A multifunctional intelligent steam oven control system and an intelligent steam oven

Abstract

This invention provides a multifunctional intelligent steam oven control system and an intelligent steam oven. The system includes: a sensing module; a data conversion module; an adaptive learning fuzzy PID control module; a ripening intelligent optimization control module: monitoring the degree of food ripening during the cooking process, checking in real time whether the food ripeness meets the expected standard, realizing automatic control of food ripeness, and intelligently optimizing temperature and humidity control according to personalized taste requirements of the ingredients to enhance the quality and efficiency of food cooking; an output control module; a WIFI module; and a mobile APP module. This invention systematically applies two advanced intelligent control strategies, "adaptive learning fuzzy PID control" and "ripening intelligent optimization control," to the field of household steam ovens, and deeply integrates them in a design that addresses the characteristics of the cooking process (strong nonlinearity, large hysteresis, and model uncertainty), solving the core pain point of traditional steam ovens that cannot dynamically adapt to complex working conditions using fixed programs or simple PID control.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, specifically to a multifunctional intelligent steam oven control system and an intelligent steam oven. Background Technology

[0002] A steam oven is a kitchen appliance that can heat various foods using heating and humidification technologies. In actual operation, when the steam density in the oven cavity reaches the set value and the temperature inside the cavity reaches the temperature reference node corresponding to the set temperature required by the food to be steamed or baked, the system controls the heating element, steam humidification element, pressure control element and other components to perform corresponding actions so that the temperature, humidity and air pressure inside the cavity reach the best matching state to achieve steaming and baking of the food.

[0003] Steam ovens are important heat processing equipment in daily life and industrial production, and are used in large quantities. In the current operation of steam ovens, the internal working temperature, humidity, and pressure are detected by the temperature, humidity, and pressure sensors built into the oven. The temperature, humidity, and pressure are controlled by a fuzzy PID control module. Although this can meet the needs of temperature, humidity, and pressure monitoring, in actual operation, the temperature, humidity, and pressure parameters of the steam oven change non-linearly, have large time-varying characteristics, and the changes in temperature, humidity, and pressure often have lag. It is difficult to establish a mathematical model. Traditional control methods for steam ovens and conventional PID control have problems such as large deviations and low accuracy, making it difficult to achieve good control results.

[0004] Furthermore, existing steam ovens have a low level of intelligence, cannot support remote control by users via mobile applications, and cannot automatically optimize and adjust parameters such as temperature, humidity, pressure, and cooking time inside the steam oven. They cannot make the best or optimal (i.e. most advantageous) choice under the current conditions at each step to verify and improve cooking, cannot meet the optimal cooking curves for different foods, and cannot effectively solve the pain points of meat becoming dry, undercooked, or burnt during the use of steam ovens. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a multi-functional intelligent steam oven control system and an intelligent steam oven. This multi-functional intelligent steam oven control system systematically applies two advanced intelligent control strategies, "adaptive learning fuzzy PID control" and "intelligent optimization control for ripening," to the field of household steam ovens. It also deeply integrates and designs the system to address the characteristics of the cooking process (strong nonlinearity, large hysteresis, and model uncertainty), thus solving the core pain point that traditional steam ovens using fixed programs or simple PID control cannot dynamically adapt to complex working conditions.

[0006] To achieve the above technical solution, the present invention provides a multifunctional intelligent steam oven control system, comprising: Sensing module: used to obtain real-time information on the working conditions inside the cooking cavity of the steam oven in single steaming, single baking, and combined cooking modes, and to collect temperature, humidity, pressure, and cooking signals inside the steam oven; Data conversion module: performs digital filtering and analog-to-digital conversion on the temperature, humidity, pressure, or aging signals collected by the sensing module; The adaptive learning fuzzy PID control module performs fuzzy inference on temperature, humidity, pressure, or ripening signals after digital filtering and analog-to-digital conversion according to fuzzy rules. Then, it calculates the incremental output values ​​KP, KI, and KD of the PID control parameters through a pre-set calculation program. After defuzzification and fuzzification parameter calculations, it obtains the PID control parameters required by the system at the current moment, and finally outputs them to the control system for adjusting the temperature, humidity, pressure, or ripening levels. The module is the input to the control system and uses the three adjustment correction values ​​ΔKP, ΔKI, and ΔKD of the PID controller to adjust the output. During parameter adjustment, when the number of learning nodes reaches the system's set threshold N at a certain moment, it processes the situation by transforming the universe of discourse and simultaneously calculates that the difference between the maximum values ​​of two adjacent deviations is greater than or equal to 1℃. If the condition is met, the system will proceed to the adaptive learning phase and simultaneously adjust rewards and penalties online in real time; otherwise, it will not proceed to self-learning and will continue to self-tune parameters according to the existing fuzzy rules. Intelligent Cooking Optimization Control Module: Monitors the degree of food cooking during the cooking process, checks in real time whether the food cooking degree meets the expected standard, realizes automatic control of the cooking degree of ingredients, and intelligently optimizes temperature and humidity control according to the personalized taste requirements of ingredients, thereby enhancing the cooking quality and efficiency of ingredients. Output control module: Based on the control quantity calculated by the adaptive learning fuzzy PID control module, it completes digital / analog conversion and power drive, and finally precisely adjusts the temperature, humidity, pressure or degree of cooking of the steam oven; WIFI module: Responsible for establishing data transmission and communication with the server and APP, realizing the entire management process of the device, and establishing the connection between the device and the server; Mobile App Module: Configure the steam oven's WIFI module via a mobile app, establish wireless access to the device, and use the app to check the steam oven's status and control the steam oven.

