Accurate heating control method, device and equipment for electric fire stove and storage medium

Abstract

The invention discloses an electric stove accurate heating control method, device and equipment and a storage medium, and relates to the field of electric stove heating control. The method comprises the following steps: constructing a target input vector according to the acquired cooker type and cooker material of a cooker arranged on the electric stove, the center point temperature of the cooker at the current moment and the working state information of a plasma electrode of the electric stove at the current moment; inputting the target input vector into a temperature prediction model trained through a historical sample data set, and predicting a target prediction temperature of the target input vector at the next moment through the temperature prediction model; and adjusting a power output strategy of a plasma electrode of the electric stove according to a relation between the target predicted temperature and the preset working temperature. According to the invention, the output power distribution of the plasma electrode can be accurately regulated and controlled according to the material of the cookware, the type of the cookware and the heating requirement.

Description

Technical Field

[0001] The present invention relates to the technical field of electric stove heating control, and in particular to an electric stove precise heating control method, device, equipment and storage medium. Background Art

[0002] An electric stove is a type of kitchen equipment that uses plasma technology to convert electrical energy into thermal energy to achieve open flame cooking. Compared with traditional gas stoves, electric stoves do not require gas and burners, but only use electricity to generate heat energy.

[0003] The heating method of traditional electric stoves is usually fixed power distribution, and the size of the fire is controlled by setting multiple different fire gears, that is, different fire gears are adapted to different output powers of plasma electrodes. For example: choose a low fire gear for stewing and low-heat cooking, choose a medium fire gear for stir-frying and general food steaming, and choose a high fire gear for rapid heating and boiling water. However, the traditional heating control method cannot be accurately adjusted according to the material, type and heating requirements of the pot, resulting in heat waste or uneven heating.

[0004] Therefore, we need a precise heating control solution for electric stoves, which combines the material of the cooker, the type of cooker, and the real-time temperature of the cooker to accurately control the power distribution of the plasma electrode of the electric stove. Summary of the invention

[0005] The present invention provides a precise heating control method, device, equipment and storage medium for an electric stove, which solves the problem that the traditional heating control method cannot accurately adjust the power distribution of the plasma electrode according to the material, type and heating requirements of the cookware.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: In a first aspect, the present invention provides a method for accurately controlling heating of an electric stove, the method comprising: Constructing a target input vector according to the acquired type of the pot, the material of the pot, the temperature of the center point of the pot at the current moment, and the working state information of the plasma electrodes of the electric stove at the current moment; the working state information includes the power output gears of all the plasma electrodes of the electric stove; Input the target input vector into a temperature prediction model trained by a historical sample data set, and predict the target predicted temperature of the target input vector at the next moment by the temperature prediction model; the historical sample data set includes the historical input vector and the actual temperature at the next moment corresponding to the historical input vector; According to the relationship between the target predicted temperature and the preset working temperature, the power output strategy of the plasma electrode of the electric stove is adjusted.

[0007] In a possible implementation, before predicting the target predicted temperature of the target input vector at the next moment by using the temperature prediction model, the method further includes: Conduct temperature measurement experiments on all combinations of working state information of all plasma electrodes of the electric stove, pot material information, and pot type information, and obtain the pot center temperature of each combination at the current moment and the actual temperature of the pot center at the next moment; Construct a historical input vector for each combination according to the type of pot, the material of the pot, the temperature of the center point of the pot at the current moment, and the working state information of the plasma electrode at the current moment; Constructing a historical sample data set according to a plurality of historical input vectors and the actual temperature of the center point of the pot at the next moment corresponding to each of the historical input vectors; The original temperature prediction model is trained using the historical sample data set until the loss value between the predicted temperature output by the temperature prediction model and the actual temperature no longer decreases, thereby obtaining a trained temperature prediction model.

[0008] In a possible implementation, the loss value is calculated by a comprehensive loss function of the temperature prediction model; The comprehensive loss function is the sum of temperature error loss and energy consumption loss; For a sample group in the historical sample data set, the temperature error loss is the mean square error between the predicted temperature and the actual temperature, and the energy consumption loss is determined according to the power output gears of all plasma electrodes of the electric stove.

[0009] In a possible implementation, the loss function of the energy consumption loss is specifically: ; in, Represents energy consumption loss, Represents the weight factor of energy consumption loss in the comprehensive loss function, Indicates the number of the current sample in the sample group. represents the total number of samples in the sample group, Indicates the number of the plasma electrode in the current sample. represents the total number of plasma electrodes, Indicates In the sample The power output level of each plasma electrode.

