Oven energy-saving control method based on multi-sensor detection and oven

Abstract

The invention discloses an oven energy-saving control method based on multi-sensor detection and an oven. The energy-saving control method comprises the steps that A, cooking set time information is received, and a monitoring module is called to record internal initial environment parameters of the oven; b, the execution module controls the heating module to start heating; c, the monitoring module collects environmental parameters in the oven in real time and transmits the environmental parameters to the processing module; d, the processing module is provided with a prediction model, whether the internal environment parameters of the oven reach a prediction model threshold value or not is monitored in real time, if the internal environment parameters reach the prediction model threshold value, the execution module is controlled to generate a heating stopping instruction, and the heating module is controlled to stop heating; and E, when the detection module monitors that the reduction rate of the temperature in the oven exceeds a threshold value, the execution module generates a heating instruction to enable the heating module to heat, the step B to the step D are repeated, and on the contrary, the cooking time is waited to be over. Compared with the prior art, the method has the advantages that the environment parameters are calculated and predicted through the prediction model, and intelligent and dynamic energy-saving control is achieved.

Description

Technical Field

[0001] This invention relates to the field of oven control, and more particularly to an oven energy-saving control method and oven based on multi-sensor detection. Background Technology

[0002] In daily life, ovens are widely used for cooking various foods. With basic functions generally met, consumers are increasingly focusing on energy efficiency when choosing an oven. Currently, common oven energy-saving technologies mainly focus on hardware improvements, such as using high-efficiency insulation materials to reduce heat loss, adding heat recovery devices to utilize the residual heat from exhaust gases, using temperature probes to stop heating once the food reaches the set temperature, or automatically switching to a fixed energy-saving program with lower power after preheating. However, these methods all have certain limitations: hardware improvements such as insulation materials and heat recovery devices increase production costs; temperature probe control requires more user intervention, and its main function is to prevent food from burning, not to actively optimize energy consumption; and fixed energy-saving programs lack flexibility and cannot be dynamically adjusted according to the specific conditions during cooking, thus limiting their energy-saving effect.

[0003] Based on the above, existing oven energy-saving technologies need further improvement. Summary of the Invention

[0004] The first objective of this invention is to overcome the shortcomings of the prior art and provide an oven energy-saving control method based on multi-sensor detection. By calculating and predicting the internal environmental parameters of the oven through a predictive model, the heating module stops heating at the stop time point, making full use of residual heat to complete the remaining cooking tasks and achieving dynamic energy saving.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: An oven energy-saving control method based on multi-sensor detection, characterized by the following steps: A. Receive cooking time setting information and retrieve the initial environmental parameters inside the oven recorded by the monitoring module; B. The execution module controls the heating module to start heating and begin the cooking task; C. The monitoring module collects environmental parameters inside the oven in real time and transmits them to the processing module; D. The processing module is equipped with a prediction model to monitor in real time whether the internal environmental parameters of the oven reach the prediction model threshold. If the prediction model threshold is reached, the execution module is controlled to generate a stop heating command and control the heating module to stop heating. E. When the detection module detects that the rate of temperature drop inside the oven exceeds the threshold, the execution module generates a heating command to heat the heating module and repeats steps B to D. Otherwise, it waits for the cooking time to end and sends a cooking completion signal.

[0006] Compared with existing technologies, this invention calculates and predicts the internal environmental parameters of the oven using a predictive model, and controls the heating module to enter a predictive shutdown state at the stop time point, thereby making full use of residual heat to complete the remaining cooking stage. This achieves intelligent and dynamic energy-saving control, which can adaptively adjust according to the actual cooking situation, effectively improving energy utilization efficiency. Moreover, this solution is implemented through software settings, without changing the original hardware structure of the oven. This not only avoids the increased costs caused by adding insulation layers, heat recovery devices, etc., but also reduces the complexity and cost of production and manufacturing, and has significant economic benefits and ease of promotion.

[0007] Preferably, the training steps of the prediction model include: a. Conduct multiple cooking experiments to collect data on food type, weight, set parameters, real-time sensor data, and raw data at the stop time point; b. Clean up outliers and missing values ​​in the original data; c. Align environmental parameters with timestamps, calculate the threshold of the prediction model, and construct the training dataset; d. Input the training dataset into the machine learning algorithm for iterative optimization and adjust the model parameters to reduce the error between the stopping point predicted by the model and the stopping point in the original data; e. Solidify the trained model into a file and store it in the processing module for later use.

