A multi-functional oven and its multi-segment programmable control method and medium

Abstract

This application relates to the field of household appliance control technology, and discloses a multi-functional oven and its multi-segment programmable control method and medium. The multi-functional oven includes an oven body with a baking cavity, a heating device and a spraying device disposed in the baking cavity, a door for opening and closing the baking cavity, and a control system. The control system includes a processor and a control panel for inputting parameters. The method includes: inputting parameters for multiple sequentially executed baking periods through the control panel; controlling the heating device and the spraying device to work sequentially according to the set order of the multiple baking periods; monitoring the humidity in the baking cavity in real time through a humidity sensor; if the humidity exceeds the humidity range set for the corresponding period, adjusting the spray volume of the spraying device to compensate for humidity; after all baking periods are completed, opening the door; monitoring the temperature in the baking cavity in real time through a temperature sensor; and controlling the door to close when the temperature drops to the cooling target temperature or the cooling time is reached.

Description

Technical Field

[0001] This application relates to the field of household appliance control technology, and in particular to a multi-functional oven and its multi-segment programmable control method and medium. Background Technology

[0002] Existing food processing equipment (such as ovens) in the home appliance sector faces significant technological bottlenecks in food preparation. Traditional ovens generally only have a constant heating function within a single temperature range, unable to automatically switch between different time periods based on the varying temperature requirements of bread, cakes, and other foods during fermentation, baking, and browning. This necessitates users to manually monitor and frequently adjust the temperature throughout the process, making operation cumbersome and susceptible to food quality issues due to temperature fluctuations. For example, the crucial "high humidity initial baking" and "low humidity later shaping" stages in European bread making cannot be controlled in stages using existing equipment. Existing spray functions are mostly manually triggered and cannot accurately match the time period requirements, directly affecting core quality indicators such as crust crispness. Furthermore, the cooling process after baking relies on manual door opening, making it difficult to standardize the timing and duration of cooling, potentially leading to problems such as softening of cookies. Moreover, the lack of a real-time feedback and adjustment mechanism for internal temperature and humidity prevents the formation of a closed-loop control system.

[0003] Therefore, a method is urgently needed to solve at least one of the above problems. Summary of the Invention

[0004] This application provides a multi-functional oven and its multi-stage programmable control method and medium, aiming to solve the problems in existing technologies where oven control logic is mostly a simple "temperature setting - heating maintenance" single-threaded mode, without addressing pre-programmed design of multi-stage programs, coordinated control of multiple actuators (heating, spraying, door), and intelligent compensation algorithms based on sensor data. For example, in traditional ovens, the spraying device and temperature control module are independent and cannot dynamically adjust the spray volume based on real-time humidity data; the door opening and closing during the cooling stage is not linked to temperature monitoring. Therefore, how to accurately match the timing of multi-stage temperature and humidity parameters with the actions of actuators through a systematic programmable control method, and combine it with intelligent feedback algorithms to improve control accuracy, is a technical problem that has not yet been solved in the prior art.

[0005] In a first aspect, embodiments of this application provide a multi-segment programmable control method applied to a multi-functional oven. The multi-functional oven includes an oven body with a baking cavity, a heating device and a spray device disposed in the baking cavity, a door for opening and closing the baking cavity, and a control system. The control system includes a processor and a control panel for inputting parameters. The method includes:

[0006] The parameters for multiple sequentially executed baking periods are input through the control panel. The parameters for each baking period include the duration of the period, the target temperature range, whether spraying is required, the start time of spraying, the duration of spraying, the amount of spray, and the target cooling temperature or cooling time of the cooling phase.

[0007] The heating device and the spraying device are controlled to work sequentially according to the set baking time period. The humidity in the baking cavity is monitored in real time by a humidity sensor. If the humidity exceeds the humidity range set for the corresponding time period, the spray volume of the spraying device is adjusted to compensate for the humidity.

[0008] After all baking periods are completed, the door is opened, and the temperature inside the baking cavity is monitored in real time by a temperature sensor. When the temperature drops to the target cooling temperature or the cooling time is reached, the door is closed. When the quality data of the baked product is abnormal, a causal inference model is used to analyze the process data, obtain the cause of the abnormality, and adjust the subsequent program parameters. The process data includes temperature curves, humidity curves, and spraying time. The subsequent program parameters include fermentation humidity and baking temperature. The quality data of the finished product includes one or more of the following: cake height, bread crust hardness, and user rating.

[0009] In some embodiments, controlling the heating device and the spraying device to operate sequentially according to a set sequence of multiple baking periods includes: controlling the heating device to start, monitoring the temperature inside the baking cavity in real time using a temperature sensor, adjusting the power output of the heating device according to the deviation between the real-time monitored temperature and the corresponding target temperature, so that the temperature inside the baking cavity is maintained within the corresponding target temperature range; if it is determined that spraying is required based on the parameters of the baking period, controlling the spraying device to start when the set spraying start time is reached, adjusting the flow rate of the spraying device according to the set spraying volume, and controlling the spraying device to stop according to the set spraying duration.

[0010] In some embodiments, adjusting the spray volume of the spraying device for humidity compensation includes: obtaining the humidity range set for the current time period and the humidity value inside the baking cavity monitored in real time, calculating the humidity deviation and the rate of change of humidity deviation, determining the corresponding spray volume adjustment level according to a preset fuzzy control rule table, and controlling the spraying device to spray with the adjusted spray volume.

[0011] In some embodiments, the step of monitoring the temperature inside the baking cavity in real time using a temperature sensor and controlling the door to close when the temperature drops to the target cooling temperature or the cooling time is reached includes: acquiring the rate of temperature drop inside the baking cavity in real time, predicting the time required to reach the target cooling temperature based on the rate of temperature drop and the current temperature, and controlling the door to close when the target cooling temperature is reached if the predicted time is less than the remaining cooling time; and controlling the door to close when the cooling time is reached if the predicted time is greater than or equal to the remaining cooling time.

[0012] In some embodiments, the method further includes: establishing a database linking food types and baking program parameters; when a user inputs a food type through the control panel, retrieving corresponding preset baking program parameters from the database; the preset baking program parameters include parameters for multiple sequentially executed baking periods and parameters for the cooling stage, so as to modify the retrieved preset baking program parameters before use.

[0013] In some embodiments, the method further includes: recording the actual temperature and humidity data and corresponding food quality evaluation data for each baking period during each baking process; establishing a prediction model of baking parameters and food quality based on a machine learning algorithm; and optimizing and recommending subsequent baking program parameters based on the prediction model.

[0014] In some embodiments, the method further includes: acquiring real-time operating status data of the heating device, the spraying device, and the door opening and closing mechanism; monitoring the operating status of the multi-functional oven based on preset fault diagnosis rules; and issuing a fault alarm message through the control panel if an abnormal state is detected.

[0015] In some embodiments, the method further includes: under the premise of meeting baking quality requirements, with the goal of minimizing energy consumption, using a genetic algorithm to optimize the target temperature, spray volume and cooling time for each baking period, and generating optimized baking program parameters.

[0016] Secondly, embodiments of this application provide a multi-functional oven, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to execute the computer program and implement the method provided in any embodiment of this application when executing the computer program.

[0017] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the method provided in any embodiment of this application.

