A cooking control method of a gas stove

Abstract

The application discloses a cooking control method of a gas stove, comprising the following steps: S1, triggering a cooking reduction operation instruction; S2, obtaining recipe cooking data and analyzing the recipe cooking data to obtain an algorithm type, a step duration, a target index, a temperature range and a fire control range of each cooking substep; S3, after a user manually ignites, starting to execute dish cooking reduction according to each cooking substep; S4, predicting a target index achievement condition of the current cooking substep after the step duration; S5, determining whether to adjust a fire gear according to the prediction result; and S6, judging whether the target index of the current cooking substep is achieved, if yes, turning to the next cooking substep, and if not, continuing to execute the step. The cooking control method ensures that each cooking substep of the cooking process can be closest to the cooking method of a chef to the maximum extent, and finally restores the cooking product of the chef.

Description

Technical Field

[0001] This invention relates to the field of stove technology, and more particularly to a cooking control method for a gas stove. Background Technology

[0002] During the cooking process, the heating power, heating time, and degree of heating of the ingredients have a crucial impact on the final cooking effect. Traditional cooking methods rely on the chef's experience and feel, making it difficult to accurately quantify and control the heating parameters, which makes the cooking process difficult to replicate.

[0003] Most existing simple cooking programs can only control and judge the time or temperature point, lacking analysis and prediction of the overall temperature trend. As a result, the cooked products often fail to reach the level of a chef's cooking skills. The consistency of repeated cooking results is poor due to factors such as the amount of food, the temperature of the cooking environment, and the power of the stove. Summary of the Invention

[0004] This invention aims to at least partially solve one of the problems existing in the prior art. To this end, this invention proposes a cooking control method for a gas stove, which ensures that each cooking sub-step of the cooking process can closely resemble the cooking techniques of a master chef, ultimately reproducing the chef's culinary output and improving the level of intelligence and cooking effect.

[0005] The cooking control method for a gas stove provided above is achieved through the following technical solution:

[0006] A cooking control method for a gas stove, the gas stove including an IoT module and a temperature measuring component, the temperature measuring component being used to detect the temperature of the pot bottom and the ambient temperature, the IoT module being communicatively connected to the temperature measuring component, and the cooking control method including the following steps:

[0007] S1, triggers the cooking restoration operation command;

[0008] S2, acquire recipe cooking data and parse the recipe cooking data to obtain the algorithm type, step duration, target index, temperature range and fire control range of each cooking sub-step;

[0009] S3: After the user manually starts the fire, the cooking process begins to reproduce the dish according to each cooking sub-step.

[0010] S4, predicts the achievement of the target indicators of the current cooking sub-step after the step duration;

[0011] S5, determine whether to adjust the firepower level based on the prediction results;

[0012] S6: Determine whether the target indicator of the current cooking sub-step has been achieved. If so, proceed to the next cooking sub-step; otherwise, continue executing this step.

[0013] In some implementations, the specific steps for creating the recipe cooking data in step S2 include:

[0014] S21, inviting chefs to cook dishes on the same gas stove;

[0015] S22 records the temperature value, power level, and current step every second during the cooking process, using it as raw data and calibrating the ambient temperature, and then uploads it to the server after cooking is completed.

[0016] S23, the server divides the data into segments according to the cooking sub-steps, classifies the algorithm types according to the different temperature change trends within the steps, and calculates the duration and type index of the temperature curve for each step according to the type.

[0017] S24, supplements auxiliary information such as fire range, temperature range, feed amount, and ambient temperature.

[0018] In some implementations, before proceeding from step S3 to step S4, the algorithm type of the current cooking sub-step is determined first, and then the real-time pot bottom temperature is obtained and judged to see if it exceeds the temperature range of the current cooking sub-step. If so, the heat level is adjusted; otherwise, the process proceeds to step S4.

[0019] In some implementations, step S4, specifically the steps for predicting the achievement of the target indicator after the current cooking sub-step's duration, include:

[0020] S41 caches the real-time pot bottom temperature over a period of time and uses this temperature data to calculate the temperature change trend.

[0021] S42, calculate and predict the predicted indicators after the step duration of the current cooking sub-step based on the temperature change trend;

[0022] S43 compares the predicted indicator with the target indicator with a certain fault tolerance ratio. If the fault tolerance ratio is exceeded, the target indicator cannot be achieved; if the fault tolerance ratio is not exceeded, the target indicator can be achieved.

