阅读说明：本技术 食用策略的确定方法及装置、存储介质、电子装置 (Edible strategy determination method and device, storage medium and electronic device ) 是由 王鹏飞 于 2021-06-24 设计创作，主要内容包括：本发明公开了一种食用策略的确定方法及装置、存储介质、电子装置,其中,上述方法包括：采集目标食品的数据信息,其中,数据信息包含以下至少之一：目标食品的保质期、目标食品的营养成分表；将数据信息输入到食品模型中,以得到目标食品的预测食用时间段,其中,食品模型为使用多组数据通过机器学习训练出的,多组数据中的每组数据均包括：食品的数据信息,以及食品的数据信息对应的食用时间段；在满足预设条件的情况下,根据预测食用时间段向目标对象确定目标食品的食用策略,解决了相关技术中,无法根据待食用的目标食品的数据信息为目标对象确定相应的食用策略等问题。(The invention discloses a method and a device for determining an eating strategy, a storage medium and an electronic device, wherein the method comprises the following steps: collecting data information of a target food, wherein the data information comprises at least one of the following: the shelf life of the target food and the nutrient content table of the target food; inputting data information into a food model to obtain a predicted eating time period of a target food, wherein the food model is trained by machine learning by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the data information of the food and the eating time period corresponding to the data information of the food are obtained; under the condition that the preset conditions are met, the eating strategy of the target food is determined for the target object according to the predicted eating time period, and the problems that in the related technology, the corresponding eating strategy cannot be determined for the target object according to the data information of the target food to be eaten and the like are solved.)

1. A method for determining a consumption strategy, comprising:

collecting data information of a target food, wherein the data information comprises at least one of the following: the shelf life of the target food and the nutrient content table of the target food;

inputting the data information into a food model to obtain a predicted eating time period of the target food, wherein the food model is trained by machine learning using a plurality of sets of data, and each set of data in the plurality of sets of data comprises: the data information of the food and the eating time period corresponding to the data information of the food are obtained;

and determining the eating strategy of the target food to the target object according to the predicted eating time period under the condition that a preset condition is met.

2. The eating strategy determining method according to claim 1, wherein in case that a preset condition is met, determining the eating strategy of the target food product to the target object according to the predicted eating time period comprises:

acquiring a diet list of the target object, wherein the diet list is used for indicating historical eating records according to the target object, and the historical eating records are used for indicating eating time periods of different foods;

acquiring a target eating time period corresponding to the target food from the diet list;

in an instance in which the predicted consumption time period is less than the target consumption time period, indicating that the target subject has processed the target food product within the predicted consumption time period.

3. The eating strategy determination method according to claim 2, wherein after obtaining the target eating time period corresponding to the target food from the diet list, the method further comprises:

in an instance in which the predicted consumption time period is greater than the target consumption time period, indicating that the target subject has processed the target food product within the target consumption time period.

4. The method of claim 2, further comprising:

determining a target eating time period corresponding to the target food is not obtained from the diet list;

instructing the target subject to finish processing the target food within the predicted consumption time period.

5. The eating strategy determination method according to claim 1, wherein after determining the eating strategy of the target food to the target object according to the predicted eating period, the method comprises:

receiving indication information sent by a terminal corresponding to a target object, wherein the indication information is used for indicating that the terminal allows receiving push information in a target time period, and the push information is used for pushing the eating strategies to the target object;

and sending the eating strategy to the terminal within the target time period.

6. The eating strategy determining method according to claim 1, wherein after determining the eating strategy of the target food to the target object according to the predicted eating time period under the condition that a preset condition is met, the method further comprises:

obtaining a quality of the target food product over a plurality of time periods, wherein the time of the plurality of time periods precedes the shelf life;

and calculating the mass weighting of the plurality of time periods to determine the optimal eating time of the target food.

7. The eating strategy determining method according to claim 1, wherein after determining the eating strategy of the target food to the target object according to the predicted eating time period under the condition that a preset condition is met, the method further comprises:

acquiring the actual time for the target object to eat the target food;

and in the case that the actual time is inconsistent with a first boundary value of the predicted eating time period, taking a time set consisting of the actual time and a second boundary value of the predicted eating time period as the eating time period of the target food, wherein the predicted eating time period comprises: the first boundary value and the second boundary value.

