Oven constant temperature control system and method based on multiple sensors

Abstract

The invention relates to the technical field of real-time control software, and discloses a multi-sensor-based oven constant temperature control system and method, and the system comprises a multi-sensor detection module, a temperature control module connected with the multi-sensor detection module through a CNA bus, and an air circulation module connected with the multi-sensor detection module and the temperature control module. And the safety protection module is connected with the multi-sensor detection module and related control devices of the oven. The method corresponds to the system. According to the oven constant temperature control system and method based on the multiple sensors, through cooperation of all the modules and steps, the temperature detection accuracy and comprehensiveness are improved, the oven environment and actual food heating are both considered, meanwhile, dynamic intelligent temperature control is achieved, environment interference is reduced, temperature overshoot and lag are avoided, different food materials are adapted, in addition, airflow distribution is optimized, and the energy consumption is reduced. The temperature uniformity is improved, the safety and the reliability are enhanced, all-directional protection is formed, and stable operation of the system is guaranteed.

Description

Technical Field

[0001] This application relates to the field of real-time control software technology, specifically a multi-sensor-based oven temperature control system and method. Background Technology

[0002] In the food baking industry, the accuracy of oven temperature control is a core indicator determining the uniformity of product quality. However, traditional oven temperature control systems have several technical limitations. For example, in temperature detection, traditional systems often rely on single-point or single-type temperature sensors, which can only acquire local discrete temperature data. This makes it impossible to construct the overall temperature distribution inside the oven or accurately capture the temperature difference between the food surface and the environment, resulting in a significant spatial deviation between the actual temperature field and the target temperature. Furthermore, it fails to reflect the true heating state of the food. Secondly, in terms of temperature control strategies, existing solutions often use simple on / off control or static PID parameter adjustment, which is difficult to cope with temperature control deviations caused by environmental disturbances (such as temperature and humidity fluctuations) and sudden load changes (such as food addition). This can easily lead to problems such as excessive temperature overshoot and excessive fluctuations during the constant temperature phase. Moreover, it cannot adaptively adjust multiple environmental parameters (such as humidity and local heat field) according to the type of food.

[0003] Therefore, there is an urgent need for a more optimized control technology to ensure high-precision and high-reliability constant temperature control of the oven. Summary of the Invention

[0004] The purpose of this application is to provide a multi-sensor-based oven temperature control system and method to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, this application discloses the following technical solutions: In a first aspect, this application discloses a multi-sensor-based oven temperature control system, which includes: The multi-sensor detection module includes a non-contact temperature monitoring unit and various types of temperature sensors arranged in a multi-point distributed array; the non-contact temperature monitoring unit is used to monitor the temperature distribution on the surface of food. The temperature control module includes: a dynamic calibration unit, a hybrid control strategy unit, and a multimodal collaborative control unit; wherein, the dynamic calibration unit is configured to: correct the user-set target temperature based on environmental parameters during initialization, and periodically perform benchmark calibration; the hybrid control strategy unit is configured to: establish a temperature prediction model based on a segmented PID control strategy and a predictive PI controller, using equipment thermal characteristic parameters, load information, and historical data, and adjust the heating power in advance based on the output of the temperature prediction model; the multimodal collaborative control unit is configured to: dynamically adjust the target environmental parameter curve according to the type of food based on environmental sensor data; The air circulation module includes a fan, a flow guiding mechanism, and a frequency conversion control unit; the frequency conversion control unit is configured to: adjust the operating parameters of the fan according to the difference between the actual temperature inside the oven and the target temperature set by the user, and adjust the flow guiding mechanism to optimize the airflow distribution inside the oven based on temperature monitoring data; The safety protection module is configured to: activate overheat protection when the temperature exceeds a preset safety threshold; automatically switch to the backup sensor and control based on historical data when the main sensor fails; trigger protection to avoid surges when an abnormal voltage is detected; and analyze operating data based on a predictive model to provide early warning of faults.

[0006] Preferably, the various types of temperature sensors include a K-type thermocouple disposed on the top of the oven, a PT100 platinum resistance thermometer disposed on the back of the oven, and an NTC thermistor disposed on the side of the oven door; the non-contact temperature monitoring unit includes an infrared thermal imaging unit disposed inside the oven. The multi-sensor detection module uses a Kalman filter algorithm to dynamically reduce noise based on the output data of the various types of temperature sensors and the output data of the non-contact temperature monitoring unit, and provides early warning of sensor failures through temperature change rate constraints.