[0010] In a possible implementation, before predicting the target predicted temperature of the target input vector at the next moment by using the temperature prediction model, the method further includes: constructing an original temperature prediction model based on a multi-layer perceptron; The original temperature prediction model includes an input layer, a first hidden layer, a second hidden layer and an output layer connected in sequence; The input layer is used to receive an input vector; the input vector is a historical input vector or a target input vector; The first hidden layer and the second hidden layer are sequentially arranged between the input layer and the output layer, and the second hidden layer is residually connected to the input layer; the first hidden layer and the second hidden layer are used to perform nonlinear transformation and feature extraction on the received input vector; The output layer is used to receive information from the second hidden layer and output the predicted temperature.

[0011] In a possible implementation, when the target predicted temperature is lower than the preset working temperature, the power output strategy of the plasma electrode of the electric stove is adjusted according to the relationship between the target predicted temperature and the preset working temperature, specifically including: Combined with the working status information of the plasma electrode at the current moment, a plasma electrode that is not turned on is randomly turned on; And / or, randomly select half of the turned-on plasma electrodes, and increase the power output level of the plasma electrodes whose power output level has not reached the highest level by one level.

[0012] In a possible implementation, when the target predicted temperature is greater than the preset working temperature, the power output strategy of the plasma electrode of the electric stove is adjusted according to the relationship between the target predicted temperature and the preset working temperature, specifically including: Combined with the working status information of the plasma electrode at the current moment, a turned-on plasma electrode is randomly turned off; And / or, randomly select half of the turned-on plasma electrodes and reduce the power output level of the plasma electrodes by one level.

[0013] In a second aspect, the present invention provides a precise heating control device for an electric stove, the device comprising: A data acquisition module, for constructing a target input vector according to the acquired type of the pot, the material of the pot, the temperature of the center point of the pot at the current moment, and the working state information of the plasma electrodes of the electric stove at the current moment; the working state information includes the power output gears of all the plasma electrodes of the electric stove; A data processing module, used for inputting the target input vector into a temperature prediction model trained by a historical sample data set, and predicting the target predicted temperature of the target input vector at the next moment by the temperature prediction model; the historical sample data set includes the historical input vector and the actual temperature at the next moment corresponding to the historical input vector; The strategy adjustment module adjusts the power output strategy of the plasma electrode of the electric stove according to the relationship between the target predicted temperature and the preset working temperature.

[0014] In a possible implementation, the electric stove precise heating control device further includes a model training module. Before predicting the target predicted temperature of the target input vector at the next moment by using the temperature prediction model, the model training module is configured to execute: Conduct temperature measurement experiments on all combinations of working state information of all plasma electrodes of the electric stove, pot material information, and pot type information, and obtain the pot center temperature of each combination at the current moment and the actual temperature of the pot center at the next moment; Construct a historical input vector for each combination according to the type of pot, the material of the pot, the temperature of the center point of the pot at the current moment, and the working state information of the plasma electrode at the current moment; Constructing a historical sample data set according to a plurality of historical input vectors and the actual temperature of the center point of the pot at the next moment corresponding to each of the historical input vectors; The original temperature prediction model is trained using the historical sample data set until the loss value between the predicted temperature output by the temperature prediction model and the actual temperature no longer decreases, thereby obtaining a trained temperature prediction model.

[0015] In a possible implementation, in the model training module, the loss value is configured to be calculated by a comprehensive loss function of the temperature prediction model; The comprehensive loss function is the sum of temperature error loss and energy consumption loss; For a sample group in the historical sample data set, the temperature error loss is the mean square error between the predicted temperature and the actual temperature, and the energy consumption loss is determined according to the power output gears of all plasma electrodes of the electric stove.

[0016] In a possible implementation, in the model training module, the loss function of the energy consumption loss is specifically configured as: ; in, Represents energy consumption loss, Represents the weight factor of energy consumption loss in the comprehensive loss function, Indicates the number of the current sample in the sample group. represents the total number of samples in the sample group, Indicates the number of the plasma electrode in the current sample. represents the total number of plasma electrodes, Indicates In the sample The power output level of each plasma electrode.

[0017] In a possible implementation, the electric stove precise heating control device further includes a model building module. Before predicting the target predicted temperature of the target input vector at the next moment by using the temperature prediction model, the model building module is configured to execute: Construct the original temperature prediction model based on multi-layer perceptron; The original temperature prediction model includes an input layer, a first hidden layer, a second hidden layer and an output layer connected in sequence; The input layer is used to receive an input vector; the input vector is a historical input vector or a target input vector; The first hidden layer and the second hidden layer are sequentially arranged between the input layer and the output layer, and the second hidden layer is residually connected to the input layer; the first hidden layer and the second hidden layer are used to perform nonlinear transformation and feature extraction on the received input vector; The output layer is used to receive information from the second hidden layer and output the predicted temperature.