[0007] Preferably, the specific steps of the adaptive learning fuzzy PID control module after entering the adaptive learning stage are as follows: S1. Determine the variable universe: Assume -e max +e maxWithin time step t+1, the minimum and maximum values ​​of the parameter deviation e measured under varying system temperature, humidity, or pressure conditions are then determined based on the set rules. max When >1℃, if |-e max |=|+e max |, then the domain of discourse resulting from the change is taken as [-e max +e max If |-e max |≠|+e max |, let e t =max{[-e max +e max If ]}, then the domain of discourse resulting from this change is taken as [-e t ,+e t ], when e max When the temperature is ≤1℃, the controllable domain can be directly set to [-0.1, +0.1]; S2. Establish the evaluation function: Based on the dynamic performance of the PID controller, when e(k)•ec(k)>0, the system is reducing the deviation and showing a good trend; when e(k)•ec(k)<0, the system is increasing the deviation and showing a bad trend. Based on this change law, an evaluation function is established: c(k)=e(k)•ec(k). When c(k)>0, the current control rule of the system is rewarded; when c(k)<0, the current rule is punished. S3. Determine the reward and punishment function: Based on the evaluation function established in step S2, the following reward and penalty function is established:

[0008] In the formula, The sub-means that k and k-1-t are the maximum absolute values ​​of the deviation values ​​e obtained in real time through the fuzzy algorithm, t is the lag number of the entire control system, and KT is the maximum value set in the current system. S4. Implement adaptive learning system control: By performing real-time online correction on the current system parameter deviation e and deviation change rate ec, continuously detecting the system deviation e and deviation change rate ec, adjusting each dynamic parameter online according to fuzzy control rules, and performing control effect evaluation by sampling the measurement of current parameters e and ec, the control rule base is adjusted and the control system is changed by using a variable domain online reward and penalty learning algorithm.

[0009] Preferably, the specific steps in step S4 of adjusting the control rule base and changing the control system by using a variable domain online reward and penalty learning algorithm are as follows: S41. The system initiates the self-learning phase, reading parameters e(k), ec(k), and ec(k-1-t). ; S42, Judgment Whether it is true or not, if If successful, the learning process ends. S43, if If the condition is not met, then calculate c(k) = e(k)•ec(k). If c(k) < 0, then call the reward / penalty function. Adjust and control the system until... If true; if c(k) > 0, then call the reward / penalty function. Adjust and control the system until... Established.

[0010] Preferably, the output control module operates as follows: (1) Read the set target value; (2) Calculate the difference ΔT between the target value and the actual value, where ΔT = target value - actual value; (3) The adaptive learning fuzzy PID control module performs fuzzy self-tuning PID calculations; (4) The actual value is adjusted by using a PID controller to adjust the voltage or power of the heating element, humidification element or pressure adjustment element by adjusting the duty cycle of the thyristor; (5) The actual value gradually approaches the target value.

[0011] Preferably, the operation method of the ripening intelligent optimization control module is as follows: (1) After visually recognizing the food placed in the steam oven by the visual recognition sensor, the food is compared with the database loaded in the system to quickly analyze the type of food and the ripening medium threshold S of the food. (2) The ripening value T of the food is sensed in real time by the ripening medium sensor. When the ripening value T differs from the ripening medium threshold S by more than 1, the control program continues to heat and humidify. The ripening medium is sensed and detected in real time and compared with the set threshold S. (3) When the maturity ripening value T differs from the maturity ripening medium threshold S by less than or equal to 1, the system sends an electrical signal according to the personalized taste and heats and humidifies the ingredients according to the personalized setting program to achieve the personalized taste requirements of the ingredients. (4) Real-time detection of maturity: When the ripening medium reaches the set threshold, the system sends an electrical signal to control the load to stop heating and humidifying, and the cooking ends.

[0012] Preferably, the sensing module specifically includes: a temperature sensor, a humidity sensor, a pressure sensor, and a aging medium sensor.

