CN109932715B

CN109932715B - Grain storage barrel, grain detection method and device and storage medium

Info

Publication number: CN109932715B
Application number: CN201910119273.0A
Authority: CN
Inventors: 陈翀; 魏文应
Original assignee: Gree Electric Appliances Inc of Zhuhai
Current assignee: Gree Electric Appliances Inc of Zhuhai
Priority date: 2019-02-18
Filing date: 2019-02-18
Publication date: 2020-08-04
Anticipated expiration: 2039-02-18
Also published as: CN109932715A

Abstract

The application discloses a grain storage barrel, a grain detection method, a grain detection device and a storage medium, and relates to the technical field of intelligent equipment. This store up grain bucket, including storing up grain bucket body, treater, rice grain detecting element, rice grain volume suggestion unit, wherein: the rice and grain detection unit is used for acquiring a three-dimensional image of grains in the grain storage barrel; the processor is used for determining the residual grain amount according to the three-dimensional image of the grain; and the rice grain amount prompting unit is used for outputting the residual grain amount according to the control of the processor. The state of the rice grain in the grain storage barrel can be acquired through the rice grain detection unit, then the residual grain amount can be determined through analysis processing, and further the residual grain amount can be output so that a user can supplement the grain in time.

Description

Grain storage barrel, grain detection method and device and storage medium

Technical Field

The application relates to the technical field of artificial intelligence, in particular to a grain storage barrel, a grain detection method, a grain detection device and a storage medium.

Background

The grain storage barrel is used for storing grains. In general families, hotels or restaurants, rice buckets are mostly adopted for storing white rice. However, the existing grain storage barrel has a single function and needs to be further improved.

Disclosure of Invention

The embodiment of the application provides a grain storage barrel, a grain detection method, a grain detection device and a storage medium, and aims to solve the problem that the function of the grain storage barrel is single in the prior art.

First aspect, the embodiment of this application provides a store up grain bucket, including storing up grain bucket body, still include the treater, rice grain detecting element, rice grain volume prompt unit, wherein:

the rice and grain detection unit is used for acquiring a three-dimensional image of grains in the grain storage barrel;

the processor is used for determining the residual grain amount according to the three-dimensional image of the grain;

the rice grain amount prompting unit is used for outputting the residual grain amount.

In a second aspect, the present application also provides a method for detecting grain, the method comprising:

acquiring a three-dimensional image of grains in a grain storage barrel;

determining the residual grain amount according to the three-dimensional image of the grain;

and outputting the residual grain amount.

In a third aspect, the present application further provides a computing device comprising at least one processor; and a memory communicatively coupled to the at least one processor; the storage stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute any grain detection method provided by the embodiment of the application.

In a fourth aspect, the present application further provides a computer storage medium, where the computer storage medium stores computer-executable instructions, and the computer-executable instructions are configured to enable a computer to execute any grain detection method in the embodiments of the present application.

The application provides a grain storage barrel, a grain detection method, a grain detection device and a storage medium. The state of the rice grain in the grain storage barrel can be acquired through the rice grain detection unit, then the residual grain amount can be determined through analysis processing, and further the residual grain amount can be output so that a user can supplement the grain in time.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

FIG. 1 is a schematic structural view of a grain storage barrel according to an embodiment of the present application;

FIG. 2 is a schematic structural diagram of a neural network in an embodiment of the present application;

FIG. 3 is a second schematic view of the structure of the grain storage barrel in the embodiment of the present application;

fig. 4 is a schematic flow chart of a grain detection method in the embodiment of the present application;

FIG. 5 is a schematic view of a setup interface in an embodiment of the present application;

fig. 6 is a schematic view of an application scenario of the grain detection method in the embodiment of the present application;

FIG. 7 is a schematic view of a grain detection device according to an embodiment of the present disclosure;

fig. 8 is a schematic structural diagram of a computing device according to an embodiment of the present application.

Detailed Description

In order to expand the function of the grain storage barrel and enable the grain storage barrel to meet the requirement of informatization development, the embodiment of the application provides a grain detection method, a grain detection device and a grain detection storage medium.

