Temperature control method and device of steaming oven, steaming oven and readable medium

Abstract

The embodiment of the invention provides a temperature control method and device of a steaming oven, the steaming oven and a readable medium, the method and device are applied to the steaming oven, the steaming oven comprises variable frequency heating equipment, the variable frequency heating equipment is graphene heating equipment and / or eddy current heating equipment, and a preset temperature sensor is used for collecting the current temperature of the steaming oven; a preset current sensor and a preset voltage sensor are used for collecting working current and working voltage of the variable-frequency heating equipment respectively; based on the current temperature, the working current, the working voltage and a preset target temperature of the steaming oven, a power control strategy of the variable-frequency heating equipment is obtained through a preset control strategy generation model; on the basis of the power control strategy, the current temperature of the steaming oven is converted into the target temperature by adjusting the output power of the variable-frequency heating equipment, the current temperature of the steaming oven can be converted into the target temperature, and the steaming oven is controlled to operate at a constant temperature according to the target temperature.

Description

Technical Field

[0001] The present invention relates to the technical field of steam ovens, and in particular to a temperature control method for a steam oven, a temperature control device for a steam oven, a steam oven and a computer-readable medium. Background Art

[0002] In the related art, the steam oven usually adopts PID (Proportional-Integral-Derivative) control algorithm for temperature control. However, the PID control algorithm makes it difficult for the steam oven to maintain a constant temperature for a long time to ensure the cooking quality. Summary of the invention

[0003] The embodiments of the present invention provide a temperature control method and device for a steam oven, a steam oven and a computer-readable storage medium to solve the problem that a PID control algorithm is difficult to keep the steam oven at a constant temperature for a long time to ensure cooking quality.

[0004] The embodiment of the present invention discloses a temperature control method for a steam oven, which is applied to the steam oven. The steam oven includes a variable frequency heating device, and the variable frequency heating device is a graphene heating device and / or an eddy current heating device. The method includes:

[0005] Using a preset temperature sensor to collect the current temperature of the steam oven;

[0006] Using a preset current sensor and voltage sensor to respectively collect the working current and working voltage of the variable frequency heating device;

[0007] Based on the current temperature, the working current, the working voltage and the preset target temperature of the steam oven, a power control strategy of the variable frequency heating device is obtained by using a preset control strategy generation model;

[0008] Based on the power control strategy, the current temperature of the steam oven is converted to the target temperature by adjusting the output power of the variable frequency heating device.

[0009] Optionally, the power control strategy of the variable frequency heating device is obtained by using a preset control strategy generation model based on the current temperature, the working current, the working voltage and the preset target temperature of the steam oven, including:

[0010] Using the working current and the working voltage, obtaining the current power of the variable frequency heating device;

[0011] The current temperature, the current power and the target temperature are input into the control strategy generation model to obtain the power control strategy of the variable frequency heating device.

[0012] Optionally, the method comprises:

[0013] Obtaining a training current temperature of the steam oven, a training target temperature, a training current frequency of the variable frequency heating device, and a training power control strategy for converting the training current temperature into the training target temperature;

[0014] The preset machine learning model is trained using the training current temperature, the training target temperature, the training current frequency and the training power control strategy to obtain the control strategy generation model.

[0015] Optionally, the using the training current temperature, the training target temperature, the training current frequency, and the training power control strategy to train a preset machine learning model to obtain the control strategy generation model includes:

[0016] During the training process of the machine learning model, obtaining the inference power control strategy of the machine learning model for the training current temperature, the training target temperature and the training current frequency output;

[0017] Obtain at least one of the transition temperature, transition speed and transition energy consumption corresponding to the inference power control strategy; the transition temperature is the temperature obtained by adjusting the output power of the variable frequency heating device by using the inference power control strategy so that the training current temperature is transformed; the transition speed is the speed at which the training current temperature is transformed into the transition temperature; the transition energy consumption is the energy consumption of the variable frequency heating device during the process of transforming the training current temperature into the transition temperature;

[0018] The machine learning model is trained based on at least one of the transition temperature, the transition speed, and the transition energy consumption.

[0019] Optionally, the training the machine learning model based on at least one of the transition temperature, the transition speed and the transition energy consumption comprises:

[0020] If the difference between the transition temperature and the training target temperature is less than a preset difference threshold, providing a preset first reward for the machine learning model;

[0021] If the transition speed is higher than a preset speed threshold, providing a preset second reward to the machine learning model;

[0022] If the conversion energy consumption is less than a preset energy consumption threshold, providing a preset third reward for the machine learning model;

[0023] The machine learning model is trained using at least one of the first reward, the second reward, and the third reward.

[0024] Optionally, the using at least one of the first reward, the second reward, and the third reward to train the machine learning model comprises:

[0025] Configuring a preset temperature weight, speed weight, and energy consumption weight for the machine learning model;

[0026] Based on the temperature weight, the first reward, the speed weight, the second reward, the energy consumption weight, and the third reward, obtaining a target reward of the machine learning model;

[0027] The machine learning model is trained using the target reward.

[0028] Optionally, the step of changing the current temperature of the steam oven to the target temperature by adjusting the output power of the variable frequency heating device includes:

[0029] Obtain at least one of a temperature to be processed, a speed to be processed, and an energy consumption to be processed corresponding to the power control strategy; the temperature to be processed is a temperature obtained by adjusting the output power of the variable frequency heating device by using the power control strategy so that the current temperature is transformed; the speed to be processed is a speed at which the current temperature is transformed into the temperature to be processed; the energy consumption to be processed is the energy consumption of the variable frequency heating device during the process of the current temperature being transformed into the temperature to be processed;

[0030] The control strategy generation model is trained based on at least one of the temperature to be processed, the speed to be processed, and the energy consumption to be processed.

[0031] Optionally, the training of the control strategy generation model based on at least one of the temperature to be processed, the speed to be processed, and the energy consumption to be processed includes:

[0032] Based on the to-be-processed temperature, the to-be-processed speed, and the to-be-processed energy consumption, adjusting at least one of the temperature weight, the speed weight, and the energy consumption weight;

[0033] Based on at least one of the adjusted temperature weight, the speed weight, and the energy consumption weight, obtaining a reward during the training process of the control strategy generation model;

[0034] The control policy generation model is trained using the reward.

[0035] Optionally, the method comprises:

[0036] Based on the power control strategy, the switching state of the graphene heating device and / or the eddy current heating device is adjusted.