[0008] Since the raw data collected may contain anomalies, it is necessary to clean and preprocess the data to make it effective data for training before it can be used for model training. Through multi-dimensional data fusion training, the prediction model can have the ability to generalize to different cooking scenarios and high-precision prediction performance, providing core algorithm support for dynamic energy-saving control and improving the intelligence level and adaptability of the system.

[0009] Preferably, the environmental parameters include at least one of the following: food type and weight, food color, set temperature and duration, real-time cavity temperature and humidity.

[0010] Preferably, the prediction model thresholds include the rate of temperature change, the rate of humidity change, the rate of weight change, and / or the rate of color change.

[0011] As the cooking process progresses, environmental parameters also change accordingly. Based on these changes, a prediction model threshold is calculated and used as the basis for deciding whether to stop heating or restart heating. With multiple parameter dimensions, the prediction is more accurate.

[0012] Preferably, the heating module includes a heating element and a cooling fan, and the execution module is connected to the heating element and the cooling fan respectively. When the execution module generates a stop heating command, the heating element and the cooling fan are turned off. The execution module realizes integrated and precise control of heating and heat dissipation functions, ensuring that the oven can quickly switch working states according to the prediction results, thus ensuring the continuity and reliability of the cooking process.

[0013] Preferably, the system further includes a data acquisition module electrically connected to the monitoring module and the processing module. The data acquisition module converts environmental parameters into digital signals and transmits them to the processing module. Since the environmental parameters are analog signals, and the processing module can only recognize digital signals, a data acquisition module is added to process and convert the analog signals.

[0014] Preferably, the processing module includes a processor MCU connected to the data acquisition module and a memory. The prediction model is stored in the memory. The execution module is connected to the processor MCU. After receiving the digital signal, the processor MCU calls the prediction model to calculate and predict the digital signal.

[0015] Preferably, the data acquisition module includes a signal circuit and an analog-to-digital converter. The signal circuit receives and processes environmental parameters, and the analog-to-digital converter converts the processed environmental parameters into digital signals and transmits them to the processor MCU.

[0016] Preferably, the monitoring module includes a temperature sensor, a humidity sensor, a weight sensor, and / or an optical sensor. The temperature sensor and humidity sensor are used to monitor the temperature and humidity inside the cavity, respectively, and the weight sensor and optical sensor are used to monitor the weight and color of the food, respectively. Multi-sensor collaborative monitoring comprehensively acquires key physical parameters during the cooking process, providing rich and accurate input data for the prediction model, thereby supporting the model to make energy-saving decisions that are more in line with the actual cooking conditions and improving control accuracy and effectiveness.

[0017] Another objective of this invention is to provide an oven that stores an oven energy-saving control method based on multi-sensor detection; it is smarter, more energy-efficient, and less costly. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the present invention.

[0019] Figure 2 This is a schematic diagram of the procedure of the present invention.

[0020] Figure 3 This is a schematic diagram of the training steps of the prediction model of the present invention. Detailed Implementation

[0021] In the description of this invention, it should be understood that the orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "horizontal", "inner", and "outer" is based on the orientation or positional relationship shown in the accompanying drawings and is only for the convenience of describing this invention and simplifying the description, and is not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0022] Example 1: See Figures 1 to 3 This embodiment discloses an oven energy-saving control method based on multi-sensor detection, the steps of which include: A. Receive cooking time setting information and retrieve the initial environmental parameters inside the oven recorded by the monitoring module; B. The execution module controls the heating module to start heating and begin the cooking task; C. The monitoring module collects environmental parameters inside the oven in real time and transmits them to the processing module; D. The processing module is equipped with a prediction model to monitor in real time whether the internal environmental parameters of the oven reach the prediction model threshold. If the prediction model threshold is reached, the execution module is controlled to generate a stop heating command and control the heating module to stop heating. E. When the detection module detects that the rate of temperature drop inside the oven exceeds the threshold, the execution module generates a heating command to heat the heating module and repeats steps B to D. Otherwise, it waits for the cooking time to end and sends a cooking completion signal.

[0023] The program for the oven energy-saving control method based on multi-sensor detection of the present invention is stored in memory for the processor MCU to call.

