Intelligent rice cooker control method and system based on dynamic temperature curve learning

Abstract

Embodiments of the present invention disclose a smart rice cooker control method and system based on dynamic temperature curve learning. The control method includes: determining an initial temperature curve based on a first user operation instruction and the type of food in the rice cooker; controlling the operation of the rice cooker based on the initial temperature curve; triggering the rice cooker to switch from a water absorption phase to a temperature rise phase when detecting that the rice cooker's operating state meets a first preset condition; controlling the rice cooker during the temperature rise phase using a fuzzy PID algorithm to trigger the rice cooker to switch from the temperature rise phase to a boiling phase; and controlling the rice cooker heating during the boiling phase using a dual closed-loop control mode. This invention improves the temperature control accuracy of the rice cooker, enables personalized temperature control, improves the judgment accuracy of phase switching, and continuously optimizes the control strategy.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of smart rice cookers, and in particular to a smart rice cooker control method based on dynamic temperature curve learning, system electronic equipment, and storage medium. Background Art

[0002] With the development of smart home appliances, users have put forward higher requirements for rice cookers, including: personalized cooking (such as brown rice, low-sugar rice, soup, mixed grains), stable taste performance, higher temperature control accuracy, etc.

[0003] Existing rice cookers primarily use timer control or single-sensor control to regulate the heating process. The specific workflow is as follows: Initially, power is applied to heat the rice to the set temperature; the temperature change at the bottom of the pot is detected to determine whether the water is boiling; and the rice switches to "keep warm" mode after reaching the set temperature or time. However, this fixed heating process persists when varying rice types, grains, or water levels, leading to significant fluctuations in rice doneness and texture. Furthermore, the system relies solely on time or a single temperature point to determine water absorption, heating, or boiling status, which can easily lead to "half-cooked" or "burned" rice due to variations in ambient temperature, water volume, or pot material. Therefore, there is an urgent need for a new type of smart rice cooker to meet user needs. Summary of the Invention

[0004] In order to overcome the shortcomings of the existing technology, the purpose of the embodiments of the present invention is to provide an intelligent rice cooker control method and system based on dynamic temperature curve learning, which can realize personalized temperature control and improve the judgment accuracy of stage switching. It solves the problems of traditional rice cooker temperature control strategies that are "fixed, rigid, and unadaptable", and have problems in control accuracy and user experience. It is an important innovative breakthrough in the high-precision temperature control system in the field of intelligent kitchen appliances.

[0005] To solve the above problems, a first aspect of an embodiment of the present invention discloses a smart rice cooker control method based on dynamic temperature curve learning, which includes the following steps:

[0006] Determining an initial temperature curve according to a first operation instruction from the user and the type of food in the rice cooker; the initial temperature curve includes target temperatures at various stages of operation of the rice cooker;

[0007] According to the initial temperature curve, the operation of the electric cooker is controlled and the operating state of the electric cooker is monitored;

[0008] When it is detected that the working state of the electric cooker meets the first preset condition, triggering and controlling the electric cooker to switch from the water absorption stage to the temperature rising stage;

[0009] During the heating stage, the rice cooker is controlled by a fuzzy PID algorithm so that the internal temperature of the rice cooker tracks the target temperature of the initial temperature curve during the heating stage;

[0010] When detecting that the operating state of the electric cooker satisfies a second preset condition, triggering and controlling the electric cooker to switch from a heating stage to a boiling stage;

[0011] During the boiling stage, the rice cooker is heated using a dual closed-loop control mode, which uses the temperature deviation at the top of the pot as the PID outer loop input and the temperature deviation at the bottom of the pot as the inner loop input.

[0012] Preferably, when it is detected that the working state of the electric cooker satisfies the first preset condition, triggering and controlling the electric cooker to switch from the water absorption stage to the heating stage comprises:

[0013] The top temperature, humidity, weight change rate and sound intensity of the rice cooker are detected respectively, and corresponding weights are set for the top temperature, humidity, weight change rate and sound intensity. The top temperature, humidity, weight change rate and sound intensity are weighted and summed to obtain a first judgment signal. When the first judgment signal is greater than a first threshold, the rice cooker is triggered to switch from a water absorption stage to a heating stage.

[0014] Preferably, when it is detected that the working state of the electric cooker satisfies the first preset condition, triggering and controlling the electric cooker to switch from the water absorption stage to the heating stage comprises:

[0015] The top temperature, humidity, weight change rate and sound intensity of the rice cooker are detected respectively, and the detected top temperature, humidity, weight change rate and sound intensity are input into an expert reasoning system. The expert reasoning system outputs a first reasoning signal to determine whether to trigger phase switching. When it is determined to be so, the rice cooker is triggered to switch from the water absorption phase to the temperature rising phase.

[0016] Preferably, the intelligent rice cooker control method further comprises:

[0017] The user's taste preference history data is obtained, the taste preference history data is mapped with the first preset condition and the second preset condition adjustment amount, and the temperature threshold in the first preset condition and / or the second preset condition is updated according to the mapping data.

