Intelligent detachable grill, control method and system thereof and storage medium

Abstract

The invention relates to an intelligent detachable grill, a control method and system thereof and a storage medium, and relates to the field of intelligent cooking, and the method comprises the steps: building a food cooking degree database which is used for storing a mapping relation between temperature-time change curves of different food types and cooking degree grades; obtaining a surface temperature distribution data change curve of each subarea of the baking tray according to the surface temperature distribution data of each subarea of the baking tray; matching the surface temperature distribution data change curve with a temperature-time change curve of a corresponding food type in a food cooking degree database to obtain a matching curve; according to a mapping relation between the matching curve and a food cooking degree database, obtaining a cooking degree grade; generating a temperature control instruction according to the cooking degree grade, wherein the temperature control instruction comprises a heat preservation instruction and a heating instruction; and the temperature control instruction is output to the heating element of each partition of the corresponding baking tray through the controller. The method has the effect of improving the cooking quality and the user experience.

Description

Technical Field

[0001] The present application relates to the field of intelligent cooking, and in particular to an intelligent detachable grill and a control method, system and storage medium thereof. Background Art

[0002] In modern intelligent detachable grill control systems, precise temperature control and cooking decisions are the core technical support for improving cooking quality and user experience.

[0003] In related technologies, intelligent cooking control systems mainly rely on preset temperature parameters and single sensor data to make simple judgments on the heating process of ingredients, match the doneness level with the heating strategy based on fixed rules, and finally output unified temperature control instructions.

[0004] Regarding the above-mentioned related technologies, there are defects in the process of food doneness identification and temperature control strategy formulation, such as large errors in doneness judgment, inaccurate zone temperature control, lack of support for user personalized needs, and insufficient response capabilities to abnormal operations. These defects lead to unstable temperature control and inconsistent doneness during the cooking process, affecting the final cooking effect and user experience. Summary of the Invention

[0005] In order to improve cooking quality and user experience, the present application provides an intelligent detachable grill and its control method, system and storage medium.

[0006] In a first aspect, the present application provides a control method for an intelligent detachable grill, which adopts the following technical solutions: A control method for an intelligent detachable grill, comprising: Establishing a food doneness database, wherein the food doneness database is used to store a mapping relationship between temperature-time variation curves of different food types and doneness levels; According to the surface temperature distribution data of each partition of the baking pan, a surface temperature distribution data variation curve of each partition of the baking pan is obtained; Matching the surface temperature distribution data change curve with the temperature-time change curve of the corresponding food type in the food doneness database to obtain a matching curve; Obtaining a doneness level according to the mapping relationship between the matching curve and the food doneness database; generating a temperature control instruction according to the doneness level, the temperature control instruction including a keep-warm instruction and a heating instruction; The temperature control instruction is outputted to the heating elements of the corresponding zones of the baking tray through the controller.

[0007] By employing this technical solution, real-time temperature data from each baking tray zone is collected. Combined with a food doneness database, this intelligently matches the optimal cooking curve and generates precise temperature control instructions. Furthermore, the heating power for each zone is automatically adjusted based on the characteristics of the ingredients, enabling intelligent cooking control. This solution significantly improves the accuracy of food doneness judgment and optimizes the efficiency of heat energy distribution. It effectively addresses the issues of uneven heating, overcooking, or undercooking that plague traditional baking processes, making the cooking process more intelligent and efficient, significantly improving the taste and quality consistency of ingredients, and providing users with a superior cooking experience.

[0008] Optionally, the surface temperature distribution data of each partition of the baking pan and a timestamp corresponding to the surface temperature distribution data are acquired through a temperature sensor array; generating discrete temperature-time data points based on the surface temperature distribution data and the timestamp; Smoothing the discrete temperature-time data points to form a continuous temperature-time curve; Based on the temperature-time variation curve, the surface temperature distribution characteristics of each partition are extracted to generate the surface temperature distribution data variation curve.

[0009] By adopting this technical solution, a temperature sensor array collects temperature data from each partition in real time and associates it with a timestamp. Discrete temperature-time data points are constructed and then smoothed to form a continuous variation curve. By extracting surface temperature distribution characteristics, a surface temperature distribution data variation curve corresponding to the baking pan partition is generated. This solution significantly improves the accuracy and continuity of temperature monitoring, effectively eliminating the local errors caused by traditional single-point temperature measurement. It can accurately capture subtle temperature variations in each partition. Data smoothing effectively suppresses environmental interference and measurement noise, improving the stability of temperature monitoring and laying a reliable technical foundation for precise temperature control.

[0010] Optionally, calculating the temperature change rate of adjacent discrete temperature-time data points; If the temperature change rate exceeds N times the average value of the historical temperature change rate, it is marked as an abnormal point; Replacing the outlier with a weighted average of the first M normal data points of the outlier to obtain a corrected data point; Smoothing the corrected data points to obtain smoothed data points; The smoothed data points are fitted to the temperature-time variation curve by interpolation.

[0011] By employing this technical solution, we accurately identify and correct abnormal temperature values ​​based on real-time temperature monitoring data and intelligent algorithm analysis, and optimize the temperature curve by combining historical data characteristics. This solution effectively improves the reliability of temperature data, reduces the impact of measurement errors and environmental interference, and significantly enhances the accuracy and stability of cooking control.

[0012] Optionally, applying a detection current to the heating element to measure the resistance value of the heating element; Calculating the deviation between the resistance value and the standard resistance value to obtain a deviation rate; If the deviation rate of the first partition exceeds a deviation threshold, marking the first partition as a failed partition; Taking the failed partition as a reference, dynamically merging it with adjacent partitions to form an extended control area, where the extended control area is obtained by merging the failed partition and at least two adjacent partitions; Acquiring temperature data detected by each temperature sensor in the extended control area; According to the distance relationship between each temperature sensor and the center of the failed partition, the temperature data is weightedly calculated to obtain surface temperature distribution data of each partition of the baking pan.

[0013] By adopting this technical solution, which leverages intelligent fault detection and zone-coordinated control, the temperature monitoring system is automatically reconfigured when a heating element anomaly occurs, integrating sensor data from adjacent zones to achieve precise temperature compensation. This solution effectively improves the grill system's fault tolerance and stability, ensuring precise temperature control even in the event of a localized fault. This solves the cooking interruption problem often associated with hardware failure in traditional equipment, significantly improving the reliability and continuity of the cooking process.

