Intelligent cooker control method, device and system, storage medium and electronic equipment
By identifying the material and type of cookware through sound wave feedback signals and combining this with a heating strategy library, intelligent cooktop control is achieved. This solves the problem that traditional cooktops cannot identify cookware types and enables energy-saving and safe cooking control.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-05
- Publication Date
- 2026-03-31
AI Technical Summary
Traditional cooktops cannot automatically identify the type of cookware, resulting in uneven heating or high-temperature use, which may lead to energy waste and health hazards. Existing identification technologies are costly and lack stability.
By acquiring the acoustic feedback signal of the cookware under the excitation of an acoustic sweep frequency signal, the vibration characteristic parameters are extracted, the cookware attributes are matched using a preset acoustic feature library, and intelligent control is achieved by combining the heating strategy library.
It achieves automated identification and intelligent heating without modifying the cookware, saving energy, avoiding abnormal situations such as boiling over, dry burning, and scorching, and improving cooking results.
Smart Images
Figure CN121761344A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of kitchen appliance technology, and in particular to intelligent cooktop control methods, devices, systems, storage media, and electronic devices. Background Technology
[0002] Cookware made of different materials and with different structures exhibits significant differences in heat conduction. Under a uniform heating mode, cookware with varying thermal conductivity often fails to achieve optimal heating results, potentially leading to energy waste and uneven heating or excessively high temperatures that negatively impact cooking performance. This is especially true for cookware made of special materials such as non-stick pans, where prolonged use at high temperatures may release harmful substances, posing health risks. Traditional cooktops typically cannot automatically identify the type of cookware used, making it difficult to provide a matching heating strategy and lacking corresponding identification and temperature control mechanisms. While users can manually select the cookware type, this method relies on personal judgment, resulting in limited accuracy and reliability. Currently, some high-end cooktops attempt to identify cookware using weight sensors or Radio Frequency Identification (RFID) tags; however, these solutions still suffer from limitations such as high cost, insufficient identification stability, and the need for specific modifications to the cookware, restricting their widespread application. Summary of the Invention
[0003] To address at least one of the aforementioned technical problems, this disclosure provides a method, apparatus, system, storage medium, and electronic device for controlling intelligent cooktops.
[0004] According to one aspect of this disclosure, a method for controlling an intelligent cooktop is provided, comprising: Acquire the acoustic feedback signal generated by the target cookware on the target stove under the excitation of the acoustic sweep frequency signal; Based on the acoustic feedback signal, feature extraction processing is performed to obtain vibration characteristic parameters; Based on the vibration characteristic parameters and the preset acoustic feature library, a matching process is performed to obtain the cookware attribute information corresponding to the target cookware, the cookware attribute information including the target material and the target type; A target heating strategy is obtained by performing a matching process based on the target material, the target type, and a preset heating strategy library. The target stove is controlled based on the target heating strategy.
[0005] In some possible implementations, the method further includes: The sample stoves are collected to generate sample feedback signals under the excitation of acoustic sweep frequency signals. The sample stoves include various types of stoves corresponding to various materials. The sample feedback signals generated by each type of stove corresponding to each material are subjected to feature analysis processing to obtain the sample characteristic parameters of the stoves corresponding to the multiple materials and multiple types of stoves; The preset acoustic feature library is constructed based on the sample characteristic parameters of various types of stoves corresponding to the various materials.
[0006] In some possible implementations, the method further includes: Obtain the historical heating strategy corresponding to the target cookware and the cooking doneness of the food corresponding to the historical heating strategy; A standard heating strategy is determined based on the historical heating strategies and the corresponding food cooking maturity levels. The preset heating strategy library is updated based on the target cookware and the standard heating strategy.
[0007] In some possible implementations, the method further includes: Obtain user taste preference information; The preset heating strategy library is updated based on the taste preference information.
[0008] In some possible implementations, the vibration characteristic parameters include natural frequency, damping coefficient, quality factor, resonant mode, boundary vibration characteristics, and vibration damping characteristics. The matching process based on the vibration characteristic parameters and a preset acoustic feature library to obtain cookware attribute information corresponding to the target cookware includes: An acoustic feature vector is constructed based on the natural frequency, the damping coefficient, the quality factor, the resonant mode, the boundary vibration characteristics, and the vibration attenuation characteristics; Based on the acoustic feature vector and the vibration characteristic vector of each type of cookware corresponding to each material in the preset acoustic feature library, confidence scores are calculated to obtain multiple confidence scores. If the highest confidence level among multiple confidence levels is greater than or equal to the first threshold, the attribute information of the cookware corresponding to the highest confidence level is determined as the cookware attribute information corresponding to the target cookware.
