Cooking control method of gas stove

Abstract

The invention discloses a cooking control method of a gas stove. The cooking control method comprises the following steps: S1, triggering a cooking restoration operation instruction; s2, obtaining menu cooking data, and analyzing the menu cooking data to obtain an algorithm type, a step duration, a target index, a temperature range and a fire control range of each cooking sub-step; s3, after the user manually ignites, starting to execute dish cooking restoration according to each cooking sub-step; s4, predicting a target index achievement condition of the current cooking sub-step after the step duration; s5, determining whether to adjust the firepower gear according to the prediction result; and S6, judging whether the target index of the current cooking sub-step is reached or not, if so, turning to the next cooking sub-step, and if not, continuing to execute the step. According to the cooking control method, it is ensured that each cooking sub-step in the cooking process can be close to the cooking technique of the kitchen to the maximum extent, and finally the cooking product of the kitchen is restored.

Description

Technical Field

[0001] The invention relates to the technical field of stoves, and in particular to a cooking control method of a gas stove. Background Art

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

[0003] Most of the existing simple cooking programs can only control and judge the duration or temperature point, and lack the analysis and prediction of the overall temperature trend. As a result, the cooking products are often difficult to reach the cooking level of a chef. Affected by factors such as the amount of food, the temperature of the cooking environment, and the power of the stove, the consistency of repeated cooking results is poor. Summary of the invention

[0004] The present invention aims to solve at least one of the problems existing in the existing related technologies to a certain extent. To this end, the present invention proposes a cooking control method for a gas stove to ensure that each cooking sub-step of the cooking process can be as close to the chef's cooking techniques as possible, ultimately restoring the chef's cooking products, and improving the intelligence level and cooking effects.

[0005] According to the cooking control method of a gas stove provided above, it is implemented through the following technical solutions:

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

[0007] S1, triggering cooking and restoring operation instructions;

[0008] S2, obtaining recipe cooking data, and parsing 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 turns on the fire, the cooking process starts according to each cooking sub-step;

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

[0011] S5, determining whether to adjust the firepower level according to the prediction result;

[0012] S6, determining whether the target index of the current cooking sub-step has been achieved, if so, proceeding to the next cooking sub-step, if not, continuing to execute this step.

[0013] In some embodiments, in step S2, the specific steps of preparing the recipe cooking data include:

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

[0015] S22, recording the temperature value, power level, and current step of the cooking process every second as raw data and calibrating the ambient temperature, and uploading to the server after the cooking is completed;

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

[0017] S24, complete auxiliary information such as firepower range, temperature range, feed amount, ambient temperature, etc.

[0018] In some embodiments, before step S3 proceeds to step S4, the algorithm type of the current cooking sub-step is first determined, and then the real-time bottom temperature of the pot is obtained and judged whether it exceeds the temperature range of the current cooking sub-step. If so, the fire power level is adjusted, otherwise, proceed to step S4.

[0019] In some embodiments, in step S4, the specific steps of predicting the achievement of the target indicator of the current cooking sub-step after the step duration include:

[0020] S41, caching the real-time pot bottom temperature in the past period of time, and calculating the temperature change trend based on the temperature data;

[0021] S42, regularly calculating and predicting the prediction index of the current cooking sub-step after the step duration according to the temperature change trend;

[0022] S43, comparing the predicted indicator with the target indicator at a certain tolerance ratio. If the tolerance ratio is exceeded, the target indicator cannot be achieved. If the tolerance ratio is not exceeded, the target indicator can be achieved.

[0023] In some embodiments, in step S5, the specific step of determining whether to adjust the firepower level according to the prediction result includes:

[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 prediction result is that it cannot be achieved, adjust the firepower level and proceed to step S6.

[0026] In some embodiments, in step S52, after adjusting the firepower level and before proceeding to step S6, it is first determined whether the adjusted firepower level exceeds the firepower 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 embodiments, in step S6, before entering the next cooking sub-step, it is first determined whether the user needs to add ingredients;

[0028] If not, directly proceed to the next cooking sub-step until all cooking sub-steps are completed;

[0029] If so, the user is prompted to perform the feeding operation of the next cooking sub-step, and adjust the fire power level to the minimum fire power level. Then, when the feeding is recognized, it automatically proceeds to the next cooking sub-step.