[0037] The embodiment of the present invention further discloses a temperature control device for a steam oven, which is applied to the steam oven. The steam oven includes a variable frequency heating device, which is a graphene heating device and / or an eddy current heating device. The device includes:

[0038] A temperature acquisition module, used to acquire the current temperature of the steam oven using a preset temperature sensor;

[0039] A voltage acquisition module, used to respectively acquire the working current and working voltage of the variable frequency heating device using a preset current sensor and a preset voltage sensor;

[0040] A strategy obtaining module, used to obtain the power control strategy of the variable frequency heating device by using a preset control strategy generation model based on the current temperature, the working current, the working voltage and the preset target temperature of the steam oven;

[0041] A power adjustment module is used to change the current temperature of the steam oven to the target temperature by adjusting the output power of the variable frequency heating device based on the power control strategy.

[0042] Optionally, the strategy obtaining module includes:

[0043] A frequency acquisition submodule, used to acquire the current power of the variable frequency heating device by using the working current and the working voltage;

[0044] The strategy acquisition submodule is used to input the current temperature, the current power and the target temperature into the control strategy generation model to obtain the power control strategy of the variable frequency heating device.

[0045] Optionally, the device comprises:

[0046] A training data acquisition module, used to acquire a training current temperature of the steam oven, a training target temperature, a training current frequency of the variable frequency heating device, and a training power control strategy for converting the training current temperature into the training target temperature;

[0047] A training module is used to train a preset machine learning model using the training current temperature, the training target temperature, the training current frequency and the training power control strategy to obtain the control strategy generation model.

[0048] Optionally, the training module includes:

[0049] An inference power control strategy acquisition submodule, used to obtain the inference power control strategy output by the machine learning model for the training current temperature, the training target temperature and the training current frequency during the training process of the machine learning model;

[0050] A transition temperature acquisition submodule is used to obtain at least one of the transition temperature, transition speed and transition energy consumption corresponding to the inference power control strategy; the transition temperature is the temperature obtained by adjusting the output power of the variable frequency heating device by using the inference power control strategy so that the training current temperature is transformed; the transition speed is the speed at which the training current temperature is transformed into the transition temperature; the transition energy consumption is the energy consumption of the variable frequency heating device during the process of transforming the training current temperature into the transition temperature;

[0051] A training submodule is used to train the machine learning model based on at least one of the transition temperature, the transition speed and the transition energy consumption.

[0052] Optionally, the training submodule includes:

[0053] a first reward providing unit, configured to provide a preset first reward to the machine learning model if the difference between the transition temperature and the training target temperature is less than a preset difference threshold;

[0054] A second reward providing unit, configured to provide a preset second reward to the machine learning model if the transition speed is higher than a preset speed threshold;

[0055] A third reward providing unit, configured to provide a preset third reward for the machine learning model if the conversion energy consumption is less than a preset energy consumption threshold;

[0056] A training unit is used to train the machine learning model using at least one of the first reward, the second reward, and the third reward.

[0057] Optionally, the training unit includes:

[0058] A weight configuration subunit, used to configure a preset temperature weight, speed weight, and energy consumption weight for the machine learning model;

[0059] a target reward acquisition subunit, configured to acquire a target reward of the machine learning model based on the temperature weight, the first reward, the speed weight, the second reward, the energy consumption weight, and the third reward;

[0060] A training subunit is used to train the machine learning model using the target reward.

[0061] Optionally, the power adjustment module includes:

[0062] The to-be-processed temperature acquisition submodule is used to obtain at least one of the to-be-processed temperature, the to-be-processed speed, and the to-be-processed energy consumption corresponding to the power control strategy; the to-be-processed temperature is the temperature obtained by adjusting the output power of the variable frequency heating device by using the power control strategy so that the current temperature is transformed; the to-be-processed speed is the speed at which the current temperature is transformed into the to-be-processed temperature; the to-be-processed energy consumption is the energy consumption of the variable frequency heating device during the process of the current temperature being transformed into the to-be-processed temperature;

[0063] The control strategy generation model training submodule is used to train the control strategy generation model based on at least one of the temperature to be processed, the speed to be processed and the energy consumption to be processed.

[0064] Optionally, the control strategy generation model training submodule includes:

[0065] A weight adjustment unit, configured to adjust at least one of the temperature weight, the speed weight, and the energy consumption weight based on the to-be-processed temperature, the to-be-processed speed, and the to-be-processed energy consumption;

[0066] A reward acquisition unit, configured to acquire a reward in a training process of the control strategy generation model based on at least one of the adjusted temperature weight, the speed weight, and the energy consumption weight;

[0067] A control strategy generation model training unit is used to train the control strategy generation model using the reward.

[0068] Optionally, the device comprises:

[0069] A switch state adjustment module is used to adjust the switch state of the graphene heating device and / or the eddy current heating device based on the power control strategy.

[0070] The embodiment of the present invention further discloses a steam oven, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus;

[0071] The memory is used to store computer programs;

[0072] The processor is used to implement the method described in the embodiment of the present invention when executing the program stored in the memory.

[0073] The embodiment of the present invention further discloses one or more computer-readable media on which instructions are stored. When executed by one or more processors, the processors are enabled to execute the method described in the embodiment of the present invention.

[0074] The embodiments of the present invention include the following advantages:

[0075] In an embodiment of the present invention, the steam oven includes a variable frequency heating device, and the variable frequency heating device is a graphene heating device and / or an eddy current heating device. The current temperature of the steam oven is collected using a preset temperature sensor, and the working current and working voltage of the variable frequency heating device are collected using a preset current sensor and a voltage sensor, respectively. Based on the current temperature, working current, working voltage and preset target temperature of the steam oven, a power control strategy of the variable frequency heating device is obtained using a preset control strategy generation model; based on the power control strategy, the current temperature of the steam oven is converted to the target temperature by adjusting the output power of the variable frequency heating device. By using the power control strategy output by the control strategy generation model, and cooperating with the variable frequency heating device to adjust the output power of the variable frequency heating device, the current temperature of the steam oven can be converted to the target temperature, and the steam oven can be controlled to operate at a constant temperature according to the target temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 is a flow chart of steps of a temperature control method of a steam oven provided in an embodiment of the present invention;

[0077] Figure 2 is a flow chart of steps of another temperature control method of a steam oven provided in an embodiment of the present invention;

[0078] Figure 3 is a structural block diagram of a temperature control device for a steam oven provided in an embodiment of the present invention;

[0079] Figure 4 is a block diagram of a steam oven provided in an embodiment of the present invention;

[0080] Figure 5 is a schematic diagram of a computer-readable medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0081] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0082] To facilitate understanding of the technical solutions and technical effects of the embodiments of the present invention, the relevant technologies of the present invention are briefly described below.