[0009] In some possible implementations, the method further includes: If the highest confidence level among multiple confidence levels is lower than the second threshold, the cookware attribute information corresponding to the target cookware is determined to be the default cookware attribute information, and the second threshold is less than the first threshold.
[0010] According to a second aspect of this disclosure, a smart cooktop control device is provided, the device comprising: The feedback signal acquisition module is used to acquire the acoustic feedback signal generated by the target cookware on the target stove under the excitation of the acoustic sweep frequency signal; The feature extraction module is used to perform feature extraction processing based on the acoustic feedback signal to obtain vibration characteristic parameters; The feature matching module is used to perform matching processing based on the vibration characteristic parameters and the preset acoustic feature library to obtain the cookware attribute information corresponding to the target cookware, wherein the cookware attribute information includes the target material and the target type. The strategy matching module is used to perform matching processing based on the target material, the target type and a preset heating strategy library to obtain the target heating strategy; The control module is used to control the target stove based on the target heating strategy.
[0011] According to a third aspect of this disclosure, an intelligent stove control system is provided, including a signal acquisition module, a control module, and a stove. The signal acquisition module and the stove are respectively connected to the control module for communication. The signal acquisition module is used to transmit a frequency sweep signal of the sound wave within a preset frequency range to the pot on the stove, and to acquire the sound wave feedback signal generated by the pot on the stove. The control module is used to execute the intelligent stove control method as described in any one of the first aspects.
[0012] According to a fourth aspect of this disclosure, an electronic device is provided, including at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements the intelligent stove control method as described in any one of the first aspects by executing the instructions stored in the memory.
[0013] According to a fifth aspect of this disclosure, a computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored therein, the at least one instruction or at least one program being loaded and executed by a processor to implement the intelligent stove control method as described in any of the first aspects.
[0014] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.
[0015] Implementing this disclosure will have the following beneficial effects: The system acquires the acoustic feedback signal generated by the target cookware on the target stove under the excitation of an acoustic sweep frequency signal; it performs feature extraction processing based on the acoustic feedback signal to obtain vibration characteristic parameters; it performs matching processing based on the vibration characteristic parameters and a preset acoustic feature library to obtain the cookware attribute information corresponding to the target cookware, including the target material and target type; without modifying the cookware, it automatically identifies the material and type of the cookware through non-contact acoustic waves, and performs matching processing based on the target material, target type, and a preset heating strategy library to obtain the target heating strategy; it controls the target stove based on the target heating strategy. This provides matching heating strategies for the characteristics of different cookware, realizing automated intelligent control of the stove, saving energy, and avoiding abnormal cooking situations such as boiling over, dry burning, and scorching.
[0016] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description
[0017] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.
[0018] Figure 1 A flowchart illustrating a smart stove control method according to an embodiment of the present disclosure is shown. Figure 2 A flowchart illustrating a method for constructing a preset acoustic feature library according to an embodiment of the present disclosure is shown. Figure 3 A flowchart illustrating a first method for updating a preset heating strategy library according to an embodiment of the present disclosure is shown. Figure 4 A flowchart illustrating a second method for updating a preset heating strategy library according to an embodiment of the present disclosure is shown. Figure 5 A flowchart illustrating a method for determining cookware attribute information according to an embodiment of the present disclosure is shown. Figure 6 A flowchart illustrating a method for determining default cookware attribute information according to an embodiment of the present disclosure is shown. Figure 7 This diagram illustrates the structure of an intelligent stove control device according to an embodiment of the present disclosure. Figure 8 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation
[0019] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.
[0021] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.
[0022] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.
[0023] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.
[0024] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.
[0025] Figure 1This diagram illustrates a flow chart of a smart stove control method according to an embodiment of the present disclosure, as shown below. Figure 1 As shown, the above method includes: S101. Obtain the acoustic feedback signal generated by the target cookware on the target stove under the excitation of the acoustic sweep frequency signal. The executing entity of this application is the control module. The intelligent stove control system includes a signal acquisition module, which includes an ultrasonic signal generator and an ultrasonic signal receiver. The ultrasonic signal generator sends a sound wave sweep frequency signal to the target stove. After the ultrasonic signal receiver receives the sound wave feedback signal, it sends it to the control module. The control module acquires the sound wave feedback signal.