[0018] In a possible implementation, when the target predicted temperature is lower than the preset operating temperature, the strategy adjustment module is configured to execute: Combined with the working status information of the plasma electrode at the current moment, a plasma electrode that is not turned on is randomly turned on; And / or, randomly select half of the turned-on plasma electrodes, and increase the power output level of the plasma electrodes whose power output level has not reached the highest level by one level.

[0019] In a possible implementation, when the target predicted temperature is greater than the preset operating temperature, the strategy adjustment module is configured to execute: Combined with the working status information of the plasma electrode at the current moment, a turned-on plasma electrode is randomly turned off; And / or, randomly select half of the turned-on plasma electrodes and reduce the power output level of the plasma electrodes by one level.

[0020] In a third aspect, the present invention provides a device for heating using plasma, comprising: Controller; a memory for storing instructions executable by the controller; Wherein, the controller is configured to execute the instructions to implement any of the above-mentioned methods for precise heating control of electric stoves.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores at least one instruction, at least one program, a code set or an instruction set. The at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by a processor to implement the electric stove precision heating control method described in any one of the above items.

[0022] In actual application, the method for precise heating control of an electric stove provided by an embodiment of the present invention first collects the type of pot, the material of the pot, the temperature of the center point of the pot at the current moment, and the power output gear of each group of plasma electrodes of the electric stove at the current moment of the pot set on the electric stove, and constructs a target input vector based on the collected information of the electric stove, wherein the output power of the plasma electrode increases with the increase of the gear; then, the target input vector is input into a trained temperature prediction model, and the temperature prediction model is used to predict the target predicted temperature of the pot at the next moment according to the target input vector, wherein the temperature prediction model uses historical data to predict the target predicted temperature of the pot at the next moment. The present invention trains the actual temperature of the next moment corresponding to the input vector and the historical input vector; finally, according to the relationship between the target predicted temperature output by the temperature prediction model and the preset working temperature required by the user, the power output strategy of the plasma electrode of the electric fire stove is adjusted; the present invention can accurately regulate the output power distribution of the plasma electrode according to the material of the cookware, the type of cookware and the heating needs of the user, thereby dynamically adjusting the temperature of the cookware, so that the temperature of the cookware is dynamically maintained at the preset working temperature, and dynamically controlling the power output and power distribution of multiple groups of plasma heads of the electric fire stove, so that the heat distribution is more uniform, the heating efficiency is improved, and the waste of heat energy is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flowchart of the steps of a precise heating control method for an electric stove provided by an embodiment of the present invention; Figure 2 A temperature curve of the temperature at the center of a pot corresponding to a precise heating control method for an electric stove provided in an embodiment of the present invention; Figure 3 This is a structural block diagram of a precise heating control device for an electric stove provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, unless otherwise specified, "multiple" means two or more. In addition, the use of "based on" or "according to" means openness and inclusiveness, because the process, steps, calculations or other actions "based on" or "according to" one or more of the conditions or values ​​may be based on additional conditions or values ​​beyond the described values ​​in practice.

[0026] In order to solve the problem that traditional heating control methods cannot accurately adjust the plasma electrode power distribution according to the cookware material, cookware type and user heating requirements, the embodiments of the present invention provide an electric stove accurate heating control method, device, equipment and storage medium.

[0027] like Figure 1 As shown, in the first aspect, an embodiment of the present invention provides a method for precise heating control of an electric stove, the method comprising: Step 101: construct a target input vector according to the acquired information of the type of the pot, the material of the pot, the temperature of the center point of the pot at the current moment, and the working state information of the plasma electrode of the electric stove at the current moment.

[0028] The working status information includes the power output gears of all plasma electrodes of the electric stove.

[0029] Specifically, each electric stove includes multiple groups of plasma electrodes, all plasma electrodes are numbered, the maximum output power of each group of plasma electrodes is divided into multiple power levels, and each power level corresponds to a power output gear.

[0030] In this embodiment, the electric stove includes 18 groups of plasma electrodes, and the 18 groups of plasma electrodes are numbered sequentially. Afterwards, the maximum output power of each group of plasma electrodes is divided into four levels. When the maximum output power of each group of plasma electrodes is U, the output powers that can be selected by each group of plasma electrodes after classification include 0, U / 3, 2U / 3, and U, and these four output powers correspond to the 0th, 1st, 2nd, and 3rd gears of the plasma electrode power output gear respectively.

[0031] The working status information is an 18-dimensional vector, which is specifically recorded as: ; in, It is used to indicate the power output position of the plasma electrode, and its value can be 0, 1, 2, or 3; The subscripts are used to indicate the number of the plasma electrodes.

[0032] Different pot types and pot materials change temperature differently when heated.