[0014] Optionally, when it is identified that the temperature change amplitude of the first partition within a preset time period exceeds a change threshold of the temperature-time change curve, an operation interference signal is generated; In response to the operation interference signal, stopping the step of obtaining the doneness level according to the mapping relationship between the matching curve and the food doneness database, and acquiring image data of the first partition; analyzing, based on the image data, whether there are operational features of human cooking; If so, continue to perform the step of obtaining the doneness level according to the mapping relationship between the matching curve and the food doneness database; If it does not exist, a device abnormality warning message is generated and the fault diagnosis process is started.

[0015] By implementing this technical solution, the cause of abnormal temperature fluctuations can be determined in real time, accurately distinguishing between human error and equipment failure. This solution effectively enhances the intelligent level of the cooking system, ensuring the continuity of the cooking process while promptly identifying potential equipment problems. This not only maintains the stable operation of the cooking system, but also enhances the safety protection capabilities of the equipment, significantly improving the intelligent level of cooking control.

[0016] Optionally, establish an operational priority matrix; Classifying the operation interference signals of each partition based on the operation priority matrix into first priority interference signals, second priority interference signals, and third priority interference signals; Initiating an image acquisition and verification process for the first-priority interference signal, wherein the verification process is to determine whether a user-intended operation occurs in a certain partition through image recognition technology; Delaying the second-priority interference signal for a preset time period before starting the image acquisition and verification process, wherein the preset time period does not exceed a preset threshold; A time-sharing verification mechanism is started for the third priority interference signal, wherein the time-sharing verification mechanism allocates image acquisition according to a principle of alternating acquisition of adjacent partitions.

[0017] By adopting the above technical solution and establishing an intelligent priority response mechanism, we achieve efficient processing of concurrent interference signals from multiple partitions. This solution intelligently prioritizes processing based on operational characteristics, responding immediately to critical operations like flipping, while appropriately delaying processing for secondary operations like adding seasoning. It also uses time-sharing verification for unknown interference. While ensuring timely response to core cooking operations, it also optimizes system resource allocation, significantly improving system response efficiency and stability in the face of multiple concurrent tasks.

[0018] Optionally, receiving temperature-time data of the food through a mobile terminal, the temperature-time data including at least one set of temperature values ​​in a time series and their corresponding doneness levels; Uploading the temperature-time data to the cloud for verification, including data format verification, temperature range rationality judgment, and cooking safety assessment; If the verification is successful, the temperature-time data is stored in the food doneness database, and a custom temperature-time curve is generated; receiving a food image uploaded by the mobile terminal, extracting features of the food image, and generating an image recognition tag; Associating and binding the image recognition tag with the custom temperature-time curve, and storing the result in the food doneness database; An image of the food to be cooked is captured, the image recognition tag is matched, and the custom temperature-time curve is used as a benchmark.

[0019] By implementing this technical solution and building a complete closed-loop system for user-defined cooking curves, the digitalization and intelligent application of cooking knowledge are achieved. This solution ensures cooking safety through a data verification process while transforming the user's unique temperature control experience into an executable cooking curve. Combined with image recognition technology, this significantly enhances the smart grill's personalized adaptation capabilities, enabling the device to accurately reproduce the user's preferred cooking results.

[0020] In a second aspect, the present application provides a control method for an intelligent detachable grill, which adopts the following technical solution: A control method for an intelligent detachable grill, comprising: An upper grill assembly, a lower grill assembly, a hinge assembly, and a controller; the upper grill assembly and the lower grill assembly are connected via the hinge assembly; the controller is disposed in the upper grill assembly; The upper grill assembly is provided with a first heating element, which is electrically connected to the controller; the lower grill assembly is provided with a second heating element, which is electrically connected to the controller; The upper grill assembly further includes an upper grill pan, an upper shell, and a first array of temperature sensors uniformly disposed between the upper grill pan and the upper shell, wherein a fixed end of the first array of temperature sensors is fixed to the upper shell, and a detection end of the first array of temperature sensors does not contact the upper grill pan; The lower grill assembly further includes a lower grill pan, a lower housing, and a second array of temperature sensors uniformly disposed between the lower grill pan and the lower housing, wherein a fixed end of the second array of temperature sensors is fixed to the lower housing, and a detection end of the second array of temperature sensors does not contact the lower grill pan; The controller is in communication with the first array temperature sensor and the second array temperature sensor.

[0021] In a third aspect, the present application provides a control system for an intelligent detachable grill, which adopts the following technical solutions: A control system for an intelligent detachable grill, comprising: An acquisition module is used to obtain the food doneness database and the surface temperature distribution data of each zone of the baking pan; A memory for storing a program for the control method of the intelligent detachable grill; The program in the memory can be loaded and executed by the processor to implement the control method of the intelligent detachable grill.

[0022] By adopting the above technical solution, the acquisition module obtains the food doneness database and the surface temperature distribution data of each zone of the baking tray. Combined with the control program pre-stored in the memory, the processor intelligently adjusts the grill, which can match the doneness of the ingredients with the heating status in real time, improve the temperature control accuracy and cooking consistency, and support independent control of the zones and adaptive adjustment in abnormal situations, thereby enhancing the intelligent control capabilities and temperature control accuracy during the cooking process.

[0023] In a fourth aspect, the present application provides a computer storage medium capable of storing corresponding programs, which is convenient for improving cooking quality and precise temperature control, and adopts the following technical solutions: A computer-readable storage medium stores a computer program capable of being loaded by a processor and executing any one of the above-mentioned control methods for an intelligent detachable grill.