[0018] The multi-functional oven and its multi-segment programmable control method and medium provided in this application embodiment achieve automatic temperature switching during fermentation, shaping, and browning stages by preset multi-time-segment temperature parameters, avoiding errors from manual adjustment and improving the consistency of baking quality. Based on real-time humidity monitoring and fuzzy control algorithms, the spray volume is dynamically adjusted to meet the humidity requirements of different foods at each stage (such as the high humidity environment in the early stage of baking European bread), optimizing the taste and appearance of the food. The cooling process is monitored in real time by a temperature sensor, automatically triggering the door opening and closing action without manual intervention, ensuring standardized cooling timing and duration, and preventing food quality degradation due to improper cooling. Through pre-programmed multi-segment program control, human operation deviations are reduced, and the reproducibility is high, making it suitable for the large-scale, standardized production needs of home kitchens and bakeries.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic flowchart illustrating the steps of a multi-segment programmable control method provided in an embodiment of this application;

[0022] Figure 2 This is a schematic diagram of a multifunctional oven structure provided in one embodiment of this application;

[0023] Figure 3 This is a schematic block diagram of a multi-segment programmable device provided in an embodiment of this application;

[0024] Figure 4 This is a schematic block diagram of the structure of a multifunctional oven provided in one embodiment of this application.

[0025] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0028] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0029] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0030] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0031] Existing food processing equipment (such as ovens) in the home appliance sector faces significant technological bottlenecks in food preparation. Traditional ovens generally only have a constant heating function within a single temperature range, unable to automatically switch between different time periods based on the varying temperature requirements of bread, cakes, and other foods during fermentation, baking, and browning. This necessitates users to manually monitor and frequently adjust the temperature throughout the process, making operation cumbersome and susceptible to food quality issues due to temperature fluctuations. For example, the crucial "high humidity initial baking" and "low humidity shaping and browning" stages in European bread making cannot be controlled in stages using existing equipment. Existing spray functions are mostly manually triggered and cannot accurately match the time period requirements, directly affecting core quality indicators such as crust crispness. Furthermore, the cooling process after baking relies on manual door opening, making it difficult to standardize the timing and duration of cooling, potentially leading to problems such as softening of cookies. Moreover, the lack of a real-time feedback and adjustment mechanism for internal temperature and humidity prevents the formation of a closed-loop control system.

[0032] In existing technologies, oven control logic is mostly a simple "temperature setting - heating maintenance" single-threaded mode, lacking pre-programmed design of multi-segment programs, coordinated control of multiple actuators (heating, spraying, door), and intelligent compensation algorithms based on sensor data. For example, the spraying device and temperature control module of traditional ovens are independent, unable to dynamically adjust the spray volume based on real-time humidity data; the door opening and closing during the cooling stage is not linked to temperature monitoring. Therefore, how to use a systematic program control method to accurately match the timing of multi-stage temperature and humidity parameters with the actions of actuators, and combine this with intelligent feedback algorithms to improve control accuracy, remains an unsolved technical challenge in existing technologies.

[0033] Therefore, a method is urgently needed to solve at least one of the above problems.

[0034] To solve the above problem, please refer to Figure 1 This application provides a multi-segment programmable method for encapsulating, for example, Figure 2 The multi-functional oven shown is an oven body with a baking cavity, a heating device and a spray device disposed in the baking cavity, a door for opening and closing the baking cavity, and a control system, the control system including a processor and a control panel for inputting parameters.

[0035] Specifically, such as Figure 1 As shown, the provided multi-segment programmable control method includes steps S101 to S103. Details are as follows:

[0036] Step S101. Input parameters for multiple sequentially executed baking periods through the control panel. The parameters for each baking period include the duration of the period, the target temperature range, whether spraying is required, the start time of spraying, the duration of spraying, the amount of spray, and the target cooling temperature or cooling time of the cooling stage.

[0037] Specifically, this step is the core pre-configuration stage, allowing users to break down the entire baking process into multiple time-series stages with independent temperature and humidity control logic, based on the processing requirements of different foods (such as European bread, cakes, and cookies). Key parameters can be set independently for each time period, enabling differentiated control over fermentation, baking, browning, and cooling processes.

[0038] Users input parameters for each time period sequentially via the control panel (such as a touchscreen or knob + display): Duration of Time Period: Set the total working time for this stage (e.g., 30 minutes for fermentation stage, 20 minutes for baking stage). Target Temperature Range: Set the temperature range to be maintained in the baking cavity during this time period (e.g., 35±2℃ for fermentation stage, 200±5℃ for browning stage).

[0039] The spray trigger condition is set by selecting "Whether spraying is needed" (e.g., high humidity spraying is needed at the beginning of the European bread season). If spraying is needed, the spray start time (e.g., start 2 minutes after the start of the time period), spray duration (e.g., spray continuously for 30 seconds), and spray volume (e.g., water output of 50ml / min per unit time) can be further set.

[0040] Cooling parameters can be set by setting the target cooling temperature (e.g., below 40°C) or cooling time (e.g., a fixed cooling time of 5 minutes).

[0041] The control system stores preset parameters as reusable program templates (such as "European bread mode" and "cake mode"), which users can directly recall later without repeated input. It supports dynamic adjustment of time-segment parameters, such as temporarily modifying parameters for periods not yet executed during baking. Through time-segmented parameter configuration, it overcomes the limitations of traditional ovens' single temperature control, enabling the equipment to automatically adapt to the temperature and humidity requirements of different food processing stages, reducing manual intervention.

[0042] Step S102. Control the heating device and the spraying device to work sequentially according to the set multiple baking time periods. Monitor the humidity in the baking cavity in real time through a humidity sensor. If the humidity exceeds the humidity range set for the corresponding time period, adjust the spray volume of the spraying device to compensate for the humidity.

[0043] Specifically, this step is the execution stage of the baking process. According to the preset program of S101, the system automatically controls the heating device, spraying device and other actuators in sequence, and dynamically adjusts the spray volume in combination with the real-time feedback data of the humidity sensor to achieve precise humidity closed-loop control.

[0044] The time-sequential execution control uses a processor to sequentially activate each stage according to a preset time period sequence: Heating control: Based on the target temperature range for the current time period, the power of the heating element is adjusted using a PID algorithm to quickly stabilize the temperature inside the cavity within the set range. Spray control: During the required spraying period, the spraying device (such as an ultrasonic atomizer) is triggered according to a preset start time, duration, and spray volume.

[0045] The humidity feedback compensation mechanism monitors the humidity inside the baking cavity in real time using a humidity sensor (such as a capacitive humidity sensor). If the real-time humidity is lower than the lower limit set for the current period (e.g., the initial humidity requirement for European bread is ≥80%, but the actual measurement is 70%), the system automatically increases the spray volume or extends the spraying time; if the humidity is higher than the upper limit (e.g., the actual measured humidity is 90%), the spray volume is reduced or the spraying is paused. The compensation algorithm needs to take into account the response characteristics of the spray device to avoid over-adjustment that could cause oscillations.

[0046] The multi-mechanism collaborative logic includes: Heating and spraying linkage: During spraying, the heating power is briefly reduced to avoid the instantaneous evaporation of water mist affecting humidity stability. Smooth transition between time periods: Between adjacent time periods, temperature changes are implemented using a gradual algorithm (e.g., increasing / decreasing temperature by 10°C per minute) to prevent food quality from being damaged by sudden temperature changes.

[0047] By using sensor data and closed-loop control of the actuator, the problem of traditional oven humidity control relying on manual operation and lacking dynamic compensation is solved. It is especially suitable for humidity-sensitive foods such as European bread, ensuring the crispness of the crust and the uniformity of the internal structure.