[0018] Preferably, the electric cooker is controlled by fuzzy PID algorithm in the heating stage so that the internal temperature of the electric cooker follows the target temperature of the initial temperature curve in the heating stage, comprising:

[0019] Obtain the target temperature of the current heating stage according to the initial temperature curve, and obtain the current bottom temperature of the rice cooker according to the temperature sensor;

[0020] Obtaining a first temperature error and a first temperature error change rate according to a target temperature in a current heating stage and a current bottom temperature;

[0021] The first temperature error and the first temperature error change rate are input into a fuzzy controller to obtain a control signal, and the heating power of the rice cooker is adjusted according to the control signal.

[0022] Preferably, determining the initial temperature curve according to the user's first operation instruction and the type of food in the rice cooker includes:

[0023] Obtain one or more preset temperature curves according to the food type and the user's second operation instruction;

[0024] The cooking process of the rice cooker is simulated according to the one or more preset temperature curves to obtain one or more simulated cooking result indicators, the simulated cooking result indicators suitable for the user's preference are determined according to the user's first operation instruction, and the initial temperature curve is determined according to the simulated cooking result indicators suitable for the user's preference.

[0025] Preferably, obtaining one or more preset temperature curves according to the food type and the user's second operation instruction includes:

[0026] Establishing a preset temperature curve database, wherein the preset temperature curve database includes temperature curves for various food types and various cooking modes;

[0027] Identify the type of food in the rice cooker and obtain the user's second operation instruction;

[0028] One or more preset temperature curves corresponding to the type of food in the current rice cooker and the second operation instruction of the user are screened out from the database.

[0029] A second aspect of an embodiment of the present invention discloses an intelligent rice cooker control system based on dynamic temperature curve learning, comprising:

[0030] a temperature curve unit, configured to determine an initial temperature curve according to a first operation instruction from a user and the type of food in the rice cooker; the initial temperature curve including target temperatures at various stages of operation of the rice cooker;

[0031] A control unit, configured to control the operation of the rice cooker and monitor the working state of the rice cooker according to the initial temperature curve;

[0032] a first triggering unit, configured to trigger and control the rice cooker to switch from a water absorption stage to a temperature rising stage upon detecting that the working state of the rice cooker satisfies a first preset condition;

[0033] A heating unit is used to control the rice cooker by a fuzzy PID algorithm during the heating stage so that the internal temperature of the rice cooker tracks the target temperature of the initial temperature curve during the heating stage;

[0034] a second triggering unit, configured to trigger and control the electric rice cooker to switch from a heating stage to a boiling stage when detecting that the operating state of the electric rice cooker satisfies a second preset condition;

[0035] The boiling unit is used to control the heating of the rice cooker during the boiling stage through a dual closed-loop control mode, wherein the dual closed-loop control mode uses the temperature deviation at the top of the pot as the PID outer loop input and the temperature deviation at the bottom of the pot as the inner loop input.

[0036] A third aspect of an embodiment of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to execute the intelligent rice cooker control method based on dynamic temperature curve learning disclosed in the first aspect of the embodiment of the present invention.

[0037] A fourth aspect of an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the intelligent rice cooker control method based on dynamic temperature curve learning disclosed in the first aspect of an embodiment of the present invention.

[0038] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0039] The present invention determines an initial temperature curve based on a first user operation instruction and the type of food in the rice cooker; controls the operation of the rice cooker based on the initial temperature curve and monitors the working status of the rice cooker; triggers and controls the rice cooker to switch from a water absorption stage to a heating stage when it is detected that the working status of the rice cooker meets a first preset condition; controls the rice cooker in the heating stage using a fuzzy PID algorithm so that the internal temperature of the rice cooker tracks the target temperature of the initial temperature curve in the heating stage; triggers and controls the rice cooker from the heating stage to a boiling stage when it is detected that the working status of the rice cooker meets a second preset condition; and controls the heating of the rice cooker in the boiling stage using a dual closed-loop control mode. The dual closed-loop control mode uses the temperature deviation of the top of the pot as the PID outer loop input and the temperature deviation of the bottom of the pot as the inner loop input, thereby achieving personalized heating control for different food ingredients and improving the accuracy and stability of the temperature control system. The fuzzy PID algorithm is also introduced to adapt to the strong thermal inertia and large load variation characteristics of the heating stage, achieving more flexible and smoother temperature control. It is more adaptable to nonlinear systems, such as the response differences of different pots / hot plates, than traditional fixed PID. At the same time, during the boiling stage, it can avoid violent evaporation caused by overshoot of top temperature and burnt pot caused by overheating of bottom temperature. In addition, the stage switching judgment is intelligent. The first and second preset conditions are no longer judged by time or single temperature alone, but are combined with multi-sensor data, such as temperature, weight change rate, humidity mutation, sound bubble detection, etc. to form a "state judgment", which improves the judgment accuracy and enables the rice to reach a balance between doneness and moisture content more stably.