[0024] In summary, this application includes at least one of the following beneficial technical effects: 1. Real-time temperature data from each baking tray zone is collected, combined with a food doneness database to intelligently match the optimal cooking curve and generate precise temperature control instructions. The system also automatically adjusts the heating power of each zone based on the characteristics of the ingredients, achieving intelligent cooking control. This solution significantly improves the accuracy of food doneness judgment, optimizes heat energy distribution efficiency, and effectively solves the problems of uneven heating, overcooking, or undercooking in traditional baking processes. This makes the cooking process more intelligent and efficient, significantly improving the taste and quality consistency of ingredients, and providing users with a superior cooking experience. 2. Based on real-time temperature monitoring data and intelligent algorithm analysis, it accurately identifies and corrects abnormal temperature values, and optimizes the temperature curve by combining historical data characteristics. This solution effectively improves the reliability of temperature data, reduces the impact of measurement errors and environmental interference, and significantly improves the accuracy and stability of cooking control; 3. Real-time determination of the cause of abnormal temperature fluctuations, accurately distinguishing between human error and equipment failure. This solution effectively enhances the intelligent level of the cooking system. While ensuring the continuity of the cooking process, it also promptly identifies potential equipment problems. This not only maintains the stable operation of the cooking system, but also enhances the safety protection capabilities of the equipment, significantly improving the intelligent level of cooking control. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of a control method for an intelligent detachable grill provided in an embodiment of the present application.

[0026] Figure 2 This is a flow chart of a method for generating a temperature change curve of a baking pan partition based on a temperature sensor array provided in an embodiment of the present application.

[0027] Figure 3This is a flow chart of a method for generating a temperature curve based on anomaly detection and weighted correction provided in an embodiment of the present application.

[0028] Figure 4 This is a flow chart of a temperature collaborative control method based on partition failure compensation provided in an embodiment of the present application.

[0029] Figure 5 This is a flow chart of a temperature control and adjustment method based on operation interference identification provided in an embodiment of the present application.

[0030] Figure 6 This is a flowchart of an image verification method based on operation priority classification provided in an embodiment of the present application.

[0031] Figure 7 This is a flow chart of a cooking control method based on a user-defined curve provided in an embodiment of the present application.

[0032] Figure 8 This is a schematic diagram of a control system of an intelligent detachable grill provided in an embodiment of the present application.

[0033] Figure 9 This is a schematic diagram of an intelligent detachable grill provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figures 1 to 9 It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0035] The present application embodiment discloses a control method for an intelligent detachable grill. Figure 1 , the method comprising: Step S101: establishing a food doneness database, which is used to store mapping relationships between temperature-time variation curves and doneness levels of different food types.

[0036] The temperature-time change curve refers to the trend of temperature change over time during the heating process of a certain type of food.

[0037] The doneness level refers to the degree of maturity of the food after heating.

[0038] For example, by collecting temperature change data of various types of food under different heating conditions, multiple sets of temperature change curves over time are formed, and the corresponding doneness level is marked for each curve, and these data are uniformly stored in the food doneness database.

[0039] Step S102: obtaining a surface temperature distribution data variation curve of each partition of the baking pan according to the surface temperature distribution data of each partition of the baking pan.

[0040] The baking tray is divided into multiple independent temperature control areas.

[0041] Surface temperature distribution data refers to the real-time temperature information collected by temperature sensors distributed in each zone of the baking pan.

[0042] The surface temperature distribution data change curve refers to a graph that depicts the temperature trend of each partition over time, with time as the horizontal axis and temperature as the vertical axis.

[0043] For example, the sensor collects temperature data of each area in real time by distributing the temperature in the left, middle and right zones of the baking pan, and generates a surface temperature distribution data change curve in combination with the timestamp.

[0044] Step S103: Match the surface temperature distribution data change curve with the temperature-time change curve of the corresponding food type in the food doneness database to obtain a matching curve.

[0045] Matching curve means finding the most similar curve by comparing the currently collected surface temperature distribution data change curve with the standard curve in the food doneness database.

[0046] For example, when the user places chicken breast, the doneness standard curve library corresponding to the chicken breast is called, and the currently collected temperature distribution data change curve is matched with the temperature-time change curve. If the similarity of a certain "eight-point cooked" curve reaches 90%, it will be used as the matching curve.

[0047] Step S104: Obtaining the doneness level according to the mapping relationship between the matching curve and the food doneness database.

[0048] The mapping relationship refers to the correspondence between each temperature-time change curve in the food doneness database and the doneness level it represents.

[0049] The doneness level refers to the final determination of the current state of maturity of the food.

[0050] If the similarity between the current curve and the "cooked" temperature-time change curve in the food doneness database is greater than or equal to the doneness threshold, it is determined that the target doneness has been reached and a 55-65°C keep-warm instruction is generated; if the similarity between the current curve and the "uncooked" temperature-time change curve in the food doneness database is greater than or equal to the doneness threshold, a 140-220°C heating instruction is generated.

[0051] For example, if the matched curve belongs to "eight-tenths cooked", it is determined that the doneness level of the current food is "eight-tenths cooked".

[0052] Step S105: Generate temperature control instructions according to the doneness level, the temperature control instructions including keep-warm instructions and heating instructions.

[0053] Temperature control instructions refer to heating instructions or insulation instructions issued according to the current state of the ingredients.

[0054] The keep warm command is to maintain the current temperature and is used when approaching or reaching the target doneness.

[0055] The heating command is to increase the temperature and is used when the target doneness has not yet been reached.

[0056] For example, if the current doneness level is "medium rare" and has not yet reached the doneness level, a heating instruction is issued to continue heating; if the current doneness level has reached the doneness level, a keep warm instruction is issued to prevent overheating.

[0057] Step S106: The controller outputs the temperature control instruction to the heating elements corresponding to each zone of the baking tray.

[0058] The controller is a control unit that receives temperature control instructions and drives the heating element to work.

[0059] Heating elements are heating devices embedded in the various sections of the baking tray that are responsible for heat output.

[0060] Independent temperature control means that each zone can independently perform heating or insulation operations according to its own status.

[0061] For example, if the food in the left area of ​​the baking tray is cooked, while the middle and right areas are still in the heating stage, the controller only sends heating instructions to the heating elements in the middle and right areas, and sends a keep warm instruction to the left area.

[0062] By employing this technical solution, real-time temperature data from each baking tray zone is collected. Combined with a food doneness database, this intelligently matches the optimal cooking curve and generates precise temperature control instructions. Furthermore, the heating power for each zone is automatically adjusted based on the characteristics of the ingredients, enabling intelligent cooking control. This solution significantly improves the accuracy of food doneness judgment and optimizes the efficiency of heat energy distribution. It effectively addresses the issues of uneven heating, overcooking, or undercooking that plague traditional baking processes, making the cooking process more intelligent and efficient, significantly improving the taste and quality consistency of ingredients, and providing users with a superior cooking experience.