[0083] As modern families have increasingly higher requirements for cooking quality, steam ovens, as integrated and intelligent kitchen appliances, have gradually become popular. In related technologies, steam ovens usually use PID (Proportional-Integral-Derivative) control algorithms for temperature control. Although the PID control algorithm achieves relative temperature stability to a certain extent when performing temperature control. However, in the face of complex and changeable cooking environments, steam ovens have problems such as slow response speed, large overshoot, and high energy consumption. Among them, slow response speed refers to the PID control algorithm's insufficient response to temperature changes, and it cannot quickly adjust the temperature of the steam oven to the target temperature; overshoot refers to the fact that in the process of the PID control algorithm adjusting the temperature to the target temperature, the temperature of the steam oven may first exceed the set target temperature, and then fall back to the target temperature; large overshoot refers to the fact that the temperature of the steam oven will first exceed the target temperature by a large margin.

[0084] In addition, the PID control algorithm makes it difficult for the steam oven to maintain a constant temperature for a long time to ensure the cooking quality. In the process of using the PID control algorithm to control the steam oven to maintain a constant temperature for cooking, it is also difficult to maximize the use of energy.

[0085] Reference Figure 1 , shows a flow chart of the steps of a temperature control method for a steam oven provided in an embodiment of the present invention, which is applied to a steam oven, wherein the steam oven includes a variable frequency heating device, wherein the variable frequency heating device is a graphene heating device and / or an eddy current heating device, and specifically may include the following steps:

[0086] Step 101, using a preset temperature sensor to collect the current temperature of the steam oven;

[0087] In the embodiment of the present invention, the steam oven includes a variable frequency heating device, which may be a graphene heating device and / or an eddy current heating device. Therefore, the steam oven is a variable frequency oven.

[0088] In the embodiment of the present invention, a temperature sensor, a humidity sensor and a current sensor may be provided inside the steam oven. The temperature sensor may collect the current temperature inside the steam oven, the humidity sensor may collect the current humidity inside the steam oven, and the current sensor may collect the current current inside the steam oven.

[0089] Step 102, using a preset current sensor and a preset voltage sensor to respectively collect the working current and the working voltage of the variable frequency heating device;

[0090] In an embodiment of the present invention, a temperature sensor, a current sensor, and a voltage sensor may be provided on the variable frequency heating device of the steam oven. Among them, the temperature sensor may collect the working temperature of the variable frequency heating device, the current sensor may collect the working current of the variable frequency heating device, and the voltage sensor may collect the working voltage of the variable frequency heating device. The current power of the variable frequency heating device may be obtained by multiplying the working voltage and the working current. The data of the working temperature, the current power, the working current, and the working voltage may reflect the real-time working status and efficiency of the variable frequency heating device. It should be noted that the data acquisition frequency of the sensors inside the steam oven and the sensors on the variable frequency heating device may be reasonably set according to the working characteristics and control requirements of the steam oven to ensure the real-time and accuracy of the data acquisition. For example, data may be collected once per second to quickly respond to environmental changes.

[0091] Step 103, based on the current temperature, the working current, the working voltage and the preset target temperature of the steam oven, a power control strategy of the variable frequency heating device is obtained by using a preset control strategy generation model;

[0092] In an embodiment of the present invention, a control strategy can be used to generate a model based on the current temperature of the steam oven, the preset target temperature, the working current and the working voltage of the variable frequency heating device to obtain a power control strategy for the variable frequency heating device.

[0093] In some embodiments of the present invention, the power control strategy of the variable frequency heating device is obtained by using a preset control strategy generation model based on the current temperature, the working current, the working voltage and the preset target temperature of the steam oven, including:

[0094] Using the working current and the working voltage, obtaining the current power of the variable frequency heating device;

[0095] The current temperature, the current power and the target temperature are input into the control strategy generation model to obtain the power control strategy of the variable frequency heating device.

[0096] In the embodiment of the present invention, the working voltage and the working current can be multiplied to obtain the current power of the variable frequency heating device. If I represents the working current, V represents the working voltage, and P represents the current power, the power calculation formula of the current power is:

[0097] P=IV

[0098] In the embodiment of the present invention, the current power of the variable frequency heating device, the current temperature of the steam oven and the preset target temperature are input into the control strategy generation model to obtain the power control strategy of the variable frequency heating device.

[0099] Among them, the current power of the variable frequency heating device, the current temperature of the steam oven and the preset target temperature can be used as the state space input of the control strategy generation model. The design of this state space should fully consider the actual operation and control requirements of the steam oven, and be used to predict and determine the next control strategy.

[0100] The power control strategy can be used as the action space output of the control strategy generation model. The design of the action space ensures that the temperature of the steam oven can be accurately controlled. The action space output also includes the power adjustment range of the face-changing heating device.

[0101] In some embodiments of the invention, the method comprises:

[0102] Obtaining a training current temperature of the steam oven, a training target temperature, a training current frequency of the variable frequency heating device, and a training power control strategy for converting the training current temperature into the training target temperature;

[0103] The preset machine learning model is trained using the training current temperature, the training target temperature, the training current frequency and the training power control strategy to obtain the control strategy generation model.

[0104] In the embodiment of the present invention, the training current temperature of the steam oven, the training target temperature, the training current frequency of the variable frequency heating device, and the training power control strategy for converting the training current temperature into the training target temperature can be obtained. It should be noted that these training data include simulation data and actual operation data of the steam oven.

[0105] Then, these training data are preprocessed by filtering, denoising and other preprocessing operations to reduce noise interference and improve data quality. At the same time, the training data is normalized to adapt it to the input requirements of the machine learning model. Then, the preprocessed training current temperature, training target temperature, training current frequency and training power control strategy are used to train the preset machine learning model to obtain the control strategy generation model. During the training process of the machine learning model, the model parameters and training strategy are continuously adjusted to improve the generalization ability and control performance of the model.

[0106] It should be noted that, according to the complexity and real-time requirements of the steam oven temperature control, advanced reinforcement learning algorithms such as Deep Q-Networks (DQN) or Proximal Policy Optimization (PPO) can be used. These algorithms can perform well in processing continuous state and action spaces. The reinforcement learning algorithms of Deep Q-Networks or Proximal Policy Optimization are embedded in the machine learning model.

