CN110811460A - Method and system for determining cleaning mode - Google Patents
Method and system for determining cleaning mode Download PDFInfo
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- CN110811460A CN110811460A CN201911046695.6A CN201911046695A CN110811460A CN 110811460 A CN110811460 A CN 110811460A CN 201911046695 A CN201911046695 A CN 201911046695A CN 110811460 A CN110811460 A CN 110811460A
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L15/00—Washing or rinsing machines for crockery or tableware
- A47L15/0018—Controlling processes, i.e. processes to control the operation of the machine characterised by the purpose or target of the control
- A47L15/0063—Controlling processes, i.e. processes to control the operation of the machine characterised by the purpose or target of the control using remote monitoring or controlling of the dishwasher operation, e.g. networking systems
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L15/00—Washing or rinsing machines for crockery or tableware
- A47L15/0018—Controlling processes, i.e. processes to control the operation of the machine characterised by the purpose or target of the control
- A47L15/0021—Regulation of operational steps within the washing processes, e.g. optimisation or improvement of operational steps depending from the detergent nature or from the condition of the crockery
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L15/00—Washing or rinsing machines for crockery or tableware
- A47L15/0018—Controlling processes, i.e. processes to control the operation of the machine characterised by the purpose or target of the control
- A47L15/0047—Energy or water consumption, e.g. by saving energy or water
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L15/00—Washing or rinsing machines for crockery or tableware
- A47L15/0018—Controlling processes, i.e. processes to control the operation of the machine characterised by the purpose or target of the control
- A47L15/0055—Metering or indication of used products, e.g. type or quantity of detergent, rinse aid or salt; for measuring or controlling the product concentration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention is suitable for the technical field of intelligent household appliances, and provides a method and a system for determining a cleaning mode, wherein the method comprises the following steps: if the intelligent dish washing machine detects that a preset reporting trigger condition is met, sending turbidity information about tableware in the cavity to the cloud server; the cloud server imports the received turbidity information into a preset cleaning parameter conversion model, calculates cleaning parameters corresponding to the turbidity information and sends the cleaning parameters to the intelligent dish washing machine; and the intelligent dishwasher adjusts a washing mode based on the received washing parameters and performs washing operation on the tableware in the adjusted washing mode. According to the invention, the cleaning mode does not need to be manually set by a user, and the automatic determination of the cleaning parameters and the adjustment of the cleaning mode through the cleaning parameters are realized through the interaction between the intelligent dish washing machine and the cloud server, so that the determination efficiency of the cleaning mode is improved, and the operation of the user is reduced.
Description
Technical Field
The invention belongs to the technical field of intelligent household appliances, and particularly relates to a method and a system for determining a cleaning mode.
Background
With the continuous development of intellectualization and automation, a dishwasher as one of intelligent household appliances has gradually entered into thousands of households. The dishwasher can provide convenient and comfortable tableware washing experience for users, and the users can select washing modes corresponding to different types of tableware to be washed in the washing process. The existing method for determining the cleaning mode mainly executes the default cleaning mode through manual setting of a user or system fixation, and cannot automatically adjust according to the dirt degree of the tableware to be cleaned, so that the operation steps of the user are increased, and the cleaning efficiency is reduced.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and a system for determining a washing mode, so as to solve the problems that the existing method for determining a washing mode is mainly configured manually by a user or the default washing mode is fixedly executed by a system, and cannot be automatically adjusted according to the dirty degree of the dishes to be washed, thereby increasing the operation steps of the user and reducing the washing efficiency.
A first aspect of an embodiment of the present invention provides a method for determining a washing pattern, which is applied to a dish washing system, the dish washing system including: the intelligent dish washing machine comprises an intelligent dish washing machine and a cloud server;
the method for determining the cleaning mode comprises the following steps:
if the intelligent dish washing machine detects that a preset reporting trigger condition is met, sending turbidity information about tableware in the cavity to the cloud server;
the cloud server imports the received turbidity information into a preset cleaning parameter conversion model, calculates cleaning parameters corresponding to the turbidity information and sends the cleaning parameters to the intelligent dish washing machine;
and the intelligent dishwasher adjusts a washing mode based on the received washing parameters and performs washing operation on the tableware in the adjusted washing mode.
