CN110953838A - Food material buying prompting method in refrigerator, storage medium and refrigerator - Google Patents
Food material buying prompting method in refrigerator, storage medium and refrigerator Download PDFInfo
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- CN110953838A CN110953838A CN201911296245.2A CN201911296245A CN110953838A CN 110953838 A CN110953838 A CN 110953838A CN 201911296245 A CN201911296245 A CN 201911296245A CN 110953838 A CN110953838 A CN 110953838A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D29/00—Arrangement or mounting of control or safety devices
- F25D29/005—Mounting of control devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D11/00—Self-contained movable devices, e.g. domestic refrigerators
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D2500/00—Problems to be solved
- F25D2500/06—Stock management
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Abstract
The invention discloses a method for prompting food material buying in a refrigerator, a storage medium and the refrigerator. The method comprises the steps of determining food material information in each type area by acquiring food material images of each type area in the refrigerator and utilizing a first neural network model, determining a food material variety to be supplemented according to the food material information in the preset normal area by judging whether the type area is a preset normal area or not when the type area is the preset normal area, and sending a purchase prompt for purchasing the food material variety to be supplemented to a user; and when the type area is not the preset equipment area, determining whether the food materials need to be supplemented according to the food material information of the type area, and when the food materials need to be supplemented to the type area, sending a food material buying prompt to the user based on the food material type of the type area. According to the scheme, the food material information in the refrigerator is identified in real time and efficiently, different buying prompt strategies are adopted for the stock areas and the non-stock areas with high use frequency, and the use experience of a user is improved.
Description
Technical Field
The invention relates to the field of household appliances, in particular to a method for prompting the purchase of food materials in a refrigerator, a storage medium and the refrigerator.
Background
In the prior art, the refrigerator on the market can only store food and achieve the aim of keeping fresh, and has single function. Meanwhile, since the eating habits of users are almost fixed, the users always have some food materials which are used by the users. Such food materials are in need of continuous purchase. However, with the acceleration of the rhythm of life at present, people are busy more and more, and people often forget to supplement food materials when going to work. In this way, a situation in which the lack of the food material is found only when the food is cooked occurs. In addition, even if the user remembers to replenish the ingredients, it may happen that the user forgets to purchase some ingredients. Under the conditions, the user needs to go to the supermarket for many times, and time and labor are wasted.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the technical problem that the condition of food materials in a refrigerator is obtained in real time and the purchasing prompt is carried out according to the condition of the food materials is solved.
In order to solve the technical problems, the invention provides a method for prompting the buying of food materials in a refrigerator, a storage medium and the refrigerator.
In a first aspect of the present application, a method for prompting food material buying in a refrigerator is provided, which includes:
acquiring food material images of various types of areas in a refrigerator;
inputting the food material image of each type area into a pre-established first neural network model to determine food material information in each type area, wherein the food material information comprises food material varieties and food material residual quantity;
judging whether the type area is a preset normal area or not;
when the type area is a preset stock area, determining the food material variety needing to be supplemented according to the food material information of the type area, and sending a food material buying prompt to a user based on the food material variety needing to be supplemented so that the user can buy the food material of the corresponding variety;
when the type area is not the preset stock area, determining whether the type area needs to be supplemented with food materials according to the food material information of the type area, and when determining that the type area needs to be supplemented with food materials, sending a food material buying prompt to a user based on the type of the food material of the type area so that the user can buy the food materials belonging to the corresponding type.
Preferably, when the type area is the preset equipment area, determining a food material variety to be supplemented according to the food material information of the type area, and sending a food material buying prompt to the user based on the food material variety to be supplemented, the method includes:
when the type area is a preset stock area, comparing the food material variety in the food material information of the type area with the food material variety in the historical storage data;
determining the food material variety to be supplemented in the type region according to the comparison result;
and sending a food material purchasing prompt to the user based on the food material variety needing to be supplemented.