[0107] In a specific example, first, a virtual environment of a steam oven is constructed using simulation software to simulate different cooking scenarios and temperature changes. Then, the machine learning model is trained in the simulation environment, and the performance of the power control strategy output by the machine learning model in temperature regulation, temperature regulation response speed, and energy consumption during temperature regulation is improved by continuously adjusting the model parameters and training strategies.

[0108] Based on the simulation training, the trained model can be verified on the actual steam oven. By collecting the current temperature of the steam oven, the current power of the variable frequency heating device and other state parameters in real time and inputting these parameters into the model, the performance of the power control strategy output by the model in temperature regulation, temperature regulation speed and energy consumption during temperature regulation is observed. At the same time, the data of the model in actual operation is recorded, including indicators such as temperature control accuracy, temperature regulation response speed and energy consumption.

[0109] According to the verification results of the model on the actual steam oven, the parameters and training strategy of the model are adjusted. For example, the weight of the reward function can be adjusted to better balance the relationship between temperature control and energy consumption; the neural network structure of the model can be optimized to improve the generalization ability of the model; the data collection frequency and preprocessing method can also be adjusted to improve the accuracy and real-time performance of the data. These adjustments can be flexibly combined and optimized according to the specific verification results and actual needs. By continuously iterating the above process, the performance of the power control strategy output by the model can be gradually optimized, so that it can achieve better constant temperature energy saving effects on the actual steam oven.

[0110] In some embodiments of the present invention, the method of training a preset machine learning model by using the training current temperature, the training target temperature, the training current frequency, and the training power control strategy to obtain the control strategy generation model includes:

[0111] During the training process of the machine learning model, obtaining the inference power control strategy of the machine learning model for the training current temperature, the training target temperature and the training current frequency output;

[0112] Obtain at least one of the transition temperature, transition speed and transition energy consumption corresponding to the inference power control strategy; the transition temperature is the temperature obtained by adjusting the output power of the variable frequency heating device by using the inference power control strategy so that the training current temperature is transformed; the transition speed is the speed at which the training current temperature is transformed into the transition temperature; the transition energy consumption is the energy consumption of the variable frequency heating device during the process of transforming the training current temperature into the transition temperature;

[0113] The machine learning model is trained based on at least one of the transition temperature, the transition speed, and the transition energy consumption.

[0114] In an embodiment of the present invention, during the training process of the machine learning model, a reasonable reward function can be designed to guide the training process of the machine learning model.

[0115] Specifically, the inference power control strategy of the machine learning model for the training current temperature, the training target temperature, and the training current frequency output can be obtained. Then, at least one of the transition temperature, transition speed, and transition energy consumption corresponding to the inference power control strategy is obtained. Among them, the transition temperature is the temperature obtained by adjusting the output power of the variable frequency heating device using the inference power control strategy so that the training current temperature is transformed; the transition speed is the speed at which the training current temperature is transformed into the transition temperature; and the transition energy consumption is the energy consumption of the variable frequency heating device during the process of transforming the training current temperature into the transition temperature.

[0116] In an embodiment of the present invention, a machine learning model can be trained based on at least one of the transition temperature, transition speed, and transition energy consumption.

[0117] In some embodiments of the present invention, the training of the machine learning model based on at least one of the transition temperature, the transition speed, and the transition energy consumption comprises:

[0118] If the difference between the transition temperature and the training target temperature is less than a preset difference threshold, providing a preset first reward for the machine learning model;

[0119] If the transition speed is higher than a preset speed threshold, providing a preset second reward to the machine learning model;

[0120] If the conversion energy consumption is less than a preset energy consumption threshold, providing a preset third reward for the machine learning model;

[0121] The machine learning model is trained using at least one of the first reward, the second reward, and the third reward.

[0122] In an embodiment of the present invention, a machine learning model can be trained based on at least one of the transition temperature, transition speed, and transition energy consumption.

[0123] Specifically, if the difference between the transition temperature and the training target temperature is less than a preset difference threshold, a preset first reward is provided for the machine learning model; if the transition speed is higher than a preset speed threshold, a preset second reward is provided for the machine learning model; if the transition energy consumption is less than a preset energy consumption threshold, a preset third reward is provided for the machine learning model; the machine learning model can be trained using at least one of the first reward, the second reward, and the third reward. The first reward, the second reward, and the third reward can be set, adjusted, and optimized according to specific control requirements and goals.

[0124] In a specific example, when the deviation between the transition temperature and the training target temperature is small, for example, the deviation is within ±1°C (degrees Celsius), a higher reward is given to encourage the machine learning model to improve the accuracy of temperature control. When the variable frequency heating device is in a low power consumption state, for example, the transition energy consumption is lower than the preset energy consumption threshold, a certain reward is given to encourage the machine learning model to reduce energy consumption. When the response time of temperature adjustment is short, for example, the time from temperature change to reaching the transition temperature is less than a certain set value, rewards can also be given to encourage the machine learning model to improve the response speed.

[0125] In some embodiments of the present invention, the using at least one of the first reward, the second reward, and the third reward to train the machine learning model includes:

[0126] Configuring a preset temperature weight, speed weight, and energy consumption weight for the machine learning model;

[0127] Based on the temperature weight, the first reward, the speed weight, the second reward, the energy consumption weight, and the third reward, obtaining a target reward of the machine learning model;

[0128] The machine learning model is trained using the target reward.

[0129] In an embodiment of the present invention, a preset temperature weight, speed weight, and energy consumption weight may be configured for the machine learning model. Then, based on the temperature weight, the first reward, the speed weight, the second reward, the energy consumption weight, and the third reward, a reward function may be used to obtain a target reward for the machine learning model.

[0130] Among them, the reward function can be:

[0131] R=W1*R1+W2*R2+W3*R3

[0132] Among them, R is the target reward, W1 is the temperature weight, R1 is the first reward, W2 is the speed weight, R2 is the second reward, W3 is the energy consumption weight, and R3 is the third reward. This reward function fully considers factors such as temperature control accuracy, response speed, and energy consumption.

[0133] In an embodiment of the present invention, target rewards can be used to train a machine learning model.

[0134] Step 104: Based on the power control strategy, the current temperature of the steam oven is converted to the target temperature by adjusting the output power of the variable frequency heating device.

[0135] In the embodiment of the present invention, the power control strategy includes a power adjustment instruction. According to the power adjustment instruction, by adjusting the output power of the variable frequency heating device, the current temperature of the steam oven can be converted to the target temperature, and constant temperature control at the target temperature can be achieved.