A second aspect of embodiments of the present invention provides a dishwashing system, comprising: the intelligent dish washing machine comprises an intelligent dish washing machine and a cloud server;
the intelligent dish washing machine is used for sending turbidity information about tableware in the cavity to the cloud server if the preset reporting trigger condition is met;
the cloud server is used for importing the received turbidity information into a preset washing parameter conversion model, calculating washing parameters corresponding to the turbidity information and sending the washing parameters to the intelligent dish washing machine;
and the intelligent dishwasher is used for adjusting a washing mode based on the received washing parameters and executing washing operation on the tableware in the adjusted washing mode.
The method and the system for determining the cleaning mode provided by the embodiment of the invention have the following beneficial effects:
according to the embodiment of the invention, the intelligent dish washing machine is used for collecting the turbidity information of the tableware to be washed and uploading the turbidity information to the cloud server, the cloud server calculates the washing parameters corresponding to the turbidity information through big data analysis and sends the washing parameters to the corresponding intelligent dish washing machine, then the intelligent dish washing machine adjusts the washing mode according to the washing parameters, and executes the corresponding washing operation through the adjusted washing mode, so that the purpose of automatically configuring the washing mode is realized. Compared with the prior art for determining the cleaning mode, the cleaning mode is not required to be manually set by a user, the cleaning parameters are automatically determined and the cleaning mode is adjusted through the cleaning parameters through interaction between the intelligent dish washing machine and the cloud server, the determining efficiency of the cleaning mode is improved, and the operation of the user is reduced. On the other hand, the calculation operation of the cleaning parameters is processed by the cloud server, so that the calculation pressure of the intelligent dish washing machine is reduced, the demand of the calculation capacity of the intelligent dish washing machine is reduced, and the counterfeiting cost of the intelligent dish washing machine is reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, 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 invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is an interactive flowchart of a cleaning mode determining method according to a first embodiment of the present invention;
fig. 2 is a flowchart of a specific implementation of a method for determining a cleaning mode according to a second embodiment of the present invention;
fig. 3 is a flowchart of a specific implementation of a method S101 for determining a cleaning mode according to a third embodiment of the present invention;
fig. 4 is a flowchart of a specific implementation of a method S101 for determining a cleaning mode according to a fourth embodiment of the present invention;
fig. 5 is a flowchart of a specific implementation of a method S101 for determining a cleaning mode according to a fifth embodiment of the present invention;
fig. 6 is a flowchart illustrating a specific implementation of a method for determining a cleaning mode according to a sixth embodiment of the present invention;
fig. 7 is a flowchart illustrating an implementation of the method S102 for determining a cleaning mode according to the seventh embodiment of the present invention;
FIG. 8 is a block diagram of a dishwasher system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
According to the embodiment of the invention, the intelligent dish washing machine is used for collecting the turbidity information of the tableware to be washed and uploading the turbidity information to the cloud server, the cloud server calculates the washing parameters corresponding to the turbidity information through big data analysis and sends the washing parameters to the corresponding intelligent dish washing machine, then the intelligent dish washing machine adjusts the washing mode according to the washing parameters and executes the corresponding washing operation through the adjusted washing mode, the purpose of automatically configuring the washing mode is achieved, and the problem of solving the existing determining method of the washing mode is solved.
In the embodiment of the present invention, the main execution body of the process is a dish washing system. The dish washing system comprises an intelligent dish washing machine and a cloud server, wherein the intelligent dish washing machine can be provided with a communication module, is communicated with the cloud server through the communication module, receives a washing parameter sent by the cloud server and adjusts a washing mode. Fig. 1 shows a flowchart of an implementation of the method for determining a cleaning mode according to the first embodiment of the present invention, which is detailed as follows:
in S101, if the intelligent dishwasher detects that a preset reporting trigger condition is met, the intelligent dishwasher sends turbidity information about the tableware in the cavity to the cloud server.