[0024] The training steps for the prediction model include: a. Conduct multiple cooking experiments to collect data on food type, weight, set parameters, real-time sensor data, and raw data at the stop time point; b. Clean up outliers and missing values ​​in the original data; c. Align environmental parameters with timestamps, calculate the threshold of the prediction model, and construct the training dataset; d. Input the training dataset into the machine learning algorithm for iterative optimization and adjust the model parameters to reduce the error between the stopping point predicted by the model and the stopping point in the original data; e. Solidify the trained model into a file and store it in the processing module for later use.

[0025] Machine learning algorithms such as XGBoost.

[0026] The quality of training data is improved through rigorous data cleaning, alignment, and feature extraction. Timestamps make each parameter at the same point in time clearly visible, enabling prediction of that time point using multiple parameters, thus enhancing the model's generalization ability and prediction stability. A fixed storage mechanism facilitates efficient model retrieval, ensuring the consistency and reliability of the prediction function in actual operation, and the retrieval does not consume the processor's MCU memory, reducing hardware requirements. Through a systematic pre-training process, the prediction model can cover diverse cooking scenarios, thereby improving its prediction accuracy and adaptability in practical applications, providing a solid algorithmic foundation for intelligent energy-saving functions.

[0027] The cooking experiments in the data acquisition steps are completed in a controlled environment to ensure accurate control of variables, and the stopping time is determined by experts.

[0028] The stop time point refers to the earliest time that experts determine can be stopped in each standard cooking experiment when the food reaches the ideal cooking effect (including but not limited to reaching the target core temperature, optimal color, and best taste). Stopping heating at this time point allows full use of the residual heat in the oven cavity, enabling the food to continue cooking precisely after external heating has stopped, achieving the expected cooking effect.

[0029] The settings include setting the total cooking time and the required temperature for the food.

[0030] The environmental parameters include at least one of the following: food type and weight, food color, set temperature and duration, real-time cavity temperature and humidity.

[0031] The prediction model thresholds include the rate of temperature change, the rate of humidity change, the rate of weight change, and / or the rate of color change.

[0032] The heating module includes a heating element and a cooling fan. The execution module is connected to both the heating element and the cooling fan. When the execution module generates a stop heating command, the heating element and the cooling fan turn off. Directly connecting both the heating element and the cooling fan to the execution module facilitates centralized control of the heating and cooling functions. This allows for simultaneous and rapid start-up and shutdown of both functions, preventing a time lag between the start-up of the heating and cooling functions that could cause rapid temperature increases and damage to system components. Simultaneous start-up also improves the accuracy and speed of control command response, enabling the oven to quickly switch operating states and ensuring the continuity and stability of the cooking process.

[0033] It also includes a data acquisition module electrically connected to the monitoring module and the processing module, which converts environmental parameters into digital signals and transmits them to the processing module.

[0034] The processing module includes a processor MCU connected to the data acquisition module and a memory. The prediction model is stored in the memory. The execution module is connected to the processor MCU. After receiving digital signals, the processor MCU calls the prediction model to calculate and predict the digital signals. The coordinated configuration of the processor MCU and the memory separates the processing and storage functions onto two hardware devices. Through interconnection via calls, the module is small in size, has a large capacity, and fast read / write speeds, enabling the prediction model to respond quickly and process in real time. This ensures the immediate execution and reliable operation of predictive energy-saving control, providing crucial hardware support for the overall system performance.

[0035] The data acquisition module includes a signal circuit and an analog-to-digital converter (ADC). The signal circuit receives and processes environmental parameters, while the ADC converts the processed environmental parameters into digital signals and transmits them to the processor MCU. Specifically, the environmental parameters are analog signals. Since the processor MCU can only recognize digital signals, the analog signals acquired by each sensor need to be converted into digital signals by the ADC before being transmitted to the processor MCU. This ensures that the sensor signals are accurately transmitted to the processor MCU, providing a high-quality, real-time, and reliable data source for subsequent model prediction and control decisions.

[0036] The monitoring module includes a temperature sensor, a humidity sensor, a weight sensor, and / or an optical sensor. The temperature and humidity sensors are used to monitor the temperature and humidity inside the cavity, respectively, while the weight and optical sensors are used to monitor the weight and color of the food, respectively. By integrating multiple sensors, the system can comprehensively and accurately capture real-time status information of the cooking environment and food, providing multi-dimensional and high-precision input data for the predictive model, thereby improving the accuracy of energy-saving control.

[0037] Specifically, optical sensors use devices such as cameras and color sensors to collect surface image data of food and capture changes in the color of the food surface, thereby determining the doneness of the food.