[0136] In some embodiments of the invention, the method comprises:

[0137] Based on the power control strategy, the switching state of the graphene heating device and / or the eddy current heating device is adjusted.

[0138] In an embodiment of the present invention, the power control strategy includes an adjustment instruction for the on / off state of the variable frequency heating device. According to the adjustment instruction for the on / off state of the variable frequency heating device, the on / off state of the graphene heating device and / or the eddy current heating device can be adjusted to convert the current temperature of the steam oven to the target temperature and realize constant temperature control at the target temperature.

[0139] In some embodiments of the present invention, the step of changing the current temperature of the steam oven to the target temperature by adjusting the output power of the variable frequency heating device comprises:

[0140] Obtain at least one of a temperature to be processed, a speed to be processed, and an energy consumption to be processed corresponding to the power control strategy; the temperature to be processed is a temperature obtained by adjusting the output power of the variable frequency heating device by using the power control strategy so that the current temperature is transformed; the speed to be processed is a speed at which the current temperature is transformed into the temperature to be processed; the energy consumption to be processed is the energy consumption of the variable frequency heating device during the process of the current temperature being transformed into the temperature to be processed;

[0141] The control strategy generation model is trained based on at least one of the temperature to be processed, the speed to be processed, and the energy consumption to be processed.

[0142] In an embodiment of the present invention, the training of the machine learning model for the steam oven is carried out at the manufacturer of the steam oven. After the machine learning model training is completed to obtain the control strategy generation model, the steam oven can be operated at the user's home. During the operation of the steam oven at the user's home, the sensors in the steam oven and the sensors of the variable frequency heating device can continue to collect data on the actual operation of the steam oven, and input these data into the control strategy generation model as feedback information, and perform a new round of iterative optimization and training on the control strategy generation model. Through continuous trial and error and adjustment, the power control strategy output by the control strategy generation model gradually approaches the optimal state.

[0143] In the embodiment of the present invention, during the training process of the control strategy generation model, the power control strategy output by the control strategy generation model can be obtained, and at least one of the to-be-processed temperature, to-be-processed speed, and to-be-processed energy consumption corresponding to the power control strategy can be obtained. The to-be-processed temperature is the temperature obtained by adjusting the output power of the variable frequency heating device using the power control strategy so that the current temperature is transformed; the to-be-processed speed is the speed at which the current temperature is transformed into the to-be-processed temperature; and the to-be-processed energy consumption is the energy consumption of the variable frequency heating device during the process of the current temperature being transformed into the to-be-processed temperature.

[0144] In the embodiment of the present invention, based on at least one of the temperature to be processed, the speed to be processed, and the energy consumption to be processed, the optimization control strategy generation model can be trained.

[0145] In some embodiments of the present invention, the step of training the control strategy generation model based on at least one of the temperature to be processed, the speed to be processed, and the energy consumption to be processed includes:

[0146] Based on the to-be-processed temperature, the to-be-processed speed, and the to-be-processed energy consumption, adjusting at least one of the temperature weight, the speed weight, and the energy consumption weight;

[0147] Based on at least one of the adjusted temperature weight, the speed weight, and the energy consumption weight, obtaining a reward during the training process of the control strategy generation model;

[0148] The control policy generation model is trained using the reward.

[0149] In the embodiment of the present invention, during the operation of the steam oven in the user's home, the sensors in the steam oven and the sensors of the variable frequency heating device can continue to collect data on the actual operation of the steam oven, and use these data to monitor the operating status of the steam oven in real time, evaluate the actual effect of the power control strategy output by the control strategy generation model, and pay attention to the changes in key indicators such as the accuracy of temperature adjustment, the response speed of temperature adjustment, and energy consumption during temperature adjustment. The control strategy generation model is optimized and adjusted according to the evaluation results of the key indicators, including adjusting the weight of the reward function, optimizing the model parameters, and adjusting the training strategy to improve the model performance. At the same time, the applicability and robustness tests in different cooking scenarios are paid attention to to ensure the wide applicability of the control strategy.

[0150] Therefore, in the training process of the control strategy generation model, at least one of the temperature weight, speed weight and energy consumption weight can be adjusted based on the temperature to be processed, the speed to be processed and the energy consumption to be processed. For example, if the difference between the temperature to be processed and the target temperature is large, the temperature weight can be increased; if the speed to be processed is slow, the speed weight can be increased; if the energy consumption to be processed is high, the energy consumption weight can be increased.

[0151] In the training process of the control strategy generation model, similar to the training process of the machine learning model, the first reward, the second reward and the third reward can be provided to the control strategy generation model according to the temperature to be processed, the speed to be processed and the energy consumption to be processed. Then, the target reward in the training process of the control strategy generation model is obtained by using the adjusted temperature weight, speed weight and energy consumption weight and the first reward, the second reward and the third reward. The control strategy generation model is trained using the target reward.

[0152] In an embodiment of the present invention, the current power and operating temperature of the variable frequency heating device, the current temperature, current humidity, current current, preset target temperature and preset target humidity of the steam oven can also be input into another control strategy generation model, and another power control strategy of the variable frequency heating device can be obtained. The other power control strategy is used to adjust the output power of the variable frequency heating device, so as to convert the current temperature into the target temperature and the current humidity into the target humidity. The state space input of the other control strategy generation model is the current power and operating temperature of the variable frequency heating device, the current temperature, current humidity, current current, preset target temperature and preset target humidity of the steam oven, and the action space output of the other control strategy generation model is the other power control strategy. The model training process corresponding to the other control strategy generation model in the manufacturer and the user's home is similar to the training process of the control strategy generation model, and the present invention will not repeat it here.

[0153] In the embodiment of the present invention, the steam oven has a safety mechanism, and when there is an abnormal situation in the steam oven, it can automatically shut down or take other protective measures to avoid safety accidents. During the operation of the steam oven, if any device on the steam oven is detected to have a fault, an alarm will be issued and fault diagnosis will be performed.

[0154] During the training process of the machine learning model or the control strategy generation model, the machine learning model or the control strategy generation model has a redundant design. The parameters of the machine learning model or the control strategy generation model before training are the initial parameters. If during the training process of the machine learning model or the control strategy generation model, the performance of the model becomes worse and worse while the initial parameters change, the initial parameters can be used to replace the changed parameters, and then continue training.