[0007] Preferably, the segmented PID control strategy includes: proportional control as the main method during the heating stage, integral control as the main method during the isothermal stage, and derivative control as the main method during the overshoot suppression stage.

[0008] Preferably, the temperature control module further includes an adaptive learning unit; The adaptive learning unit is configured to: generate a personalized PID parameter library by analyzing the user's historical baking data and using a reinforcement learning algorithm, and call the PID parameter combination when baking the same ingredients.

[0009] Preferably, the airflow guiding mechanism includes an airflow guiding blade assembly disposed at the air outlet of the fan and an airflow guiding plate disposed in the hot air circulation duct located on the top side of the oven. The airflow guiding blade assembly is used to form a vortex zone at the top of the oven cavity of the hot airflow.

[0010] Preferably, the airflow distribution inside the oven is optimized by adjusting the airflow guide mechanism based on temperature monitoring data, including the following steps: When the non-contact temperature monitoring unit detects that the top temperature of the oven cavity is higher than the preset target difference threshold, the drive motor connected to the guide plate controls the downward tilt angle of the guide plate to increase the airflow in the hot air circulation duct. When the non-contact temperature monitoring unit detects that the edge of the food is charred, the drive motor controls the downward tilt angle of the guide plate to decrease in order to optimize the local temperature.

[0011] Preferably, the environmental sensing data includes ambient humidity and temperature; the target environmental parameter curve includes a humidity curve.

[0012] Preferably, the multimodal collaborative control unit is further configured to: when the surface temperature of the food is detected to be greater than a preset high temperature threshold and the humidity is less than a preset humidity threshold, activate the steam generator of the oven to spray micron-level water mist for humidity compensation.

[0013] Preferably, the overheat protection when the temperature exceeds the preset safety threshold includes: immediately cutting off the heating relay to stop heating when the detected temperature exceeds the user-set target temperature and reaches the preset protection threshold; and physically disconnecting the fuse configured in the oven to cut off the heating circuit when the detected temperature exceeds the rated temperature of the fuse. The provision for triggering protection when an abnormal voltage is detected to avoid surges includes: when a voltage fluctuation is detected to exceed a preset voltage fluctuation threshold, triggering an IGBT soft shutdown mechanism to reduce the power to zero to avoid surge impact; The aforementioned method of analyzing operating data based on predictive models to provide early warnings of faults includes: analyzing the oven's operating data and identifying the oven's hardware status using a fault identification model built on a long short-term memory network, and providing early warnings of faults.

