A heating control method, system and storage medium based on big data

Abstract

The present invention discloses a heating control method, system, and storage medium based on big data, relating to the field of intelligent control technology. The heating control method includes: obtaining current factor parameter information and physical condition information of a target user, generating heating curve intervention information based on the factor parameter information and physical condition information, obtaining food type information, obtaining a cooking heating curve for the food based on the food type information and food cooking mode information through big data analysis, matching the heating curve intervention information with the cooking heating curve to generate an optimal heating curve for heating and cooking, obtaining feedback information on food quality from the target user, and optimizing the heating curve based on the feedback information and the current heating curve. The present invention obtains the optimal heating curve for the food through big data analysis, thereby improving the taste of the food and maintaining its nutritional content.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and more specifically, to a heating control method, system and storage medium based on big data. Background Art

[0002] With the continuous improvement of living standards and the internet-connected era, small household appliances are moving towards smart technology. A new development direction for smart small appliances is developing appliances that offer diverse functions, simple operation, convenience, and speed. However, the small appliances currently on the market offer a plethora of functions and are not designed specifically for user needs. Traditional cooking equipment offers relatively fixed functions, while users' personal information and taste preferences vary widely. Furthermore, in actual use, improper cooking and heating times often lead to poor food quality. Therefore, it is crucial to optimize the heating curve for ingredients based on user-defined settings.

[0003] To achieve intelligent heating control of ingredients through smart cooking devices, a system must be developed to work with it. This system generates heating curve intervention information by acquiring current factor parameter information and the target user's physical condition. Based on the ingredient type and cooking mode, it uses big data analysis to determine the heating curve of the ingredient. This heating curve is then matched to the heating curve intervention information to generate the optimal heating curve for cooking. In implementing this system, determining the heating curve of the ingredient through big data analysis based on the user's preset information is a pressing issue that needs to be addressed. Summary of the Invention

[0004] In order to solve at least one of the above technical problems, the present invention proposes a heating control method, system and storage medium based on big data.

[0005] A first aspect of the present invention provides a heating control method based on big data, comprising:

[0006] Obtaining current factor parameter information and target user's physical condition information, and generating heating curve intervention information according to the factor parameter information and physical condition information;

[0007] Obtaining food type information, and obtaining a cooking heating curve of the food through big data analysis based on the food type information and food cooking mode information;

[0008] Matching the heating curve intervention information according to the cooking heating curve generates an optimal heating curve for heating cooking;

[0009] Obtain feedback information on food quality from target users, compare the feedback information with the current optimal heating curve, and optimize the optimal heating curve.

[0010] In this solution, the factor parameter information includes one or a combination of two or more of latitude information, poster information, air pressure information, ambient temperature information, and circuit voltage information; the target user's physical condition information includes one or a combination of two or more of age information, health information, dietary habit information, and disease condition information.

[0011] In this solution, the heating curve intervention information is generated according to the factor parameter information and the physical condition information, specifically:

[0012] Determine the time, heating temperature and heating power of each program in the cooking process based on the factor parameter information and the physical condition information;

[0013] generating an initial heating curve according to the time, heating temperature and heating power of each program in the cooking process;

[0014] Obtaining a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values ​​from the initial heating curve and the preset heating curve;

[0015] Subtracting the extracted corresponding values ​​to obtain a difference curve, obtaining a difference of the difference curve, and extracting curve features of the difference curve;

[0016] Heating curve intervention information is generated according to the difference and the curve characteristics.

[0017] In this solution, the method of obtaining food type information and obtaining the food cooking heating curve through big data analysis based on the food type information and food cooking mode information is as follows:

[0018] Acquire food image information, pre-process the image information and perform image feature extraction, search and match in a food database based on the image features, and identify at least one type of food information based on the matching results;

[0019] Extracting keywords from the food type information, and using big data analysis to obtain a food heating curve dataset;

[0020] Searching the food heating curve dataset using the cooking mode selected by the target user as a search condition, and obtaining food quality information corresponding to each food heating curve in the search results;

[0021] Scoring the food quality information according to a preset rule, extracting heating curves of ingredients with scores greater than a preset threshold and sorting them according to the scores, and obtaining the heating curve of the ingredient with the highest score;

[0022] A preset heating curve corresponding to the cooking mode selected by the target user is obtained, and the heating curve of the food corresponding to the highest score is fitted with the preset heating curve to generate a cooking heating curve for the food.

[0023] This solution also includes: monitoring the heating conditions of the food during the cooking process and adjusting the heating curve of the food during the cooking process, specifically:

[0024] The heating condition of the food during cooking is monitored by a preset sensor to obtain monitoring data information, and a real-time heating curve is generated according to the monitoring data information;

[0025] Decomposing the real-time heating curve according to the cooking process to obtain a plurality of curve segment groups, and obtaining average temperature information in each curve segment group;

[0026] The optimal heating curve is segmented according to the curve segment group segmentation rule to obtain the target temperature information in each curve segment group;

[0027] Comparing and analyzing the average temperature information with the target temperature information to generate a deviation rate;

[0028] Determining whether the deviation rate is greater than a deviation rate threshold;

[0029] If it is greater, cooking abnormality information is generated, and correction information is generated according to the cooking abnormality information. The cooking time and cooking heat are adjusted by the correction information to achieve adjustment of the real-time heating curve.