[0155] In an embodiment of the present invention, the steam oven includes a variable frequency heating device, and the variable frequency heating device is a graphene heating device and / or an eddy current heating device. The current temperature of the steam oven is collected using a preset temperature sensor, and the working current and working voltage of the variable frequency heating device are collected using a preset current sensor and a voltage sensor, respectively. Based on the current temperature, working current, working voltage and preset target temperature of the steam oven, a power control strategy of the variable frequency heating device is obtained using a preset control strategy generation model; based on the power control strategy, the current temperature of the steam oven is converted to the target temperature by adjusting the output power of the variable frequency heating device. By using the power control strategy output by the control strategy generation model, and cooperating with the variable frequency heating device to adjust the output power of the variable frequency heating device, the current temperature of the steam oven can be converted to the target temperature, and the steam oven can be controlled to operate at a constant temperature according to the target temperature.

[0156] In the embodiment of the present invention, the control strategy generation model is obtained by training the machine learning model. In the training process of the machine learning model and the optimization process of the control strategy generation model, the temperature regulation accuracy of the steam oven, the response speed of the temperature regulation and the energy consumption during the temperature regulation process are considered, and the problems of slow response speed, large overshoot, high energy consumption, etc. in the temperature control method of the steam oven are solved. By introducing a constant temperature energy-saving method based on reinforcement learning and combining graphene heating devices and eddy current heat changing devices of different powers, fast, accurate and energy-saving temperature control of the steam oven in a complex cooking environment is achieved.

[0157] In an embodiment of the present invention, by training the model with a reinforcement learning algorithm, it is possible to continuously try and error and optimize the control strategy, and by combining the variable frequency heating technology, the steam oven can maintain a more stable and accurate temperature during the cooking process. In addition, the reinforcement learning algorithm during the training process can quickly adapt to environmental changes, reduce overshoot, and combine with efficient heating devices (such as graphene heating devices and eddy current heat devices) to shorten the response time of temperature regulation. In an embodiment of the present invention, by optimizing the control strategy to generate a model, the power control strategy can automatically adjust the power and switch state of the graphene heating device and the eddy current heat device according to the needs of different cooking stages, ensuring that the steam oven can maintain the optimal temperature state in different cooking stages, and reducing unnecessary energy waste, achieving heating on demand, and achieving energy saving and consumption reduction of the steam oven during constant temperature cooking.

[0158] Reference Figure 2 , shows a flow chart of steps of another temperature control method of a steam oven provided in an embodiment of the present invention, which may specifically include the following steps:

[0159] Step 201: data collection and preprocessing.

[0160] Obtain the training current temperature of the steam oven, the training target temperature, the training current frequency of the variable frequency heating device, and the training power control strategy for converting the training current temperature into the training target temperature.

[0161] Step 202: Model construction and training.

[0162] By training the current temperature, training the target temperature, training the current frequency and training the power control strategy, a preset machine learning model is trained to obtain a control strategy generation model.

[0163] Step 203: Model embedded deployment.

[0164] Embed the control strategy generation model into the steam oven.

[0165] Step 204: The user uses the system and uses the data from the user's use process as training data to train the model.

[0166] When the control strategy generation model is running in the user's home, the state space input data of the running process is collected and used to train and optimize the control strategy generation model.

[0167] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0168] Reference Figure 3 , shows a structural block diagram of a temperature control device for a steam oven provided in an embodiment of the present invention, which is applied to a steam oven. The steam oven includes a variable frequency heating device, which is a graphene heating device and / or an eddy current heating device, and specifically may include the following modules:

[0169] The temperature acquisition module 301 is used to acquire the current temperature of the steam oven using a preset temperature sensor;

[0170] The voltage acquisition module 302 is used to respectively acquire the working current and working voltage of the variable frequency heating device using a preset current sensor and a preset voltage sensor;

[0171] A strategy obtaining module 303 is used to obtain a power control strategy of the variable frequency heating device using a preset control strategy generation model based on the current temperature, the working current, the working voltage and the preset target temperature of the steam oven;

[0172] The power adjustment module 304 is used to change the current temperature of the steam oven to the target temperature by adjusting the output power of the variable frequency heating device based on the power control strategy.

[0173] In an optional embodiment of the present invention, the strategy obtaining module includes:

[0174] A frequency acquisition submodule, used to acquire the current power of the variable frequency heating device by using the working current and the working voltage;

[0175] The strategy acquisition submodule is used to input the current temperature, the current power and the target temperature into the control strategy generation model to obtain the power control strategy of the variable frequency heating device.

[0176] In an optional embodiment of the present invention, the device comprises:

[0177] A training data acquisition module, used to acquire a training current temperature of the steam oven, a training target temperature, a training current frequency of the variable frequency heating device, and a training power control strategy for converting the training current temperature into the training target temperature;

[0178] A training module is used to train a preset machine learning model using the training current temperature, the training target temperature, the training current frequency and the training power control strategy to obtain the control strategy generation model.

[0179] In an optional embodiment of the present invention, the training module includes:

[0180] An inference power control strategy acquisition submodule, used to obtain the inference power control strategy output by the machine learning model for the training current temperature, the training target temperature and the training current frequency during the training process of the machine learning model;

[0181] A transition temperature acquisition submodule is used to obtain at least one of the transition temperature, transition speed and transition energy consumption corresponding to the inference power control strategy; the transition temperature is the temperature obtained by adjusting the output power of the variable frequency heating device by using the inference power control strategy so that the training current temperature is transformed; the transition speed is the speed at which the training current temperature is transformed into the transition temperature; the transition energy consumption is the energy consumption of the variable frequency heating device during the process of transforming the training current temperature into the transition temperature;

[0182] A training submodule is used to train the machine learning model based on at least one of the transition temperature, the transition speed and the transition energy consumption.

[0183] In an optional embodiment of the present invention, the training submodule includes:

[0184] a first reward providing unit, configured to provide a preset first reward to the machine learning model if the difference between the transition temperature and the training target temperature is less than a preset difference threshold;

[0185] A second reward providing unit, configured to provide a preset second reward to the machine learning model if the transition speed is higher than a preset speed threshold;

[0186] A third reward providing unit, configured to provide a preset third reward for the machine learning model if the conversion energy consumption is less than a preset energy consumption threshold;

[0187] A training unit is used to train the machine learning model using at least one of the first reward, the second reward, and the third reward.

[0188] In an optional embodiment of the present invention, the training unit includes:

[0189] A weight configuration subunit, used to configure a preset temperature weight, speed weight, and energy consumption weight for the machine learning model;

[0190] a target reward acquisition subunit, configured to acquire a target reward of the machine learning model based on the temperature weight, the first reward, the speed weight, the second reward, the energy consumption weight, and the third reward;

[0191] A training subunit is used to train the machine learning model using the target reward.