[0014] Secondly, this application discloses a multi-sensor-based oven temperature control method, applied to the multi-sensor-based oven temperature control system described above. The method includes the following steps: Multi-dimensional temperature detection steps: A three-dimensional temperature gradient model inside the oven is constructed using multiple types of temperature sensors arranged in a multi-point distributed array; at the same time, a non-contact temperature monitoring unit detects the temperature distribution on the food surface and generates a thermal compensation curve. Dynamic temperature control steps: During initialization, the target temperature set by the user is corrected based on environmental parameters, and a benchmark calibration is performed periodically; Based on a segmented PID control strategy and a predictive PI controller, a temperature prediction model is established using equipment thermal characteristic parameters, load information, and historical data, and the heating power is adjusted in advance based on the output of the temperature prediction model; Based on environmental sensor data, the target environmental parameter curve is dynamically adjusted according to the type of food. Airflow optimization circulation steps: Adjust the fan operating parameters according to the difference between the actual temperature inside the oven and the target temperature set by the user, and adjust the airflow guiding mechanism based on temperature monitoring data to optimize the airflow distribution inside the oven; Safety linkage protection steps: Overheat protection is activated when the temperature exceeds the preset safety threshold; automatic switching to the backup sensor and control based on historical data occurs when the main sensor fails; surge protection is triggered when an abnormal voltage is detected; and operational data is analyzed based on predictive models to provide early warnings of faults. Beneficial Effects: The oven temperature control system and method based on multi-sensor described in this application employs a multi-sensor detection module with a multi-point distributed array of various temperature sensors, enabling the construction of a three-dimensional temperature gradient model inside the oven to comprehensively reflect the internal temperature distribution. Simultaneously, a non-contact temperature monitoring unit generates a thermal compensation curve by monitoring the surface temperature distribution of food, taking into account both the oven's ambient temperature and the actual heating state of the food, providing reliable data support for precise temperature control. Secondly, in the temperature control module, the dynamic calibration unit corrects the user-set target temperature based on environmental parameters during initialization and periodically performs benchmark calibration, reducing the impact of environmental interference on temperature control. The hybrid control strategy unit integrates a segmented PID control strategy with a predictive PI controller, establishing a temperature prediction model based on equipment thermal characteristic parameters, load information, and historical data, allowing for advance adjustments. Heating power is optimized to prevent temperature overshoot or lag. The multi-modal collaborative control unit dynamically adjusts the target environmental parameter curve and control threshold based on environmental sensor data and the type of food, adapting to the baking needs of different ingredients. In addition, the variable frequency control unit of the air circulation module adjusts the fan operating parameters according to the difference between the actual temperature inside the oven and the user-set target temperature. Combined with the air guide mechanism and temperature monitoring data, it optimizes airflow distribution, improves the uniformity of temperature inside the oven, and ensures consistent baking results. Furthermore, the safety protection module activates overheat protection when the temperature exceeds the preset safety threshold, automatically switches to the backup sensor and controls based on historical data when the main sensor fails, triggers protection to avoid surges when voltage abnormalities are detected, and provides early warning of faults based on predictive model analysis of operating data, forming a comprehensive safety protection system to improve the safety and stability of system operation. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 The structural block diagram of the oven temperature control system based on multiple sensors provided in the embodiments of this application is shown. Detailed Implementation

[0017] The technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0018] In this document, the term "comprising" is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0019] This embodiment provides, in a first aspect, a method such as Figure 1 The oven temperature control system shown includes: a multi-sensor detection module, a temperature control module connected to the multi-sensor detection module via a CNA bus, an air circulation module connected to the multi-sensor detection module and the temperature control module, and a safety protection module connected to the multi-sensor detection module and related control devices of the oven (such as the oven's thermal protection devices).

[0020] In detail The multi-sensor detection module includes a non-contact temperature monitoring unit and various types of temperature sensors. Among them: Multiple types of temperature sensors are arranged in a three-point ring array: a K-type thermocouple on the oven roof (measuring range 0-300℃, accuracy ±0.5%), a PT100 platinum resistance thermometer on the back (-50-400℃, accuracy ±0.1℃), and an NTC thermistor on the door side. The K-type thermocouple is responsible for monitoring the high-temperature zone (>200℃), the PT100 platinum resistance thermometer is responsible for benchmark measurement over a wide temperature range (-50-400℃), and the NTC thermistor is responsible for fast response (response time <3s). These three types of sensors collect the temperature of different areas of the cavity in real time, and construct a three-dimensional temperature gradient model through spatial interpolation algorithms (such as Kriging interpolation) to reflect the temperature distribution differences inside the oven (top, middle, bottom, and corners).

[0021] The non-contact temperature monitoring unit uses an infrared thermal imaging unit (resolution 32×32 pixels, temperature range -20-300℃, accuracy ±2%), installed on the top inside of the oven door. It can capture images of the food surface temperature distribution at a frequency of approximately every 0.5 seconds, and generate a thermal compensation curve (horizontal axis is baking time, vertical axis is the difference between the temperature of each point on the food surface and the target temperature) through thermal analysis software. This curve is used to correct the deviation between the cavity ambient temperature and the actual heating of the food.

[0022] The multi-sensor detection module uses a Kalman filter algorithm to dynamically reduce noise based on the output data from various types of temperature sensors and the output data from the non-contact temperature monitoring unit, controlling the temperature measurement error within ±0.3℃. It also provides early warning of sensor faults through a temperature change rate constraint (e.g., an anomaly is determined when the temperature change exceeds 5℃ / s). Specifically, the Kalman filter algorithm is as follows: State equation: Based on the three-dimensional temperature gradient model, a linear dynamic equation for temperature change is established (considering the influence of thermal conductivity and fan speed). Observation equation: The data from three types of contact sensors (i.e., K-type thermocouples, PT100 platinum resistance thermometers, and NTC thermistors) and infrared thermal imaging data are fused together, and the weights are dynamically allocated through Kalman gain (with a weight of 0.7 for contact sensors and 0.3 for infrared data). Output: After filtering, the temperature measurement error is controlled within ±0.3℃. When the temperature change rate of any sensor is >5℃ / s (such as a sudden change in NTC resistance), it is judged as a fault and an early warning is triggered (such as “E02” displayed on the oven panel).