[0013] Preferably, the mobile APP module specifically includes: Registration and login module: Allows users to register, log in to the APP, and pair with the steam oven; Add Device Module: Establish a network connection between the mobile app and the steam oven, and bind the new device; Control function module: includes single steaming, single baking and combined cooking modes, and manages cloud recipes through mobile APP to realize IoT remote management mode; The reservation function module allows users to set the clock for the steam oven via a mobile app, specifying the cooking program, cooking time, and reservation time. Query function module: Allows users to query the status of the steam oven and display the status via a mobile app.

[0014] The present invention also provides a multi-functional intelligent steam oven, specifically comprising: a steam oven body and the aforementioned multi-functional intelligent steam oven control system mounted on the steam oven body.

[0015] The beneficial effects of the multifunctional intelligent steam oven control system and intelligent steam oven provided by the invention are as follows: (1) This multi-functional intelligent steam oven control system systematically applies two advanced intelligent control strategies, namely "adaptive learning fuzzy PID control" and "mature intelligent optimization control", to the field of household steam ovens. It also deeply integrates the design for the characteristics of the cooking process (strong nonlinearity, large lag, and model uncertainty), solving the core pain point that traditional steam ovens cannot dynamically adapt to complex working conditions by using fixed programs or simple PID control.

[0016] (2) This multi-functional intelligent steam oven control system integrates environmental perception (temperature, humidity, pressure, cooking), intelligent algorithm (adaptive fuzzy PID, cooking optimization), precise execution and remote interconnection (WIFI / APP) into one, and constructs a closed-loop intelligent control system, realizing full automation and optimization of steaming and baking cooking.

[0017] (3) This multi-functional intelligent steam oven control system is clearly applicable to multiple modes such as single steaming, single baking, and combined cooking. Its core algorithm has universality and can cope with the complex control requirements of temperature, humidity and pressure for different cooking processes, thus improving the functional coverage and practicality of the equipment.

[0018] (4) This multi-functional intelligent steam oven control system greatly reduces the fluctuation of cooking effect caused by differences in initial conditions (such as food temperature and weight) or environmental interference through real-time sensing and intelligent control, ensuring the stability and repeatability of the quality of the dishes. Attached Figure Description

[0019] Figure 1 This is a block diagram of the multifunctional intelligent steam oven control system of the present invention.

[0020] Figure 2 This is a flowchart of the adaptive learning fuzzy PID control module in this invention.

[0021] Figure 3 This is a functional block diagram of the mobile APP module in this invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0023] Example 1: A multifunctional intelligent steam oven control system.

[0024] Reference Figures 1 to 3 As shown, a multi-functional intelligent steam oven control system specifically includes: (I) Sensing Module 1: This module is used to obtain real-time information on the operating conditions inside the cooking cavity of the steam oven in single steaming, single baking, and combined cooking modes, and to collect temperature, humidity, pressure, and cooking signals within the steam oven. Specifically, the sensing module includes a temperature sensor, a humidity sensor, a pressure sensor, and a cooking medium sensor. By accurately sensing the parameters of the cooking process, the sensing module performs optimal intelligent optimization control and matching, achieving fresh steaming and automatic control of cooking doneness, effectively improving the steam oven cooking experience.

[0025] (II) Data Conversion Module 2: This module performs digital filtering and analog-to-digital conversion on the temperature, humidity, pressure, or ripening signals collected by the sensing module. The most important task of the data conversion module is programming the data processing. It mainly involves three parts: temperature sampling and processing, digital filtering of the system, and conversion of proportional relationships. The sampling and processing of temperature, humidity, pressure, or ripening signals begins with acquiring analog signals from the operating steam oven and performing analog-to-digital conversion using hardware. Multiple methods are employed to prevent environmental interference.

[0026] (III) Adaptive Learning Fuzzy PID Control Module 3: The adaptive learning fuzzy PID control module is the core control module of this system. Its function is to: perform fuzzy inference on the temperature, humidity, pressure, or ripening signals after digital filtering and analog-to-digital conversion according to fuzzy rules; then calculate the incremental output values ​​KP, KI, and KD of the PID control parameters through a pre-set calculation program; then calculate the PID control parameters required by the system at the current moment through defuzzification and fuzzification parameter calculation; finally, output the parameters to the control system for adjusting the temperature, humidity, pressure, or ripening level; and use the three adjustment correction values ​​ΔKP, ΔKI, and ΔKD of the PID controller to adjust the input and output of the system. During the parameter adjustment process, when the number of learning nodes at a certain moment reaches the system-set threshold N (N=10 in this system), the universe of discourse is transformed to handle the situation, and the difference between the maximum values ​​of two adjacent deviations is calculated to be greater than or equal to 1℃. If the condition is met, the system will proceed to the adaptive learning phase, while simultaneously adjusting rewards and penalties online in real time. Otherwise, it will not proceed to self-learning and will continue to self-tun the parameters according to the existing fuzzy rules.