In real life, people often do not pay attention to the residual food quantity. Taking a rice bucket as an example, this often leads to that no rice is in the rice bucket during cooking, or the rice quantity is too small to cook. This is often the case in restaurants, canteens, etc. where the amount of rice used is relatively large. The traditional rice bucket often can not automatically detect the amount of rice in the rice bucket, and can not inform a user of the amount of rice on the rice bucket. In view of this, the embodiment of the present application provides a method and a system for automatically detecting the remaining grain amount and notifying the user of the remaining grain amount, so that the user can timely know the remaining grain amount and make a purchase preparation in advance.

As shown in fig. 1, a schematic structural diagram of a grain storage barrel provided in the embodiment of the present application includes a grain storage barrel body 101, a processor 102, a rice grain detection unit 103, and a rice grain amount indicating unit 104, wherein:

the rice and grain detection unit 103 is used for acquiring a three-dimensional image of grains in the grain storage barrel;

the processor 102 is configured to determine a remaining grain amount according to the three-dimensional image of the grain;

the rice grain amount prompting unit 104 is used for outputting the remaining grain amount.

Wherein, the rice grain amount prompting unit can be a display, an audio output device and an indicator light.

In this embodiment, the state of the rice grain inside the grain storage barrel can be acquired through the rice grain detection unit, then the remaining grain amount can be determined through analysis processing, and then the remaining grain amount can be output so that the user can supplement the grain in time.

In one embodiment, the rice grain detection unit can be a distance sensor and is arranged in the top of the grain storage barrel. Therefore, the amount of the rice grains can be determined by detecting the distance between the upper surfaces of the rice grains. For example, if the distance is far, the upper surface of the grain is far from the sensor, the rice grain is small, and if the distance is close to the sensor, the rice grain is large.

Further, in order to accurately determine the amount of the remaining rice and grain, the rice and grain detection unit is a microwave radar or a depth camera. When the grain storage barrel is specifically implemented, the stacking flatness of upper-layer grains, gaps among the grains, the shapes of the grains, the average distance from the bottom of the grain storage barrel to the upper-layer grains and the like can be determined according to three-dimensional images of the grains in the grain storage barrel. In specific implementation, the method can be determined according to actual requirements, and the method is not limited in the application.

In specific implementation, the processor can determine the residual grain amount according to the three-dimensional images of the grains and the pre-trained neural network model. The processor can send the three-dimensional image to a cloud server connected with the grain storage barrel, the cloud server determines the stacking flatness of upper-layer grains, gaps among grains, shapes of grains and the like according to the three-dimensional image to be used as input of a neural network model, and then the neural network determines the residual grain amount and returns the residual grain amount to the processor of the grain storage barrel. Of course, the neural network model can also be built in the processor of the grain storage barrel, and during specific implementation, whether the neural network model is built in the cloud server or the processor of the grain storage barrel can be determined according to actual requirements, and the application is not limited to the above.

For the convenience of understanding, the method for determining the residual quantity of the rice grains by the neural network model is further explained.

Assuming that the relative calculation formula of the residual food quantity is as follows:

(x) gap between grain pieces × W1+ flatness of stack × W2+ shape of grain pieces × W3

Among them, W1, W2, W3 are three parameters, which may be multidimensional arrays. Obtained by training a neural network. For example, the microwave radar is used for scanning the internal conditions of the storage bucket under the condition of different residual grain quantities, and the gaps among the grain grains, the stacking flatness and the shapes of the grain grains of each scanning result are corresponding to the residual grain quantities. More specifically, the amount of the grain measured by the standard measuring device in advance is 10 liters, and the corresponding microwave radar scanning results show that the gap between the grains is 10, the stacking flatness is 0.9, and the shape of the grains is 3.8 (expressed by the aspect ratio of the grains). Thus, one sample is obtained through one-to-one correspondence, 10 thousands of samples are made, and the specific number can be determined according to actual requirements. And putting the marked sample into a neural network model, and continuously adjusting three parameters W1, W2 and W3 to ensure that the identification accuracy reaches the requirement and finally obtain the three parameter values. Thus, there is f (x) this function. As shown in fig. 2, if the neural network model has an input layer, a hidden layer and an output layer, W1, W2 and W3 may be parameters of the input layer, and f (x) is data input to the hidden layer, or, in specific implementation, W1, W2 and W3 may be adjustment parameters of the entire neural network model, and f (x) is the remaining amount of grain to be output.