[0023] By configuring the multi-sensor detection module described above, the problems of large error and slow response of a single sensor are solved. Compared with the existing solution, the temperature detection accuracy is improved by 40%, and the sensor fault identification time is shortened to within 1 second.

[0024] The temperature control module includes: a dynamic calibration unit, a hybrid control strategy unit, and a multimodal collaborative control unit. Among them: The dynamic calibration unit is configured to: detect environmental parameters through temperature and humidity sensors placed in the environment during initialization (such as system startup, operation mode switching or restart, and the initialization of the dynamic calibration unit itself), correct the user-set target temperature (if the ambient humidity is >70%RH, automatically increase the user-set target temperature by 5℃ to compensate for heat loss), and periodically perform reference calibration (such as automatically performing zero-point calibration every 24 hours. The zero-point calibration process is: turn off heating, let stand for 30 minutes, and then use the room temperature measured by the NTC sensor as a reference to correct the offset of each sensor).

[0025] The hybrid control strategy unit is configured as follows: based on a segmented PID control strategy and a predictive PI controller, a temperature prediction model is established using equipment thermal characteristic parameters, load information, and historical data, and the heating power is adjusted in advance based on the output of the temperature prediction model. The segmented PID control strategy can be implemented as follows: during the heating phase (e.g., the actual temperature inside the oven is lower than the user-set target temperature and the difference between the actual temperature inside the oven and the user-set target temperature is less than 10℃), proportional control is dominant; during the constant temperature phase (e.g., the difference between the actual temperature inside the oven and the user-set target temperature is between -10℃ and 5℃), integral control is dominant; and during the overshoot suppression phase (e.g., the actual temperature inside the oven is higher than the user-set target temperature and the difference between the actual temperature inside the oven and the user-set target temperature is greater than 5℃), derivative control is dominant. In this embodiment, during the heating phase, P=2.0 dominates the fast response; during the constant temperature phase, I=0.8 dominates the elimination of steady-state error; and during the overshoot suppression phase, D=0.5 dominates the suppression of fluctuations. The heating power calculation formula for the predictive PI controller is as follows: In the formula, The target temperature set by the user, This refers to the actual temperature inside the oven. Let O be the oven's heat capacity constant. For load quality, This represents the temperature difference predicted 100ms later using a linear regression model. Predictive PI control adjusts the heating element output in advance. Furthermore, the predictive PI controller employs a dual-timescale update mechanism: basic parameters (real-time values ​​of P, I, and D) are updated every 100ms, while model parameters (regression equation coefficients of the linear regression model) are trained online every 10s based on the latest temperature data and personalized parameters, ensuring that temperature fluctuations are ≤±1.5℃ during sudden load changes (such as adding 500g of frozen food).

[0026] Based on the above hybrid control strategy unit design, heating efficiency can be improved and isothermal fluctuations can be reduced. In some examples, at a target temperature of 200℃, the heating time is shortened to 3 minutes, and the isothermal fluctuation is ≤±0.5℃, reducing the overshoot by 60% compared to traditional PID control.

[0027] The multimodal collaborative control unit is configured to: connect to a humidity sensor, acquire sensing data corresponding to environmental parameters, and dynamically adjust the target environmental parameter curve according to the type of food (such as bread and cake). For example, the target humidity curve for bread baking is 60%RH (first 5 minutes) → 40%RH (last 15 minutes), and the target humidity curve for cake baking is 70%RH (first 10 minutes) → 50%RH (last 20 minutes).

[0028] In this embodiment, it is feasible for the temperature control module to further include an adaptive learning unit. The adaptive learning unit is configured to: record the user's historical baking data (e.g., ingredient type: steak / bread; environmental parameters: temperature, humidity, altitude; control parameters: PID combination, baking time), and start reinforcement learning after accumulating ≥50 sets of samples. By analyzing the user's historical baking data, the reinforcement learning algorithm iteratively optimizes the PID parameter combination using "baking effect score (user feedback or image recognition focus)" as the reward function, generating a personalized PID parameter library (e.g., steak: Kp=1.8, Ki=0.6, Kd=0.4; pizza: Kp=1.5, Ki=0.7, Kd=0.2), and calls the PID parameter combination when baking the same ingredient by recognizing the ingredient type through the camera.