[0063] The embodiment of the present application discloses a method for generating a temperature change curve of a baking pan partition based on a temperature sensor array. Figure 2 , the method comprising: Step S201: obtaining surface temperature distribution data of each partition of the baking pan and a timestamp corresponding to the surface temperature distribution data through a temperature sensor array.

[0064] The temperature sensor array refers to multiple temperature sensors distributed in different heating areas of the baking tray according to a certain layout, which is used to collect temperature information of each zone.

[0065] The timestamp refers to the specific moment when each temperature sampling is recorded and is used to construct data on temperature changes over time.

[0066] For example, during operation of the grill, temperature sensors are respectively arranged in the left, middle and right sections, and temperature data is collected every 5 seconds, and the corresponding collection time and temperature are recorded.

[0067] Step S202: Generate discrete temperature-time data points based on the surface temperature distribution data and the timestamp.

[0068] Discrete temperature-time data points refer to a set of independent data points formed by combining the collected temperature data with the corresponding time, expressed in the form of (time, temperature).

[0069] For example, if the temperature of a partition is 25 degrees at the 0th second, 80 degrees at the 5th second, and 130 degrees at the 10th second, the discrete temperature-time data points formed are (0, 25), (5, 80), and (10, 130).

[0070] Step S203: Smoothing the discrete temperature-time data points to form a continuous temperature-time variation curve by fitting.

[0071] Smoothing is the process of removing abnormal fluctuations in data caused by sensor errors or external interference.

[0072] A continuous temperature-time variation curve refers to connecting discrete points into a continuous temperature-time variation curve through mathematical algorithms, such as interpolation.

[0073] An adaptive weighted sliding average algorithm is used to smooth discrete temperature-time data points. The weights are dynamically adjusted according to the confidence of the data points to remove abnormal fluctuations. The corrected data points are then connected by interpolation to form a continuous temperature-time change curve.

[0074] If the discrete temperature-time data points are not fitted into a continuous temperature-time change curve, the system will find it difficult to accurately reflect the temperature change trend during the heating process of the food, and will be easily interfered by sensor noise or abnormal data, resulting in inaccurate doneness judgment.

[0075] Step S204: extracting the surface temperature distribution characteristics of each partition based on the temperature-time variation curve, and generating a surface temperature distribution data variation curve.

[0076] The surface temperature distribution characteristics refer to the information reflecting the temperature differences between different zones of the baking tray and their changing patterns, including temperature gradient, heating rate, hot spot distribution, etc.

[0077] For example, multiple temperature sensors are arranged in a partition. Due to uneven heating or differences in the placement of ingredients, the temperatures detected by each sensor may be different. The system analyzes these temperature data, extracts the surface temperature distribution characteristics of the partition, and combines the time information to generate a curve representing the change of the surface temperature distribution data of the partition.

[0078] By adopting this technical solution, the temperature sensor array collects temperature data from each partition in real time and associates it with a timestamp. Discrete temperature-time data points are constructed and then smoothed to form a continuous variation curve. By extracting the surface temperature distribution characteristics, a surface temperature distribution data variation curve corresponding to the baking pan partition is generated. This solution significantly improves the accuracy and continuity of temperature monitoring, effectively eliminating the local errors caused by traditional single-point temperature measurement, and can accurately capture the subtle temperature variation characteristics of each partition. Data smoothing effectively suppresses environmental interference and measurement noise, improving the stability of temperature monitoring and laying a reliable technical foundation for precise temperature control.

[0079] The embodiment of the present application discloses a method for generating a temperature curve based on anomaly detection and weighted correction. Figure 3 , the method comprising: Step S301: Calculate the temperature change rate of adjacent discrete temperature-time data points.

[0080] The temperature change rate refers to the magnitude of temperature change per unit time.

[0081] The temperature change rate of two adjacent discrete temperature-time data points is calculated by subtracting the temperature of the previous point from the temperature of the latter point and dividing it by the time difference between the two points.

[0082] For example, the temperature of a certain partition is measured to be 80° C. at the 5th second and 120° C. at the 6th second, and the temperature change rate between these two adjacent points is 40° C. / second.

[0083] Step S302: If the temperature change rate exceeds N times the average value of the historical temperature change rate, it is marked as an abnormal point.

[0084] The historical average temperature change rate refers to the average temperature change rate recorded over a period of time when the system is in normal working condition.

[0085] N times refers to a preset threshold value, which can be set according to the actual application scenario. Here, N=3.

[0086] Anomalies refer to unreasonable temperature changes caused by sensor interference or external environment fluctuations.

[0087] For example, if the historical average temperature change rate of the partition is 10° C. / second, and the change rate of a current data point reaches 35° C. / second and N=3, it is determined to be an abnormal point.

[0088] Step S303: Replace the outlier point based on the weighted average of the first M normal data points of the outlier point to obtain a corrected data point.

[0089] The M normal data points refer to the most recent M data points that are not marked as abnormal before the abnormal point.

[0090] The weighted average refers to the average value calculated by assigning different weights to each point based on its distance from the outlier. The closer the data point is to the outlier, the higher the weight.

[0091] The weighted average of the M normal data points before the abnormal point is calculated by assigning different weights to each data point according to its distance from the abnormal point. The closer the distance, the higher the weight. The temperature value of each point is multiplied by its corresponding weight and then summed up. The result is used to replace the abnormal point to form a corrected data point.

[0092] For example, assuming M=3, the first three normal points are 75°C, 80°C, and 85°C respectively. The weighted average value obtained by weighting 0.2, 0.3, and 0.5 is 82°C. This value is used to replace the original abnormal point of 120°C to form a corrected data point.

[0093] Step S304: Smoothing the corrected data points to obtain smoothed data points.

[0094] Smoothing data points means applying an adaptive weighted sliding average algorithm to the corrected data points, dynamically adjusting the weight according to the confidence level of each data point, and performing weighted average calculation on adjacent data points, thereby reducing data fluctuations and noise impacts and obtaining a more stable and continuous temperature change trend.