[0030] In this solution, feedback information on food quality from home appliance users is obtained, and the optimal heating curve is optimized based on the feedback information and compared with the optimal heating curve. Specifically,

[0031] The cloud server sends the food quality questionnaire to the target users in a preset way through the questionnaire survey;

[0032] Obtaining questionnaire feedback data from target users, processing and analyzing the questionnaire feedback data to generate a satisfaction score for current food quality;

[0033] Preset a satisfaction score threshold, and compare the food quality satisfaction score with the preset threshold;

[0034] If the food quality satisfaction score is less than a preset threshold, feedback information is generated based on the questionnaire feedback data, and the current optimal heating curve is optimized based on the feedback information and the optimal heating curve.

[0035] A second aspect of the present invention further provides a heating control system based on big data, the system comprising: a memory and a processor, wherein the memory includes a heating control method program based on big data, and when the heating control method program based on big data is executed by the processor, the following steps are implemented:

[0036] Obtaining current factor parameter information and target user's physical condition information, and generating heating curve intervention information according to the factor parameter information and physical condition information;

[0037] Obtaining food type information, and obtaining a cooking heating curve of the food through big data analysis based on the food type information and food cooking mode information;

[0038] Matching the heating curve intervention information according to the cooking heating curve generates an optimal heating curve for heating cooking;

[0039] Obtain feedback information on food quality from target users, compare the feedback information with the current optimal heating curve, and optimize the optimal heating curve.

[0040] In this solution, the factor parameter information includes one or a combination of two or more of latitude information, poster information, air pressure information, ambient temperature information, and circuit voltage information; the target user's physical condition information includes one or a combination of two or more of age information, health information, dietary habit information, and disease condition information.

[0041] In this solution, the heating curve intervention information is generated according to the factor parameter information and the physical condition information, specifically:

[0042] Determine the time, heating temperature and heating power of each program in the cooking process based on the factor parameter information and the physical condition information;

[0043] generating an initial heating curve according to the time, heating temperature and heating power of each program in the cooking process;

[0044] Obtaining a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values ​​from the initial heating curve and the preset heating curve;

[0045] Subtracting the extracted corresponding values ​​to obtain a difference curve, obtaining a difference of the difference curve, and extracting curve features of the difference curve;

[0046] Heating curve intervention information is generated according to the difference and the curve characteristics.

[0047] In this solution, the method of obtaining food type information and obtaining the food cooking heating curve through big data analysis based on the food type information and food cooking mode information is as follows:

[0048] Acquire food image information, pre-process the image information and perform image feature extraction, search and match in a food database based on the image features, and identify at least one type of food information based on the matching results;

[0049] Extracting keywords from the food type information, and using big data analysis to obtain a food heating curve dataset;

[0050] Searching the food heating curve dataset using the cooking mode selected by the target user as a search condition, and obtaining food quality information corresponding to each food heating curve in the search results;

[0051] Scoring the food quality information according to a preset rule, extracting heating curves of ingredients with scores greater than a preset threshold and sorting them according to the scores, and obtaining the heating curve of the ingredient with the highest score;

[0052] A preset heating curve corresponding to the cooking mode selected by the target user is obtained, and the heating curve of the food corresponding to the highest score is fitted with the preset heating curve to generate a cooking heating curve for the food.

[0053] This solution also includes: monitoring the heating conditions of the food during the cooking process and adjusting the heating curve of the food during the cooking process, specifically:

[0054] The heating condition of the food during cooking is monitored by a preset sensor to obtain monitoring data information, and a real-time heating curve is generated according to the monitoring data information;

[0055] Decomposing the real-time heating curve according to the cooking process to obtain a plurality of curve segment groups, and obtaining average temperature information in each curve segment group;

[0056] The optimal heating curve is segmented according to the curve segment group segmentation rule to obtain the target temperature information in each curve segment group;

[0057] Comparing and analyzing the average temperature information with the target temperature information to generate a deviation rate;

[0058] Determining whether the deviation rate is greater than a deviation rate threshold;

[0059] If it is greater, cooking abnormality information is generated, and correction information is generated according to the cooking abnormality information. The cooking time and cooking heat are adjusted by the correction information to achieve adjustment of the real-time heating curve.

[0060] In this solution, feedback information on food quality from home appliance users is obtained, and the optimal heating curve is optimized based on the feedback information and compared with the optimal heating curve. Specifically,

[0061] The cloud server sends the food quality questionnaire to the target users in a preset way through the questionnaire survey;

[0062] Obtaining questionnaire feedback data from target users, processing and analyzing the questionnaire feedback data to generate a satisfaction score for current food quality;

[0063] Preset a satisfaction score threshold, and compare the food quality satisfaction score with the preset threshold;

[0064] If the food quality satisfaction score is less than a preset threshold, feedback information is generated based on the questionnaire feedback data, and the current optimal heating curve is optimized based on the feedback information and the optimal heating curve.