[0033] Among them, the types of pots include soup pots, woks, frying pans and steamers, and the materials of pots include iron, aluminum and stainless steel.

[0034] In this embodiment, the material of the pot is represented by a three-dimensional vector: ; in, , , They represent different cookware materials, and their values ​​can be 1 or 0. For example, the vector corresponding to iron is (1, 0, 0), the vector corresponding to aluminum is (0, 1, 0), and the vector corresponding to stainless steel is (0, 0, 1).

[0035] The type of cookware is represented by a four-dimensional vector: ; in, , , , They represent different types of pots and can be 1 or 0. For example, the vector corresponding to a soup pot is (1, 0, 0, 0), the vector corresponding to a wok is (0, 1, 0, 0), the vector corresponding to a frying pan is (0, 0, 1, 0), and the vector corresponding to a steamer is (0, 0, 0, 1).

[0036] Based on this, the target input vector is determined according to the type of pot, the material of the pot, the vector corresponding to the power output gear of each plasma electrode of the electric stove, and the center point temperature of the pot at the current moment measured by the thermometer.

[0037] Step 102: Input the target input vector into the temperature prediction model trained by the historical sample data set, and predict the target predicted temperature of the target input vector at the next moment by the temperature prediction model.

[0038] The historical sample data set includes the historical input vector and the actual temperature at the next moment corresponding to the historical input vector.

[0039] In this embodiment of the present invention, the target input vector Depend on , , as well as These four groups of variables consist of is an 18-dimensional vector, is a 3-dimensional vector, is a 4-dimensional vector, is a 1-dimensional vector.

[0040] Enter the target vector Input into the trained temperature prediction model prediction, and the target input vector can be accurately output The target predicted temperature at the next moment.

[0041] Step 103: adjusting the power output strategy of the plasma electrode of the electric stove according to the relationship between the target predicted temperature and the preset working temperature.

[0042] Specifically, the preset working temperature can be set by the function button of the electric stove. In actual application, under the heating of the plasma electrode, the temperature of the pot continues to rise and eventually reaches the preset working temperature, and the temperature needs to be controlled within the preset working temperature.

[0043] In other words, the temperature control of the electric stove is a dynamic balance process. When the target predicted temperature is lower than the preset working temperature, it is necessary to increase the output power of some plasma electrodes or turn on more groups of plasma electrodes to make the temperature of the electric stove gradually approach the preset working temperature during actual operation; when the target predicted temperature is higher than the preset working temperature, it is necessary to reduce the output power of the plasma electrodes or turn off some plasma electrodes to make the temperature of the electric stove gradually approach the preset working temperature during actual operation, and finally reach a dynamic balance, thereby realizing precise control of the heating of the cooker.

[0044] like Figure 2 As shown, the temperature at the center of the pot gradually increases from the initial room temperature. When the temperature at the center of the pot reaches near the preset working temperature T0, the output power of the plasma electrode of the electric stove is dynamically adjusted through the precise heating control method of the electric stove of the present invention, and the temperature at the center of the pot is dynamically maintained near the preset working temperature T0, thereby achieving stable control of the pot temperature.

[0045] When the electric stove precise heating control method provided by the embodiment of the present invention is actually applied, the type of cookware, the material of the cookware, the temperature of the center point of the cookware at the current moment, and the power output gear of each group of plasma electrodes of the electric stove at the current moment of the cookware set on the electric stove are first collected, and a target input vector is constructed according to the collected information of the electric stove, wherein as the gear increases, the output power of the plasma electrode increases accordingly; then the target input vector is input into a trained temperature prediction model, and the temperature prediction model is used to predict the target predicted temperature of the cookware at the next moment according to the target input vector, wherein the temperature prediction model is trained by the historical input vector and the actual temperature at the next moment corresponding to the historical input vector; finally, according to the relationship between the target predicted temperature output by the temperature prediction model and the preset working temperature required by the user, the power output strategy of the plasma electrode of the electric stove is adjusted.

[0046] The present invention can accurately regulate the output power distribution of the plasma electrode according to the material of the cookware, the type of cookware and the heating needs of the user, thereby dynamically adjusting the temperature of the cookware, so that the temperature of the cookware is dynamically maintained at a preset working temperature, and dynamically controlling the power output and power distribution of multiple groups of plasma heads of the electric stove to make the heat distribution more uniform, improve the heating efficiency and reduce the waste of heat energy.

