Cooking control method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of digital household appliances, in particular to a cooking control method and device, electronic equipment and a storage medium, the method is applied to a control unit in a cooking system, and the cooking system further comprises a range hood and stove all-in-one machine, a first temperature measuring mechanism and a second temperature measuring mechanism; the first temperature measuring mechanism is arranged at the bottom of the electromagnetic range panel and used for detecting the temperature of the pot bottom. The infrared sensor is integrated on the island platform range hood lifting push rod, close-range direct detection of the temperature of the pot body is achieved, the defects that infrared temperature measurement of a traditional range hood is interfered by steam and measurement is not accurate are overcome, double verification is carried out based on accurate pot body temperature data in combination with the pot bottom temperature, and the accuracy of temperature measurement is improved. According to the method, the accuracy and the response speed of dry burning judgment are remarkably improved, further, user habits are learned through the AI algorithm, the temperature control curve is dynamically optimized, the risk of dry burning is effectively avoided while the cooking effect is ensured, and safe and accurate self-adaptive intelligent cooking control is achieved.

Description

Technical Field

[0001] This invention relates to the field of digital home appliance technology, and in particular to a cooking control method, device, electronic device, and storage medium. Background Technology

[0002] To achieve intelligent cooking and anti-dry-burning functions, existing integrated range hoods and cooktops typically incorporate infrared temperature sensors in the range hood area for non-contact detection of the pot or food temperature. However, this approach has inherent drawbacks: due to the considerable distance between the range hood, cooktop, and pot, the infrared sensor needs to perform long-distance, wide-area measurements, making it susceptible to interference from environmental factors such as steam and fumes, leading to inaccurate temperature readings. More importantly, this method primarily detects the temperature of the pot's interior or the food surface, rather than directly and accurately monitoring the temperature changes of the pot itself (especially the side walls). For thin-walled cookware (such as 304 / 430 stainless steel pots), under dry-heating or high-stirring conditions, the side wall temperature can rise rapidly to a dangerous level within a short time (e.g., more than 10 seconds), causing deformation or damage. Traditional long-distance infrared temperature measurement methods cannot capture the instantaneous temperature rise of the pot's side walls in a timely and accurate manner, resulting in significant detection blind spots and response lag. This makes it impossible to provide effective instantaneous dry-burning protection for thin pots and also hinders precise cooking control based on the actual temperature changes of the pot.

[0003] Therefore, there is a lack of existing technologies that can directly, accurately, and reliably detect the temperature of the pot body and thereby achieve rapid dry-burn protection and intelligent cooking control. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a cooking control method, device, electronic device, and storage medium to improve the accuracy of cooking temperature detection.

[0005] In a first aspect, embodiments of the present invention provide a cooking control method applied to a control unit in a cooking system. The cooking system further includes an integrated range hood and cooktop, a first temperature measuring mechanism, and a second temperature measuring mechanism. The first temperature measuring mechanism is disposed at the bottom of the induction cooktop panel and is used to detect the temperature of the bottom of the pot. The second temperature measuring mechanism is an infrared sensor disposed on the range hood lifting push rod of the integrated range hood and cooktop and is used to detect the temperature of the pot body. The method includes: The temperature of the pot body measured by the second temperature measuring mechanism and the temperature of the pot bottom measured by the first temperature measuring mechanism are obtained. The type of cookware is determined based on the temperature of the pot body and the temperature of the pot bottom; Determine whether the cookware is in a dry-heat state based on the type of cookware, the temperature of the cookware body, and the temperature of the bottom of the cookware; If not, input the current heating mode and pot body temperature into the pre-trained AI model. The AI ​​model generates and outputs optimized temperature control parameters based on user habit data. The heating power curve of the integrated range hood and cooktop is dynamically adjusted based on optimized temperature control parameters.

[0006] In conjunction with the first aspect, the steps for determining the type of cookware based on the temperature of the pot body and the temperature of the pot bottom include: Calculate the temperature difference between the pot body and the pot bottom. The thickness of the cookware is determined based on the preset relationship between temperature difference and thickness. The type of cookware is determined by comparing the thickness of the cookware with the threshold.

[0007] Based on the first aspect, the cookware type is the first type of cookware; The steps to determine whether a cookware is dry-heated, based on the type of cookware, the temperature of the cookware body, and the temperature of the bottom of the cookware, include: If the temperature of the pot reaches the first temperature threshold and remains there for a period of time, it is determined that the pot is in a dry-burning state.

[0008] In conjunction with the first aspect, cookware types also include second cookware; The steps to determine whether a cookware is dry-heated, based on the type of cookware, the temperature of the cookware body, and the temperature of the bottom of the cookware, include: If the temperature of the pot body reaches the first temperature threshold and continues for a second time, and the temperature rise slope calculated based on the temperature of the bottom of the pot is greater than the slope threshold; Alternatively, if the temperature at the bottom of the pot reaches the first temperature threshold and remains there for a third time, it is determined that the pot is in a dry-burning state.

