CN113951734A - Heating control method and system based on big data and storage medium - Google Patents
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Abstract
本发明公开了一种基于大数据的加热控制方法、系统和存储介质，涉及智能控制技术领域，其中加热控制方法包括：获取当前因素参数信息及目标用户的身体状况信息，根据所述因素参数信息及身体状况信息生成加热曲线干预信息，获取食材种类信息，根据所述食材种类信息及食材烹饪模式信息通过大数据分析获取食材的烹饪加热曲线，并根据所述烹饪加热曲线匹配加热曲线干预信息生成最佳加热曲线进行加热烹饪，获取目标用户对食物品质的反馈信息，根据所述反馈信息与当前加热曲线比较，对加热曲线进行优化。本发明通过大数据分析获取食材的最佳加热曲线，实现食材的口感提升，并保持营养成分。
The invention discloses a heating control method, system and storage medium based on big data, and relates to the technical field of intelligent control. The heating control method includes: acquiring current factor parameter information and physical condition information of a target user, and physical condition information to generate heating curve intervention information, obtain ingredient type information, obtain the cooking heating curve of the ingredient through big data analysis according to the ingredient type information and ingredient cooking mode information, and generate the heating curve intervention information according to the cooking heating curve matching heating curve The best heating curve is used for heating and cooking, the feedback information of the target user on the food quality is obtained, and the heating curve is optimized according to the comparison between the feedback information and the current heating curve. The present invention obtains the best heating curve of the ingredients through big data analysis, realizes the improvement of the taste of the ingredients, and maintains the nutritional components.
Description
技术领域technical field
本发明涉及智能控制技术领域，更具体的，涉及一种基于大数据的加热控制方法、系统和存储介质。The invention relates to the technical field of intelligent control, and more particularly, to a heating control method, system and storage medium based on big data.
背景技术Background technique
随着生活水平的不断提升,大环境网络时代的背景下,小家电正在朝智能化的方向发展。而在智能小家电研发中，开发功能多样、操作简单、方便、快捷的智能小家电成为各种智能小家电开发的新方向。然而目前市场上的小家电产品功能繁杂,并没有针对用户需求进行设计。在小家电品类中传统的烹饪设备具备的烹饪功能比较固定，而用户的个人基本状况信息及口味偏好信息千差万别，并且在烹饪设备的实际使用中用户往往因为烹饪加热时间把握不当导致食物品质不佳，因此根据用户的预设信息为食材匹配最佳加热曲线显得尤为重要。With the continuous improvement of living standards, under the background of the era of large environment and network, small household appliances are developing in the direction of intelligence. In the research and development of smart small household appliances, the development of smart small household appliances with diverse functions, simple operation, convenience and speed has become a new direction for the development of various smart small household appliances. However, the functions of small household appliances on the market are complicated, and they are not designed according to the needs of users. In the category of small household appliances, traditional cooking equipment has relatively fixed cooking functions, while users' personal basic status information and taste preference information vary widely, and in the actual use of cooking equipment, users often lead to poor food quality due to improper cooking and heating time. , so it is particularly important to match the optimal heating curve for the ingredients according to the user's preset information.
为了能够通过智能烹饪设备实现对食材的智能加热控制，需要开发一款系统与之配合进行实现，该系统通过获取当前因素参数信息及目标用户的身体状况信息生成加热曲线干预信息，根据食材种类信息及食材烹饪模式信息通过大数据分析获取食材的加热曲线，根据加热曲线匹配加热曲线干预信息生成最佳加入曲线进行加热烹饪。在该系统的实现过程中，如何基于用户的预设信息通过大数据分析获取食材的加热曲线是亟不可待需要解决的问题。In order to realize the intelligent heating control of ingredients through intelligent cooking equipment, it is necessary to develop a system to cooperate with it. And the cooking mode information of the ingredients is obtained through big data analysis to obtain the heating curve of the ingredients, and the optimal addition curve is generated according to the heating curve matching the heating curve intervention information for heating and cooking. In the implementation process of the system, how to obtain the heating curve of the ingredients through big data analysis based on the user's preset information is an urgent problem that needs to be solved.
发明内容SUMMARY OF THE INVENTION
为了解决上述至少一个技术问题，本发明提出了一种基于大数据的加热控制方法、系统及存储介质。In order to solve at least one of the above technical problems, the present invention provides a heating control method, system and storage medium based on big data.
本发明第一方面提供了一种基于大数据的加热控制方法，包括：A first aspect of the present invention provides a heating control method based on big data, comprising:
获取当前因素参数信息及目标用户的身体状况信息，根据所述因素参数信息及身体状况信息生成加热曲线干预信息；Obtain current factor parameter information and target user's physical condition information, and generate heating curve intervention information according to the factor parameter information and physical condition information;
获取食材种类信息，根据所述食材种类信息及食材烹饪模式信息通过大数据分析获取食材的烹饪加热曲线；Obtaining ingredient type information, and obtaining the cooking heating curve of the ingredient through big data analysis according to the ingredient type information and the ingredient cooking mode information;
根据所述烹饪加热曲线匹配加热曲线干预信息生成最佳加热曲线进行加热烹饪；According to the cooking heating curve matching heating curve intervention information, generate an optimal heating curve for heating and cooking;
获取目标用户对食物品质的反馈信息，根据所述反馈信息与当前最佳加热曲线比较，对最佳加热曲线进行优化。The feedback information on the food quality from the target user is obtained, and the optimal heating curve is optimized according to the feedback information compared with the current optimal heating curve.
本方案中，所述因素参数信息包括纬度信息、海报信息、气压信息、环境温度信息、电路电压信息的一种或两种及以上的组合；所述目标用户的身体状况信息包括年龄信息、健康信息、膳食习惯信息、患病情况信息的一种或两种以上的组合。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 physical condition information of the target user includes age information, health One or a combination of two or more of information, dietary habits information, and disease information.
本方案中，所述的根据所述因素参数信息及身体状况信息生成加热曲线干预信息，具体为：In this solution, the generation of the heating curve intervention information according to the factor parameter information and the physical condition information is specifically:
通过所述因素参数信息及身体状况信息确定烹饪过程中各项程序的时间、加热温度及加热功率；Determine the time, heating temperature and heating power of each program in the cooking process through the factor parameter information and the physical condition information;
根据所述烹饪过程中各项程序的时间、加热温度及加热功率生成初始加热曲线；Generate an initial heating curve according to the time, heating temperature and heating power of each program in the cooking process;
获取预设烹饪模式对应的预设加热曲线，将所述初始加热曲线及所述预设加热曲线进行对应值提取；obtaining a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values of the initial heating curve and the preset heating curve;
将提取到的对应值进行相减得到差值曲线，获取差值曲线差值，并提取差值曲线的曲线特征；Subtract the extracted corresponding values to obtain the difference curve, obtain the difference value of the difference curve, and extract the curve characteristics of the difference curve;
根据所述差值及所述曲线特征生成加热曲线干预信息。Heating curve intervention information is generated according to the difference value and the curve characteristic.