[0047] Furthermore, before predicting the target predicted temperature of the target input vector at the next moment by the temperature prediction model, the method further includes: Conduct temperature measurement experiments on all combinations of working state information of all plasma electrodes of the electric stove, pot material information, and pot type information, and obtain the pot center temperature of each combination at the current moment and the actual temperature of the pot center at the next moment; Construct a historical input vector for each combination according to the type of pot, the material of the pot, the temperature of the center point of the pot at the current moment, and the working state information of the plasma electrode at the current moment; Constructing a historical sample data set according to multiple historical input vectors and the actual temperature of the center point of the pot at the next moment corresponding to each historical input vector; The original temperature prediction model is trained using the historical sample data set until the loss value between the predicted temperature output by the temperature prediction model and the actual temperature no longer decreases, thereby obtaining a trained temperature prediction model.

[0048] Among them, after the cooker type, cooker material and the output power of each plasma electrode are vectorized and encoded, the temperature measurement experiment is carried out on all combinations of the above three groups of vectors, that is, the working state information of the plasma electrode of the electric stove at the current moment is , the pot material is , cookware type is In the case of using a thermometer to measure the temperature data of the center point of the pot at the current moment , and the actual temperature of the center point of the pot at the next moment. Thus, a historical sample data set is obtained, which includes multiple historical input vectors and their corresponding actual temperatures at the next moment.

[0049] When constructing the historical sample data set, the time interval between the next moment and the current moment is 10 seconds. That is, the center temperature of the pot after 10 seconds is related to the pot type, pot material, output power level of each plasma electrode 10 seconds ago, and the center temperature of the pot 10 seconds ago.

[0050] When training the model, the historical sample data set is divided into a training set, a validation set, and a test set, with the division ratios of 70%, 20%, and 10%, respectively. The parameters and weights of the temperature prediction model are trained through the training set; the hyperparameters in the model, such as learning rate, regularization parameters, etc., are adjusted through the validation set to improve the generalization performance of the model, that is, the performance of the validation set is used to determine whether the temperature prediction model is overfitting or underfitting. The test set is used to finally evaluate the performance of the temperature prediction model and check the generalization ability of the temperature prediction model for data that has never been seen. The loss function is minimized through the Adam optimizer. The gradient of the loss function for each parameter is calculated through the back propagation algorithm, and the parameters are updated to reduce the loss.

[0051] In an embodiment of the present invention, the performance of the temperature prediction model is monitored through a validation set until the loss value between the predicted temperature of the validation set and the actual temperature of the temperature prediction model in several consecutive batch sizes no longer decreases, and the training is stopped to avoid overfitting of the model, thereby obtaining a trained temperature prediction model.

[0052] Furthermore, the loss value is calculated by the comprehensive loss function of the temperature prediction model.

[0053] The comprehensive loss function is the sum of temperature error loss and energy consumption loss.

[0054] For a sample group in the historical sample data set, the temperature error loss is the mean square error between the predicted temperature and the actual temperature, and the energy consumption loss is determined according to the power output gears of all plasma electrodes of the electric stove.

[0055] Specifically, the comprehensive loss function is: ; in, It represents comprehensive loss; represents the temperature error loss, which is used to represent the error of the temperature prediction model in predicting the pot temperature; Indicates energy consumption loss.

[0056] The temperature error loss function is specifically: ; in, Represents the total number of samples in the sample group, which is used to calculate the average loss to ensure that the loss value is independent of the number of samples.

[0057] Indicates The temperature vector of the center point of the pot measured at the current moment for each training sample.

[0058] Indicates The predicted temperature of the training samples at the next moment.

[0059] Furthermore, the loss function of energy consumption loss is specifically: ; in, Indicates energy consumption loss.

[0060] Represents the weight factor of energy consumption loss in the comprehensive loss function, which is used to balance the relative importance of temperature error loss and energy consumption loss. The value of can control the trade-off between optimizing temperature accuracy and energy efficiency in the temperature prediction model.

[0061] Indicates the number of the current sample in the sample group. represents the total number of samples in the sample group, Indicates the number of the plasma electrode in the current sample. represents the total number of plasma electrodes, Indicates In the sample The power output level of each plasma electrode.

[0062] The comprehensive loss function of the present invention is composed of a temperature error loss part and an energy consumption loss part, so that the temperature prediction model can better balance temperature accuracy and energy consumption during the training process.

[0063] Specifically, on the basis of the traditional mean square error, a penalty term for energy consumption is added to form a comprehensive loss function. By introducing the loss term for energy consumption, the temperature prediction model can not only focus on the accuracy of temperature prediction during training, but also consider the energy efficiency of power setting, which helps to achieve the dual goals of temperature control and energy optimization, and improve the overall performance and user satisfaction of the electric stove.

[0064] Furthermore, before predicting the target predicted temperature of the target input vector at the next moment through the temperature prediction model, the method also includes: constructing an original temperature prediction model based on a multi-layer perceptron.

[0065] The original temperature prediction model includes an input layer, a first hidden layer, a second hidden layer, and an output layer which are connected in sequence.

[0066] The input layer is used to receive input vectors.