[0095] For example, for the corrected data points, the weights are automatically adjusted according to the credibility of each data point. For example, the most recent data can be given more weight, while the older data can be given less weight. Then, these points are weighted and averaged to obtain more stable data points.

[0096] Step S305: Fitting the smoothed data points into a temperature-time variation curve by interpolation.

[0097] Interpolation is used to estimate the value of unknown points between known discrete points to make the data more continuous and complete.

[0098] For example, the processed smoothed temperature data points are connected into a smooth line using interpolation, for example, the intermediate temperature value is automatically added between the two points of 80° C. and 85° C. to form a continuous temperature-time change curve.

[0099] By employing this technical solution, we accurately identify and correct abnormal temperature values ​​based on real-time temperature monitoring data and intelligent algorithm analysis, and optimize the temperature curve by combining historical data characteristics. This solution effectively improves the reliability of temperature data, reduces the impact of measurement errors and environmental interference, and significantly enhances the accuracy and stability of cooking control.

[0100] The embodiment of the present application discloses a temperature collaborative control method based on partition failure compensation. Figure 4 , the method comprising: Step S401: applying a detection current to the heating element to measure the resistance value of the heating element.

[0101] The detection current refers to the small current applied by the system to the heating element during the equipment startup or self-test phase to detect its resistance status.

[0102] Resistance is a physical parameter that reflects the internal conductive performance of the heating element. Abnormal resistance may indicate that the element is aging or damaged.

[0103] Exemplarily, the detection current is applied to the heating elements of the left, middle and right partitions in sequence, and the resistance values ​​measured are 10, 10.2 and 15.8 respectively.

[0104] Step S402: Calculate the deviation between the resistance value and the standard resistance value to obtain a deviation rate.

[0105] The standard resistance value refers to the resistance value of the heating element under normal working conditions.

[0106] Deviation refers to the difference between the measured resistance value and the standard value.

[0107] The deviation rate refers to the ratio of the deviation value to the standard value, which is used to measure whether the heating element is in an abnormal state.

[0108] For example, if the standard resistance value is 10 and the resistance measured in a certain partition is 15.8, the deviation value is 5.8 and the deviation rate is 58%.

[0109] Step S403: If the deviation rate of the first partition exceeds the deviation threshold, the first partition is marked as a failed partition.

[0110] The deviation threshold refers to a reference value preset by the system to determine whether the heating element has failed, such as being set to 20%.

[0111] Failure zones refer to areas where the deviation rate exceeds the allowable range and may contain heating anomalies or malfunction.

[0112] For example, if the deviation threshold is set to 20%, and the deviation rate of the first partition reaches 58%, the system determines that the first partition is a failed partition.

[0113] Step S404: Taking the failed partition as a reference, dynamically merge it with adjacent partitions to form an extended control area. The extended control area is obtained by merging the failed partition and at least two adjacent partitions.

[0114] The extended control area refers to a new heating control area formed by merging the failed partition with the adjacent normal partition, which is used to achieve overall temperature control.

[0115] Dynamic merging means that when a partition becomes an invalid partition, it is automatically merged with two normal partitions adjacent to it on the left or right or above and below to form an extended control area covering multiple partitions.

[0116] For example, if the left zone is determined to be a failed zone, it is merged with the right and lower zones to form an extended control zone covering the three zones, and temperature control is performed uniformly.

[0117] Step S405: Acquire temperature data detected by each temperature sensor in the extended control area.

[0118] Temperature data refers to the real-time temperature values ​​collected by all sensors in the extended control area.

[0119] Exemplarily, the temperature data acquired from the temperature sensor are 130° C., 135° C., and 140° C., respectively.

[0120] Step S406: performing weighted calculation on the temperature data according to the distance relationship between each temperature sensor and the center of the failed partition to obtain the surface temperature distribution data of each partition of the baking pan.

[0121] The distance relationship refers to the distance between each temperature sensor and the center point of the failure zone.

[0122] Weighted calculation means assigning different weights based on the location of different sensors. The closer the sensor is to the center of the failure zone, the greater the weight of the temperature data. The weight coefficient = 1 / (distance from the sensor to the fault center), where 1 is a normalized reference value. The surface temperature distribution data of the baking pan = weighted temperature sum / total weight.

[0123] For example, assume that the center point of the failure zone is located in the left zone, and the three sensors are 1 cm, 3 cm, and 5 cm away from the center point, respectively. The corresponding temperatures are 130°C, 135°C, and 140°C. The weight coefficients are 1, 1 / 3, and 1 / 5, respectively. The temperature distribution data is (130×1+135×1 / 3+140×1 / 5=130+45+28) / (23 / 15)=132.39.

[0124] By adopting this technical solution, which leverages intelligent fault detection and zone-coordinated control, the temperature monitoring system is automatically reconfigured when a heating element anomaly occurs, integrating sensor data from adjacent zones to achieve precise temperature compensation. This solution effectively improves the grill system's fault tolerance and stability, ensuring precise temperature control even in the event of a localized fault. This solves the cooking interruption problem often associated with hardware failure in traditional equipment, significantly improving the reliability and continuity of the cooking process.

[0125] The embodiment of the present application discloses a temperature control method based on operation interference identification. Figure 5 , the method comprising: Step S501: When it is identified that the temperature variation amplitude of the first partition within a preset time period exceeds a variation threshold of the temperature-time variation curve, an operation interference signal is generated.

[0126] The preset duration refers to a set time window, such as 1 second or 2 seconds.

[0127] The temperature variation refers to the absolute value of the temperature increase or decrease of the first partition within a preset time period.

[0128] The change threshold refers to the preset upper limit of temperature fluctuation, which is used to determine whether abnormal changes occur.

[0129] Operation interference signal refers to the signal generated after the system detects possible human operation.

[0130] For example, when the temperature of the first partition drops suddenly from 150° C. to 80° C. within 1 second, and the change threshold set by the system is 30° C., the change is determined to be an abnormal fluctuation, and an operation interference signal is generated.

[0131] Step S502: In response to the operation interference signal, the step of obtaining the doneness level according to the mapping relationship between the matching curve and the food doneness database is stopped, and image data of the first partition is acquired.

[0132] Image data refers to real-time images of the baking tray partitions obtained through a camera or other image acquisition device.

[0133] Exemplarily, after the operation interference signal is generated, the doneness level determination process is suspended and the first subarea image is captured.