[0038] Compared with existing technologies, this invention calculates and predicts the internal environmental parameters of the oven using a predictive model, and controls the heating module to enter a predictive shutdown state at the stop time point, thereby making full use of residual heat to complete the remaining cooking stage. This achieves intelligent and dynamic energy-saving control, which can adaptively adjust according to the actual cooking situation, effectively improving energy utilization efficiency. Moreover, this solution is implemented through software settings, without changing the original hardware structure of the oven. This not only avoids the increased costs caused by adding insulation layers, heat recovery devices, etc., but also reduces the complexity and cost of production and manufacturing, and has significant economic benefits and ease of promotion.

[0039] Example 2: This embodiment discloses an oven that stores an oven energy-saving control method based on multi-sensor detection. Compared with the prior art, the oven of the present invention, due to the application of the above-mentioned solution, can intelligently adapt to the utilization of waste heat in various cooking situations, saving energy and reducing consumption, and is economical and easy to promote.

[0040] Based on the disclosure and teachings of the foregoing specification, those skilled in the art can make changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the present invention should also fall within the protection scope of the claims of the present invention. Furthermore, although some specific terms are used in this specification, these terms are only for convenience of explanation and do not constitute any limitation on the present invention.

Claims

1. A method for energy-saving control of an oven based on multi-sensor detection, characterized in that the steps include... include: A. Receive cooking time setting information and retrieve the initial environmental parameters inside the oven recorded by the monitoring module; B. The execution module controls the heating module to start heating and begin the cooking task; C. The monitoring module collects environmental parameters inside the oven in real time and transmits them to the processing module; D. The processing module is equipped with a prediction model to monitor in real time whether the internal environmental parameters of the oven reach the prediction model threshold. If the prediction model threshold is reached, the execution module is controlled to generate a stop heating command and control the heating module to stop heating. E. When the detection module detects that the rate of temperature drop inside the oven exceeds the threshold, the execution module generates a heating command to heat the heating module and repeats steps B to D. Otherwise, it waits for the cooking time to end and sends a cooking completion signal.

2. The method according to claim 1, characterized in that, The training steps for the prediction model include: a. Conduct multiple cooking experiments to collect data on food type, weight, set parameters, real-time sensor data, and raw data at the stop time point; b. Clean up outliers and missing values ​​in the original data; c. Align environmental parameters with timestamps, calculate the threshold of the prediction model, and construct the training dataset; d. Input the training dataset into the machine learning algorithm for iterative optimization and adjust the model parameters to reduce the error between the stopping point predicted by the model and the stopping point in the original data; e. Solidify the trained model into a file and store it in the processing module for later use.

3. The method according to claim 1, characterized in that, The environmental parameters include at least one of the following: food type and weight, food color, set temperature and duration, real-time cavity temperature and humidity.

4. The method according to claim 1, characterized in that, The prediction model thresholds include the rate of temperature change, the rate of humidity change, the rate of weight change, and / or the rate of color change.

5. The method according to claim 1, characterized in that, The heating module includes a heating element and a cooling fan. The execution module is connected to the heating element and the cooling fan respectively. When the execution module generates a stop heating command, the heating element and the cooling fan are turned off.

6. The method according to claim 1, characterized in that, It also includes a data acquisition module electrically connected to the monitoring module and the processing module, which converts environmental parameters into digital signals and transmits them to the processing module.

7. The method according to claim 6, characterized in that, The processing module includes a processor MCU connected to the data acquisition module and a memory. The prediction model is stored in the memory. The execution module is connected to the processor MCU. After receiving the digital signal, the processor MCU calls the prediction model to calculate and predict the digital signal.

8. The method according to claim 7, characterized in that, The data acquisition module includes a signal circuit and an analog-to-digital converter. The signal circuit receives and processes environmental parameters, and the analog-to-digital converter converts the processed environmental parameters into digital signals and transmits them to the processor MCU.

9. The method according to claim 1, characterized in that, The monitoring module includes a temperature sensor, a humidity sensor, a weight sensor, and / or an optical sensor. The temperature sensor and humidity sensor are used to monitor the temperature and humidity inside the cavity, respectively, and the weight sensor and optical sensor are used to monitor the weight and color of the food, respectively.

10. An oven, characterized in that, The oven is equipped with an oven energy-saving control method based on multi-sensor detection as described in any one of claims 1-9.

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