[0009] In conjunction with the first aspect, the second temperature measuring mechanism is a dot matrix infrared sensor; The steps for obtaining the pot body temperature measured by the second temperature measuring mechanism include: Temperature data at multiple points on the side wall of the cookware are obtained using a dot matrix infrared sensor. Based on temperature data from multiple locations, characteristic values ​​representing the overall temperature of the pot body are calculated.

[0010] In conjunction with the first aspect, the method also includes: During the constant heating power stage, temperature change curves at multiple points were obtained; If more than a specified number of points show a continuous upward trend in temperature, it is determined that the moisture inside the cookware has decreased. Switch to the reduction stage and adjust the heating power curve.

[0011] In conjunction with the first aspect, the AI ​​model is trained through the following steps: Collect historical cooking data, including cookware type, heating mode, real-time temperature sequence, and user-confirmed cooking effect rating; Using cookware type, heating mode, and real-time temperature sequence as inputs, and the user's preferred temperature control curve as the optimization target, the neural network model is trained under supervision until an AI model that meets the preset conditions is obtained.

[0012] Secondly, this application also provides a cooking control device applied to a control unit in a cooking system. The cooking system further includes an integrated range hood and cooktop, a first temperature measuring mechanism, and a second temperature measuring mechanism. The first temperature measuring mechanism is disposed at the bottom of the induction cooktop panel and is used to detect the temperature of the bottom of the pot. The second temperature measuring mechanism is an infrared sensor disposed on the range hood lifting push rod of the integrated range hood and cooktop and is used to detect the temperature of the pot body. The device includes: The acquisition module is used to acquire the pot body temperature measured by the second temperature measuring mechanism and the pot bottom temperature measured by the first temperature measuring mechanism; The determination module is used to determine the type of cookware based on the temperature of the pot body and the temperature of the pot bottom; The judgment module is used to determine whether the cookware is in a dry-heat state based on the cookware type, the temperature of the cookware body, and the temperature of the bottom of the cookware. The output module is used to input the current heating mode and pot body temperature into the pre-trained AI model when the pot is not in a dry-heating state. The AI ​​model generates and outputs optimized temperature control parameters based on user habit data. The adjustment module is used to dynamically adjust the heating power curve of the integrated range hood and cooktop based on optimized temperature control parameters.

[0013] Thirdly, this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor runs the computer program to cause the electronic device to perform the above-described method.

[0014] Fourthly, this application provides a storage medium storing computer program instructions, which are read and executed by a processor to perform the above-described method.

[0015] The embodiments of this invention bring the following beneficial effects: The cooking control method, device, electronic device, and storage medium provided in this application are applied to the control unit of a cooking system. The cooking system also includes an integrated range hood and cooktop, a first temperature measuring mechanism, and a second temperature measuring mechanism. The first temperature measuring mechanism is disposed at the bottom of the induction cooktop panel and is used to detect the temperature of the bottom of the pot. The second temperature measuring mechanism is an infrared sensor disposed on the range hood lifting push rod of the integrated range hood and cooktop and is used to detect the temperature of the pot body. The method includes: acquiring the pot body temperature measured by the second temperature measuring mechanism and the pot bottom temperature measured by the first temperature measuring mechanism; determining the pot type based on the pot body temperature and the pot bottom temperature; determining whether the pot is in a dry-burning state based on the pot type, pot body temperature, and pot bottom temperature; if not, inputting the current heating mode and pot body temperature into a pre-trained AI model, the AI ​​model generating and outputting optimized temperature control parameters based on user habit data; and dynamically adjusting the heating power curve of the integrated range hood and cooktop according to the optimized temperature control parameters.

[0016] This application integrates an infrared sensor into the lifting push rod of the island range hood, enabling direct close-range detection of the pot's temperature. This overcomes the shortcomings of traditional range hood infrared temperature measurement, which is susceptible to steam interference and inaccurate. Based on accurate pot temperature data, combined with the pot bottom temperature for dual verification, the accuracy and response speed of dry burning judgment are significantly improved. Furthermore, by learning user habits through AI algorithms, the temperature control curve is dynamically optimized, effectively avoiding the risk of dry burning while ensuring cooking results, thus achieving safe and precise adaptive intelligent cooking control.

[0017] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 A schematic flowchart of the cooking temperature measurement method provided in an embodiment of the present invention; Figure 2This is a schematic diagram of the structure of the lifting push rod performing the upward movement in the cooking temperature measurement method provided in the embodiment of the present invention; Figure 3 This is a schematic diagram of the temperature change curve of a thin pan during the initial heating stage in the cooking temperature measurement method provided in this embodiment of the invention. Figure 4 This is a schematic diagram of the temperature change curve of a thick-walled pan during the initial heating stage in the cooking temperature measurement method provided in this embodiment of the invention. Figure 5 This is a schematic diagram of the structure of the cooking temperature measuring device provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the electronic device structure provided in an embodiment of the present invention.

