Temperature control method and device of intelligent cooking equipment and intelligent cooking equipment

Abstract

This disclosure relates to a temperature control method, apparatus, and intelligent cooking equipment. The method includes acquiring cavity temperature information corresponding to the intelligent cooking equipment; determining target heating rate data based on multiple preheating cavity temperature data; determining a target load based on the target heating rate data and a preset load prediction model; if the multiple cooking cavity temperature data meet preset temperature conditions, determining target temperature fluctuation data based on the multiple cooking cavity temperature data; determining the target number of food layers based on the target load, target temperature fluctuation data, and a preset layer prediction model; and controlling the heating module of the intelligent cooking equipment based on the target load and target number of layers to perform temperature control. This disclosure can automatically control the temperature for different food loads and different numbers of layers, achieving cooking precision and avoiding problems such as uneven browning due to a constant heating ratio when placing different layers, which leads to poor cooking results.

Description

Technical Field

[0001] This disclosure relates to the field of smart kitchen appliances technology, and in particular to temperature control methods, devices, and smart cooking appliances for smart cooking equipment. Background Technology

[0002] With the improvement of living standards, the current mainstream temperature control methods for smart cooking equipment, such as electric ovens, steam ovens, and air fryers, mainly rely on user experience or simple single-point temperature feedback. They fail to fully consider the actual heat field distribution and heat load changes within the cooking cavity. However, due to the different number of baking trays, the actual heating temperature of the food will vary, thus affecting the cooking effect. Some technical solutions install multiple temperature probes at different heights on the side wall of the inner cavity to identify the number of baking trays and perform targeted temperature control. However, multiple temperature probes and their associated signal processing circuits increase the material and manufacturing costs of the equipment. At the same time, the probes on the side wall are at risk of being bumped or damaged when pushing in or removing the baking trays, affecting the reliability and service life of the equipment. Furthermore, the side wall probes cannot accurately reflect the true temperature of the core area where the food is located, and the temperature control itself is biased. Existing technologies generally ignore the influence of the heat load of the food itself, making it difficult to achieve precise temperature control and ultimately affecting the cooking quality of the food. Summary of the Invention

[0003] To address at least one of the aforementioned technical problems, this disclosure provides a temperature control method, apparatus, and intelligent cooking device for intelligent cooking equipment.

[0004] According to one aspect of this disclosure, a temperature control method for an intelligent cooking device is provided, comprising: Obtain the cavity temperature information corresponding to the intelligent cooking device, the cavity temperature information including multiple preheating cavity temperature data corresponding to the preheating process and multiple cooking cavity temperature data corresponding to the current time period; The target heating rate data is determined based on the temperature data of the multiple preheating chambers, and the target heating rate data is the maximum heating rate data during the preheating process. The target load is determined based on the target heating rate data and the preset load prediction model. If the temperature data of the multiple cooking cavities meet the preset temperature conditions, the target temperature fluctuation data is determined based on the temperature data of the multiple cooking cavities. The target temperature fluctuation data is the maximum temperature fluctuation data corresponding to the current time period. The target number of food layers is determined based on the target load, the target temperature fluctuation data, and the preset layer prediction model. The heating module of the intelligent cooking device is controlled based on the target load and the target number of layers to perform temperature control.

[0005] In some possible implementations, determining the target heating rate data based on the temperature data of the plurality of preheating cavities includes: At least one heating rate data in the preheating process is determined based on the temperature data of the multiple preheating chambers; The largest heating rate among the at least one heating rate data is taken as the target heating rate data.

[0006] In some possible implementations, the multiple cooking cavity temperature data include the highest temperature data and the corresponding lowest temperature data corresponding to multiple preset sub-time periods in the current time period, and the determination of target temperature fluctuation data based on the multiple cooking cavity temperature data includes: The maximum temperature difference data corresponding to the multiple preset sub-time periods is determined based on the highest temperature data and the lowest temperature data corresponding to the multiple preset sub-time periods. Obtain the average power data of the target heating element corresponding to the multiple preset sub-time periods; The temperature fluctuation data corresponding to the multiple preset sub-time periods is determined based on the maximum temperature difference data and the average power data corresponding to the multiple preset sub-time periods. The maximum value among the temperature fluctuation data corresponding to the multiple preset sub-time periods is taken as the target temperature fluctuation data.

[0007] In some possible implementations, the heating module includes a top heating unit and a bottom heating unit, and the heating module that controls the smart cooking device based on the target load and the target number of layers includes: The set temperature and current cavity temperature data corresponding to the intelligent cooking device are obtained, wherein the set temperature includes upper layer set temperature data and lower layer set temperature data; The first heating ratio corresponding to the top heating unit is determined based on the upper layer temperature data, the current cavity temperature data, the target load, and the target number of layers; The second heating ratio corresponding to the bottom heating unit is determined based on the lower layer temperature data, the target load, and the target number of layers; The top heating unit and the bottom heating unit are controlled based on the first heating ratio and the second heating ratio, respectively.