8. An apparatus for determining a consumption strategy, comprising:

the acquisition module is used for acquiring data information of the target food, wherein the data information comprises at least one of the following: the shelf life of the target food and the nutrient content table of the target food;

a prediction module, configured to input the data information into a food model to obtain a predicted eating time period of the target food, where the food model is trained through machine learning by using multiple sets of data, and each set of data in the multiple sets of data includes: the data information of the food and the eating time period corresponding to the data information of the food are obtained;

and the determining module is used for determining the eating strategy of the target food to the target object according to the predicted eating time period under the condition that a preset condition is met.

9. A computer-readable storage medium, comprising a stored program, wherein the program is operable to perform the method of any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method of any of claims 1 to 7 by means of the computer program.

Technical Field

The invention relates to the field of communication, in particular to a method and a device for determining an eating strategy, a storage medium and an electronic device.

Background

At present, the prediction of refrigerator fresh-keeping behaviors (including the optimal food eating time and the eating habits of users) is based on a machine. Limited by a Read-only Memory (ROM) and the capacity of a Memory, the storage data volume is limited, the optimal eating time of the food cannot be separated, and the optimal diet matching cannot be recommended to the user in time. In addition, the refrigerator fresh-keeping behavior of the related art is only based on a period of time data, the most reasonable eating time and matching mode of food cannot be recommended to the user at the optimal time point, the related prediction algorithm does not combine the actual eating habits of the user and is not suitable for the user, and when the eating behavior of the user changes, the prediction algorithm cannot automatically repair and adjust according to the actual variable.

Aiming at the problems that the corresponding eating strategy can not be determined for the target object according to the data information of the target food to be eaten in the related technology, and the like, an effective solution is not provided.

Disclosure of Invention

The embodiment of the invention provides a method and a device for determining an eating strategy, a storage medium and an electronic device, which are used for at least solving the problems that the corresponding eating strategy cannot be determined for a target object according to data information of target food to be eaten in the related technology and the like.

According to an embodiment of the present invention, there is provided a method for determining a food consumption strategy, including: collecting data information of a target food, wherein the data information comprises at least one of the following: the shelf life of the target food and the nutrient content table of the target food; inputting the data information into a food model to obtain a predicted eating time period of the target food, wherein the food model is trained by machine learning using a plurality of sets of data, and each set of data in the plurality of sets of data comprises: the data information of the food and the eating time period corresponding to the data information of the food are obtained; and determining the eating strategy of the target food to the target object according to the predicted eating time period under the condition that a preset condition is met.

In an exemplary embodiment, in case that a preset condition is satisfied, determining the eating strategy of the target food to the target object according to the predicted eating time period comprises: acquiring a diet list of the target object, wherein the diet list is used for indicating historical eating records according to the target object, and the historical eating records are used for indicating eating time periods of different foods; acquiring a target eating time period corresponding to the target food from the diet list; in an instance in which the predicted consumption time period is less than the target consumption time period, indicating that the target subject has processed the target food product within the predicted consumption time period.

In an exemplary embodiment, after obtaining the target consumption time period corresponding to the target food from the diet list, the method further comprises: in an instance in which the predicted consumption time period is greater than the target consumption time period, indicating that the target subject has processed the target food product within the target consumption time period.

In an exemplary embodiment, the method further includes: determining a target eating time period corresponding to the target food is not obtained from the diet list; instructing the target subject to finish processing the target food within the predicted consumption time period.

In an exemplary embodiment, after determining the consumption strategy of the target food product to the target subject according to the predicted consumption time period, the method comprises: receiving indication information sent by a terminal corresponding to a target object, wherein the indication information is used for indicating that the terminal allows receiving push information in a target time period, and the push information is used for pushing the eating strategies to the target object; and sending the eating strategy to the terminal within the target time period.

In an exemplary embodiment, after determining the eating strategy of the target food to the target object according to the predicted eating time period in case that a preset condition is satisfied, the method further comprises: obtaining a quality of the target food product over a plurality of time periods, wherein the time of the plurality of time periods precedes the shelf life; and calculating the mass weighting of the plurality of time periods to determine the optimal eating time of the target food.