[0134] Step S503: Analyze whether there are any human cooking operation features based on the image data.

[0135] The operational features of manual cooking refer to the image features of the user's actions such as turning over the ingredients, adding seasonings, moving or replacing ingredients during the cooking process.

[0136] The collected image data is subjected to feature extraction through image recognition algorithms to identify whether there are typical human operations such as turning over ingredients, adding seasonings or replacing ingredients in the picture.

[0137] For example, image recognition analysis is performed on the collected images, and if there are image features of human turning over food or changing ingredients, it is determined that there are operation features of human cooking.

[0138] Step S504: If yes, continue to execute the step of obtaining the doneness level according to the mapping relationship between the matching curve and the food doneness database.

[0139] For example, if there is a human cooking operation, the process of determining the degree of doneness of the food is continued.

[0140] Step S505: If not, generate device abnormality warning information and start the fault diagnosis process.

[0141] Device abnormality warning information refers to the prompt information that feedback device abnormality to the user through display screen or voice prompt.

[0142] The fault diagnosis process refers to a series of detection operations performed by the system to identify equipment problems such as sensor failure and heating anomalies.

[0143] For example, if the image analysis does not detect any human operation, but the temperature still fluctuates violently, the system will determine that the equipment is abnormal, pop up a prompt "Temperature abnormality, please check the equipment", and enter the self-test mode to detect whether the temperature sensor or heating element is faulty.

[0144] By implementing this technical solution, the cause of abnormal temperature fluctuations can be determined in real time, accurately distinguishing between human error and equipment failure. This solution effectively enhances the intelligent level of the cooking system, ensuring the continuity of the cooking process while promptly identifying potential equipment problems. This not only maintains the stable operation of the cooking system, but also enhances the safety protection capabilities of the equipment, significantly improving the intelligent level of cooking control.

[0145] The embodiment of the present application discloses an image verification method based on operation priority classification. Figure 6 , the method comprising: Step S601: Establish an operation priority matrix.

[0146] The operation priority matrix refers to a priority classification model constructed by the system based on the type of human operation and its impact on temperature changes.

[0147] If an operation priority matrix is ​​not established, the system will not be able to effectively classify and respond to different types of interference signals, resulting in a lack of targeted initiation of the image verification process, which may cause waste of resources, delayed response, or misjudgment of operation intentions.

[0148] Step S602: Classify the operation interference signals of each partition based on the operation priority matrix into first priority interference signals, second priority interference signals and third priority interference signals.

[0149] The first priority interference signal refers to an operation signal that is recognized as significantly changing the heating state, such as turning over food.

[0150] The second priority interference signal refers to an operation signal that is identified as having a smaller impact, such as adding seasoning, but still requires verification.

[0151] The third priority interference signal refers to an interference signal whose specific operation behavior is not clearly identified.

[0152] For example, if the temperature in a certain zone suddenly drops and image recognition indicates that a user is stirring food, this is marked as a first-priority interference signal. If the user is adding seasoning, this is marked as a second-priority interference signal. If an object is detected approaching but a specific operation is not recognized, this is marked as a third-priority interference signal.

[0153] Step S603: starting an image acquisition and verification process for the first priority interference signal. The verification process is to determine whether a user-intended operation occurs in a certain partition through image recognition technology.

[0154] For example, when a sudden temperature drop in a certain area is detected and it is determined that it may be a flipping operation, image acquisition is immediately triggered to take a photo. After analysis confirms that the user has indeed flipped the food, the process of determining the food doneness level is continued.

[0155] Step S604: delaying the second priority interference signal for a preset time period before starting the image acquisition and verification process, where the preset time period does not exceed a preset threshold.

[0156] For example, a slight temperature fluctuation is detected in a certain zone, and the system initially determines that it is a seasoning addition operation, so it starts the image acquisition process for verification after a delay of 0.3 seconds, and completes the judgment without affecting the overall temperature control rhythm.

[0157] Step S605: starting a time-sharing verification mechanism for the third priority interference signal. The time-sharing verification mechanism allocates image acquisition according to the principle of alternating acquisition of adjacent partitions.

[0158] The time-sharing verification mechanism refers to the verification of low-priority signals using non-real-time, alternating image acquisition.

[0159] If the temperature change rate of the third priority zone exceeds the safety threshold, its priority will be automatically raised to the second priority; if the third priority zone fails to complete verification for more than 2 seconds, the verification process will be supplemented.

[0160] During the image acquisition process, the operating status of each partition is monitored in real time. When a fault such as communication interruption or temperature data abnormality is detected in the adjacent partition of the current target partition, the system dynamically selects the adjacent partition with the fault as an alternative target according to the preset rules, and continues to execute the image acquisition task while maintaining the original alternating acquisition logic and priority sorting.

[0161] For example, during the operation of a grill, the system originally captured images of the upper, lower, left, and right zones according to the principle of alternating adjacent zones. Before a certain capture, the upper zone's temperature sensor was found to be abnormal. The system determined that it was in a faulty state and immediately activated a replacement strategy, transferring the task of capturing the upper zone to the adjacent, functioning right zone. Without changing the alternating sequence of upper-lower-left-right, the upper zone was temporarily skipped and images of the zones adjacent to the upper zone were prioritized. Once the upper zone recovered, it was automatically added to the capture queue.

[0162] By adopting the above technical solution and establishing an intelligent priority response mechanism, we achieve efficient processing of concurrent interference signals from multiple partitions. This solution intelligently prioritizes processing based on operational characteristics, responding immediately to critical operations like flipping, while appropriately delaying processing for secondary operations like adding seasoning. It also uses time-sharing verification for unknown interference. While ensuring timely response to core cooking operations, it also optimizes system resource allocation, significantly improving system response efficiency and stability in the face of multiple concurrent tasks.

[0163] The present application embodiment discloses a cooking control method based on a user-defined curve. Figure 7 , the method comprising: Step S701: receiving temperature-time data of food through a mobile terminal, where the temperature-time data includes at least one set of temperature values ​​in a time series and their corresponding doneness levels.

[0164] For example, the user inputs a set of steak cooking data through the APP, such as 25°C at the 0th minute, 80°C at the 3rd minute, 140°C at the 6th minute, and 180°C at the 9th minute, and marks the degree of doneness corresponding to this set of data as "medium rare".