本方案中，所述的获取食材种类信息，根据所述食材种类信息及食材烹饪模式信息通过大数据分析获取食材的烹饪加热曲线，具体为：In this solution, in the acquisition of the type information of the ingredients, the cooking heating curve of the ingredients is obtained through big data analysis according to the type information of the ingredients and the cooking mode information of the ingredients, specifically:
获取食材图像信息，对图像信息进行预处理并进行图像特征提取，根据所述图像特征在食材数据库中检索并进行匹配，根据匹配结果识别出至少一种食材种类信息；Obtaining food image information, preprocessing the image information and extracting image features, retrieving and matching in the food database according to the image features, and identifying at least one food type information according to the matching result;
提取所述食材种类信息中的关键词，将所述关键词借助大数据分析获取食材加热曲线数据集；Extracting the keywords in the type information of the ingredients, and using the keywords to analyze the heating curve data set of the ingredients with the help of big data;
根据目标用户选择的烹饪模式作为检索条件在所述食材加热曲线数据集中进行检索，获取检索结果中各食材加热曲线对应的食物品质信息；According to the cooking mode selected by the target user as the retrieval condition, the retrieval is performed in the food material heating curve data set, and the food quality information corresponding to each food material heating curve in the retrieval result is obtained;
将所述食物品质信息按照预设规则进行评分，将评分大于预设阈值的食材加热曲线进行提取并按照评分进行排序，获取最高评分对应的食材加热曲线；Scoring the food quality information according to a preset rule, extracting the heating curves of the ingredients whose scores are greater than a preset threshold, and sorting them according to the scores, to obtain the heating curves of the ingredients corresponding to the highest scores;
获取目标用户选择的烹饪模式对应的预设加热曲线，将所述最高评分对应的食材加热曲线与预设加热曲线进行拟合，生成食材的烹饪加热曲线。The preset heating curve corresponding to the cooking mode selected by the target user is acquired, and the heating curve of the ingredient corresponding to the highest score is fitted with the preset heating curve to generate the cooking heating curve of the ingredient.
本方案中，还包括：在食材烹饪过程中对食材的受热状况进行监测，对食材在烹饪过程中的加热曲线进行调整，具体为：This solution also includes: monitoring the heating state of the ingredients during the cooking process, and adjusting the heating curve of the ingredients during the cooking process, specifically:
根据预设传感器对食材烹饪过程中的受热状况进行监测，得到监测数据信息，根据所述监测数据信息生成实时加热曲线；Monitoring the heating state of the ingredients during the cooking process according to a preset sensor, obtaining monitoring data information, and generating a real-time heating curve according to the monitoring data information;
将所述实时加热曲线根据烹饪过程进行分解，得到多个曲线段组，获取各曲线段组中的平均温度信息；Decomposing the real-time heating curve according to the cooking process, obtaining a plurality of curve segment groups, and acquiring the average temperature information in each curve segment group;
根据曲线段组分割规则对最佳加热曲线进行分段，获取各曲线段组中的目标温度信息；Segment the optimal heating curve according to the curve segment group segmentation rule, and obtain the target temperature information in each curve segment group;
将所述平均温度信息与所述目标温度信息进行对比分析，生成偏差率；Carrying out comparative analysis on the average temperature information and the target temperature information to generate a deviation rate;
判断所述偏差率是否大于偏差率阈值；judging whether the deviation rate is greater than the deviation rate threshold;
若大于，则生成烹饪异常信息，根据所述烹饪异常信息生成修正信息，通过修正信息调整烹饪时间和烹饪火候大小，实现对实时加热曲线的调整。If it is greater than the value, cooking abnormality information is generated, correction information is generated according to the cooking abnormality information, and the cooking time and the cooking heat are adjusted by the correction information, so as to realize the adjustment of the real-time heating curve.
本方案中，获取家电设备使用者对食物品质的反馈信息，根据所述反馈信息与最佳加热曲线比较，对最佳加热曲线进行优化，具体为：In this solution, the feedback information on the food quality from the user of the household appliance is obtained, and the optimal heating curve is optimized according to the feedback information compared with the optimal heating curve, specifically:
云端服务器通过问卷调查方式将食物品质调查问卷通过预设方式发送给目标用户；The cloud server sends the food quality questionnaire to the target user in a preset way by means of a questionnaire survey;
获取目标用户的问卷反馈数据，将所述问卷反馈数据进行处理分析，生成对当前食物品质满意度得分；Obtain the questionnaire feedback data of the target user, process and analyze the questionnaire feedback data, and generate a satisfaction score for the current food quality;
预设满意度得分阈值，将所述食物品质满意度得分与预设阈值进行比较；Presetting a satisfaction score threshold, and comparing the food quality satisfaction score with a preset threshold;
若食物品质满意度得分小于预设阈值，则根据问卷反馈数据生成反馈信息，根据所述反馈信息与最佳加热曲线比较，对当前最佳加热曲线进行优化。If the food quality satisfaction score is less than the preset threshold, feedback information is generated according to the questionnaire feedback data, and the current optimal heating curve is optimized according to the feedback information compared with the optimal heating curve.
本发明第二方面还提供了一种基于大数据的加热控制系统，该系统包括：存储器、处理器，所述存储器中包括一种基于大数据的加热控制方法程序，所述一种基于大数据的加热控制方法程序被所述处理器执行时实现如下步骤：A second aspect of the present invention also provides a heating control system based on big data, the system comprising: a memory and a processor, wherein the memory includes a program of a heating control method based on big data, the big data-based heating control method program When the program of the heating control method is executed by the processor, the following steps are realized:
获取当前因素参数信息及目标用户的身体状况信息，根据所述因素参数信息及身体状况信息生成加热曲线干预信息；Obtain current factor parameter information and target user's physical condition information, and generate heating curve intervention information according to the factor parameter information and physical condition information;
获取食材种类信息，根据所述食材种类信息及食材烹饪模式信息通过大数据分析获取食材的烹饪加热曲线；Obtaining ingredient type information, and obtaining the cooking heating curve of the ingredient through big data analysis according to the ingredient type information and the ingredient cooking mode information;
根据所述烹饪加热曲线匹配加热曲线干预信息生成最佳加热曲线进行加热烹饪；According to the cooking heating curve matching heating curve intervention information, generate an optimal heating curve for heating and cooking;
获取目标用户对食物品质的反馈信息，根据所述反馈信息与当前最佳加热曲线比较，对最佳加热曲线进行优化。The feedback information on the food quality from the target user is obtained, and the optimal heating curve is optimized according to the feedback information compared with the current optimal heating curve.
本方案中，所述因素参数信息包括纬度信息、海报信息、气压信息、环境温度信息、电路电压信息的一种或两种及以上的组合；所述目标用户的身体状况信息包括年龄信息、健康信息、膳食习惯信息、患病情况信息的一种或两种以上的组合。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 physical condition information of the target user includes age information, health One or a combination of two or more of information, dietary habits information, and disease information.
本方案中，所述的根据所述因素参数信息及身体状况信息生成加热曲线干预信息，具体为：In this solution, the generation of the heating curve intervention information according to the factor parameter information and the physical condition information is specifically:
通过所述因素参数信息及身体状况信息确定烹饪过程中各项程序的时间、加热温度及加热功率；Determine the time, heating temperature and heating power of each program in the cooking process through the factor parameter information and the physical condition information;
根据所述烹饪过程中各项程序的时间、加热温度及加热功率生成初始加热曲线；Generate an initial heating curve according to the time, heating temperature and heating power of each program in the cooking process;
获取预设烹饪模式对应的预设加热曲线，将所述初始加热曲线及所述预设加热曲线进行对应值提取；obtaining a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values of the initial heating curve and the preset heating curve;
将提取到的对应值进行相减得到差值曲线，获取差值曲线差值，并提取差值曲线的曲线特征；Subtract the extracted corresponding values to obtain the difference curve, obtain the difference value of the difference curve, and extract the curve characteristics of the difference curve;
根据所述差值及所述曲线特征生成加热曲线干预信息。Heating curve intervention information is generated according to the difference value and the curve characteristic.