[0023] In some implementations, step S5, the specific steps of determining whether to adjust the firepower level based on the prediction result, include:

[0024] S51, If ​​the prediction result is that it can be achieved, maintain the current firepower level and proceed to step S6;

[0025] S52, if the predicted result is not achievable, adjust the firepower level and proceed to step S6.

[0026] In some implementations, in step S52, after adjusting the heat level and before proceeding to step S6, it is first determined whether the adjusted heat level exceeds the heat control range of the current cooking sub-step. If not, proceed to step S6; if so, adjust the step duration of the current cooking sub-step.

[0027] In some implementations, in step S6, before proceeding to the next cooking sub-step, it is determined whether the user needs to add ingredients;

[0028] Otherwise, proceed directly to the next cooking sub-step until all cooking sub-steps are completed;

[0029] If so, the user will be prompted to perform the next cooking sub-step's ingredient addition operation, and the heat level will be adjusted to the minimum heat level. Then, once the ingredient addition is detected, the process will automatically proceed to the next cooking sub-step.

[0030] In some implementations, the algorithm type includes any one of temperature rise type algorithm, continuous type algorithm, and heat type algorithm;

[0031] The temperature rise type algorithm is guided by the target temperature. The target is determined to be achieved after the temperature of the bottom of the pot is reached and maintained for a period of time. The target index is the target temperature.

[0032] The continuous type algorithm is time-oriented. The goal is determined to be achieved when the temperature of the pot bottom is within a certain temperature range and the heating time reaches the required time. The target indicator is the duration.

[0033] The heat type algorithm is temperature and time-oriented. Based on the heat formula Q=mcΔT, it performs discrete integration on the temperature difference between the real-time pot bottom temperature and the ambient temperature over a time range. When the integral value reaches a set value, the target is determined to be achieved, where the target index is the total heat value.

[0034] In some implementations, the specific steps of discretely integrating the temperature difference between the real-time pot bottom temperature and the ambient temperature over a time range include: dividing the total real-time pot bottom temperature over the time range into multiple different temperature ranges, and applying different weighted integration ratios to the temperature difference between different temperature ranges.

[0035] In some implementations, when a calorie-type algorithm is used, the real-time calorie content of the food is calculated using the following formula:

[0036]

[0037] In the formula: T e For ambient temperature, T r (t) represents the real-time temperature, W(T) r (t) is the weighted integral, t0 is the start time, t1 is the current time, m is the meat portion, and c is the specific heat capacity.

[0038] Compared with the prior art, the present invention has at least the following beneficial effects:

[0039] The cooking control method of the gas stove of the present invention first analyzes the cooking data of the recipe, then predicts the indicators and adjusts the fire level according to the prediction results, thereby ensuring that each cooking sub-step of the cooking process can closely resemble the cooking techniques of the chef, and finally reproduce the chef's cooking product. This improves the consistency of repeated cooking, enhances the level of intelligence and cooking effect, and improves the user experience. Attached Figure Description

[0040] Figure 1 This is a flowchart of the cooking control method in an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of the connection of the gas stove in an embodiment of the present invention. Detailed Implementation

[0042] The following embodiments illustrate the present invention, but the present invention is not limited to these embodiments. Modifications to the specific embodiments of the present invention or equivalent substitutions for some technical features, without departing from the spirit of the present invention, should all be covered within the scope of the technical solutions claimed in the present invention.

[0043] refer to Figure 2 This embodiment provides a cooking control method for a gas stove. The gas stove includes an IoT module and a temperature measuring component. The IoT module is communicatively connected to the temperature measuring component and a server. An algorithm is integrated into the IoT module, which has network connectivity, the ability to control the gas stove's firepower levels, and scene recognition capabilities. The temperature measuring component has temperature detection capabilities for detecting the temperature of the pot bottom and the ambient temperature. In this embodiment, the temperature measuring component includes a pot bottom temperature sensor for detecting the pot bottom temperature and an ambient temperature sensor for detecting the ambient temperature.