[0048] Step S103. After all baking periods are completed, open the door and monitor the temperature inside the baking cavity in real time using a temperature sensor. When the temperature drops to the target cooling temperature or the cooling time is reached, control the door to close. When the quality data of the baked product is abnormal, use a causal inference model to analyze the process data, obtain the cause of the abnormality, and adjust the subsequent program parameters. The process data includes temperature curves, humidity curves, and spraying time. The subsequent program parameters include fermentation humidity and baking temperature. The quality data of the finished product includes one or more of the following: cake height, bread crust hardness, and user rating.

[0049] Specifically, this step addresses the cooling process after baking. By using temperature sensors to monitor and coordinate with the door's movement, the cooling process is automated and standardized, preventing food quality degradation due to improper manual operation.

[0050] After all baking periods are completed, the system automatically controls the door drive mechanism (such as a motor) to open the door to a preset angle (such as 15°), forming a controllable heat dissipation channel.

[0051] The cooling termination condition can be selected from two options: Temperature-based: The temperature inside the cavity is monitored in real time by a temperature sensor, and the door automatically closes when the temperature drops to the preset cooling target temperature (e.g., 40°C). Time-based: If the user sets a fixed cooling time (e.g., 5 minutes), the door will close regardless of the temperature after the time is reached.

[0052] Temperature monitoring with safety redundancy ensures that even in time-based cooling mode, the system continues to monitor the temperature. If the cavity temperature rises abnormally (e.g., due to residual heat recirculation), the door can be reopened for cooling to prevent overheating. Once cooling is complete, the door closes to minimize heat loss and facilitate a quick start to the next baking cycle. This solves the problem of traditional ovens relying on manual judgment of cooling timing, preventing quality issues such as cookies becoming soft due to insufficient cooling or cakes collapsing due to sudden cooling, while also improving ease of operation and consistency of results.

[0053] When the quality of the baked product is abnormal (such as "the cake did not rise" or "the bread crust is too hard"), the causal inference model is used to analyze the process data (such as temperature curves, humidity curves, and spraying time) to find the cause of the abnormality (such as "the humidity is too low during fermentation" or "the baking temperature is too high"), and the subsequent program parameters are automatically adjusted (such as increasing the fermentation humidity and decreasing the baking temperature) to avoid repeating the error.

[0054] Data collection involves recording process data (such as temperature, humidity, spray time, and heating power) and finished product quality data (such as cake height, bread crust hardness, and user ratings) for each baking session and storing them in a time-series database.

[0055] Causal models are established by using structural causal models (SCM) to establish causal relationships between process variables (such as fermentation temperature, humidity, and time) and quality variables (such as cake height). For example: fermentation temperature ↑ → fermentation degree ↑ → cake height ↑; fermentation humidity ↓ → dough water loss ↑ → fermentation degree ↓ → cake height ↓.

[0056] Anomaly Detection and Root Cause Analysis: When the finished product quality is abnormal (e.g., "cake height is 20% lower than preset"), the system activates the anomaly detection module. It compares the process data with the normal procedure (e.g., "fermentation temperature 38℃ / 60min, humidity 70%)" to identify differences (e.g., "fermentation humidity is only 60%, 10% lower than normal"). It uses DO-calculation (e.g., "DO(fermentation humidity = 70%)") to simulate whether the cake height would reach the preset value if the fermentation humidity were normal. If the simulation result shows that "the cake height will reach the preset value", the root cause is that the fermentation humidity is too low. If the simulation result shows that "the cake height still does not reach the preset value", other variables are further analyzed (e.g., "whether the fermentation temperature is too low").

[0057] Parameter adjustment and verification are achieved through automatic adjustment of subsequent program parameters by the system (e.g., increasing fermentation humidity from 60% to 70%). The adjusted program is then executed, process data and finished product quality data are collected, and the root cause analysis is verified to be correct (e.g., if "cake height returns to preset value," the root cause is correct; otherwise, reanalysis is performed). The system stores the root cause-adjustment strategy in the "Abnormal Handling Knowledge Base" (e.g., "cake didn't rise → check if fermentation humidity is too low → increase humidity by 5%) for direct use in subsequent similar abnormalities.

[0058] In some embodiments, controlling the heating device and the spraying device to operate sequentially according to a set sequence of multiple baking periods includes: controlling the heating device to start, monitoring the temperature inside the baking cavity in real time using a temperature sensor, adjusting the power output of the heating device according to the deviation between the real-time monitored temperature and the corresponding target temperature, so that the temperature inside the baking cavity is maintained within the corresponding target temperature range; if it is determined that spraying is required based on the parameters of the baking period, controlling the spraying device to start when the set spraying start time is reached, adjusting the flow rate of the spraying device according to the set spraying volume, and controlling the spraying device to stop according to the set spraying duration.

[0059] This embodiment details the basic control logic of the heating and spraying device in step S102. The core lies in achieving precise closed-loop temperature control through feedback from a temperature sensor, and strictly triggering and stopping the spraying action according to preset timing parameters.

[0060] Temperature closed-loop control includes: Start-up and monitoring: Upon entering a certain baking period, the processor immediately controls the heating device (such as upper and lower heating elements, hot air fan) to start. Temperature sensors (such as thermocouples or thermistors) continuously monitor the real-time temperature inside the baking cavity and feed the data back to the processor. Power adjustment: The processor calculates the deviation between the real-time temperature and the set target temperature for that period. Using a PID (proportional-integral-derivative) control algorithm, the power output of the heating device is dynamically adjusted according to the magnitude of the deviation (e.g., through PWM pulse width modulation). For example, when the actual temperature is much lower than the target temperature, the system will heat at full power or high power; when approaching the target temperature, the power is reduced to prevent overshoot, ultimately stabilizing the cavity temperature within the preset target temperature range.

[0061] Spray timing control includes: Conditional judgment: The system checks the parameters for the current time period. If the "Does spray need to be sprayed?" flag is "Yes", it enters the spray preparation state. Triggering and stopping: The system's internal timer starts working. When the timer reaches the preset "spray start time", the processor sends a start signal to the spray device (such as a water pump + ultrasonic atomizer) and sets the initial spray flow rate according to the "spray volume" parameter (which may correspond to different water pump speeds or atomizer power levels). The spray device continues to work until the set "spray duration" is reached, at which point the processor sends a stop signal to shut down the spray device.

[0062] This embodiment achieves basic temperature stability and automates the spraying operation within each baking period, ensuring the timing accuracy of process execution.

[0063] In some embodiments, adjusting the spray volume of the spraying device for humidity compensation includes: obtaining the humidity range set for the current time period and the humidity value inside the baking cavity monitored in real time, calculating the humidity deviation and the rate of change of humidity deviation, determining the corresponding spray volume adjustment level according to a preset fuzzy control rule table, and controlling the spraying device to spray with the adjusted spray volume.

[0064] This embodiment refines the humidity compensation mechanism in step S102. It employs a fuzzy control algorithm, rather than simple on / off control, to address the nonlinearity and hysteresis of humidity changes within the baking cavity, thereby achieving gentler and more precise humidity regulation.

[0065] Data acquisition involves real-time monitoring of the humidity value inside the cavity using a humidity sensor. The processor acquires the target humidity range set for the current time period (e.g., upper limit H_high and lower limit H_low), and calculates the deviation (e) between the real-time humidity and the midpoint of the target range, as well as the rate of change of the deviation per unit time (ec, i.e., the humidity change trend).