[0021] Figure label: 1-Combined range hood and cooktop, 2-Induction cooktop panel, 3-Range hood, 4-Lifting push rod, 5-First temperature measuring mechanism, 6-Second temperature measuring mechanism; 10 - Acquisition Module, 20 - Confirmation Module, 30 - Judgment Module, 40 - Output Module, 50 - Adjustment Module; 130 - Processor, 131 - Memory, 132 - Bus, 133 - Communication interface. Detailed Implementation

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

[0023] To facilitate understanding of this embodiment, the application scenarios and design concepts of this application embodiment will be briefly introduced below.

[0024] Traditional range hood and cooktop combos typically use infrared sensors in the range hood for long-distance temperature measurement, which is susceptible to steam interference and cannot accurately detect the temperature of the cookware. Especially for thin-walled cookware, it is difficult to respond promptly to rapid temperature rises, posing a risk of dry burning and damage to the cookware, and it also cannot achieve precise cooking control.

[0025] Based on this, embodiments of this application provide a cooking control method, device, electronic device, and storage medium to improve the accuracy of temperature detection.

[0026] Example 1 This application provides a cooking control method applied to a control unit in a cooking system, the cooking system further including a range hood and cooktop combo unit 1, a first temperature measuring mechanism 5, and a second temperature measuring mechanism 6; combined with Figure 1 As shown, the first temperature measuring mechanism 5 is located at the bottom of the induction cooker panel 2 and is used to detect the temperature of the bottom of the pot; the second temperature measuring mechanism 6 is an infrared sensor, which is located on the lifting push rod 4 of the range hood 3 of the integrated range hood and cooker 1 and is used to detect the temperature of the pot body.

[0027] The first temperature measuring mechanism 5 is located at the bottom of the induction cooktop panel 2 and is used to directly detect the temperature of the bottom of the pot. The second temperature measuring mechanism 6 is an infrared sensor fixed to the end of the lifting push rod 4 of the range hood 3, which can dynamically align with the center area of ​​the pot as the lifting push rod 4 moves up and down. After the integrated range hood and cooktop 1 is started, the lifting push rod 4 automatically rises to the preset temperature measuring position, so that the infrared sensor is accurately aligned with the center of the pot. At the same time, the first temperature measuring mechanism 5 continuously monitors the temperature of the bottom of the pot, and the dual temperature data is transmitted to the control unit in real time. The control unit identifies the type of cookware based on the temperature difference between the pot body and the bottom of the pot, and adopts a differentiated judgment strategy: for thin-walled cookware, the judgment is mainly based on the instantaneous response characteristics of the pot body temperature, and protection is immediately triggered when an abnormal temperature rise is detected; for thick-walled cookware, both the pot body temperature and the temperature rise slope of the bottom of the pot are used for dual verification. If the system detects that the heating is in a dry-burning state, it immediately cuts off the heating power and issues an alarm. If the dry-burning threshold has not been reached, it inputs the real-time temperature data and the current heating mode into an AI model trained on user habit data. The AI ​​algorithm dynamically outputs optimized temperature control parameters and adjusts the heating power curve in real time to achieve safe, accurate and adaptive intelligent cooking control.

[0028] Combination Figure 2 As shown, the method includes: S110, acquire the pot body temperature measured by the second temperature measuring mechanism and the pot bottom temperature measured by the first temperature measuring mechanism.

[0029] S120 determines the type of cookware based on the temperature of the pot body and the temperature of the pot bottom.

[0030] S130 determines whether the cookware is in a dry-heat state based on the type of cookware, the temperature of the cookware body, and the temperature of the bottom of the cookware.

[0031] If not, proceed to step S140; if yes, proceed to step S160.

[0032] S140 inputs the current heating mode and pot body temperature into a pre-trained AI model. The AI ​​model generates and outputs optimized temperature control parameters based on user habit data. The S150 dynamically adjusts the heating power curve of the integrated range hood and cooktop based on optimized temperature control parameters.

[0033] S160, control the integrated range hood and stove to stop heating and execute the dry burning protection mode, and at the same time issue an alarm to remind the user.

