CN113951734A - Heating control method and system based on big data and storage medium - Google Patents
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- 238000010438 heat treatment Methods 0.000 title claims abstract description 389
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- A47J—KITCHEN EQUIPMENT; COFFEE MILLS; SPICE MILLS; APPARATUS FOR MAKING BEVERAGES
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Abstract
The invention discloses a heating control method, a heating control system and a storage medium based on big data, and relates to the technical field of intelligent control, wherein the heating control method comprises the following steps: the method comprises the steps of obtaining current factor parameter information and physical condition information of a target user, generating heating curve intervention information according to the factor parameter information and the physical condition information, obtaining food material type information, obtaining a cooking heating curve of food materials through big data analysis according to the food material type information and the food material cooking mode information, generating an optimal heating curve according to the fact that the cooking heating curve is matched with the heating curve intervention information to perform heating cooking, obtaining feedback information of the target user on food quality, and optimizing the heating curve according to the comparison between the feedback information and the current heating curve. According to the method, the optimal heating curve of the food material is obtained through big data analysis, so that the taste of the food material is improved, and the nutritional ingredients are kept.
Description
Technical Field
The invention relates to the technical field of intelligent control, in particular to a heating control method and system based on big data and a storage medium.
Background
With the continuous improvement of living standard, small household appliances are developing towards intellectualization under the background of the large environmental network era. In the development of small intelligent household appliances, the small intelligent household appliances with various development functions, simple operation, convenience and quickness become a new direction for developing various small intelligent household appliances. However, small household electrical appliances in the current market are complex in function and are not designed according to the requirements of users. In small household appliances, the traditional cooking equipment has a relatively fixed cooking function, personal basic condition information and taste preference information of users are different, and in the actual use of the cooking equipment, the users often have poor food quality due to improper cooking and heating time, so that it is important to match an optimal heating curve for food materials according to preset information of the users.
In order to realize intelligent heating control of food materials through intelligent cooking equipment, a system needs to be developed to be matched with the intelligent cooking equipment for realization, the system generates heating curve intervention information by acquiring current factor parameter information and physical condition information of a target user, acquires a heating curve of the food materials through big data analysis according to food material type information and food material cooking mode information, and generates an optimal joining curve according to the heating curve matching with the heating curve intervention information for heating and cooking. In the implementation process of the system, how to obtain the heating curve of the food material through big data analysis based on the preset information of the user is a problem which needs to be solved urgently.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides a heating control method, a heating control system and a storage medium based on big data.
The invention provides a heating control method based on big data in a first aspect, which comprises the following steps:
acquiring current factor parameter information and physical condition information of a target user, and generating heating curve intervention information according to the factor parameter information and the physical condition information;
acquiring food material type information, and acquiring a cooking heating curve of food materials through big data analysis according to the food material type information and the food material cooking mode information;
matching heating curve intervention information according to the cooking heating curve to generate an optimal heating curve for heating and cooking;
feedback information of the target user on the food quality is obtained, and the optimal heating curve is optimized according to the comparison between the feedback information and the current optimal heating curve.
In the scheme, the factor parameter information comprises one or a combination of two or more of latitude information, poster information, air pressure information, environment temperature information and circuit voltage information; the physical condition information of the target user comprises one or the combination of more than two of age information, health information, meal habit information and illness condition information.
In this scheme, the generating of the heating curve intervention information according to the factor parameter information and the physical condition information specifically includes:
determining time, heating temperature and heating power of each program in the cooking process according to the factor parameter information and the physical condition information;
generating an initial heating curve according to the time, the heating temperature and the heating power of each program in the cooking process;
acquiring a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values of the initial heating curve and the preset heating curve;
subtracting the extracted corresponding values to obtain a difference curve, acquiring a difference value of the difference curve, and extracting curve characteristics of the difference curve;
and generating heating curve intervention information according to the difference value and the curve characteristics.
In this scheme, the obtaining of the food material type information obtains the cooking heating curve of the food material through big data analysis according to the food material type information and the food material cooking mode information, and specifically includes:
acquiring food material image information, preprocessing the image information, extracting image characteristics, searching and matching in a food material database according to the image characteristics, and identifying at least one food material type information according to a matching result;
extracting key words in the food material type information, and analyzing the key words by means of big data to obtain a food material heating curve data set;
searching in the food heating curve data set according to a cooking mode selected by a target user as a searching condition, and acquiring food quality information corresponding to each food heating curve in a searching result;
scoring the food quality information according to a preset rule, extracting food material heating curves with scores larger than a preset threshold value, and sorting the food material heating curves according to the scores to obtain food material heating curves corresponding to the highest scores;
and acquiring a preset heating curve corresponding to the cooking mode selected by the target user, and fitting the food material heating curve corresponding to the highest score with the preset heating curve to generate a cooking heating curve of the food material.