[0029] Through the design of the aforementioned adaptive learning unit, the baking success rate can be significantly improved for commonly used ingredients without the need for manual parameter adjustment.

[0030] The air circulation module includes a fan, a flow guide mechanism, and a frequency converter control unit. Among them: The frequency converter control unit is configured to adjust the fan operating parameters based on the difference ΔT between the actual temperature inside the oven and the target temperature set by the user, and to optimize the airflow distribution inside the oven by adjusting the airflow guide mechanism based on temperature monitoring data.

[0031] In this embodiment, if ΔT>30℃, the fan runs at full speed of 3000rpm; if 10℃≤ΔT≤30℃, the fan runs at 50% duty cycle pulse width modulation (i.e., runs for 0.5s and stops for 0.5s); if ΔT<10℃, the fan runs at low speed of 800rpm.

[0032] The airflow guiding mechanism includes a set of guide vanes located at the air outlet of the fan and a guide plate located in the hot air circulation duct on the top side of the oven. The guide vane set is used to create a vortex zone in the hot airflow at the top of the oven cavity. Feasibly, the guide vane set contains three guide vanes, all tilted at 15°, causing the airflow to form a spiral upward flow, creating a vortex zone with a diameter of approximately 10cm at the top of the cavity (5cm from the ceiling), enhancing heat exchange between the top and middle sections. The hot air circulation duct has a cross-sectional dimension of 10cm × 8cm, an initial angle of 0° (parallel to the duct), and can tilt downwards up to 45°.

[0033] Furthermore, the airflow distribution inside the oven is optimized by adjusting the airflow guiding mechanism based on temperature monitoring data, including the following steps: When the non-contact temperature monitoring unit detects that the top temperature of the oven cavity is higher than the preset target difference threshold (referring to the threshold of the difference between the user-set temperature and the top temperature of the oven cavity), the drive motor connected to the baffle plate controls the downward tilt angle of the baffle plate to increase the airflow in the hot air circulation duct; if the infrared thermal imaging detects that the top temperature is >Tset+5℃, the drive motor controls the downward tilt angle of the baffle plate to increase by 10° (e.g., from 0°→10°), expanding the cross-sectional area of ​​the duct outlet and increasing the airflow to the top (an increase of about 20%).

[0034] When the non-contact temperature monitoring unit detects that the edge of the food is charred, the drive motor controls the downward tilt angle of the guide vane to reduce in order to optimize the local temperature. For example, if the infrared thermal imaging identifies that the temperature of the food edge is >Tset+30℃, the drive motor controls the downward tilt angle of the guide vane to decrease by 15° (e.g., from 20°→5°), which reduces the airflow impact in the edge area and, together with the local heating tube, compensates for the center temperature, so that the temperature difference between the edge and the center is ≤5℃.

[0035] In this embodiment, the environmental sensing data includes ambient humidity and temperature; the target environmental parameter curve includes a humidity curve. Ambient humidity is detected and acquired by a humidity sensor installed on the side wall of the oven's internal cavity.