[0066] Fuzzy inference includes: Fuzzification: Converting precise deviation (e) and deviation change rate (ec) values ​​into fuzzy linguistic variables such as "negative large", "negative small", "zero", "positive small", and "positive large". Rule matching: Querying a preset fuzzy control rule table. This rule table is based on expert experience, for example: "If the humidity deviation is negative large (too dry) and the deviation change rate is negative large (drying rapidly), then the spray volume adjustment is positive large (significantly increasing the spray volume)". Defuzzification converts the output of fuzzy inference (such as "increase the spray volume to medium") into precise control quantities, i.e., specific spray device flow adjustment levels or power percentages. The processor dynamically adjusts the operating parameters of the spray device based on the defuzzified results, making it operate at the new spray volume until the next sampling cycle for a new round of adjustment. The implementation significantly improves the stability and adaptability of humidity control, effectively suppressing over-spraying or under-compensation, avoiding humidity fluctuations, and is particularly suitable for high-quality baking that is sensitive to humidity.

[0067] In some embodiments, the step of monitoring the temperature inside the baking cavity in real time using a temperature sensor and controlling the door to close when the temperature drops to the target cooling temperature or the cooling time is reached includes: acquiring the rate of temperature drop inside the baking cavity in real time, predicting the time required to reach the target cooling temperature based on the rate of temperature drop and the current temperature, and controlling the door to close when the target cooling temperature is reached if the predicted time is less than the remaining cooling time; and controlling the door to close when the cooling time is reached if the predicted time is greater than or equal to the remaining cooling time.

[0068] This embodiment optimizes the cooling termination judgment logic in step S103. It combines temperature-based precise control with time-based fault-tolerant control, and achieves a more intelligent and reliable door closing decision through a predictive algorithm.

[0069] Data monitoring and prediction: During the cooling phase, after the door is opened, the system continuously monitors the rate of temperature decrease (V_temp) inside the cavity. Combining the current temperature (T_current) and the set cooling target temperature (T_target), the system predicts the time required to reach the target temperature (T_predict = (T_current - T_target) / V_temp).

[0070] The adaptive decision-making logic reads the user-defined cooling time (T_set) simultaneously through the system. Case 1 (Temperature Priority): If the predicted time T_predict is less than the remaining cooling time (T_set - already cooled time), it indicates a rapid temperature drop, and the temperature condition can be prioritized. The system will immediately close the door when the measured temperature drops to T_target. Case 2 (Time Priority): If the predicted time T_predict is greater than or equal to the remaining cooling time, it indicates a slow temperature drop, and the target temperature may not be reached within the set time. To ensure the process ends on time (and avoid indefinite door opening), the system will close the door when the set T_set is reached, even if the temperature has not completely dropped to T_target at this point. This improves the robustness of the program and user experience while ensuring cooling effectiveness, avoiding prolonged device waiting due to slow temperature drops in certain cases.

[0071] In some embodiments, the method further includes: establishing a database linking food types and baking program parameters; when a user inputs a food type through the control panel, retrieving corresponding preset baking program parameters from the database; the preset baking program parameters include parameters for multiple sequentially executed baking periods and parameters for the cooling stage, so as to modify the retrieved preset baking program parameters before use.

[0072] This embodiment is an enhancement of step S101. By establishing a pre-built program library, the user's operating threshold is reduced, while retaining room for personalized adjustments.

[0073] The database is established by creating an associated database in the control system's memory. Each record contains a "food type" (such as "standard European bread", "chiffon cake", "cookie") and a corresponding set of "preset baking program parameters", which are all the parameters such as temperature, humidity, spray, and cooling at multiple time periods described in the embodiment.

[0074] Users don't need to set all the parameters from scratch; they simply select the desired "food type" through the control panel. The system automatically retrieves the corresponding preset parameters from the database and displays them on the control panel. Users can view and modify these preset parameters according to their preferences, ingredient differences, or environmental conditions (such as increasing the browning temperature by 10°C). After confirmation, the personalized program can be launched. This greatly simplifies user operation, achieving the convenience of "one-click baking," while also catering to the customization needs of advanced users.

[0075] In some embodiments, the method further includes: recording the actual temperature and humidity data and corresponding food quality evaluation data for each baking period during each baking process; establishing a prediction model of baking parameters and food quality based on a machine learning algorithm; and optimizing and recommending subsequent baking program parameters based on the prediction model.

[0076] This embodiment incorporates machine learning capabilities, enabling the oven to continuously optimize the baking program based on historical baking data and feedback, thereby improving the quality of the food.

[0077] Data collection involves the system automatically recording actual temperature curves, humidity curves, and equipment operating parameters at each stage of the baking process. After baking, users are encouraged to input "quality evaluation data" for the food (such as a score of 0-10 for crust color, 0-10 for internal texture, and overall satisfaction) via the control panel or mobile app.

[0078] Model training involves collecting sufficient data and then using machine learning algorithms (such as linear regression, decision trees, or neural networks) to train a "baking parameter-food quality" predictive model. This model can learn complex patterns, such as "initial humidity exceeding 85% for 5 minutes is strongly positively correlated with crust crispness." When the user selects the same food type again, the system not only retrieves preset parameters but also analyzes the data based on this model. It might suggest, "Based on historical data, lowering the temperature in the second stage by 5°C is expected to improve the internal humidity score." The user can then choose to adopt this optimization suggestion. This example enables the oven to continuously learn and evolve, adapting personally to the user's taste preferences and kitchen environment, ultimately becoming smarter with use.

[0079] In some embodiments, the method further includes: acquiring real-time operating status data of the heating device, the spraying device, and the door opening and closing mechanism; monitoring the operating status of the multi-functional oven based on preset fault diagnosis rules; and issuing a fault alarm message through the control panel if an abnormal state is detected.

[0080] This embodiment adds a device health management function, which monitors the status of key components to detect potential faults in advance, ensuring safe use and extending the device's lifespan.

[0081] Status data acquisition is achieved by real-time collection of the current and resistance values ​​of the heating device; the operating current and whether the spray pump is stalled; and the limit switch signal and operating current of the door motor.

[0082] The rule-based judgment is based on a preset fault diagnosis rule base. For example: Heating element failure: If the output power is 100%, but the temperature sensor detects a temperature rise below the threshold within a predetermined time, it is determined that the heating element may be damaged or open-circuited. Spray device failure: If a spray command is issued, but the humidity sensor does not detect a significant increase in humidity within a subsequent period, it is determined that the water pump or atomizer is malfunctioning. Door failure: After receiving a door closing command, if the door position sensor does not return a "closed" signal within the timeout range, it is determined that the door is jammed or the motor is malfunctioning. Alarm prompt: Once an abnormal state matching the above rules is detected, the processor immediately displays the specific fault code and text prompt (such as "E01: Heating abnormal, please check the heating element") on the control panel screen, possibly accompanied by an audible alarm, and simultaneously interrupts the current baking program to ensure safety. This embodiment realizes predictive maintenance of the equipment, improves product safety and reliability, and facilitates users to promptly identify problems and contact after-sales service.

[0083] In some embodiments, the method further includes: under the premise of meeting baking quality requirements, with the goal of minimizing energy consumption, using a genetic algorithm to optimize the target temperature, spray volume and cooling time for each baking period, and generating optimized baking program parameters.

[0084] This embodiment introduces an energy-saving optimization algorithm while ensuring baking quality. By globally optimizing the parameters of the baking program, it aims to reduce energy consumption.

[0085] First, the objective function is defined as "minimizing total energy consumption." Energy consumption can be calculated as the sum of the integrals of heating power over time for each period. Simultaneously, a constraint is set: "food quality score not lower than a certain threshold," to ensure that energy saving does not come at the expense of quality.