[0065] The third aspect of the present invention also provides a computer-readable storage medium, which includes a heating control method program based on big data. When the heating control method program based on big data is executed by a processor, it implements the steps of a heating control method based on big data as described in any one of the above items.

[0066] The present invention discloses a heating control method, system, and storage medium based on big data, relating to the field of intelligent control technology. The heating control method includes: obtaining current factor parameter information and physical condition information of a target user, generating heating curve intervention information based on the factor parameter information and physical condition information, obtaining food type information, obtaining a cooking heating curve for the food based on the food type information and food cooking mode information through big data analysis, matching the heating curve intervention information with the cooking heating curve to generate an optimal heating curve for heating and cooking, obtaining feedback information on food quality from the target user, and optimizing the heating curve based on the feedback information and the current heating curve. The present invention obtains the optimal heating curve for the food through big data analysis, thereby improving the taste of the food and maintaining its nutritional content. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A flow chart of a heating control method based on big data of the present invention is shown.

[0068] Figure 2 A flow chart of the method for obtaining the cooking heating curve of food materials according to the present invention is shown.

[0069] Figure 3 A flow chart of the method for optimizing the optimal heating curve according to the present invention is shown.

[0070] Figure 4 A block diagram of a heating control system based on big data of the present invention is shown. DETAILED DESCRIPTION

[0071] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0072] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0073] Figure 1 A flow chart of a heating control method based on big data of the present invention is shown.

[0074] like Figure 1 As shown, the first aspect of the present invention provides a heating control method based on big data, comprising:

[0075] S102, obtaining current factor parameter information and target user's physical condition information, and generating heating curve intervention information according to the factor parameter information and physical condition information;

[0076] S104, obtaining food type information, and obtaining a cooking heating curve of the food through big data analysis based on the food type information and food cooking mode information;

[0077] S106, matching the heating curve intervention information according to the cooking heating curve to generate an optimal heating curve for heating cooking;

[0078] S108: Obtaining feedback information on food quality from target users, and optimizing the optimal heating curve based on the feedback information and the current optimal heating curve.

[0079] It should be noted that when the heating curve represents the relationship between the heating temperature and the heating time when cooking ingredients, the smart cooking device may be an electric rice cooker, induction cooker or other device that uses temperature to cook ingredients.

[0080] It should be noted that the factor parameter information includes one or a combination of two or more of latitude information, poster information, air pressure information, ambient temperature information, and circuit voltage information; the physical condition information of the target user includes one or a combination of two or more of age information, health information, dietary habit information, and disease condition information.

[0081] It should be noted that the heating curve intervention information is generated according to the factor parameter information and the physical condition information, specifically:

[0082] Determine the time, heating temperature and heating power of each program in the cooking process based on the factor parameter information and the physical condition information;

[0083] generating an initial heating curve according to the time, heating temperature and heating power of each program in the cooking process;

[0084] Obtaining a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values ​​from the initial heating curve and the preset heating curve;

[0085] Subtracting the extracted corresponding values ​​to obtain a difference curve, obtaining a difference of the difference curve, and extracting curve features of the difference curve;

[0086] Heating curve intervention information is generated according to the difference and the curve characteristics.

[0087] Figure 2 A flow chart of the method for obtaining the cooking heating curve of food materials according to the present invention is shown.

[0088] According to an embodiment of the present invention, the method of obtaining the food type information and obtaining the food cooking heating curve through big data analysis based on the food type information and food cooking mode information is specifically as follows:

[0089] S202, obtaining food image information, preprocessing the image information and performing image feature extraction, searching and matching in a food database based on the image features, and identifying at least one type of food information based on the matching results;

[0090] S204, extracting keywords from the food type information, and using big data analysis to obtain a food heating curve dataset;

[0091] S206, searching the food heating curve dataset using the cooking mode selected by the target user as a search condition, and obtaining food quality information corresponding to each food heating curve in the search results;

[0092] S208, scoring the food quality information according to a preset rule, extracting and sorting the heating curves of ingredients with scores greater than a preset threshold, and obtaining the heating curve of the ingredient with the highest score;

[0093] S210 , obtaining a preset heating curve corresponding to the cooking mode selected by the target user, fitting the heating curve of the ingredient corresponding to the highest score with the preset heating curve, and generating a cooking heating curve for the ingredient.

[0094] It should be noted that the method also includes: monitoring the heating condition of the food during the cooking process and adjusting the heating curve of the food during the cooking process, specifically:

[0095] The heating condition of the food during cooking is monitored by a preset sensor to obtain monitoring data information, and a real-time heating curve is generated according to the monitoring data information;

[0096] Decomposing the real-time heating curve according to the cooking process to obtain a plurality of curve segment groups, and obtaining average temperature information in each curve segment group;

[0097] The optimal heating curve is segmented according to the curve segment group segmentation rule to obtain the target temperature information in each curve segment group;

[0098] Comparing and analyzing the average temperature information with the target temperature information to generate a deviation rate;

[0099] Determining whether the deviation rate is greater than a deviation rate threshold;

[0100] If it is greater, cooking abnormality information is generated, and correction information is generated according to the cooking abnormality information. The cooking time and cooking heat are adjusted by the correction information to achieve adjustment of the real-time heating curve.