[0034] First, in step S110, the infrared sensor on the lifting push rod 4 directly acquires the pot body temperature, combined with the pot bottom temperature detection, to construct a dual-source temperature sensing system. Then, in step S120, based on the temperature difference between the pot body and the pot bottom, the system can accurately identify the pot type, laying the foundation for subsequent differentiated control. In step S130, multi-parameter fusion judgment is performed based on the pot type, pot body temperature, and pot bottom temperature to achieve differentiated dry-burn protection for different pots. Especially for thin-walled pots, the instantaneous response of the pot body temperature can quickly cut off heating before the critical point of dry burning occurs in step S160, effectively preventing pot damage. When no dry-burning state is detected, step S140 inputs the current heating mode and pot body temperature into the AI ​​model, generating optimized temperature control parameters based on user habit data. In step S150, the heating power curve is dynamically adjusted, achieving personalized and adaptive precise temperature control. Thus, through a complete control process from temperature detection and status judgment to intelligent response, a safety assurance system integrating prevention, early warning, and protection is established, significantly improving cooking safety and user experience.

[0035] In conjunction with the first aspect, step S120 includes: S121, calculate the temperature difference between the pot body temperature and the pot bottom temperature to obtain the temperature difference.

[0036] The temperature T of the pot body is simultaneously acquired at a preset sampling period (e.g., 0.5-1 seconds). side With the temperature T of the bottom of the pot bottom And calculate the instantaneous temperature difference ΔT = |T side - T bottom | and continuously record the temperature difference change curve during the heating process.

[0037] S122, based on the preset correspondence between temperature difference and thickness, determine the thickness of the cookware.

[0038] Understandably, the control unit stores a pre-stored temperature difference-thickness mapping table. This table, established through extensive experimentation, matches the currently measured temperature difference data with the table to determine the optimal corresponding cookware thickness value. It records the correspondence between temperature difference and thickness for cookware of different materials under standard heating conditions. The pre-stored temperature difference-thickness mapping table is then invoked to match the currently measured temperature difference data with the table, determining the optimal corresponding cookware thickness value.

[0039] S123, Determine the cookware type based on the comparison relationship between cookware thickness and threshold.

[0040] It is understandable that a thickness threshold (e.g., 2.0 mm) is preset in the control unit. If d < d0, it is determined as the first type of cookware (thin-walled cookware); if d ≥ d0, it is determined as the second type of cookware (thick-walled cookware). Among them, the above threshold d0 is determined according to material safety performance (to ensure that thin-walled cookware can be protected in time under dry-burning conditions), heat conduction characteristics (based on the difference in heat response time of cookware with different thicknesses), and user usage data (statistical analysis of the thickness distribution of commonly used cookware). Ensure that thin-walled cookware can be protected in time under dry-burning conditions. In addition, a confidence verification mechanism is also set up: when the determination results in three consecutive sampling periods are the same, the final cookware type is confirmed to avoid misidentification caused by temperature fluctuations and improve the classification accuracy.

[0041] Combined with the first aspect, the cookware type is the first cookware type.

[0042] Step S130 includes: S131, if the temperature of the cookware body reaches the first temperature threshold and lasts for the first time, it is determined that the cookware is in a dry-burning state.

[0043] In this embodiment, the first cookware type is thin-walled cookware (such as lightweight stainless steel cookware, stoneware cookware). It has a small heat capacity, small thermal inertia, extremely rapid temperature rise, fast response, and is extremely sensitive to power changes, and is extremely prone to overheating. If continuous high-power heating is carried out, the temperature of the cookware will soar, but the heat may not have time to be transferred to the interior of the food, resulting in "the cookware is red, but the dish is raw" or instantaneous dry-burning. Therefore, pulsed (intermittent) heating must be used for thin cookware. That is: it switches between the two states of "full-power heating" and "stop heating" at an extremely high frequency (such as tens of thousands of times per second). For thin-walled cookware, the temperatures of the cookware body and the bottom of the pot are both room temperature (e.g., Figure 3 、 Figure 4 the 25 °C shown in Figure 3 before heating. In the initial stage of heating (0 - t1), the temperature of the cookware body of the thin pot soars and reaches the first temperature threshold T1 at time t1 (as shown at point A in Figure 3 ), but at this time, the temperature of the bottom of the pot is much lower than T1 (as shown at point C in Figure 3 ). It takes more time for the bottom of the pot to rise to T1 (point B in

[0044] The dry-burning state judgment in step S130 adopts the following mechanism: In the initial stage of heating, the system continuously monitors the temperature T of the cookware body obtained by the second temperature measurement mechanism side . When it is detected that T side reaches the first temperature threshold T1 (preferably 250 °C), the duration timing is started. If the temperature of the cookware body continuously remains at T1 or above for the first time t1 (preferably 2 - 3 seconds), it is immediately determined that the cookware is in a dry-burning state.

[0045] The technical principle of this judgment mechanism is based on the unique thermophysical properties of thin-walled cookware: thin-walled cookware made of materials such as 304 / 430 stainless steel has the significant characteristics of small heat capacity and fast heat conduction. When heated empty or without oil, due to the lack of a buffering medium, the temperature of the pot will rise rapidly, and can quickly rise from room temperature to the dangerous temperature range within 10 seconds.