Preferably, when the type area is not the preset stock area, determining whether the type area needs to be supplemented with food materials according to the food material information of the type area, and when it is determined that the type area needs to be supplemented with food materials, sending a food material buying prompt to the user based on the food material type of the type area, including:
when the type area is not the preset stock area, calculating the sum of the residual quantities of the food materials of all food material varieties in the type area according to the residual quantities of the food materials in the food material information of the type area;
judging whether the sum of the residual quantity of the food materials of all the food material varieties in the type area is smaller than a preset quantity or not;
when the sum of the residual quantities of the food materials of all food material varieties in the type area is smaller than the preset quantity, determining that the food materials need to be supplemented to the type area;
and sending a food material buying prompt to the user based on the food material type of the type area.
Preferably, when the type area is not the preset stock area, determining whether the type area needs to be supplemented with food materials according to the food material information of the type area, and when it is determined that the type area needs to be supplemented with food materials, after sending a food material buying prompt to the user based on the food material type of the type area, the method further includes:
acquiring any one of the region information of the refrigerator, historical storage data and date of food materials in the region of the type;
acquiring a food material variety belonging to the type area through a pre-established second neural network model based on any one of the regional information of the refrigerator, historical storage data and date of the food material in the type area;
and recommending the food material varieties which belong to the type area and are obtained by utilizing the second neural network model to the user.
Preferably, the historical storage data comprises food material varieties, historical quantities, historical purchase times and historical consumption speeds.
Preferably, the method further comprises: and storing the determined food material information in each type area to update historical storage data, and updating the first neural network model and the second neural network model based on the updated historical storage data.
Preferably, the first neural network model is constructed based on a YOLO algorithm.
Preferably, the sending of the food material buying prompt to the user comprises: and sending information containing food material buying prompts to a mobile terminal of a user.
In a second aspect of the present application, a storage medium is provided, where a computer program is stored, and the computer program, when executed by a processor, can implement the method for prompting food material buying in a refrigerator as described in any one of the above.
In a third aspect of the present application, a refrigerator is provided, which includes a memory and a controller connected to the memory, wherein the controller is configured to execute a computer program in the memory, and when the computer program is executed by the controller, the method for prompting food material buying in the refrigerator as described in any one of the above aspects can be implemented.
Preferably, the refrigerator further comprises: an image acquisition device and a communication device, wherein the image acquisition device and the communication device are respectively connected with the controller,
the image acquisition device is used for acquiring food material images of various types of regions in the refrigerator;
the communication device is used for sending information containing food material buying prompts to the mobile terminal so as to realize interaction between the refrigerator and the mobile terminal.
Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:
by applying the method for prompting the buying of the food materials in the refrigerator, the food material images of all types of areas in the refrigerator are acquired and input into the pre-established first neural network model, so that the food material information in all types of areas is determined by using the first neural network model, and by judging whether the type areas are preset regular areas or not, when the type areas are the preset regular areas, the food material varieties needing to be supplemented are determined according to the food material information of the type areas, and the buying prompt for buying the food material varieties needing to be supplemented is directly sent to a user, so that the user can buy the food materials of the corresponding varieties; when the type area is not the preset equipment area, determining whether food materials need to be supplemented according to the food material information of the type area, and when determining that the food materials need to be supplemented to the type area, sending a food material buying prompt to a user based on the food material type of the type area so that the user can buy the food materials belonging to the corresponding type. According to the scheme, the food material information in the refrigerator is identified in real time and efficiently, different purchasing prompt strategies are adopted for the stock area and the non-stock area with high use frequency, the situation that a user forgets or does not determine to purchase types is avoided, prompting and recommending can be carried out by combining user habits, and the use experience of the user is improved.
Drawings
The scope of the present disclosure may be better understood by reading the following detailed description of exemplary embodiments in conjunction with the accompanying drawings. Wherein the included drawings are:
fig. 1 shows a flow chart of a method for prompting food material buying in a refrigerator according to an embodiment of the application.
Fig. 2 is a schematic flow chart illustrating a process of sending a food material buying prompt to a user based on a food material variety to be supplemented when a type area is a preset stock area in the embodiment of the present application.
Fig. 3 is a flowchart illustrating that when the type area is not the preset stock area, a food material buying prompt is sent to a user based on the food material type of the type area in the embodiment of the present application.
