CN111435610A - Method and device for recommending food and cooking appliance - Google Patents
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Abstract
The invention discloses a method and a device for recommending food and a cooking appliance. Wherein, the method comprises the following steps: acquiring user data associated with a target object, wherein the user data comprises at least one of: taste data and physical condition data for different foods; the user data is analyzed using a neural network model to determine the type of food suitable for the target object. According to the scheme, the user data are analyzed through the neural network model, food which is more suitable for the user and healthier is obtained finally and recommended to the user, and the technical problem that the recommendation result is inaccurate due to the fact that the scheme for recommending food is single in the prior art is solved.
Description
Technical Field
The invention relates to the field of intelligent small household appliances, in particular to a method and a device for recommending food and a cooking appliance.
Background
With the development of artificial intelligence technology, more and more household products are added into a team of intelligent applications one after another, so that the daily life of people is more convenient. The electric cooker is an essential cooking tool in life, the control mode of the electric cooker is developed from simple mechanical control to the current microcomputer control, fuzzy control and the like, the function is also developed from a single cooking function to multiple purposes, different cooking modes are provided, the user requirements are fully met, and different types of rice are cooked.
However, different types of rice have different tastes and nutritional ingredients. Therefore, how to select the rice type and what influence different rice has on different users, that is, how to select rice scientifically and healthily, is a problem that healthy diet is concerned more and more. At present, the way for users to obtain rice that suits their tastes and benefits their bodies has been attempted only through web search or life experience.
Aiming at the problem that the recommendation result is inaccurate due to a single scheme for recommending food in the prior art, no effective solution is provided at present.
Disclosure of Invention
The embodiment of the invention provides a method and a device for recommending food and a cooking appliance, and at least solves the technical problem that in the prior art, the recommendation result is inaccurate due to single scheme for recommending food.
According to an aspect of an embodiment of the present invention, there is provided a method of recommending food, including: acquiring user data associated with a target object, wherein the user data comprises at least one of: taste data and physical condition data for different foods; the user data is analyzed using a neural network model to determine the type of food suitable for the target object.
Optionally, where the user data comprises taste data of different foods, collecting user data associated with the target object comprises: acquiring the types of different foods eaten by the target object within a preset time period through the cooking appliance, and determining the characteristics of the foods according to the types of the foods, wherein the characteristics of the foods comprise: cooking and nutritional characteristics; the cooking appliance records cooking modes of different foods in a preset time period, and/or receives feedback information of the target object on the different foods eaten, and determines taste data of the target object based on the cooking modes and/or the feedback information.
Optionally, where the user data includes physical condition data, acquiring user data associated with the target subject includes: acquiring physical condition data of a target object transmitted by an external medical device, wherein different physical condition data correspond to different recommended foods, and the external medical device comprises at least one of the following components: a hospital's computer to store cases, medical instruments, and wearable body detection devices.
Optionally, analyzing the user data using a neural network model to determine a food category suitable for the target object, comprising: analyzing the user data using a neural network model, determining at least one food matched with the physical condition data of the target subject, and outputting a food category of the matched food; wherein, in case that it is determined that there is a match between a plurality of foods and the physical condition data of the target subject, the matching degree of different foods is output, and the matching degree of each food is taken as the recommendation probability of the different kinds of foods.
Optionally, before analyzing the user data using the neural network model, the method further comprises: acquiring sample data, wherein the sample data comprises: food characteristics of different foods and physical condition data of different target subjects; and inputting the sample data into a neural network for training to obtain a neural network model.
Optionally, according to the recommendation probabilities of different kinds of foods output by the output layer of the neural network model, a predetermined number of foods are sequentially selected for recommendation according to the order of the recommendation probabilities from high to low.
Optionally, after analyzing the user data using the neural network model to determine a food category suitable for the target object, the method further comprises: outputting recommendation information, wherein the recommendation information is a food type recommended to the target object; in the case where the kind of food to be cooked is identified, judging whether the kind of food to be cooked matches the kind of food recommended to the target object; if the food type to be cooked matches the food type recommended to the target object, feedback information of the target object is received.
Optionally, if the type of food to be cooked does not match the type of food recommended to the target object, the type of food currently cooked and/or feedback information is collected, and the collected result is used as training data to train the neural network model.