[0046] This embodiment of the application uses an infrared sensor mounted on the lifting push rod 4 to directly monitor the pot body temperature. Utilizing its close-range alignment advantage, it can accurately capture this instantaneous temperature rise characteristic. A continuous judgment time window of 2-3 seconds ensures both timely protection response and effectively avoids false triggering caused by momentary interference or normal cooking fluctuations.

[0047] Furthermore, during the judgment and execution process, multiple verification mechanisms are implemented simultaneously: real-time monitoring of the temperature change trend of the pot bottom to ensure that its temperature rise curve conforms to the dry-burning characteristics; algorithmic filtering to eliminate abnormal readings caused by instantaneous interference from the sensor; specifically, when the ambient humidity sensor detects humidity greater than 85%, the judgment time is automatically extended by 0.5 seconds to compensate for the potential influence of steam on infrared temperature measurement. This rapid judgment strategy designed for thin-walled cookware fundamentally solves the response lag problem caused by the long heat transfer path in traditional pot bottom temperature detection schemes by directly monitoring the temperature change of the pot body.

[0048] In conjunction with the first aspect, cookware types also include second cookware; Step S130 includes: S132, if the temperature of the pot body reaches the first temperature threshold and continues for a second time, and the temperature rise slope calculated based on the temperature of the bottom of the pot is greater than the slope threshold. Alternatively, if the temperature at the bottom of the pot reaches the first temperature threshold and remains there for a third time, it is determined that the pot is in a dry-burning state.

[0049] For the second type of cookware (thick-walled cookware, such as cast iron pots and composite bottom pots), thick-walled cookware has a large heat capacity and thermal inertia. Once heated to the target temperature, the stored heat is sufficient to maintain stable cooking. Even with brief power fluctuations, the temperature is not prone to sudden drops. Therefore, the most effective strategy for thick-walled pots is to continuously output a high and stable power, allowing them to reach and stabilize at the target cooking temperature as quickly as possible. Continuous power input can overcome their large thermal inertia, without worrying about them overheating instantly like thin-walled pots. For thick-walled cookware, combined with... Figure 4 As shown, the temperature of the pot body and bottom before heating is room temperature (e.g., Figure 4 As shown in the figure, at 25°C, the temperature of the pan body rises in the initial stage of heating (0-t1), but compared to... Figure 3 The temperature rise slope (i.e., the rate of temperature change) is low, and the first temperature threshold T1 is reached at time t1 (in combination with...). Figure 4(As shown at point A), but at this time the temperature of the pot bottom is less than but relatively close to T1 (in conjunction with...) Figure 4 As shown at point C), it takes more time for the bottom of the pot to heat up to T1 (in conjunction with...). Figure 4 Point B in the middle.

[0050] Step S130 employs a multi-parameter fusion-based composite judgment mechanism to address the detection challenges posed by the thermal conductivity characteristics of thick-walled cookware. This mechanism ensures the comprehensiveness and reliability of dry-burn protection through parallel judgment along two paths. Specifically, in the first judgment path, the system requires three conditions to be met simultaneously: the pot body temperature reaches a first temperature threshold of 250℃, this temperature is maintained for a second time period of 5-8 seconds, and the real-time temperature rise slope calculated based on the pot bottom temperature is greater than a preset threshold of 0.8-1.2℃ / second. This combination of conditions fully considers the thermal characteristics of thick-walled cookware—due to its large heat capacity and slow thermal conductivity, the temperature change is relatively gradual, and a single temperature threshold is insufficient to accurately reflect the actual situation. The introduction of the temperature rise slope effectively distinguishes the differences in thermodynamic characteristics between normal cooking and dry-burning conditions.

[0051] In the second judgment path, the system sets an independent standard for judging the bottom temperature of the pot: when the bottom temperature of the pot reaches 250℃ and remains so for 10-15 seconds in a third time period, it is directly judged as a dry-burning state. This path is mainly for slow but continuous dry-burning processes, making up for the possibility of missed detection in the first path.

[0052] To achieve accurate judgment, a comprehensive model for calculating the temperature rise slope was established: based on the temperature difference at the bottom of the pot between two consecutive sampling periods, a sliding window algorithm is used to smooth the data from multiple consecutive sampling points, effectively eliminating interference from instantaneous fluctuations. Simultaneously, the system also integrates an environmental temperature compensation mechanism to ensure that the accuracy of the slope calculation is unaffected by environmental factors.

[0053] Understandably, the dry-burn protection can be triggered when any of the conditions are met. This allows for both rapid response to sudden temperature changes and effective identification of slow-developing dry-burning processes, enabling comprehensive monitoring of the dry-burning status of thick-walled cookware.