[0044] refer to Figure 1 The cooking control method includes the following steps:

[0045] S1, triggers the cooking restoration operation command;

[0046] Specifically, a local button is installed on the gas stove, or network control commands are provided on the server. The cooking restoration operation is triggered via the network control commands or the local button.

[0047] S2, acquire recipe cooking data and parse the recipe cooking data to obtain the algorithm type, step duration, target index, temperature range and fire control range of each cooking sub-step;

[0048] Specifically, the IoT module downloads recipe cooking data from the server, then breaks down the recipe cooking data into several cooking sub-steps, and parses out the algorithm type, step duration, target indicators, temperature range, and gear control range of each cooking sub-step.

[0049] S3: After the user manually starts the fire, the cooking process begins to reproduce the dish according to each cooking sub-step.

[0050] S4, predict the achievement of the target indicators of the current cooking sub-step after the step duration based on the temperature change trend;

[0051] S5, determine whether to adjust the firepower level based on the prediction results;

[0052] S6: Determine whether the target indicator of the current cooking sub-step has been achieved. If so, proceed to the next cooking sub-step; otherwise, continue executing this step.

[0053] As can be seen, by first analyzing the cooking data of the recipe, then predicting the indicators and adjusting the heat level according to the prediction results, it is ensured that each cooking sub-step can closely resemble the chef's cooking techniques, ultimately reproducing the chef's cooking output. Based on the analysis and prediction of real-time temperature, closed-loop control of cooking time and heat level is achieved, which improves the consistency of repeated cooking, enhances the level of intelligence and cooking effect, and improves the user experience.

[0054] Furthermore, in step S2, the specific steps for creating the recipe cooking data include:

[0055] S21, inviting chefs to cook dishes on the same gas stove;

[0056] S22 records the temperature value, power level, and current step every second during the cooking process, using it as raw data and calibrating the ambient temperature, and then uploads it to the server after cooking is completed.

[0057] S23, the server divides the data into segments according to the cooking sub-steps, classifies the algorithm types according to the different temperature change trends within the steps, and calculates the duration and type index of the temperature curve for each step according to the type.

[0058] S24, supplements auxiliary information such as fire range, temperature range, feed amount, and ambient temperature.

[0059] Furthermore, before proceeding from step S3 to step S4, the algorithm type of the current cooking sub-step is first determined. Then, it is acquired and judged whether the real-time pot bottom temperature exceeds the temperature range of the current cooking sub-step. If so, the heat level is adjusted; otherwise, the process proceeds to step S4. Specifically, after determining the algorithm type of the current cooking sub-step, the algorithm continuously monitors the real-time pot bottom temperature. When it exceeds the temperature range of the current cooking sub-step, the IoT module adjusts the heat level; when it does not exceed the temperature range of the current cooking sub-step, the process proceeds to step S4 for prediction and judgment. Thus, based on real-time data analysis and intelligent decision-making algorithms, intelligent control of the heat level during the cooking process is achieved, improving the intelligence level of the cooking equipment.

[0060] In addition, after adjusting the heat level, it is determined whether the adjusted heat level exceeds the heat control range of the current cooking sub-step. If not, proceed to step S6 to determine whether the target indicator of the current cooking sub-step has been achieved. If so, the step duration of the current cooking sub-step is adjusted. At this time, there is no room for heat adjustment, so it is necessary to extend or shorten the cooking time to correct the problem and ensure the quality of the cooked product.

[0061] Furthermore, in step S4, the specific steps for predicting the achievement of the target indicator after the current cooking sub-step's duration based on the temperature change trend include:

[0062] S41, cache the real-time temperature of the bottom of the pot over a period of time (e.g., 30 seconds) and use this temperature data to calculate the temperature change trend;

[0063] S42, calculate and predict the predicted index after the current cooking sub-step time based on the temperature change trend (e.g., every 500ms);

[0064] S43. Compare the predicted indicator with the target indicator with a certain tolerance ratio (e.g., 5±2%). If the tolerance ratio is exceeded, it indicates that the target indicator cannot be achieved. At this time, the fire level needs to be adjusted so that the fire power of the gas stove can reach the same or similar fire power as the recorded fire power. If the tolerance ratio is not exceeded, it indicates that the target indicator can be achieved.