[0030] In some embodiments, the algorithm type includes any one of a temperature rise type algorithm, a continuous type algorithm, and a thermal type algorithm;

[0031] The temperature rise type algorithm is guided by the target temperature. When the temperature of the bottom of the pot reaches and is maintained for more than a period of time, the target is determined to be achieved, wherein the target indicator is the target temperature;

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

[0033] The heat type algorithm is guided by temperature and time. According to the heat formula Q=mcΔT, the temperature difference between the real-time bottom temperature of the pot and the ambient temperature is discretely integrated within the time range. When the integral value reaches the set value, it is determined that the target is achieved, where the target indicator is the total heat value.

[0034] In some embodiments, the specific step of discretely integrating the temperature difference between the real-time bottom temperature of the pot and the ambient temperature within a time range includes: dividing all real-time bottom temperatures of the pot within the time range into a plurality of different temperature intervals, and using integration ratios with different weights for the temperature difference between different temperature intervals.

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

[0036]

[0037] Where: T e is the ambient temperature, T r (t) is 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 recipe cooking data, then predicts the indicators and adjusts the firepower level according to the prediction results, thereby ensuring that each cooking sub-step of the cooking process can be as close to the chef's cooking technique as possible, and finally restoring the chef's cooking products, thereby improving the consistency of repeated cooking, improving the intelligence level and cooking effect, and enhancing the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of a cooking control method according to an embodiment of the present invention;

[0041] Figure 2 Schematic diagram of the connection of the gas stove in the embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following examples illustrate the present invention, but the present invention is not limited to these examples. Modifications to the specific embodiments of the present invention or equivalent replacement of some technical features without departing from the spirit of the present invention should be included in the scope of the technical solution claimed by the present invention.

[0043] refer to Figure 2 This embodiment provides a cooking control method for a gas stove, wherein the gas stove includes an IOT module and a temperature measuring component, wherein the IOT module is respectively connected to the temperature measuring component and a server for communication. An algorithm is integrated on the IOT module, and the IOT module has the ability to connect to the Internet and control the fire level of the gas stove, and of course, also has the ability to recognize scenes. The temperature measuring component has a temperature detection capability, which is used to detect the temperature of the bottom of the pot and the ambient temperature. In this embodiment, the temperature measuring component includes a bottom temperature sensor for detecting the temperature of the bottom of the pot and an ambient temperature sensor for detecting the ambient temperature.

[0044] refer to Figure 1 , the cooking control method comprises the following steps:

[0045] S1, triggering cooking and restoring operation instructions;

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

[0047] S2, obtaining recipe cooking data, and parsing 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 the recipe cooking data from the server, then splits the recipe cooking data into several cooking sub-steps, and parses out the algorithm type, step duration, target indicator, temperature range and gear control range of each cooking sub-step.

[0049] S3, after the user manually turns on the fire, the cooking process starts according to each cooking sub-step;

[0050] S4, predicting the achievement of the target indicator of the current cooking sub-step after the step duration according to the temperature change trend;

[0051] S5, determining whether to adjust the firepower level according to the prediction result;

[0052] S6, determining whether the target index of the current cooking sub-step has been achieved, if so, proceeding to the next cooking sub-step, if not, continuing to execute this step.

[0053] It can be seen that by first analyzing the recipe cooking data, then predicting the indicators and adjusting the fire level according to the prediction results, it is ensured that each cooking sub-step of the cooking process can be as close to the chef's cooking method as possible, and finally restore the chef's cooking products. Based on the analysis and prediction of real-time temperature, closed-loop control of cooking time and firepower is achieved, which improves the consistency of repeated cooking, improves the level of intelligence and cooking effects, and enhances user experience.

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

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

[0056] S22, recording the temperature value, power level, and current step of the cooking process every second as raw data and calibrating the ambient temperature, and uploading to the server after the cooking is completed;

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

[0058] S24, complete auxiliary information such as firepower range, temperature range, feed amount, ambient temperature, etc.

[0059] Furthermore, before step S3 turns to step S4, the algorithm type of the current cooking sub-step is first determined, and then the real-time pot bottom temperature is obtained and judged whether it exceeds the temperature range of the current cooking sub-step. If so, the fire power level is adjusted, otherwise, it turns 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 fire power level; when it does not exceed the temperature range of the current cooking sub-step, it turns to step S4 for prediction and judgment. Therefore, based on real-time data analysis and intelligent decision-making algorithms, intelligent control of the fire power level of the cooking process is realized, and the intelligence level of the cooking equipment is improved.