[0086] The optimization using a genetic algorithm includes: Encoding: Encoding all adjustable parameters of the entire baking process (such as target temperature, spray volume, and cooling time at each stage) into a "chromosome" (parameter combination). Initialization and Evaluation: Randomly generating multiple parameter combinations to form an initial "population". Using the quality prediction model established in Example 5, evaluating the estimated quality and estimated energy consumption corresponding to each parameter combination. Selection, Crossover, and Mutation: Following the principle of "survival of the fittest", selecting parameter combinations with low energy consumption and meeting quality standards as "parents", and generating new "offspring" parameter combinations through crossover (exchanging some parameters) and mutation (randomly fine-tuning a parameter). Iteration: Repeating the evaluation and evolution process, after multiple iterations, the optimal parameter combination in the population is the optimized baking program with the lowest energy consumption while meeting quality requirements. The system can recommend this optimized program as an "energy-saving mode" for this food type to users.

[0087] In some embodiments, by integrating a high-definition camera and image processing unit into the oven cavity, computer vision technology is used to analyze the changes in the shape, color, and volume of the food surface in real time, and dynamically adjust the parameters of the baking stage to achieve true "personalized instruction" and consistent results.

[0088] High-temperature resistant, oil-resistant, high-definition cameras are installed on the inner wall of the baking cavity, and supplementary lighting is provided to ensure image quality. An image processing unit (which can be a separate coprocessor or integrated into the main processor) runs a deep learning model.

[0089] Feature extraction and state recognition include: During the fermentation stage, continuous imaging is used to analyze the rate of change in the projected area of ​​the bread dough, accurately determining whether fermentation has reached its optimal state (e.g., doubling in size), rather than simply relying on a fixed time. During the coloring stage, the color histogram of the food surface is analyzed in real time and compared with an "ideal coloring" model. When the preset golden color is detected, the system can automatically end the current high-temperature coloring period early and proceed to the next stage to prevent burning. For foods such as cakes, the system monitors the height of the rise and surface cracking. If excessive expansion is detected and there is a risk of cracking, the temperature rise rate can be fine-tuned for intervention. The processor compares the visual analysis results with the currently running program. For example, if the dough completes fermentation prematurely, the system will immediately interrupt the fermentation stage and enter the baking stage early; if one side colors too quickly, the power of the heating element on that side can be adjusted individually to achieve uniform coloring. This is equivalent to introducing a high-priority real-time feedback loop during the execution of S102.

[0090] By upgrading the control logic from "time-driven" to "state-driven", the machine can "see" and understand the baking process, fundamentally solving the problem of product differences caused by factors such as the initial state of ingredients and environmental temperature differences.

[0091] In some embodiments, by introducing reinforcement learning algorithms, the oven is treated as an agent, the baking process as an environment, and user feedback as a reward. Through continuous interaction with the user, the oven can learn autonomously and recommend baking programs that match the user's unique taste, and even help the user discover new preferences.

[0092] The reinforcement learning framework includes: State: All parameters of the current baking program (temperature, humidity, time, etc. at each stage). Action: Fine-tuning of program parameters (e.g., ±5°C, ±1 minute). Reward: The rating given by the user after consumption via the app or control panel (e.g., 1-5 stars).

[0093] The learning and exploration cycle involves evaluating a user's first experience baking bread using a preset program. The system (agent) then generates a slightly different "exploratory" program (e.g., increasing the initial humidity by 5%) based on the current strategy (e.g., a neural network) and recommends it to the user for their next attempt. If the user gives a 5-star rating after trying the new program, the system receives a positive reward, reinforcing the strategy that generated that "action"; if they give a 3-star rating, they receive a negative reward, weakening the strategy. Through multiple interactions, the system develops unique baking strategies for different users (identifiable by their account). For example, the strategy learned for "User A" might be "longer, lower-temperature baking" to achieve extreme internal moisture, while the strategy for "User B" might be "a higher final temperature" to achieve an extremely crisp crust.

[0094] The oven has transformed from a tool for executing commands into a partner for exploring culinary delights with its users. It no longer pursues "standard" flavors, but proactively uncovers and solidifies users' "personalized" preferences, achieving truly personalized cooking.

[0095] In some embodiments, a high-fidelity digital twin is created for the physical oven. Before placing real ingredients in the oven, users can "virtually bake" the food using virtual ingredients on the digital twin, previewing the finished product and performing non-destructive testing and optimization of program parameters, achieving a zero-waste, zero-failure cooking experience.

[0096] The digital twin model was constructed by creating a complex simulation model that incorporates thermodynamics, fluid dynamics, and food chemical reactions. This model can simulate the entire process inside the oven, including hot air circulation, heat penetration, moisture evaporation, and the Maillard reaction (coloring). The model parameters were calibrated and standardized using extensive physical experimental data.

[0097] The virtual baking operation allows users to enter "virtual baking" mode after selecting a real food program. The system displays a 3D oven and food model on the screen. Users can modify any parameters (such as increasing the temperature by 20°C) and then start the simulation. The digital twin will quickly calculate and display the expansion and browning process of the virtual food in an accelerated animation, ultimately providing a predicted image of the finished product and key indicators (such as core temperature and moisture loss rate). If the virtual result is unsatisfactory (e.g., it shows that it will burn), the user can return to adjust the parameters and perform virtual baking again until a satisfactory predicted result is achieved. The final confirmed program parameters will then be sent to the physical oven for execution.

[0098] By applying digital twin technology from the industrial sector to home appliances, cooking is transformed from a "trial and error" model to a "prediction and verification" model, greatly improving the success rate and user experience while reducing food waste.

[0099] In some embodiments, the focus is on smart home ecosystems and energy management. The oven is viewed as an energy-consuming "intelligent agent" that communicates and collaborates with other smart devices in the home (such as photovoltaic power generation systems, energy storage batteries, and smart meters) to automatically select the time of day with the lowest energy cost or the most environmentally friendly conditions for baking, and to optimize the energy consumption of the baking process.

[0100] After the user sets the baking program (such as "start baking bread in 3 hours"), the oven does not start immediately. Instead, it queries the home energy management system (HEMS) for electricity information for the next few hours, including grid time-of-use pricing, home solar power generation forecasts, and current battery charge levels.

[0101] The system automatically calculates and selects when photovoltaic power generation is in surplus (electricity prices are at their lowest or even negative) or when grid electricity prices are at their lowest. To optimize energy use, the system flexibly adjusts the program while ensuring core quality (such as thorough fermentation and complete baking). For example, it may use a "low-temperature slow baking" mode (lower overall power but slightly longer time) to match the stable curve of solar power generation; or briefly reduce the insulation power during peak grid periods (within the allowable temperature fluctuation range). The system recommends options such as "Economy Mode" (saving 30% on electricity costs, increasing baking time by 15 minutes) or "Eco Mode" (100% solar power usage), giving users the power to choose their energy source.

[0102] By optimizing oven control from a single device level to a home system level, making it an active participant in the smart energy network, a combination of functionality and sustainability is achieved.

[0103] In some embodiments, the state of ingredients is perceived through multiple dimensions of image, weight, and texture. A deep learning model is used to automatically identify the type of ingredients (such as "whole wheat bread dough" and "chiffon cake batter") and freshness (such as "over-fermented dough" and "unmixed cake batter"). The baking program parameters are dynamically adjusted (such as extending the fermentation time and lowering the baking temperature) without requiring manual input from the user, thus achieving "ingredient self-adaptation".

[0104] A high-temperature resistant wide-angle camera is installed inside the oven door (to capture images of the food and identify its color, shape, and texture); a high-precision weight sensor is installed at the bottom of the baking tray (to measure the weight of the food and determine if the portion size meets the preset); and a humidity sensor is installed on the inner wall of the oven (to help determine the moisture content of the food, such as "the dough is too dry").