In an exemplary embodiment, after determining the eating strategy of the target food to the target object according to the predicted eating time period in case that a preset condition is satisfied, the method further comprises: acquiring the actual time for the target object to eat the target food; and in the case that the actual time is inconsistent with a first boundary value of the predicted eating time period, taking a time set consisting of the actual time and a second boundary value of the predicted eating time period as the eating time period of the target food, wherein the predicted eating time period comprises: the first boundary value and the second boundary value.

According to another embodiment of the present invention, there is also provided a food consumption strategy determining apparatus, including: the acquisition module is used for acquiring data information of the target food, wherein the data information comprises at least one of the following: the shelf life of the target food and the nutrient content table of the target food; a prediction module, configured to input the data information into a food model to obtain a predicted eating time period of the target food, where the food model is trained through machine learning by using multiple sets of data, and each set of data in the multiple sets of data includes: the data information of the food and the eating time period corresponding to the data information of the food are obtained; and the determining module is used for determining the eating strategy of the target food to the target object according to the predicted eating time period under the condition that a preset condition is met.

In an exemplary embodiment, the determining module is configured to obtain a diet list of the target object, where the diet list is used to indicate a historical consumption record according to the target object, and the historical consumption record is used to indicate consumption time periods of different food products; acquiring a target eating time period corresponding to the target food from the diet list; in an instance in which the predicted consumption time period is less than the target consumption time period, indicating that the target subject has processed the target food product within the predicted consumption time period.

In an exemplary embodiment, the determining module is further configured to instruct the target object to finish processing the target food within the target consumption time period if the predicted consumption time period is greater than the target consumption time period.

In an exemplary embodiment, the determining module is further configured to determine that the target consumption time period corresponding to the target food is not obtained from the diet list; instructing the target subject to finish processing the target food within the predicted consumption time period.

In an exemplary embodiment, the apparatus further includes: the indication module is used for receiving indication information sent by a terminal corresponding to a target object, wherein the indication information is used for indicating that the terminal allows to receive push information in a target time period, and the push information is used for pushing the eating strategy to the target object; and sending the eating strategy to the terminal within the target time period.

In an exemplary embodiment, the apparatus further includes: a quality module for obtaining a quality of the target food product over a plurality of time periods, wherein the time of the plurality of time periods precedes the shelf life; and calculating the mass weighting of the plurality of time periods to determine the optimal eating time of the target food.

In an exemplary embodiment, the apparatus further comprises: the comparison module is used for acquiring the actual time for the target object to eat the target food; and in the case that the actual time is inconsistent with a first boundary value of the predicted eating time period, taking a time set consisting of the actual time and a second boundary value of the predicted eating time period as the eating time period of the target food, wherein the predicted eating time period comprises: the first boundary value and the second boundary value.

According to a further embodiment of the present invention, a computer-readable storage medium is also provided, in which a computer program is stored, wherein the computer program is configured to carry out the steps of any of the above-described method embodiments when executed.

According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.

By the invention, data information of the target food is collected, wherein the data information comprises at least one of the following data: the shelf life of the target food and the nutrient content table of the target food; inputting data information into a food model to obtain a predicted eating time period of a target food, wherein the food model is trained by machine learning by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the data information of the food and the eating time period corresponding to the data information of the food are obtained; under the condition of meeting the preset conditions, determining an eating strategy of the target food to the target object according to the predicted eating time period, namely analyzing data information of the target food through a food model, and further determining an eating strategy to be executed by the target object for the target food.

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 block diagram of a hardware structure of a cloud platform of a method for determining a food consumption policy according to an embodiment of the present invention;

FIG. 2 is a flow chart of a method of determining a consumption strategy according to an embodiment of the present invention;

FIG. 3 is a schematic flow chart of an intelligent device based edible strategy determination according to an alternative embodiment of the present invention;

FIG. 4 is a schematic diagram of the construction of a food product model according to an alternative embodiment of the invention;

FIG. 5 is a schematic flow chart for determining optimal consumption time according to an alternative embodiment of the present invention;

fig. 6 is a block diagram of a device for determining a food consumption policy according to an embodiment of the present invention.

Detailed Description

The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.

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.