[0026] In some embodiments, the frequency band of the acoustic sweep signal can be 20kHz-100kHz. Target cookware includes, but is not limited to, iron woks, ceramic woks, stainless steel woks, iron frying pans, ceramic frying pans, stainless steel frying pans, earthenware pots, stainless steel saucepans, cast iron pots, and stainless steel pressure cookers.
[0027] S102. Based on the acoustic feedback signal, feature extraction processing is performed to obtain vibration characteristic parameters; Vibration characteristic parameters include, but are not limited to, natural frequency, damping coefficient, quality factor, resonance mode, boundary vibration characteristics, vibration attenuation characteristics, and the number and distribution of resonance peaks. The frequency shift, amplitude fluctuation and phase change of the acoustic feedback signal are analyzed, and then combined with the preset physical model for back-calculation to obtain the vibration characteristic parameters corresponding to the target cookware.
[0028] In some embodiments, the modulation intensity corresponding to the acoustic feedback signal is determined based on amplitude modulation or Doppler frequency shift. The modulation intensity is the ratio of frequency shift to amplitude fluctuation, i.e., the resonance enhancement effect when the frequency of the acoustic sweep signal matches the natural frequency. The natural frequency is determined based on the acoustic sweep signal and the modulation intensity. The natural frequency is used to characterize the characteristic frequency of the free vibration of the target stove. The damping coefficient is used to characterize the vibration decay rate of the target stove. The quality factor is used to characterize the core parameters of the energy loss characteristics of the vibration system and resonance system corresponding to the target stove. The resonance mode is used to characterize the spatial shape and energy distribution of the vibration of the target stove. The boundary vibration characteristics are used to characterize the constraint state and energy transfer of the boundary of the target stove. The vibration decay characteristics are used to characterize the energy loss rate and material damping of the vibration of the target stove. The number and distribution of resonance peaks are used to characterize the natural frequency spectrum and structural stiffness distribution of the target stove.
[0029] S103. Based on the vibration characteristic parameters and the preset acoustic feature library, a matching process is performed to obtain the cookware attribute information corresponding to the target cookware. The cookware attribute information includes the target material and the target type. The preset acoustic feature library is a database pre-built based on the vibration characteristic parameters of different types of cookware corresponding to different materials. The vibration characteristic parameters of the target cookware are compared one by one with the vibration characteristic parameters of different types of cookware in different materials in the preset acoustic feature library. The material and type of the cookware with the highest similarity exceeding the preset similarity are taken as the material and type of the target cookware, thus obtaining the target material and target type.
[0030] In some embodiments, the target material includes, but is not limited to, iron, stainless steel, aluminum pots, copper pots, ceramic, glass, and composite non-stick pans; the target type includes, but is not limited to, frying pans, saucepans, milk pots, pressure cookers, enamel pots, and steamers. S104. Based on the target material, target type and preset heating strategy library, a matching process is performed to obtain the target heating strategy; The preset heating strategy library is a pre-built database based on heating strategies for different types of cookware corresponding to different materials. The target heating strategy is obtained by querying the preset heating strategy library according to the target material and type of the target cookware. The target heating strategy is used to characterize the heat adjustment method of the target cookware.
[0031] In some embodiments, different types of cookware corresponding to different materials have different heating methods. Cast iron pots are slow-heating pots, preheating over medium heat initially and then heating steadily later. Stainless steel pots are fast-heating pots, avoiding localized overheating with linear power increases. Aluminum pots are gentle-heating pots, preventing sudden temperature changes with gradual power control. Non-stick pots require temperature limiting protection, with a maximum temperature not exceeding 260°C. Ceramic pots are ultra-gentle-heating pots, requiring strict temperature control to prevent thermal shock cracking. Woks require concentrated heat for rapid heating. Soup pots require even heating to prevent sticking. Small flat-bottomed pans require small-area heating for energy efficiency. Clay pots require ultra-low-speed heating for thorough preheating. For example, the heating strategy for iron woks is concentrated heat for rapid heating, while the heating strategy for cast iron enamel pots is medium-low heat with slow heating. The heating strategy for stainless steel woks is medium-high heat for the first two minutes, then reduced to low heat later.
[0032] In some embodiments, the heating strategy may also take into account the characteristics of the ingredients, determining the heating strategy by comprehensively considering the material and type of the target cookware and the characteristics of the ingredients within the target cookware. A preset heating strategy library is constructed based on the cookware material, cookware type, and ingredient type. The type of target ingredient in the target cookware is identified, and the preset heating strategy library is matched to determine the target heating strategy.