[0060] In addition, after adjusting the firepower level, determine whether the adjusted firepower level exceeds the firepower 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, adjust the step duration of the current cooking sub-step. At this time, there is no room for firepower adjustment, so it is necessary to extend or shorten the step cooking time for correction to ensure the cooking product.

[0061] Further, in step S4, the specific steps of predicting the achievement of the target indicator of the current cooking sub-step after the step duration according to the temperature change trend include:

[0062] S41, caching the real-time pot bottom temperature within a period of time (e.g., 30 seconds) in the past, and calculating the temperature change trend based on the temperature data;

[0063] S42, calculating and predicting the prediction index of the current cooking sub-step after the step duration according to the temperature change trend (e.g., every 500 ms);

[0064] S43, compare the predicted index with the target index at a certain tolerance ratio (for example, 5±2%). If the tolerance ratio is exceeded, it indicates that the target index cannot be achieved. At this time, it is necessary to adjust the firepower level so that the firepower of the gas stove can reach the same or similar firepower as the recorded level. If the tolerance ratio is not exceeded, it indicates that the target index can be achieved.

[0065] Further, in step S5, the specific steps of determining whether to adjust the firepower level according to the prediction result include: S51, if the prediction result is achievable, maintain the current firepower level and proceed to step S6; S52, if the prediction result is unachievable, adjust the firepower level and proceed to step S6.

[0066] Furthermore, in step S52, after adjusting the firepower level and before proceeding to step S6, first determine whether the adjusted firepower level exceeds the firepower 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 firepower adjustment, so it is necessary to extend or shorten the step cooking time for correction.

[0067] It can be seen that based on the analysis of temperature change trends and the prediction of indicators, closed-loop control of cooking time and firepower is achieved, which improves the consistency of repeated cooking and achieves the same cooking effect under the influence of complex external factors.

[0068] Furthermore, in step S6, before the next cooking sub-step is transferred to, it is first determined whether there is a next cooking sub-step. If not, it indicates that all cooking sub-steps have been completed, and the cooking reduction operation is terminated. If there is a next cooking sub-step, it is determined whether the user needs to feed; if not (that is, if the user does not need to feed), it is directly transferred to the next cooking sub-step until all cooking sub-steps are completed; if yes (that is, if the user needs to feed), the user is prompted to perform the feeding operation of the next cooking sub-step, and the firepower level is adjusted to the minimum firepower level to prevent the feeding process from being too hot and causing the ingredients to burn. When the IOT module recognizes the feeding, it automatically transfers to the next cooking sub-step, that is, re-predicts the achievement of the target indicator of the next cooking sub-step until all cooking sub-steps are executed.

[0069] Furthermore, the IOT module divides the algorithm types according to the attributes of each sub-step and the temperature change trend, and the algorithm types include any one of a temperature rise type algorithm, a continuous type algorithm and a heat type algorithm.

[0070] The temperature rise type algorithm is guided by the target temperature. When the temperature of the pot bottom reaches and is maintained for more than a period of time (for example, 2 seconds), the target is determined to be achieved, where the target indicator is the target temperature. For example, this determination method can be used in the hot pot and hot oil stages.

[0071] The continuous type algorithm is time-oriented. If the temperature of the pot bottom is within a certain temperature range (for example, ±5°C) and the heating time reaches the required time, the target is determined to be achieved, where the target indicator is the duration. For example, this determination method can be used in the stewing stage.

[0072] The heat type algorithm is guided by temperature and time. According to the heat formula Q=mcΔT, the temperature difference between the real-time bottom temperature of the pot and the ambient temperature is discretely integrated within the time range. When the integral value reaches the set value, it is determined that the target is achieved, where the target indicator is the total heat value. For example, this determination method can be used in the stage of stir-frying meat and vegetables.

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

[0074] After breaking down the cooking process by steps, the temperature change trends within the steps are mostly obvious. When it comes to the step of adding materials, the temperature basically shows a trend of first dropping and then rising, so the heat type algorithm will be used in more step scenarios. In order to more accurately calculate the heat transferred from the gas stove fire to the food and obtain more accurate indicators, it is also necessary to refine the discrete integration rules, that is, to divide all real-time pot bottom temperatures within the time range into multiple different temperature intervals, and use different weighted integration ratios for the temperature difference in different temperature intervals.