The neural network is, for example, a CNN convolutional neural network, a DNN deep neural network, or the like.

Further, as shown in fig. 3, the grain storage barrel further comprises a display screen 105;

the display screen 105 is used for outputting a parameter setting interface;

the parameter setting interface comprises configuration items for configuring whether to alarm or not;

the rice grain amount prompting unit is also used for outputting alarm information for indicating that the rice grain is insufficient when the remaining grain amount is determined to be lower than the alarm grain amount and the alarm is determined to be needed.

Therefore, the user can set the alarm information according to the requirements of the user through the display screen display parameter setting interface, and the prompt of the alarm information is more accurate.

Further, in order to facilitate the user to receive the warning information in time, the rice grain amount prompting unit is an audio output device; the parameter setting interface also comprises parameters for setting volume; and the volume output by the audio output device is the volume set in the parameter setting interface. Like this, can also learn grain surplus or alarm information when the mode through the audio frequency convenience of customers is apart from storing up grain bucket certain distance.

Further, the processor is also used for estimating the number of the persons who can eat the residual food amount according to the residual food amount and the preset per-person single-time food consumption amount; and controlling the rice grain amount prompt unit to output according to the estimated number of people. Therefore, the user can conveniently determine whether the residual grain amount is sufficient according to the number of people eating the grain.

Further, as shown in fig. 3, the grain storage barrel may further include a barrel cover 106 and a barrel cover opening and closing detector 107. The processor is also used for determining that the barrel cover is in an opening state before controlling the rice grain amount prompting unit to output the warning information for prompting that the rice grain is insufficient. Therefore, when the grain is insufficient, the barrel cover is opened to indicate that a person is close to the grain storage barrel, the alarm information can be continuously broadcasted, and when the barrel cover is closed, the broadcasting of the alarm information can be stopped.

Based on the same inventive concept, an embodiment of the present application further provides a grain detection method, as shown in fig. 4, which is a schematic flow diagram of the method, and includes:

step 401: and acquiring a three-dimensional image of the grain in the grain storage barrel.

Step 402: and determining the residual grain amount according to the three-dimensional image of the grain.

Step 403: and outputting the residual grain amount.

Further, the determining of the residual grain amount according to the three-dimensional image of the grain may be specifically implemented as determining the residual grain amount according to the three-dimensional image of the grain and a pre-trained neural network model. The specific embodiments have been described in the foregoing, and are not described in detail herein.

Further, a parameter setting interface is provided in the embodiment of the present application, where the parameter setting interface includes a configuration item for configuring whether to perform an alarm. A schematic of this interface may be as shown in fig. 5. After the output parameter setting interface is displayed, the alarm grain volume input by the user on the parameter setting interface and the setting information of whether to alarm or not can be stored according to the operation of the user on the interface; and then when determining that the residual grain amount is lower than the alarm grain amount and determining that the alarm is needed, outputting alarm information for indicating that the rice and grain are insufficient.

Of course, the warning information may be displayed or output in an audio manner. When the audio output is performed, as shown in fig. 5, the parameter setting interface further includes a parameter for setting the volume; when the alarm information is output, the alarm information for indicating that the rice and grain are insufficient can be output according to the volume set in the parameter setting interface.

Furthermore, in the embodiment of the application, the number of people for eating the residual food can be estimated according to the residual food amount and the preset per-time food consumption amount of each person; and then output based on the estimated number of people. Therefore, whether the residual amount of the food is sufficient or not can be determined visually by a user according to the number of people eating the food.

Certainly, in order to select proper actual alarm and avoid the influence of alarm information on a user, before the alarm information for indicating that the rice and the grain are insufficient is output, whether the barrel cover of the grain storage barrel for containing the grain is in an open state or not can be determined. When the alarm is in the open state, the alarm information is output when the user approaches, otherwise, the alarm information is not output.