[0008] In some possible implementations, determining the second heating ratio corresponding to the bottom heating unit based on the lower layer temperature data, the target load, and the target number of layers includes: Obtain the total number of placement layers corresponding to the intelligent cooking device; The target layer distance is determined based on the total number of placement layers and the target number of layers; The second heating ratio corresponding to the bottom heating tube is determined based on the lower layer temperature, the target load, and the target layer distance.

[0009] In some possible implementations, the method further includes: The temperature probe of the intelligent cooking device is calibrated based on the target load and the target number of layers to obtain calibration data. The heating module of the smart cooking device is controlled based on the calibration data.

[0010] In some possible implementations, the method further includes: Obtain the training dataset corresponding to the intelligent cooking device, the training dataset including data on the number of food placement layers, food load data, and cavity temperature fluctuation data; The initial neural network is trained under supervision based on the data on the number of food layers, the food load, and the cavity temperature fluctuation to obtain the preset layer prediction model.

[0011] According to a second aspect of this disclosure, a temperature control device for a smart cooking appliance is provided, the device comprising: The temperature data acquisition module is used to acquire the cavity temperature information corresponding to the intelligent cooking device. The cavity temperature information includes multiple preheating cavity temperature data corresponding to the preheating process and multiple cooking cavity temperature data corresponding to the current time period. The heating rate determination module is used to determine target heating rate data based on the temperature data of the multiple preheating chambers, wherein the target heating rate data is the maximum heating rate data during the preheating process. The load determination module is used to determine the target load based on the target heating rate data and the preset load prediction model. The temperature fluctuation data determination module is used to determine target temperature fluctuation data based on the multiple cooking cavity temperature data if the multiple cooking cavity temperature data meet the preset temperature conditions. The target temperature fluctuation data is the maximum temperature fluctuation data corresponding to the current time period. The target layer number determination module is used to determine the target layer number for food placement based on the target load, the target temperature fluctuation data, and a preset layer number prediction model. A temperature control module is used to control the heating module of the smart cooking device based on the target load and the target number of layers to perform temperature control.

[0012] According to a third aspect of this disclosure, an intelligent cooking device is provided, including a temperature monitoring module and a heating module, wherein the temperature monitoring module is disposed at the center of the top of the cavity of the intelligent cooking device, and the heating module is disposed inside the cavity of the intelligent cooking device; The temperature monitoring module is used to monitor the cavity temperature data of the intelligent cooking device; The heating module is used to heat the cavity of the intelligent cooking device; The intelligent cooking device also includes the temperature control device as described in the second aspect; The intelligent cooking device is either a steam oven or an oven.

[0013] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided that stores at least one instruction or at least one program, the at least one instruction or at least one program being loaded and executed by a processor to implement a temperature control method for an intelligent cooking device as described in any of the first aspects.

[0014] According to a fifth aspect of this disclosure, an electronic device is provided, including at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements a temperature control method for an intelligent cooking device as described in any one of the first aspects by executing the instructions stored in the memory.

[0015] 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 disclosure.

[0016] Implementing this disclosure will have the following beneficial effects: The system acquires the cavity temperature information of the intelligent cooking device, including temperature data from multiple preheating cavities during the preheating process and multiple cooking cavities during the current time period. Based on the multiple preheating cavity temperature data, a target heating rate is determined, which is the maximum heating rate during the preheating process. A target load is then determined based on the target heating rate and a preset load prediction model. Since different loads absorb different amounts of heat, different loads placed inside the cavity result in different rates of temperature change. The heating rate during cooking accurately identifies the food load. If the multiple cooking cavity temperature data meet the preset temperature conditions, a target temperature fluctuation is determined, which is the maximum temperature fluctuation during the current time period. The target number of food layers is determined based on the target load, target temperature fluctuation data, and a preset layer prediction model. Different layers result in different temperature fluctuations inside the cavity, and the load also affects the stabilized temperature fluctuations. Therefore, combining the load and temperature fluctuations accurately identifies the number of food layers. The heating module of the intelligent cooking device is then controlled based on the target load and target layer number for temperature control. It automatically controls the temperature for different food loads and different numbers of layers, achieving precise cooking and avoiding problems such as uneven browning caused by maintaining a constant heating ratio when placing different layers, which leads to poor cooking results.

[0017] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A schematic flowchart of a temperature control method for an intelligent cooking device according to an embodiment of the present disclosure is shown. Figure 2 A flowchart illustrating a method for determining a target heating rate according to an embodiment of the present disclosure is shown. Figure 3 A flowchart illustrating a method for determining target temperature fluctuation data according to an embodiment of the present disclosure is shown. Figure 4 A schematic flowchart of a heating unit control method according to an embodiment of the present disclosure is shown; Figure 5 A flowchart illustrating a second heating ratio determination method according to an embodiment of the present disclosure is shown. Figure 6 A schematic flowchart of a heating module calibration method according to an embodiment of the present disclosure is shown; Figure 7 A flowchart illustrating a method for constructing a layer prediction model according to an embodiment of the present disclosure is shown. Figure 8 A schematic diagram of the structure of a temperature control device for an intelligent cooking appliance according to an embodiment of the present disclosure is shown. Figure 9 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0020] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all 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.