The method provided by the embodiment of the application can be executed in a computer terminal, a cloud platform or a similar operation device. Taking an example of the method running on a cloud platform, fig. 1 is a hardware structure block diagram of the cloud platform of the method for determining an eating strategy according to the embodiment of the present invention. As shown in fig. 1, the cloud platform may include one or more (only one shown in fig. 1) processors 102 (the processors 102 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, and in an exemplary embodiment, may further include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those of ordinary skill in the art that the structure shown in fig. 1 is merely illustrative and is not intended to limit the structure of the cloud platform. For example, the cloud platform may also include more or fewer components than shown in FIG. 1, or have a different configuration with equivalent functionality to that shown in FIG. 1 or with more functionality than that shown in FIG. 1.

The memory 104 may be used to store a computer program, for example, a software program and a module of an application software, such as a computer program corresponding to the method for determining a food consumption policy in the embodiment of the present invention, and the processor 102 executes the computer program stored in the memory 104 to perform various functional applications and data processing, i.e., implement the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the cloud platform via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of a cloud platform. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

In this embodiment, a method for determining a food strategy is provided, and applied to the cloud platform, and fig. 2 is a flowchart of a method for determining a food strategy according to an embodiment of the present invention, where the flowchart includes the following steps:

step S202, collecting data information of the target food, wherein the data information comprises at least one of the following: the shelf life of the target food and the nutrient content table of the target food;

step S204, inputting the data information into a food model to obtain a predicted eating time period of the target food, wherein the food model is trained by machine learning using multiple sets of data, and each set of data in the multiple sets of data includes: the data information of the food and the eating time period corresponding to the data information of the food are obtained;

and step S206, determining the eating strategy of the target food to the target object according to the predicted eating time period under the condition that the preset condition is met.

Through the technical scheme, the data information of the target food is collected, wherein the data information comprises at least one of the following data: the shelf life of the target food and the nutrient content table of the target food; inputting data information into a food model to obtain a predicted eating time period of a target food, wherein the food model is trained by machine learning by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the data information of the food and the eating time period corresponding to the data information of the food are obtained; under the condition of meeting the preset conditions, determining an eating strategy of the target food to the target object according to the predicted eating time period, namely analyzing data information of the target food through a food model, and further determining an eating strategy to be executed by the target object for the target food.

In an exemplary embodiment, in case that a preset condition is satisfied, determining the eating strategy of the target food to the target object according to the predicted eating time period comprises: acquiring a diet list of the target object, wherein the diet list is used for indicating historical eating records according to the target object, and the historical eating records are used for indicating eating time periods of different foods; acquiring a target eating time period corresponding to the target food from the diet list; in an instance in which the predicted consumption time period is less than the target consumption time period, indicating that the target subject has processed the target food product within the predicted consumption time period.

In short, in order to better realize recommendation of eating strategies of different target foods, historical eating records of target objects stored in the cloud platform are processed, food categories and eating time in the historical eating records are extracted, the eating time corresponding to different foods is in one-to-one correspondence, the eating time period of each food is determined, then a diet list of the target object related to the household appliance is determined according to the historical eating record of the target object transmitted to the cloud platform by the household appliance storing the food, further matching the predicted eating time period of the target food predicted by the food model with the target eating time period of the diet list, in the event that the predicted consumption time period is less than the target consumption time period, the target subject is instructed to finish processing the target food within the predicted consumption time period to make the predicted consumption time period more in line with the consumption habits of the target subject.

It should be noted that, when a plurality of home appliances for storing food are bound to a target object at the same time, as long as the current device can upload historical usage data of the target object for the food to the cloud platform, a food model determined based on historical consumption records and used for predicting that the target object consumes the target food next time will include the current device, so that the food model can cover more eating ways of the target object.

In an exemplary embodiment, after obtaining the target consumption time period corresponding to the target food from the diet list, the method further comprises: in an instance in which the predicted consumption time period is greater than the target consumption time period, indicating that the target subject has processed the target food product within the target consumption time period.

It can be understood that, when the predicted eating time period is longer than the target eating time period, it indicates that the eating habits of the target object are changed, or the current historical eating records corresponding to the home appliance device include eating records of a plurality of target objects, and at this time, in order to ensure consistency of the eating patterns of the target objects, the target object is instructed to finish processing the target food in the target eating time period matched in the eating list.

In an exemplary embodiment, the method further includes: determining a target eating time period corresponding to the target food is not obtained from the diet list; instructing the target subject to finish processing the target food within the predicted consumption time period.