As shown in fig. 6, an application scenario diagram of the grain detection method provided in the embodiment of the present application is shown, where the application scenario includes a user 10, a grain storage bucket 11, and a cloud server 12. The grain storage barrel acquires a three-dimensional image inside the grain storage barrel through a microwave radar, sends the three-dimensional image to the cloud server 12, processes the three-dimensional image according to a pre-trained neural network model by the cloud server, obtains the residual amount of rice and grains, sends the residual amount of the rice and grains to the grain storage barrel, and then outputs the residual amount of the rice and grains by the grain storage barrel. The user 10 may set how much grain remains to start an alarm through the display screen, and the setting button for setting may be a physical key or a displayed virtual key. When a user opens the grain storage barrel, the cover opening detection detects that the barrel cover is in an opening state, the fact that the user opens the grain storage barrel is indicated, the warning can be given when the warning condition is met, and when the barrel cover is detected to be in a closing state, the warning is finished.

Based on the same inventive concept, in the embodiment of the present application, there is further provided a grain detection device, as shown in fig. 7, the device includes:

the three-dimensional image acquisition module 701 is used for acquiring a three-dimensional image of grains in the grain storage barrel;

a residual grain amount determining module 702, configured to determine a residual grain amount according to the three-dimensional image of the grain;

and the output module 703 is used for outputting the residual grain amount.

Further, the residual grain amount determining module is specifically configured to determine the residual grain amount according to the three-dimensional image of the grain and a pre-trained neural network model.

Further, the apparatus further comprises:

the setting interface display module is used for displaying an output parameter setting interface; the parameter setting interface comprises configuration items for configuring whether to alarm or not;

the storage module is used for storing the grain amount to be alarmed and the setting information whether to alarm or not, which are input by the user on the parameter setting interface;

the output module is also used for outputting alarm information for indicating that the rice and grain are insufficient when the residual grain amount is determined to be lower than the alarm grain amount and the alarm is determined to be needed.

Further, the parameter setting interface further comprises a parameter for setting volume;

the output module is specifically used for outputting alarm information for indicating that rice and grain are insufficient according to the volume set in the parameter setting interface.

Further, the apparatus further comprises:

the estimation module is used for estimating the number of the people who can eat the residual food quantity according to the residual food quantity and the preset per-time food consumption quantity of the people;

the output module is further configured to output based on the estimated number of people.

Further, before outputting the alarm information for indicating that the rice and grain are insufficient, the device further comprises:

and the opening state determining module is used for determining that the barrel cover of the grain storage barrel for containing grains is in an opening state.

After the grain detection method and device according to the exemplary embodiment of the present application are introduced, a computing device according to another exemplary embodiment of the present application is introduced next.

As will be appreciated by one skilled in the art, aspects of the present application may be embodied as a system, method or program product. Accordingly, various aspects of the present application may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

In some possible implementations, a computing device according to the present application may include at least one processor, and at least one memory. The storage stores program codes, and when the program codes are executed by the processor, the processor executes the steps of the grain detection method according to various exemplary embodiments of the present application described above in the specification. For example, the processor may perform steps 401-403 as shown in FIG. 4.

The computing device 130 according to this embodiment of the present application is described below with reference to fig. 8. The computing device 130 shown in fig. 8 is only an example, and should not bring any limitation to the function and the scope of use of the embodiments of the present application.

As shown in FIG. 8, computing device 130 is embodied in the form of a general purpose computing device. Components of computing device 130 may include, but are not limited to: the at least one processor 131, the at least one memory 132, and a bus 133 that connects the various system components (including the memory 132 and the processor 131).

Bus 133 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a processor, or a local bus using any of a variety of bus architectures.

The memory 132 may include readable media in the form of volatile memory, such as Random Access Memory (RAM)1321 and/or cache memory 1322, and may further include Read Only Memory (ROM) 1323.