[0021] 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.

[0022] Various exemplary embodiments, features, and aspects of this disclosure 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.

[0023] 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.

[0024] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: 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 elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0025] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced 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 disclosure.

[0026] Figure 1 This diagram illustrates a flow chart of a temperature control method for an intelligent cooking device according to an embodiment of the present disclosure. Figure 1 As shown, the above method includes: S101. Obtain the cavity temperature information corresponding to the intelligent cooking device. The cavity temperature information includes multiple preheating cavity temperature data corresponding to the preheating process and multiple cooking cavity temperature data corresponding to the current time period. Intelligent cooking equipment is a type of smart device that features a sealed inner cavity, multi-layered supports on the side walls of the inner cavity for placing food or baking trays, and heating modules located at the top, bottom, or back. A temperature monitoring module is located at the top of the cavity of the intelligent cooking equipment. This module detects the internal temperature of the cavity and sends it to the controller. The controller receives the cavity temperature data from the temperature detection module, combines it with the time of acquisition, and stores it to obtain the cavity temperature information. The preheating process includes multiple preheating cavity temperature data points collected at preset time intervals during the preheating process. The current time period is the period after the preheating process ends and the cooking stage begins; the multiple cooking cavity temperature data points are also multiple cooking cavity temperature data points corresponding to multiple preset sub-time periods within the current time period.

[0027] In some embodiments, the smart cooking device includes, but is not limited to, a steam oven, a regular oven, and an air fryer; this application uses a steam oven as an example. The temperature monitoring module includes, but is not limited to, a temperature sensor. The temperature monitoring module is located at the center of the top of the cavity of the smart cooking device, and the heating module is located inside the cavity of the smart cooking device. The temperature monitoring module, the heating module, and the control module are communicatively connected. After the preheating process of the smart cooking device is completed, i.e., after the cavity temperature reaches the set temperature, the corresponding cavity temperature information of the smart cooking device is acquired.

[0028] S102. Determine the target heating rate data based on the temperature data of multiple preheating chambers. The target heating rate data is the maximum heating rate data during the preheating process. The system determines multiple heating rate data corresponding to the preheating process based on the temperatures of multiple preheating chambers, and takes the largest heating rate data as the target heating rate data.

[0029] In some embodiments, the multiple preheating cavity temperature data can be multiple preheating cavity temperature data collected at unit time intervals during the preheating process, and the heating rate data can be the heating rate data corresponding to a unit time. After the smart cooking device is started, the initial temperature difference within the cavity will cause differences in the heating rate. Therefore, based on the maximum heating rate data per unit time during the heating process, the food load can be determined more accurately.

[0030] S103. Determine the target load based on the target heating rate data and the preset load prediction model; The preset load prediction model is obtained by training an initial neural network based on a load training set, which includes maximum heating rate data and load data. The target heating rate data is input into the preset load prediction model for prediction processing to obtain the target load.

[0031] In some embodiments, the preset load prediction model may be obtained based on neural network training.

[0032] S104. If the temperature data of multiple cooking cavities meet the preset temperature conditions, determine the target temperature fluctuation data based on the temperature data of multiple cooking cavities. The target temperature fluctuation data is the maximum temperature fluctuation data corresponding to the current time period. The preset temperature condition allows the temperature fluctuation data of multiple cooking cavities to fall within a preset fluctuation range. This multiple cooking cavity temperature data includes temperature data from multiple preset sub-time periods within the current time period. If the temperature data of multiple cooking cavities do not meet the preset temperature condition, the heating module will maintain its original control method.

[0033] In some embodiments, the average power of the target heating unit in each preset sub-time period is obtained, the maximum temperature change in each preset sub-time period is determined based on the temperature data of multiple cooking cavities in multiple preset sub-time periods, the temperature fluctuation value in each preset sub-time period is determined based on the average power in each preset sub-time period and the maximum temperature change in each preset sub-time period, i.e., the temperature fluctuation data, and the maximum value among the temperature fluctuation data in multiple preset sub-time periods is taken as the target temperature fluctuation data.

[0034] S105. Determine the target number of food layers based on the target load, target temperature fluctuation data, and a preset layer prediction model; The side wall of the intelligent cooking equipment is equipped with multiple layers of supports for placing food or baking trays. The target load and target temperature fluctuation data are input into the preset layer prediction model for prediction to obtain the target number of layers.

[0035] In some embodiments, the temperature fluctuation thresholds corresponding to each layer after temperature stabilization are adjusted according to the target load to adapt to the impact of load on temperature fluctuations. Based on the temperature fluctuation thresholds corresponding to each layer and the target temperature fluctuation data and the temperature fluctuation thresholds corresponding to each layer, the target layer where the food is located is determined.