[0040] Furthermore, the user's taste preference history data is obtained, and the taste preference history data is mapped with the adjustment amount of the first preset condition and the adjustment amount of the second preset condition. The temperature threshold in the first preset condition and / or the second preset condition is updated according to the mapping data, and the user's subjective evaluation, such as "hard" or "a little wet", is fed back to the control logic, which can continuously optimize the control strategy and improve the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 1 is a flow chart of an intelligent rice cooker control method based on dynamic temperature curve learning provided by one embodiment of the present invention;

[0042] Figure 2 1 is a schematic structural diagram of an intelligent rice cooker control system based on dynamic temperature curve learning provided by one embodiment of the present invention;

[0043] Figure 3 It is a structural diagram of an electronic device disclosed in one embodiment of the present invention. DETAILED DESCRIPTION

[0044] This specific implementation manner is merely an explanation of an embodiment of the present invention, and it is not a limitation of the embodiment of the present invention. After reading this specification, those skilled in the art may make non-creative modifications to the embodiment as needed, but as long as it is within the scope of the claims of the embodiment of the present invention, it is protected by patent law.

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the embodiments of the present invention.

[0046] The term "comprise" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units expressly listed, but may include other steps or units not expressly listed or inherent to such process, method, product or apparatus.

[0047] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0048] Example 1

[0049] Please refer to Figure 1-3 As shown in Figure 1, a smart rice cooker control method based on dynamic temperature curve learning is shown in Figure 1. Figure 1 As shown, it includes the following steps:

[0050] Step S110: determining an initial temperature curve according to the user's first operation instruction and the type of food in the rice cooker; the initial temperature curve includes target temperatures of the rice cooker at various operating stages;

[0051] In this embodiment, the first operation instruction may refer to the cooking mode (such as quick cooking, slow cooking, keeping warm, etc.), time and temperature settings, etc. selected by the user. For example, the initial temperature curve setting will be different depending on the cooking mode selected.

[0052] Ingredient types can include specific rice varieties, soups, porridges, and other ingredients. Different ingredient types will require different heating times and cooking temperatures, and thus different initial temperature curve settings. For example, when cooking rice, different heating times and cooking temperatures will be required for different types of rice and brown rice. When making porridge, different heating times and cooking temperatures will be required for different types of black rice, purple rice, and oatmeal.

[0053] The step S110 may include:

[0054] Step S1101: obtaining one or more preset temperature curves according to the food type and the user's second operation instruction;

[0055] Step S1102: simulating a cooking process of the rice cooker according to the one or more preset temperature curves to obtain one or more simulated cooking result indicators;

[0056] Step S1103: determining a simulated cooking result index that suits the user's preference according to the user's first operation instruction, and determining an initial temperature curve according to the simulated cooking result index that suits the user's preference.

[0057] In this embodiment, the initial temperature curve is a preset temperature curve corresponding to the simulated cooking result index that suits the user's preference.

[0058] The step S1101 may specifically include:

[0059] Step S11011: establishing a preset temperature curve database, wherein the preset temperature curve database includes temperature curves for various food types and various cooking modes;

[0060] Step S11012: Identify the type of food in the rice cooker and obtain a second operation instruction from the user;

[0061] Step S11013: Filter out one or more preset temperature curves corresponding to the type of food in the current rice cooker and the second operation instruction of the user from the database.

[0062] Step S120: According to the initial temperature curve, control the operation of the electric cooker and monitor the operating state of the electric cooker;

[0063] In the above implementation process, dynamic temperature curves and refined phased control are used to ensure that the taste of each cooking is highly repeatable and is not affected by factors such as user water addition errors and ambient temperature changes. At the same time, it can adapt to the "soft / hard / dry / wet" taste preferences of different users, truly achieving "user-customized taste".

[0064] Moreover, users do not need to select complex modes. The system can automatically generate the optimal heating path based on operating instructions and food types, such as rice type identification, thus enhancing the intelligent experience.

[0065] Step S130: When it is detected that the working state of the electric cooker satisfies the first preset condition, triggering and controlling the electric cooker to switch from the water absorption stage to the temperature rising stage;

[0066] In this step, the first preset condition may be one or more of the temperature of the rice cooker, the humidity in the pot, the weight change rate in the pot, and the sound intensity in the pot reaching a temperature threshold, a humidity threshold, the weight change rate in the pot, and the sound intensity in the pot.

[0067] As an embodiment, step S130 may include:

[0068] Step S1301: Detecting the top temperature, humidity, weight change rate, and sound intensity of the rice cooker pot, respectively, and setting corresponding weights for the top temperature, humidity, weight change rate, and sound intensity;

[0069] In practice, we need to determine the degree of influence of each sensor's data on the phase transition. Different sensors contribute differently to determining the phase transition, so we need to assign an appropriate weight to each sensor. The specific weight settings can be as follows:

[0070] The weight of the top temperature in the pot is w1=0.4. Temperature is the most direct indicator, especially when switching between stages. The change in temperature directly reflects the moisture and temperature status in the pot; the humidity in the pot is w2=0.25, and the weight of the weight change rate in the pot is w3=0.2; the sound intensity in the pot is w4=0.15. Sound change is a more indirect indicator, mainly used to detect changes in moisture evaporation and bubbling sound.