In short, when it is determined that the target eating time period corresponding to the target food is not obtained from the diet list, it is stated that the historical eating history used in establishing the current food model does not include the historical eating history of the target food, and further, after the predicted eating time period of the target food is predicted by the food model, the target object is recommended to finish processing the target food in the predicted eating time period.

In an exemplary embodiment, after determining the consumption strategy of the target food product to the target subject according to the predicted consumption time period, the method comprises: receiving indication information sent by a terminal corresponding to a target object, wherein the indication information is used for indicating that the terminal allows receiving push information in a target time period, and the push information is used for pushing the eating strategies to the target object; and sending the eating strategy to the terminal within the target time period.

For example, the target object may not receive the eating strategy within 24 hours, and eat the target food according to the eating strategy, so that the eating strategy needs to be pushed according to the pushable time period set by the target object, so that the eating strategy determined by the eating model can be successfully acquired by the target object, and disturbance of pushing information on the target object in non-pushing time is reduced.

In an exemplary embodiment, after determining the eating strategy of the target food to the target object according to the predicted eating time period in case that a preset condition is satisfied, the method further comprises: obtaining a quality of the target food product over a plurality of time periods, wherein the time of the plurality of time periods precedes the shelf life; and calculating the mass weighting of the plurality of time periods to determine the optimal eating time of the target food.

For example, when the optimal eating time of food in a refrigerator needs to be determined currently, the food fresh-keeping data in the time period of the previous n days are counted to obtain the optimal eating time of the food. And dynamically adjusting the refrigerator to the optimal temperature, and recommending the most reasonable food collocation for the user according to the determined optimal eating time of the food and the eating habit of the user in the previous n days.

In an exemplary embodiment, after determining the eating strategy of the target food to the target object according to the predicted eating time period in case that a preset condition is satisfied, the method further comprises: acquiring the actual time for the target object to eat the target food; and in the case that the actual time is inconsistent with a first boundary value of the predicted eating time period, taking a time set consisting of the actual time and a second boundary value of the predicted eating time period as the eating time period of the target food, wherein the predicted eating time period comprises: the first boundary value and the second boundary value.

In order to ensure the accuracy of the food model prediction, when the actual time of the target object eating the target food exceeds the maximum boundary value (i.e. the first boundary value) of the predicted eating time period, the actual time is taken as the maximum boundary value of the predicted eating time period after the food, and the time set of the eating time period of the target food is updated by combining the minimum boundary value (i.e. the second boundary value) of the predicted eating time period, so that the food model can be flexibly adjusted and corrected according to the actual situation.

In order to better understand the process of the method for determining the eating strategy, the following describes a flow of the method for determining the eating strategy with reference to an optional embodiment, but the flow is not limited to the technical solution of the embodiment of the present invention.

In an optional implementation manner, a method for determining an eating strategy is provided, and fig. 3 is a schematic flow chart of determining an eating strategy based on an intelligent device according to an optional embodiment of the present invention, which specifically includes the following steps:

step S302: extracting initial data of food, extracting the initial data of food according to RFID (radio Frequency Identification, RFID for short) technology, and reading and writing a recording medium (such as an electronic tag or a radio Frequency card) carrying food data information.

Step S304: and (4) establishing a food model, and as shown in fig. 4, schematically constructing the food model according to the alternative embodiment of the invention.

For example, when food consumption in a refrigerator needs to be predicted at present, a food model is established for the current refrigerator based on historical user diet data stored on a cloud server and a common modeling tool resume is applied, further, a next diet mode is predicted according to the food model of the refrigerator, after next actual diet data is obtained, the established diet model is continuously corrected according to actual values, and diet behaviors can enter a database of historical food consumption records for optimizing the food model.

Step S306: predicting the optimal food consumption time, determining the eating habits of the user (corresponding to the target object in the embodiment of the invention), and recommending the most reasonable food collocation for the user at the optimal food consumption time according to the eating habits of the user, as shown in fig. 5, which is a schematic flow chart for determining the optimal food consumption time in the alternative embodiment of the invention.