[0033] S105. Control the target stove based on the target heating strategy.
[0034] The control module communicates with the target stove and controls the firepower of the target stove according to the target heating strategy, thereby heating the target pot and cooking the food inside.
[0035] For example, if the target cooktop is a stainless steel soup pot, the specific identification and control process is as follows: The acoustic feedback signal generated by the target pot under the excitation of a 50-70kHz acoustic sweep frequency signal is obtained. The frequency response curve corresponding to the acoustic feedback signal is extracted. The obtained vibration characteristic parameters show that the main resonance peak is located at 58kHz, the quality factor Q value is calculated to be 85, which is within a relatively high quality factor range, and there are multiple harmonic resonance peaks. The vibration modes are axisymmetrically distributed, and the size is estimated to be of medium capacity. Based on the vibration characteristic parameters, a preset acoustic feature library is queried to determine that the target cooktop is a stainless steel soup pot, i.e., the target type is a soup pot, and the target material is stainless steel. Based on the target material and target type of the target cooktop, a preset heating strategy library is queried. The obtained target heating strategy is as follows: For the first two minutes, the target cooktop is controlled to operate at 60% power for rapid heating; after two minutes, the target cooktop is controlled to operate at 80% power for stable heating; after the first preset time, after boiling is determined, the target cooktop is controlled to adjust to 60% power to maintain a gentle boil and prevent overflow; after the second preset time, the target cooktop is controlled to turn off the heat to prevent dry burning or scorching. If the target cooktop is a cast iron wok, the vibration characteristic parameters of the target cooktop are as follows: the main resonance peak is 38kHz, the Q value is 45, the damping is relatively large, the frequency response curve is smooth, and the harmonics are few. Based on the vibration characteristic parameters, the preset acoustic feature library is consulted to determine that the target cooktop is a cast iron wok, that is, the target type is "exceeding" and the target material is iron. Based on the target material and target type of the target cooktop, the preset heating strategy library is consulted to obtain the target heating strategy as follows: for the first four minutes, the target cooktop is controlled to run at 50% power for sufficient preheating; after four minutes, the target cooktop is controlled to run at 75% power for rapid heating; after eight minutes, the target cooktop is controlled to run at 65% power to maintain heating; after the third preset time, the target cooktop is controlled to turn off the heat to prevent dry burning or scorching.
[0036] The above technical solution requires no modification to the cookware. It automatically and accurately identifies the material and type of the cookware using non-contact sound waves, and provides matching heating strategies for the characteristics of different cookware. This enables automated and intelligent control of the stove, saves energy, and avoids abnormal cooking situations such as boiling over, dry burning, and scorching.
[0037] Please see Figure 2 In some embodiments, the method further includes: S201. Collect the sample feedback signal generated by the sample stove under the excitation of the acoustic sweep frequency signal. The sample stove includes various types of stoves corresponding to various materials. S202. Perform feature analysis on the sample feedback signals generated by each type of stove corresponding to each material to obtain sample characteristic parameters of stoves corresponding to multiple materials and multiple types of stoves. S203. Construct a preset acoustic feature library based on the sample characteristic parameters of various types of stoves corresponding to various materials.
[0038] The sample cookware includes cookware of various types corresponding to various materials. For each type of cookware corresponding to each material, multiple acoustic wave sweep signals are sent, and the vibration characteristics of the sample feedback signals generated multiple times are analyzed to obtain the sample characteristic parameters corresponding to each type of cookware corresponding to each material, which are then stored in a preset acoustic feature library.
[0039] In some embodiments, different types of cookware made of different materials have different vibration characteristics. For example, among cookware made of different materials, cast iron pots have the following vibration characteristics: obvious low-frequency resonance peaks, relatively large damping, and a medium quality factor (Q value); stainless steel pots have the following vibration characteristics: rich high-frequency resonance components, sharp resonance peaks, and a relatively high quality factor (Q value); aluminum pots have the following vibration characteristics: moderate resonance frequency, relatively small damping, and sensitive response; ceramic pots have the following vibration characteristics: the highest resonance frequency, usually >80kHz, sharp resonance peaks but low amplitude, and unique damping characteristics; composite material pots have the following characteristics: multiple resonance peaks and complex features; establishing a database of acoustic characteristics of cookware categories: among different types of cookware, woks have the following vibration characteristics: shallow bottom and wide mouth, specific resonance mode distribution; soup pots have the following vibration characteristics: deep bottom and narrow mouth, different boundary vibration characteristics; flat-bottomed pots have the following vibration characteristics: flat bottom, uniform vibration propagation; and earthenware pots have the following vibration characteristics: thick-walled structure, unique vibration attenuation characteristics.