[0071] Step S1302: The pot top temperature, pot humidity, pot weight change rate, pot sound intensity weighted sum to obtain a first determination signal;

[0072] Specifically, the first determination signal R1 = w1*temperature at the top of the pot+w2*humidity in the pot+w3*weight change rate in the pot+w4*sound intensity in the pot.

[0073] Step S1303: When the first determination signal is greater than the first threshold value, the rice cooker is triggered to switch from the water absorption stage to the temperature rising stage.

[0074] In specific implementation, the first threshold value can be formulated based on experience and can be adjusted through experiments in actual use. For example, the first threshold value Rt is set to 80 (this value is an empirical value and can be adjusted through experiments in actual use).

[0075] When the first determination signal is ≥80, the rice cooker is triggered to switch from the water absorption stage to the temperature rise stage. When the first determination signal is less than 80, the stage switch is not triggered.

[0076] For example, the top temperature in the pot = 80, the humidity in the pot = 88, the weight change rate in the pot = 12, and the sound intensity in the pot = 15. The first judgment signal R1 is calculated to be 0.4*85+0.3*88+0.2*12+0.1*15=64.3<80. When the first judgment signal R1 is less than 80, the control of the rice cooker from the water absorption stage to the heating stage is not triggered. It is necessary to continue heating or wait until the values ​​of other sensors increase to make the judgment signal reach a higher value.

[0077] As another embodiment, step S130 may include:

[0078] Step S13011: Detecting the top temperature, humidity, weight change rate, and sound intensity of the rice cooker pot;

[0079] Step S13012: Input the detected pot top temperature, pot humidity, pot weight change rate, and pot sound intensity into the expert reasoning system;

[0080] In specific implementation, the expert reasoning system can be established in the following ways:

[0081] In the specific implementation, the rule base of the expert agent system is pre-set: a rule base consisting of "IF-THEN" rules is defined to determine whether specific conditions are met based on sensor data.

[0082] Specifically, the rule base can be set as follows:

[0083] Rule 31: If the bottom temperature Ttop ≥ 80°C and the bottom temperature Ttop ≤ 85°C, then switch to the heating stage;

[0084] Rule 32: IF humidity H ≥ 80% AND humidity H ≤ 85% THEN switch to the warming stage;

[0085] Rule 33: If the rate of change of the weight in the pot is small (e.g. less than 5% / minute) AND the weight tends to be stable THEN switch to the heating stage;

[0086] Rule 34: IF the sound characteristics (such as bubbling sound) are weak or not significantly enhanced THEN remain in the water absorption stage.

[0087] Step S13012: According to the first inference signal output by the expert reasoning system, determine whether to trigger the phase switch. When it is determined to be yes, trigger the control of the rice cooker to switch from the water absorption phase to the heating phase.

[0088] In practice, the expert reasoning system's reasoning mechanism is as follows: Based on the input data of the rice cooker's top temperature, humidity, weight change rate, and sound intensity, and a rule base, it infers whether the first pre-set condition is met. The output decision is as follows: Based on the inference results, a control decision is made to determine whether to switch phases and thus adjust the heating power.

[0089] For example, if the top temperature reaches 80°C to 85°C, the humidity is between 80% and 85%, the weight change rate tends to be stable, and the sound is weak or has no significant change, the expert reasoning system will infer that the water absorption stage is over and is ready to enter the heating stage.

[0090] Step S140: During the heating stage, the electric cooker is controlled by a fuzzy PID algorithm so that the internal temperature of the electric cooker tracks the target temperature of the initial temperature curve during the heating stage;

[0091] In this embodiment, the fuzzy PID control algorithm enables the rice cooker to accurately track the target temperature curve during the heating phase. The fuzzy PID algorithm combines the flexibility of fuzzy control with the precision of PID control, enabling the system to adapt to different cooking conditions and smoothly control the heating process, improving the efficiency and quality of rice cooking.

[0092] As an embodiment, the step S140 may include:

[0093] Step S1401: Obtain the target temperature of the current heating stage according to the initial temperature curve, and obtain the current bottom temperature of the rice cooker according to the temperature sensor;

[0094] Specifically, the target temperature is obtained through the initial temperature curve.

[0095] For example, the initial temperature curve is as follows: 0-10 minutes: increase from room temperature to 60°C; 10-20 minutes: increase from 60°C to 80°C; 20-30 minutes: increase from 80°C to 95°C; after 30 minutes: maintain at 95°C.