本方案中，所述的获取食材种类信息，根据所述食材种类信息及食材烹饪模式信息通过大数据分析获取食材的烹饪加热曲线，具体为：In this solution, in the acquisition of the type information of the ingredients, the cooking heating curve of the ingredients is obtained through big data analysis according to the type information of the ingredients and the cooking mode information of the ingredients, specifically:
获取食材图像信息，对图像信息进行预处理并进行图像特征提取，根据所述图像特征在食材数据库中检索并进行匹配，根据匹配结果识别出至少一种食材种类信息；Obtaining food image information, preprocessing the image information and extracting image features, retrieving and matching in the food database according to the image features, and identifying at least one food type information according to the matching result;
提取所述食材种类信息中的关键词，将所述关键词借助大数据分析获取食材加热曲线数据集；Extracting the keywords in the type information of the ingredients, and using the keywords to analyze the heating curve data set of the ingredients with the help of big data;
根据目标用户选择的烹饪模式作为检索条件在所述食材加热曲线数据集中进行检索，获取检索结果中各食材加热曲线对应的食物品质信息；According to the cooking mode selected by the target user as the retrieval condition, the retrieval is performed in the food material heating curve data set, and the food quality information corresponding to each food material heating curve in the retrieval result is obtained;
将所述食物品质信息按照预设规则进行评分，将评分大于预设阈值的食材加热曲线进行提取并按照评分进行排序，获取最高评分对应的食材加热曲线；Scoring the food quality information according to a preset rule, extracting the heating curves of the ingredients whose scores are greater than a preset threshold, and sorting them according to the scores, to obtain the heating curves of the ingredients corresponding to the highest scores;
获取目标用户选择的烹饪模式对应的预设加热曲线，将所述最高评分对应的食材加热曲线与预设加热曲线进行拟合，生成食材的烹饪加热曲线。The preset heating curve corresponding to the cooking mode selected by the target user is acquired, and the heating curve of the ingredient corresponding to the highest score is fitted with the preset heating curve to generate the cooking heating curve of the ingredient.
本方案中，还包括：在食材烹饪过程中对食材的受热状况进行监测，对食材在烹饪过程中的加热曲线进行调整，具体为：This solution also includes: monitoring the heating state of the ingredients during the cooking process, and adjusting the heating curve of the ingredients during the cooking process, specifically:
根据预设传感器对食材烹饪过程中的受热状况进行监测，得到监测数据信息，根据所述监测数据信息生成实时加热曲线；Monitoring the heating state of the ingredients during the cooking process according to a preset sensor, obtaining monitoring data information, and generating a real-time heating curve according to the monitoring data information;
将所述实时加热曲线根据烹饪过程进行分解，得到多个曲线段组，获取各曲线段组中的平均温度信息；Decomposing the real-time heating curve according to the cooking process, obtaining a plurality of curve segment groups, and acquiring the average temperature information in each curve segment group;
根据曲线段组分割规则对最佳加热曲线进行分段，获取各曲线段组中的目标温度信息；Segment the optimal heating curve according to the curve segment group segmentation rule, and obtain the target temperature information in each curve segment group;
将所述平均温度信息与所述目标温度信息进行对比分析，生成偏差率；Carrying out comparative analysis on the average temperature information and the target temperature information to generate a deviation rate;
判断所述偏差率是否大于偏差率阈值；judging whether the deviation rate is greater than the deviation rate threshold;
若大于，则生成烹饪异常信息，根据所述烹饪异常信息生成修正信息，通过修正信息调整烹饪时间和烹饪火候大小，实现对实时加热曲线的调整。If it is greater than the value, cooking abnormality information is generated, correction information is generated according to the cooking abnormality information, and the cooking time and the cooking heat are adjusted by the correction information, so as to realize the adjustment of the real-time heating curve.
本方案中，获取家电设备使用者对食物品质的反馈信息，根据所述反馈信息与最佳加热曲线比较，对最佳加热曲线进行优化，具体为：In this solution, the feedback information on the food quality from the user of the household appliance is obtained, and the optimal heating curve is optimized according to the feedback information compared with the optimal heating curve, specifically:
云端服务器通过问卷调查方式将食物品质调查问卷通过预设方式发送给目标用户；The cloud server sends the food quality questionnaire to the target user in a preset way by means of a questionnaire survey;
获取目标用户的问卷反馈数据，将所述问卷反馈数据进行处理分析，生成对当前食物品质满意度得分；Obtain the questionnaire feedback data of the target user, process and analyze the questionnaire feedback data, and generate a satisfaction score for the current food quality;
预设满意度得分阈值，将所述食物品质满意度得分与预设阈值进行比较；Presetting a satisfaction score threshold, and comparing the food quality satisfaction score with a preset threshold;
若食物品质满意度得分小于预设阈值，则根据问卷反馈数据生成反馈信息，根据所述反馈信息与最佳加热曲线比较，对当前最佳加热曲线进行优化。If the food quality satisfaction score is less than the preset threshold, feedback information is generated according to the questionnaire feedback data, and the current optimal heating curve is optimized according to the feedback information compared with the optimal heating curve.
本发明第三方面还提供一种计算机可读存储介质，所述计算机可读存储介质中包括一种基于大数据的加热控制方法程序，所述一种基于大数据的加热控制方法程序被处理器执行时，实现如上述任一项所述的一种基于大数据的加热控制方法的步骤。A third aspect of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a big data-based heating control method program, and the big data-based heating control method program is processed by a processor When executed, the steps of a heating control method based on big data as described in any one of the above are realized.
本发明公开了一种基于大数据的加热控制方法、系统和存储介质，涉及智能控制技术领域，其中加热控制方法包括：获取当前因素参数信息及目标用户的身体状况信息，根据所述因素参数信息及身体状况信息生成加热曲线干预信息，获取食材种类信息，根据所述食材种类信息及食材烹饪模式信息通过大数据分析获取食材的烹饪加热曲线，并根据所述烹饪加热曲线匹配加热曲线干预信息生成最佳加热曲线进行加热烹饪，获取目标用户对食物品质的反馈信息，根据所述反馈信息与当前加热曲线比较，对加热曲线进行优化。本发明通过大数据分析获取食材的最佳加热曲线，实现食材的口感提升，并保持营养成分。The invention discloses a heating control method, system and storage medium based on big data, and relates to the technical field of intelligent control. The heating control method includes: acquiring current factor parameter information and physical condition information of a target user, and physical condition information to generate heating curve intervention information, obtain ingredient type information, obtain the cooking heating curve of the ingredient through big data analysis according to the ingredient type information and ingredient cooking mode information, and generate the heating curve intervention information according to the cooking heating curve matching heating curve The best heating curve is used for heating and cooking, the feedback information of the target user on the food quality is obtained, and the heating curve is optimized according to the comparison between the feedback information and the current heating curve. The invention obtains the best heating curve of the ingredients through big data analysis, realizes the improvement of the taste of the ingredients, and maintains the nutritional components.
附图说明Description of drawings
图1示出了本发明一种基于大数据的加热控制方法的流程图。Fig. 1 shows a flow chart of a heating control method based on big data of the present invention.
图2示出了本发明获取食材的烹饪加热曲线的方法流程图。Fig. 2 shows a flow chart of a method for obtaining a cooking heating curve of an ingredient according to the present invention.
图3示出了本发明对最佳加热曲线进行优化的方法流程图。Fig. 3 shows a flow chart of the method of the present invention for optimizing the optimum heating curve.
图4示出了本发明一种基于大数据的加热控制系统的框图。Fig. 4 shows a block diagram of a heating control system based on big data of the present invention.
具体实施方式Detailed ways
为了能够更清楚地理解本发明的上述目的、特征和优点，下面结合附图和具体实施方式对本发明进行进一步的详细描述。需要说明的是，在不冲突的情况下，本申请的实施例及实施例中的特征可以相互组合。In order to understand the above objects, features and advantages of the present invention more clearly, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments may be combined with each other in the case of no conflict.
在下面的描述中阐述了很多具体细节以便于充分理解本发明，但是，本发明还可以采用其他不同于在此描述的其他方式来实施，因此，本发明的保护范围并不受下面公开的具体实施例的限制。Many specific details are set forth in the following description to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific details disclosed below. Example limitations.
图1示出了本发明一种基于大数据的加热控制方法的流程图。Fig. 1 shows a flow chart of a heating control method based on big data of the present invention.