[0027] In actual cooking, system load (amount and type of ingredients), environmental conditions, and actuator status all change. By setting this adaptive learning fuzzy PID control module, the PID parameters (KP, KI, KD) can be tuned online in real time based on the deviation (e) and the rate of change of deviation (ec) through fuzzy inference, ensuring that the controller always operates with the current optimal response characteristics (such as speed, stability, and no overshoot). Compared to the rigidity and inadequacy of traditional fixed PID parameters in dealing with different cooking stages (such as preheating, constant temperature, and reducing sauce), this adaptive learning fuzzy PID control module ensures that key parameters such as temperature, humidity, pressure, and degree of doneness can track the set curve faster, more smoothly, and more accurately, fundamentally improving the uniformity and consistency of cooking. Moreover, this adaptive learning fuzzy PID control module sets clear learning trigger conditions (number of nodes N and deviation change threshold). When the system is running smoothly and the deviation is small, it does not easily initiate complex online learning, but instead uses verified fuzzy rules for fine-tuning. This design avoids unnecessary consumption of computational resources and parameter oscillations that may be caused by continuous learning, ensuring the system's operating efficiency and stability most of the time. Deep learning is only activated for optimization when there is a genuine need (i.e., when control performance tends to deteriorate), reflecting an intelligent trade-off in engineering.

[0028] This adaptive learning fuzzy PID control module summarizes the characteristics of common PID control and fuzzy control based on analysis and comparison. It attempts to solve the time-varying and nonlinear control problems of a steam oven control system by utilizing the advantages of both pure PID and fuzzy algorithms. The key to fuzzy PID control is calculating the optimal PID parameters. This is achieved by using a fuzzy algorithm to calculate the PID control parameters, specifically by using pre-set fuzzy rules to perform real-time linear correction of the system deviation (e) and the rate of change of deviation (ec). To achieve this dynamic adjustment process, the fuzzy relationship between e and ec in the control system must first be found. Secondly, continuous monitoring of the system deviation and the rate of change of deviation is essential during actual operation. Then, the dynamic parameters are adjusted online according to the fuzzy control rules to adapt to the different requirements of different control systems for parameters such as deviation and rate of change of deviation, thereby giving the PID controller good dynamic and static performance.

[0029] In this embodiment, the specific steps of the adaptive learning fuzzy PID control module after entering the adaptive learning stage are as follows: S1. Determine the variable universe: Assume -e max +e max Within time step t+1, the minimum and maximum values ​​of the parameter deviation e measured under varying system temperature, humidity, or pressure conditions are then determined based on the set rules. max When >1℃, if |-e max |=|+e max |, then the domain of discourse resulting from the change is taken as [-e max +e max If |-e max |≠|+e max |, let e t =max{[-e max +e max If ]}, then the domain of discourse resulting from this change is taken as [-e t ,+e t ], when e max When the temperature is ≤1℃, the controllable domain can be directly set to [-0.1, +0.1]; S2. Establish the evaluation function: Based on the dynamic performance of the PID controller, when e(k)•ec(k)>0, the system is reducing the deviation and showing a good trend; when e(k)•ec(k)<0, the system is increasing the deviation and showing a bad trend. Based on this change law, an evaluation function is established: c(k)=e(k)•ec(k). When c(k)>0, the current control rule of the system is rewarded; when c(k)<0, the current rule is punished. S3. Determine the reward and punishment function: Based on the evaluation function established in step S2, the following reward and penalty function is established:

[0030] In the formula, , The sub-means that k and k-1-t are the maximum absolute values ​​of the deviation values ​​e obtained in real time through the fuzzy algorithm, t is the lag number of the entire control system, and KT is the maximum value set in the current system. This reward and penalty function uses the difference in the absolute value of the maximum deviation within adjacent learning cycles as the basis for the severity of the reward or penalty, directly relating to the core indicator of control effectiveness—whether the maximum deviation has been reduced. Simultaneously, the system lag time (t) is introduced to compensate for the impact of the large lag characteristics of the cooking process on the effect evaluation. This allows the learning process to accurately and fairly evaluate the effectiveness of the control rules from the previous stage and make precise adjustments, representing a customized innovation for objects with large inertia and large lag, such as steam ovens.

[0031] S4. Implement adaptive learning system control: By performing real-time online correction on the current system parameter deviation e and deviation change rate ec, continuously detecting the system deviation e and deviation change rate ec, adjusting each dynamic parameter online according to fuzzy control rules, and performing control effect evaluation by sampling the measurement of current parameters e and ec, the control rule base is adjusted and the control system is changed by using a variable domain online reward and penalty learning algorithm.

[0032] The specific steps in step S4 of adjusting the control rule base and changing the control system by using a variable domain online reward and penalty learning algorithm are as follows: S41. The system initiates the self-learning phase, reading parameters e(k), ec(k), and ec(k-1-t). ; S42, Judgment Whether it is true or not, if If successful, the learning process ends. S43, if If the condition is not met, then calculate c(k) = e(k)•ec(k). If c(k) < 0, then call the reward / penalty function. Adjust and control the system until... If true; if c(k) > 0, then call the reward / penalty function. Adjust and control the system until ≤1 Established.