[0036] S106. A heating module for intelligent cooking equipment that controls temperature based on target load and target number of layers.

[0037] The heating module controls each heating unit according to the target load and the target number of layers, so that the cooking temperature on the surface of the food with the target load placed on the target number of layers is consistent with the set temperature, thus achieving uniform heating of the food.

[0038] In some embodiments, if the baking tray for placing food is placed on the upper layer (first layer) inside the cavity of the intelligent cooking device, the heat from the top heating unit acts more on the upper surface of the food, while the bottom heating unit is farther away from the food load. In this case, the top heating unit is controlled to reduce its heating power, and the bottom heating unit is controlled to increase its heating power, thereby reducing the cooking temperature of the upper surface of the food and increasing the cooking temperature of the lower surface of the food. If the baking tray for placing food is placed on the bottom layer (last layer) inside the cavity of the intelligent cooking device, the top heating unit is farther away from the food load, and the bottom heating unit is closer to the food load. In this case, the top heating unit is controlled to increase its heating power, and the bottom heating unit is controlled to decrease its heating power, thereby increasing the cooking temperature of the upper surface of the food and decreasing the cooking temperature of the lower surface of the food. The temperatures of the upper and lower surfaces of the food are adjusted to ensure that the temperature at the load point is consistent with the set temperature.

[0039] The above technical solution addresses the issue that when different loads are placed inside the cavity, the temperature at the food placement area varies significantly due to the different heat absorbed under different load conditions. However, the temperature probe, being far from the center of the food load, detects only minor temperature changes. Therefore, by adjusting the temperature at the center of the cavity based on the amount of food load and the number of layers, the temperature at the load point can be kept consistent with the set temperature regardless of the load or the layer on which it is placed. This ensures precise cooking under different food loads within the cavity and avoids temperature differences at the food placement area caused by different loads and layers, which could affect the cooking results.

[0040] Please see Figure 2 In some embodiments, the target heating rate data is determined based on multiple preheating chamber temperature data, including: S1021. Determine at least one heating rate data during the preheating process based on multiple preheating chamber temperature data; S1022. Take the largest heating rate data among at least one heating rate data as the target heating rate data.

[0041] Multiple preheating chamber temperature data can be the temperatures of multiple preheating chambers collected based on a preset time interval during the preheating process. Multiple heating rate data corresponding to multiple time intervals are calculated based on the multiple preheating chamber temperature data, and the maximum heating rate among the multiple heating rate data is taken as the target heating rate data.

[0042] In some embodiments, the temperature data of the multiple preheating chambers are multiple preheating chamber temperature data collected based on a unit time interval, and the target heating rate data is the heating rate data with the largest temperature rise per unit time during the preheating process.

[0043] The above technical solution uses the maximum heating rate data during cooking as the basis for determining the load, reducing the influence of the initial temperature of the food and the ambient temperature, and accurately identifying the food load.

[0044] Please see Figure 3 In some embodiments, the multiple cooking cavity temperature data include the highest temperature data and the corresponding lowest temperature data corresponding to multiple preset sub-time periods in the current time period. Determining the target temperature fluctuation data based on the multiple cooking cavity temperature data includes: S1041. Determine the maximum temperature difference data corresponding to multiple preset sub-time periods based on the highest temperature data and the lowest temperature data corresponding to multiple preset sub-time periods. S1042. Obtain the average power data of the target heating element in multiple preset sub-time periods; S1043. Determine the temperature fluctuation data corresponding to multiple preset sub-periods based on the maximum temperature difference data and the average power data corresponding to multiple preset sub-periods. S1044. Take the maximum value among the temperature fluctuation data corresponding to multiple preset sub-time periods as the target temperature fluctuation data.

[0045] The system acquires at least one power of the target heating unit in each preset sub-period. Based on the at least one power, it calculates the average power data of the target heating unit of the smart cooking device in each preset sub-period. Based on the ratio of the maximum temperature difference data and the average power data in each preset sub-period, it obtains the temperature fluctuation data in each preset sub-period. The maximum value among the temperature fluctuation data in multiple sub-periods is taken as the target temperature fluctuation data in the current period.

[0046] In some embodiments, the target heating unit may be a top heating element, and the multiple cooking cavity temperature data may be multiple cooking cavity temperature data collected at one-minute intervals within five minutes. The maximum temperature difference data corresponding to each minute within five minutes is determined, the average power data corresponding to each minute within five minutes is determined, and the temperature fluctuation data corresponding to each minute is determined based on the maximum temperature difference data and the average power data corresponding to each minute. The maximum temperature fluctuation data within five minutes is taken as the target temperature fluctuation data.

[0047] The above technical solution, after the intelligent cooking equipment finishes preheating and enters the cooking process, determines the temperature fluctuation data by comprehensively considering the power of the heating unit and the temperature difference, which can more accurately reflect the number of layers of food placed in the food.