In this scheme, still include: the heating condition of the food materials is monitored in the food material cooking process, and the heating curve of the food materials in the cooking process is adjusted, specifically:
monitoring the heating condition of the food material in the cooking process according to a preset sensor to obtain monitoring data information, and generating a real-time heating curve according to the monitoring data information;
decomposing the real-time heating curve according to a cooking process to obtain a plurality of curve segment groups, and acquiring average temperature information in each curve segment group;
segmenting the optimal heating curve according to a curve segment group segmentation rule to obtain target temperature information in each curve segment group;
comparing and analyzing the average temperature information and the target temperature information to generate a deviation ratio;
judging whether the deviation rate is greater than a deviation rate threshold value or not;
if the cooking time is larger than the preset cooking time, generating abnormal cooking information, generating correction information according to the abnormal cooking information, and adjusting the cooking time and the cooking duration through the correction information to realize the adjustment of the real-time heating curve.
In this scheme, acquire the feedback information of household electrical appliances user to food quality, according to feedback information and best heating curve compare, optimize best heating curve, specifically do:
the cloud server sends the food quality questionnaire to the target user in a preset mode in a questionnaire survey mode;
obtaining questionnaire feedback data of a target user, processing and analyzing the questionnaire feedback data, and generating a current food quality satisfaction degree score;
presetting a satisfaction score threshold, and comparing the food quality satisfaction score with the preset threshold;
and if the food quality satisfaction score is smaller than a preset threshold value, generating feedback information according to questionnaire feedback data, and comparing the feedback information with the optimal heating curve to optimize the current optimal heating curve.
The second aspect of the present invention also provides a big data based heating control system, comprising: the heating control method based on the big data comprises a memory and a processor, wherein the memory comprises a program of the heating control method based on the big data, and when the program of the heating control method based on the big data is executed by the processor, the following steps are realized:
acquiring current factor parameter information and physical condition information of a target user, and generating heating curve intervention information according to the factor parameter information and the physical condition information;
acquiring food material type information, and acquiring a cooking heating curve of food materials through big data analysis according to the food material type information and the food material cooking mode information;
matching heating curve intervention information according to the cooking heating curve to generate an optimal heating curve for heating and cooking;
feedback information of the target user on the food quality is obtained, and the optimal heating curve is optimized according to the comparison between the feedback information and the current optimal heating curve.
In the scheme, the factor parameter information comprises one or a combination of two or more of latitude information, poster information, air pressure information, environment temperature information and circuit voltage information; the physical condition information of the target user comprises one or the combination of more than two of age information, health information, meal habit information and illness condition information.
In this scheme, the generating of the heating curve intervention information according to the factor parameter information and the physical condition information specifically includes:
determining time, heating temperature and heating power of each program in the cooking process according to the factor parameter information and the physical condition information;
generating an initial heating curve according to the time, the heating temperature and the heating power of each program in the cooking process;
acquiring a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values of the initial heating curve and the preset heating curve;
subtracting the extracted corresponding values to obtain a difference curve, acquiring a difference value of the difference curve, and extracting curve characteristics of the difference curve;
and generating heating curve intervention information according to the difference value and the curve characteristics.
In this scheme, the obtaining of the food material type information obtains the cooking heating curve of the food material through big data analysis according to the food material type information and the food material cooking mode information, and specifically includes:
acquiring food material image information, preprocessing the image information, extracting image characteristics, searching and matching in a food material database according to the image characteristics, and identifying at least one food material type information according to a matching result;
extracting key words in the food material type information, and analyzing the key words by means of big data to obtain a food material heating curve data set;
searching in the food heating curve data set according to a cooking mode selected by a target user as a searching condition, and acquiring food quality information corresponding to each food heating curve in a searching result;
scoring the food quality information according to a preset rule, extracting food material heating curves with scores larger than a preset threshold value, and sorting the food material heating curves according to the scores to obtain food material heating curves corresponding to the highest scores;
and acquiring a preset heating curve corresponding to the cooking mode selected by the target user, and fitting the food material heating curve corresponding to the highest score with the preset heating curve to generate a cooking heating curve of the food material.
In this scheme, still include: the heating condition of the food materials is monitored in the food material cooking process, and the heating curve of the food materials in the cooking process is adjusted, specifically:
monitoring the heating condition of the food material in the cooking process according to a preset sensor to obtain monitoring data information, and generating a real-time heating curve according to the monitoring data information;
decomposing the real-time heating curve according to a cooking process to obtain a plurality of curve segment groups, and acquiring average temperature information in each curve segment group;
segmenting the optimal heating curve according to a curve segment group segmentation rule to obtain target temperature information in each curve segment group;
comparing and analyzing the average temperature information and the target temperature information to generate a deviation ratio;
judging whether the deviation rate is greater than a deviation rate threshold value or not;
if the cooking time is larger than the preset cooking time, generating abnormal cooking information, generating correction information according to the abnormal cooking information, and adjusting the cooking time and the cooking duration through the correction information to realize the adjustment of the real-time heating curve.