[0040] The above technical solution constructs a preset acoustic feature library based on the vibration characteristics of different types of cookware corresponding to different materials, so as to accurately and quickly query the corresponding material and type after identifying the vibration characteristics of the target cookware.
[0041] Please see Figure 3 In some embodiments, the method further includes: S301. Obtain the historical heating strategy corresponding to the target cookware and the cooking degree of the ingredients corresponding to the historical heating strategy. S302. Determine the standard heating strategy based on historical heating strategies and the cooking maturity of ingredients corresponding to historical heating strategies; S303. Update the preset heating strategy library based on the target cookware and standard heating strategy.
[0042] Record the heating strategy corresponding to each cooking session of the target cookware, for example, the preheating power and time, and the cooking power and time. Simultaneously, record the doneness of the food after each cooking session, either automatically from the cookware's operation records or through user input. The types of food cooked in each type of cookware corresponding to each material are relatively fixed or similar; for example, frying pans are generally used for pan-frying meats, while earthenware pots are used for stewing meats or porridges that require long cooking times.
[0043] In some embodiments, the historical heating strategies corresponding to the target cookware and the corresponding food cooking maturity are obtained. The recognition algorithm is dynamically optimized based on the food cooking maturity to determine the optimal heating strategy, i.e., the standard heating strategy, corresponding to the target type and target material of the target cookware. The heating strategy corresponding to the target type of cookware corresponding to the target material in the preset heating strategy library is updated based on the standard heating strategy.
[0044] In other embodiments, historical heating measurements of cookware of the target material and the cooking maturity of the food can be obtained, and heating strategies for cookware of the target material and the target type in the preset heating strategy library can be optimized based on the historical heating measurements of cookware of the target material and the cooking maturity of the food.
[0045] The above technical solution optimizes the heating strategies in the preset heating strategy library by considering the historical heating strategies of the target cookware and the cooking maturity of the ingredients. This provides a more precise heating strategy that is more suitable for the target cookware, improves the accuracy of cookware control, and optimizes the cooking effect.
[0046] Please see Figure 4 In some embodiments, the method further includes: S401. Obtain user's taste preference information; S402. Update the preset heating strategy library based on taste preference information.
[0047] Taste preference information is used to characterize a user's preference for the softness or tenderness of food. Taste preference information can be determined through the control records of the stove or by user input.
[0048] In some embodiments, the cooking time in the preset heating strategy library is updated according to taste preference information. For example, if the taste preference information indicates that the user prefers a soft and tender taste, the cooking time of each type of cookware corresponding to each material in the preset heating strategy library is extended; if the taste preference information indicates that the user prefers a firmer taste, the cooking time of each type of cookware corresponding to each material in the preset heating strategy library is reduced.
[0049] The above technical solution adjusts the heating strategy parameters according to the user's taste preferences, making the control of the stove more user-friendly and improving the user experience.
[0050] Please see Figure 5 In some embodiments, the vibration characteristic parameters include natural frequency, damping coefficient, quality factor, resonant mode, boundary vibration characteristics, and vibration attenuation characteristics. Matching processing is performed based on the vibration characteristic parameters and a preset acoustic feature library to obtain the cookware attribute information corresponding to the target cookware, including: S1031. Construct acoustic feature vectors based on natural frequency, damping coefficient, quality factor, resonant mode, boundary vibration characteristics, and vibration attenuation characteristics; S1032. Calculate the confidence level based on the acoustic feature vector and the vibration characteristic vector of each type of cookware corresponding to each material in the preset acoustic feature library to obtain multiple confidence levels. S1033. If the highest confidence level among multiple confidence levels is greater than or equal to the first threshold, determine the attribute information of the cookware corresponding to the highest confidence level as the attribute information of the target cookware.
[0051] An acoustic feature vector is constructed based on the vibration characteristic parameters corresponding to the target cookware. Elements within the acoustic feature vector include, but are not limited to, elements corresponding to natural frequency, damping coefficient, quality factor, resonant mode, boundary vibration characteristics, and vibration attenuation characteristics. The weighted distance or similarity between the acoustic feature vector corresponding to the target cookware and the vibration characteristic vectors of each type of cookware corresponding to each material in a pre-defined acoustic feature library is calculated and converted into a confidence score between 0 and 100% to determine the cookware attribute information corresponding to the target cookware.