如图1所示，本发明第一方面提供了一种基于大数据的加热控制方法，包括：As shown in FIG. 1, a first aspect of the present invention provides a heating control method based on big data, including:
S102，获取当前因素参数信息及目标用户的身体状况信息，根据所述因素参数信息及身体状况信息生成加热曲线干预信息；S102, obtaining current factor parameter information and physical condition information of the target user, and generating heating curve intervention information according to the factor parameter information and physical condition information;
S104，获取食材种类信息，根据所述食材种类信息及食材烹饪模式信息通过大数据分析获取食材的烹饪加热曲线；S104, obtain the type information of the ingredients, and obtain the cooking heating curve of the ingredients through big data analysis according to the type information of the ingredients and the cooking mode information of the ingredients;
S106，根据所述烹饪加热曲线匹配加热曲线干预信息生成最佳加热曲线进行加热烹饪；S106, generating an optimal heating curve for heating and cooking according to the cooking heating curve matching the heating curve intervention information;
S108，获取目标用户对食物品质的反馈信息，根据所述反馈信息与当前最佳加热曲线比较，对最佳加热曲线进行优化。S108: Obtain feedback information on the food quality from the target user, and optimize the optimal heating curve according to the feedback information compared with the current optimal heating curve.
需要说明的是，在加热曲线表示的是食材烹饪时的加热温度和加热时长的关系时，智能烹饪设备可以是利用温度高低对食材进行烹饪的电饭煲、电磁炉等设备。It should be noted that, when the heating curve represents the relationship between the heating temperature and the heating duration of the ingredients during cooking, the intelligent cooking device may be an electric rice cooker, an induction cooker, or other devices that use the temperature to cook the ingredients.
需要说明的是，所述因素参数信息包括纬度信息、海报信息、气压信息、环境温度信息、电路电压信息的一种或两种及以上的组合；所述目标用户的身体状况信息包括年龄信息、健康信息、膳食习惯信息、患病情况信息的一种或两种以上的组合。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 age information, One or a combination of two or more of health information, dietary habit information, and disease information.
需要说明的是，所述的根据所述因素参数信息及身体状况信息生成加热曲线干预信息，具体为：It should be noted that the generation of the heating curve intervention information according to the factor parameter information and the physical condition information is specifically:
通过所述因素参数信息及身体状况信息确定烹饪过程中各项程序的时间、加热温度及加热功率；Determine the time, heating temperature and heating power of each program in the cooking process through the factor parameter information and the physical condition information;
根据所述烹饪过程中各项程序的时间、加热温度及加热功率生成初始加热曲线；Generate an initial heating curve according to the time, heating temperature and heating power of each program in the cooking process;
获取预设烹饪模式对应的预设加热曲线，将所述初始加热曲线及所述预设加热曲线进行对应值提取；obtaining a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values of the initial heating curve and the preset heating curve;
将提取到的对应值进行相减得到差值曲线，获取差值曲线差值，并提取差值曲线的曲线特征；Subtract the extracted corresponding values to obtain the difference curve, obtain the difference value of the difference curve, and extract the curve characteristics of the difference curve;
根据所述差值及所述曲线特征生成加热曲线干预信息。Heating curve intervention information is generated according to the difference value and the curve characteristic.
图2示出了本发明获取食材的烹饪加热曲线的方法流程图。Fig. 2 shows a flow chart of a method for obtaining a cooking heating curve of an ingredient according to the present invention.
根据本发明实施例，所述的获取食材种类信息，根据所述食材种类信息及食材烹饪模式信息通过大数据分析获取食材的烹饪加热曲线，具体为：According to an embodiment of the present invention, in the acquisition of the type information of the ingredients, the cooking and heating curves of the ingredients are obtained through big data analysis according to the type information of the ingredients and the cooking mode information of the ingredients, specifically:
S202，获取食材图像信息，对图像信息进行预处理并进行图像特征提取，根据所述图像特征在食材数据库中检索并进行匹配，根据匹配结果识别出至少一种食材种类信息；S202, acquiring image information of food materials, preprocessing the image information and extracting image features, retrieving and matching in the food materials database according to the image features, and identifying at least one food type information according to the matching results;
S204，提取所述食材种类信息中的关键词，将所述关键词借助大数据分析获取食材加热曲线数据集；S204, extracting the keywords in the type information of the ingredients, and obtaining the heating curve data set of ingredients by analyzing the keywords with the help of big data;
S206，根据目标用户选择的烹饪模式作为检索条件在所述食材加热曲线数据集中进行检索，获取检索结果中各食材加热曲线对应的食物品质信息；S206, according to the cooking mode selected by the target user as the retrieval condition, perform retrieval in the data set of the heating curve of the ingredients, and obtain the food quality information corresponding to the heating curve of each ingredient in the retrieval result;
S208，将所述食物品质信息按照预设规则进行评分，将评分大于预设阈值的食材加热曲线进行提取并按照评分进行排序，获取最高评分对应的食材加热曲线；S208, scoring the food quality information according to a preset rule, extracting the heating curves of the ingredients whose scores are greater than a preset threshold, and sorting them according to the scores, to obtain the heating curves of the ingredients corresponding to the highest scores;
S210，获取目标用户选择的烹饪模式对应的预设加热曲线，将所述最高评分对应的食材加热曲线与预设加热曲线进行拟合，生成食材的烹饪加热曲线。S210: Acquire a preset heating curve corresponding to the cooking mode selected by the target user, and fit the heating curve of the ingredient corresponding to the highest score with the preset heating curve to generate a cooking heating curve of the ingredient.
需要说明的是，还包括：在食材烹饪过程中对食材的受热状况进行监测，对食材在烹饪过程中的加热曲线进行调整，具体为：It should be noted that it also includes: monitoring the heating condition of the ingredients during the cooking process, and adjusting the heating curve of the ingredients during the cooking process, specifically:
根据预设传感器对食材烹饪过程中的受热状况进行监测，得到监测数据信息，根据所述监测数据信息生成实时加热曲线；Monitoring the heating state of the ingredients during the cooking process according to a preset sensor, obtaining monitoring data information, and generating a real-time heating curve according to the monitoring data information;
将所述实时加热曲线根据烹饪过程进行分解，得到多个曲线段组，获取各曲线段组中的平均温度信息；Decomposing the real-time heating curve according to the cooking process, obtaining a plurality of curve segment groups, and acquiring the average temperature information in each curve segment group;
根据曲线段组分割规则对最佳加热曲线进行分段，获取各曲线段组中的目标温度信息；Segment the optimal heating curve according to the curve segment group segmentation rule, and obtain the target temperature information in each curve segment group;
将所述平均温度信息与所述目标温度信息进行对比分析，生成偏差率；Carrying out comparative analysis on the average temperature information and the target temperature information to generate a deviation rate;
判断所述偏差率是否大于偏差率阈值；judging whether the deviation rate is greater than the deviation rate threshold;
若大于，则生成烹饪异常信息，根据所述烹饪异常信息生成修正信息，通过修正信息调整烹饪时间和烹饪火候大小，实现对实时加热曲线的调整。If it is greater than the value, cooking abnormality information is generated, correction information is generated according to the cooking abnormality information, and the cooking time and the cooking heat are adjusted by the correction information, so as to realize the adjustment of the real-time heating curve.
需要说明的是，当加热曲线用于表示加热温度和加热时长的关系时，加热曲线就是每个时段的加热温度和加热时长的组合。将烹饪加热曲线分割为多个曲线段，并根据烹饪加热曲线中多个曲线段中各个时间点分别对应的温度，确定该曲线段组的目标温度。其中，根据烹饪加热曲线中多个曲线段中各个时间点分别对应的温度，确定该曲线段组的目标温度，具体为：提取每个曲线段组中每个时间点对应温度，对每个曲线段组中每个时间点对应的温度进行累加，将温度累加值与该曲线段组中的时间节点数量相除，将得到的温度平均值作为该曲线段组对应的目标温度。It should be noted that when the heating curve is used to represent the relationship between the heating temperature and the heating duration, the heating curve is the combination of the heating temperature and the heating duration in each period. The cooking heating curve is divided into a plurality of curve segments, and the target temperature of the curve segment group is determined according to the temperature corresponding to each time point in the multiple curve segments in the cooking heating curve. The target temperature of the curve segment group is determined according to the respective temperatures corresponding to each time point in the multiple curve segments in the cooking heating curve, specifically: extracting the temperature corresponding to each time point in each curve segment group, and for each curve segment The temperature corresponding to each time point in the segment group is accumulated, the accumulated temperature value is divided by the number of time nodes in the curve segment group, and the average temperature obtained is used as the target temperature corresponding to the curve segment group.