In this scheme, acquire the feedback information of household electrical appliances user to food quality, according to feedback information and best heating curve compare, optimize best heating curve, specifically do:
the cloud server sends the food quality questionnaire to the target user in a preset mode in a questionnaire survey mode;
obtaining questionnaire feedback data of a target user, processing and analyzing the questionnaire feedback data, and generating a current food quality satisfaction degree score;
presetting a satisfaction score threshold, and comparing the food quality satisfaction score with the preset threshold;
and if the food quality satisfaction score is smaller than a preset threshold value, generating feedback information according to questionnaire feedback data, and comparing the feedback information with the optimal heating curve to optimize the current optimal heating curve.
The third aspect of the present invention further provides a computer-readable storage medium, which includes a big data-based heating control method program, and when the big data-based heating control method program is executed by a processor, the steps of the big data-based heating control method described in any one of the above are implemented.
The invention discloses a heating control method, a heating control system and a storage medium based on big data, and relates to the technical field of intelligent control, wherein the heating control method comprises the following steps: the method comprises the steps of obtaining current factor parameter information and physical condition information of a target user, generating heating curve intervention information according to the factor parameter information and the physical condition information, obtaining food material type information, obtaining a cooking heating curve of food materials through big data analysis according to the food material type information and the food material cooking mode information, generating an optimal heating curve according to the fact that the cooking heating curve is matched with the heating curve intervention information to perform heating cooking, obtaining feedback information of the target user on food quality, and optimizing the heating curve according to the comparison between the feedback information and the current heating curve. According to the method, the optimal heating curve of the food material is obtained through big data analysis, so that the taste of the food material is improved, and the nutritional ingredients are kept.
Drawings
FIG. 1 shows a flow chart of a big data based heating control method of the present invention.
Fig. 2 shows a flow chart of a method for obtaining a cooking heating curve of a food material according to the present invention.
Fig. 3 shows a flow chart of a method for optimizing an optimal heating profile according to the present invention.
FIG. 4 shows a block diagram of a big data based heating control system of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
FIG. 1 shows a flow chart of a big data based heating control method of the present invention.
As shown in fig. 1, a first aspect of the present invention provides a heating control method based on big data, including:
s102, acquiring current factor parameter information and physical condition information of a target user, and generating heating curve intervention information according to the factor parameter information and the physical condition information;
s104, acquiring food material type information, and acquiring a cooking heating curve of a food material through big data analysis according to the food material type information and the food material cooking mode information;
s106, generating an optimal heating curve according to the cooking heating curve matching heating curve intervention information to perform heating cooking;
and S108, acquiring feedback information of the target user on the food quality, and optimizing the optimal heating curve according to the comparison between the feedback information and the current optimal heating curve.
When the heating curve indicates the relationship between the heating temperature and the heating time during cooking of the food material, the intelligent cooking device may be a device such as an electric cooker or an induction cooker that cooks the food material according to the temperature.
The factor parameter information includes one or a combination of two or more of latitude information, poster information, air pressure information, environment temperature information and circuit voltage information; the physical condition information of the target user comprises one or the combination of more than two of age information, health information, meal habit information and illness condition information.
It should be noted that the generating of the heating curve intervention information according to the factor parameter information and the physical condition information specifically includes:
determining time, heating temperature and heating power of each program in the cooking process according to the factor parameter information and the physical condition information;
generating an initial heating curve according to the time, the heating temperature and the heating power of each program in the cooking process;
acquiring a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values of the initial heating curve and the preset heating curve;
subtracting the extracted corresponding values to obtain a difference curve, acquiring a difference value of the difference curve, and extracting curve characteristics of the difference curve;
and generating heating curve intervention information according to the difference value and the curve characteristics.
Fig. 2 shows a flow chart of a method for obtaining a cooking heating curve of a food material according to the present invention.
According to the embodiment of the present invention, the obtaining of the food material type information and the obtaining of the cooking heating curve of the food material through big data analysis according to the food material type information and the food material cooking mode information specifically include:
s202, acquiring food material image information, preprocessing the image information, extracting image features, searching and matching in a food material database according to the image features, and identifying at least one food material type information according to a matching result;
s204, extracting keywords in the food material category information, and analyzing the keywords by means of big data to obtain a food material heating curve data set;
s206, searching in the food heating curve data set according to the cooking mode selected by the target user as a searching condition, and acquiring food quality information corresponding to each food heating curve in a searching result;
s208, scoring the food quality information according to a preset rule, extracting food material heating curves with scores larger than a preset threshold value, and sorting the food material heating curves according to the scores to obtain food material heating curves corresponding to the highest scores;
s210, acquiring a preset heating curve corresponding to the cooking mode selected by the target user, and fitting the food material heating curve corresponding to the highest score with the preset heating curve to generate a cooking heating curve of the food material.