[0105] Model training involves collecting multiple data points on ingredients (including images, weight, and moisture content) and labeling them with ingredient types (such as "whole wheat bread" and "chocolate cake") and states (such as "unfermented", "fermented", and "too dry").

[0106] A convolutional neural network (CNN) is used to train an image recognition model (to identify food type and texture), and a multilayer perceptron (MLP) is used to train a weight-moisture correlation model (e.g., "dough weight 1kg + moisture 30% → requires 15 minutes of extended fermentation"). The outputs of the two models are then combined to obtain the final food status judgment.

[0107] The system dynamically adjusts parameters by automatically activating the camera and weight sensor to acquire image, weight, and moisture data after the user places the food in and closes the oven door.

[0108] The deep learning model identifies "whole wheat bread dough (unfermented) + weight 20% heavier than preset + slightly lower moisture content", then retrieves the basic program (fermentation 38℃ / 60min, baking 180℃ / 30min) from the associated database and dynamically adjusts it: the fermentation time is extended by 10 minutes (due to increased weight); the fermentation humidity is increased by 5% (due to slightly lower moisture content); and the baking temperature is decreased by 5℃ (due to the dough being too moist, to prevent the crust from burning).

[0109] The user confirms that the adjusted program parameters (such as "fermentation 38℃ / 70min, baking 175℃ / 30min") will be displayed on the control panel. The user can choose to "execute directly" or "manually modify" (such as "increase the baking time by 5 minutes").

[0110] By upgrading from "user input of ingredient type" to "system perception of ingredient status", the problems of product inconsistencies caused by "user unfamiliarity with ingredient characteristics" and "fluctuations in ingredient status (such as the degree of dough fermentation)" have been solved, achieving precise matching of "ingredient-program".

[0111] In some embodiments, by treating humidity control as a reinforcement learning (RL) problem, the oven acts as an "agent" that can adjust the spray volume in advance by predicting humidity change trends (rather than just responding to the current humidity), achieving "no overshoot and no lag" humidity control, which is particularly suitable for humidity-sensitive ingredients (such as chiffon cakes and baguettes).

[0112] The RL framework design includes: State: real-time humidity value, humidity change rate, current baking stage (e.g., "fermentation stage" or "browning stage"), food type (e.g., "cake" or "bread"), and ambient temperature (e.g., kitchen humidity 20%); Action: spray volume adjustment (e.g., "increase by 1 level", "decrease by 1 level", or "remain unchanged", each level corresponding to a flow rate change of 10 ml / min); Reward: humidity within the target range: +1 / second (positive reward); humidity exceeding the target range: -2 / second (negative reward); predicting humidity exceeding the range in advance and successfully intervening: +5 (additional reward, encouraging "predictive control").

[0113] The model training uses a deep Q-network (DQN) to train the agent, simulating over 100,000 baking scenarios (such as "humidity drops from 60% to 50% during cake fermentation" and "humidity rises from 40% to 50% during bread baking"). The training objective is to enable the agent to predict humidity change trends (such as "current humidity is 55%, change rate -2% / min → will be lower than the target value of 50% in 5 minutes") and adjust the spray volume in advance (such as "increase the spray level by 1, and the humidity will remain at 52% in 5 minutes").

[0114] The real-time control process includes: during the "chiffon cake fermentation stage" (target humidity 70%-80%), the system monitors the current humidity at 75%, with a change rate of -1% / min (i.e., a decrease of 1% per minute); the RL agent predicts that "the humidity will drop to 70% (target lower limit) in 5 minutes", so it increases the spray level by 1 (the flow rate increases from 20ml / min to 30ml / min); after 5 minutes, the humidity stabilizes at 73% (within the target range), and the system receives a reward of +1 / second × 300 seconds + +5 for early intervention, thus reinforcing the strategy.

[0115] In some embodiments, by building a digital twin for the oven, the baking process of the food (such as expansion, browning, and moisture loss) can be previewed virtually before the user performs actual baking, and program parameters (such as adjusting temperature and time) can be optimized to avoid "trial and error" cooking and reduce food waste.

[0116] The digital twin model is built based on the hardware characteristics of the oven (such as heating element power, cavity volume, and fan speed) and thermodynamic principles (such as heat transfer and moisture evaporation) to establish a high-fidelity simulation model; it integrates food databases (such as the thermal conductivity of "whole wheat bread" and the expansion coefficient of "cake") to simulate the state changes of different foods under different programs (such as "baking at 180℃ for 30 minutes → the center temperature of the bread reaches 95℃, and 15% moisture is lost").

[0117] The virtual baking process includes: the user inputs the target ingredient (e.g., "chocolate chiffon cake") and preferences (e.g., "soft" and "crispy crust") through the control panel; the system retrieves the basic program (fermentation 38℃ / 60min, baking 160℃ / 40min) from the associated database and inputs it into the digital twin model; the model displays the changes of the virtual ingredient with accelerated animation (e.g., 10x speed): fermentation stage: the dough expands to twice its size (as expected); baking stage: at the 20-minute mark, the cake surface is too dark (exceeding the user's preference for "light golden"); finally: the virtual finished product's "center temperature", "moisture loss rate", "crust hardness" and other indicators do not meet the user's requirements.

[0118] Parameter optimization: The digital twin model automatically adjusts program parameters (e.g., reducing the baking temperature from 160℃ to 150℃ and extending the time by 5 minutes) and resimulates; the second simulation shows that: during the baking stage, the cake surface is evenly colored (meeting the "light golden" standard); the final indicators (center temperature 90℃, moisture loss 10%) meet user preferences; the system recommends the optimized program (fermentation 38℃ / 60min, baking 150℃ / 45min) to the user.

[0119] The system executes the optimized program after user confirmation, and sends the parameters to the physical oven for execution. The error between the virtual simulation result and the actual baking result is ≤5% (e.g., the virtual prediction baking time is 45 minutes, while the actual baking time is 43 minutes).

[0120] In some embodiments, the oven is regarded as an "energy smart body" in the smart home, working in conjunction with the photovoltaic power generation system, energy storage battery and smart meter in the home to automatically select the time of day with the lowest energy cost (such as the off-peak electricity price) or the most environmentally friendly time (such as 100% solar energy) for baking, and flexibly adjusting program parameters (such as extending the baking time and reducing the power) to achieve a balance between "energy saving and quality".

[0121] The oven obtains the following information through a Home Energy Management System (HEMS): the grid's time-of-use electricity price (e.g., peak hour ¥1.5 / kWh, off-peak hour ¥0.5 / kWh); the home's photovoltaic power generation forecast (e.g., strong solar radiation in the next 2 hours, estimated power generation of 2 kWh); and the current battery charge (e.g., 50% remaining, capable of releasing 1 kWh). Collaborative decision-making is achieved by the system automatically selecting the baking time based on user-defined priorities (e.g., "prioritize energy saving" or "prioritize on-time baking").

[0122] Case 1 (Prioritizing Energy Saving): The user sets "start baking bread in 3 hours". HEMS query shows that in the next 3 hours, the second and third hours are the off-peak hours of the grid (0.5 yuan / kWh) + the photovoltaic power generation is in surplus (expected to generate 1.5 kWh). The system recommends "energy saving mode": postpone the baking start time by 1 hour (start in the second hour) and adjust the program parameters (such as reducing the baking temperature from 180℃ to 170℃ and extending the time by 10 minutes) to match the smooth curve of solar power generation (avoid using grid electricity during peak hours).