According to another aspect of an embodiment of the present invention, there is also provided an apparatus for recommending food, including: an acquisition module to acquire user data associated with a target object, wherein the user data includes at least one of: taste data and physical condition data for different foods; and the analysis module is used for analyzing the user data by using the neural network model and determining the food type suitable for the target object.
Optionally, the acquisition module comprises: the first acquisition module is used for acquiring the types of different foods eaten by the target object within a preset time period through the cooking appliance and determining the characteristics of the foods according to the types of the foods, wherein the characteristics of the foods comprise: cooking and nutritional characteristics; the first determination module is used for recording cooking modes of different foods in a preset time period through the cooking appliance and/or receiving feedback information of the target object on the different foods eaten, and determining the taste data of the target object based on the cooking modes and/or the feedback information.
Optionally, the acquisition module comprises: the second acquisition module is used for acquiring physical condition data of the target object transmitted by the external medical equipment, wherein different physical condition data correspond to different recommended foods, and the external medical equipment comprises at least one of the following components: a hospital's computer to store cases, medical instruments, and wearable body detection devices.
Optionally, the analysis module comprises: an analysis sub-module for analyzing the user data using the neural network model, determining at least one food matched with the physical condition data of the target subject, and outputting a food category of the matched food; and the matching module is used for outputting the matching degrees of different foods under the condition that the plurality of foods are determined to be matched with the physical condition data of the target object, and taking the matching degree of each food as the recommendation probability of different foods.
Optionally, the apparatus further comprises: an obtaining module, configured to obtain sample data before analyzing user data using a neural network model, where the sample data includes: food characteristics of different foods and physical condition data of different target subjects; and the training module is used for inputting the sample data into the neural network for training to obtain the neural network model.
Optionally, the apparatus further comprises: and the sequencing module is used for sequentially selecting a predetermined number of foods to recommend according to the recommendation probabilities of different types of foods output by the output layer of the neural network model from high to low.
Optionally, the apparatus further comprises: the output module is used for outputting recommendation information after analyzing the user data by using the neural network model and determining the food type suitable for the target object, wherein the recommendation information is the food type recommended to the target object; the judging module is used for judging whether the type of the food to be cooked is matched with the type of the food recommended to the target object or not under the condition that the type of the food to be cooked is identified; and the receiving module is used for receiving the feedback information of the target object if the food type to be cooked is matched with the food type recommended to the target object.
Optionally, the apparatus further comprises: and the training submodule is used for collecting the type of the currently cooked food and/or feedback information if the type of the food to be cooked is not matched with the type of the food recommended to the target object, and training the neural network model by using the collected result as training data.
According to another aspect of the embodiments of the present invention, there is also provided a storage medium including a stored program, wherein when the program runs, the apparatus on which the storage medium is located is controlled to execute any one of the above methods for recommending food.
According to another aspect of the embodiments of the present invention, there is also provided a processor for executing a program, where the program executes any one of the above methods for recommending food.
According to another aspect of the embodiment of the invention, a cooking appliance is also provided, which comprises any one of the above food recommending devices.
In an embodiment of the present invention, user data associated with a target object is collected, wherein the user data includes at least one of: taste data and physical condition data for different foods; the user data is analyzed using a neural network model to determine the type of food suitable for the target object. According to the scheme, the user data are analyzed through the neural network model, and finally, the food which is more suitable for the user and healthier is obtained and recommended to the user. According to the using condition of the recommended food by the user, data are continuously updated along with time, so that the food which is more suitable for the user to eat is found, and the technical problem that the recommended result is inaccurate due to the fact that the scheme of recommending the food is single in the prior art is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of an alternative method of recommending food according to an embodiment of the present application;
FIG. 2 is a block diagram of an alternative deep neural network recommended food in accordance with an embodiment of the present application;
FIG. 3 is an overall flow diagram of an alternative food recommendation system according to an embodiment of the present application;
FIG. 4 is an alternative follow-up flow chart of food recommendation according to an embodiment of the present application; and
fig. 5 is a schematic view of an alternative food recommending apparatus according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, system, article, or device that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, apparatus, article, or device.
Example 1
In accordance with an embodiment of the present invention, there is provided an embodiment of a method for recommending food items, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a flowchart of a method for recommending food according to an embodiment of the present invention, as shown in fig. 1, the method includes the steps of:
step S102, collecting user data associated with a target object, wherein the user data comprises at least one of the following: taste data and physical condition data for different foods.
