阅读说明：本技术 一种马桶及其冲水控制方法和装置 (Closestool and flushing control method and device thereof ) 是由 王远城 于 2019-09-05 设计创作，主要内容包括：一种马桶的冲水控制方法包括：获取冲水指令,以及获取所述便池内的排泄物参数；将所述排泄物参数作为已训练的神经网络的输入,输出所述排泄物参数对应的冲水参数；根据所述冲水参数控制所述马桶进行冲水。在对排泄物进行冲水时,能够提高操作的便利性,并且能够节约水资源,有利于提升冲水效果。(A flushing control method of a closestool comprises the following steps: acquiring a flushing instruction and acquiring excrement parameters in the excrement pool; taking the excrement parameters as the input of a trained neural network, and outputting flushing parameters corresponding to the excrement parameters; and controlling the closestool to flush according to the flushing parameters. When flushing the excrement, can improve the convenience of operation to can the water economy resource, be favorable to promoting the bath effect.)

1. A flushing control method of a toilet bowl is characterized by comprising the following steps:

acquiring a flushing instruction and acquiring excrement parameters in the excrement pool;

taking the excrement parameters as the input of a trained neural network, and outputting flushing parameters corresponding to the excrement parameters;

and controlling the closestool to flush according to the flushing parameters.

2. The flushing control method of the toilet according to claim 1, wherein the parameters of the excrement comprise one or more of excrement amount, excrement hardness, excrement thickness and excrement length, and the flushing parameters comprise one or more of flushing water amount and flushing water speed.

3. The flush control method of a toilet according to claim 2, wherein the flush rate comprises a dynamically varying flush rate.

4. The flushing control method of a toilet according to claim 1 or 2, wherein the step of obtaining the parameters of the excrement in the toilet bowl comprises:

acquiring an excrement image in the excrement pool;

identifying the waste parameter from the waste image.

5. The flushing control method of a toilet according to claim 4, wherein the step of acquiring an image of the excrement in the toilet bowl comprises:

when the excrement is detected to fall into the urinal, acquiring an image of the excrement falling into the urinal;

and acquiring the excrement parameters corresponding to the excrement accumulated in the urinal according to the image of the excrement falling into the urinal.

6. The flush control method for a toilet according to claim 1, wherein before the step of outputting the flush parameter corresponding to the excretion parameter as an input of the trained neural network, the method further comprises:

obtaining a training sample of flushing values of various excrement parameters under the operation of various flushing parameters;

selecting training samples with flushing scores larger than a preset value from the training samples as positive samples;

and taking the excrement parameters of the positive sample as the input of a preset neural network, taking the flushing parameters as labels, training the neural network, and obtaining the trained neural network.

7. The flush control method of a toilet according to claim 1, further comprising:

acquiring an excrement image;

determining the probability of the excrement remaining in the body of the user according to the excrement image;

and selecting a predetermined type of sterilizing liquid to wash the body of the user according to the probability.

8. A flush control device for a toilet, the flush control device comprising:

the excrement parameter acquisition unit is used for acquiring a flushing instruction and acquiring excrement parameters in the excrement pool;

the neural network computing unit is used for taking the excrement parameters as the input of the trained neural network and outputting flushing parameters corresponding to the excrement parameters;

and the flushing control unit is used for controlling the closestool to flush according to the flushing parameters.

9. A toilet comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, carries out the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.

Technical Field

The application belongs to the field of toilets, and particularly relates to a toilet and a flushing control method and device thereof.

Background

With the improvement of living standard of people, people pay more and more attention to the quality, health and safety of life of individuals. More and more households have toilets installed in their restrooms. When people use the closestool, the smell of the toilet can be effectively reduced, and the use convenience of people is greatly improved.

In the process of using the closestool, a certain amount of water needs to be reserved in the closestool, and when a user flushes water after using the closestool, the amount of water is increased in the urinal, so that excrement in the urinal and water flush into a sewer together, and the flushing operation of the closestool is completed.

However, in the current toilet flushing operation, a user generally controls the flushing amount according to a flushing switch, and the user is limited by the experience of the user, when the toilet is flushed, the user may control excessive flushing, waste water resources, or control less flushing, need to repeatedly flush, and is troublesome to use.