It should be noted that the method further includes: the heating condition of the food materials is monitored in the food material cooking process, and the heating curve of the food materials in the cooking process is adjusted, specifically:
monitoring the heating condition of the food material in the cooking process according to a preset sensor to obtain monitoring data information, and generating a real-time heating curve according to the monitoring data information;
decomposing the real-time heating curve according to a cooking process to obtain a plurality of curve segment groups, and acquiring average temperature information in each curve segment group;
segmenting the optimal heating curve according to a curve segment group segmentation rule to obtain target temperature information in each curve segment group;
comparing and analyzing the average temperature information and the target temperature information to generate a deviation ratio;
judging whether the deviation rate is greater than a deviation rate threshold value or not;
if the cooking time is larger than the preset cooking time, generating abnormal cooking information, generating correction information according to the abnormal cooking information, and adjusting the cooking time and the cooking duration through the correction information to realize the adjustment of the real-time heating curve.
It should be noted that, when a heating curve is used to express the relationship between the heating temperature and the heating time period, the heating curve is a combination of the heating temperature and the heating time period for each period. And dividing the cooking heating curve into a plurality of curve segments, and determining the target temperature of the curve segment group according to the temperature corresponding to each time point in the plurality of curve segments in the cooking heating curve. The method comprises the following steps of determining a target temperature of a curve segment group according to temperatures corresponding to each time point in a plurality of curve segments in a cooking heating curve, wherein the target temperature specifically comprises the following steps: and extracting the corresponding temperature of each time point in each curve segment group, accumulating the corresponding temperature of each time point in each curve segment group, dividing the accumulated value of the temperature by the number of time nodes in the curve segment group, and taking the obtained average temperature value as the target temperature corresponding to the curve segment group.
Fig. 3 shows a flow chart of a method for optimizing an optimal heating profile according to the present invention.
According to the embodiment of the invention, feedback information of a household appliance user on food quality is obtained, and the optimal heating curve is optimized according to the comparison between the feedback information and the optimal heating curve, which specifically comprises the following steps:
s302, the cloud server sends a food quality questionnaire to a target user in a preset mode in a questionnaire mode;
s304, obtaining questionnaire feedback data of a target user, processing and analyzing the questionnaire feedback data, and generating a current food quality satisfaction degree score;
s306, presetting a satisfaction score threshold, and comparing the food quality satisfaction score with the preset threshold;
and S308, if the food quality satisfaction score is smaller than a preset threshold, generating feedback information according to questionnaire feedback data, and comparing the feedback information with the optimal heating curve to optimize the current optimal heating curve.
According to the embodiment of the invention, the heating condition of the food material in the cooking process is monitored according to the preset sensor, the food quality is represented through the monitoring data information of each cooking stage, and a functional relation is formed according to the time and the average temperature in the cooking stage, wherein the function of the food quality can be represented as:
wherein,
which is indicative of the quality of the food,
the number of items of the cooking stage is represented,
indicating the total number of cooking stages,
the deformation coefficient of the food material after being heated is shown,
which represents the average temperature during the cooking phase,
a reference temperature representing the cooking phase is indicated,
shows the food material mature quality factor, reflects the sensitivity of food material to temperature change,
indicating the time parameter corresponding to the cooking phase.
According to the embodiment of the invention, the method further comprises the step of realizing the required adjustment on the cooking heating curve of the food material according to the taste requirement of the target user, and specifically comprises the following steps:
generating a demand label according to the demand of a target user, and capturing a demand data set in a database through the demand label;
generating preference characteristics through the demand data set, and determining a similarity threshold interval according to the preference characteristics;
calculating the similarity between a data set and a demand data set in a database by a preset calculation method, and taking the data set falling in the similarity threshold interval as a demand data set;
extracting a cooking heating curve in a demand data set, and extracting heating temperature information and cooking stage time information according to the cooking heating curve;
and updating heating curve intervention information according to the heating temperature information and the cooking stage time information, and matching the cooking heating curve according to the updated heating curve intervention information to generate an optimal heating curve.
According to the embodiment of the invention, when the cooking equipment cooks a plurality of food materials, the cooking heating curves of the plurality of food materials are aggregated, specifically:
generating recipe information according to the requirements of a target user, extracting food materials required for cooking according to the recipe information, and obtaining a cooking heating curve of the food materials required through big data analysis;
acquiring cooking time of different cooking stages of different food materials according to the cooking heating curve, sequencing the cooking time in a preset cooking stage, generating a putting sequence of the different food materials according to the sequencing, and displaying the putting sequence according to a preset mode;
extracting curve characteristic points of the cooking heating curve of the required food material, generating characteristic point matching pairs, calculating included angle angles between a connecting line of the characteristic points in the matching pairs and the horizontal direction and distances between the characteristic points, and generating an included angle set and a distance set;
and performing curve fitting according to the angle combination of the included angles and the distance set, and performing curve correction to generate an optimal heating curve.