[0054] As a preferred approach, a dynamic verification mechanism was also established: before triggering the dry-burn protection, the working status of the two temperature sensors is cross-verified to eliminate false alarms caused by single sensor failure; at the same time, the temperature change curve for 120 seconds before triggering is recorded to provide data support for subsequent algorithm optimization. While ensuring the protective effect, the false alarm rate for thick-walled cookware is controlled below 5%, significantly improving the practicality and reliability of the system and providing a targeted intelligent protection solution for different types of cookware.

[0055] In conjunction with the first aspect, the second temperature measuring mechanism 6 is a dot matrix infrared sensor.

[0056] Step S110, which involves obtaining the pot body temperature measured by the second temperature measuring mechanism, includes: S111 acquires temperature data at multiple points on the side wall of the cookware through a dot matrix infrared sensor; S112, based on temperature data from multiple points, calculates characteristic values ​​representing the overall temperature of the pot body.

[0057] In this embodiment, the second temperature measuring mechanism 6 employs a dot-matrix infrared sensor, which, through its unique spatial temperature sensing capability, achieves high-precision measurement of the temperature field of the pot body. As an example, this dot-matrix infrared sensor, with a sampling frequency of 10Hz, simultaneously acquires temperature data from 35 independent detection points distributed in a 5×7 dot matrix on the side wall of the pot. These detection points are arranged in an equally spaced matrix, effectively covering a rectangular monitoring range of 20cm×28cm in the central area of ​​the pot's side surface.

[0058] During data acquisition, each detection unit operates independently, converting the infrared radiation energy of its corresponding area into digital temperature values ​​to form complete temperature field information. The system first verifies the validity of the raw data by comparing temperature gradient changes at adjacent points, automatically identifying and eliminating abnormal data points caused by momentary water vapor obstruction, oil splashes, or reflective interference. Subsequently, a spatial filtering algorithm based on a Gaussian kernel function is used to smooth the valid temperature data, eliminating random measurement noise while preserving the true temperature distribution characteristics.

[0059] In the feature value calculation stage, a three-level fusion strategy can be adopted: First, the temperature values ​​of each point are calculated by weighted average, with the central area points assigned a higher weight of 0.6 and the edge area points decreasing to 0.2, to reflect the difference in importance of different areas of the pot body in the thermal state assessment; Second, the detection area is divided into three sub-regions: upper, middle, and lower, and the average temperature of each region is calculated, with the maximum value selected as the auxiliary feature value; Finally, a comprehensive evaluation function is constructed by combining the standard deviation of the temperature distribution and the maximum temperature gradient to ensure a comprehensive characterization of the overall thermal distribution state of the pot body.

[0060] As a preferred approach, a dynamic optimization mechanism was also established. During the initial heating phase, full-matrix data was used for baseline measurements. Once the temperature stabilized, 15-20 representative points were automatically selected for focused monitoring based on the consistency of the temperature distribution. When an abnormal increase in local temperature was detected, a high-density sampling mode was automatically activated, adding temporary detection points around the abnormal area, increasing the sampling density to twice that of the standard mode, thus achieving precise monitoring of the abnormal area.

[0061] Understandably, by employing this multi-point measurement, layered processing, and dynamic optimization approach, the system effectively overcomes the inherent flaw of single-point infrared temperature measurement being susceptible to interference from local factors. It controls the overall error of pot body temperature measurement within ±2.5℃, improving measurement stability and providing a more reliable and accurate data foundation for subsequent dry-burning judgment and intelligent cooking control.

[0062] In conjunction with the first aspect, the method also includes: S210, during the constant heating power stage, acquires temperature change curves at multiple points.

[0063] S220, if more than a specified number of points show a continuous upward trend in temperature, it is determined that the moisture in the cookware has decreased.

[0064] S230, switch to the reduction stage and adjust the heating power curve.

[0065] During the constant heating power stage, the temperature data of multiple points acquired by the dot matrix infrared sensor are continuously monitored, and temperature change curves of each point are generated. When it is detected that more than a set percentage (e.g., 70%) of the points show a continuous upward trend in temperature, and this trend is maintained for more than a preset time (e.g., 30 seconds), it is determined that the moisture in the pot has been significantly reduced, and the food has entered the reducing stage.

[0066] By employing a sliding window-based trend recognition algorithm, the temperature data sequence at each location is linearly fitted. When the fitting slope remains positive and exceeds a set threshold, the location is determined to be in a heating state. Simultaneously, instantaneous temperature fluctuations caused by sudden changes in heat or displacement of the cookware are excluded to ensure the accuracy of the judgment.