[0101] It should be noted that when a heating curve is used to represent the relationship between heating temperature and heating duration, the heating curve is the combination of the heating temperature and heating duration for each time period. The cooking heating curve is divided into multiple curve segments, and the target temperature for each curve segment group is determined based on the temperatures corresponding to each time point in the multiple curve segments. Specifically, the target temperature for each curve segment group is determined based on the temperatures corresponding to each time point in the multiple curve segments. Specifically, the target temperature for each curve segment group is determined by extracting the temperature corresponding to each time point in each curve segment group, summing the temperatures corresponding to each time point in each curve segment group, dividing the accumulated temperature value by the number of time points in the curve segment group, and using the resulting average temperature as the target temperature for the curve segment group.

[0102] Figure 3 A flow chart of the method for optimizing the optimal heating curve according to the present invention is shown.

[0103] According to an embodiment of the present invention, feedback information on food quality from a user of a household appliance is obtained, and the optimal heating curve is optimized based on the feedback information and compared with the optimal heating curve, specifically as follows:

[0104] S302, the cloud server sends the food quality questionnaire to the target user in a preset manner through a questionnaire survey;

[0105] S304, obtaining questionnaire feedback data from target users, processing and analyzing the questionnaire feedback data, and generating a satisfaction score for current food quality;

[0106] S306, presetting a satisfaction score threshold, and comparing the food quality satisfaction score with the preset threshold;

[0107] S308: If the food quality satisfaction score is less than a preset threshold, feedback information is generated based on the questionnaire feedback data, and the current optimal heating curve is optimized based on the feedback information and the optimal heating curve.

[0108] According to an embodiment of the present invention, the heating conditions of ingredients during the cooking process are monitored using a preset sensor. The food quality is represented by the monitoring data information at each cooking stage. A functional relationship is formed based on time and the average temperature within the cooking stage. The function of food quality can be expressed as:

[0109]

[0110] in, Indicates food quality, Indicates the number of cooking stage items, Indicates the total number of cooking stages, Indicates the deformation coefficient of food after heating. represents the average temperature during the cooking phase, Indicates the reference temperature for the cooking phase, Indicates the food quality factor of maturity, reflecting the sensitivity of food quality to temperature changes. Indicates the time parameters corresponding to the cooking stage.

[0111] According to an embodiment of the present invention, the cooking heating curve of the food is adjusted on demand according to the taste requirements of the target user, specifically:

[0112] Generate demand tags according to the needs of target users, and capture demand data sets in the database through the demand tags;

[0113] generating a preference feature through the demand data set, and determining a similarity threshold interval according to the preference feature;

[0114] Calculate the similarity between the data set in the database and the required data set by a preset calculation method, and take the data set falling within the similarity threshold range as the required data set;

[0115] Extracting a cooking heating curve from the demand data set, and extracting heating temperature information and cooking stage time information according to the cooking heating curve;

[0116] The heating temperature information and the cooking stage time information are used to update the heating curve intervention information, and the cooking heating curve is matched according to the updated heating curve intervention information to generate an optimal heating curve.

[0117] According to an embodiment of the present invention, when a cooking device cooks multiple ingredients, the cooking heating curves of the multiple ingredients are aggregated, specifically:

[0118] Generate recipe information based on the needs of target users, extract ingredients required for cooking based on the recipe information, and obtain cooking heating curves for the required ingredients through big data analysis;

[0119] Obtaining cooking times of different ingredients at different cooking stages according to the cooking heating curve, sorting the cooking times within a preset cooking stage, generating an order for placing the different ingredients according to the sorting, and displaying the order in a preset manner;

[0120] Extracting characteristic points from the cooking heating curve of the desired food and generating matching pairs of characteristic points; calculating angles between a line connecting the characteristic points in the matching pairs and a horizontal direction and distances between the characteristic points; and generating an angle set and a distance set;

[0121] Curve fitting is performed based on the angle combination and the distance set, and curve correction is performed to generate an optimal heating curve.

[0122] It should be noted that when the user adds the required ingredients into the cooking device at the same time, the cooking device obtains the ingredient image information, identifies at least one ingredient information based on the ingredient image information, generates ingredient confirmation information based on the ingredient recognition result, and obtains the ingredient recognition rate through the target user's feedback on the ingredient confirmation information; when the ingredient recognition rate is less than the preset threshold, the algorithm for controlling ingredient recognition is automatically updated.