图3示出了本发明对最佳加热曲线进行优化的方法流程图。Fig. 3 shows a flow chart of the method of the present invention for optimizing the optimum heating curve.
根据本发明实施例，获取家电设备使用者对食物品质的反馈信息，根据所述反馈信息与最佳加热曲线比较，对最佳加热曲线进行优化，具体为：According to the embodiment of the present invention, the feedback information on the food quality from the user of the household appliance is obtained, and the optimal heating curve is optimized according to the feedback information compared with the optimal heating curve, specifically:
S302，云端服务器通过问卷调查方式将食物品质调查问卷通过预设方式发送给目标用户；S302, the cloud server sends the food quality questionnaire to the target user in a preset manner by means of a questionnaire survey;
S304，获取目标用户的问卷反馈数据，将所述问卷反馈数据进行处理分析，生成对当前食物品质满意度得分；S304, obtain the questionnaire feedback data of the target user, process and analyze the questionnaire feedback data, and generate a satisfaction score for the current food quality;
S306，预设满意度得分阈值，将所述食物品质满意度得分与预设阈值进行比较；S306, preset a satisfaction score threshold, and compare the food quality satisfaction score with a preset threshold;
S308，若食物品质满意度得分小于预设阈值，则根据问卷反馈数据生成反馈信息，根据所述反馈信息与最佳加热曲线比较，对当前最佳加热曲线进行优化。S308, if the food quality satisfaction score is less than the preset threshold, generate feedback information according to the questionnaire feedback data, and optimize the current optimal heating curve according to the feedback information compared with the optimal heating curve.
根据本发明实施例，根据预设传感器对食材烹饪过程中的受热状况进行监测，通过各烹饪阶段的监测数据信息对食物品质进行表示，根据时间和烹饪阶段内的平均温度构成函数关系，其中食物品质的函数可以表示为：According to the embodiment of the present invention, the heating condition of the ingredients during the cooking process is monitored according to the preset sensor, the quality of the food is represented by the monitoring data information of each cooking stage, and the time and the average temperature in the cooking stage form a functional relationship, wherein the food The function of quality can be expressed as:
其中，
根据本发明实施例，还包括根据目标用户的口感需求，对食材的烹饪加热曲线实现需求性调整，具体为：According to the embodiment of the present invention, it also includes the need to adjust the cooking and heating curve of the ingredients according to the taste requirements of the target user, specifically:
根据目标用户的需求生成需求标签，通过所述需求标签捕捉数据库中的需求数据集合；Generate a demand label according to the demand of the target user, and capture the demand data set in the database through the demand label;
通过所述需求数据集合生成偏好特征，根据所述偏好特征确定相似度阈值区间；Generate a preference feature through the demand data set, and determine a similarity threshold interval according to the preference feature;
通过预设计算方法计算数据库中数据集合与需求数据集合的相似度，将落在所述相似度阈值区间中的数据集合作为需求数据集合；Calculate the similarity between the data set in the database and the demand data set by a preset calculation method, and use the data set falling in the similarity threshold interval as the demand data set;
提取需求数据集合中的烹饪加热曲线，根据所述烹饪加热曲线提取加热温度信息及烹饪阶段时间信息；extracting the cooking heating curve in the demand data set, and extracting heating temperature information and cooking stage time information according to the cooking heating curve;
将所述加热温度信息及烹饪阶段时间信息更新加热曲线干预信息，根据更新后的加热曲线干预信息匹配烹饪加热曲线生成最佳加热曲线。The heating curve intervention information is updated with the heating temperature information and the cooking stage time information, and the optimal heating curve is generated by matching the cooking heating curve according to the updated heating curve intervention information.
根据本发明实施例，当烹饪设备烹饪多种食材时，将多种食材的烹饪加热曲线进行聚合，具体为：According to the embodiment of the present invention, when the cooking device cooks multiple ingredients, the cooking heating curves of the multiple ingredients are aggregated, specifically:
通过目标用户的需求生成食谱信息，根据所述食谱信息提取烹饪所需食材，通过大数据分析获取所述所需食材的烹饪加热曲线；Generate recipe information according to the needs of the target user, extract the ingredients required for cooking according to the recipe information, and obtain the cooking heating curve of the required ingredients through big data analysis;
根据所述烹饪加热曲线获取不同食材的不同烹饪阶段的烹饪时间，并在预设烹饪阶段内将所述烹饪时间进行排序，并根据所述排序生成不同食材的放入顺序，并按照预设方式显示；The cooking times of different ingredients in different cooking stages are acquired according to the cooking heating curve, and the cooking times are sorted within the preset cooking stages, and the order of placing the different ingredients is generated according to the sorting, and in a preset manner show;
将所述所需食材的烹饪加热曲线进行曲线特征点提取，并生成特征点匹配对，计算所述匹配对中特征点连线与水平方向的夹角角度及特征点之间的距离，并生成夹角角度集合及距离集合；Perform curve feature point extraction on the cooking heating curve of the required ingredients, and generate a matching pair of feature points, calculate the angle between the line connecting the feature points in the matching pair and the angle between the feature points and the distance between the feature points, and generate Angle set and distance set;
根据所述夹角角度结合及距离集合进行曲线拟合，并进行曲线修正，生成最佳加热曲线。Curve fitting is performed according to the angle combination and the distance set, and curve correction is performed to generate an optimal heating curve.
需要说明的是，在用户同时将所需食材加入烹饪设备中的情况下，烹饪设备获取食材图像信息，并根据所述食材图像信息识别出至少一种食材信息，将食材识别结果生成食材确认信息，通过目标用户对食材确认信息的反馈得到食材识别率；在所述食材识别率小于预设阈值时，对控制食材识别的算法进行自动更新。It should be noted that, when the user adds the required ingredients into the cooking device at the same time, the cooking device acquires image information of the ingredients, identifies at least one ingredient information according to the image information of the ingredients, and generates ingredient confirmation information based on the identification result of the ingredients. , the recognition rate of the food material is obtained through the feedback of the target user on the confirmation information of the food material; when the recognition rate of the food material is less than the preset threshold, the algorithm for controlling the recognition of the food material is automatically updated.
根据本发明实施例，烹饪设备在进行食材识别时，识别食材的冷冻状态，根据所述冷冻状态匹配最佳加热曲线，具体为：According to the embodiment of the present invention, when the cooking device recognizes the ingredients, it identifies the freezing state of the ingredients, and matches the optimal heating curve according to the freezing state, specifically:
获取烹饪设备中的食材的高光谱图像信息，将所述高光谱图像信息进行预处理，并选取感兴趣区域，获取所述感兴趣区域在预设波长的光谱反射率；acquiring hyperspectral image information of the ingredients in the cooking device, preprocessing the hyperspectral image information, selecting a region of interest, and acquiring the spectral reflectance of the region of interest at a preset wavelength;
根据所述光谱反射率识别新鲜食材和冷冻食材，并设置光谱反射率阈值区间，通过食材的色泽度及所述光谱反射率所落在的阈值区间判断所述冷冻食材的冷冻程度，根据所述冷冻程度获取食材的烹饪加热曲线；Identify fresh ingredients and frozen ingredients according to the spectral reflectance, set a spectral reflectance threshold interval, and determine the degree of freezing of the frozen ingredients according to the color of the ingredients and the threshold interval in which the spectral reflectance falls. The degree of freezing obtains the cooking heating curve of the ingredients;
通过大数据根据所述冷冻程度确定食材的解冻阶段的时长及解冻加热温度，将所述解冻阶段自定义加入食材的烹饪过程，结合食材的烹饪加热曲线生成食材的最佳加热曲线；Determine the duration of the thawing stage and the thawing heating temperature of the ingredients according to the freezing degree through big data, add the thawing stage to the cooking process of the ingredients, and generate the best heating curve of the ingredients in combination with the cooking heating curve of the ingredients;
在根据所述最佳加热曲线进行烹饪的过程中，对食材的成熟度进行监测，根据所述成熟度对最佳加热曲线进行实时优化修正。During the cooking process according to the optimal heating curve, the maturity of the ingredients is monitored, and the optimal heating curve is optimized and corrected in real time according to the maturity.