[0065] Furthermore, in step S5, the specific steps for determining whether to adjust the firepower level based on the prediction result include: S51, if the prediction result is that it can be achieved, then maintain the current firepower level and proceed to step S6; S52, if the prediction result is that it cannot be achieved, then adjust the firepower level and proceed to step S6.

[0066] Furthermore, in step S52, after adjusting the heat level and before proceeding to step S6, it is first determined whether the adjusted heat level exceeds the heat control range of the current cooking sub-step. If not, proceed to step S6; if so, adjust the step duration of the current cooking sub-step. At this time, there is no room for heat adjustment, so it is necessary to extend or shorten the cooking time of the step to make corrections.

[0067] It is evident that by analyzing temperature change trends and predicting indicators, closed-loop control of cooking time and heat intensity can be achieved, improving the consistency of repeated cooking and enabling the same cooking results to be achieved even under the influence of complex external factors.

[0068] Further, in step S6, before proceeding to the next cooking sub-step, it is determined whether there is another cooking sub-step. If not, it indicates that all cooking sub-steps have been completed, and the cooking restoration operation ends. If there is another cooking sub-step, it is determined whether the user needs to add ingredients. If not (i.e., if the user does not need to add ingredients), it proceeds directly to the next cooking sub-step until all cooking sub-steps are completed. If yes (i.e., if the user needs to add ingredients), the user is prompted to perform the ingredient addition operation for the next cooking sub-step, and the heat level is adjusted to the minimum to prevent the food from burning due to excessive heat during ingredient addition. Once the IoT module recognizes the ingredient addition, it automatically proceeds to the next cooking sub-step, i.e., it re-predicts the achievement of the target indicators for the next cooking sub-step, until all cooking sub-steps are completed.

[0069] Furthermore, the IoT module classifies algorithm types according to the different attributes of each sub-step and the temperature change trend. The algorithm types include any one of the following: temperature rise type algorithm, continuous type algorithm, and heat type algorithm.

[0070] The temperature rise type algorithm is guided by the target temperature. The target is considered achieved if the temperature at the bottom of the pot reaches and is maintained for a certain period (e.g., 2 seconds). The target indicator is the target temperature. For example, this method can be used in the stages of heating the pot or heating the oil.

[0071] The continuous type algorithm is time-oriented. It determines that the goal has been achieved if the pot bottom temperature is within a certain range (e.g., ±5℃) and the heating time reaches the required duration. The goal metric is the duration. For example, this method can be used in the simmering / stewing stage.

[0072] The heat type algorithm is temperature and time-oriented. Based on the heat formula Q=mcΔT, it performs discrete integration on the temperature difference between the real-time pot bottom temperature and the ambient temperature over a time range. The goal is considered achieved when the integral value reaches a set value, where the goal indicator is the total heat value. For example, this determination method can be used in the stir-frying of meat and vegetables.

[0073] In this embodiment, the integration rule of the heat type algorithm is as follows:

[0074] After breaking down the food preparation process step by step, the temperature change trends within each step are mostly distinctive. When it comes to the ingredient addition step, the temperature generally shows a trend of first decreasing and then rising again. Therefore, heat type algorithms are used in many of these steps. To more accurately calculate the heat transferred from the gas stove's heat to the food and obtain more precise indicators, the discrete integration rule needs to be refined. This involves dividing the real-time pot bottom temperature within the time range into multiple different temperature intervals and applying different weighted integration ratios to the temperature differences between different intervals.

[0075] For example, three temperature zones are defined: a low-temperature zone (below 90℃), a medium-temperature zone (90℃-180℃), and a high-temperature zone (above 180℃). As the real-time temperature of the pot bottom increases, the thermal potential difference between the pot and the food gradually increases, leading to increased heat transfer. Therefore, the discrete heat integral is calculated with weights of 70%, 80%, and 90% for the low-temperature, medium-temperature, and high-temperature zones, respectively. Furthermore, after the user adds ingredients during cooking, the temperature difference between the pot and the food is usually large, and a stable thermal potential equilibrium has not yet been reached. More heat is lost through thermal radiation. Therefore, the integral weight needs to be reduced during the cooling phase (e.g., adjusted to 50%). It is evident that the gas stove possesses cooking scene recognition capabilities, enabling it to identify differences in factors such as ingredient addition and quantity based on temperature change trends. This allows for timely adjustments to the cooking process, optimizing cooking results and ensuring that even under complex external influences, it can achieve cooking techniques similar to or comparable to those of a professional chef, effectively guaranteeing the quality of the cooked food.