It should be noted that, when a user adds a required food material to the cooking device at the same time, the cooking device acquires food material image information, identifies at least one type of food material information according to the food material image information, generates food material identification information from a food material identification result, and obtains a food material identification rate through feedback of a target user on the food material identification information; and when the food material recognition rate is smaller than a preset threshold value, automatically updating an algorithm for controlling food material recognition.
According to the embodiment of the invention, when the cooking equipment identifies the food materials, the freezing state of the food materials is identified, and the optimal heating curve is matched according to the freezing state, specifically:
the method comprises the steps of obtaining hyperspectral image information of food materials in cooking equipment, preprocessing the hyperspectral image information, selecting an interested area, and obtaining the spectral reflectivity of the interested area at a preset wavelength;
identifying fresh food materials and frozen food materials according to the spectral reflectivity, setting a spectral reflectivity threshold interval, judging the freezing degree of the frozen food materials according to the color degree of the food materials and the threshold interval in which the spectral reflectivity falls, and acquiring a cooking heating curve of the food materials according to the freezing degree;
determining the time length of a thawing stage and the thawing heating temperature of the food material according to the freezing degree through big data, adding the thawing stage into the cooking process of the food material in a self-defined mode, and generating an optimal heating curve of the food material by combining the cooking heating curve of the food material;
and in the process of cooking according to the optimal heating curve, monitoring the maturity of the food materials, and carrying out real-time optimization and correction on the optimal heating curve according to the maturity.
It should be noted that optionally, the cooking equipment and the intelligent refrigerator are networked and connected through the internet of things technology, after the cooking equipment completes food identification, food identification results are sent to the intelligent refrigerator, the intelligent refrigerator extracts storage time of food materials in the refrigerator through the food identification results, freshness of the food materials is generated according to the storage time, the freshness is fed back to the cloud server, and the cloud server generates an optimal heating curve according to the freshness of the food materials and a cooking heating curve of the food materials.
FIG. 4 shows a block diagram of a big data based heating control system of the present invention.
The second aspect of the present invention also provides a big data based heating control system 4, comprising: a memory 41 and a processor 42, wherein the memory includes a big data based heating control method program, and when the processor executes the big data based heating control method program, the following steps are implemented:
acquiring current factor parameter information and physical condition information of a target user, and generating heating curve intervention information according to the factor parameter information and the physical condition information;
acquiring food material type information, and acquiring a cooking heating curve of food materials through big data analysis according to the food material type information and the food material cooking mode information;
matching heating curve intervention information according to the cooking heating curve to generate an optimal heating curve for heating and cooking;
feedback information of the target user on the food quality is obtained, and the optimal heating curve is optimized according to the comparison between the feedback information and the current optimal heating curve.
When the heating curve indicates the relationship between the heating temperature and the heating time during cooking of the food material, the intelligent cooking device may be a device such as an electric cooker or an induction cooker that cooks the food material according to the temperature.
The factor parameter information includes one or a combination of two or more of latitude information, poster information, air pressure information, environment temperature information and circuit voltage information; the physical condition information of the target user comprises one or the combination of more than two of age information, health information, meal habit information and illness condition information.
It should be noted that the generating of the heating curve intervention information according to the factor parameter information and the physical condition information specifically includes:
determining time, heating temperature and heating power of each program in the cooking process according to the factor parameter information and the physical condition information;
generating an initial heating curve according to the time, the heating temperature and the heating power of each program in the cooking process;
acquiring a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values of the initial heating curve and the preset heating curve;
subtracting the extracted corresponding values to obtain a difference curve, acquiring a difference value of the difference curve, and extracting curve characteristics of the difference curve;
and generating heating curve intervention information according to the difference value and the curve characteristics.
According to the embodiment of the present invention, the obtaining of the food material type information and the obtaining of the cooking heating curve of the food material through big data analysis according to the food material type information and the food material cooking mode information specifically include:
acquiring food material image information, preprocessing the image information, extracting image characteristics, searching and matching in a food material database according to the image characteristics, and identifying at least one food material type information according to a matching result;
extracting key words in the food material type information, and analyzing the key words by means of big data to obtain a food material heating curve data set;
searching in the food heating curve data set according to a cooking mode selected by a target user as a searching condition, and acquiring food quality information corresponding to each food heating curve in a searching result;
scoring the food quality information according to a preset rule, extracting food material heating curves with scores larger than a preset threshold value, and sorting the food material heating curves according to the scores to obtain food material heating curves corresponding to the highest scores;
and acquiring a preset heating curve corresponding to the cooking mode selected by the target user, and fitting the food material heating curve corresponding to the highest score with the preset heating curve to generate a cooking heating curve of the food material.