[0048] Please see Figure 4 In some embodiments, the heating module includes a top heating unit and a bottom heating unit. The heating module of the intelligent cooking device, which controls the target load and target number of layers, includes: S1061. Obtain the set temperature and current cavity temperature data corresponding to the intelligent cooking device. The set temperature includes the upper layer set temperature data and the lower layer set temperature data. S1062. Determine the first heating ratio corresponding to the top heating unit based on the upper layer temperature data, the current cavity temperature data, the target load, and the target number of layers. S1063. Determine the second heating ratio corresponding to the bottom heating unit based on the lower layer temperature data, target load, and target number of layers; S1064. Control the top heating unit and the bottom heating unit based on the first heating ratio and the second heating ratio respectively.

[0049] The top heating unit is located at the top of the cavity, and the bottom heating unit is located at the bottom of the cavity. The first heating ratio is used to characterize the proportion of the heating time of the top heating unit to the total heating time, and the second heating ratio is used to characterize the proportion of the heating time of the bottom heating unit to the total heating time. Based on the upper layer set temperature data, the current cavity temperature data, the target load, and the target number of layers, linear processing is performed to obtain the first heating ratio corresponding to the top heating unit.

[0050] In some embodiments, the top heating unit and the bottom heating unit include, but are not limited to, heating tubes, heating wires, and heating rods. First heating ratio The calculation formula is as follows:

[0051] in, This is the current cavity temperature data. Set the temperature for the upper layer. For the target load, denoted as the target layer number, and m, n, p, and q as experimental parameters.

[0052] The above technical solution adjusts the proportion of heating tubes according to the load and number of food layers to ensure uniform heating under different layers. In addition, the proportion of bottom heating tubes is adjusted according to information such as the set temperature, load, and number of layers to ensure the temperature of the lower half of the cavity, thereby ensuring the cooking effect. The heating time ratio of the top heating unit is determined by comprehensively considering the current cavity temperature data, target load, and target number of layers, so as to accurately control the top heating unit and avoid temperature differences caused by different loads and different numbers of layers, thereby improving the cooking effect.

[0053] Please see Figure 5 In some embodiments, determining the second heating ratio corresponding to the bottom heating unit based on the lower layer temperature data, the target load, and the target number of layers includes: S10631. Obtain the total number of placement layers corresponding to the intelligent cooking equipment; S10632. Determine the target layer distance based on the total number of layers and the target layer; S10633. Determine the second heating ratio corresponding to the bottom heating tube based on the lower layer set temperature, target load, and target layer distance.

[0054] Based on the lower layer temperature setting, target load, and target layer distance, a linear processing is performed to obtain the second heating ratio corresponding to the bottom heating tube.

[0055] In some embodiments, the second heating ratio The calculation formula is as follows:

[0056] in, Set the temperature for the lower layer. For the target load, This represents the total number of layers. denoted as the target layer number, and a, b, c, and d as experimental parameters.

[0057] The above technical solution comprehensively considers the target load, the total number of layers, and the target number of layers to determine the heating time ratio of the bottom heating unit, accurately controls the bottom heating unit, avoids temperature differences caused by different loads and different numbers of layers, and improves the cooking effect.

[0058] Please see Figure 6 In some embodiments, the method further includes: S201. Based on the target load and target number of layers, perform calibration calculations on the temperature probe of the intelligent cooking device to obtain calibration data; S202, Heating module for intelligent cooking equipment controlled based on calibration data.

[0059] The calibration data is used to characterize the cavity temperature data detected by the smart cooking device. By controlling the heating module to calibrate the cavity temperature data, the temperature of the food can be adjusted.

[0060] In some embodiments, a target load calibration coefficient is determined based on the target load and a first preset relationship, and a target layer calibration coefficient is determined based on the target number of layers and a second preset relationship. The second preset relationship is used to characterize the relationship between the number of food placement layers and the layer calibration coefficient. Calibration data is determined based on the target load calibration coefficient and the target layer calibration coefficient. The heating module is adjusted based on the calibration coefficient to adjust the cavity temperature so that the temperature at the load location, i.e., the food placement location, is consistent with the set temperature.

[0061] In some embodiments, calibration data The calculation formula is as follows: +C in, For load calibration coefficient, A, B, and C are the layer calibration coefficients, and A, B, and C are the experimental coefficients.

[0062] The above technical solution adjusts the temperature difference between the temperature detected by the cavity temperature probe and the temperature at the center point of the cavity according to the load and number of layers, thereby adjusting the temperature at the center point of the cavity. By adjusting the heating module through a calibration coefficient, the temperature at the food can be adjusted so that the temperature at the food is basically consistent with the temperature set by the user when the load and number of layers are different, thus improving the accuracy of cooking temperature.

[0063] Please see Figure 7 In some embodiments, the method further includes: S301. Obtain the training dataset corresponding to the smart cooking device. The training dataset includes data on the number of food placement layers, food load data, and cavity temperature fluctuation data. S302. Based on the data of the number of food placement layers, the data of food load, and the data of cavity temperature fluctuation, the initial neural network is trained under supervision to obtain the preset layer prediction model.