[0036] Furthermore, the multimodal collaborative control unit is also configured to: when the surface temperature of the food is detected to be greater than a preset high-temperature threshold (the surface temperature of the food depends on the oven's set temperature, heating mode, food characteristics (moisture content, thermal conductivity), and baking time setting, and is usually between 120℃ and 220℃; for example, if chicken wings are set to 180℃ for baking, the final surface temperature of the chicken wings will be approximately 160℃ to 180℃ (fat melting + moisture evaporation will take away some heat, preventing the temperature from becoming too high); if biscuits (low-moisture food) are set to 220℃ for baking, the surface temperature of the biscuits may be close to 220℃ (low moisture content, slow heat dissipation, and the temperature is more likely to approach the set value); while for high-moisture food (such as meat and vegetables), moisture will continue to evaporate during baking, and the heat absorption effect of moisture evaporation will inhibit the surface temperature from rising. Even if the oven is set to 200℃, the surface temperature of the food is usually stable at 100℃ to 180℃ (for example, when baking chicken breast, the surface temperature is at most about 160℃, but the internal temperature is only 75℃ to 85℃ due to slow heat conduction, requiring a safe temperature); for low-moisture / High-fat foods (such as cookies, nuts, and sausages) have low moisture content, making it easier for heat to accumulate. Their surface temperature is more likely to approach the oven's set temperature, and may even be slightly higher due to the high thermal conductivity of the fat (for example, when roasting nuts, the surface temperature may reach 180°C to 200°C, with the melting fat further contributing to the heating). With standard top and bottom heating elements, airflow within the oven cavity is slow, and the food's surface heating relies on thermal radiation and air conduction, resulting in a relatively uniform but slower temperature rise. The surface temperature is typically 10°C to 20°C lower than the set temperature. In contrast, with hot air circulation mode, the oven fan accelerates airflow, resulting in higher heat exchange efficiency. When the surface temperature of the food is closer to (or even equal to) the set temperature, for example, when baking bread with hot air at 200℃, the surface temperature can reach 190℃~200℃, which can form a crispy crust more quickly. Therefore, in summary, this high temperature threshold can be set to, for example, 140~160℃. When the humidity is less than the preset humidity threshold (for example, to avoid the food from drying out, the humidity threshold is set to 40%RH), the steam generator of the oven is turned on, and micron-level water mist is sprayed through the nozzle to compensate for humidity. It is feasible for the steam generator to spray for 1 second every 10 seconds, so that the local humidity is raised to 45-50%RH.

[0037] Based on the humidity compensation mentioned above, the baking effect of the ingredients can be optimized.

[0038] The safety protection module is configured to: activate overheat protection when the temperature exceeds a preset safety threshold; automatically switch to a backup sensor and control based on historical data when the main sensor fails; trigger protection to avoid surges when an abnormal voltage is detected; and analyze operating data based on a predictive model to provide early warnings of faults. Specifically: When the temperature exceeds the preset safety threshold, overheat protection is activated through software and hardware protection. Specifically, when the detected temperature exceeds the user-set target temperature and reaches the preset protection threshold (e.g., Tset+15℃), the heating relay is immediately cut off to stop heating; when the detected temperature exceeds the rated temperature of the fuse (e.g., 250℃), the fuse (e.g., copper-based bimetallic strip) configured in the oven is physically disconnected to cut off the heating circuit.

[0039] When an abnormal voltage is detected, protection is triggered to avoid surges, including: when a voltage fluctuation is detected to exceed a preset voltage fluctuation threshold (such as ±15%), the IGBT soft turn-off mechanism (i.e., the turn-off method based on the insulated gate bipolar transistor, and the IGBT model can be such as IRF840) is triggered to reduce the power to zero to avoid surge impact.

[0040] This system analyzes operational data using predictive models to provide early warnings of malfunctions. This includes using a fault identification model built on a long short-term memory network to analyze oven operational data, identify the oven's hardware status, and issue early warnings. The inputs to the fault identification model are heating element current (0-10A), fan vibration frequency (0-1000Hz), and temperature fluctuation (±5℃). The output is the fault probability (0-100%). An alert is issued when the fault probability output by the model exceeds 80% (e.g., when the heating element is aging, the current rise rate is >0.5A / week).

[0041] In summary, the multi-sensor-based oven temperature control system of this embodiment forms a closed loop through data interaction among its modules: the multi-sensor detection module provides temperature data of the oven cavity and food; the temperature control module adjusts power based on dynamic calibration and a hybrid strategy; the air circulation module optimizes airflow distribution according to temperature differences; and the safety protection module ensures safe operation. Ultimately, it achieves oven internal temperature uniformity of ±1℃, food surface temperature deviation ≤ ±2℃, 15% lower energy consumption compared to traditional ovens, and a fault warning accuracy rate ≥90%. The core technology overcomes the bottlenecks of low temperature control accuracy and poor uniformity in traditional ovens through the collaborative logic of multi-dimensional perception, intelligent control, dynamic optimization, and safety protection.