[0067] Once the reduction stage is determined, the following control strategies are automatically executed: First, the heating power is gradually reduced from the current constant value to the reduction power, with the reduction rate dynamically adjusted according to the type of cookware and the initial power; then, it is switched to intermittent heating mode, using pulsed power output with a working cycle of 10-30 seconds to prevent the food from burning; at the same time, a timer is started, and when the preset reduction time is reached or a specific temperature characteristic is detected, it automatically switches to the heat preservation state.

[0068] This cooking stage identification mechanism effectively overcomes the limitations of traditional single sensors in accurately judging changes in the state of matter inside the pot through multi-point temperature trend analysis, achieving a seamless switch from stewing to reducing sauce, and significantly improving the automation level of the cooking process and the quality of the finished product.

[0069] In conjunction with the first aspect, the AI ​​model in step S140 is trained through the following steps: Collect historical cooking data, including cookware type, heating mode, real-time temperature sequence, and user-confirmed cooking effect rating; Using cookware type, heating mode, and real-time temperature sequence as inputs, and the user's preferred temperature control curve as the optimization target, the neural network model is trained under supervision until an AI model that meets the preset conditions is obtained.

[0070] First, multi-dimensional data collection was conducted, gathering historical cooking data encompassing four key dimensions: cookware type (including thin-walled and thick-walled cookware), heating mode (such as stir-frying, stewing, and deep-frying), real-time temperature sequences (including temperature curves of the cookware body and bottom), and user-confirmed cooking performance ratings (using a 1-5 star rating system). In the data preprocessing stage, the raw data underwent the following processing: data cleaning to remove outliers and invalid data segments; temperature sequence standardization to eliminate biases caused by changes in ambient temperature; feature extraction to extract feature parameters including heating rate, temperature stability, and peak temperature. Model training employed a deep neural network architecture, using cookware type, heating mode, and real-time temperature sequences as input features, and the user-preferred ideal temperature control curve as the training objective. Supervised learning was used during training, employing a mean squared error loss function and an adaptive moment estimation optimizer, continuously adjusting network parameters through backpropagation.

[0071] As an implementation method, model validation employs k-fold cross-validation to ensure the model has good generalization ability. The training termination condition is set so that the validation set loss no longer decreases significantly over multiple consecutive training epochs, while the model's prediction accuracy on the test set must reach above 85%.

[0072] Through continuous training and optimization, a fully trained AI model is obtained. This AI model has the following characteristics: it can generate personalized temperature control curves based on different cookware types and heating modes; it can analyze the current temperature sequence in real time and dynamically adjust the heating strategy; and it can learn user preferences and gradually optimize the cooking effect.

[0073] Secondly, this application also provides a cooking control device applied to a control unit in a cooking system. The cooking system further includes a range hood and cooktop integrated unit 1, a first temperature measuring mechanism 5, and a second temperature measuring mechanism 6. The first temperature measuring mechanism 5 is disposed at the bottom of the induction cooktop panel 2 and is used to detect the temperature of the pot bottom. The second temperature measuring mechanism 6 is an infrared sensor disposed on the lifting push rod 4 of the range hood 3 of the range hood and cooktop integrated unit 1 and is used to detect the temperature of the pot body. Figure 5 As shown, the device includes: an acquisition module 10, a determination module 20, a judgment module 30, an output module 40, and an adjustment module 50.

[0074] The acquisition module 10 is used to acquire the pot body temperature measured by the second temperature measuring mechanism 6 and the pot bottom temperature measured by the first temperature measuring mechanism 5.

[0075] The determination module 20 is used to determine the type of cookware based on the temperature of the pot body and the temperature of the pot bottom.

[0076] The judgment module 30 is used to determine whether the cookware is in a dry-burning state based on the cookware type, the temperature of the cookware body, and the temperature of the bottom of the cookware.

[0077] The output module 40 is used to input the current heating mode and pot body temperature into a pre-trained AI model when the pot is not in a dry-burning state. The AI ​​model generates and outputs optimized temperature control parameters based on user habit data. The adjustment module 50 is used to dynamically adjust the heating power curve of the integrated range hood and stove 1 according to the optimized temperature control parameters.

[0078] Thirdly, embodiments of this application provide an electronic device, combined with Figure 6 As shown, the electronic device includes a memory 131 and a processor 130. The memory 131 stores a computer program, and the processor 130 runs the computer program to make the electronic device perform the above-described method.

[0079] Furthermore, combined Figure 6 The electronic device shown also includes a bus 132 and a communication interface 133, with the processor 130, the communication interface 133 and the memory 131 connected via the bus 132.

[0080] The memory 131 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 133 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 132 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0081] Processor 130 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 130 or by instructions in software form. Processor 130 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 131, and processor 130 reads the information in memory 131 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0082] Fourthly, embodiments of this application provide a readable storage medium storing computer program instructions, which are read and executed by a processor to perform the above-described method.