[0064] Data on the number of food layers corresponding to different cavity temperature fluctuations of smart cooking devices with different food loads are obtained. Based on the relationship between the three, the initial neural network is trained under supervised optimization to obtain a preset layer prediction model.

[0065] In some embodiments, the initial neural network includes, but is not limited to, convolutional neural networks and recurrent neural networks.

[0066] The above technical solution constructs a layer prediction model based on data on the number of food layers, food load, and cavity temperature fluctuations, thereby improving the accuracy and rationality of layer prediction, achieving accurate temperature control, and improving cooking results and user experience.

[0067] Please see Figure 8 According to a second aspect of this disclosure, a temperature control device for an intelligent cooking appliance is provided, the device comprising: The temperature data acquisition module 10 is used to acquire the cavity temperature information corresponding to the smart cooking device. The cavity temperature information includes multiple preheating cavity temperature data corresponding to the preheating process and multiple cooking cavity temperature data corresponding to the current time period. The heating rate determination module 20 is used to determine the target heating rate data based on the temperature data of multiple preheating chambers. The target heating rate data is the maximum heating rate data during the preheating process. The load determination module 30 is used to determine the target load based on the target heating rate data and the preset load prediction model. The temperature fluctuation data determination module 40 is used to determine the target temperature fluctuation data based on the multiple cooking cavity temperature data if the temperature data of multiple cooking cavities meet the preset temperature conditions. The target temperature fluctuation data is the maximum temperature fluctuation data corresponding to the current time period. The target layer number determination module 50 is used to determine the target layer number for food placement based on the target load, target temperature fluctuation data, and a preset layer number prediction model. Temperature control module 60 is used to control the heating module of the smart cooking device based on the target load and the target number of layers for temperature control.

[0068] In some embodiments, the heating rate determining module 20 includes: A rate determination unit is used to determine at least one heating rate data during the preheating process based on multiple preheating chamber temperature data. The target rate determination unit is used to select the largest heating rate data among at least one heating rate data as the target heating rate data.

[0069] In some embodiments, the multiple cooking cavity temperature data include the highest temperature data and the corresponding lowest temperature data corresponding to multiple preset sub-time periods in the current time period. The temperature fluctuation data determination module 40 includes: The temperature difference determination unit is used to determine the maximum temperature difference data corresponding to multiple preset sub-time periods based on the highest temperature data and the lowest temperature data corresponding to multiple preset sub-time periods. The average power determination unit is used to obtain the average power data of the target heating tube in multiple preset sub-time periods; The fluctuation data determination unit is used to determine the temperature fluctuation data corresponding to multiple preset sub-periods based on the maximum temperature difference data and the average power data corresponding to multiple preset sub-periods. The target fluctuation data determination unit is used to take the maximum value of the temperature fluctuation data corresponding to multiple preset sub-periods as the target temperature fluctuation data.

[0070] In some embodiments, the heating module includes a top heating unit and a bottom heating unit, and the temperature control module 60 includes: The temperature data acquisition unit is used to acquire the set temperature and current cavity temperature data of the smart cooking device. The set temperature includes upper layer set temperature data and lower layer set temperature data. The first heating ratio determination unit is used to determine the first heating ratio corresponding to the top heating unit based on the upper layer set temperature data, the current cavity temperature data, the target load and the target number of layers. The second heating ratio determination unit is used to determine the second heating ratio corresponding to the bottom heating unit based on the lower layer temperature data, target load and target number of layers. The control unit is used to control the top heating unit and the bottom heating unit based on the first heating ratio and the second heating ratio, respectively.

[0071] In some embodiments, the second heating ratio determining unit includes: The layer acquisition unit is used to obtain the total number of layers corresponding to the smart cooking device; The layer distance determination unit is used to determine the target layer distance based on the total number of placed layers and the target number of layers; The ratio determination unit is used to determine the second heating ratio corresponding to the bottom heating tube based on the lower layer set temperature, target load, and target layer distance.

[0072] In some embodiments, the apparatus further includes: The calibration data determination module is used to perform calibration calculations on the temperature probe of the smart cooking device based on the target load and the target number of layers to obtain calibration data. The calibration module is used to control the heating module of intelligent cooking equipment based on calibration data.

[0073] In some embodiments, the apparatus further includes: The training set acquisition module is used to acquire the training dataset corresponding to the smart cooking equipment. The training dataset includes data on the number of food placement layers, food load data, and cavity temperature fluctuation data. The training module is used to supervise the training of the initial neural network based on data on the number of food placement layers, food load, and cavity temperature fluctuation, so as to obtain a prediction model with a preset number of layers.