[0067] Among them, the input vector is a historical input vector or a target input vector.

[0068] The first hidden layer and the second hidden layer are sequentially arranged between the input layer and the output layer, and the second hidden layer is residually connected to the input layer.

[0069] Among them, the first hidden layer and the second hidden layer are used to perform nonlinear transformation and feature extraction on the received input vector, and the activation functions of the first hidden layer and the second hidden layer are both ReLU functions. The ReLU function allows the hidden layer to introduce nonlinear relationships, so that the temperature prediction model can learn complex patterns and features.

[0070] The output layer is used to receive the information from the second hidden layer and output the predicted temperature.

[0071] In this embodiment, a multi-layer perceptron is selected to construct the original temperature prediction model, and residual connections are added between hidden layers, so that the output of the second hidden layer depends not only on the output of the first hidden layer, but also includes parts from the input layer. The present invention allows the gradient to flow directly through the multi-layer perceptron through residual connections, which alleviates the gradient vanishing problem in the deep network of the multi-layer perceptron. This structure creates a direct "jump" path between layers, reduces information loss, and improves the training efficiency of the network. In addition, it promotes the efficient propagation of information in the network, enabling the model to learn more complex feature representations and improve prediction performance.

[0072] Furthermore, when the target predicted temperature is lower than the preset working temperature, the power output strategy of the plasma electrode of the electric stove is adjusted according to the relationship between the target predicted temperature and the preset working temperature, specifically including: Combined with the working status information of the plasma electrode at the current moment, a plasma electrode that is not turned on is randomly turned on; And / or, randomly select half of the turned-on plasma electrodes, and increase the power output level of the plasma electrodes whose power output level has not reached the highest level by one level.

[0073] Furthermore, when the target predicted temperature is greater than the preset working temperature, the power output strategy of the plasma electrode of the electric stove is adjusted according to the relationship between the target predicted temperature and the preset working temperature, specifically including: Combined with the working status information of the plasma electrode at the current moment, a turned-on plasma electrode is randomly turned off; And / or, randomly select half of the turned-on plasma electrodes and reduce the power output level of the plasma electrodes by one level.

[0074] In this embodiment, when the electric stove starts working, half of the plasma electrodes are randomly turned on, and the power output gear of the turned-on plasma electrodes is randomly set. The target input vector at the current moment is input into the trained temperature prediction model, and the target predicted temperature at the next moment output by the temperature prediction model can be obtained. By comparing the relationship between the target predicted temperature and the preset working temperature, the power output of the plasma electrode can be dynamically adjusted.

[0075] When the target predicted temperature is lower than the preset operating temperature, a plasma electrode is randomly turned on and the gear of the plasma electrode is set to gear 2. If all plasma electrodes are turned on at this time, this operation is not required. Afterwards, half of the plasma electrodes that have been turned on are randomly selected and their power output gear is increased by one gear. If the selected plasma electrode is already at the highest gear, this operation is not required.

[0076] When the target predicted temperature is greater than the preset operating temperature, a plasma electrode that is turned on is randomly turned off first. If all plasma electrodes are turned off at this time, this operation is not required. After that, half of the plasma electrodes that are turned on are randomly selected and their power output gear is lowered by one gear. If the selected plasma electrode is already at the lowest gear, this operation is not required.

[0077] By adjusting the power output strategy through this solution, the cookware can be better stabilized within the preset working stability range after being heated from room temperature to the preset working stability.

[0078] like Figure 3 As shown, in a second aspect, an embodiment of the present invention provides a precise heating control device for an electric stove, the device comprising: The data acquisition module 201 is used to construct a target input vector according to the acquired type of the pot, the material of the pot, the temperature of the center point of the pot at the current moment, and the working state information of the plasma electrode of the electric stove at the current moment; the working state information includes the power output gear of all the plasma electrodes of the electric stove; The data processing module 202 is used to input the target input vector into the temperature prediction model trained by the historical sample data set, and predict the target predicted temperature of the target input vector at the next moment by the temperature prediction model; the historical sample data set includes the historical input vector and the actual temperature at the next moment corresponding to the historical input vector; The strategy adjustment module 203 adjusts the power output strategy of the plasma electrode of the electric stove according to the relationship between the target predicted temperature and the preset working temperature.

[0079] Furthermore, the electric stove precise heating control device further includes a model training module. Before predicting the target predicted temperature of the target input vector at the next moment through the temperature prediction model, the model training module is configured to execute: Conduct temperature measurement experiments on all combinations of working state information of all plasma electrodes of the electric stove, pot material information, and pot type information, and obtain the pot center temperature of each combination at the current moment and the actual temperature of the pot center at the next moment; Construct a historical input vector for each combination according to the type of pot, the material of the pot, the temperature of the center point of the pot at the current moment, and the working state information of the plasma electrode at the current moment; Constructing a historical sample data set according to multiple historical input vectors and the actual temperature of the center point of the pot at the next moment corresponding to each historical input vector; The original temperature prediction model is trained using the historical sample data set until the loss value between the predicted temperature output by the temperature prediction model and the actual temperature no longer decreases, thereby obtaining a trained temperature prediction model.