It should be noted that the method further includes: the heating condition of the food materials is monitored in the food material cooking process, and the heating curve of the food materials in the cooking process is adjusted, specifically:
monitoring the heating condition of the food material in the cooking process according to a preset sensor to obtain monitoring data information, and generating a real-time heating curve according to the monitoring data information;
decomposing the real-time heating curve according to a cooking process to obtain a plurality of curve segment groups, and acquiring average temperature information in each curve segment group;
segmenting the optimal heating curve according to a curve segment group segmentation rule to obtain target temperature information in each curve segment group;
comparing and analyzing the average temperature information and the target temperature information to generate a deviation ratio;
judging whether the deviation rate is greater than a deviation rate threshold value or not;
if the cooking time is larger than the preset cooking time, generating abnormal cooking information, generating correction information according to the abnormal cooking information, and adjusting the cooking time and the cooking duration through the correction information to realize the adjustment of the real-time heating curve.
It should be noted that, when a heating curve is used to express the relationship between the heating temperature and the heating time period, the heating curve is a combination of the heating temperature and the heating time period for each period. And dividing the cooking heating curve into a plurality of curve segments, and determining the target temperature of the curve segment group according to the temperature corresponding to each time point in the plurality of curve segments in the cooking heating curve. The method comprises the following steps of determining a target temperature of a curve segment group according to temperatures corresponding to each time point in a plurality of curve segments in a cooking heating curve, wherein the target temperature specifically comprises the following steps: and extracting the corresponding temperature of each time point in each curve segment group, accumulating the corresponding temperature of each time point in each curve segment group, dividing the accumulated value of the temperature by the number of time nodes in the curve segment group, and taking the obtained average temperature value as the target temperature corresponding to the curve segment group.
According to the embodiment of the invention, feedback information of a household appliance user on food quality is obtained, and the optimal heating curve is optimized according to the comparison between the feedback information and the optimal heating curve, which specifically comprises the following steps:
the cloud server sends the food quality questionnaire to the target user in a preset mode in a questionnaire survey mode;
obtaining questionnaire feedback data of a target user, processing and analyzing the questionnaire feedback data, and generating a current food quality satisfaction degree score;
presetting a satisfaction score threshold, and comparing the food quality satisfaction score with the preset threshold;
and if the food quality satisfaction score is smaller than a preset threshold value, generating feedback information according to questionnaire feedback data, and comparing the feedback information with the optimal heating curve to optimize the current optimal heating curve.
According to the embodiment of the invention, the heating condition of the food material in the cooking process is monitored according to the preset sensor, the food quality is represented through the monitoring data information of each cooking stage, and a function relation is formed according to the time and the average temperature in the cooking stage, wherein the function of the food quality can be represented as follows:
wherein p represents the food quality, i represents the number of items in the cooking stage, n represents the total number of the cooking stages, λ represents the deformation coefficient of the heated food material, T represents the average temperature in the cooking stage, and T represents the average temperature in the cooking stagecA reference temperature representing a cooking stage, beta a food material ripening quality factorReflecting the sensitivity of the food to temperature changes, and t represents the corresponding time parameter of the cooking stage.
According to the embodiment of the invention, the method further comprises the step of realizing the required adjustment on the cooking heating curve of the food material according to the taste requirement of the target user, and specifically comprises the following steps:
generating a demand label according to the demand of a target user, and capturing a demand data set in a database through the demand label;
generating preference characteristics through the demand data set, and determining a similarity threshold interval according to the preference characteristics;
calculating the similarity between a data set and a demand data set in a database by a preset calculation method, and taking the data set falling in the similarity threshold interval as a demand data set;
extracting a cooking heating curve in a demand data set, and extracting heating temperature information and cooking stage time information according to the cooking heating curve;
and updating heating curve intervention information according to the heating temperature information and the cooking stage time information, and matching the cooking heating curve according to the updated heating curve intervention information to generate an optimal heating curve.
According to the embodiment of the invention, when the cooking equipment cooks a plurality of food materials, the cooking heating curves of the plurality of food materials are aggregated, specifically:
generating recipe information according to the requirements of a target user, extracting food materials required for cooking according to the recipe information, and obtaining a cooking heating curve of the food materials required through big data analysis;
acquiring cooking time of different cooking stages of different food materials according to the cooking heating curve, sequencing the cooking time in a preset cooking stage, generating a putting sequence of the different food materials according to the sequencing, and displaying the putting sequence according to a preset mode;
extracting curve characteristic points of the cooking heating curve of the required food material, generating characteristic point matching pairs, calculating included angle angles between a connecting line of the characteristic points in the matching pairs and the horizontal direction and distances between the characteristic points, and generating an included angle set and a distance set;
and performing curve fitting according to the angle combination of the included angles and the distance set, and performing curve correction to generate an optimal heating curve.
It should be noted that, when a user adds a required food material to the cooking device at the same time, the cooking device acquires food material image information, identifies at least one type of food material information according to the food material image information, generates food material identification information from a food material identification result, and obtains a food material identification rate through feedback of a target user on the food material identification information; and when the food material recognition rate is smaller than a preset threshold value, automatically updating an algorithm for controlling food material recognition.