Fig. 4 shows a flow chart of a method for prompting purchasing of food materials in a refrigerator according to a specific example of the present application.
Fig. 5 shows a schematic structural diagram of a refrigerator provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the following will describe in detail an implementation method of the present invention with reference to the accompanying drawings and embodiments, so that how to apply technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented.
In the prior art, the refrigerator on the market can only store food and achieve the aim of keeping fresh, and has single function. Meanwhile, since the eating habits of users are almost fixed, the users always have some food materials which are used by the users. Such food materials are in need of continuous purchase. However, with the acceleration of the rhythm of life at present, people are busy more and more, and people often forget to supplement food materials when going to work. In this way, a situation in which the lack of the food material is found only when the food is cooked occurs. In addition, even if the user remembers to replenish the ingredients, it may happen that the user forgets to purchase some ingredients. Under the conditions, the user needs to go to the supermarket for many times, and time and labor are wasted.
In view of the above, the application provides a method for prompting food material buying in a refrigerator, a storage medium and a refrigerator. The method comprises the steps that food material images of various types of regions in a refrigerator are obtained, the food material images of the various types of regions are input into a pre-established first neural network model, so that food material information in the various types of regions is determined by the first neural network model, whether the type regions are preset constant regions or not is judged, when the type regions are the preset constant regions, food material varieties needing to be supplemented are determined according to the food material information of the type regions, and a purchase prompt for purchasing the food material varieties needing to be supplemented is directly sent to a user, so that the user can purchase food materials of corresponding varieties; when the type area is not the preset equipment area, determining whether food materials need to be supplemented according to the food material information of the type area, and when determining that the food materials need to be supplemented to the type area, sending a food material buying prompt to a user based on the food material type of the type area so that the user can buy the food materials belonging to the corresponding type. According to the scheme, the food material information in the refrigerator is identified in real time and efficiently, different purchasing prompt strategies are adopted for the stock area and the non-stock area with high use frequency, the situation that a user forgets or does not determine to purchase types is avoided, prompting and recommending can be carried out by combining user habits, and the use experience of the user is improved.
Example one
Referring to fig. 1, fig. 1 shows a method for prompting food material buying in a refrigerator according to an embodiment of the present application, which includes steps S101 to S105.
In step S101, food material images of each type area in the refrigerator are acquired.
The step can be specifically that food material images of various types of regions in the refrigerator are acquired through an image acquisition device, such as a camera, arranged in the refrigerator; or the food material images in various types of regions in the refrigerator can be obtained by the refrigerator by reading data stored in the cloud.
As a specific example, the regions in the refrigerator are divided according to the types to which the food materials belong, the types to which the food materials belong can be divided into ingredients including onion, garlic and the like, fruits including apples, bananas and the like, meats, dairy products and the like, and then the regions in the refrigerator can be correspondingly divided into ingredient regions, fruit regions, meat regions, dairy product regions and the like.
In step S102, the food material image of each type region is input into a first neural network model established in advance to determine the food material information in each type region, wherein the food material information includes the food material variety and the remaining number of food materials.
In a complex scenario, when a plurality of targets need to be processed in real time, automatic target extraction and identification are particularly important.
In the embodiment of the present application, as a preferred example, the first neural network model is constructed based on a YOLO algorithm, and the first neural network model constructed by the YOLO algorithm includes an input layer, a plurality of convolutional layers, a plurality of pooling layers, a connection layer, and an output layer.
The process of determining the food material information in each type area by using the first neural network model constructed based on the YOLO algorithm is as follows:
dividing an input image into n × n meshes;
predicting a plurality of bounding boxes in each grid, and a trust value and a category probability value corresponding to each bounding box;
calculating the confidence of each bounding box based on the trust value and the category probability value corresponding to each bounding box;
respectively judging whether the confidence of each boundary box is smaller than a preset threshold value, and filtering the boundary box with the confidence smaller than the preset threshold value;
and respectively carrying out non-maximum suppression on all the reserved bounding boxes, and outputting the result of the target bounding box.