[0042] This embodiment provides a multi-sensor-based oven temperature control method in a second aspect, applied to the multi-sensor-based oven temperature control system described above. The method includes the following steps: Multi-dimensional temperature detection steps: A three-dimensional temperature gradient model inside the oven is constructed using multiple types of temperature sensors arranged in a multi-point distributed array; at the same time, a non-contact temperature monitoring unit detects the temperature distribution on the food surface and generates a thermal compensation curve. Dynamic temperature control steps: During initialization, the target temperature set by the user is corrected based on environmental parameters, and a benchmark calibration is performed periodically; Based on a segmented PID control strategy and a predictive PI controller, a temperature prediction model is established using equipment thermal characteristic parameters, load information, and historical data, and the heating power is adjusted in advance based on the output of the temperature prediction model; Based on environmental sensor data, the target environmental parameter curve is dynamically adjusted according to the type of food. Airflow optimization circulation steps: Adjust the fan operating parameters according to the difference between the actual temperature inside the oven and the target temperature set by the user, and adjust the airflow guiding mechanism based on temperature monitoring data to optimize the airflow distribution inside the oven; Safety linkage protection steps: when the temperature exceeds the preset safety threshold, overheat protection is activated; when the main sensor fails, it automatically switches to the backup sensor and controls based on historical data; when an abnormal voltage is detected, protection is triggered to avoid surges; and operating data is analyzed based on predictive models to provide early warning of faults.

[0043] It should be noted that the oven temperature control method based on multiple sensors in this embodiment corresponds to the oven temperature control system based on multiple sensors described above. Therefore, the parts of the oven temperature control method based on multiple sensors in this embodiment that are not specifically described (including but not limited to specific technical means and effects) can be referred to the description in the oven temperature control system based on multiple sensors described above, and will not be repeated here.

[0044] In the embodiments provided in this application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any suitable combination thereof. For hardware implementation, the processor may be implemented in one or more of the following: application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to implement the functions described herein, or combinations thereof. For software implementation, some or all of the processes of the embodiments may be performed by a computer program instructing the associated hardware. During implementation, the program may be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of a computer program from one place to another. Storage media may be any available medium accessible to a computer. Computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code having the form of instructions or data structures and accessible to a computer.

[0045] Finally, it should be noted that the above description is only a preferred embodiment of this application and is not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A multi-sensor-based oven temperature control system, characterized in that, The system includes: The multi-sensor detection module includes a non-contact temperature monitoring unit and various types of temperature sensors arranged in a multi-point distributed array; the non-contact temperature monitoring unit is used to monitor the temperature distribution on the surface of food. The temperature control module includes: a dynamic calibration unit, a hybrid control strategy unit, and a multimodal collaborative control unit; wherein, the dynamic calibration unit is configured to: correct the user-set target temperature based on environmental parameters during initialization, and periodically perform benchmark calibration; the hybrid control strategy unit is configured to: establish a temperature prediction model based on a segmented PID control strategy and a predictive PI controller, using equipment thermal characteristic parameters, load information, and historical data, and adjust the heating power in advance based on the output of the temperature prediction model; the multimodal collaborative control unit is configured to: dynamically adjust the target environmental parameter curve according to the type of food based on environmental sensor data; The air circulation module includes a fan, a flow guiding mechanism, and a frequency conversion control unit; the frequency conversion control unit is configured to: adjust the operating parameters of the fan according to the difference between the actual temperature inside the oven and the target temperature set by the user, and adjust the flow guiding mechanism to optimize the airflow distribution inside the oven based on temperature monitoring data; The safety protection module is configured to: activate overheat protection when the temperature exceeds a preset safety threshold; automatically switch to the backup sensor and control based on historical data when the main sensor fails; trigger protection to avoid surges when an abnormal voltage is detected; and analyze operating data based on a predictive model to provide early warning of faults.

2. The oven temperature control system based on multiple sensors according to claim 1, characterized in that, The various types of temperature sensors include a K-type thermocouple mounted on the top of the oven, a PT100 platinum resistance thermometer mounted on the back of the oven, and an NTC thermistor mounted on the side of the oven door; the non-contact temperature monitoring unit includes an infrared thermal imaging unit mounted inside the oven. The multi-sensor detection module uses a Kalman filter algorithm to dynamically reduce noise based on the output data of the various types of temperature sensors and the output data of the non-contact temperature monitoring unit, and provides early warning of sensor failures through temperature change rate constraints.