[0075] For example, three temperature intervals are divided: low temperature zone (below 90℃), medium temperature zone (90℃-180℃), and high temperature zone (above 180℃). As the real-time bottom temperature of the pot increases, the thermal potential difference between the pot and the food gradually increases, and the heat transfer will increase. Therefore, the discrete heat integral is accumulated and calculated with weights of 70%, 80%, and 90% for the low temperature zone, medium temperature zone, and high temperature zone, respectively. In addition, when the user feeds the ingredients during cooking, the temperature difference between the pot and the food is usually large, and a stable thermal potential equilibrium state has not yet been reached. More heat will be lost in the form of thermal radiation. Therefore, the integral weight needs to be lowered (for example, adjusted to 50%) during the cooling stage. It can be seen that the gas stove has the ability to recognize cooking scenes, and can identify differences in factors such as feeding actions and portions based on temperature change trends, make timely adjustments to the cooking process, optimize the cooking effect, and ensure that the same or similar cooking methods as the chef can be achieved under the influence of complex external factors, effectively guaranteeing the cooking results.

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

[0077]

[0078] Where: T e is the ambient temperature, T r (t) is 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] In the process of executing the cooking restoration of the dish, assuming that the step length of the current cooking sub-step in the recipe cooking data is ts, and the historical temperature value T0 at time t2 is T r (t2), based on the temperature trend, the temperature at the next time point t can be predicted using a linear relationship as follows:

[0080]

[0081] The prediction index can be obtained as follows:

[0082]

[0083] Then compare the target index Q of the recipe cooking data t With the prediction index Q p , and based on the relationship between the comparison result and the fault tolerance ratio (for example, 5%), adjust the firepower level or cooking time parameters, thereby realizing closed-loop control of cooking time and firepower based on real-time temperature analysis and prediction, and realizing intelligent control of the cooking process based on real-time data analysis and intelligent algorithms, thereby improving the intelligence level of cooking equipment.

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

[0085] Feeding action recognition: The temperature drop rate can be deduced through the temperature drop amplitude within a certain period of time. If the temperature drop rate is greater than the threshold within a certain time interval before the step starts, it can be determined that the feeding action is triggered, and the algorithm automatically starts the algorithm cooking of this step.

[0086] Identification of portion size differences: After adding materials, the temperature drops to the lowest point and then continues to rise. By comparing the current lowest temperature point with the lowest temperature point of the recorded data, the difference in portion size can be roughly determined. If a calorie type algorithm is used, the target indicator can be adjusted according to the portion size difference. For example, if the lowest temperature is reduced by 10%, it is likely that the portion size has increased, so the target (calorie) indicator can be increased by 10% to ensure the cooking results of different portions.

[0087] Identification of completion of juice collection: The speed of temperature rise can be deduced from the temperature rise amplitude within a certain period of time. The temperature characteristic of the juice collection scene is generally a steady heating process. When the juice begins to decrease, the temperature increases sharply. Therefore, in the latter part of the step, when the temperature begins to rise after a period of stability, the temperature rise speed is judged. If it is greater than the threshold, it can be determined that the juice collection is completed.

[0088] Dry-burn protection identification: When the temperature is greater than a certain threshold (e.g. 280°C), and the temperature rise rate is greater than the threshold (e.g. 5°C / s) or the duration is greater than the threshold (e.g. 10 seconds), the algorithm identifies it as a dry-burn state and needs to be immediately controlled to shut down for protection.

[0089] It can be seen that the algorithm in the IOT module also has scene recognition capabilities. It can identify different actions or cooking changes through temperature changes and act on the entire cooking algorithm calculation process. It can not only restore the chef's cooking techniques and improve cooking results, but also reduce the number of user confirmation interactions, improve product experience, and ensure the safety of the entire cooking process.