The method can rapidly and real-timely identify the types and the quantity of the food materials based on the YOLO algorithm. After the food material information is obtained by using the first network model based on the YOLO algorithm, the food material information in the refrigerator can be classified and stored according to the type of the food material variety, so as to update the historical storage data of the refrigerator. The historical storage data comprises food material varieties, historical quantities, historical purchase times and historical consumption speeds.
In step S103, it is determined whether the type area is a preset stock area.
The step may specifically be to set a certain type of area with a high frequency of use as a preset stock area, for example, to set an ingredient area for storing green onions, garlic, and the like as the preset stock area, and a fruit area, a meat area, and a dairy product area as the common area, and then different purchase prompting strategies may be adopted for the preset stock area and the common area, so that a purchase prompt may be flexibly performed according to a user habit. When the type area is the preset stock area, executing step S104; when the type area is not the preset stock area, step S105 is performed.
In step S104, when the type area is the preset stock area, determining a food material variety to be supplemented according to the food material information in the type area, and sending a food material buying prompt to the user based on the food material variety to be supplemented, so that the user purchases a food material of the corresponding variety.
Referring to fig. 2, this step may be implemented by the following steps S1041 to S1043:
s1041: when the type area is a preset stock area, comparing the food material variety in the food material information of the type area with the food material variety in the historical storage data;
s1042: determining the food material variety to be supplemented in the type region according to the comparison result;
s1043: and sending a food material purchasing prompt to the user based on the food material variety needing to be supplemented.
As a specific example, when the food material variety in the preset common area is only garlic, and the food material variety in the preset common area in the history storage data is garlic, shallot, or ginger, the determined food material variety in the preset common area may be compared with the food material variety in the history storage data to determine that the missing food material variety is shallot or ginger, and at this time, a prompt for buying shallot or ginger may be sent to the user.
The food material buying prompt can be a prompt for the user to buy by utilizing a display screen or a voice broadcasting function on the refrigerator, or a prompt for buying by sending characters or voice to a handheld terminal of the user.
In step S105, when the type area is not the preset stock area, it is determined whether the type area needs to be supplemented with the food material according to the food material information of the type area, and when it is determined that the type area needs to be supplemented with the food material, a food material buying prompt is sent to the user based on the food material type of the type area, so that the user can buy the food material belonging to the corresponding type.
Referring to fig. 3, this step may be implemented by the following steps S1051 to S1054:
s1051: and when the type area is not the preset stock area, calculating the sum of the residual quantities of the food materials of all food material varieties in the type area according to the residual quantities of the food materials in the food material information of the type area.
S1052: and judging whether the sum of the residual quantity of the food materials of all the food material varieties in the type area is less than the preset quantity or not.
S1053: and when the sum of the residual quantity of the food materials of all the food material varieties in the type area is smaller than the preset quantity, determining that the food materials need to be supplemented to the type area.
S1054: and sending a food material buying prompt to the user based on the food material type of the type area.
The preset number can be a preset number set according to eating habits or purchasing habits of a user, a corresponding preset number can be set for each type area of the non-preset stock area, the same preset number can also be set, and for each type area, when the sum of the residual numbers of the food materials of all food material varieties in the area type is less than the preset number, the type area is determined to be supplemented with the food materials.
As a specific example, when it is determined that the non-preset regular area has a fruit area and a dairy area, the preset number of the two types of areas is 3, and the fruit area is determined to include 1 apple, 1 orange and 3 bananas by identifying the image of the fruit area; by identifying the image of the dairy area, it was determined that the dairy area includes 1 bottle of milk. The sum of the residual quantity of various fruits in the fruit outlet area can be calculated to be 5, the residual quantity of the dairy product area is 1, and then the sum of the residual quantity of the food materials in the dairy product area is smaller than the preset quantity, so that the food materials needing to be supplemented to dairy products can be determined, a user can be prompted to buy the dairy products by utilizing a display screen or a voice broadcast function on a refrigerator, and prompt information can also be sent to a handheld terminal of the user, such as a mobile phone or a tablet computer, in a text or voice mode.