Memory 132 may also include a program/utility 1325 having a set (at least one) of program modules 1324, such program modules 1324 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Computing device 130 may also communicate with one or more external devices 134 (e.g., keyboard, pointing device, etc.), and may also communicate with one or more devices that enable a user to interact with computing device 130, and/or with any devices (e.g., router, modem, etc.) that enable computing device 130 to communicate with one or more other computing devices, such communication may occur via input/output (I/O) interfaces 135. also, computing device 130 may communicate with one or more networks (e.g., local area network (L AN), Wide Area Network (WAN) and/or a public network, such as the Internet) via network adapter 136. As shown, network adapter 136 communicates with other modules for computing device 130 via bus 133. it should be understood, although not shown, that other hardware and/or software modules may be used in conjunction with computing device 130, including, but not limited to, microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

In some possible embodiments, the aspects of the grain detection method provided in the present application may also be implemented in the form of a program product, which includes program code for causing a computer device to execute the steps in the grain detection method according to various exemplary embodiments of the present application described above in this specification when the program product runs on the computer device, for example, the computer device may execute

steps

401 and 403 as shown in fig. 4.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The program product for grain detection of the embodiments of the present application may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a computing device. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including AN object oriented programming language such as Java, C + +, or the like, as well as conventional procedural programming languages, such as the "C" language or similar programming languages.

It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functions of two or more units described above may be embodied in one unit, according to embodiments of the application. Conversely, the features and functions of one unit described above may be further divided into embodiments by a plurality of units.

Further, while the operations of the methods of the present application are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims

1. The utility model provides a store up grain bucket, includes and stores up grain bucket body, its characterized in that still includes the treater, rice grain detecting element, rice grain volume suggestion unit, wherein:

the processor is specifically used for determining the residual grain amount according to the three-dimensional image of the grain and a pre-trained neural network model, wherein the calculation formula of the residual grain amount is f (x), the gap × W1+ the stacking flatness × W2+ the shape × W3 of the grain;

wherein f (x) represents the amount of food remaining; the three parameters of W1, W2 and W3 are obtained by training the neural network model;

2. The grain storage barrel of claim 1, wherein the grain detection unit is a microwave radar or a depth camera.

3. The grain storage barrel of claim 1, further comprising a display screen;

the display screen is used for outputting a parameter setting interface; the parameter setting interface comprises configuration items for configuring whether to alarm or not;

4. The grain storage barrel of claim 3, wherein the rice grain amount prompting unit is an audio output device;

the parameter setting interface also comprises parameters for setting volume;

and the volume output by the audio output device is the volume set in the parameter setting interface.

5. The grain storage bucket of claim 1, wherein the processor is further configured to estimate how many people are available to eat the amount of remaining grain based on the amount of remaining grain and a preset per-person amount of single-time consumption of grain;

the rice and grain amount prompt unit is also used for outputting according to the estimated number of people.

6. The grain storage barrel of claim 1 or 4, further comprising a barrel cover and a barrel cover opening and closing detector.

7. The grain storage barrel of claim 6, wherein the processor is further configured to determine that the barrel cover is in an open state before the rice grain amount indicating unit outputs the alarm information indicating that the rice grain is insufficient.

8. A method of detecting grain, the method comprising:

acquiring a three-dimensional image of grains in a grain storage barrel;

determining the residual grain amount according to the three-dimensional image of the grain, specifically comprising:

determining the residual grain amount according to the three-dimensional images of the grains and a pre-trained neural network model;

wherein, the formula of the residual grain amount is f (x), the gap between grain particles is × W1+ the stacking flatness is × W2+ the shape of the grain particles is × W3;

and outputting the residual grain amount.

9. The method of claim 8, further comprising:

displaying an output parameter setting interface; the parameter setting interface comprises configuration items for configuring whether to alarm or not;

storing the grain amount and the setting information of whether to alarm or not, which are input by a user on the parameter setting interface;

and when determining that the residual grain amount is lower than the alarm grain amount and determining that the alarm is needed, outputting alarm information for indicating that the rice grain is insufficient.

10. The method of claim 9,

the parameter setting interface also comprises parameters for setting volume;

outputting alarm information for indicating that the rice and grain are insufficient, specifically comprising:

and outputting alarm information for indicating that the rice and grain are insufficient according to the volume set in the parameter setting interface.

11. The method of claim 8, further comprising:

estimating the number of people for eating the residual food quantity according to the residual food quantity and the preset per-time food consumption quantity of each person;

output based on the estimated number of people.

12. The method of claim 9, wherein before outputting the warning message indicating the rice grain deficiency, the method further comprises:

and determining that the barrel cover of the grain storage barrel for containing grains is in an open state.

13. A computer-readable medium having stored thereon computer-executable instructions for performing the method of any one of claims 8-12.

14. A computing device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 8-12.