[0076] In this embodiment, when using a calorie-type algorithm, the real-time calorie content of the food is calculated using the following formula:

[0077]

[0078] In the formula: T e For ambient temperature, T r (t) represents the real-time temperature, W(T) r (t) is the integral weight, t0 is the start time, t1 is the current time, m is the meat portion, and c is the specific heat capacity.

[0079] During the process of recreating a dish's cooking process, assuming the duration of the current cooking sub-step in the recipe's cooking data is ts, and the historical temperature value at time t2 is T0 = T r (t2) can be used to predict the temperature at time t using a linear relationship based on the temperature trend:

[0080]

[0081] Therefore, the predictive indicators can be obtained as follows:

[0082]

[0083] Then compare the target indicator Q of the recipe cooking data. t With the forecast indicator Q p Based on the comparison results and the fault tolerance ratio (e.g., 5%), the power level or cooking time parameters are adjusted. Based on the analysis and prediction of real-time temperature, closed-loop control of cooking time and power level is achieved. Furthermore, based on real-time data analysis and intelligent algorithms, intelligent control of the cooking process is realized, improving the intelligence level of the cooking equipment.

[0084] In this embodiment, the algorithm type also includes a scene recognition algorithm, which includes any one of feeding action recognition, weight difference recognition, juice reduction completion recognition, and dry burning protection recognition.

[0085] Feeding action recognition: By observing the temperature drop over a certain period of time, the rate of temperature drop can be deduced. If the rate of temperature drop exceeds a threshold within a certain time interval before the start of a step, it can be determined that the feeding action is triggered, and the algorithm will automatically start the algorithm cooking for that step.

[0086] Weight difference identification: After the ingredients are added, the temperature will continue to rise after dropping to the lowest point. By comparing the current lowest temperature with the lowest temperature recorded in the data, the weight difference can be roughly determined. If a heat type algorithm is used, the target index can be adjusted according to the weight difference. For example, if the lowest temperature decreases by 10%, it is likely due to an increase in weight. The target (heat) index can be increased by 10% to ensure the cooking output under different weights.

[0087] Juice reduction completion recognition: By observing the temperature rise over a certain period of time, the rate of temperature rise can be deduced. The temperature characteristics of the juice reduction scenario are generally a stable heating process. After the juice begins to decrease, the temperature increases sharply. Therefore, in the later stage of the process, when the temperature starts to rise again after a period of stability, the rate of temperature rise is judged. If it exceeds the threshold, it can be determined that the juice reduction is complete.

[0088] Dry burning protection identification: When the temperature exceeds a certain threshold (e.g., 280℃), and the rate of temperature rise exceeds the threshold (e.g., 5℃ / s) or the duration exceeds the threshold (e.g., 10 seconds), the algorithm identifies it as a dry burning state, requiring immediate control to shut off the flame for protection.

[0089] It is evident that the algorithms in the IoT module also possess scene recognition capabilities. By detecting temperature changes, they can identify different actions or cooking variations and apply them to the entire cooking algorithm calculation process. This not only allows the algorithm to replicate the chef's cooking techniques and improve the final product, but also reduces the number of user confirmation interactions, enhances the product experience, and ensures the safety of the entire cooking process.

[0090] The above descriptions are merely some embodiments of the present invention. Those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the scope of protection of the present invention.