[0074] In some embodiments, a smart cooking device is provided, including a temperature monitoring module and a heating module. The temperature monitoring module is disposed at the center of the top of the cavity of the smart cooking device, and the heating module is disposed inside the cavity of the smart cooking device. The temperature monitoring module is used to monitor the cavity temperature data of the intelligent cooking device; The heating module is used to heat the cavity of the intelligent cooking device; The intelligent cooking device also includes the temperature control device as described in the second aspect; The intelligent cooking device is either a steam oven or an oven.

[0075] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0076] This application provides a temperature control device for an intelligent cooking device. The device can be a terminal or a server. The temperature control device for the intelligent cooking device includes a processor and a memory. The memory stores at least one instruction or at least one program. The at least one instruction or at least one program is loaded and executed by the processor to implement the temperature control method for the intelligent cooking device provided in the above method embodiment.

[0077] Memory is used to store software programs and modules. The processor executes these stored software programs and modules to perform various functional applications and data processing. Memory can primarily consist of a program storage area and a data storage area. The program storage area stores the operating system, application programs required for functionality, etc.; the data storage area stores data created based on device usage, etc. Furthermore, memory can include high-speed random access memory (RAM) and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, memory can also include a memory controller to provide the processor with access to the memory.

[0078] The methods and embodiments provided in this application can be executed in electronic devices such as mobile terminals, computer terminals, servers, or similar computing devices. Figure 9 This is a hardware structure block diagram of an electronic device for a temperature control method of an intelligent cooking device provided in an embodiment of this application. For example... Figure 9As shown, the electronic device 900 can vary considerably due to differences in configuration or performance. It may include one or more Central Processing Units (CPUs) 910 (CPUs 910 may include, but are not limited to, microprocessors such as MCUs or programmable logic devices such as FPGAs), a memory 930 for storing data, and one or more storage media 920 (e.g., one or more mass storage devices) for storing application programs 923 or data 922. The memory 930 and storage media 920 may be temporary or persistent storage. The program stored in the storage media 920 may include one or more modules, each module may include a series of instruction operations on the electronic device. Furthermore, the CPU 910 may be configured to communicate with the storage media 920 and execute the series of instruction operations in the storage media 920 on the electronic device 900. Electronic device 900 may also include one or more power supplies 960, one or more wired or wireless network interfaces 950, one or more input / output interfaces 940, and / or one or more operating systems 921, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0079] The input / output interface 940 can be used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the electronic device 900. In one example, the input / output interface 940 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the input / output interface 940 may be a radio frequency (RF) module used for wireless communication with the Internet.

[0080] Those skilled in the art will understand that Figure 9 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device 900 may also include... Figure 9 The more or fewer components shown, or having the same Figure 9 The different configurations shown.

[0081] Embodiments of this application also provide a computer-readable storage medium, which can be disposed in an electronic device to store at least one instruction or at least one program related to implementing a temperature control method for an intelligent cooking device in the method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the temperature control method for the intelligent cooking device provided in the above method embodiment.

[0082] Optionally, in this embodiment, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0083] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0084] As can be seen from the embodiments of the temperature control method, device, equipment, terminal, server, storage medium, or computer program of the intelligent cooking equipment provided in this application, this application obtains the cavity temperature information corresponding to the intelligent cooking equipment. The cavity temperature information includes multiple preheating cavity temperature data corresponding to the preheating process and multiple cooking cavity temperature data corresponding to the current time period; determines the target heating rate data based on the multiple preheating cavity temperature data, the target heating rate data being the maximum heating rate data during the preheating process; determines the target load based on the target heating rate data and a preset load prediction model; since the heat absorbed under different load conditions is different, when different loads are placed in the cavity, the temperature change rate in the cavity is different, and the food load can be accurately identified by the heating rate during the cooking process. If the temperature data from multiple cooking cavities meet the preset temperature conditions, the target temperature fluctuation data is determined based on this data. This target temperature fluctuation data is the maximum temperature fluctuation data corresponding to the current time period. The target number of food layers is determined based on the target load, the target temperature fluctuation data, and a preset layer prediction model. When food is placed on different layers, the temperature fluctuation within the cavities will differ, and the load will also affect the stabilized temperature fluctuation. Therefore, combining the load and temperature fluctuation allows for accurate identification of the food layer. The heating module of the intelligent cooking device is controlled based on the target load and target number of layers for temperature control. Automatic temperature control is implemented for different food loads and different layer numbers to achieve cooking precision and avoid problems such as uneven browning due to a constant heating ratio when different layers are placed, resulting in poor cooking effects.

[0085] 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, the above description focuses on specific embodiments of this application. 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 results. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0086] The various embodiments in this application 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 device, equipment, and storage medium 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.