In addition, when the type area is not the preset stock area, determining whether the type area needs to be supplemented with food materials according to the food material information of the type area, and when it is determined that the type area needs to be supplemented with food materials, after a food material buying prompt is sent to the user based on the food material type of the type area, the method may further include:
acquiring at least one of region information of the refrigerator, historical storage data and date of food materials in the region of the type;
acquiring a food material variety belonging to the type area through a pre-established second neural network model based on at least one of the regional information of the refrigerator, historical storage data and date of the food material in the type area;
and recommending the food material varieties which belong to the type area and are obtained by utilizing the second neural network model to the user.
The second neural network model can be a BP neural network model, a convolutional neural network model or a recurrent neural network model, statistics is carried out according to historical storage data of food materials in the type region, and the taste, the historical purchase times, the historical consumption speed and the like of the user can be determined. By combining date and region information, season vegetables, fruits and the like in the season can be obtained under the condition of networking.
As an example, the acquisition of the food material variety belonging to the type area needing to be supplemented through the second neural network model may be that the BP neural network model is trained by selecting first sample data of which the historical data includes regional information of a refrigerator, user taste, historical purchase times, historical consumption speed and date. The method comprises the steps of utilizing a trained BP neural network model, taking regional information, user taste, historical purchase times, historical consumption speed and date of a refrigerator as an input layer of the BP neural network model, determining the number of neurons of the input layer according to the obtained number of attributes, enabling the output layer to be recommended food material varieties, and enabling the specific prediction process of the BP neural network model to be the method in the prior art and not to be specifically described in the embodiment of the application. The method can realize intelligent recommendation according to the purchasing habits and tastes of the users, and is beneficial to improving the use experience of the users.
It should be noted that, in the embodiment of the present application, the execution order of step S104 and step S105 is not limited, step S104 may be executed first and then step S105 may be executed, step S105 may be executed first and then step S104 may be executed, or step S104 and step S105 may be executed simultaneously.
The method for prompting the buying of the food material in the refrigerator comprises the steps of obtaining food material images of various types of areas in the refrigerator, inputting the food material images of the various types of areas into a pre-established first neural network model, determining food material information in the various types of areas by using the first neural network model, determining whether the type areas are preset equipment areas or not, determining food material varieties needing to be supplemented according to the food material information of the various types of areas when the type areas are the preset equipment areas, and directly sending a buying prompt for buying the food material varieties needing to be supplemented to a user so that the user can buy the food materials of the corresponding varieties; when the type area is not the preset stock area, determining whether the type of the food materials needing to be supplemented is determined according to the food material information of the type area, and when the food materials needing to be supplemented to the type area are determined, sending a food material buying prompt to a user based on the type of the food materials of the type area so that the user can buy the food materials belonging to the corresponding type. According to the scheme, the food material information in the refrigerator is identified in real time and efficiently, different purchasing prompt strategies are adopted for the stock area and the non-stock area with high use frequency, the situation that a user forgets or does not determine to purchase types is avoided, prompting and recommending can be carried out by combining user habits, and the use experience of the user is improved.
Specific examples
The method for prompting food material buying in a refrigerator provided by the embodiment is described below by taking a specific application scenario in which a user obtains a buying prompt through a mobile terminal as an example. Referring to fig. 4, the method includes steps S201 to S207:
in step S201, the user inputs the time of departure from work from the mobile terminal.
In step S202, within a preset time before the next shift time, food material images of various types of areas in the refrigerator are acquired through an image acquisition device in the refrigerator.
The type and the quantity of the food materials can be rapidly identified by using the YOLO algorithm, so that the preset time can be set based on the performance, the processing speed and the like of the image acquisition device.
In step S203, the food material image of each type region is input into a first neural network model pre-established based on the YOLO algorithm to determine the food material information in each type region, wherein the food material information includes the variety and the remaining amount of the food material.
In step S204, it is determined whether the type area is a preset stock area.
When the type area is determined to be the preset stock area, step S205 is executed; when it is determined that the type area is not the preset stock area, step S206 is performed.
In step S205, when the type area is the preset stock area, a food material variety to be supplemented is determined according to the food material information of the type area, and based on the food material variety to be supplemented, information including a food material buying prompt is sent to the mobile terminal of the user when the time arrives at the next shift, so that the user can buy the food material of the corresponding variety.