[0033] In actual cooking, when significant disturbances occur (such as suddenly opening a door or adding a large amount of frozen food) causing a sharp increase in deviation, a fixed fuzzy universe of discourse may fail to cover the current state, leading to control output saturation or rule failure. The variable universe of discourse mechanism of this adaptive learning fuzzy PID control module can adjust the control output based on the maximum and minimum measured deviations ([-e...). max , +e max The domain of the input variables is dynamically adjusted. This is equivalent to automatically expanding the controller's "field of view" and "adjustment range," ensuring that effective control quantities can still be generated under large deviations, quickly bringing the system back on track, and greatly enhancing the system's anti-interference ability and stability.

[0034] The "adaptive learning" of this adaptive learning fuzzy PID control module is not a one-time tuning, but a continuous process. When the system detects that the number of learning nodes has reached a threshold (N=10) and the deviation fluctuates significantly (|e... max (k) - e max When (k-1)|≥ 1℃, a deep "reward and punishment learning" process is triggered. The trend of the control effect is judged by the evaluation function c(k)=e(k)·ec(k): if c(k)>0 (the deviation is decreasing), the current control rule is "rewarded" to strengthen the effective strategy; if c(k)<0 (the deviation is increasing), the rule is "punished" and adjusted to avoid repeating the same mistake. This allows the control system to learn from historical operations, becoming "smarter" with use, and better able to handle unfamiliar or atypical cooking scenarios, much like an experienced chef.

[0035] Traditional PID controllers have fixed parameters, making it difficult to maintain optimal performance throughout the cooking process. This adaptive learning fuzzy PID control module tunes the PID parameters online through fuzzy inference and learns "rewards / penalties" based on the deviation trend (e(k)·ec(k)), enabling the controller to automatically adjust to follow the system state (such as changes in the heat absorption and release stages of the ingredients), achieving faster, smoother, and less overshoot control.

[0036] When the deviation is too large (emax > 1℃), the domain of fuzzy control is automatically adjusted, avoiding control failure caused by fixed rules under abnormal or large disturbance conditions, thus enhancing the robustness and control range of the system. It is difficult to establish an accurate mathematical model for the cooking process. This solution combines fuzzy logic (handling expert experience) and adaptive learning (online optimization), achieving excellent control without requiring an accurate model of the object, making it ideal for complex controlled objects such as steam ovens.

[0037] Traditional fuzzy PID control relies on a fixed rule base once set, making its effectiveness highly dependent on expert experience. This adaptive learning fuzzy PID control module, through its learning mechanism, can dynamically modify and optimize the fuzzy rule base online based on actual control performance. This means the system's control strategy is no longer preset and rigid, but rather evolving and adaptable. The initial rule base can be a general or empirical foundation. Over long-term use, it can self-optimize based on the specific user's habits, the hardware characteristics of the device, and local environmental conditions, ultimately forming a unique optimal control strategy, achieving a leap from "general intelligence" to "personalized intelligence."

[0038] This adaptive learning fuzzy PID control module does not simply superimpose fuzzy control with adaptive learning, but cleverly designs a hierarchical decision-making architecture: First layer (normal layer): The fuzzy self-tuning PID controller runs continuously to handle daily adjustments.

[0039] The second layer (learning trigger layer) is based on the dual conditions of "learning node count" and "change in maximum deviation value" to determine whether to enter deep learning.

[0040] The third layer (learning and execution layer): Online adjustment of rules based on reward and punishment functions, which is initiated after being triggered.

[0041] This architecture effectively solves the key question of "when to learn," making learning targeted and avoiding system instability caused by blind learning.

[0042] (iv) Intelligent Optimization Control Module 4: During the cooking process, it monitors the degree of food ripening, checks in real time whether the food ripeness meets the expected standard, realizes the self-control of the ripeness of ingredients, and intelligently optimizes the temperature and humidity control according to the personalized taste requirements of ingredients, thereby enhancing the quality and efficiency of food cooking.

[0043] The working method of the ripening intelligent optimization control module is as follows: (1) After visually recognizing the food placed in the steam oven by the visual recognition sensor, the food is compared with the database loaded in the system to quickly analyze the type of food and the ripening medium threshold S of the food. (2) The ripening value T of the food is sensed in real time by the ripening medium sensor. When the ripening value T differs from the ripening medium threshold S by more than 1, the control program continues to heat and humidify. The ripening medium is sensed and detected in real time and compared with the set threshold S. (3) When the maturity ripening value T differs from the maturity ripening medium threshold S by less than or equal to 1, the system sends an electrical signal according to the personalized taste and heats and humidifies the ingredients according to the personalized setting program to achieve the personalized taste requirements of the ingredients. (4) Real-time detection of maturity: When the ripening medium reaches the set threshold, the system sends an electrical signal to control the load to stop heating and humidifying, and the cooking ends.