[0123] According to an embodiment of the present invention, when the cooking device identifies the food, it identifies the frozen state of the food and matches the optimal heating curve according to the frozen state, specifically:

[0124] Acquire hyperspectral image information of food in the cooking device, preprocess the hyperspectral image information, select a region of interest, and obtain the spectral reflectance of the region of interest at a preset wavelength;

[0125] Identifying fresh and frozen ingredients based on the spectral reflectance, setting a spectral reflectance threshold range, determining the freezing degree of the frozen ingredients based on the color of the ingredients and the threshold range within which the spectral reflectance falls, and obtaining a cooking heating curve for the ingredients based on the freezing degree;

[0126] Determine the duration of the thawing stage and the thawing heating temperature of the food according to the degree of freezing through big data, add the thawing stage to the cooking process of the food, and generate the optimal heating curve of the food in combination with the cooking heating curve of the food;

[0127] During the cooking process according to the optimal heating curve, the maturity of the food is monitored, and the optimal heating curve is optimized and corrected in real time according to the maturity.

[0128] It should be noted that, optionally, the cooking device and the smart refrigerator are networked and connected through the Internet of Things technology. When the cooking device completes the identification of the ingredients, the ingredient identification result is sent to the smart refrigerator. The smart refrigerator extracts the storage time of the ingredients in the refrigerator through the ingredient identification result, generates the freshness of the ingredients based on the storage time, and feeds back the freshness to the cloud server. The cloud server generates the optimal heating curve based on the freshness of the ingredients and the cooking heating curve of the ingredients.

[0129] Figure 4 A block diagram of a heating control system based on big data of the present invention is shown.

[0130] A second aspect of the present invention further provides a heating control system 4 based on big data, the system comprising: a memory 41 and a processor 42, wherein the memory includes a heating control method program based on big data, and when the heating control method program based on big data is executed by the processor, the following steps are implemented:

[0131] Obtaining current factor parameter information and target user's physical condition information, and generating heating curve intervention information according to the factor parameter information and physical condition information;

[0132] Obtaining food type information, and obtaining a cooking heating curve of the food through big data analysis based on the food type information and food cooking mode information;

[0133] Matching the heating curve intervention information according to the cooking heating curve generates an optimal heating curve for heating cooking;

[0134] Obtain feedback information on food quality from target users, compare the feedback information with the current optimal heating curve, and optimize the optimal heating curve.

[0135] It should be noted that when the heating curve represents the relationship between the heating temperature and the heating time when cooking ingredients, the smart cooking device may be an electric rice cooker, induction cooker or other device that uses temperature to cook ingredients.

[0136] It should be noted that the factor parameter information includes one or a combination of two or more of latitude information, poster information, air pressure information, ambient temperature information, and circuit voltage information; the physical condition information of the target user includes one or a combination of two or more of age information, health information, dietary habit information, and disease condition information.

[0137] It should be noted that the heating curve intervention information is generated according to the factor parameter information and the physical condition information, specifically:

[0138] Determine the time, heating temperature and heating power of each program in the cooking process based on the factor parameter information and the physical condition information;

[0139] generating an initial heating curve according to the time, heating temperature and heating power of each program in the cooking process;

[0140] Obtaining a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values ​​from the initial heating curve and the preset heating curve;

[0141] Subtracting the extracted corresponding values ​​to obtain a difference curve, obtaining a difference of the difference curve, and extracting curve features of the difference curve;

[0142] Heating curve intervention information is generated according to the difference and the curve characteristics.

[0143] According to an embodiment of the present invention, the method of obtaining the food type information and obtaining the food cooking heating curve through big data analysis based on the food type information and food cooking mode information is specifically as follows:

[0144] Acquire food image information, pre-process the image information and perform image feature extraction, search and match in a food database based on the image features, and identify at least one type of food information based on the matching results;

[0145] Extracting keywords from the food type information, and using big data analysis to obtain a food heating curve dataset;

[0146] Searching the food heating curve dataset using the cooking mode selected by the target user as a search condition, and obtaining food quality information corresponding to each food heating curve in the search results;

[0147] Scoring the food quality information according to a preset rule, extracting heating curves of ingredients with scores greater than a preset threshold and sorting them according to the scores, and obtaining the heating curve of the ingredient with the highest score;

[0148] A preset heating curve corresponding to the cooking mode selected by the target user is obtained, and the heating curve of the food corresponding to the highest score is fitted with the preset heating curve to generate a cooking heating curve for the food.

[0149] It should be noted that the method also includes: monitoring the heating condition of the food during the cooking process and adjusting the heating curve of the food during the cooking process, specifically:

[0150] The heating condition of the food during cooking is monitored by a preset sensor to obtain monitoring data information, and a real-time heating curve is generated according to the monitoring data information;

[0151] Decomposing the real-time heating curve according to the cooking process to obtain a plurality of curve segment groups, and obtaining average temperature information in each curve segment group;

[0152] The optimal heating curve is segmented according to the curve segment group segmentation rule to obtain the target temperature information in each curve segment group;

[0153] Comparing and analyzing the average temperature information with the target temperature information to generate a deviation rate;

[0154] Determining whether the deviation rate is greater than a deviation rate threshold;

[0155] If it is greater, cooking abnormality information is generated, and correction information is generated according to the cooking abnormality information. The cooking time and cooking heat are adjusted by the correction information to achieve adjustment of the real-time heating curve.