Claims

1. A cooking control method for a gas stove, characterized in that, The gas stove includes an IoT module and a temperature measuring component. The temperature measuring component is used to detect the temperature of the pot bottom and the ambient temperature. The IoT module is communicatively connected to the temperature measuring component. The cooking control method includes the following steps: S1, triggers the cooking restoration operation command; S2, acquire recipe cooking data, and break down the recipe cooking data into several cooking sub-steps to obtain the algorithm type, step duration, target index, temperature range and fire control range of each cooking sub-step; S3: After the user manually starts the fire, the cooking process begins to reproduce the dish according to each cooking sub-step. S4, predicts the achievement of the target indicators of the current cooking sub-step after the step duration; S5, determine whether to adjust the firepower level based on the prediction results; S6, determine whether the target indicator of the current cooking sub-step has been achieved. If yes, proceed to the next cooking sub-step; otherwise, continue executing this step. In step S4, the specific steps for predicting the achievement of the target indicator after the current cooking sub-step's duration include: S41 caches the real-time pot bottom temperature over a period of time and uses this temperature data to calculate the temperature change trend. S42, calculate and predict the predicted indicators after the step duration of the current cooking sub-step based on the temperature change trend; S43 compares the predicted indicator with the target indicator with a certain fault tolerance ratio. If the fault tolerance ratio is exceeded, the target indicator cannot be achieved; if the fault tolerance ratio is not exceeded, the target indicator can be achieved.

2. The cooking control method for a gas stove according to claim 1, characterized in that, In step S2, the specific steps for creating the recipe cooking data include: S21, inviting chefs to cook dishes on the same gas stove; S22 records the temperature value, power level, and current step every second during the cooking process, using it as raw data and calibrating the ambient temperature, and then uploads it to the server after cooking is completed. S23, the server divides the data into segments according to the cooking sub-steps, classifies the algorithm types according to the different temperature change trends within the steps, and calculates the duration and type index of the temperature curve for each step according to the type. S24, supplements auxiliary information such as fire range, temperature range, feed amount, and ambient temperature.

3. The cooking control method for a gas stove according to claim 1, characterized in that, Before proceeding from step S3 to step S4, first determine the algorithm type of the current cooking sub-step, then obtain and determine whether the real-time pot bottom temperature exceeds the temperature range of the current cooking sub-step. If so, adjust the heat level; otherwise, proceed to step S4.

4. The cooking control method for a gas stove according to claim 1, characterized in that, In step S5, the specific steps for determining whether to adjust the firepower level based on the prediction result include: S51, If ​​the prediction result is that it can be achieved, maintain the current firepower level and proceed to step S6; S52, if the predicted result is not achievable, adjust the firepower level and proceed to step S6.

5. The cooking control method for a gas stove according to claim 4, characterized in that, In step S52, after adjusting the heat level and before proceeding to step S6, it is first determined whether the adjusted heat level exceeds the heat control range of the current cooking sub-step. If not, proceed to step S6; if so, adjust the duration of the current cooking sub-step.

6. The cooking control method for a gas stove according to claim 1, characterized in that, In step S6, before proceeding to the next cooking sub-step, it is first determined whether the user needs to add ingredients; Otherwise, proceed directly to the next cooking sub-step until all cooking sub-steps are completed; If so, the user will be prompted to perform the next cooking sub-step's ingredient addition operation, and the heat level will be adjusted to the minimum heat level. Then, once the ingredient addition is detected, the process will automatically proceed to the next cooking sub-step.

7. The cooking control method for a gas stove according to claim 1, characterized in that, The algorithm type includes any one of the following: temperature rise type algorithm, continuous type algorithm, and heat type algorithm; The temperature rise type algorithm is guided by the target temperature. The target is determined to be achieved after the temperature of the bottom of the pot is reached and maintained for a period of time. The target index is the target temperature. The continuous type algorithm is time-oriented. The goal is determined to be achieved when the temperature of the pot bottom is within a certain temperature range and the heating time reaches the required time. The target indicator is the duration. The heat type algorithm is temperature and time-oriented. Based on the heat formula Q=mcΔT, it performs discrete integration on the temperature difference between the real-time pot bottom temperature and the ambient temperature over a time range. When the integral value reaches a set value, the target is determined to be achieved, where the target index is the total heat value.

8. A cooking control method for a gas stove according to claim 7, characterized in that, The specific steps for discretely integrating the temperature difference between the real-time pot bottom temperature and the ambient temperature over a time range include: The real-time temperature of the pot bottom within the time range is divided into multiple different temperature ranges, and the temperature difference between different temperature ranges is integrated with different weights.

9. A cooking control method for a gas stove according to claim 8, characterized in that, When using a calorie-based algorithm, the real-time calorie content of food is calculated using the following formula: ， In the formula: For ambient temperature, For real-time temperature, Let t0 be the weighted integral, t1 be the start time, t1 be the current time, m be the meat portion, and c be the specific heat capacity.

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