[0096] Step S1402: obtaining a first temperature error and a first temperature error change rate according to the target temperature of the current heating stage and the current bottom temperature;

[0097] In this embodiment, the target temperature of the current heating stage and the current bottom temperature satisfy the following formula: E(t)=Ttarget(t)-Tactual(t);

[0098] Where: Ttarget(t) is the target temperature at the current moment, given according to the initial temperature curve; Tactual(t) is the actual temperature of the rice cooker at the current moment.

[0099] In this embodiment, the first temperature error change rate is used to control the prediction part of the system, ΔE(t)=E(t)-E(t-1);

[0100] The first temperature error change rate ΔE(t) is the difference between the current error and the error at the previous moment, which reflects the rate of temperature change.

[0101] Step S1403: Input the first temperature error and the first temperature error change rate into a fuzzy controller to obtain a control signal, and adjust the heating power of the rice cooker according to the control signal.

[0102] In this embodiment, the calculated first temperature error E(t) and the first temperature error change rate ΔE(t) are input into the fuzzy controller, and the fuzzy controller processes them according to fuzzy rules.

[0103] The specific fuzzy rules are as follows:

[0104] Rule 41: IF E(t) is positive AND ΔE(t)is small, THEN increase heatingpower moderately.

[0105] Rule 42: IF E(t) is negative AND ΔE(t) is large, THEN decrease heatingpower sharply.

[0106] Rule 43: IF E(t)is zero AND ΔE(t) is zero, THEN maintain heatingpower.

[0107] In the above implementation process, the module rules correspond the first temperature error and the first error change rate to fuzzy language (such as "negative", "zero", "positive", "large", and "small"), and then obtain the output of the controller through fuzzy reasoning. According to the output of the fuzzy control, the proportional gain Kp, integral time Ti, and differential time Td of the PID controller are dynamically adjusted, so that the system can accurately control the heating power according to the actual temperature changes, so that its temperature can follow the temperature curve as quickly and smoothly as possible.

[0108] Step S150: When it is detected that the operating state of the electric cooker satisfies the second preset condition, triggering and controlling the electric cooker to switch from the heating stage to the boiling stage;

[0109] As a specific embodiment, the second preset condition can be set to: less than or equal to one or more of the conditions of the rice cooker top temperature threshold, humidity threshold, weight change rate threshold in the pot, bubble sound threshold in the pot, etc.

[0110] For example, the second setting condition is set to meet the following: when one or more of the following conditions are met: the temperature at the top of the rice cooker is between 95°C and 100°C, the humidity is between 85% and 90%, the weight change rate in the pot is greater than 10% / min, or the sound of bubbles in the pot is significantly enhanced, it is deemed that the second preset condition is met.

[0111] As another embodiment, step S150 may also specifically include:

[0112] Detect the top temperature of the rice cooker, the humidity inside the pot, the weight change rate inside the pot, and the sound intensity inside the pot respectively;

[0113] Set corresponding weights for the pot top temperature, pot humidity, pot weight change rate, and pot sound intensity;

[0114] The top temperature in the pot, the humidity in the pot, the weight change rate in the pot, and the sound intensity in the pot are weighted and summed to obtain a second judgment signal. When the second judgment signal is greater than a second threshold, the rice cooker is triggered to switch from the heating stage to the boiling stage.

[0115] In this step, the temperature at the top of the rice cooker is detected.

[0116] As another embodiment, step S150 may also specifically include:

[0117] The top temperature, humidity, weight change rate and sound intensity of the rice cooker are detected respectively, and the detected top temperature, humidity, weight change rate and sound intensity are input into an expert reasoning system. The expert reasoning system outputs a second reasoning signal to determine whether to trigger stage switching. When it is determined to be so, the rice cooker is triggered to switch from the heating stage to the boiling stage.

[0118] Step S160: In the boiling stage, the rice cooker is heated by a dual closed-loop control mode, in which the top temperature deviation in the pot is used as the PID outer loop input and the bottom temperature deviation in the pot is used as the inner loop input.

[0119] The temperature deviation at the top of the pot = the target temperature on the temperature curve - the actual temperature at the top of the pot;

[0120] The temperature deviation of the bottom of the pot = the temperature setting value of the bottom of the pot (output by the outer loop PID) - the actual temperature of the bottom of the pot;

[0121] In this embodiment, the top temperature deviation is used as the PID outer loop input, and the bottom temperature deviation is used as the inner loop input, so that temperature overshoot can be suppressed through cascade control.

[0122] In this step, the outer-loop PID controller outputs the target pot bottom temperature, which is set as the target of the inner loop. This can steadily control the top water surface temperature and prevent violent evaporation. The inner-loop PID controller outputs the actual heating power, which can accurately track the heat output of the pot bottom and indirectly regulate the top temperature.

[0123] In specific implementation, the parameters Kp, Ki, and Kd of the outer loop PID can be set to 2.0, 0.1, and 0.5, respectively, for slow response to avoid violent fluctuations in the top steam. The parameters Kp, Ki, and Kd of the inner loop PID can be set to 4.5, 0.2, and 1.0, respectively, for fast response to track the bottom temperature.