[0052] In some embodiments, a comprehensive evaluation system based on multi-feature fusion, probabilistic models, and decision boundaries transforms the matching process into a nearest neighbor search problem in a multi-dimensional feature space. Vibration characteristic parameters for each type of cookware corresponding to each material are constructed into a vibration characteristic vector. These parameters include, but are not limited to, natural frequency, damping coefficient, quality factor, resonant mode, boundary vibration characteristics, and vibration attenuation characteristics. A preset acoustic feature library stores the average feature vector and its standard deviation vector for each type of cookware corresponding to each material, obtained from a large number of samples. For the target cookware, the Mahalanobis distance or weighted Euclidean distance between its acoustic feature vector and each type of cookware corresponding to each material in the library is calculated. This distance is converted into a confidence score; the closer the distance, the higher the confidence score. If the highest confidence score is greater than or equal to a first threshold (instantly, the first threshold can be 75%), the material and type of the cookware corresponding to the first threshold are directly output as the target material and target type, respectively.
[0053] In some embodiments, the confidence score is directly output by a pre-trained machine learning classifier, such as a support vector machine or a neural network. This classifier takes vibration characteristic parameters as input and outputs the probability of belonging to each cookware category, with the highest probability value being used as the confidence score.
[0054] The above technical solution improves the accuracy and efficiency of attribute recognition by quickly querying the cookware attribute information corresponding to the target stove in the preset acoustic feature database based on confidence level.
[0055] Please see Figure 6 In some embodiments, the method further includes: S1035. If the highest confidence level among multiple confidence levels is lower than the second threshold, the cookware attribute information corresponding to the target cookware is determined to be the default cookware attribute information, and the second threshold is less than the first threshold.
[0056] If the highest confidence score is lower than the second threshold, the target cookware is determined to be an unknown cookware, and the cookware attribute information corresponding to the target cookware is determined as the default cookware attribute information. The heating strategy corresponding to the default cookware attribute information in the preset heating strategy library is the default and safe heating scheme.
[0057] In some embodiments, the highest confidence score is greater than or equal to the second threshold but lower than the first threshold. For example, if the second threshold is 50%, fuzzy recognition processing is initiated. The processing methods include: prompting the user for confirmation, combining the cookware weight information for auxiliary judgment, or enabling a more complex classifier for secondary recognition.
[0058] The above technical solution, if the target stove cannot be identified by type, is confirmed as the default type, thereby enabling the adoption of a universal and safe heating strategy to ensure the safety of stove control.
[0059] Please see Figure 7 According to a second aspect of this disclosure, an intelligent cooktop control device is provided, the device comprising: Feedback signal acquisition module 10 is used to acquire the acoustic feedback signal generated by the target cookware on the target stove under the excitation of the acoustic sweep frequency signal; Feature extraction module 20 is used to perform feature extraction processing based on acoustic feedback signals to obtain vibration characteristic parameters; The feature matching module 30 is used to perform matching processing based on vibration characteristic parameters and a preset acoustic feature library to obtain cookware attribute information corresponding to the target cookware. The cookware attribute information includes the target material and the target type. The strategy matching module 40 is used to perform matching processing based on the target material, target type and preset heating strategy library to obtain the target heating strategy; The control module 50 is used to control the target stove based on the target heating strategy.
[0060] In some embodiments, the apparatus further includes: The sample feedback signal acquisition module is used to acquire the sample feedback signal generated by the sample stove under the excitation of the acoustic sweep frequency signal. The sample stove includes various types of stoves corresponding to various materials. The sample analysis and processing module is used to perform feature analysis and processing on the sample feedback signals generated by each type of stove corresponding to each material, so as to obtain the sample characteristic parameters of stoves corresponding to multiple materials and multiple types of stoves. The feature library construction module is used to build a preset acoustic feature library based on the sample characteristic parameters of various types of stoves corresponding to various materials.
[0061] In some embodiments, the apparatus further includes: The historical data acquisition module is used to acquire the historical heating strategy corresponding to the target cookware and the cooking degree of the food corresponding to the historical heating strategy. The standard heating strategy determination module is used to determine the standard heating strategy based on historical heating strategies and the cooking maturity of the ingredients corresponding to the historical heating strategies. The first heating strategy library update module is used to update the preset heating strategy library based on the target cookware and the standard heating strategy.
[0062] In some embodiments, the apparatus further includes: The taste preference information acquisition module is used to acquire users' taste preference information; The second heating strategy library update module is used to update the preset heating strategy library based on taste preference information.