[0123] Case 2 (Prioritizing Timeliness): The user sets "baking must be completed in 1 hour". HEMS query shows that the next hour is the peak time of the power grid (1.5 yuan / kWh), but the energy storage battery has enough power (can release 1 kWh of electricity). The system recommends "energy storage mode": use the power of the energy storage battery for baking, and keep the program parameters unchanged (such as 180℃ / 30 minutes) to avoid using the peak power grid.

[0124] The program execution and feedback are handled by the oven according to the collaboratively decided program (e.g., "Energy Saving Mode": 170℃ / 40 minutes). During execution, the system monitors energy consumption (e.g., the use of 1.2 kWh of solar power) and finished product quality (e.g., the crispness of the bread crust and the center temperature) in real time. If the quality meets the requirements, the system saves the program to the "Energy Saving Program Library" for future use. If the quality does not meet the standards (e.g., the bread is not fully baked), the parameters are adjusted (e.g., the temperature is increased by 5℃), and "failure cases" are recorded to optimize the next decision. This upgrade from "single device-level control" to "home system-level optimization" solves the problems of "high oven energy consumption" and "disconnection from the home energy system," achieving a balance between "energy saving + quality + punctuality."

[0125] The multi-functional oven and its multi-segment programmable control method and medium provided in this application embodiment achieve automatic temperature switching during fermentation, shaping, and browning stages by preset multi-time-segment temperature parameters, avoiding errors from manual adjustment and improving the consistency of baking quality. Based on real-time humidity monitoring and fuzzy control algorithms, the spray volume is dynamically adjusted to meet the humidity requirements of different foods at each stage (such as the high humidity environment in the early stage of baking European bread), optimizing the taste and appearance of the food. The cooling process is monitored in real time by a temperature sensor, automatically triggering the door opening and closing action without manual intervention, ensuring standardized cooling timing and duration, and preventing food quality degradation due to improper cooling. Through pre-programmed multi-segment program control, human operation deviations are reduced, and the reproducibility is high, making it suitable for the large-scale, standardized production needs of home kitchens and bakeries.

[0126] Please see Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of the multi-segment programmable device 200 provided in the embodiments of this application. The multi-segment programmable device 200 is used to execute the steps of the multi-segment programmable method shown in the above embodiments. The multi-segment programmable device 200 can be a single server or a server cluster, or the multi-segment programmable device 200 can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

[0127] like Figure 3 As shown, the multi-segment programmable control device 200 includes:

[0128] The parameter input unit 201 is used to input parameters for multiple sequentially executed baking periods through the control panel. The parameters for each baking period include the duration of the period, the target temperature range, whether spraying is required, the start time of spraying, the duration of spraying, the amount of spraying, and the target cooling temperature or cooling time of the cooling stage.

[0129] The working setting unit 202 is used to control the heating device and the spraying device to work sequentially according to the order of multiple set baking time periods. The humidity sensor monitors the humidity in the baking cavity in real time. If the humidity exceeds the humidity range set for the corresponding time period, the spray volume of the spraying device is adjusted to compensate for the humidity.

[0130] The door control unit 203 is used to open the door after all baking periods are completed, monitor the temperature inside the baking cavity in real time through a temperature sensor, and control the door to close when the temperature drops to the target cooling temperature or the cooling time is reached. When the quality data of the baked product is abnormal, the process data is analyzed using a causal inference model to obtain the cause of the abnormality and adjust the subsequent program parameters. The process data includes temperature curves, humidity curves, and spraying time, and the subsequent program parameters include fermentation humidity and baking temperature. The quality data of the finished product includes one or more of the following: cake height, bread crust hardness, and user rating.

[0131] In some embodiments, controlling the heating device and the spraying device to operate sequentially according to a set sequence of multiple baking periods includes: controlling the heating device to start, monitoring the temperature inside the baking cavity in real time using a temperature sensor, adjusting the power output of the heating device according to the deviation between the real-time monitored temperature and the corresponding target temperature, so that the temperature inside the baking cavity is maintained within the corresponding target temperature range; if it is determined that spraying is required based on the parameters of the baking period, controlling the spraying device to start when the set spraying start time is reached, adjusting the flow rate of the spraying device according to the set spraying volume, and controlling the spraying device to stop according to the set spraying duration.

[0132] In some embodiments, adjusting the spray volume of the spraying device for humidity compensation includes: obtaining the humidity range set for the current time period and the humidity value inside the baking cavity monitored in real time, calculating the humidity deviation and the rate of change of humidity deviation, determining the corresponding spray volume adjustment level according to a preset fuzzy control rule table, and controlling the spraying device to spray with the adjusted spray volume.

[0133] In some embodiments, the step of monitoring the temperature inside the baking cavity in real time using a temperature sensor and controlling the door to close when the temperature drops to the target cooling temperature or the cooling time is reached includes: acquiring the rate of temperature drop inside the baking cavity in real time, predicting the time required to reach the target cooling temperature based on the rate of temperature drop and the current temperature, and controlling the door to close when the target cooling temperature is reached if the predicted time is less than the remaining cooling time; and controlling the door to close when the cooling time is reached if the predicted time is greater than or equal to the remaining cooling time.

[0134] In some embodiments, the method further includes: establishing a database linking food types and baking program parameters; when a user inputs a food type through the control panel, retrieving corresponding preset baking program parameters from the database; the preset baking program parameters include parameters for multiple sequentially executed baking periods and parameters for the cooling stage, so as to modify the retrieved preset baking program parameters before use.

[0135] In some embodiments, the method further includes: recording the actual temperature and humidity data and corresponding food quality evaluation data for each baking period during each baking process; establishing a prediction model of baking parameters and food quality based on a machine learning algorithm; and optimizing and recommending subsequent baking program parameters based on the prediction model.

[0136] In some embodiments, the method further includes: acquiring real-time operating status data of the heating device, the spraying device, and the door opening and closing mechanism; monitoring the operating status of the multi-functional oven based on preset fault diagnosis rules; and issuing a fault alarm message through the control panel if an abnormal state is detected.

[0137] In some embodiments, the method further includes: under the premise of meeting baking quality requirements, with the goal of minimizing energy consumption, using a genetic algorithm to optimize the target temperature, spray volume and cooling time for each baking period, and generating optimized baking program parameters.

[0138] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the multi-segment programmable device and each module described above can be referred to the corresponding processes in the multi-segment programmable method embodiments described above, and will not be repeated here.

[0139] The aforementioned multi-segment programmable control method can be implemented as a computer program, which can, for example... Figure 3 It runs on the device shown.

[0140] Please see Figure 4 , Figure 4 This is a schematic block diagram of the structure of a multi-functional oven provided in an embodiment of this application. The multi-functional oven includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0141] The storage medium can store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any multi-segment programmable method.

[0142] The processor provides computing and control capabilities to support the operation of the entire multi-functional oven.

[0143] Internal memory provides an environment for the execution of computer programs stored in non-volatile storage media. When these computer programs are executed by a processor, the processor can perform any multi-segment programmable method.

[0144] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal to which the present application is applied. A specific multi-functional oven may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0145] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0146] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0147] The parameters for multiple sequentially executed baking periods are input through the control panel. The parameters for each baking period include the duration of the period, the target temperature range, whether spraying is required, the start time of spraying, the duration of spraying, the amount of spray, and the target cooling temperature or cooling time of the cooling phase.

[0148] The heating device and the spraying device are controlled to work sequentially according to the set baking time period. The humidity in the baking cavity is monitored in real time by a humidity sensor. If the humidity exceeds the humidity range set for the corresponding time period, the spray volume of the spraying device is adjusted to compensate for the humidity.