[0124] During specific implementation, a reinforcement learning mechanism can also be introduced to dynamically adjust the proportional coefficient (Kp) and integral time (Ti) in the PID parameters based on feedback data such as the moisture content and taste score of the rice cooked each time.

[0125] During the above implementation process, the bottom temperature is precisely controlled by the inner ring, and the bottom of the pot will not overheat, effectively preventing the pot from sticking and aging of the pot inner shell. At the same time, the temperature rise of the bottom of the pot is limited, and the life of the hot plate component and coating is protected, the probability of sticking is reduced, and the life of the pot inner shell is increased.

[0126] The method of the present invention may further include:

[0127] Step 170: Obtain the user's taste preference history data, map the taste preference history data with the adjustment amount of the first preset condition and the adjustment amount of the second preset condition, and update the temperature threshold in the first preset condition and / or the second preset condition according to the mapping data.

[0128] In specific implementation, it may include:

[0129] Step 1701: Collect historical data on user taste preferences, which may specifically include cooking result feedback, such as user ratings, moisture content, and taste characteristics, to obtain user satisfaction data.

[0130] In this step, the user's taste preference history data may include cooking result feedback such as "softer", "drier", "non-stick pan", "rice core without hard core", etc.

[0131] Step 1702: establishing a user preference model, mapping the adjustment amounts of the first preset condition and the second preset condition of the rice cooker to the user satisfaction in the cooking result feedback;

[0132] In this step, a mapping function is established between the user's taste preference and the first preset condition and the second preset condition, that is, the first threshold of the first preset condition is adjusted, and / or the second threshold of the second preset condition is adjusted, so that the first preset condition and the second preset condition of the rice cooker are adjusted and mapped to the user satisfaction in the cooking result feedback. Specifically, the following steps are included:

[0133] Step S17021: First, characterize the user feedback data with feature data.

[0134] Specifically, the user's feedback on hardness, "hard, normal, soft" is mapped to the values ​​in the data [-1, 0, +1], i.e., if the user feels hard: -1, normal: 0, soft: +1;

[0135] The wetness feedback data wetness_feedback reported by users ranges from hard, normal, and soft. The data is represented by a numerical value, where user-reported dryness is -1, normal is 0, and wetness is +1. The wetness_feedback is [-1, 0, +1].

[0136] The rawness and cookedness feedback data of user feedback is represented by [-1, 0, +1], where rawness is -1, normal is 0, and cookedness is +1.

[0137] The user feedback data burn_feedback for pan sticking is represented by [0, 1], i.e., if the pan sticks: 1, if the pan doesn’t stick: 0.

[0138] Step S17022: Establish a mapping function between the user's taste preference and the first preset condition and the adjustment amount of the second preset condition.

[0139] Softer ones absorb water longer, while harder ones absorb water earlier. Wet ones absorb water longer, while dry ones absorb water earlier. Raw ones boil earlier, while cooked ones boil later.

[0140] As an example, the mapping function may be as follows:

[0141] ΔT1=a1*softness_feedback+a2*wetness_feedback;

[0142] Where ΔT1 is the adjustment amount of the first threshold, softness_feedback is the hardness feedback data reported by the user, and wetness_feedback is the dryness feedback data reported by the user. Specifically, a1=-1.5, a2=1.0;

[0143] ΔT2=b1*doneness_feedback+b2*burn_feedback;

[0144] Wherein, ΔT2 is the adjustment amount of the second threshold, doneness_feedback is the user's feedback data on doneness, burn_feedback is the user's feedback data on sticking to the pan, b1=-2.0, b2=2.5.

[0145] The specific mapping function is not limited here, it is just an example.

[0146] Step 1703: Update the temperature threshold in the first preset condition and / or the second preset condition according to the mapping data.

[0147] For example, after each use, the user provides taste feedback (such as soft / hard / dry / wet / rating), and the system updates the preference model parameters, such as generating dynamic adjustment amounts at the start of the next cooking, for example:

[0148] First preset condition temperature adjustment: T1 = T1_base + ΔT1;

[0149] Second preset condition temperature adjustment: T2 = T2_base + ΔT2;

[0150] Among them, when the first preset condition and the second preset condition are that the internal temperature of the rice cooker (the internal temperature can be specifically selected as the top temperature or the bottom temperature) meets the temperature threshold, ΔT1 and ΔT2 are respectively the temperature adjustment amounts for the temperature threshold in the first preset condition and the second preset condition.

[0151] For example, if the user prefers harder / drier rice, the water absorption phase is shortened, and the temperature threshold is lowered. Specifically, if the top temperature is ≥ 78°C and the weight change rate dW / dt is close to 0 before the adjustment, the top temperature can be adjusted to ≥ 75°C.

[0152] For example, if the user prefers softer / wetter rice → the water absorption phase is longer → the temperature threshold is increased.

[0153] In the above implementation process, a closed loop between user feedback and control behavior is achieved, which solves the problem that traditional rice cookers cannot feed back users' subjective evaluations (such as "too hard" or "a little wet") into the control logic, resulting in the inability to continuously optimize the control strategy.