需要说明的是，可选的，通过物联网技术将烹饪设备与智能冰箱进行组网相连，当烹饪设备完成食材识别后，将食材识别结果发送到智能冰箱，智能冰箱通过食材识别结果提取食材在冰箱中的存放时间，根据所述存放时间生成食材的新鲜程度，将所述新鲜程度反馈到云端服务器，云端服务器根据食材的新鲜程度结合食材的烹饪加热曲线，生成最佳加热曲线。It should be noted that, optionally, the cooking equipment and the smart refrigerator are connected to a network through the Internet of Things technology. After the cooking equipment completes the identification of the ingredients, it sends the identification results of the ingredients to the smart refrigerator, and the smart refrigerator extracts the ingredients from the identification results. For the storage time in the refrigerator, the freshness of the ingredients is generated according to the storage time, and the freshness is fed back to the cloud server, and the cloud server generates the optimal heating curve according to the freshness of the ingredients and the cooking heating curve of the ingredients.
图4示出了本发明一种基于大数据的加热控制系统的框图。Fig. 4 shows a block diagram of a heating control system based on big data of the present invention.
本发明第二方面还提供了一种基于大数据的加热控制系统4，该系统包括：存储器41、处理器42，所述存储器中包括一种基于大数据的加热控制方法程序，所述一种基于大数据的加热控制方法程序被所述处理器执行时实现如下步骤：The second aspect of the present invention also provides a heating control system 4 based on big data, the system includes: a memory 41 and a processor 42, the memory includes a heating control method program based on big data, the one When the program of the heating control method based on big data is executed by the processor, the following steps are implemented:
获取当前因素参数信息及目标用户的身体状况信息，根据所述因素参数信息及身体状况信息生成加热曲线干预信息；Obtain current factor parameter information and target user's physical condition information, and generate heating curve intervention information according to the factor parameter information and physical condition information;
获取食材种类信息，根据所述食材种类信息及食材烹饪模式信息通过大数据分析获取食材的烹饪加热曲线；Obtaining ingredient type information, and obtaining the cooking heating curve of the ingredient through big data analysis according to the ingredient type information and the ingredient cooking mode information;
根据所述烹饪加热曲线匹配加热曲线干预信息生成最佳加热曲线进行加热烹饪；According to the cooking heating curve matching heating curve intervention information, generate an optimal heating curve for heating and cooking;
获取目标用户对食物品质的反馈信息，根据所述反馈信息与当前最佳加热曲线比较，对最佳加热曲线进行优化。The feedback information on the food quality from the target user is obtained, and the optimal heating curve is optimized according to the feedback information compared with the current optimal heating curve.
需要说明的是，在加热曲线表示的是食材烹饪时的加热温度和加热时长的关系时，智能烹饪设备可以是利用温度高低对食材进行烹饪的电饭煲、电磁炉等设备。It should be noted that, when the heating curve represents the relationship between the heating temperature and the heating duration of the ingredients during cooking, the intelligent cooking device may be an electric rice cooker, an induction cooker, or other devices that use the temperature to cook the ingredients.
需要说明的是，所述因素参数信息包括纬度信息、海报信息、气压信息、环境温度信息、电路电压信息的一种或两种及以上的组合；所述目标用户的身体状况信息包括年龄信息、健康信息、膳食习惯信息、患病情况信息的一种或两种以上的组合。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 age information, One or a combination of two or more of health information, dietary habit information, and disease information.
需要说明的是，所述的根据所述因素参数信息及身体状况信息生成加热曲线干预信息，具体为：It should be noted that the generation of the heating curve intervention information according to the factor parameter information and the physical condition information is specifically:
通过所述因素参数信息及身体状况信息确定烹饪过程中各项程序的时间、加热温度及加热功率；Determine the time, heating temperature and heating power of each program in the cooking process through the factor parameter information and the physical condition information;
根据所述烹饪过程中各项程序的时间、加热温度及加热功率生成初始加热曲线；Generate an initial heating curve according to the time, heating temperature and heating power of each program in the cooking process;
获取预设烹饪模式对应的预设加热曲线，将所述初始加热曲线及所述预设加热曲线进行对应值提取；obtaining a preset heating curve corresponding to a preset cooking mode, and extracting corresponding values of the initial heating curve and the preset heating curve;
将提取到的对应值进行相减得到差值曲线，获取差值曲线差值，并提取差值曲线的曲线特征；Subtract the extracted corresponding values to obtain the difference curve, obtain the difference value of the difference curve, and extract the curve characteristics of the difference curve;
根据所述差值及所述曲线特征生成加热曲线干预信息。Heating curve intervention information is generated according to the difference value and the curve characteristic.
根据本发明实施例，所述的获取食材种类信息，根据所述食材种类信息及食材烹饪模式信息通过大数据分析获取食材的烹饪加热曲线，具体为：According to an embodiment of the present invention, in the acquisition of the type information of the ingredients, the cooking and heating curves of the ingredients are obtained through big data analysis according to the type information of the ingredients and the cooking mode information of the ingredients, specifically:
获取食材图像信息，对图像信息进行预处理并进行图像特征提取，根据所述图像特征在食材数据库中检索并进行匹配，根据匹配结果识别出至少一种食材种类信息；Obtaining food image information, preprocessing the image information and extracting image features, retrieving and matching in the food database according to the image features, and identifying at least one food type information according to the matching result;
提取所述食材种类信息中的关键词，将所述关键词借助大数据分析获取食材加热曲线数据集；Extracting the keywords in the type information of the ingredients, and using the keywords to analyze the heating curve data set of the ingredients with the help of big data;
根据目标用户选择的烹饪模式作为检索条件在所述食材加热曲线数据集中进行检索，获取检索结果中各食材加热曲线对应的食物品质信息；According to the cooking mode selected by the target user as the retrieval condition, the retrieval is performed in the food material heating curve data set, and the food quality information corresponding to each food material heating curve in the retrieval result is obtained;
将所述食物品质信息按照预设规则进行评分，将评分大于预设阈值的食材加热曲线进行提取并按照评分进行排序，获取最高评分对应的食材加热曲线；Scoring the food quality information according to a preset rule, extracting the heating curves of the ingredients whose scores are greater than a preset threshold, and sorting them according to the scores, to obtain the heating curves of the ingredients corresponding to the highest scores;
获取目标用户选择的烹饪模式对应的预设加热曲线，将所述最高评分对应的食材加热曲线与预设加热曲线进行拟合，生成食材的烹饪加热曲线。The preset heating curve corresponding to the cooking mode selected by the target user is acquired, and the heating curve of the ingredient corresponding to the highest score is fitted with the preset heating curve to generate the cooking heating curve of the ingredient.
需要说明的是，还包括：在食材烹饪过程中对食材的受热状况进行监测，对食材在烹饪过程中的加热曲线进行调整，具体为：It should be noted that it also includes: monitoring the heating condition of the ingredients during the cooking process, and adjusting the heating curve of the ingredients during the cooking process, specifically:
根据预设传感器对食材烹饪过程中的受热状况进行监测，得到监测数据信息，根据所述监测数据信息生成实时加热曲线；Monitoring the heating state of the ingredients during the cooking process according to a preset sensor, obtaining monitoring data information, and generating a real-time heating curve according to the monitoring data information;
将所述实时加热曲线根据烹饪过程进行分解，得到多个曲线段组，获取各曲线段组中的平均温度信息；Decomposing the real-time heating curve according to the cooking process, obtaining a plurality of curve segment groups, and acquiring the average temperature information in each curve segment group;
根据曲线段组分割规则对最佳加热曲线进行分段，获取各曲线段组中的目标温度信息；Segment the optimal heating curve according to the curve segment group segmentation rule, and obtain the target temperature information in each curve segment group;
将所述平均温度信息与所述目标温度信息进行对比分析，生成偏差率；Carrying out comparative analysis on the average temperature information and the target temperature information to generate a deviation rate;
判断所述偏差率是否大于偏差率阈值；judging whether the deviation rate is greater than the deviation rate threshold;
若大于，则生成烹饪异常信息，根据所述烹饪异常信息生成修正信息，通过修正信息调整烹饪时间和烹饪火候大小，实现对实时加热曲线的调整。If it is greater than the value, cooking abnormality information is generated, correction information is generated according to the cooking abnormality information, and the cooking time and the cooking heat are adjusted by the correction information, so as to realize the adjustment of the real-time heating curve.