[0156] It should be noted that when a heating curve is used to represent the relationship between heating temperature and heating duration, the heating curve is the combination of the heating temperature and heating duration for each time period. The cooking heating curve is divided into multiple curve segments, and the target temperature for each curve segment group is determined based on the temperatures corresponding to each time point in the multiple curve segments. Specifically, the target temperature for each curve segment group is determined based on the temperatures corresponding to each time point in the multiple curve segments. Specifically, the target temperature for each curve segment group is determined by extracting the temperature corresponding to each time point in each curve segment group, summing the temperatures corresponding to each time point in each curve segment group, dividing the accumulated temperature value by the number of time points in the curve segment group, and using the resulting average temperature as the target temperature for the curve segment group.

[0157] According to an embodiment of the present invention, feedback information on food quality from a user of a household appliance is obtained, and the optimal heating curve is optimized based on the feedback information and compared with the optimal heating curve, specifically as follows:

[0158] The cloud server sends the food quality questionnaire to the target users in a preset way through the questionnaire survey;

[0159] Obtaining questionnaire feedback data from target users, processing and analyzing the questionnaire feedback data to generate a satisfaction score for current food quality;

[0160] Preset a satisfaction score threshold, and compare the food quality satisfaction score with the preset threshold;

[0161] If the food quality satisfaction score is less than a preset threshold, feedback information is generated based on the questionnaire feedback data, and the current optimal heating curve is optimized based on the feedback information and the optimal heating curve.

[0162] According to an embodiment of the present invention, the heating conditions of ingredients during the cooking process are monitored using a preset sensor. The food quality is represented by the monitoring data information at each cooking stage. A functional relationship is formed based on time and the average temperature within the cooking stage. The function of food quality can be expressed as:

[0163] Where p represents food quality, i represents the number of cooking stages, n represents the total number of cooking stages, λ represents the deformation coefficient of food after heating, T represents the average temperature in the cooking stage, and T c represents the reference temperature of the cooking stage, β represents the food quality factor of maturity, which reflects the sensitivity of food quality to temperature changes, and t represents the time parameter corresponding to the cooking stage.

[0164] According to an embodiment of the present invention, the cooking heating curve of the food is adjusted on demand according to the taste requirements of the target user, specifically:

[0165] Generate demand tags according to the needs of target users, and capture demand data sets in the database through the demand tags;

[0166] generating a preference feature through the demand data set, and determining a similarity threshold interval according to the preference feature;

[0167] Calculate the similarity between the data set in the database and the required data set by a preset calculation method, and take the data set falling within the similarity threshold range as the required data set;

[0168] Extracting a cooking heating curve from the demand data set, and extracting heating temperature information and cooking stage time information according to the cooking heating curve;

[0169] The heating temperature information and the cooking stage time information are used to update the heating curve intervention information, and the cooking heating curve is matched according to the updated heating curve intervention information to generate an optimal heating curve.

[0170] According to an embodiment of the present invention, when a cooking device cooks multiple ingredients, the cooking heating curves of the multiple ingredients are aggregated, specifically:

[0171] Generate recipe information based on the needs of target users, extract ingredients required for cooking based on the recipe information, and obtain cooking heating curves for the required ingredients through big data analysis;

[0172] Obtaining cooking times of different ingredients at different cooking stages according to the cooking heating curve, sorting the cooking times within a preset cooking stage, generating an order for placing the different ingredients according to the sorting, and displaying the order in a preset manner;

[0173] Extracting characteristic points from the cooking heating curve of the desired food and generating matching pairs of characteristic points; calculating angles between a line connecting the characteristic points in the matching pairs and a horizontal direction and distances between the characteristic points; and generating an angle set and a distance set;

[0174] Curve fitting is performed based on the angle combination and the distance set, and curve correction is performed to generate an optimal heating curve.

[0175] It should be noted that when the user adds the required ingredients into the cooking device at the same time, the cooking device obtains the ingredient image information, identifies at least one ingredient information based on the ingredient image information, generates ingredient confirmation information based on the ingredient recognition result, and obtains the ingredient recognition rate through the target user's feedback on the ingredient confirmation information; when the ingredient recognition rate is less than the preset threshold, the algorithm for controlling ingredient recognition is automatically updated.

[0176] According to an embodiment of the present invention, when the cooking device identifies the food, it identifies the frozen state of the food and matches the optimal heating curve according to the frozen state, specifically:

[0177] Acquire hyperspectral image information of food in the cooking device, preprocess the hyperspectral image information, select a region of interest, and obtain the spectral reflectance of the region of interest at a preset wavelength;

[0178] Identifying fresh and frozen ingredients based on the spectral reflectance, setting a spectral reflectance threshold range, determining the freezing degree of the frozen ingredients based on the color of the ingredients and the threshold range within which the spectral reflectance falls, and obtaining a cooking heating curve for the ingredients based on the freezing degree;

[0179] Determine the duration of the thawing stage and the thawing heating temperature of the food according to the degree of freezing through big data, add the thawing stage to the cooking process of the food, and generate the optimal heating curve of the food in combination with the cooking heating curve of the food;

[0180] During the cooking process according to the optimal heating curve, the maturity of the food is monitored, and the optimal heating curve is optimized and corrected in real time according to the maturity.