[0044] This intelligent ripening optimization control module breaks through the traditional control mode that uses "time" or "core temperature" as a single endpoint. Through visual recognition and ripening sensors, it directly or indirectly senses changes in the "ripeness" of the ingredients or the "ripening medium," achieving cooking endpoint control based on the actual state of the ingredients, preventing overcooking or undercooking. The system not only determines "whether it's cooked through," but also performs precise temperature and humidity control adjustments (such as "tender roasting" or "crispy roasting") when the ingredients are close to the ripeness threshold, based on "personalized taste requirements," greatly enhancing the user experience and product added value. Users do not need professional knowledge to select complex parameters; they only need to put in the ingredients and select the desired texture, and the system will automatically complete the entire process from recognition to optimized cooking, truly achieving "one-click" intelligent cooking.

[0045] This intelligent optimization control module for cooking proposes a comparative control logic between the "cooking medium threshold S" and the "real-time cooking value T," and uses "visual recognition" to determine the type and initial state of the ingredients, combining this with subsequent intelligent optimization control. This control paradigm, which uses "ingredient state perception" as its core to replace or assist "cavity environment parameter control," is a significant innovation in the control concept of cooking appliances, enabling the machine to "observe" and "understand" the cooking process.

[0046] (v) Output control module 5: Based on the control quantity calculated by the adaptive learning fuzzy PID control module, it completes digital / analog conversion and power drive, and finally precisely adjusts the temperature, humidity, pressure or degree of cooking of the steam oven.

[0047] The output control module operates as follows: (1) Read the set target value; (2) Calculate the difference ΔT between the target value and the actual value, where ΔT = target value - actual value; (3) The adaptive learning fuzzy PID control module performs fuzzy self-tuning PID calculations; (4) The actual value is adjusted by using a PID controller to adjust the voltage or power of the heating element, humidification element or pressure adjustment element by adjusting the duty cycle of the thyristor; (5) The actual value gradually approaches the target value.

[0048] This output control module, as a direct downstream of the adaptive learning fuzzy PID control module, has the core task of applying the optimized digital control quantities (such as duty cycle setpoints) calculated by the algorithm to actuators such as heating elements, steam generators, and pressure valves without delay or distortion through digital-to-analog conversion and power drive. This achieves a lossless conversion and efficient closed loop from "intelligent decision-making" to "precise execution." This allows the complex preceding processes of perception, calculation, learning, and optimization to ultimately translate into precise physical changes to the cooking environment (temperature, humidity, and pressure), forming a complete intelligent closed loop from "perception-thinking-decision-execution-re-perception." By adjusting the duty cycle through methods such as thyristor zero-crossing triggering or phase angle control, continuous and stepless fine adjustment of power can be achieved. This allows for precise following of subtle changes in upstream control commands, enabling "smooth" adjustment of parameters such as temperature and humidity, avoiding the negative impact of abrupt changes on food taste and equipment lifespan.

[0049] (vi) WIFI module 6: responsible for establishing data transmission and communication with the server and APP, realizing the entire management process of the device, and establishing the connection between the device and the server.

[0050] (vii) Mobile APP module 7: Configure the steam oven WIFI module through the mobile APP, establish wireless access to the device, and use the APP to check the status of the steam oven and control the steam oven.

[0051] The mobile APP module specifically includes: Registration and login module: Allows users to register, log in to the APP, and pair with the steam oven; Add Device Module: Establish a network connection between the mobile app and the steam oven, and bind the new device; Control function module: includes single steaming, single baking and combined cooking modes, and manages cloud recipes through mobile APP to realize IoT remote management mode; The reservation function module allows users to set the clock for the steam oven via a mobile app, specifying the cooking program, cooking time, and reservation time. Query function module: Allows users to query the status of the steam oven and display the status via a mobile app.

[0052] In actual use, all operations such as starting, pausing, switching modes, and adjusting parameters can be remotely completed through this mobile APP module. Combined with the query function module, users can view the cavity temperature, remaining cooking time, and food cooking progress in real time, providing unprecedented freedom and convenience. Users can start cooking on their way home or easily adjust the cooking program in the living room, achieving "whole-house intelligent interaction" and perfectly integrating into modern smart home living scenarios. The control function module integrates all modes such as single steaming, single baking, and combined cooking, and encapsulates complex temperature, humidity, and time parameter settings in an intuitive graphical interface and cloud recipes. Users do not need to remember cumbersome operation steps and parameters; they only need to select a cloud recipe or preset mode on the APP, and the system will automatically complete the optimal control. This makes professional-grade cooking technology "one-click," greatly expanding the user base and allowing even cooking novices to easily make delicious dishes. The reservation function module allows users to flexibly set cooking programs and start times, enabling users to plan cooking tasks in advance according to their lifestyle. For example, users can schedule breakfast steaming and set dinner to be ready on time, fully coordinating cooking time with other tasks, maximizing the savings of time spent in the kitchen and improving life efficiency.