需要说明的是，当加热曲线用于表示加热温度和加热时长的关系时，加热曲线就是每个时段的加热温度和加热时长的组合。将烹饪加热曲线分割为多个曲线段，并根据烹饪加热曲线中多个曲线段中各个时间点分别对应的温度，确定该曲线段组的目标温度。其中，根据烹饪加热曲线中多个曲线段中各个时间点分别对应的温度，确定该曲线段组的目标温度，具体为：提取每个曲线段组中每个时间点对应温度，对每个曲线段组中每个时间点对应的温度进行累加，将温度累加值与该曲线段组中的时间节点数量相除，将得到的温度平均值作为该曲线段组对应的目标温度。It should be noted that when the heating curve is used to represent the relationship between the heating temperature and the heating duration, the heating curve is the combination of the heating temperature and the heating duration in each period. The cooking heating curve is divided into a plurality of curve segments, and the target temperature of the curve segment group is determined according to the respective temperatures corresponding to each time point in the multiple curve segments in the cooking heating curve. The target temperature of the curve segment group is determined according to the temperature corresponding to each time point in a plurality of curve segments in the cooking heating curve, specifically: extracting the temperature corresponding to each time point in each curve segment group, and for each curve segment The temperature corresponding to each time point in the segment group is accumulated, the accumulated temperature value is divided by the number of time nodes in the curve segment group, and the average temperature obtained is used as the target temperature corresponding to the curve segment group.
根据本发明实施例，获取家电设备使用者对食物品质的反馈信息，根据所述反馈信息与最佳加热曲线比较，对最佳加热曲线进行优化，具体为：According to the embodiment of the present invention, the feedback information on the food quality from the user of the household appliance is obtained, and the optimal heating curve is optimized according to the feedback information compared with the optimal heating curve, specifically:
云端服务器通过问卷调查方式将食物品质调查问卷通过预设方式发送给目标用户；The cloud server sends the food quality questionnaire to the target user in a preset way by means of a questionnaire survey;
获取目标用户的问卷反馈数据，将所述问卷反馈数据进行处理分析，生成对当前食物品质满意度得分；Obtain the questionnaire feedback data of the target user, process and analyze the questionnaire feedback data, and generate a satisfaction score for the current food quality;
预设满意度得分阈值，将所述食物品质满意度得分与预设阈值进行比较；Presetting a satisfaction score threshold, and comparing the food quality satisfaction score with a preset threshold;
若食物品质满意度得分小于预设阈值，则根据问卷反馈数据生成反馈信息，根据所述反馈信息与最佳加热曲线比较，对当前最佳加热曲线进行优化。If the food quality satisfaction score is less than the preset threshold, feedback information is generated according to the questionnaire feedback data, and the current optimal heating curve is optimized according to the feedback information compared with the optimal heating curve.
根据本发明实施例，根据预设传感器对食材烹饪过程中的受热状况进行监测，通过 各烹饪阶段的监测数据信息对食物品质进行表示，根据时间和烹饪阶段内的平均温度构成函 数关系，其中食物品质的函数可以表示为：
其中，p表示食物品质，i表示烹饪阶段项数，n表示烹饪阶段总数，λ表示食材受热后的形 变系数，T表示烹饪阶段中的平均温度，Tc表示烹饪阶段的参考温度，β表示食材成熟品质 因子，反应食物品质对温度变化的敏感程度，t表示烹饪阶段对应的时间参数。Among them, p is the food quality, i is the number of cooking stages, n is the total number of cooking stages, λ is the deformation coefficient of the food after heating, T is the average temperature in the cooking stage, T c is the reference temperature in the cooking stage, and β is the food The maturity quality factor reflects the sensitivity of food quality to temperature changes, and t represents the time parameter corresponding to the cooking stage.
根据本发明实施例，还包括根据目标用户的口感需求，对食材的烹饪加热曲线实现需求性调整，具体为：According to the embodiment of the present invention, it also includes the need to adjust the cooking and heating curve of the ingredients according to the taste requirements of the target user, specifically:
根据目标用户的需求生成需求标签，通过所述需求标签捕捉数据库中的需求数据集合；Generate a demand label according to the demand of the target user, and capture the demand data set in the database through the demand label;
通过所述需求数据集合生成偏好特征，根据所述偏好特征确定相似度阈值区间；Generate a preference feature through the demand data set, and determine a similarity threshold interval according to the preference feature;
通过预设计算方法计算数据库中数据集合与需求数据集合的相似度，将落在所述相似度阈值区间中的数据集合作为需求数据集合；Calculate the similarity between the data set in the database and the demand data set by a preset calculation method, and use the data set falling in the similarity threshold interval as the demand data set;
提取需求数据集合中的烹饪加热曲线，根据所述烹饪加热曲线提取加热温度信息及烹饪阶段时间信息；extracting the cooking heating curve in the demand data set, and extracting heating temperature information and cooking stage time information according to the cooking heating curve;
将所述加热温度信息及烹饪阶段时间信息更新加热曲线干预信息，根据更新后的加热曲线干预信息匹配烹饪加热曲线生成最佳加热曲线。The heating curve intervention information is updated with the heating temperature information and the cooking stage time information, and the optimal heating curve is generated by matching the cooking heating curve according to the updated heating curve intervention information.
根据本发明实施例，当烹饪设备烹饪多种食材时，将多种食材的烹饪加热曲线进行聚合，具体为：According to the embodiment of the present invention, when the cooking device cooks multiple ingredients, the cooking heating curves of the multiple ingredients are aggregated, specifically:
通过目标用户的需求生成食谱信息，根据所述食谱信息提取烹饪所需食材，通过大数据分析获取所述所需食材的烹饪加热曲线；Generate recipe information according to the needs of the target user, extract the ingredients required for cooking according to the recipe information, and obtain the cooking heating curve of the required ingredients through big data analysis;
根据所述烹饪加热曲线获取不同食材的不同烹饪阶段的烹饪时间，并在预设烹饪阶段内将所述烹饪时间进行排序，并根据所述排序生成不同食材的放入顺序，并按照预设方式显示；The cooking times of different ingredients in different cooking stages are acquired according to the cooking heating curve, and the cooking times are sorted within the preset cooking stages, and the placing order of the different ingredients is generated according to the sorting, and the order of the different ingredients is generated according to the preset method. show;
将所述所需食材的烹饪加热曲线进行曲线特征点提取，并生成特征点匹配对，计算所述匹配对中特征点连线与水平方向的夹角角度及特征点之间的距离，并生成夹角角度集合及距离集合；Perform curve feature point extraction on the cooking heating curve of the required ingredients, and generate a matching pair of feature points, calculate the angle between the line connecting the feature points in the matching pair and the angle between the feature points and the distance between the feature points, and generate Angle set and distance set;
根据所述夹角角度结合及距离集合进行曲线拟合，并进行曲线修正，生成最佳加热曲线。Curve fitting is performed according to the angle combination and the distance set, and curve correction is performed to generate an optimal heating curve.