Disclosure of Invention

In view of this, embodiments of the present disclosure provide a toilet and a flushing control method and device thereof, so as to solve the problem in the prior art that water resources may be wasted or the toilet is more troublesome to use when flushing.

A first aspect of embodiments of the present application provides a flush control method for a toilet, including:

acquiring a flushing instruction and acquiring excrement parameters in the excrement pool;

taking the excrement parameters as the input of a trained neural network, and outputting flushing parameters corresponding to the excrement parameters;

and controlling the closestool to flush according to the flushing parameters.

With reference to the first aspect, in a first possible implementation manner of the first aspect, the excrement parameter includes one or more of excrement amount, excrement hardness, excrement thickness and excrement length, and the flushing parameter includes one or more of flushing water amount and flushing water speed.

With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, the flushing speed includes a flushing speed with a dynamically changing speed magnitude.

With reference to the first aspect or the first possible implementation manner of the first aspect, in a third possible implementation manner of the first aspect, the step of obtaining the excrement parameter in the excrement pool includes:

acquiring an excrement image in the excrement pool;

identifying the waste parameter from the waste image.

With reference to the third possible implementation manner of the first aspect, in a fourth possible implementation manner of the first aspect, the step of acquiring an excrement image in the excrement pool includes:

when the excrement is detected to fall into the urinal, acquiring an image of the excrement falling into the urinal;

and acquiring the excrement parameters corresponding to the excrement accumulated in the urinal according to the image of the excrement falling into the urinal.

With reference to the first aspect, in a fifth possible implementation manner of the first aspect, before the step of outputting the flushing parameter corresponding to the excretion parameter by using the excretion parameter as an input of the trained neural network, the method further includes:

obtaining a training sample of flushing values of various excrement parameters under the operation of various flushing parameters;

selecting training samples with flushing scores larger than a preset value from the training samples as positive samples;

and taking the excrement parameters of the positive sample as the input of a preset neural network, taking the flushing parameters as labels, training the neural network, and obtaining the trained neural network.

With reference to the first aspect, in a sixth possible implementation manner of the first aspect, the method further includes:

acquiring an excrement image;

determining the probability of the excrement remaining in the body of the user according to the excrement image;

and selecting a predetermined type of sterilizing liquid to wash the body of the user according to the probability.

A second aspect of embodiments of the present application provides a flush control device of a toilet, including:

the excrement parameter acquisition unit is used for acquiring a flushing instruction and acquiring excrement parameters in the excrement pool;

the neural network computing unit is used for taking the excrement parameters as the input of the trained neural network and outputting flushing parameters corresponding to the excrement parameters;

and the flushing control unit is used for controlling the closestool to flush according to the flushing parameters.

A third aspect of embodiments of the present application provides a toilet comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to any one of the first aspect when executing the computer program.

A fourth aspect of embodiments of the present application provides a computer-readable storage medium, in which a computer program is stored, which, when executed by a processor, performs the steps of the method according to any one of the first aspect.

Compared with the prior art, the embodiment of the application has the advantages that: when the closestool acquires a flushing instruction, the excrement parameters in the excrement pool are acquired, the excrement parameters are used as the input of the trained neural network, the flushing parameters corresponding to the excrement parameters are output, and the closestool is controlled to flush according to the flushing parameters, so that the convenience of operation can be improved and the flushing effect can be improved when the excrement is flushed at every time.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.

Fig. 1 is a schematic flow chart illustrating an implementation of a flush control method for a toilet according to an embodiment of the present disclosure;

fig. 2 is a schematic flow chart illustrating an implementation of a neural network training method according to an embodiment of the present application;

fig. 3 is a schematic flow chart illustrating an implementation of a flushing control method according to an embodiment of the present application;

FIG. 4 is a schematic view of a flush control device of a toilet according to an embodiment of the present disclosure;

fig. 5 is a schematic view of a toilet provided by an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.