[0154] Example 2

[0155] An intelligent rice cooker control system based on dynamic temperature curve learning is disclosed in an embodiment of the present invention. Figure 2 As shown, Figure 2 It is an intelligent rice cooker control system based on dynamic temperature curve learning, including:

[0156] The temperature curve unit 210 is used to determine an initial temperature curve according to the user's first operation instruction and the type of food in the rice cooker; the initial temperature curve includes the target temperature of the rice cooker at each operating stage;

[0157] A control unit 220 is used to control the operation of the rice cooker and monitor the working state of the rice cooker according to the initial temperature curve;

[0158] The first trigger unit 230 is used to trigger and control the rice cooker to switch from the water absorption stage to the temperature rising stage when detecting that the working state of the rice cooker meets the first preset condition;

[0159] A heating unit 240 is used to control the rice cooker by a fuzzy PID algorithm during the heating stage so that the internal temperature of the rice cooker tracks the target temperature of the initial temperature curve during the heating stage;

[0160] The second trigger unit 250 is used to trigger the rice cooker to switch from the heating stage to the boiling stage when it is detected that the working state of the rice cooker meets the second preset condition;

[0161] The boiling unit 260 is used to control the heating of the rice cooker during the boiling stage through a dual closed-loop control mode, wherein the dual closed-loop control mode uses the temperature deviation at the top of the pot as the PID outer loop input and the temperature deviation at the bottom of the pot as the inner loop input.

[0162] The first trigger unit 230 may specifically include:

[0163] A detection unit is used to detect the top temperature of the rice cooker pot, the humidity in the pot, the weight change rate in the pot, and the sound intensity in the pot;

[0164] a weighting unit, configured to set corresponding weights for the temperature of the pot top, the humidity of the pot, the rate of change of the weight of the pot, and the intensity of the sound in the pot, and to perform a weighted summation of the temperature of the pot top, the humidity of the pot, the rate of change of the weight of the pot, and the intensity of the sound in the pot to obtain a first determination signal;

[0165] The determination unit is used to trigger and control the rice cooker to switch from the water absorption stage to the temperature rise stage when the first determination signal is greater than a first threshold value.

[0166] Example 3

[0167] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. Figure 3 As shown, the electronic device may include:

[0168] A memory 310 storing executable program code;

[0169] a processor 320 coupled to the memory 310;

[0170] The processor 320 calls the executable program code stored in the memory 310 to execute part or all of the steps in the intelligent rice cooker control method based on dynamic temperature curve learning in Example 1.

[0171] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute some or all of the steps in a smart rice cooker control method based on dynamic temperature curve learning in embodiment 1.

[0172] An embodiment of the present invention further discloses a computer program product, wherein when the computer program product is run on a computer, the computer is caused to execute some or all of the steps in the intelligent rice cooker control method based on dynamic temperature curve learning in embodiment one.

[0173] An embodiment of the present invention also discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product. When the computer program product is run on a computer, the computer executes some or all of the steps in the intelligent rice cooker control method based on dynamic temperature curve learning in Example 1.

[0174] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the processes does not necessarily mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0175] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the objectives of this embodiment as needed.

[0176] In addition, the functional units in the embodiments of the present invention may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The integrated unit may be implemented in the form of hardware or software functional units.

[0177] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several requests for causing a computer device (which can be a personal computer, server, or network device, specifically a processor in the computer device) to execute some or all of the steps of the method described in various embodiments of the present invention.

[0178] In the embodiments provided herein, it should be understood that "B corresponding to A" means that B is associated with A and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B based solely on A; B can also be determined based on A and / or other information.

[0179] Those skilled in the art will appreciate that some or all of the steps in the various methods of the embodiments may be performed by instructing related hardware through a program. The program may be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0180] The above is a detailed introduction to the intelligent rice cooker control method, device, electronic device and storage medium based on dynamic temperature curve learning disclosed in the embodiments of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core concept. At the same time, for those skilled in the art, according to the concept of the present invention, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. An intelligent rice cooker control method based on dynamic temperature curve learning, characterized in that, These include the following: Determining an initial temperature curve according to a first operation instruction from the user and the type of food in the rice cooker; the initial temperature curve includes target temperatures at various stages of operation of the rice cooker; According to the initial temperature curve, the operation of the electric cooker is controlled and the operating state of the electric cooker is monitored; When it is detected that the working state of the electric cooker meets the first preset condition, triggering and controlling the electric cooker to switch from the water absorption stage to the temperature rising stage; During the heating stage, the rice cooker is controlled by a fuzzy PID algorithm so that the internal temperature of the rice cooker tracks the target temperature of the initial temperature curve during the heating stage; When detecting that the operating state of the electric cooker satisfies a second preset condition, triggering and controlling the electric cooker to switch from a heating stage to a boiling stage; During the boiling stage, the rice cooker is heated using a dual closed-loop control mode, which uses the temperature deviation at the top of the pot as the PID outer loop input and the temperature deviation at the bottom of the pot as the inner loop input.