For example, when the optimal eating time of food in a refrigerator needs to be determined currently, the food fresh-keeping data in the time period of the previous n days are counted to obtain the optimal eating time of the food. And dynamically adjusting the refrigerator to the optimal temperature, and recommending the most reasonable food collocation for the user according to the determined optimal eating time of the food and the eating habit of the user in the previous n days.

It should be noted that the food model may be constructed in a cloud platform, or may be constructed in other servers capable of performing data interaction with the device, which is not limited in this disclosure.

According to the optional embodiment of the invention, when the eating strategy is determined, the method is realized based on cloud big data, is not limited by the data volume of historical eating records, and can cover all diet data during calculation; the related algorithm model can be continuously optimized and corrected according to the actual result; the food consumption time is weighted and calculated on the basis of time period prediction, and the specific consumption time point can be detailed and predicted. The reasonable diet collocation cannot be recommended in a corresponding time period without combining the diet habit condition of the user, the storage and operation pressure of the equipment terminal is reduced, the most suitable food collocation is recommended for the user in the optimal food eating time, the user experience is improved, and the more accurate prediction of the eating time of the target food is realized.

Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.

In this embodiment, a device for determining an eating strategy is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, which have already been described and will not be described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.

FIG. 6 is a block diagram of an apparatus for determining a consumption strategy according to an embodiment of the present invention; as shown in fig. 6, includes:

a collecting module 62, configured to collect data information of a target food, where the data information includes at least one of: the shelf life of the target food and the nutrient content table of the target food;

a prediction module 64, configured to input the data information into a food model to obtain a predicted eating time period of the target food, where the food model is trained through machine learning by using multiple sets of data, and each set of data in the multiple sets of data includes: the data information of the food and the eating time period corresponding to the data information of the food are obtained;

a determining module 66, configured to determine an eating strategy of the target food to the target object according to the predicted eating time period if a preset condition is met.

Through the technical scheme, the data information of the target food is collected, wherein the data information comprises at least one of the following data: the shelf life of the target food and the nutrient content table of the target food; inputting data information into a food model to obtain a predicted eating time period of a target food, wherein the food model is trained by machine learning by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the data information of the food and the eating time period corresponding to the data information of the food are obtained; under the condition of meeting the preset conditions, determining an eating strategy of the target food to the target object according to the predicted eating time period, namely analyzing data information of the target food through a food model, and further determining an eating strategy to be executed by the target object for the target food.

In an exemplary embodiment, the determining module is configured to obtain a diet list of the target object, where the diet list is used to indicate a historical consumption record according to the target object, and the historical consumption record is used to indicate consumption time periods of different food products; acquiring a target eating time period corresponding to the target food from the diet list; in an instance in which the predicted consumption time period is less than the target consumption time period, indicating that the target subject has processed the target food product within the predicted consumption time period.

In short, in order to better realize recommendation of eating strategies of different target foods, historical eating records of target objects stored in the cloud platform are processed, food categories and eating time in the historical eating records are extracted, the eating time corresponding to different foods is in one-to-one correspondence, the eating time period of each food is determined, then a diet list of the target object related to the household appliance is determined according to the historical eating record of the target object transmitted to the cloud platform by the household appliance storing the food, further matching the predicted eating time period of the target food predicted by the food model with the target eating time period of the diet list, in the event that the predicted consumption time period is less than the target consumption time period, the target subject is instructed to finish processing the target food within the predicted consumption time period to make the predicted consumption time period more in line with the consumption habits of the target subject.

It should be noted that, when a plurality of home appliances for storing food are bound to a target object at the same time, as long as the current device can upload historical usage data of the target object for the food to the cloud platform, a food model determined based on historical consumption records and used for predicting that the target object consumes the target food next time will include the current device, so that the food model can cover more eating ways of the target object.

In an exemplary embodiment, the determining module is further configured to instruct the target object to finish processing the target food within the target consumption time period if the predicted consumption time period is greater than the target consumption time period.

It can be understood that, when the predicted eating time period is longer than the target eating time period, it indicates that the eating habits of the target object are changed, or the current historical eating records corresponding to the home appliance device include eating records of a plurality of target objects, and at this time, in order to ensure consistency of the eating patterns of the target objects, the target object is instructed to finish processing the target food in the target eating time period matched in the eating list.

In an exemplary embodiment, the determining module is further configured to determine that the target consumption time period corresponding to the target food is not obtained from the diet list; instructing the target subject to finish processing the target food within the predicted consumption time period.