[0080] Further, in the model training module, the loss value is configured to be calculated by a comprehensive loss function of the temperature prediction model; The comprehensive loss function is the sum of temperature error loss and energy consumption loss; For a sample group in the historical sample data set, the temperature error loss is the mean square error between the predicted temperature and the actual temperature, and the energy consumption loss is determined according to the power output gears of all plasma electrodes of the electric stove.

[0081] Furthermore, in the model training module, the loss function of energy consumption loss is specifically configured as: ; in, Represents energy consumption loss, Represents the weight factor of energy consumption loss in the comprehensive loss function, Indicates the number of the current sample in the sample group. represents the total number of samples in the sample group, Indicates the number of the plasma electrode in the current sample. represents the total number of plasma electrodes, Indicates In the sample The power output level of each plasma electrode.

[0082] Furthermore, the electric stove precise heating control device further includes a model building module. Before predicting the target predicted temperature of the target input vector at the next moment by the temperature prediction model, the model building module is configured to execute: Construct the original temperature prediction model based on multi-layer perceptron; The original temperature prediction model includes an input layer, a first hidden layer, a second hidden layer, and an output layer connected in sequence; The input layer is used to receive an input vector; the input vector is a historical input vector or a target input vector; The first hidden layer and the second hidden layer are sequentially arranged between the input layer and the output layer, and the second hidden layer is residually connected to the input layer; the first hidden layer and the second hidden layer are used to perform nonlinear transformation and feature extraction on the received input vector; The output layer is used to receive the information from the second hidden layer and output the predicted temperature.

[0083] Furthermore, when the target predicted temperature is lower than the preset operating temperature, the strategy adjustment module 203 is configured to execute: Combined with the working status information of the plasma electrode at the current moment, a plasma electrode that is not turned on is randomly turned on; And / or, randomly select half of the turned-on plasma electrodes, and increase the power output level of the plasma electrodes whose power output level has not reached the highest level by one level.

[0084] Furthermore, when the target predicted temperature is greater than the preset operating temperature, the strategy adjustment module 203 is configured to execute: Combined with the working status information of the plasma electrode at the current moment, a turned-on plasma electrode is randomly turned off; And / or, randomly select half of the turned-on plasma electrodes and reduce the power output level of the plasma electrodes by one level.

[0085] The electric fire stove precise heating control device provided by the embodiment of the present invention is used to execute the above-mentioned electric fire stove precise heating control method, and thus can achieve the same effect as the above-mentioned electric fire stove precise heating control method.

[0086] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0087] In a third aspect, an embodiment of the present invention further provides a device for plasma heating, comprising: Controller; a memory for storing instructions executable by the controller; Wherein, the controller is configured to execute the instructions to implement any of the above-mentioned methods for precise heating control of electric stoves.

[0088] In this embodiment, the trained temperature prediction model is deployed on the embedded processor or edge computing device of the electric stove to achieve real-time temperature prediction.

[0089] In actual applications, the user sets the type and material of the pot according to the actual situation, collects the temperature of the center point of the pot in real time through the temperature sensor, and collects the power output gear of each plasma electrode through the embedded processor. The target input vector containing the type of pot, the material of the pot, the temperature of the center point of the pot at the current moment, and the working state information of the plasma electrode of the electric stove at the current moment is input into the temperature prediction model trained by the historical sample data set. The temperature prediction model can output the target predicted temperature at the next moment in real time. Finally, the embedded processor or edge computing device dynamically adjusts the power output gear of the plasma electrode of the electric stove according to the relationship between the target predicted temperature and the preset working temperature, thereby realizing the dynamic adjustment of the pot temperature.

[0090] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, in which at least one instruction, at least one program, a code set or an instruction set is stored, and at least one instruction, at least one program, a code set or an instruction set is loaded and executed by a processor to implement the electric stove precise heating control method in an embodiment of the present invention.

[0091] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by the computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0092] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A precise heating control method for an electric stove, characterized in that: include: Constructing a target input vector according to the acquired type of the pot, the material of the pot, the temperature of the center point of the pot at the current moment, and the working state information of the plasma electrodes of the electric stove at the current moment; the working state information includes the power output gears of all the plasma electrodes of the electric stove; Input the target input vector into a temperature prediction model trained by a historical sample data set, and predict the target predicted temperature of the target input vector at the next moment by the temperature prediction model; the historical sample data set includes the historical input vector and the actual temperature at the next moment corresponding to the historical input vector; According to the relationship between the target predicted temperature and the preset working temperature, the power output strategy of the plasma electrode of the electric stove is adjusted.