In step S206, when the type area is not the preset stock area, it is determined whether to supplement the food material to the type area according to the food material information of the type area, and when it is determined that the food material needs to be supplemented to the type area, information including a food material buying prompt is sent to the mobile terminal of the user when the next shift time is reached based on the food material type of the type area, so that the user can buy the food material belonging to the corresponding type.
In step S207, the user receives a prompt including food material buying through the mobile terminal.
The mobile terminal can prompt the user to buy food materials in a text or voice broadcasting mode, and in addition, the user can push the received food material buying prompt to the family to prompt the family to buy the food materials.
Through linking refrigerator and user's mobile terminal, can realize acquireing the edible material information in the refrigerator in real time to through the setting time, realize the effect of fixed point collection and suggestion, solved the user because of busy or forget the surplus condition of eating the material in the refrigerator, and forgot the problem of buying the edible material.
In another aspect of the present application, a storage medium is provided, where a computer program is stored, and when the computer program is executed by a processor, the method for prompting food material buying in a refrigerator includes:
acquiring food material images of various types of areas in a refrigerator;
inputting the food material image of each type area into a pre-established first neural network model to determine food material information in each type area, wherein the food material information comprises food material varieties and food material residual quantity;
judging whether the type area is a preset normal area or not;
when the type area is a preset stock area, determining the food material variety needing to be supplemented according to the food material information of the type area, and sending a food material buying prompt to a user based on the food material variety needing to be supplemented so that the user can buy the food material of the corresponding variety;
when the type area is not the preset stock area, determining whether the type area needs to be supplemented with food materials according to the food material information of the type area, and when determining that the type area needs to be supplemented with food materials, sending a food material buying prompt to a user based on the type of the food material of the type area so that the user can buy the food materials belonging to the corresponding type.
The processes, functions, methods, and/or software described above may be recorded, stored, or fixed in one or more computer-readable storage media that include program instructions to be implemented by a computer to cause a processor to execute the program instructions. The media may also include program instructions, data files, data structures, etc., alone or in combination. The media or program instructions may be those specially designed and constructed for the purposes of the computer software industry, or they may be of the kind well known and available to those having skill in the computer software arts. Examples of computer readable media include: magnetic media such as hard disks, floppy disks, and magnetic tape; optical media, such as CDROM disks and DVDs; magneto-optical media, e.g., optical disks; and hardware devices specifically configured to store and execute program instructions, such as Read Only Memory (ROM), Random Access Memory (RAM), flash memory, and the like. Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter. The described hardware devices may be configured to act as one or more software modules to perform the operations and methods described above, and vice versa. In addition, computer readable storage media may be distributed over network coupled computer systems and may store and execute computer readable code or program instructions in a distributed fashion.
Referring to fig. 5, in another aspect of the present application, there is provided a refrigerator including a memory 51 and a controller 52 connected to the memory 51, wherein the controller 52 is configured to execute a computer program in the memory 51, and when the computer program is executed by the controller 52, the following method for prompting food material buying in a refrigerator can be implemented, which includes:
acquiring food material images of various types of areas in a refrigerator;
inputting the food material image of each type area into a pre-established first neural network model to determine food material information in each type area, wherein the food material information comprises food material varieties and food material residual quantity;
judging whether the type area is a preset normal area or not;
when the type area is a preset stock area, determining the food material variety needing to be supplemented according to the food material information of the type area, and sending a food material buying prompt to a user based on the food material variety needing to be supplemented so that the user can buy the food material of the corresponding variety;
when the type area is not the preset stock area, determining whether the type area needs to be supplemented with food materials according to the food material information of the type area, and when determining that the type area needs to be supplemented with food materials, sending a food material buying prompt to a user based on the type of the food material of the type area so that the user can buy the food materials belonging to the corresponding type.