[0192] In an optional embodiment of the present invention, the power adjustment module includes:

[0193] The to-be-processed temperature acquisition submodule is used to obtain at least one of the to-be-processed temperature, the to-be-processed speed, and the to-be-processed energy consumption corresponding to the power control strategy; the to-be-processed temperature is the temperature obtained by adjusting the output power of the variable frequency heating device by using the power control strategy so that the current temperature is transformed; the to-be-processed speed is the speed at which the current temperature is transformed into the to-be-processed temperature; the to-be-processed energy consumption is the energy consumption of the variable frequency heating device during the process of the current temperature being transformed into the to-be-processed temperature;

[0194] The control strategy generation model training submodule is used to train the control strategy generation model based on at least one of the temperature to be processed, the speed to be processed and the energy consumption to be processed.

[0195] In an optional embodiment of the present invention, the control strategy generation model training submodule includes:

[0196] A weight adjustment unit, configured to adjust at least one of the temperature weight, the speed weight, and the energy consumption weight based on the to-be-processed temperature, the to-be-processed speed, and the to-be-processed energy consumption;

[0197] A reward acquisition unit, configured to acquire a reward in a training process of the control strategy generation model based on at least one of the adjusted temperature weight, the speed weight, and the energy consumption weight;

[0198] A control strategy generation model training unit is used to train the control strategy generation model using the reward.

[0199] In an optional embodiment of the present invention, the device comprises:

[0200] A switch state adjustment module is used to adjust the switch state of the graphene heating device and / or the eddy current heating device based on the power control strategy.

[0201] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0202] In addition, the embodiment of the present invention further provides a steam oven, such as Figure 4 As shown, it includes a processor 401, a communication interface 402, a memory 403 and a communication bus 404, wherein the processor 401, the communication interface 402, and the memory 403 communicate with each other through the communication bus 404.

[0203] Memory 403, used for storing computer programs;

[0204] The processor 401 is used to execute the program stored in the memory 403 to implement the following steps:

[0205] Using a preset temperature sensor to collect the current temperature of the steam oven;

[0206] Using a preset current sensor and voltage sensor to respectively collect the working current and working voltage of the variable frequency heating device;

[0207] Based on the current temperature, the working current, the working voltage and the preset target temperature of the steam oven, a power control strategy of the variable frequency heating device is obtained by using a preset control strategy generation model;

[0208] Based on the power control strategy, the current temperature of the steam oven is converted to the target temperature by adjusting the output power of the variable frequency heating device.

[0209] In an optional embodiment of the present invention, the power control strategy of the variable frequency heating device is obtained by using a preset control strategy generation model based on the current temperature, the working current, the working voltage and the preset target temperature of the steam oven, including:

[0210] Using the working current and the working voltage, obtaining the current power of the variable frequency heating device;

[0211] The current temperature, the current power and the target temperature are input into the control strategy generation model to obtain the power control strategy of the variable frequency heating device.

[0212] In an optional embodiment of the present invention, the method includes:

[0213] Obtaining a training current temperature of the steam oven, a training target temperature, a training current frequency of the variable frequency heating device, and a training power control strategy for converting the training current temperature into the training target temperature;

[0214] The preset machine learning model is trained using the training current temperature, the training target temperature, the training current frequency and the training power control strategy to obtain the control strategy generation model.

[0215] In an optional embodiment of the present invention, the method of training a preset machine learning model by using the training current temperature, the training target temperature, the training current frequency, and the training power control strategy to obtain the control strategy generation model includes:

[0216] During the training process of the machine learning model, obtaining the inference power control strategy of the machine learning model for the training current temperature, the training target temperature and the training current frequency output;

[0217] Obtain at least one of the transition temperature, transition speed and transition energy consumption corresponding to the inference power control strategy; the transition temperature is the temperature obtained by adjusting the output power of the variable frequency heating device by using the inference power control strategy so that the training current temperature is transformed; the transition speed is the speed at which the training current temperature is transformed into the transition temperature; the transition energy consumption is the energy consumption of the variable frequency heating device during the process of transforming the training current temperature into the transition temperature;

[0218] The machine learning model is trained based on at least one of the transition temperature, the transition speed, and the transition energy consumption.

[0219] In an optional embodiment of the present invention, the training of the machine learning model based on at least one of the transition temperature, the transition speed, and the transition energy consumption comprises:

[0220] If the difference between the transition temperature and the training target temperature is less than a preset difference threshold, providing a preset first reward for the machine learning model;

[0221] If the transition speed is higher than a preset speed threshold, providing a preset second reward to the machine learning model;

[0222] If the conversion energy consumption is less than a preset energy consumption threshold, providing a preset third reward for the machine learning model;

[0223] The machine learning model is trained using at least one of the first reward, the second reward, and the third reward.

[0224] In an optional embodiment of the present invention, the using at least one of the first reward, the second reward, and the third reward to train the machine learning model includes:

[0225] Configuring a preset temperature weight, speed weight, and energy consumption weight for the machine learning model;

[0226] Based on the temperature weight, the first reward, the speed weight, the second reward, the energy consumption weight, and the third reward, obtaining a target reward of the machine learning model;

[0227] The machine learning model is trained using the target reward.

[0228] In an optional embodiment of the present invention, the step of changing the current temperature of the steam oven to the target temperature by adjusting the output power of the variable frequency heating device comprises:

[0229] Obtain at least one of a temperature to be processed, a speed to be processed, and an energy consumption to be processed corresponding to the power control strategy; the temperature to be processed is a temperature obtained by adjusting the output power of the variable frequency heating device by using the power control strategy so that the current temperature is transformed; the speed to be processed is a speed at which the current temperature is transformed into the temperature to be processed; the energy consumption to be processed is the energy consumption of the variable frequency heating device during the process of the current temperature being transformed into the temperature to be processed;

[0230] The control strategy generation model is trained based on at least one of the temperature to be processed, the speed to be processed, and the energy consumption to be processed.

[0231] In an optional embodiment of the present invention, the training of the control strategy generation model based on at least one of the temperature to be processed, the speed to be processed, and the energy consumption to be processed includes:

[0232] Based on the to-be-processed temperature, the to-be-processed speed, and the to-be-processed energy consumption, adjusting at least one of the temperature weight, the speed weight, and the energy consumption weight;

[0233] Based on at least one of the adjusted temperature weight, the speed weight, and the energy consumption weight, obtaining a reward during the training process of the control strategy generation model;

[0234] The control policy generation model is trained using the reward.