2. The electric stove precise heating control method according to claim 1, characterized in that: Before predicting the target predicted temperature of the target input vector at the next moment by using the temperature prediction model, the method further includes: Conduct temperature measurement experiments on all combinations of working state information of all plasma electrodes of the electric stove, pot material information, and pot type information, and obtain the pot center temperature of each combination at the current moment and the actual temperature of the pot center at the next moment; Construct a historical input vector for each combination according to the type of pot, the material of the pot, the temperature of the center point of the pot at the current moment, and the working state information of the plasma electrode at the current moment; Constructing a historical sample data set according to a plurality of historical input vectors and the actual temperature of the center point of the pot at the next moment corresponding to each of the historical input vectors; The original temperature prediction model is trained using the historical sample data set until the loss value between the predicted temperature output by the temperature prediction model and the actual temperature no longer decreases, thereby obtaining a trained temperature prediction model.

3. The electric stove precise heating control method according to claim 2, characterized in that: The loss value is calculated by the comprehensive loss function of the temperature prediction model; The comprehensive loss function is the sum of temperature error loss and energy consumption loss; For a sample group in the historical sample data set, the temperature error loss is the mean square error between the predicted temperature and the actual temperature, and the energy consumption loss is determined according to the power output gears of all plasma electrodes of the electric stove.

4. The electric stove precise heating control method according to claim 3, characterized in that: The loss function of the energy consumption loss is specifically: ； in, Represents energy consumption loss, Represents the weight factor of energy consumption loss in the comprehensive loss function, Indicates the number of the current sample in the sample group. represents the total number of samples in the sample group, Indicates the number of the plasma electrode in the current sample. represents the total number of plasma electrodes, Indicates In the sample The power output level of each plasma electrode.

5. The electric stove precise heating control method according to claim 1, characterized in that: Before predicting the target predicted temperature of the target input vector at the next moment by the temperature prediction model, the method further includes: constructing an original temperature prediction model based on a multi-layer perceptron; The original temperature prediction model includes an input layer, a first hidden layer, a second hidden layer and an output layer connected in sequence; The input layer is used to receive an input vector; the input vector is a historical input vector or a target input vector; The first hidden layer and the second hidden layer are sequentially arranged between the input layer and the output layer, and the second hidden layer is residually connected to the input layer; the first hidden layer and the second hidden layer are used to perform nonlinear transformation and feature extraction on the received input vector; The output layer is used to receive information from the second hidden layer and output the predicted temperature.

6. The electric stove precise heating control method according to claim 1, characterized in that: When the target predicted temperature is lower than the preset working temperature, the power output strategy of the plasma electrode of the electric stove is adjusted according to the relationship between the target predicted temperature and the preset working temperature, specifically including: Combined with the working status information of the plasma electrode at the current moment, a plasma electrode that is not turned on is randomly turned on; And / or, randomly select half of the turned-on plasma electrodes, and increase the power output level of the plasma electrodes whose power output level has not reached the highest level by one level.

7. The electric stove precise heating control method according to claim 1, characterized in that: When the target predicted temperature is greater than the preset working temperature, the power output strategy of the plasma electrode of the electric stove is adjusted according to the relationship between the target predicted temperature and the preset working temperature, specifically including: Combined with the working status information of the plasma electrode at the current moment, a turned-on plasma electrode is randomly turned off; And / or, randomly select half of the turned-on plasma electrodes and reduce the power output level of the plasma electrodes by one level.

8. A precise heating control device for an electric stove, characterized in that: include: A data acquisition module, for constructing a target input vector according to the acquired type of the pot, the material of the pot, the temperature of the center point of the pot at the current moment, and the working state information of the plasma electrodes of the electric stove at the current moment; the working state information includes the power output gears of all the plasma electrodes of the electric stove; A data processing module, used for inputting the target input vector into a temperature prediction model trained by a historical sample data set, and predicting the target predicted temperature of the target input vector at the next moment by the temperature prediction model; the historical sample data set includes the historical input vector and the actual temperature at the next moment corresponding to the historical input vector; The strategy adjustment module adjusts the power output strategy of the plasma electrode of the electric stove according to the relationship between the target predicted temperature and the preset working temperature.

9. A device using plasma heating, characterized in that: include: Controller; a memory for storing instructions executable by the controller; Wherein, the controller is configured to execute the instructions to implement the electric stove precise heating control method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement the electric stove precise heating control method as described in any one of claims 1-7.

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