In an alternative, the acquisition device may be a mobile phone, and the acquisition of the associated user data is realized by running an APP on the mobile phone; of course, the device may be a smart chip or a cloud server of any other form of local device. The food may be rice, soybean, potato, rhizoma Dioscoreae, etc. The taste data may be viscosity, hardness, gelatinization degree, etc.
Step S104, analyzing the user data by using a neural network model, and determining the food type suitable for the target object.
In an alternative, the neural network model may be a convolutional neural network model.
The convolutional neural network belongs to a supervised learning algorithm, is a special case in a deep neural network, and has the advantages of small weight number, high training speed and the like compared with a deep artificial neural network.
The convolutional neural network is mainly composed of three parts, namely an input layer, a hidden layer and an output layer. The input layer and the output layer are only one layer, the hidden layer can be a plurality of layers, and the deep neural network is a neural network with a plurality of hidden layers. The input layer of the convolutional neural network is data associated with the user, and the sample data is calculated by using the convolutional neural network to output food which meets the taste and/or physical condition of the user.
Fig. 2 is a diagram of a deep neural network recommended food structure according to an embodiment of the present application. As shown in fig. 2, the input layer of the deep neural network is characterized by viscosity, hardness, gelatinization temperature, dyspepsia, weakness of the spleen and the stomach, and the like, and the type of food which meets both the taste and the physical condition of the user can be recommended to the user through analysis and calculation of the deep neural network.
In an optional embodiment, the user A is the old, the staple food mainly comprises rice, the blood pressure is high, the digestion capacity is high, the data information is input into the convolutional neural network model, the model can output buckwheat to the user, the aim of reducing the blood pressure of the user is achieved while nutrition is guaranteed, and convenient personalized recommendation service is provided for the user.
Based on the scheme provided by the above embodiment of the present application, user data associated with the target object is collected, where the user data includes at least one of: taste data and physical condition data for different foods; the user data is analyzed using a neural network model to determine the type of food suitable for the target object. According to the scheme, the user data are analyzed through the neural network model, food which is more suitable for the user and healthier is obtained finally and recommended to the user, and the technical problem that the recommendation result is inaccurate due to the fact that the scheme for recommending food is single in the prior art is solved.
Optionally, where the user data comprises taste data of different foods, collecting user data associated with the target object comprises: acquiring the types of different foods eaten by the target object within a preset time period through the cooking appliance, and determining the characteristics of the foods according to the types of the foods, wherein the characteristics of the foods comprise: cooking and nutritional characteristics; the cooking appliance records cooking modes of different foods in a preset time period, and/or receives feedback information of the target object on the different foods eaten, and determines taste data of the target object based on the cooking modes and/or the feedback information.
In an alternative, the cooking appliance may be an electric cooker, an electric pressure cooker, or the like. The above-mentioned mode of obtaining the kind of different food can utilize the inside top-mounted image acquisition device of cooking utensil to obtain. The cooking characteristic may be viscosity, hardness, aroma, gelatinization temperature, and the nutritional characteristic may be amylose content, protein content, fat content, etc. The feedback information may be soft, hard, slightly fuzzy, etc.
In an optional embodiment, the related information of the rice eaten by the user in the last month is obtained through the electric cooker. The rice cooker can identify the type of rice eaten by a user, and can find out general characteristics of the rice, such as viscosity, hardness, aroma, gelatinization temperature, amylose content and the like according to the type of the rice. The electric rice cooker can collect the taste preference of the user to the rice, adjust the cooking mode and record preference information. If the preference information of the user is not recorded, the characteristics of the rice are searched according to the type of the rice eaten, and if the preference information exists, the corresponding characteristics are replaced by the preference information.
Optionally, where the user data includes physical condition data, acquiring user data associated with the target subject includes: acquiring physical condition data of a target object transmitted by an external medical device, wherein different physical condition data correspond to different recommended foods, and the external medical device comprises at least one of the following components: a hospital's computer to store cases, medical instruments, and wearable body detection devices.
In an alternative, the physical condition data may be health indicators such as blood pressure, heart rate, digestive system, and spleen and stomach conditions.
The physical condition data can list the influence of corresponding food which is currently medically determined on the physical condition, such as dyspepsia, high blood pressure, weakness of spleen and stomach, and the like, and the physical condition data of the user can be obtained by means of hospital data sharing, user self-input physical condition, wearable physical detection equipment and the like.