Fig. 1 is a schematic flow chart illustrating an implementation of a toilet flushing control method according to an embodiment of the present application, which is detailed as follows:

in step S101, a flushing instruction is obtained, and parameters of excrement in the urinal are obtained;

specifically, the flushing instruction can be generated according to the triggering instruction by detecting the triggering instruction of the flushing button. Alternatively, the volume of excrement in the excrement pool can be monitored, for example, if the volume of excrement in the excrement pool reaches a preset value, a flushing instruction is triggered to be generated; or through image analysis, when the hardness of the excrement exceeds a preset value and the orientation of the excrement meets the preset orientation requirement, a flushing instruction can be triggered. Wherein, the orientation of the excrement accords with the preset orientation requirement, and can comprise that when the axial direction of the excrement is matched with the axial direction of a toilet sewer, a flushing instruction can be triggered.

Wherein the excrement parameter can comprise one or more of excrement amount, excrement hardness, excrement thickness and excrement length. The excrement amount may be a volume of excrement when the hardness of excrement is less than a predetermined value. When the hardness of the excrement exceeds a predetermined value, the excrement parameter can comprise one or more of the quantity of excrement, the length of excrement, the thickness of excrement and the like.

The hardness of the excrement can be matched and identified according to the image characteristics of the excrement. For example, the fecal images corresponding to different hardness values may be preset, the currently acquired fecal image may be matched with a preset fecal image set, and the fecal hardness corresponding to the currently acquired fecal image may be determined according to the images in the matched fecal image set. For example, the hardness of the excrement may be in the range of 0 to 10, the hardness of the excrement may be set to 0 for a deformed thin excrement, and the maximum hardness may be set to 10.

The excrement in the embodiment of the application can be excrement excreted by a user of the closestool in the using process. When the excrement parameters are acquired, analysis and identification can be carried out according to a static image of excrement in the urinal, an image of the excrement falling into the urinal can be acquired dynamically, the amount of the excrement in the urinal can be accumulated according to the acquired image of the excrement falling into the urinal, and therefore the excrement parameters can be acquired more accurately.

In step S102, the excretion parameters are used as input of the trained neural network, and flush parameters corresponding to the excretion parameters are output;

before the neural network is used for flushing parameter calculation, the method may include a step of training the neural network, and specifically may include the step of training the neural network as shown in fig. 2:

in step S201, a training sample of flush scores obtained by a plurality of excrement parameters under different flush parameter operations is obtained;

specifically, the plurality of excrement parameters may include different excrement amounts, different excrement hardnesses, different excrement thicknesses, or different excrement lengths, and excrement in a plurality of different states may be selected according to the kinds of the excrement parameters.

After the excrement states of various different parameter values are determined, flushing statistics can be carried out on the excrement of each parameter value, and flushing scores obtained under different flushing water amounts, different flushing speeds or different curve flushing speeds are recorded.

The flushing speed in the flushing parameters may include a flushing speed of flushing at a constant speed or a flushing speed of flushing at a curve. For example, the flush rate may be controlled to gradually increase, or the flush rate may be controlled to increase or decrease in a pulsed manner, or the flush rate may be controlled to be a pulsed flush rate.

The flush score may be evaluated based on parameters such as the amount of flush water used, the cleanliness of the flush water, and the like. For example, the method can be divided into different grades according to the cleanliness, and each grade corresponds to different cleanliness scores; the flush water quality control method is characterized in that different grades are divided according to water consumption, each grade corresponds to different water consumption values, and the flush water value at this time can be determined by combining the cleanliness value and the water consumption value.

In step S202, a training sample with a flush score greater than a predetermined value among the training samples is selected as a positive sample;

for the flushing score in the training sample being smaller than the predetermined value, for example, the water consumption is high, or the cleanness after flushing is not enough, the obtained flushing score is low. And screening the training samples according to the set preset value to obtain the training sample with higher flushing score as a positive sample for training.

In step S203, the excrement parameter of the positive example sample is used as an input of a preset neural network, and the flushing parameter is used as a label, and the neural network is trained to obtain a trained neural network.

And taking the excrement parameters of the normal sample as the input of the neural network, and taking the flushing parameters as the output of the neural network for supervised learning to obtain the trained neural network.