[0087] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware, or by a program instructing the relevant hardware to implement them. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0088] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A temperature control method for an intelligent cooking device, characterized in that, The method includes: Obtain the cavity temperature information corresponding to the intelligent cooking device, the cavity temperature information including multiple preheating cavity temperature data corresponding to the preheating process and multiple cooking cavity temperature data corresponding to the current time period; The target heating rate data is determined based on the temperature data of the multiple preheating chambers, and the target heating rate data is the maximum heating rate data during the preheating process. The target load is determined based on the target heating rate data and the preset load prediction model. If the temperature data of the multiple cooking cavities meet the preset temperature conditions, the target temperature fluctuation data is determined based on the temperature data of the multiple cooking cavities. The target temperature fluctuation data is the maximum temperature fluctuation data corresponding to the current time period. The target number of food layers is determined based on the target load, the target temperature fluctuation data, and the preset layer prediction model. The heating module of the intelligent cooking device is controlled based on the target load and the target number of layers to perform temperature control.

2. The method according to claim 1, characterized in that, The determination of the target heating rate data based on the temperature data of the multiple preheating chambers includes: At least one heating rate data in the preheating process is determined based on the temperature data of the multiple preheating chambers; The largest heating rate among the at least one heating rate data is taken as the target heating rate data.

3. The method according to claim 1, characterized in that, Multiple cooking cavity temperature data include the highest temperature data and the corresponding lowest temperature data corresponding to multiple preset sub-time periods in the current time period. Determining the target temperature fluctuation data based on the multiple cooking cavity temperature data includes: The maximum temperature difference data corresponding to the multiple preset sub-time periods is determined based on the highest temperature data and the lowest temperature data corresponding to the multiple preset sub-time periods. Obtain the average power data of the target heating element corresponding to the multiple preset sub-time periods; The temperature fluctuation data corresponding to the multiple preset sub-time periods is determined based on the maximum temperature difference data and the average power data corresponding to the multiple preset sub-time periods. The maximum value among the temperature fluctuation data corresponding to the multiple preset sub-time periods is taken as the target temperature fluctuation data.

4. The method according to claim 1, characterized in that, The heating module includes a top heating unit and a bottom heating unit. The heating module that controls the intelligent cooking device based on the target load and the target number of layers includes: The set temperature and current cavity temperature data corresponding to the intelligent cooking device are obtained, wherein the set temperature includes upper layer set temperature data and lower layer set temperature data; The first heating ratio corresponding to the top heating unit is determined based on the upper layer temperature data, the current cavity temperature data, the target load, and the target number of layers; The second heating ratio corresponding to the bottom heating unit is determined based on the lower layer temperature data, the target load, and the target number of layers; The top heating unit and the bottom heating unit are controlled based on the first heating ratio and the second heating ratio, respectively.

5. The method according to claim 1, characterized in that, The step of determining the second heating ratio corresponding to the bottom heating unit based on the lower layer temperature data, the target load, and the target number of layers includes: Obtain the total number of placement layers corresponding to the intelligent cooking device; The target layer distance is determined based on the total number of placement layers and the target number of layers; The second heating ratio corresponding to the bottom heating tube is determined based on the lower layer set temperature, the target load, and the target layer distance.

6. The method according to claim 1, characterized in that, The method further includes: The temperature probe of the intelligent cooking device is calibrated based on the target load and the target number of layers to obtain calibration data. The heating module of the smart cooking device is controlled based on the calibration data.

7. The method according to claim 1, characterized in that, The method further includes: Obtain the training dataset corresponding to the intelligent cooking device, the training dataset including data on the number of food placement layers, food load data, and cavity temperature fluctuation data; The initial neural network is trained under supervision based on the data on the number of food layers, the data on the food load, and the data on the temperature fluctuation of the cavity, so as to obtain the preset layer prediction model.

8. A temperature control device for an intelligent cooking appliance, characterized in that, The device includes: The temperature data acquisition module is used to acquire the cavity temperature information corresponding to the intelligent cooking device. The cavity temperature information includes multiple preheating cavity temperature data corresponding to the preheating process and multiple cooking cavity temperature data corresponding to the current time period. The heating rate determination module is used to determine target heating rate data based on the temperature data of the multiple preheating chambers, wherein the target heating rate data is the maximum heating rate data during the preheating process. The load determination module is used to determine the target load based on the target heating rate data and the preset load prediction model. The temperature fluctuation data determination module is used to determine target temperature fluctuation data based on the multiple cooking cavity temperature data if the multiple cooking cavity temperature data meet the preset temperature conditions. The target temperature fluctuation data is the maximum temperature fluctuation data corresponding to the current time period. The target layer number determination module is used to determine the target layer number for food placement based on the target load, the target temperature fluctuation data, and a preset layer number prediction model. A temperature control module is used to control the heating module of the smart cooking device based on the target load and the target number of layers to perform temperature control.

9. A smart cooking device, characterized in that, It includes a temperature monitoring module and a heating module. The temperature monitoring module is located at the center of the top of the cavity of the intelligent cooking device, and the heating module is located inside the cavity of the intelligent cooking device. The temperature monitoring module is used to monitor the cavity temperature data of the intelligent cooking device; The heating module is used to heat the cavity of the intelligent cooking device; The intelligent cooking device also includes the temperature control device as described in claim 8; The intelligent cooking device is either a steam oven or an oven.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the temperature control method of the intelligent cooking device as described in any one of claims 1-7.

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