According to the embodiment of the invention, when the cooking equipment identifies the food materials, the freezing state of the food materials is identified, and the optimal heating curve is matched according to the freezing state, specifically:
the method comprises the steps of obtaining hyperspectral image information of food materials in cooking equipment, preprocessing the hyperspectral image information, selecting an interested area, and obtaining the spectral reflectivity of the interested area at a preset wavelength;
identifying fresh food materials and frozen food materials according to the spectral reflectivity, setting a spectral reflectivity threshold interval, judging the freezing degree of the frozen food materials according to the color degree of the food materials and the threshold interval in which the spectral reflectivity falls, and acquiring a cooking heating curve of the food materials according to the freezing degree;
determining the time length of a thawing stage and the thawing heating temperature of the food material according to the freezing degree through big data, adding the thawing stage into the cooking process of the food material in a self-defined mode, and generating an optimal heating curve of the food material by combining the cooking heating curve of the food material;
and in the process of cooking according to the optimal heating curve, monitoring the maturity of the food materials, and carrying out real-time optimization and correction on the optimal heating curve according to the maturity.
It should be noted that optionally, the cooking equipment and the intelligent refrigerator are networked and connected through the internet of things technology, after the cooking equipment completes food identification, food identification results are sent to the intelligent refrigerator, the intelligent refrigerator extracts storage time of food materials in the refrigerator through the food identification results, freshness of the food materials is generated according to the storage time, the freshness is fed back to the cloud server, and the cloud server generates an optimal heating curve according to the freshness of the food materials and a cooking heating curve of the food materials.
The third aspect of the present invention further provides a computer-readable storage medium, which includes a big data-based heating control method program, and when the big data-based heating control method program is executed by a processor, the steps of the big data-based heating control method described in any one of the above are implemented.
The invention discloses a heating control method, a heating control system and a storage medium based on big data, and relates to the technical field of intelligent control, wherein the heating control method comprises the following steps: the method comprises the steps of obtaining current factor parameter information and physical condition information of a target user, generating heating curve intervention information according to the factor parameter information and the physical condition information, obtaining food material type information, obtaining a cooking heating curve of food materials through big data analysis according to the food material type information and the food material cooking mode information, generating an optimal heating curve according to the fact that the cooking heating curve is matched with the heating curve intervention information to perform heating cooking, obtaining feedback information of the target user on food quality, and optimizing the heating curve according to the comparison between the feedback information and the current heating curve. According to the method, the optimal heating curve of the food material is obtained through big data analysis, so that the taste of the food material is improved, and the nutritional ingredients are kept.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A heating control method based on big data is characterized by comprising the following steps:
acquiring current factor parameter information and physical condition information of a target user, and generating heating curve intervention information according to the factor parameter information and the physical condition information;
acquiring food material type information, and acquiring a cooking heating curve of food materials through big data analysis according to the food material type information and the food material cooking mode information;
matching heating curve intervention information according to the cooking heating curve to generate an optimal heating curve for heating and cooking;
feedback information of the target user on the food quality is obtained, and the optimal heating curve is optimized according to the comparison between the feedback information and the current optimal heating curve.
2. The big data based heating control method according to claim 1, wherein the factor parameter information comprises one or a combination of two or more of latitude information, altitude information, barometric pressure information, ambient temperature information, and circuit voltage information; the physical condition information of the target user comprises one or the combination of more than two of age information, health information, meal habit information and illness condition information.
3. The heating control method based on big data as claimed in claim 1, wherein the generating of the heating curve intervention information according to the factor parameter information and the physical condition information comprises:
determining time, heating temperature and heating power of each program in the cooking process according to the factor parameter information and the physical condition information;
generating an initial heating curve according to the time, the heating temperature and the heating power of each program in the cooking process;
acquiring a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values of the initial heating curve and the preset heating curve;
subtracting the extracted corresponding values to obtain a difference curve, acquiring a difference value of the difference curve, and extracting curve characteristics of the difference curve;
and generating heating curve intervention information according to the difference value and the curve characteristics.
4. The big-data-based heating control method according to claim 1, wherein the obtaining of the food material type information is used for obtaining a cooking heating curve of the food material through big data analysis according to the food material type information and the food material cooking mode information, and specifically comprises:
acquiring food material image information, preprocessing the image information, extracting image characteristics, searching and matching in a food material database according to the image characteristics, and identifying at least one food material type information according to a matching result;
extracting key words in the food material type information, and analyzing the key words by means of big data to obtain a food material heating curve data set;
searching in the food heating curve data set according to a cooking mode selected by a target user as a searching condition, and acquiring food quality information corresponding to each food heating curve in a searching result;
scoring the food quality information according to a preset rule, extracting food material heating curves with scores larger than a preset threshold value, and sorting the food material heating curves according to the scores to obtain food material heating curves corresponding to the highest scores;
and acquiring a preset heating curve corresponding to the cooking mode selected by the target user, and fitting the food material heating curve corresponding to the highest score with the preset heating curve to generate a cooking heating curve of the food material.