And after the trained neural network is obtained, taking the currently acquired excrement parameters as the input of the neural network, and calculating by combining the trained neural network to obtain the flushing parameters corresponding to the currently acquired excrement parameters. That is, according to current excrement parameter, can obtain the higher bath parameter of the score of washing by water, therefore can make according to the bath parameter of selecting, it is better to wash by water the degree of obtaining the cleanliness, and the bath effect of water economy resource that can be better.

In step S103, the toilet is controlled to flush according to the flush parameters.

The closestool is controlled to flush according to the calculated flushing parameters, the defect that a user needs to judge flushing according to experience can be overcome, and the appropriate flushing parameters including flushing speed and flushing amount can be intelligently selected to flush, so that the flushing efficiency is improved while resources are saved.

In addition, as an embodiment optimized by the present application, the method may further include a cleaning step as shown in fig. 3:

in step S301, an excrement image is acquired;

the excrement image can be an image before excrement falls to a urinal, and more accurate analysis data can be provided before the excrement is soaked by water.

In step S302, determining a probability that the excrement remains on the body of the user according to the excrement image;

and comparing the excrement image with a preset excrement residual possibility image set to determine the probability that the currently acquired excrement image possibly causes residue on the body of the user.

In step S303, a predetermined type of sterilizing agent is selected according to the probability to flush the body of the user.

According to the residual probability of difference, can select different kinds of bactericidal liquid medicine to wash user's health, even when making to remain the bacterium more, can be better carry out the operation of disinfecting to user's health to the testing process need not gather user's health privacy position image, is favorable to improving the security of using, protects user's privacy.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

Fig. 4 is a schematic structural diagram of a flush control device of a toilet according to an embodiment of the present disclosure, and as shown in fig. 4, the flush control device of the toilet includes:

an excrement parameter acquiring unit 401, configured to acquire a flushing instruction and acquire an excrement parameter in the toilet;

a neural network computing unit 402, configured to use the excrement parameter as an input of a trained neural network, and output a flushing parameter corresponding to the excrement parameter;

a flushing control unit 403, configured to control the toilet to flush according to the flushing parameter.

The flush control device of the toilet bowl shown in fig. 4 corresponds to the flush control method of the toilet bowl shown in fig. 1.

FIG. 5 is a schematic view of a toilet provided by an embodiment of the present application. As shown in fig. 5, the toilet bowl 5 of this embodiment includes: a processor 50, a memory 51 and a computer program 52 stored in said memory 51 and executable on said processor 50, such as a flushing control program of a toilet. The processor 50, when executing the computer program 52, implements the steps in the above-described embodiments of the flush control method for each toilet. Alternatively, the processor 50 implements the functions of the modules/units in the above-described device embodiments when executing the computer program 52.

Illustratively, the computer program 52 may be partitioned into one or more modules/units, which are stored in the memory 51 and executed by the processor 50 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 52 in the toilet 5. For example, the computer program 52 may be divided into:

the excrement parameter acquisition unit is used for acquiring a flushing instruction and acquiring excrement parameters in the excrement pool;

the neural network computing unit is used for taking the excrement parameters as the input of the trained neural network and outputting flushing parameters corresponding to the excrement parameters;

and the flushing control unit is used for controlling the closestool to flush according to the flushing parameters.

The toilet may include, but is not limited to, a processor 50, a memory 51. Those skilled in the art will appreciate that fig. 5 is merely an example of a toilet 5, and does not constitute a limitation of the toilet 5, and may include more or fewer components than shown, or some components in combination, or different components, e.g., the toilet may also include input and output devices, network access devices, buses, etc.

The Processor 50 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 51 may be an internal storage unit of the toilet bowl 5, such as a hard disk or a memory of the toilet bowl 5. The memory 51 may also be an external storage device of the toilet 5, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the toilet 5. Further, the memory 51 may also include both an internal storage unit and an external storage device of the toilet bowl 5. The memory 51 is used for storing the computer program and other programs and data required by the toilet. The memory 51 may also be used to temporarily store data that has been output or is to be output.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.