[0149] After all baking periods are completed, the door is opened, and the temperature inside the baking cavity is monitored in real time by a temperature sensor. When the temperature drops to the target cooling temperature or the cooling time is reached, the door is closed. When the quality data of the baked product is abnormal, a causal inference model is used to analyze the process data, obtain the cause of the abnormality, and adjust the subsequent program parameters. The process data includes temperature curves, humidity curves, and spraying time. The subsequent program parameters include fermentation humidity and baking temperature. The quality data of the finished product includes one or more of the following: cake height, bread crust hardness, and user rating.

[0150] In some embodiments, controlling the heating device and the spraying device to operate sequentially according to a set sequence of multiple baking periods includes: controlling the heating device to start, monitoring the temperature inside the baking cavity in real time using a temperature sensor, adjusting the power output of the heating device according to the deviation between the real-time monitored temperature and the corresponding target temperature, so that the temperature inside the baking cavity is maintained within the corresponding target temperature range; if it is determined that spraying is required based on the parameters of the baking period, controlling the spraying device to start when the set spraying start time is reached, adjusting the flow rate of the spraying device according to the set spraying volume, and controlling the spraying device to stop according to the set spraying duration.

[0151] In some embodiments, adjusting the spray volume of the spraying device for humidity compensation includes: obtaining the humidity range set for the current time period and the humidity value inside the baking cavity monitored in real time, calculating the humidity deviation and the rate of change of humidity deviation, determining the corresponding spray volume adjustment level according to a preset fuzzy control rule table, and controlling the spraying device to spray with the adjusted spray volume.

[0152] In some embodiments, the step of monitoring the temperature inside the baking cavity in real time using a temperature sensor and controlling the door to close when the temperature drops to the target cooling temperature or the cooling time is reached includes: acquiring the rate of temperature drop inside the baking cavity in real time, predicting the time required to reach the target cooling temperature based on the rate of temperature drop and the current temperature, and controlling the door to close when the target cooling temperature is reached if the predicted time is less than the remaining cooling time; and controlling the door to close when the cooling time is reached if the predicted time is greater than or equal to the remaining cooling time.

[0153] In some embodiments, the method further includes: establishing a database linking food types and baking program parameters; when a user inputs a food type through the control panel, retrieving corresponding preset baking program parameters from the database; the preset baking program parameters include parameters for multiple sequentially executed baking periods and parameters for the cooling stage, so as to modify the retrieved preset baking program parameters before use.

[0154] In some embodiments, the method further includes: recording the actual temperature and humidity data and corresponding food quality evaluation data for each baking period during each baking process; establishing a prediction model of baking parameters and food quality based on a machine learning algorithm; and optimizing and recommending subsequent baking program parameters based on the prediction model.

[0155] In some embodiments, the method further includes: acquiring real-time operating status data of the heating device, the spraying device, and the door opening and closing mechanism; monitoring the operating status of the multi-functional oven based on preset fault diagnosis rules; and issuing a fault alarm message through the control panel if an abnormal state is detected.

[0156] In some embodiments, the method further includes: under the premise of meeting baking quality requirements, with the goal of minimizing energy consumption, using a genetic algorithm to optimize the target temperature, spray volume and cooling time for each baking period, and generating optimized baking program parameters.

[0157] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the steps of the multi-segment programmable method provided in any embodiment of this application.

[0158] The computer-readable storage medium can be the internal storage unit of the multi-functional oven described in the foregoing embodiments, such as the hard drive or memory of the multi-functional oven. Alternatively, the computer-readable storage medium can be an external storage device of the multi-functional oven, such as a plug-in hard drive, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card.

[0159] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A multi-segment programming method, characterized by, The method is applied to a multifunctional oven, which comprises an oven body with a baking cavity, a heating device and a spraying device arranged in the baking cavity, a door body for opening and closing the baking cavity, and a control system comprising a processor and a control panel for inputting parameters; the method comprises: inputting parameters of a plurality of sequentially executed baking periods through the control panel, each parameter of the baking period comprising a period duration, a target temperature range, whether spraying is required, a spraying start time, a spraying duration, a spraying amount, and a cooling target temperature or a cooling time of a cooling stage; controlling the heating device and the spraying device to work in sequence according to the set order of the plurality of baking periods, and monitoring the humidity in the baking cavity in real time through a humidity sensor, and adjusting the spraying amount of the spraying device for humidity compensation if the humidity exceeds the set humidity range of the corresponding period; after all the baking periods are completed, opening the door body, monitoring the temperature in the baking cavity in real time through a temperature sensor, and controlling the door body to be closed when the temperature drops to the cooling target temperature or the cooling time is reached, comprising: acquiring the temperature drop rate in the baking cavity in real time, predicting the time required to reach the cooling target temperature according to the temperature drop rate and the current temperature, controlling the door body to be closed when the cooling target temperature is reached if the predicted time is less than the remaining cooling time, and controlling the door body to be closed when the cooling time is reached if the predicted time is greater than or equal to the remaining cooling time; when the product quality data is abnormal, using a causal inference model to analyze the process data to obtain the abnormal reason, and adjusting the subsequent program parameters; the process data comprises a temperature curve, a humidity curve and a spraying time, and the subsequent program parameters comprise a fermentation humidity and a baking temperature; the product quality data comprises one or more of a cake height, a bread crust hardness and a user score.

2. The method of claim 1, wherein, The method further comprises: controlling the heating device to start, monitoring the temperature in the baking cavity in real time through a temperature sensor, adjusting the power output of the heating device according to the deviation of the real-time monitored temperature from the corresponding target temperature, and maintaining the temperature in the baking cavity within the corresponding target temperature range; if it is determined according to the parameters of the baking period that spraying is required, controlling the spraying device to start when the set spraying start time is reached, adjusting the flow of the spraying device according to the set spraying amount, and controlling the spraying device to stop according to the set spraying duration.

3. The method of claim 1, wherein, The method further comprises: acquiring the set humidity range of the current period and the real-time monitored humidity value in the baking cavity, calculating the humidity deviation and the humidity deviation change rate, determining the corresponding spraying amount adjustment gear according to a pre-set fuzzy control rule table, and controlling the spraying device to spray at the adjusted spraying amount.

4. The method of claim 1, wherein, The method further comprises: A database of food type and baking program parameter association is established, when a user inputs a food type through the control panel, corresponding preset baking program parameters are called from the database of association, the preset baking program parameters include parameters of a plurality of sequentially executed baking periods and parameters of a cooling stage, and the called preset baking program parameters are used after being modified.

5. The method of claim 1, wherein, The method further comprises: During each baking process, actual temperature and humidity data of each baking period and corresponding food quality evaluation data are recorded, a baking parameter and food quality prediction model is established based on a machine learning algorithm, and subsequent baking program parameters are optimized and recommended according to the prediction model.

6. The method of claim 1, wherein, The method further comprises: Real-time operation state data of the heating device, the spraying device and the door opening and closing mechanism are acquired, the operation state of the multifunctional oven is monitored based on a preset fault diagnosis rule, and if an abnormal state is detected, a fault alarm information is sent through the control panel.

7. The method of claim 1, wherein, The method further comprises: Under the premise of meeting the baking quality requirement, a genetic algorithm is used to optimize target temperature, spraying amount and cooling time of each baking period to minimize energy consumption, and optimized baking program parameters are generated.

8. A multi-functional oven characterized by, The multifunctional oven comprises a memory and a processor; The memory is used to store a computer program; The processor is used to execute the computer program and implement the method of any one of claims 1 to 7 when the computer program is executed.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to make the processor implement the method of any one of claims 1 to 7.

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