[0090] The above are only some embodiments of the present invention. For those skilled in the art, several modifications and improvements can be made without departing from the creative concept of the present invention, which all belong to the protection scope 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 bottom of the pot and the ambient temperature, the IOT module is communicatively connected with the temperature measuring component, and the cooking control method includes the following steps: S1, triggering cooking and restoring operation instructions; S2, obtaining recipe cooking data, and parsing the recipe cooking data 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 turns on the fire, the cooking process starts according to each cooking sub-step; S4, predicting the achievement of the target indicator of the current cooking sub-step after the step duration; S5, determining whether to adjust the firepower level according to the prediction result; S6, determining whether the target index of the current cooking sub-step has been achieved, if so, proceeding to the next cooking sub-step, if not, continuing to execute this step.

2. A cooking control method for a gas stove according to claim 1, characterized in that: In step S2, the specific steps of preparing the recipe cooking data include: S21, invited chefs to cook dishes on the same gas stove; S22, recording the temperature value, power level, and current step of the cooking process every second as raw data and calibrating the ambient temperature, and uploading to the server after the cooking is completed; S23, the server divides the data segments according to the cooking sub-steps, divides the algorithm types according to the different temperature change trends within the steps, and calculates the duration and type index of the temperature curve of each step according to the type; S24, complete auxiliary information such as firepower range, temperature range, feed amount, ambient temperature, etc.

3. The cooking control method of a gas stove according to claim 1, characterized in that: Before step S3 turns to step S4, first determine the algorithm type of the current cooking sub-step, then obtain and judge whether the real-time bottom temperature of the pot exceeds the temperature range of the current cooking sub-step, if so, adjust the fire power level, if not, turn to step S4.

4. A cooking control method for a gas stove according to claim 1 or 3, characterized in that: In step S4, the specific steps of predicting the achievement of the target indicator of the current cooking sub-step after the step duration include: S41, caching the real-time pot bottom temperature in the past period of time, and calculating the temperature change trend based on the temperature data; S42, regularly calculating and predicting the prediction index of the current cooking sub-step after the step duration according to the temperature change trend; S43, comparing the predicted indicator with the target indicator at a certain tolerance ratio. If the tolerance ratio is exceeded, the target indicator cannot be achieved. If the tolerance ratio is not exceeded, the target indicator can be achieved.

5. The cooking control method of a gas stove according to claim 1, characterized in that: In step S5, the specific steps of determining whether to adjust the firepower level according to 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 prediction result is that it cannot be achieved, adjust the firepower level and proceed to step S6.

6. A cooking control method for a gas stove according to claim 5, characterized in that: In step S52, after adjusting the firepower level and before proceeding to step S6, first determine whether the adjusted firepower level exceeds the firepower 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.

7. The cooking control method of a gas stove according to claim 1, characterized in that: In step S6, before entering the next cooking sub-step, it is first determined whether the user needs to add ingredients; If not, directly proceed to the next cooking sub-step until all cooking sub-steps are completed; If so, the user is prompted to perform the feeding operation of the next cooking sub-step, and adjust the fire power level to the minimum fire power level. Then, when the feeding is recognized, it automatically proceeds to the next cooking sub-step.

8. A cooking control method for a gas stove according to any one of claims 1 to 7, characterized in that: The algorithm type includes any one of a temperature rise type algorithm, a continuous type algorithm and a heat type algorithm; The temperature rise type algorithm is guided by the target temperature. When the temperature of the bottom of the pot reaches and is maintained for more than a period of time, the target is determined to be achieved, wherein the target indicator is the target temperature; The continuous type algorithm is time-oriented, and the target is determined to be achieved when the temperature of the pan bottom is within a certain temperature range and the heating time reaches the required time, wherein the target indicator is the duration; The heat type algorithm is guided by temperature and time. According to the heat formula Q=mcΔT, the temperature difference between the real-time bottom temperature of the pot and the ambient temperature is discretely integrated within the time range. When the integral value reaches the set value, it is determined that the target is achieved, where the target indicator is the total heat value.

9. A cooking control method for a gas stove according to claim 8, characterized in that: The specific steps of discretely integrating the temperature difference between the real-time pot bottom temperature and the ambient temperature within a time range include: The entire real-time pot bottom temperature within the time range is divided into a plurality of different temperature intervals, and the temperature difference in different temperature intervals is subjected to an integral ratio with different weights.

10. A cooking control method for a gas stove according to claim 9, characterized in that: When the calorie type algorithm is used, the real-time calorie content of the food is calculated using the following formula: Where: T e is the ambient temperature, T r (t) is 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.

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