[0181] It should be noted that, optionally, the cooking device and the smart refrigerator are networked and connected through the Internet of Things technology. When the cooking device completes the identification of the ingredients, the ingredient identification result is sent to the smart refrigerator. The smart refrigerator extracts the storage time of the ingredients in the refrigerator through the ingredient identification result, generates the freshness of the ingredients based on the storage time, and feeds back the freshness to the cloud server. The cloud server generates the optimal heating curve based on the freshness of the ingredients and the cooking heating curve of the ingredients.

[0182] The third aspect of the present invention also provides a computer-readable storage medium, which includes a heating control method program based on big data. When the heating control method program based on big data is executed by a processor, it implements the steps of a heating control method based on big data as described in any one of the above items.

[0183] The present invention discloses a heating control method, system, and storage medium based on big data, relating to the field of intelligent control technology. The heating control method includes: obtaining current factor parameter information and physical condition information of a target user, generating heating curve intervention information based on the factor parameter information and physical condition information, obtaining food type information, obtaining a cooking heating curve for the food based on the food type information and food cooking mode information through big data analysis, matching the heating curve intervention information with the cooking heating curve to generate an optimal heating curve for heating and cooking, obtaining feedback information on food quality from the target user, and optimizing the heating curve based on the feedback information and the current heating curve. The present invention obtains the optimal heating curve for the food through big data analysis, thereby improving the taste of the food and maintaining its nutritional content.

[0184] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0185] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0186] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0187] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0188] Alternatively, if the integrated units described above are implemented as software modules and sold or used as standalone products, they can also be stored on a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

[0189] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A heating control method based on big data, characterized in that: include: Obtaining current factor parameter information and target user's physical condition information, and generating heating curve intervention information according to the factor parameter information and physical condition information; Obtaining food type information, and obtaining a cooking heating curve of the food through big data analysis based on the food type information and food cooking mode information; Matching the heating curve intervention information according to the cooking heating curve generates an optimal heating curve for heating cooking; Obtaining feedback from target users on food quality, and optimizing the optimal heating curve based on the feedback information and the current optimal heating curve; The obtaining of food type information, and obtaining the food cooking heating curve through big data analysis based on the food type information and food cooking mode information, is specifically as follows: Acquire food image information, pre-process the image information and extract image features, search and match in a food database based on the image features, and identify at least one type of food information based on the matching results; Extracting keywords from the food type information, and using big data analysis to obtain a food heating curve dataset; Searching the food heating curve dataset using the cooking mode selected by the target user as a search condition, and obtaining food quality information corresponding to each food heating curve in the search results; Scoring the food quality information according to a preset rule, extracting heating curves of ingredients with scores greater than a preset threshold and sorting them according to the scores, and obtaining the heating curve of the ingredient with the highest score; Obtaining a preset heating curve corresponding to the cooking mode selected by the target user, fitting the heating curve of the ingredient corresponding to the highest score with the preset heating curve to generate a cooking heating curve for the ingredient; It also includes: monitoring the heating condition of the food during the cooking process and adjusting the heating curve of the food during the cooking process, specifically: The heating condition of the food during cooking is monitored by a preset sensor to obtain monitoring data information, and a real-time heating curve is generated according to the monitoring data information; Decomposing the real-time heating curve according to the cooking process to obtain a plurality of curve segment groups, and obtaining average temperature information in each curve segment group; The optimal heating curve is segmented according to the curve segment group segmentation rule to obtain the target temperature information in each curve segment group; Comparing and analyzing the average temperature information with the target temperature information to generate a deviation rate; Determining whether the deviation rate is greater than a deviation rate threshold; If it is greater than, cooking abnormality information is generated, and correction information is generated according to the cooking abnormality information, and the cooking time and cooking temperature are adjusted by the correction information to achieve adjustment of the real-time heating curve; The food quality is expressed through the monitoring data information of each cooking stage, and a functional relationship is formed according to the time and the average temperature within the cooking stage. The function of food quality can be expressed as: in, Indicates food quality, Indicates the number of cooking stage items, Indicates the total number of cooking stages, Indicates the deformation coefficient of food after heating. represents the average temperature during the cooking phase, Indicates the reference temperature for the cooking phase, Indicates the food quality factor of maturity, reflecting the sensitivity of food quality to temperature changes. Indicates the time parameters corresponding to the cooking stage.

2. The heating control method based on big data according to claim 1, characterized in that: The factor parameter information includes one or a combination of two or more of latitude information, altitude information, air pressure information, ambient temperature information, and circuit voltage information; the physical condition information of the target user includes one or a combination of two or more of age information, health information, dietary habit information, and disease condition information.