[0053] This multi-functional intelligent steam oven control system systematically applies two advanced intelligent control strategies—"adaptive learning fuzzy PID control" and "intelligent optimization control for ripening"—to the field of household steam ovens. It is deeply integrated into the design to address the characteristics of the cooking process (strong nonlinearity, large hysteresis, and model uncertainty), solving the core pain point of traditional steam ovens that use fixed programs or simple PID control, which cannot dynamically adapt to complex operating conditions. Traditional steam ovens use fixed PID or segmented PID, and when faced with the coupling of multiple physical fields such as steam, thermal radiation, and strong convection, overshoot of 1.5~2℃ and steady-state error of ±1℃ are common. This system, through "adaptive learning fuzzy PID," reduces the overshoot to 0.3℃ and the steady-state error to ±0.1℃, directly achieving commercial-grade accuracy at the level of "low-temperature slow cooking ±0.1℃".

[0054] This multi-functional intelligent steam oven control system integrates environmental sensing (temperature, humidity, pressure, and cooking), intelligent algorithms (adaptive fuzzy PID, cooking optimization), precise execution, and remote interconnection (WIFI / APP) into a single closed-loop intelligent control system, achieving full automation and optimization of steaming and baking cooking. This multi-functional intelligent steam oven control system is specifically applicable to various modes such as single steaming, single baking, and combined cooking. Its core algorithm is universally applicable, capable of handling the complex control requirements of temperature, humidity, and pressure for different cooking processes, thus enhancing the functional coverage and practicality of the equipment. Through real-time sensing and intelligent control, this multi-functional intelligent steam oven control system greatly reduces fluctuations in cooking results caused by differences in initial conditions (such as food temperature and weight) or environmental interference, ensuring the stability and repeatability of the quality of the dishes.

[0055] Example 2: A multifunctional intelligent steam oven.

[0056] A multi-functional intelligent steam oven specifically includes a steam oven body, on which a multi-functional intelligent steam oven control system as described in Example 1 is installed.

[0057] The above description is only a preferred embodiment of the present invention, but the present invention should not be limited to the content disclosed in the embodiments and drawings. Therefore, any equivalent or modified embodiments made without departing from the spirit of the present invention shall fall within the protection scope of the present invention.

Claims

1. A multifunctional intelligent steam oven control system, characterized in that... include: Sensing module: used to obtain real-time information on the working conditions inside the cooking cavity of the steam oven in single steaming, single baking, and combined cooking modes, and to collect temperature, humidity, pressure, and cooking signals inside the steam oven; Data conversion module: performs digital filtering and analog-to-digital conversion on the temperature, humidity, pressure, or aging signals collected by the sensing module; The adaptive learning fuzzy PID control module performs fuzzy inference on temperature, humidity, pressure, or ripening signals after digital filtering and analog-to-digital conversion according to fuzzy rules. Then, it calculates the incremental output values ​​KP, KI, and KD of the PID control parameters through a pre-set calculation program. After defuzzification and fuzzification parameter calculations, it obtains the PID control parameters required by the system at the current moment, and finally outputs them to the control system for adjusting the temperature, humidity, pressure, or ripening levels. The module is the input to the control system and uses the three adjustment correction values ​​ΔKP, ΔKI, and ΔKD of the PID controller to adjust the output. During parameter adjustment, when the number of learning nodes reaches the system's set threshold N at a certain moment, it processes the situation by transforming the universe of discourse and simultaneously calculates that the difference between the maximum values ​​of two adjacent deviations is greater than or equal to 1℃. If the condition is met, the system will proceed to the adaptive learning phase, while simultaneously adjusting rewards and penalties online in real time. Otherwise, it will not enter self-learning and will still perform parameter self-tuning according to the existing fuzzy rules; Intelligent cooking optimization control module: Monitors the degree of food cooking during the cooking process, checks in real time whether the food cooking degree meets the expected standard, realizes automatic control of the cooking degree of ingredients, and intelligently optimizes temperature and humidity control according to the personalized taste requirements of ingredients. Output control module: Based on the control quantity calculated by the adaptive learning fuzzy PID control module, it completes digital / analog conversion and power drive, and finally precisely adjusts the temperature, humidity, pressure or degree of cooking of the steam oven; WIFI module: Responsible for establishing data transmission and communication with the server and APP, realizing the entire management process of the device, and establishing the connection between the device and the server; Mobile App Module: Configure the steam oven's WIFI module via a mobile app, establish wireless access to the device, and use the app to check the steam oven's status and control the steam oven; The specific steps of the adaptive learning fuzzy PID control module after entering the adaptive learning stage are as follows: S1. Determine the variable universe: Assume -e max +e max Within time step t+1, the minimum and maximum values ​​of the parameter deviation e measured under varying system temperature, humidity, or pressure conditions are then determined based on the set rules. max When >1℃, if |-e max |=|+e max |, then the domain of discourse resulting from the change is taken as [-e max +e max If |-e max |≠|+e max |, let e t =max{[-e max +e max If ]}, then the domain of discourse resulting from this change is taken as [-e t ,+e t ], when e max When the temperature is ≤1℃, the controllable domain can be directly set to [-0.1, +0.1]; S2. Establish the evaluation function: Based on the dynamic performance of the PID controller, when e(k)•ec(k)>0, the system is reducing the deviation and showing a good trend; when e(k)•ec(k)<0, the system is increasing the deviation and showing a bad trend. Based on this change law, an evaluation function is established: c(k)=e(k)•ec(k). When c(k)>0, the current control rule of the system is rewarded; when c(k)<0, the current rule is punished. S3. Determine the reward and punishment function: Based on the evaluation function established in step S2, the following reward and penalty function is established: Official 1 In the formula, , , The maximum absolute value of the deviation value e obtained in real time through the fuzzy algorithm at times k, k-1, and k-1-t, respectively, where t is the lag number of the entire control system, and KT is the maximum value set in the current system. S4. Implement adaptive learning system control: By performing real-time online correction on the current system parameter deviation e and deviation change rate ec, continuously detecting the system deviation e and deviation change rate ec, adjusting each dynamic parameter online according to fuzzy control rules, and performing control effect evaluation by sampling the measurement of current parameters e and ec, the control rule base is adjusted and the control system is changed by using a variable domain online reward and penalty learning algorithm.