In short, when it is determined that the target eating time period corresponding to the target food is not obtained from the diet list, it is stated that the historical eating history used in establishing the current food model does not include the historical eating history of the target food, and further, after the predicted eating time period of the target food is predicted by the food model, the target object is recommended to finish processing the target food in the predicted eating time period.

In an exemplary embodiment, the apparatus further includes: the indication module is used for receiving indication information sent by a terminal corresponding to a target object, wherein the indication information is used for indicating that the terminal allows to receive push information in a target time period, and the push information is used for pushing the eating strategy to the target object; and sending the eating strategy to the terminal within the target time period.

For example, the target object may not receive the eating strategy within 24 hours, and eat the target food according to the eating strategy, so that the eating strategy needs to be pushed according to the pushable time period set by the target object, so that the eating strategy determined by the eating model can be successfully acquired by the target object, and disturbance of pushing information on the target object in non-pushing time is reduced.

In an exemplary embodiment, the apparatus further includes: a quality module for obtaining a quality of the target food product over a plurality of time periods, wherein the time of the plurality of time periods precedes the shelf life; and calculating the mass weighting of the plurality of time periods to determine the optimal eating time of the target food.

For example, when the optimal eating time of food in a refrigerator needs to be determined currently, the food fresh-keeping data in the time period of the previous n days are counted to obtain the optimal eating time of the food. And dynamically adjusting the refrigerator to the optimal temperature, and recommending the most reasonable food collocation for the user according to the determined optimal eating time of the food and the eating habit of the user in the previous n days.

In an exemplary embodiment, the apparatus further comprises: the comparison module is used for acquiring the actual time for the target object to eat the target food; and in the case that the actual time is inconsistent with a first boundary value of the predicted eating time period, taking a time set consisting of the actual time and a second boundary value of the predicted eating time period as the eating time period of the target food, wherein the predicted eating time period comprises: the first boundary value and the second boundary value.

In order to ensure the accuracy of the food model prediction, when the actual time of the target object eating the target food exceeds the maximum boundary value (i.e. the first boundary value) of the predicted eating time period, the actual time is taken as the maximum boundary value of the predicted eating time period after the food, and the time set of the eating time period of the target food is updated by combining the minimum boundary value (i.e. the second boundary value) of the predicted eating time period, so that the food model can be flexibly adjusted and corrected according to the actual situation.

An embodiment of the present invention further provides a storage medium including a stored program, wherein the program executes any one of the methods described above.

In an exemplary embodiment, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:

s1, collecting data information of the target food, wherein the data information comprises at least one of the following: the shelf life of the target food and the nutrient content table of the target food;

s2, inputting the data information into a food model to obtain the predicted eating time period of the target food, wherein the food model is trained by machine learning by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the data information of the food and the eating time period corresponding to the data information of the food are obtained;

and S3, determining the eating strategy of the target food to the target object according to the predicted eating time period under the condition that the preset condition is met.

In an exemplary embodiment, in the present embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.

Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.

In an exemplary embodiment, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.

In an exemplary embodiment, in the present embodiment, the processor may be configured to execute the following steps by a computer program:

s1, collecting data information of the target food, wherein the data information comprises at least one of the following: the shelf life of the target food and the nutrient content table of the target food;

s2, inputting the data information into a food model to obtain the predicted eating time period of the target food, wherein the food model is trained by machine learning by using a plurality of groups of data, and each group of data in the plurality of groups of data comprises: the data information of the food and the eating time period corresponding to the data information of the food are obtained;

and S3, determining the eating strategy of the target food to the target object according to the predicted eating time period under the condition that the preset condition is met.

In an exemplary embodiment, for specific examples in this embodiment, reference may be made to the examples described in the above embodiments and optional implementation manners, and details of this embodiment are not described herein again.

It will be apparent to those skilled in the art that the various modules or steps of the invention described above may be implemented using a general purpose computing device, which may be centralized on a single computing device or distributed across a network of computing devices, and in one exemplary embodiment may be implemented using program code executable by a computing device, such that the steps shown and described may be executed by a computing device stored in a memory device and, in some cases, executed in a sequence different from that shown and described herein, or separately fabricated into individual integrated circuit modules, or multiple ones of them fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.