The refrigerator further includes: an image acquisition device 53 and a communication device 54, wherein the image acquisition device 53 and the communication device 54 are respectively connected with the controller 52,
the image acquisition device 53 is used for acquiring food material images of various types of areas in the refrigerator;
the communication device 54 is used for sending information containing food material buying prompts to the mobile terminal so as to realize interaction between the refrigerator and the mobile terminal.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (11)
1. A method for prompting food material buying in a refrigerator is characterized by comprising the following steps:
acquiring food material images of various types of areas in a refrigerator;
inputting the food material image of each type area into a pre-established first neural network model to determine food material information in each type area, wherein the food material information comprises food material varieties and food material residual quantity;
judging whether the type area is a preset normal area or not;
when the type area is a preset stock area, determining the food material variety needing to be supplemented according to the food material information of the type area, and sending a food material buying prompt to a user based on the food material variety needing to be supplemented so that the user can buy the food material of the corresponding variety;
when the type area is not the preset stock area, determining whether the type area needs to be supplemented with food materials according to the food material information of the type area, and when determining that the type area needs to be supplemented with food materials, sending a food material buying prompt to a user based on the type of the food material of the type area so that the user can buy the food materials belonging to the corresponding type.
2. The method of claim 1, wherein when the type area is a preset equipment area, determining a food material variety to be supplemented according to the food material information of the type area, and sending a food material buying prompt to a user based on the food material variety to be supplemented comprises:
when the type area is a preset stock area, comparing the food material variety in the food material information of the type area with the food material variety in the historical storage data;
determining the food material variety to be supplemented in the type region according to the comparison result;
and sending a food material purchasing prompt to the user based on the food material variety needing to be supplemented.
3. The method of claim 1, wherein when the type area is not a preset stock area, determining whether the type area needs to be supplemented with food materials according to the food material information of the type area, and when it is determined that the type area needs to be supplemented with food materials, sending a food material buying prompt to the user based on the food material type of the type area comprises:
when the type area is not the preset stock area, calculating the sum of the residual quantities of the food materials of all food material varieties in the type area according to the residual quantities of the food materials in the food material information of the type area;
judging whether the sum of the residual quantity of the food materials of all the food material varieties in the type area is smaller than a preset quantity or not;
when the sum of the residual quantities of the food materials of all food material varieties in the type area is smaller than the preset quantity, determining that the food materials need to be supplemented to the type area;
and sending a food material buying prompt to the user based on the food material type of the type area.
4. The method of claim 3, wherein when the type area is not the preset stock area, determining whether the type area needs to be supplemented with food materials according to the food material information of the type area, and when it is determined that the type area needs to be supplemented with food materials, after sending a food material buying prompt to the user based on the food material type of the type area, further comprising:
acquiring any one of the region information of the refrigerator, historical storage data and date of food materials in the region of the type;
acquiring a food material variety belonging to the type area through a pre-established second neural network model based on any one of the regional information of the refrigerator, historical storage data and date of the food material in the type area;
and recommending the food material varieties which belong to the type area and are obtained by utilizing the second neural network model to the user.
5. The method of claim 2 or 4, wherein the historically stored data comprises food material variety, historical quantity, historical purchase times and historical consumption rate.
6. The method of claim 5, further comprising: and storing the determined food material information in each type area to update historical storage data, and updating the first neural network model and the second neural network model based on the updated historical storage data.
7. The method of claim 1, wherein the first neural network model is constructed based on a YOLO algorithm.
8. The method of claim 1, wherein sending a food material buying prompt to a user comprises: and sending information containing food material buying prompts to a mobile terminal of a user.
9. A storage medium, wherein the storage medium stores a computer program, and the computer program is capable of implementing the method for prompting the buying of food in a refrigerator according to any of claims 1 to 8 when executed by a processor.
10. A refrigerator, comprising a memory and a controller connected to the memory, wherein the controller is configured to execute a computer program stored in the memory, and when the computer program is executed by the controller, the method for prompting the buying of food materials in the refrigerator according to any of claims 1 to 8 can be implemented.
11. The refrigerator according to claim 10, further comprising: an image acquisition device and a communication device, wherein the image acquisition device and the communication device are respectively connected with the controller,
the image acquisition device is used for acquiring food material images of various types of regions in the refrigerator;
the communication device is used for sending information containing food material buying prompts to the mobile terminal so as to realize interaction between the refrigerator and the mobile terminal.
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