[0063] In one embodiment, the vibration characteristic parameters include natural frequency, damping coefficient, quality factor, resonant mode, boundary vibration characteristics, and vibration damping characteristics. The feature matching module 30 includes: The feature vector construction unit is used to construct acoustic feature vectors based on natural frequency, damping coefficient, quality factor, resonant mode, boundary vibration characteristics, and vibration attenuation characteristics. The confidence level determination unit is used to calculate the confidence level based on the acoustic feature vector and the vibration characteristic vector of each type of cookware corresponding to each material in the preset acoustic feature library, and obtain multiple confidence levels. The confidence level determination unit is used to determine the attribute information of the cookware corresponding to the highest confidence level as the attribute information of the target cookware if the highest confidence level among multiple confidence levels is greater than or equal to the first threshold.
[0064] In some embodiments, the apparatus further includes: The default attribute information determination module is used to determine the pot attribute information corresponding to the target pot as the default pot attribute information if the highest confidence among multiple confidence levels is lower than the second threshold, and the second threshold is less than the first threshold.
[0065] According to a third aspect of this disclosure, an intelligent stove control system is provided, including a signal acquisition module, a control module, and a stove. The signal acquisition module and the stove are respectively connected to the control module for communication. The signal acquisition module is used to transmit a frequency sweep signal of sound waves within a preset frequency range to the pot on the stove, and to acquire the sound wave feedback signal generated by the pot on the stove. The control module is used to execute the intelligent stove control method as described in any of the first aspects.
[0066] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.
[0067] This application provides an intelligent stove control device, which can be a terminal or a server. The intelligent stove control device includes a processor and a memory. The memory stores at least one instruction or at least one program. The at least one instruction or at least one program is loaded and executed by the processor to implement the intelligent stove control method provided in the above method embodiments.
[0068] Memory is used to store software programs and modules. The processor executes these stored software programs and modules to perform various functional applications and data processing. Memory can primarily consist of a program storage area and a data storage area. The program storage area stores the operating system, application programs required for functionality, etc.; the data storage area stores data created based on device usage, etc. Furthermore, memory can include high-speed random access memory (RAM) and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, memory can also include a memory controller to provide the processor with access to the memory.
[0069] The methods and embodiments provided in this application can be executed in electronic devices such as mobile terminals, computer terminals, servers, or similar computing devices. Figure 8 This is a hardware structure block diagram of an electronic device for an intelligent stove control method provided in an embodiment of this application. For example... Figure 8As shown, the electronic device 900 can vary considerably due to differences in configuration or performance. It may include one or more Central Processing Units (CPUs) 910 (CPUs 910 may include, but are not limited to, microprocessors such as MCUs or programmable logic devices such as FPGAs), a memory 930 for storing data, and one or more storage media 920 (e.g., one or more mass storage devices) for storing application programs 923 or data 922. The memory 930 and storage media 920 may be temporary or persistent storage. The program stored in the storage media 920 may include one or more modules, each module may include a series of instruction operations on the electronic device. Furthermore, the CPU 910 may be configured to communicate with the storage media 920 and execute the series of instruction operations in the storage media 920 on the electronic device 900. Electronic device 900 may also include one or more power supplies 960, one or more wired or wireless network interfaces 950, one or more input / output interfaces 940, and / or one or more operating systems 921, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.
[0070] The input / output interface 940 can be used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the electronic device 900. In one example, the input / output interface 940 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the input / output interface 940 may be a radio frequency (RF) module used for wireless communication with the Internet.
[0071] Those skilled in the art will understand that Figure 8 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device 900 may also include... Figure 8 The more or fewer components shown, or having the same Figure 8 The different configurations shown.
[0072] Embodiments of this application also provide a computer-readable storage medium, which can be disposed in an electronic device to store at least one instruction or at least one program related to implementing an intelligent stove control method in the method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the intelligent stove control method provided in the above method embodiment.
[0073] Optionally, in this embodiment, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.
[0074] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.
[0075] As can be seen from the embodiments of the intelligent stove control method, device, equipment, terminal, server, storage medium, or computer program provided in this application, this application obtains the acoustic feedback signal generated by the target cookware on the target stove under the excitation of an acoustic sweep frequency signal; performs feature extraction processing based on the acoustic feedback signal to obtain vibration characteristic parameters; performs matching processing based on the vibration characteristic parameters and a preset acoustic feature library to obtain cookware attribute information corresponding to the target cookware, including target material and target type; without modifying the cookware, the material and type of the cookware are automatically identified by non-contact acoustic waves, and a target heating strategy is obtained by matching processing based on the target material, target type, and a preset heating strategy library; the target stove is controlled based on the target heating strategy. It provides matching heating strategies for the characteristics of different cookware, realizing automated intelligent control of the stove, saving energy, and avoiding abnormal cooking situations such as boiling over, dry burning, and scorching.