Optionally, analyzing the user data using a neural network model to determine a food category suitable for the target object, comprising: analyzing the user data using a neural network model, determining at least one food matched with the physical condition data of the target subject, and outputting a food category of the matched food; wherein, in case that it is determined that there is a match between a plurality of foods and the physical condition data of the target subject, the matching degree of different foods is output, and the matching degree of each food is taken as the recommendation probability of the different kinds of foods.
Different foods have different nutritional characteristics, so the influence on the body is different. Therefore, the dietary structure is changed in a dietary therapy mode, and healthier data indexes can be obtained. In the above steps, by analyzing the user data through the neural network model, at least one food capable of improving the physical condition of the user can be determined, each food corresponds to a recommendation probability, and the probability that the output layer is recommended for different food types can be determined.
Fig. 3 is an overall flowchart of a food recommendation system according to an embodiment of the present application, as shown in the figure, the system first needs to acquire data, acquire taste data of a user through a cooking appliance, and simultaneously acquire physical condition data of the user through an external medical device, and after analyzing and calculating the user data by a deep neural network model, recommend to the user a food category that simultaneously meets the taste and the physical condition of the user.
Optionally, before analyzing the user data using the neural network model, the method further comprises: acquiring sample data, wherein the sample data comprises: food characteristics of different foods and physical condition data of different target subjects; and inputting the sample data into a neural network for training to obtain a neural network model.
Before recommending food to a user by using a neural network model, the neural network model needs to be trained, food characteristics corresponding to a large number of different types of food and physical condition data of the user are used as samples and input into the network for training, and the trained model data can analyze user information and recommend food types suitable for the taste and the physical condition of the user.
Optionally, according to the recommendation probabilities of different kinds of foods output by the output layer of the neural network model, a predetermined number of foods are sequentially selected for recommendation according to the order of the recommendation probabilities from high to low.
In an alternative, the predetermined number may be three, and too many choices may result in the user not knowing how to choose.
And sorting the food types from high to low according to the probability corresponding to different food types of the output layer, and selecting the three food types with the highest probability to recommend to the user.
Optionally, after analyzing the user data using the neural network model to determine a food category suitable for the target object, the method further comprises: outputting recommendation information, wherein the recommendation information is a food type recommended to the target object; in the case where the kind of food to be cooked is identified, judging whether the kind of food to be cooked matches the kind of food recommended to the target object; if the food type to be cooked matches the food type recommended to the target object, feedback information of the target object is received.
In an alternative, the recommendation information may be displayed on an outer surface display panel of the cooking appliance or may be displayed through a mobile phone APP associated with the user.
After recommending a food category to the user, the recommended food category is recorded, and then it is determined whether the user uses the recommended food category through the cooking appliance. If the fact that the user uses the recommended food is detected, after a period of time, feedback data of the user on the recommended food is collected and input into the deep neural network model to search for the food more suitable for the user.
Optionally, if the type of food to be cooked does not match the type of food recommended to the target object, the type of food currently cooked and/or feedback information is collected, and the collected result is used as training data to train the neural network model.
If the cooking appliance detects that the user does not use the recommended food type, the food type and/or feedback information actually cooked by the user is collected after a period of time and input into the deep neural network model, and rice more suitable for the health of the user is continuously recommended for the user. Through continuous training, the neural network model tends to be perfect continuously, and the food types recommended to the user more and more meet the user expectation.
Fig. 4 is a flow chart of a follow-up food recommendation according to an embodiment of the application. As shown in fig. 4, after the display panel of the cooking appliance recommends food to the user, the user may select to eat or not eat the food. If the image acquisition device in the cooking appliance detects that the user does not eat recommended food, the type of the food actually eaten by the user and the feedback information of the user to the food are collected and input into the deep neural network model for training; if the user eats the recommended food, the feedback information of the user to the food is collected, the sample data is updated, and the sample data is input into the deep neural network again for training so as to recommend the food category which is more in line with the taste and the health condition of the user to the user.
Example 2
According to an embodiment of the present invention, there is provided an apparatus for recommending food, fig. 5 is a schematic view of an apparatus for recommending food according to an embodiment of the present application, and as shown in fig. 5, the apparatus 500 includes:
an acquisition module 502 for acquiring user data associated with the target object, wherein the user data comprises at least one of: taste data and physical condition data for different foods.
An analysis module 504 for analyzing the user data using the neural network model to determine a food category suitable for the target object.