需要说明的是，在用户同时将所需食材加入烹饪设备中的情况下，烹饪设备获取食材图像信息，并根据所述食材图像信息识别出至少一种食材信息，将食材识别结果生成食材确认信息，通过目标用户对食材确认信息的反馈得到食材识别率；在所述食材识别率小于预设阈值时，对控制食材识别的算法进行自动更新。It should be noted that, when the user adds the required ingredients into the cooking device at the same time, the cooking device acquires image information of the ingredients, identifies at least one ingredient information according to the image information of the ingredients, and generates ingredient confirmation information based on the identification result of the ingredients. , the recognition rate of the food material is obtained through the feedback of the target user on the confirmation information of the food material; when the recognition rate of the food material is less than the preset threshold, the algorithm for controlling the recognition of the food material is automatically updated.
根据本发明实施例，烹饪设备在进行食材识别时，识别食材的冷冻状态，根据所述冷冻状态匹配最佳加热曲线，具体为：According to the embodiment of the present invention, when the cooking device recognizes the ingredients, it identifies the freezing state of the ingredients, and matches the optimal heating curve according to the freezing state, specifically:
获取烹饪设备中的食材的高光谱图像信息，将所述高光谱图像信息进行预处理，并选取感兴趣区域，获取所述感兴趣区域在预设波长的光谱反射率；acquiring hyperspectral image information of the ingredients in the cooking device, preprocessing the hyperspectral image information, selecting a region of interest, and acquiring the spectral reflectance of the region of interest at a preset wavelength;
根据所述光谱反射率识别新鲜食材和冷冻食材，并设置光谱反射率阈值区间，通过食材的色泽度及所述光谱反射率所落在的阈值区间判断所述冷冻食材的冷冻程度，根据所述冷冻程度获取食材的烹饪加热曲线；Identify fresh ingredients and frozen ingredients according to the spectral reflectance, set a spectral reflectance threshold interval, and determine the degree of freezing of the frozen ingredients according to the color of the ingredients and the threshold interval in which the spectral reflectance falls. The degree of freezing obtains the cooking heating curve of the ingredients;
通过大数据根据所述冷冻程度确定食材的解冻阶段的时长及解冻加热温度，将所述解冻阶段自定义加入食材的烹饪过程，结合食材的烹饪加热曲线生成食材的最佳加热曲线；Determine the duration of the thawing stage and the thawing heating temperature of the ingredients according to the freezing degree through big data, add the thawing stage to the cooking process of the ingredients, and generate the best heating curve of the ingredients in combination with the cooking heating curve of the ingredients;
在根据所述最佳加热曲线进行烹饪的过程中，对食材的成熟度进行监测，根据所述成熟度对最佳加热曲线进行实时优化修正。During the cooking process according to the optimal heating curve, the maturity of the ingredients is monitored, and the optimal heating curve is optimized and corrected in real time according to the maturity.
需要说明的是，可选的，通过物联网技术将烹饪设备与智能冰箱进行组网相连，当烹饪设备完成食材识别后，将食材识别结果发送到智能冰箱，智能冰箱通过食材识别结果提取食材在冰箱中的存放时间，根据所述存放时间生成食材的新鲜程度，将所述新鲜程度反馈到云端服务器，云端服务器根据食材的新鲜程度结合食材的烹饪加热曲线，生成最佳加热曲线。It should be noted that, optionally, the cooking equipment and the smart refrigerator are connected to a network through the Internet of Things technology. After the cooking equipment completes the identification of the ingredients, it sends the identification results of the ingredients to the smart refrigerator, and the smart refrigerator extracts the ingredients from the identification results. For the storage time in the refrigerator, the freshness of the ingredients is generated according to the storage time, and the freshness is fed back to the cloud server, and the cloud server generates the optimal heating curve according to the freshness of the ingredients and the cooking heating curve of the ingredients.
本发明第三方面还提供一种计算机可读存储介质，所述计算机可读存储介质中包括一种基于大数据的加热控制方法程序，所述一种基于大数据的加热控制方法程序被处理器执行时，实现如上述任一项所述的一种基于大数据的加热控制方法的步骤。A third aspect of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a big data-based heating control method program, and the big data-based heating control method program is processed by a processor When executed, the steps of a heating control method based on big data as described in any one of the above are realized.
本发明公开了一种基于大数据的加热控制方法、系统和存储介质，涉及智能控制技术领域，其中加热控制方法包括：获取当前因素参数信息及目标用户的身体状况信息，根据所述因素参数信息及身体状况信息生成加热曲线干预信息，获取食材种类信息，根据所述食材种类信息及食材烹饪模式信息通过大数据分析获取食材的烹饪加热曲线，并根据所述烹饪加热曲线匹配加热曲线干预信息生成最佳加热曲线进行加热烹饪，获取目标用户对食物品质的反馈信息，根据所述反馈信息与当前加热曲线比较，对加热曲线进行优化。本发明通过大数据分析获取食材的最佳加热曲线，实现食材的口感提升，并保持营养成分。The invention discloses a heating control method, system and storage medium based on big data, and relates to the technical field of intelligent control, wherein the heating control method includes: acquiring current factor parameter information and physical condition information of a target user, and physical condition information to generate heating curve intervention information, obtain ingredient type information, obtain the cooking heating curve of the ingredient through big data analysis according to the ingredient type information and ingredient cooking mode information, and generate the heating curve intervention information according to the cooking heating curve matching heating curve The best heating curve is used for heating and cooking, the feedback information of the target user on the food quality is obtained, and the heating curve is optimized according to the comparison between the feedback information and the current heating curve. The invention obtains the best heating curve of the ingredients through big data analysis, realizes the improvement of the taste of the ingredients, and maintains the nutritional components.
在本申请所提供的几个实施例中，应该理解到，所揭露的设备和方法，可以通过其它的方式实现。以上所描述的设备实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，如：多个单元或组件可以结合，或可以集成到另一个系统，或一些特征可以忽略，或不执行。另外，所显示或讨论的各组成部分相互之间的耦合、或直接耦合、或通信连接可以是通过一些接口，设备或单元的间接耦合或通信连接，可以是电性的、机械的或其它形式的。In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined, or Can be integrated into another system, or some features can be ignored, or not implemented. In addition, the coupling, or direct coupling, or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical or other forms. of.
上述作为分离部件说明的单元可以是、或也可以不是物理上分开的，作为单元显示的部件可以是、或也可以不是物理单元；既可以位于一个地方，也可以分布到多个网络单元上；可以根据实际的需要选择其中的部分或全部单元来实现本实施例方案的目的。The unit described above as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit; it may be located in one place or distributed to multiple network units; Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
另外，在本发明各实施例中的各功能单元可以全部集成在一个处理单元中，也可以是各单元分别单独作为一个单元，也可以两个或两个以上单元集成在一个单元中；上述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件功能单元的形式实现。In addition, each functional unit in each embodiment of the present invention may all 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 integration The unit can be implemented either in the form of hardware or in the form of hardware plus software functional units.
本领域普通技术人员可以理解：实现上述方法实施例的全部或部分步骤可以通过程序指令相关的硬件来完成，前述的程序可以存储于计算机可读取存储介质中，该程序在执行时，执行包括上述方法实施例的步骤；而前述的存储介质包括：移动存储设备、只读存储器（ROM，Read-Only Memory）、随机存取存储器（RAM，Random Access Memory）、磁碟或者光盘等各种可以存储程序代码的介质。Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by program instructions related to hardware, the aforementioned program can be stored in a computer-readable storage medium, and when the program is executed, the execution includes: The steps of the above method embodiment; and the aforementioned storage medium includes: a removable storage device, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, etc. A medium on which program code is stored.
或者，本发明上述集成的单元如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明实施例的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备（可以是个人计算机、服务器、或者网络设备等）执行本发明各个实施例所述方法的全部或部分。而前述的存储介质包括：移动存储设备、ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention may be embodied in the form of software products in essence or the parts that make contributions to the prior art. The computer software products are stored in a storage medium and include several instructions for A computer device (which may be a personal computer, a server, or a network device, etc.) is caused to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic disk or an optical disk and other mediums that can store program codes.
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以所述权利要求的保护范围为准。The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention. should be included within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.
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