In this embodiment, a reporting trigger condition is preset in the intelligent dishwasher, and the reporting trigger condition is specifically that the intelligent dishwasher feeds back turbidity information to the cloud server, so that the cloud server issues a cleaning parameter according to the turbidity information. The reporting trigger condition may be a time trigger condition, in which case, the intelligent dishwasher may be preconfigured with a plurality of reporting time nodes, and when detecting that the current time reaches the reporting time node, it is identified that the reporting trigger condition is satisfied. Specifically, the reporting time node may be a plurality of time points obtained based on a reporting period. Certainly, the reporting trigger condition may be an event trigger condition, and the intelligent dishwasher may obtain and report the turbidity information when the washing operation needs to be executed or at a certain time before the washing operation is executed according to the dish washing flow, so as to determine the current washing operation that needs to be executed or the washing mode of the washing operation that is to be executed.
In this embodiment, the intelligent dishwasher can acquire turbidity information of the tableware in the cavity by means of an image or a turbidity sensor and the like. The turbidity information is used to characterize the degree of soiling of the dishes, such as the turbidity level or turbidity value returned by the turbidity sensor. Optionally, the intelligent dishwasher may further directly send the obtained raw data to the cloud server, where the raw data may specifically be data that may be used to represent the degree of soiling of the dishware, such as an environmental image that is captured with soiling on the surface of the dishware, a turbidity parameter input by a user, or a turbidity value of a water body after the dishware is pre-cleaned. The cloud server can analyze the original data after receiving the original data sent by the intelligent dish washing machine, convert the original data into a turbidity level, and then deliver the data processing operation to the cloud server for execution, so that the data processing pressure of the intelligent dish washing machine is further reduced.
Optionally, in this embodiment, the intelligent dishwasher identifies a communication interface between the current and the cloud server, and determines the data amount of the collected turbidity information based on the communication interface. Different communication interfaces are configured with rated data volume, and because the data transmission rates of different communication interfaces are inconsistent, in order to ensure the uploading efficiency, the intelligent dishwasher can adjust the data volume of the turbidity information according to the difference of the communication interfaces. For example, for a communication interface with a low transmission rate, such as a GPRS interface, the data volume of the corresponding turbidity information is small, and at this time, the intelligent dishwasher may only feed back the turbidity value fed back by the turbidity sensor, that is, the turbidity information only contains the turbidity value; and for a communication interface with a higher transmission rate, such as a 5G interface or a wireless communication network interface, the data volume of the corresponding turbidity information is larger, and at this time, the intelligent dishwasher can feed back the turbidity value fed back by the turbidity sensor and a plurality of environment images about the dishware shot by the camera module.
Optionally, in this embodiment, the intelligent dishwasher requests the cloud server to issue the cleaning parameters, and has a request validity period, and in this request validity period, the intelligent dishwasher may upload the turbidity parameters to the cloud server, and issue the cleaning parameters through the cloud server to realize the purpose of automatically adjusting the cleaning mode, and outside the request validity period, the cloud server may refuse to return the cleaning parameters, and in this case, the intelligent dishwasher performs the cleaning operation on the tableware in the cavity according to the default cleaning mode. Based on the above, when the intelligent dishwasher sends the turbidity information, the authorization code can be added into the turbidity information, and the cloud server can determine the validity period information associated with the intelligent washing machine by analyzing the authorization code, judge whether the current validity period is within the validity period, and execute corresponding response operation based on the judgment result.
In S102, the cloud server imports the received turbidity information into a preset cleaning parameter conversion model, calculates cleaning parameters corresponding to the turbidity information, and sends the cleaning parameters to the intelligent dish washing machine.