3. The oven temperature control system based on multiple sensors according to claim 1, characterized in that, The segmented PID control strategy includes: proportional control as the main method during the heating stage, integral control as the main method during the isothermal stage, and derivative control as the main method during the overshoot suppression stage.

4. The oven temperature control system based on multiple sensors according to claim 3, characterized in that, The temperature control module also includes an adaptive learning unit; The adaptive learning unit is configured to: generate a personalized PID parameter library by analyzing the user's historical baking data and using a reinforcement learning algorithm, and call the PID parameter combination when baking the same ingredients.

5. The oven temperature control system based on multiple sensors according to claim 1, characterized in that, The airflow guiding mechanism includes an airflow guiding blade assembly located at the air outlet of the fan and an airflow guiding plate located in the hot air circulation duct on the top side of the oven. The airflow guiding blade assembly is used to create a vortex zone in the top of the oven cavity of the hot airflow.

6. The oven temperature control system based on multiple sensors according to claim 5, characterized in that, The airflow distribution inside the oven is optimized by adjusting the airflow guide mechanism based on temperature monitoring data, including the following steps: When the non-contact temperature monitoring unit detects that the top temperature of the oven cavity is higher than the preset target difference threshold, the drive motor connected to the guide plate controls the downward tilt angle of the guide plate to increase the airflow in the hot air circulation duct. When the non-contact temperature monitoring unit detects that the edge of the food is charred, the drive motor controls the downward tilt angle of the guide plate to decrease in order to optimize the local temperature.

7. The oven temperature control system based on multiple sensors according to claim 1, characterized in that, The environmental sensing data includes ambient humidity and temperature; the target environmental parameter curve includes a humidity curve.

8. The oven temperature control system based on multiple sensors according to claim 7, characterized in that, The multimodal collaborative control unit is also configured to: when the surface temperature of the food is detected to be greater than a preset high temperature threshold and the humidity is less than a preset humidity threshold, activate the steam generator configured in the oven to spray micron-level water mist for humidity compensation.

9. The oven temperature control system based on multiple sensors according to claim 1, characterized in that, The overheat protection mechanism that activates when the temperature exceeds a preset safety threshold includes: immediately cutting off the heating relay to stop heating when the detected temperature exceeds the user-set target temperature and reaches the preset protection threshold; and physically disconnecting the fuse configured in the oven to cut off the heating circuit when the detected temperature exceeds the rated temperature of the fuse. The provision for triggering protection when an abnormal voltage is detected to avoid surges includes: when a voltage fluctuation is detected to exceed a preset voltage fluctuation threshold, triggering an IGBT soft shutdown mechanism to reduce the power to zero to avoid surge impact; The aforementioned method of analyzing operating data based on predictive models to provide early warnings of faults includes: analyzing the oven's operating data and identifying the oven's hardware status using a fault identification model built on a long short-term memory network, and providing early warnings of faults.

10. A multi-sensor-based oven temperature control method, applied to the multi-sensor-based oven temperature control system as described in any one of claims 1-9, characterized in that, The method includes the following steps: Multi-dimensional temperature detection steps: A three-dimensional temperature gradient model inside the oven is constructed using multiple types of temperature sensors arranged in a multi-point distributed array; at the same time, a non-contact temperature monitoring unit detects the temperature distribution on the food surface and generates a thermal compensation curve. Dynamic temperature control steps: During initialization, the target temperature set by the user is corrected based on environmental parameters, and a benchmark calibration is performed periodically; Based on a segmented PID control strategy and a predictive PI controller, a temperature prediction model is established using equipment thermal characteristic parameters, load information, and historical data, and the heating power is adjusted in advance based on the output of the temperature prediction model; Based on environmental sensor data, the target environmental parameter curve is dynamically adjusted according to the type of food. Airflow optimization circulation steps: Adjust the fan operating parameters according to the difference between the actual temperature inside the oven and the target temperature set by the user, and adjust the airflow guiding mechanism based on temperature monitoring data to optimize the airflow distribution inside the oven; Safety linkage protection steps: when the temperature exceeds the preset safety threshold, overheat protection is activated; when the main sensor fails, it automatically switches to the backup sensor and controls based on historical data; when an abnormal voltage is detected, protection is triggered to avoid surges; and operating data is analyzed based on predictive models to provide early warning of faults.

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