2. The intelligent rice cooker control method based on dynamic temperature curve learning according to claim 1, wherein The method of triggering and controlling the electric rice cooker to switch from the water absorption stage to the temperature rising stage when detecting that the working state of the electric rice cooker satisfies the first preset condition comprises: The top temperature, humidity, weight change rate and sound intensity of the rice cooker are detected respectively, and corresponding weights are set for the top temperature, humidity, weight change rate and sound intensity. The top temperature, humidity, weight change rate and sound intensity are weighted and summed to obtain a first judgment signal. When the first judgment signal is greater than a first threshold, the rice cooker is triggered to switch from a water absorption stage to a heating stage.

3. The intelligent rice cooker control method based on dynamic temperature curve learning according to claim 1, wherein The method of triggering and controlling the electric rice cooker to switch from the water absorption stage to the temperature rising stage when detecting that the working state of the electric rice cooker satisfies the first preset condition comprises: The top temperature, humidity, weight change rate and sound intensity of the rice cooker are detected respectively, and the detected top temperature, humidity, weight change rate and sound intensity are input into an expert reasoning system. The expert reasoning system outputs a first reasoning signal to determine whether to trigger phase switching. When it is determined to be so, the rice cooker is triggered to switch from the water absorption phase to the temperature rising phase.

4. The intelligent rice cooker control method based on dynamic temperature curve learning according to claim 2, wherein Also includes: The user's taste preference history data is obtained, the taste preference history data is mapped with the first preset condition and the second preset condition adjustment amount, and the temperature threshold in the first preset condition and / or the second preset condition is updated according to the mapping data.

5. The intelligent rice cooker control method based on dynamic temperature curve learning according to claim 1, characterized in that, Described in the heating stage, by fuzzy PID algorithm control electric cooker, so that the internal temperature of the electric cooker follows the target temperature of the initial temperature curve in the heating stage, comprising: Obtain the target temperature of the current heating stage according to the initial temperature curve, and obtain the current bottom temperature of the rice cooker according to the temperature sensor; Obtaining a first temperature error and a first temperature error change rate according to a target temperature in a current heating stage and a current bottom temperature; The first temperature error and the first temperature error change rate are input into a fuzzy controller to obtain a control signal, and the heating power of the rice cooker is adjusted according to the control signal.

6. The intelligent rice cooker control method based on dynamic temperature curve learning according to claim 1, characterized in that, The step of determining the initial temperature curve according to the first operation instruction of the user and the type of food in the rice cooker includes: Obtain one or more preset temperature curves according to the food type and the user's second operation instruction; The cooking process of the rice cooker is simulated according to the one or more preset temperature curves to obtain one or more simulated cooking result indicators, the simulated cooking result indicators suitable for the user's preference are determined according to the user's first operation instruction, and the initial temperature curve is determined according to the simulated cooking result indicators suitable for the user's preference.

7. The intelligent rice cooker control method based on dynamic temperature curve learning according to claim 6, characterized in that: The step of obtaining one or more preset temperature curves according to the food type and the second user operation instruction includes: Establishing a preset temperature curve database, wherein the preset temperature curve database includes temperature curves for various food types and various cooking modes; Identify the type of food in the rice cooker and obtain the user's second operation instruction; One or more preset temperature curves corresponding to the type of food in the current rice cooker and the second operation instruction of the user are screened out from the database.

8. An intelligent rice cooker control system based on dynamic temperature curve learning, characterized in that: It includes: a temperature curve unit, configured to determine an initial temperature curve according to a first operation instruction from the user and the type of food in the rice cooker; The initial temperature curve includes the target temperature of the rice cooker at each stage of operation; A control unit, configured to control the operation of the electric cooker and monitor the working state of the electric cooker according to the initial temperature curve; a first triggering unit, configured to trigger and control the rice cooker to switch from a water absorption stage to a temperature rising stage upon detecting that the working state of the rice cooker satisfies a first preset condition; a heating unit for controlling the rice cooker by a fuzzy PID algorithm during a heating stage so that the internal temperature of the rice cooker tracks a target temperature of the initial temperature curve during the heating stage; a second triggering unit, configured to trigger and control the electric rice cooker to switch from a heating stage to a boiling stage when detecting that the working state of the electric rice cooker satisfies a second preset condition; The boiling unit is used to control the heating of the rice cooker during the boiling stage through a dual closed-loop control mode, wherein the dual closed-loop control mode uses the temperature deviation at the top of the pot as the PID outer loop input and the temperature deviation at the bottom of the pot as the inner loop input.

9. An electronic device, characterized in that: The method comprises: a memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to execute the intelligent rice cooker control method based on dynamic temperature curve learning according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that It stores a computer program, wherein the computer program enables a computer to execute the intelligent rice cooker control method based on dynamic temperature curve learning according to any one of claims 1 to 7.

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