[0165] Step S702: Upload the temperature-time data to the cloud for verification. The cloud verification includes data format verification, temperature range rationality judgment and cooking safety assessment.

[0166] Cloud verification refers to sending the data uploaded by users to a remote server for logical and security checks.

[0167] For example, after receiving the temperature-time data uploaded by the user, it is first checked whether the time is an increasing sequence, then it is determined whether the maximum temperature exceeds the upper limit of 220°C allowed by the equipment, and finally it is evaluated whether the overall curve has the risk of causing the food to burn.

[0168] Step S703: If the verification is successful, the temperature-time data is stored in the food doneness database, and a custom temperature-time curve is generated.

[0169] A custom temperature-time curve refers to a temperature change curve that is set by the user and generated after verification by the system to represent the heating characteristics of specific ingredients.

[0170] For example, after the steak data uploaded by the user passes the cloud verification, the system saves it to the food doneness database and generates a temperature-time curve named "User-defined_Steak_70% Rare".

[0171] Step S704: receiving the food image uploaded by the mobile terminal, extracting features of the food image, and generating an image recognition tag.

[0172] Image recognition tags refer to identifiers generated after analyzing image content and are used to mark the type of food.

[0173] Extracting food image features is to use convolutional neural networks to identify visual features such as color, texture, and shape in the image, compare these features with the features in the known food image database, and generate the most matching image recognition label to identify the current food type.

[0174] For example, before starting to cook, the user takes a photo of a steak with a mobile phone and uploads it to the system. The system uses a deep learning model to recognize that the image content is "steak" and generates a corresponding image recognition label.

[0175] Step S705: Associating and binding the image recognition tag with the custom temperature-time curve, and storing it in the food doneness database.

[0176] Association binding refers to establishing a connection between the image recognition result of a certain food ingredient and the corresponding temperature-time curve.

[0177] For example, the image recognition tag of "steak" is bound to the temperature-time curve of "user-defined_steak_medium-rare" previously uploaded by the user, and this combined information is stored in the food doneness database as a reference for identifying steak.

[0178] Step S706: Capture images of ingredients to be cooked, match image recognition tags, and call a custom temperature-time curve as a benchmark.

[0179] The collected images of the ingredients to be cooked are analyzed for features through image recognition algorithms to extract key information such as their color, texture, and shape. These information is then compared with existing image recognition tags in the food doneness database. Once the tag with the highest matching degree is found, the custom temperature-time curve associated with the tag is automatically called as the heating control benchmark for the current cooking process.

[0180] For example, the user places a piece of steak again to prepare for cooking. The system camera automatically captures the image and identifies it as "steak". Then, the temperature-time curve of "User-defined_Steak_70% Rare" previously set by the user is retrieved from the food doneness database as the benchmark for this heating control.

[0181] By implementing this technical solution and building a complete closed-loop system for user-defined cooking curves, the digitalization and intelligent application of cooking knowledge are achieved. This solution ensures cooking safety through a data verification process while transforming the user's unique temperature control experience into an executable cooking curve. Combined with image recognition technology, this significantly enhances the smart grill's personalized adaptation capabilities, enabling the device to accurately reproduce the user's preferred cooking results.

[0182] Based on the same inventive concept, the present embodiment provides a control system for an intelligent detachable grill, comprising: An acquisition module 801 is used to acquire a food doneness database and surface temperature distribution data of each zone of the baking pan; Memory 802, used for storing a program of the control method of the intelligent detachable grill; The processor 803 can load and execute the program in the memory to implement the control method of the intelligent detachable grill.

[0183] By adopting the above technical solution, the acquisition module obtains the food doneness database and the surface temperature distribution data of each zone of the baking tray. Combined with the control program pre-stored in the memory, the processor intelligently adjusts the grill, which can match the doneness of the ingredients with the heating status in real time, improve the temperature control accuracy and cooking consistency, and support independent control of the zones and adaptive adjustment in abnormal situations, thereby enhancing the intelligent control capabilities and temperature control accuracy during the cooking process.

[0184] The present application provides an intelligent detachable grill, which is used to implement the solution described in any of the above embodiments, including: an upper grill assembly 91, a lower grill assembly 92, a hinge assembly 93, and a controller 94; the upper grill assembly 91 and the lower grill assembly 92 are connected by the hinge assembly 93; the controller 94 is connected to the upper grill assembly 91; The upper grill assembly 91 is provided with a first heating element 911, which is electrically connected to the controller 94; the lower grill assembly 92 is provided with a second heating element 921, which is electrically connected to the controller 94; The upper grill assembly 91 further includes an upper grill pan, an upper housing, and a first array of temperature sensors 912 evenly disposed between the upper grill pan and the upper housing. The fixed ends of the first array of temperature sensors 912 are fixed to the upper housing, and the detection ends of the first array of temperature sensors 912 do not contact the upper grill pan. The lower grill assembly 92 further includes a lower grill pan, a lower housing, and a second array of temperature sensors 922 evenly disposed between the lower grill pan and the lower housing. The fixed ends of the second array of temperature sensors 922 are fixed to the lower housing, and the detection ends of the second array of temperature sensors 922 do not contact the lower grill pan. The controller 94 is in communication with the first array temperature sensor 911 and the second array temperature sensor 921 .

[0185] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the division of the above-mentioned functional modules is only used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-mentioned systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0186] An embodiment of the present application provides a computer-readable storage medium storing a computer program capable of being loaded by a processor and executed by a method for controlling an intelligent detachable grill.

[0187] Computer storage media include, for example, various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0188] Based on the same inventive concept, an embodiment of the present application provides a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute a control method for a smart detachable grill.

[0189] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the division of the above-mentioned functional modules is only used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-mentioned systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0190] The above are all preferred embodiments of the present application and are not intended to limit the scope of protection of this application. Unless otherwise specified, any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features. In other words, unless otherwise specified, each feature is merely an example of a series of equivalent or similar features.