Optionally, the acquisition module comprises: the first acquisition module is used for acquiring the types of different foods eaten by the target object within a preset time period through the cooking appliance and determining the characteristics of the foods according to the types of the foods, wherein the characteristics of the foods comprise: cooking and nutritional characteristics; the first determination module is used for recording cooking modes of different foods in a preset time period through the cooking appliance and/or receiving feedback information of the target object on the different foods eaten, and determining the taste data of the target object based on the cooking modes and/or the feedback information.
Optionally, the acquisition module comprises: the second acquisition module is used for acquiring physical condition data of the target object transmitted by the external medical equipment, wherein different physical condition data correspond to different recommended foods, and the external medical equipment comprises at least one of the following components: a hospital's computer to store cases, medical instruments, and wearable body detection devices.
Optionally, the analysis module comprises: an analysis sub-module for analyzing the user data using the neural network model, determining at least one food matched with the physical condition data of the target subject, and outputting a food category of the matched food; and the matching module is used for outputting the matching degrees of different foods under the condition that the plurality of foods are determined to be matched with the physical condition data of the target object, and taking the matching degree of each food as the recommendation probability of different foods.
Optionally, the apparatus further comprises: an obtaining module, configured to obtain sample data before analyzing user data using a neural network model, where the sample data includes: food characteristics of different foods and physical condition data of different target subjects; and the training module is used for inputting the sample data into the neural network for training to obtain the neural network model.
Optionally, the apparatus further comprises: and the sequencing module is used for sequentially selecting a predetermined number of foods to recommend according to the recommendation probabilities of different types of foods output by the output layer of the neural network model from high to low.
Optionally, the apparatus further comprises: the output module is used for outputting recommendation information after analyzing the user data by using the neural network model and determining the food type suitable for the target object, wherein the recommendation information is the food type recommended to the target object; the judging module is used for judging whether the type of the food to be cooked is matched with the type of the food recommended to the target object or not under the condition that the type of the food to be cooked is identified; and the receiving module is used for receiving the feedback information of the target object if the food type to be cooked is matched with the food type recommended to the target object.
Optionally, the apparatus further comprises: and the training submodule is used for collecting the type of the currently cooked food and/or feedback information if the type of the food to be cooked is not matched with the type of the food recommended to the target object, and training the neural network model by using the collected result as training data.
It should be noted that, reference may be made to the relevant description in embodiment 1 for optional or preferred embodiments of this embodiment, but the present invention is not limited to the disclosure in embodiment 1, and is not described herein again.
Example 3
According to an embodiment of the present invention, there is provided a storage medium including a stored program, wherein the apparatus in which the storage medium is located is controlled to perform the method of recommending food in embodiment 1 when the program is executed.
Example 4
According to an embodiment of the present invention, there is provided a processor for executing a program, wherein the program executes the method for recommending food in embodiment 1.
Example 5
According to an embodiment of the present invention, there is provided a cooking appliance including the device for recommending food in embodiment 2 described above.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit may be a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
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, may be located in one place, or may be distributed on a plurality of 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, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes 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 steps of the apparatus according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that it is obvious to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should also be considered as the protection scope of the present invention.
Claims (19)
1. A method of recommending food, comprising:
acquiring user data associated with a target object, wherein the user data comprises at least one of: taste data and physical condition data for different foods;
analyzing the user data using a neural network model to determine a food category suitable for the target object.
2. The method of claim 1, wherein in the event that the user data comprises taste data for different foods, acquiring user data associated with a target object comprises:
acquiring the types of different foods eaten by the target object in a preset time period through a cooking appliance, and determining food characteristics according to the types of the foods, wherein the food characteristics comprise: cooking and nutritional characteristics;
the cooking appliance records cooking modes of different foods in the preset time period, and/or receives feedback information of the target object to the different foods eaten, and determines taste data of the target object based on the cooking modes and/or the feedback information.
3. The method of claim 1, wherein, in the event that the user data comprises physical condition data, acquiring user data associated with a target subject comprises:
acquiring physical condition data of the target object transmitted by an external medical device, wherein different physical condition data correspond to different recommended foods, and the external medical device comprises at least one of the following components: a hospital's computer to store cases, medical instruments, and wearable body detection devices.
4. The method of claim 1, wherein analyzing the user data using a neural network model to determine a food category suitable for the target object comprises:
analyzing the user data using a neural network model, determining at least one food matching the physical condition data of the target subject, and outputting a food category of the matched food;
wherein, in a case where it is determined that there is a plurality of kinds of food matching the physical status data of the target subject, the matching degree of different foods is output, and the matching degree of each food is taken as the recommendation probability of the different kinds of food.
