corresponding to the target food ingredient to obtain the target radiation energy distribution data, the method further includes: The moisture content data, temperature data, and category data of the ingredients are input into a state extraction model to extract the baking state features corresponding to the target ingredients. The state extraction model is trained on a preset model based on the sample state data.
9. An intelligent baking device, characterized in that, The intelligent baking equipment includes a controller, which is used to execute the baking control method as described in any one of claims 1 to 8.
10. An intelligent control system characterized by, The intelligent control system includes a server and an intelligent baking device. The server is communicatively connected to the intelligent baking device and can interact with the intelligent baking device for data exchange. The intelligent baking device is used to execute the baking control method as described in any one of claims 1 to 8.