Claims

1. A control method for an intelligent detachable grill, characterized in that: include: Establishing a food doneness database, wherein the food doneness database is used to store a mapping relationship between temperature-time variation curves of different food types and doneness levels; According to the surface temperature distribution data of each partition of the baking pan, a surface temperature distribution data variation curve of each partition of the baking pan is obtained; Matching the surface temperature distribution data change curve with the temperature-time change curve of the corresponding food type in the food doneness database to obtain a matching curve; Obtaining a doneness level according to the mapping relationship between the matching curve and the food doneness database; generating a temperature control instruction according to the doneness level, the temperature control instruction including a keep-warm instruction and a heating instruction; The temperature control instruction is outputted to the heating elements of the corresponding zones of the baking tray through the controller.

2. The control method of the intelligent detachable grill according to claim 1, characterized in that: The method of obtaining a surface temperature distribution data variation curve of each partition of the baking pan according to the surface temperature distribution data of each partition of the baking pan comprises: Acquire the surface temperature distribution data of each partition of the baking pan and a timestamp corresponding to the surface temperature distribution data through a temperature sensor array; generating discrete temperature-time data points based on the surface temperature distribution data and the timestamp; Smoothing the discrete temperature-time data points to form a continuous temperature-time curve; Based on the temperature-time variation curve, the surface temperature distribution characteristics of each partition are extracted to generate the surface temperature distribution data variation curve.

3. The control method of the intelligent detachable grill according to claim 2, characterized in that: The step of smoothing the discrete temperature-time data points and fitting them to form a continuous temperature-time variation curve comprises: Calculating the temperature change rate of adjacent discrete temperature-time data points; If the temperature change rate exceeds N times the average value of the historical temperature change rate, it is marked as an abnormal point; Replacing the outlier with a weighted average of the first M normal data points of the outlier to obtain a corrected data point; Smoothing the corrected data points to obtain smoothed data points; The smoothed data points are fitted to the temperature-time variation curve by interpolation.

4. The control method of the intelligent detachable grill according to claim 1, characterized in that: Before obtaining the surface temperature distribution data of the baking pan, the method further includes: applying a detection current to the heating element and measuring the resistance value of the heating element; Calculating the deviation between the resistance value and the standard resistance value to obtain a deviation rate; If the deviation rate of the first partition exceeds a deviation threshold, marking the first partition as a failed partition; Taking the failed partition as a reference, dynamically merging it with adjacent partitions to form an extended control area, where the extended control area is obtained by merging the failed partition and at least two adjacent partitions; Acquiring temperature data detected by each temperature sensor in the extended control area; According to the distance relationship between each temperature sensor and the center of the failed partition, the temperature data is weightedly calculated to obtain surface temperature distribution data of each partition of the baking pan.

5. The control method of the intelligent detachable grill according to claim 1, characterized in that: Also includes: generating an operation interference signal when identifying that a temperature change amplitude of the first partition within a preset time period exceeds a change threshold of the temperature-time change curve; In response to the operation interference signal, stopping the step of obtaining the doneness level according to the mapping relationship between the matching curve and the food doneness database, and acquiring image data of the first partition; analyzing, based on the image data, whether there are operational features of human cooking; If so, continue to perform the step of obtaining the doneness level according to the mapping relationship between the matching curve and the food doneness database; If it does not exist, a device abnormality warning message is generated and the fault diagnosis process is started.

6. The control method of the intelligent detachable grill according to claim 5, characterized in that: The method further comprises: Establish an operational priority matrix; Classifying the operation interference signals of each partition based on the operation priority matrix into first priority interference signals, second priority interference signals, and third priority interference signals; Initiating an image acquisition and verification process for the first-priority interference signal, wherein the verification process is to determine whether a user-intended operation occurs in a certain partition through image recognition technology; Delaying the second-priority interference signal for a preset time period before starting the image acquisition and verification process, wherein the preset time period does not exceed a preset threshold; A time-sharing verification mechanism is started for the third priority interference signal, wherein the time-sharing verification mechanism allocates image acquisition according to a principle of alternating acquisition of adjacent partitions.

7. The control method of the intelligent detachable grill according to claim 1, characterized in that: The food doneness database supports the addition and calling of user-defined temperature-time curves, including: receiving, via a mobile terminal, temperature-time data of food, the temperature-time data comprising at least one set of temperature values ​​in a time series and their corresponding doneness levels; Uploading the temperature-time data to the cloud for verification, including data format verification, temperature range rationality judgment, and cooking safety assessment; If the verification is successful, the temperature-time data is stored in the food doneness database, and a custom temperature-time curve is generated; receiving a food image uploaded by the mobile terminal, extracting features of the food image, and generating an image recognition tag; Associating and binding the image recognition tag with the custom temperature-time curve, and storing the result in the food doneness database; An image of the food to be cooked is captured, the image recognition tag is matched, and the custom temperature-time curve is used as a benchmark.

8. An intelligent detachable grill, characterized in that: The intelligent detachable grill is used to execute the control method of the intelligent detachable grill according to any one of claims 1 to 7, comprising: an upper grill assembly, a lower grill assembly, a hinge assembly, and a controller; the upper grill assembly and the lower grill assembly are connected by the hinge assembly; the controller is connected to the upper grill assembly; The upper grill assembly is provided with a first heating element, which is electrically connected to the controller; the lower grill assembly is provided with a second heating element, which is electrically connected to the controller; The upper grill assembly further includes an upper grill pan, an upper shell, and a first array of temperature sensors uniformly disposed between the upper grill pan and the upper shell, wherein a fixed end of the first array of temperature sensors is fixed to the upper shell, and a detection end of the first array of temperature sensors does not contact the upper grill pan; The lower grill assembly further includes a lower grill pan, a lower housing, and a second array of temperature sensors uniformly disposed between the lower grill pan and the lower housing, wherein a fixed end of the second array of temperature sensors is fixed to the lower housing, and a detection end of the second array of temperature sensors does not contact the lower grill pan; The controller is in communication with the first array temperature sensor and the second array temperature sensor.

9. An intelligent detachable grill system, characterized in that: The system is used to execute a control method for an intelligent detachable grill according to any one of claims 1 to 7, comprising: An acquisition module is used to obtain the food doneness database and the surface temperature distribution data of each zone of the baking pan; A memory for storing a program for the control method of the intelligent detachable grill; The program in the memory can be loaded and executed by the processor to implement the control method of the intelligent detachable grill.

10. A computer-readable storage medium, characterized in that A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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