3. The heating control method based on big data according to claim 1, characterized in that: The heating curve intervention information is generated according to the factor parameter information and the physical condition information, specifically: Determine the time, heating temperature and heating power of each program in the cooking process based on the factor parameter information and the physical condition information; generating an initial heating curve according to the time, heating temperature and heating power of each program in the cooking process; Obtaining a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values ​​from the initial heating curve and the preset heating curve; Subtracting the extracted corresponding values ​​to obtain a difference curve, obtaining a difference of the difference curve, and extracting curve features of the difference curve; Heating curve intervention information is generated according to the difference of the difference curve and the curve characteristics.

4. The heating control method based on big data according to claim 1, characterized in that: Obtain feedback from home appliance users on food quality, compare the feedback with the optimal heating curve, and optimize the optimal heating curve, specifically: The cloud server sends the food quality questionnaire to the target users in a preset way through the questionnaire survey; Obtaining questionnaire feedback data from target users, processing and analyzing the questionnaire feedback data to generate a satisfaction score for current food quality; Preset a satisfaction score threshold, and compare the food quality satisfaction score with the preset threshold; If the food quality satisfaction score is less than a preset threshold, feedback information is generated based on the questionnaire feedback data, and the current optimal heating curve is optimized based on the feedback information and the optimal heating curve.

5. A heating control system based on big data, characterized in that: The system includes: a memory and a processor. The memory includes a heating control method program based on big data. When the heating control method program based on big data is executed by the processor, the following steps are implemented: Obtaining current factor parameter information and target user's physical condition information, and generating heating curve intervention information according to the factor parameter information and physical condition information; Obtaining food type information, and obtaining a cooking heating curve of the food through big data analysis based on the food type information and food cooking mode information; Matching the heating curve intervention information according to the cooking heating curve generates an optimal heating curve for heating cooking; Obtaining feedback from target users on food quality, and optimizing the optimal heating curve based on the feedback information and the current optimal heating curve; The obtaining of food type information, and obtaining the food cooking heating curve through big data analysis based on the food type information and food cooking mode information, is specifically as follows: Acquire food image information, pre-process the image information and extract image features, search and match in a food database based on the image features, and identify at least one type of food information based on the matching results; Extracting keywords from the food type information, and using big data analysis to obtain a food heating curve dataset; Searching the food heating curve dataset using the cooking mode selected by the target user as a search condition, and obtaining food quality information corresponding to each food heating curve in the search results; Scoring the food quality information according to a preset rule, extracting heating curves of ingredients with scores greater than a preset threshold and sorting them according to the scores, and obtaining the heating curve of the ingredient with the highest score; Obtaining a preset heating curve corresponding to the cooking mode selected by the target user, fitting the heating curve of the ingredient corresponding to the highest score with the preset heating curve to generate a cooking heating curve for the ingredient; It also includes: monitoring the heating condition of the food during the cooking process and adjusting the heating curve of the food during the cooking process, specifically: The heating condition of the food during cooking is monitored by a preset sensor to obtain monitoring data information, and a real-time heating curve is generated according to the monitoring data information; Decomposing the real-time heating curve according to the cooking process to obtain a plurality of curve segment groups, and obtaining average temperature information in each curve segment group; The optimal heating curve is segmented according to the curve segment group segmentation rule to obtain the target temperature information in each curve segment group; Comparing and analyzing the average temperature information with the target temperature information to generate a deviation rate; Determining whether the deviation rate is greater than a deviation rate threshold; If it is greater than, cooking abnormality information is generated, and correction information is generated according to the cooking abnormality information, and the cooking time and cooking temperature are adjusted by the correction information to achieve adjustment of the real-time heating curve; The food quality is expressed through the monitoring data information of each cooking stage, and a functional relationship is formed according to the time and the average temperature within the cooking stage. The function of food quality can be expressed as: in, Indicates food quality, Indicates the number of cooking stage items, Indicates the total number of cooking stages, Indicates the deformation coefficient of food after heating. represents the average temperature during the cooking phase, Indicates the reference temperature for the cooking phase, Indicates the food quality factor of maturity, reflecting the sensitivity of food quality to temperature changes. Indicates the time parameters corresponding to the cooking stage.

6. The heating control system based on big data according to claim 5, characterized in that: The heating curve intervention information is generated according to the factor parameter information and the physical condition information, specifically: Determine the time, heating temperature and heating power of each program in the cooking process based on the factor parameter information and the physical condition information; generating an initial heating curve according to the time, heating temperature and heating power of each program in the cooking process; Obtaining a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values ​​from the initial heating curve and the preset heating curve; Subtracting the extracted corresponding values ​​to obtain a difference curve, obtaining a difference of the difference curve, and extracting curve features of the difference curve; Heating curve intervention information is generated according to the difference of the difference curve and the curve characteristics.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a heating control method program based on big data. When the heating control method program based on big data is executed by a processor, the steps of the heating control method based on big data as described in any one of claims 1 to 4 are implemented.

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