5. The method of claim 1, wherein prior to analyzing the user data using a neural network model, the method further comprises:
obtaining sample data, wherein the sample data comprises: food characteristics of different foods and physical condition data of different target subjects;
and inputting the sample data into a neural network for training to obtain the neural network model.
6. The method of claim 4, wherein a predetermined number of foods are selected in sequence from high to low according to the recommendation probability of different foods output by the output layer of the neural network model.
7. The method of any one of claims 1 to 6, wherein after analyzing the user data using a neural network model to determine a food category suitable for the target object, the method further comprises:
outputting recommendation information, wherein the recommendation information is a food category recommended to the target object;
in the case that the kind of food to be cooked is identified, judging whether the kind of food to be cooked matches with the kind of food recommended to the target object;
and receiving feedback information of the target object if the food type to be cooked is matched with the food type recommended to the target object.
8. The method according to claim 7, wherein if the food type to be cooked does not match the food type recommended to the target object, the currently cooked food type and/or feedback information is collected, and the collected result is used as training data to train the neural network model.
9. An apparatus for recommending food, comprising:
an acquisition module to acquire user data associated with a target object, wherein the user data includes at least one of: taste data and physical condition data for different foods;
an analysis module for analyzing the user data using a neural network model to determine a food category suitable for the target object.
10. The apparatus of claim 9, wherein the acquisition module comprises:
the first acquisition module is used for acquiring the types of different foods eaten by the target object within a preset time period through a cooking appliance and determining food characteristics according to the types of the foods, wherein the food characteristics comprise: cooking and nutritional characteristics;
the first determination module is used for recording cooking modes of different foods in the preset time period through a cooking appliance and/or receiving feedback information of the target object on the different foods eaten, and determining the taste data of the target object based on the cooking modes and/or the feedback information.
11. The apparatus of claim 9, wherein the acquisition module comprises:
the second acquisition module is used for acquiring the physical condition data of the target object transmitted by the external medical equipment, wherein different physical condition data correspond to different recommended foods, and the external medical equipment comprises at least one of the following components: a hospital's computer to store cases, medical instruments, and wearable body detection devices.
12. The apparatus of claim 9, wherein the analysis module comprises:
an analysis sub-module for analyzing the user data using a neural network model, determining at least one food matching the physical condition data of the target subject, and outputting a food category of the matched food;
and the matching module is used for outputting the matching degrees of different foods under the condition that the plurality of foods are determined to be matched with the physical condition data of the target object, and taking the matching degree of each food as the recommendation probability of different foods.
13. The apparatus of claim 9, further comprising:
an obtaining module configured to obtain sample data before analyzing the user data using a neural network model, wherein the sample data includes: food characteristics of different foods and physical condition data of different target subjects;
and the training module is used for inputting the sample data into a neural network for training to obtain the neural network model.
14. The apparatus of claim 12, further comprising:
and the sequencing module is used for sequentially selecting a predetermined number of foods to recommend according to the recommendation probability of different types of foods output by the output layer of the neural network model from high to low.
15. The apparatus of any one of claims 9 to 14, further comprising:
the output module is used for outputting recommendation information after analyzing the user data by using a neural network model and determining a food type suitable for the target object, wherein the recommendation information is the food type recommended to the target object;
the judging module is used for judging whether the type of the food to be cooked is matched with the type of the food recommended to the target object or not under the condition that the type of the food to be cooked is identified;
and the receiving module is used for receiving the feedback information of the target object if the food type to be cooked is matched with the food type recommended to the target object.
16. The apparatus of claim 15, further comprising:
and the training submodule is used for collecting the type of the currently cooked food and/or feedback information if the type of the food to be cooked is not matched with the type of the food recommended to the target object, and training the neural network model by taking the collected result as training data.
17. A storage medium, characterized in that the storage medium comprises a stored program, wherein when the program runs, the storage medium is controlled by a device to execute the method for recommending food according to any one of claims 1 to 8.
18. A processor, characterized in that the processor is configured to run a program, wherein the program when running performs the method of recommending food of any of claims 1 to 8.
19. A cooking appliance comprising the device for recommending food of any of claims 9 to 16.
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Application publication date: 20200721 |