5. The big data based heating control method according to claim 1, further comprising: the heating condition of the food materials is monitored in the food material cooking process, and the heating curve of the food materials in the cooking process is adjusted, specifically:
monitoring the heating condition of the food material in the cooking process according to a preset sensor to obtain monitoring data information, and generating a real-time heating curve according to the monitoring data information;
decomposing the real-time heating curve according to a cooking process to obtain a plurality of curve segment groups, and acquiring average temperature information in each curve segment group;
segmenting the optimal heating curve according to a curve segment group segmentation rule to obtain target temperature information in each curve segment group;
comparing and analyzing the average temperature information and the target temperature information to generate a deviation ratio;
judging whether the deviation rate is greater than a deviation rate threshold value or not;
if the cooking time is larger than the preset cooking time, generating abnormal cooking information, generating correction information according to the abnormal cooking information, and adjusting the cooking time and the cooking duration through the correction information to realize the adjustment of the real-time heating curve.
6. The heating control method according to claim 1, wherein feedback information of a user of the home appliance on the quality of the food is obtained, and the optimal heating curve is optimized by comparing the feedback information with the optimal heating curve, specifically:
the cloud server sends the food quality questionnaire to the target user in a preset mode in a questionnaire survey mode;
obtaining questionnaire feedback data of a target user, processing and analyzing the questionnaire feedback data, and generating a current food quality satisfaction degree score;
presetting a satisfaction score threshold, and comparing the food quality satisfaction score with the preset threshold;
and if the food quality satisfaction score is smaller than a preset threshold value, generating feedback information according to questionnaire feedback data, and comparing the feedback information with the optimal heating curve to optimize the current optimal heating curve.
7. A big data based heating control system, the system comprising: the heating control method based on big data comprises a memory and a processor, wherein the memory comprises a program of the heating control method based on big data, and when the program of the heating control method based on big data is executed by the processor, the following steps are realized:
acquiring current factor parameter information and physical condition information of a target user, and generating heating curve intervention information according to the factor parameter information and the physical condition information;
acquiring food material type information, and acquiring a cooking heating curve of food materials through big data analysis according to the food material type information and the food material cooking mode information;
matching heating curve intervention information according to the cooking heating curve to generate an optimal heating curve for heating and cooking;
feedback information of the target user on the food quality is obtained, and the optimal heating curve is optimized according to the comparison between the feedback information and the current optimal heating curve.
8. The big data based heating control system according to claim 7, wherein the heating curve intervention information is generated according to the factor parameter information and the physical condition information, and specifically comprises:
determining time, heating temperature and heating power of each program in the cooking process according to the factor parameter information and the physical condition information;
generating an initial heating curve according to the time, the heating temperature and the heating power of each program in the cooking process;
acquiring a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values of the initial heating curve and the preset heating curve;
subtracting the extracted corresponding values to obtain a difference curve, acquiring a difference value of the difference curve, and extracting curve characteristics of the difference curve;
and generating heating curve intervention information according to the difference value and the curve characteristics.
9. The big-data-based heating control system of claim 7, wherein the obtaining of the food material type information is used for obtaining a cooking heating curve of the food material through big data analysis according to the food material type information and the food material cooking mode information, and specifically comprises:
acquiring food material image information, preprocessing the image information, extracting image characteristics, searching and matching in a food material database according to the image characteristics, and identifying at least one food material type information according to a matching result;
extracting key words in the food material type information, and analyzing the key words by means of big data to obtain a food material heating curve data set;
searching in the food heating curve data set according to a cooking mode selected by a target user as a searching condition, and acquiring food quality information corresponding to each food heating curve in a searching result;
scoring the food quality information according to a preset rule, extracting food material heating curves with scores larger than a preset threshold value, and sorting the food material heating curves according to the scores to obtain food material heating curves corresponding to the highest scores;
and acquiring a preset heating curve corresponding to the cooking mode selected by the target user, and fitting the food material heating curve corresponding to the highest score with the preset heating curve to generate a cooking heating curve of the food material.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium includes a big data-based heating control method program, and when the big data-based heating control method program is executed by a processor, the steps of a big data-based heating control method according to any one of claims 1 to 6 are implemented.
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| CN114711644A (en) * | 2022-04-01 | 2022-07-08 | 广东美的厨房电器制造有限公司 | Control method and control device of cooking device, storage medium and cooking device |
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| CN114711644A (en) * | 2022-04-01 | 2022-07-08 | 广东美的厨房电器制造有限公司 | Control method and control device of cooking device, storage medium and cooking device |
| CN114711644B (en) * | 2022-04-01 | 2023-09-22 | 广东美的厨房电器制造有限公司 | Control method and control device for cooking device, storage medium and cooking device |
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