[0083] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0084] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0085] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0086] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0087] Finally, it should be noted that the above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A cooking control method, characterized in that, A control unit is applied to a cooking system, the cooking system further including a range hood and cooktop, a first temperature measuring mechanism, and a second temperature measuring mechanism; the first temperature measuring mechanism is disposed at the bottom of the induction cooktop panel and is used to detect the temperature of the bottom of the pot; the second temperature measuring mechanism is an infrared sensor disposed on the range hood lifting push rod of the range hood and cooktop, and is used to detect the temperature of the pot body; the method includes: The temperature of the pot body measured by the second temperature measuring mechanism and the temperature of the pot bottom measured by the first temperature measuring mechanism are obtained. The type of cookware is determined based on the temperature of the pot body and the temperature of the pot bottom. Based on the type of cookware, the temperature of the cookware body, and the temperature of the bottom of the cookware, determine whether the cookware is in a dry-burning state; If not, the current heating mode and the pot body temperature are input into the pre-trained AI model, which generates and outputs optimized temperature control parameters based on user habit data; The heating power curve of the integrated range hood and cooktop is dynamically adjusted based on the optimized temperature control parameters.

2. The method according to claim 1, characterized in that, The step of determining the cookware type based on the temperature of the pot body and the temperature of the pot bottom includes: Calculate the temperature difference between the pot body temperature and the pot bottom temperature to obtain the temperature difference; The thickness of the cookware is determined based on a preset relationship between temperature difference and thickness. The type of cookware is determined based on the comparison between the cookware thickness and the threshold.

3. The method according to claim 1, characterized in that, The cookware type is the first cookware type; The step of determining whether the cookware is in a dry-heat state based on the cookware type, the temperature of the cookware body, and the temperature of the cookware bottom includes: If the temperature of the pot body reaches the first temperature threshold and remains there for a first time, it is determined that the pot is in a dry-burning state.

4. The method according to claim 1, characterized in that, The cookware type also includes a second cookware; The step of determining whether the cookware is in a dry-heat state based on the cookware type, the temperature of the cookware body, and the temperature of the cookware bottom includes: If the temperature of the pot body reaches the first temperature threshold and continues for a second time, and the temperature rise slope calculated based on the temperature of the pot bottom is greater than the slope threshold; Alternatively, if the temperature of the bottom of the pot reaches the first temperature threshold and remains there for a third time, it is determined that the pot is in a dry-burning state.

5. The method according to claim 1, characterized in that, The second temperature measuring mechanism is a dot matrix infrared sensor; The step of obtaining the pot body temperature measured by the second temperature measuring mechanism includes: Temperature data at multiple points on the side wall of the cookware are acquired using the dot matrix infrared sensor. Based on the temperature data from the multiple points, characteristic values ​​representing the overall temperature of the pot body are calculated.

6. The method according to claim 1, characterized in that, The method further includes: During the constant heating power stage, temperature change curves at multiple points were obtained; If more than a specified number of points show a continuous upward trend in temperature, it is determined that the moisture inside the cookware has decreased. Switch to the reduction stage and adjust the heating power curve.

7. The method according to claim 1, characterized in that, The AI ​​model is trained through the following steps: Collect historical cooking data, including cookware type, heating mode, real-time temperature sequence, and user-confirmed cooking effect rating; Using the cookware type, heating mode, and real-time temperature sequence as inputs, and the user-preferred temperature control curve as the optimization target, the neural network model is trained under supervision until the AI ​​model that meets the preset conditions is obtained.

8. A cooking control device, characterized in that, A control unit is applied to a cooking system, the cooking system further including a range hood and cooktop, a first temperature measuring mechanism, and a second temperature measuring mechanism; the first temperature measuring mechanism is disposed at the bottom of the induction cooktop panel and is used to detect the temperature of the bottom of the pot; the second temperature measuring mechanism is an infrared sensor disposed on the range hood lifting push rod of the range hood and cooktop, and is used to detect the temperature of the pot body; the device includes: The acquisition module is used to acquire the pot body temperature measured by the second temperature measuring mechanism and the pot bottom temperature measured by the first temperature measuring mechanism; The determination module is used to determine the type of cookware based on the temperature of the pot body and the temperature of the pot bottom; The judgment module is used to determine whether the cookware is in a dry-burning state based on the cookware type, the temperature of the cookware body, and the temperature of the cookware bottom. The output module is used to input the current heating mode and the temperature of the pot body to a pre-trained AI model when the pot is not in a dry-burning state. The AI ​​model generates and outputs optimized temperature control parameters based on user habit data. The adjustment module is used to dynamically adjust the heating power curve of the integrated range hood and cooktop based on the optimized temperature control parameters.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program and the processor running the computer program to cause the electronic device to perform the method of any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores computer program instructions, which, when read and executed by a processor, perform the method described in any one of claims 1 to 7.

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