[0076] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.
[0077] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device, equipment, and storage medium embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.
[0078] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware, or by a program instructing the relevant hardware to implement them. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.
[0079] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A method for controlling an intelligent stove, characterized in that, The method includes: Acquire the acoustic feedback signal generated by the target cookware on the target stove under the excitation of the acoustic sweep frequency signal; Based on the acoustic feedback signal, feature extraction processing is performed to obtain vibration characteristic parameters; Based on the vibration characteristic parameters and the preset acoustic feature library, a matching process is performed to obtain the cookware attribute information corresponding to the target cookware, the cookware attribute information including the target material and the target type; A target heating strategy is obtained by performing a matching process based on the target material, the target type, and a preset heating strategy library. The target stove is controlled based on the target heating strategy.
2. The method according to claim 1, characterized in that, The method further includes: The sample stoves are collected to generate sample feedback signals under the excitation of acoustic sweep frequency signals. The sample stoves include various types of stoves corresponding to various materials. The sample feedback signals generated by each type of stove corresponding to each material are subjected to feature analysis processing to obtain the sample characteristic parameters of the stoves corresponding to the multiple materials and multiple types of stoves; The preset acoustic feature library is constructed based on the sample characteristic parameters of various types of stoves corresponding to the various materials.
3. The method according to claim 1, characterized in that, The method further includes: Obtain the historical heating strategy corresponding to the target cookware and the cooking doneness of the food corresponding to the historical heating strategy; A standard heating strategy is determined based on the historical heating strategies and the corresponding food cooking maturity levels. The preset heating strategy library is updated based on the target cookware and the standard heating strategy.
4. The method according to claim 3, characterized in that, The method further includes: Obtain user taste preference information; The preset heating strategy library is updated based on the taste preference information.
5. The method according to claim 1, characterized in that, The vibration characteristic parameters include natural frequency, damping coefficient, quality factor, resonant mode, boundary vibration characteristics, and vibration attenuation characteristics. The matching process based on these vibration characteristic parameters and a preset acoustic feature library yields the cookware attribute information corresponding to the target cookware, including: An acoustic feature vector is constructed based on the natural frequency, the damping coefficient, the quality factor, the resonant mode, the boundary vibration characteristics, and the vibration attenuation characteristics; Based on the acoustic feature vector and the vibration characteristic vector of each type of cookware corresponding to each material in the preset acoustic feature library, confidence scores are calculated to obtain multiple confidence scores. If the highest confidence level among multiple confidence levels is greater than or equal to the first threshold, the attribute information of the cookware corresponding to the highest confidence level is determined as the cookware attribute information corresponding to the target cookware.
6. The method according to claim 5, characterized in that, The method further includes: If the highest confidence level among multiple confidence levels is lower than the second threshold, the cookware attribute information corresponding to the target cookware is determined to be the default cookware attribute information, and the second threshold is less than the first threshold.
7. A smart stove control device, characterized in that, The device includes: The feedback signal acquisition module is used to acquire the acoustic feedback signal generated by the target cookware on the target stove under the excitation of the acoustic sweep frequency signal; The feature extraction module is used to perform feature extraction processing based on the acoustic feedback signal to obtain vibration characteristic parameters; The feature matching module is used to perform matching processing based on the vibration characteristic parameters and the preset acoustic feature library to obtain the cookware attribute information corresponding to the target cookware, wherein the cookware attribute information includes the target material and the target type. The strategy matching module is used to perform matching processing based on the target material, the target type and a preset heating strategy library to obtain the target heating strategy; The control module is used to control the target stove based on the target heating strategy.
8. A smart stove control system, characterized in that, Includes a signal acquisition module, a control module, and a stove; The signal acquisition module and the stove are respectively connected to the control module for communication. The signal acquisition module is used to transmit a frequency sweep signal of the sound wave within a preset frequency range to the pot on the stove, and to acquire the sound wave feedback signal generated by the pot on the stove. The control module is used to execute the intelligent stove control method as described in any one of claims 1-6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the intelligent stove control method as described in any one of claims 1-6.
10. An electronic device, characterized in that, The system includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements the intelligent stove control method as described in any one of claims 1-6 by executing the instructions stored in the memory.