[0235] In an optional embodiment of the present invention, the method includes:

[0236] Based on the power control strategy, the switching state of the graphene heating device and / or the eddy current heating device is adjusted.

[0237] The communication bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0238] The communication interface is used for communication between the above terminal and other devices.

[0239] The memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0240] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0241] like Figure 5 As shown, in another embodiment provided by the present invention, a computer-readable storage medium 501 is also provided, in which instructions are stored. When the computer-readable storage medium 501 is run on a computer, the computer executes a temperature control method for a steam oven described in the above embodiment.

[0242] In another embodiment of the present invention, a computer program product including instructions is provided. When the computer program product is run on a computer, the computer executes the temperature control method of a steam oven described in the above embodiment.

[0243] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by 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 a computer, the process or function described in 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.). The computer-readable storage medium can be any available medium that a computer can access 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.

[0244] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0245] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0246] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A method for controlling the temperature of a steam oven, characterized in that: Applied to a steam oven, the steam oven includes a variable frequency heating device, the variable frequency heating device is a graphene heating device and / or an eddy current heating device, and the method includes: Using a preset temperature sensor to collect the current temperature of the steam oven; Using a preset current sensor and voltage sensor to respectively collect the working current and working voltage of the variable frequency heating device; Based on the current temperature, the working current, the working voltage and the preset target temperature of the steam oven, a power control strategy of the variable frequency heating device is obtained by using a preset control strategy generation model; Based on the power control strategy, the current temperature of the steam oven is converted to the target temperature by adjusting the output power of the variable frequency heating device.

2. The method according to claim 1, characterized in that The power control strategy of the variable frequency heating device is obtained by using a preset control strategy generation model based on the current temperature, the working current, the working voltage and the preset target temperature of the steam oven, including: Using the working current and the working voltage, obtaining the current power of the variable frequency heating device; The current temperature, the current power and the target temperature are input into the control strategy generation model to obtain the power control strategy of the variable frequency heating device.

3. The method according to claim 1, characterized in that The method comprises: Obtaining a training current temperature of the steam oven, a training target temperature, a training current frequency of the variable frequency heating device, and a training power control strategy for converting the training current temperature into the training target temperature; The preset machine learning model is trained using the training current temperature, the training target temperature, the training current frequency and the training power control strategy to obtain the control strategy generation model.

4. The method according to claim 3, characterized in that The method of training a preset machine learning model by using the training current temperature, the training target temperature, the training current frequency, and the training power control strategy to obtain the control strategy generation model includes: During the training process of the machine learning model, obtaining the inference power control strategy of the machine learning model for the training current temperature, the training target temperature and the training current frequency output; Obtain at least one of the transition temperature, transition speed and transition energy consumption corresponding to the inference power control strategy; the transition temperature is the temperature obtained by adjusting the output power of the variable frequency heating device by using the inference power control strategy so that the training current temperature is transformed; the transition speed is the speed at which the training current temperature is transformed into the transition temperature; the transition energy consumption is the energy consumption of the variable frequency heating device during the process of transforming the training current temperature into the transition temperature; The machine learning model is trained based on at least one of the transition temperature, the transition speed, and the transition energy consumption.

5. The method according to claim 4, characterized in that The training of the machine learning model based on at least one of the transition temperature, the transition speed, and the transition energy consumption comprises: If the difference between the transition temperature and the training target temperature is less than a preset difference threshold, providing a preset first reward for the machine learning model; If the transition speed is higher than a preset speed threshold, providing a preset second reward to the machine learning model; If the conversion energy consumption is less than a preset energy consumption threshold, providing a preset third reward for the machine learning model; The machine learning model is trained using at least one of the first reward, the second reward, and the third reward.

6. The method according to claim 5, characterized in that The using at least one of the first reward, the second reward, and the third reward to train the machine learning model comprises: Configuring a preset temperature weight, speed weight, and energy consumption weight for the machine learning model; Based on the temperature weight, the first reward, the speed weight, the second reward, the energy consumption weight, and the third reward, obtaining a target reward of the machine learning model; The machine learning model is trained using the target reward.

7. The method according to claim 6, characterized in that The step of changing the current temperature of the steam oven to the target temperature by adjusting the output power of the variable frequency heating device comprises: Obtain at least one of a temperature to be processed, a speed to be processed, and an energy consumption to be processed corresponding to the power control strategy; the temperature to be processed is a temperature obtained by adjusting the output power of the variable frequency heating device by using the power control strategy so that the current temperature is transformed; the speed to be processed is a speed at which the current temperature is transformed into the temperature to be processed; the energy consumption to be processed is the energy consumption of the variable frequency heating device during the process of the current temperature being transformed into the temperature to be processed; The control strategy generation model is trained based on at least one of the temperature to be processed, the speed to be processed, and the energy consumption to be processed.

8. The method according to claim 7, characterized in that The step of training the control strategy generation model based on at least one of the temperature to be processed, the speed to be processed, and the energy consumption to be processed comprises: Based on the to-be-processed temperature, the to-be-processed speed, and the to-be-processed energy consumption, adjusting at least one of the temperature weight, the speed weight, and the energy consumption weight; Based on at least one of the adjusted temperature weight, the speed weight, and the energy consumption weight, obtaining a reward during the training process of the control strategy generation model; The control policy generation model is trained using the reward.

9. The method according to claim 1, characterized in that: The method comprises: Based on the power control strategy, the switching state of the graphene heating device and / or the eddy current heating device is adjusted.

10. A temperature control device for a steam oven, characterized in that: Applied to a steam oven, the steam oven includes a variable frequency heating device, the variable frequency heating device is a graphene heating device and / or an eddy current heating device, and the device includes: A temperature acquisition module, used to acquire the current temperature of the steam oven using a preset temperature sensor; A voltage acquisition module, used to respectively acquire the working current and working voltage of the variable frequency heating device using a preset current sensor and voltage sensor; A strategy obtaining module, used to obtain the power control strategy of the variable frequency heating device by using a preset control strategy generation model based on the current temperature, the working current, the working voltage and the preset target temperature of the steam oven; A power adjustment module is used to change the current temperature of the steam oven to the target temperature by adjusting the output power of the variable frequency heating device based on the power control strategy.

11. A steam oven, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used to store computer programs; The processor is used to implement the method according to any one of claims 1 to 9 when executing the program stored in the memory.

12. One or more computer-readable media having instructions stored thereon, which when executed by one or more processors cause the processors to perform the method according to any one of claims 1 to 9.

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