2. The multifunctional intelligent steam oven control system as described in claim 1, characterized in that, The specific steps in step S4 of adjusting the control rule base and changing the control system by using a variable domain online reward and penalty learning algorithm are as follows: S41. The system initiates the self-learning phase, reading parameters e(k), ec(k), and ec(k-1-t). ; S42, Judgment Whether it is true or not, if If successful, the learning process ends. S43, if If the condition is not met, then calculate c(k) = e(k)•ec(k). If c(k) < 0, then call the reward / penalty function. Adjust and control the system until... If true; if c(k) > 0, then call the reward / penalty function. Adjust and control the system until... Established.

3. The multifunctional intelligent steam oven control system as described in claim 1, characterized in that, The output control module operates as follows: (1) Read the set target value; (2) Calculate the difference ΔT between the target value and the actual value, where ΔT = target value - actual value; (3) The adaptive learning fuzzy PID control module performs fuzzy self-tuning PID calculations; (4) The actual value is adjusted by using a PID controller to adjust the voltage or power of the heating element, humidification element or pressure adjustment element by adjusting the duty cycle of the thyristor; (5) The actual value gradually approaches the target value.

4. The multifunctional intelligent steam oven control system as described in claim 1, characterized in that, The working method of the ripening intelligent optimization control module is as follows: (1) After visually recognizing the food placed in the steam oven by the visual recognition sensor, the food is compared with the database loaded in the system to quickly analyze the type of food and the ripening medium threshold S of the food. (2) The ripening value T of the food is sensed in real time by the ripening medium sensor. When the ripening value T differs from the ripening medium threshold S by more than 1, the control program continues to heat and humidify. The ripening medium is sensed and detected in real time and compared with the set threshold S. (3) When the maturity ripening value T differs from the maturity ripening medium threshold S by less than or equal to 1, the system sends an electrical signal according to the personalized taste and heats and humidifies the ingredients according to the personalized setting program to achieve the personalized taste requirements of the ingredients. (4) Real-time detection of maturity: When the ripening medium reaches the set threshold, the system sends an electrical signal to control the load to stop heating and humidifying, and the cooking ends.

5. The multifunctional intelligent steam oven control system as described in claim 1, characterized in that, The sensing module specifically includes: a temperature sensor, a humidity sensor, a pressure sensor, and a aging medium sensor.

6. The multifunctional intelligent steam oven control system as described in claim 1, characterized in that, The mobile APP module specifically includes: Registration and login module: Allows users to register, log in to the APP, and pair with the steam oven; Add Device Module: Establish a network connection between the mobile app and the steam oven, and bind the new device; Control function module: includes single steaming, single baking and combined cooking modes, and manages cloud recipes through mobile APP to realize IoT remote management mode; The reservation function module allows users to set the clock for the steam oven via a mobile app, specifying the cooking program, cooking time, and reservation time. Query function module: Allows users to query the status of the steam oven and display the status via a mobile app.

7. A multifunctional intelligent steam oven, characterized in that... include: The steam oven body and the control system mounted on the steam oven body as described in any one of claims 1-6.

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