In this embodiment, the cloud server can receive the turbidity information that each intelligent dish washer sent to according to the washing parameter that turbidity information confirms corresponds, and return and give the intelligent dish washer who sends this turbidity information. In this case, the cloud server may create a plurality of virtual interfaces, and establish communication links with a plurality of intelligent dishwashers through different virtual interfaces at the same time. Optionally, the cloud server may set a maximum virtual interface according to the amount of hardware resources, and if the number of intelligent dishwashers requiring online requests currently is greater than the maximum virtual interface number, sequentially connect to each intelligent dishwasher according to the sending time of each connection request, generate a connection response queue, and respond to each connection request based on the order of each connection request in the connection response queue. It should be noted that the dish washing system is configured with a plurality of cloud servers, and each cloud server is used for managing all intelligent dish washing machines in different areas. If the current connection request of a certain cloud server is larger than the preset maximum virtual interface, the cloud server can acquire the load conditions of the cloud servers in other areas, and if the load value of the cloud server in one other area is smaller than the preset load threshold, the destination address of the unresponsive connection request is changed to change the connection object of the connection request, and the connection object is set to be the cloud server in the other area with the load value smaller than the load threshold, so that the purpose of load balancing is achieved.
In this embodiment, the cloud server may be configured with a cleaning parameter transformation model, which may be a hash function. The cloud server imports a plurality of turbidity parameters contained in the received turbidity information into a hash function, and different turbidity parameters are used for generating different cleaning parameters, for example, a turbidity area can be used for generating a washing angle; turbidity levels can be used to generate wash duration, detergent dosage, etc. The cloud server can determine and configure the cleaning parameter conversion model through big data analysis, and perform posterior adjustment on the cleaning parameter conversion model based on the cleaning results fed back by each intelligent dish washing machine in the using process, so that the accuracy of the cleaning parameter conversion model is improved.
Optionally, in this embodiment, the turbidity information includes the equipment model of intelligent dish washer, and the built-in operating parameter of different equipment models is different, and in order to improve washing parameter calculation efficiency, the high in the clouds server can select the washing parameter calculation model that matches with the equipment model according to this equipment model to make the calculation mode and the equipment model phase-match of washing parameter.
In S103, the intelligent dishwasher adjusts a washing mode based on the received washing parameters, and performs a washing operation on the dishes in the adjusted washing mode.
In this embodiment, the cloud server may issue the washing parameters to the intelligent dishwasher after calculating the washing parameters. The cleaning parameters may include at least one of: the amount of inlet water, washing time, washing temperature, the rotating speed of a spray arm, the dosage of the detergent and the like.
In this embodiment, the intelligent dishwasher may be configured with a cleaning template, the cleaning template specifically defines an execution flow of each cleaning operation, and after the intelligent dishwasher acquires the cleaning parameters, the cleaning parameters may be introduced into the cleaning template, actual operation parameters of each cleaning operation are set, so as to obtain the above-mentioned cleaning mode, and the cleaning operation is performed on the tableware in the cavity according to the cleaning mode.
As can be seen from the above, in the method for determining the washing mode provided by the embodiment of the invention, the intelligent dish washer acquires the turbidity information of the tableware to be washed, and uploads the turbidity information to the cloud server, the cloud server calculates the washing parameters corresponding to the turbidity information through big data analysis, and sends the washing parameters to the corresponding intelligent dish washer, and then the intelligent dish washer adjusts the washing mode according to the washing parameters, and executes the corresponding washing operation through the adjusted washing mode, thereby achieving the purpose of automatically configuring the washing mode. Compared with the prior art for determining the cleaning mode, the cleaning mode is not required to be manually set by a user, the cleaning parameters are automatically determined and the cleaning mode is adjusted through the cleaning parameters through interaction between the intelligent dish washing machine and the cloud server, the determining efficiency of the cleaning mode is improved, and the operation of the user is reduced. On the other hand, the calculation operation of the cleaning parameters is processed by the cloud server, so that the calculation pressure of the intelligent dish washing machine is reduced, the demand of the calculation capacity of the intelligent dish washing machine is reduced, and the counterfeiting cost of the intelligent dish washing machine is reduced.
Fig. 2 is a flowchart illustrating a specific implementation of a method for determining a cleaning mode according to a second embodiment of the present invention. Referring to fig. 2, with respect to the embodiment shown in fig. 1, the method for determining a cleaning mode provided in this embodiment further includes: s201 to S205 are specifically detailed as follows:
further, before the cloud server imports the received turbidity information into a preset cleaning parameter conversion model and calculates the cleaning parameters corresponding to the turbidity information, the method further comprises:
in S201, the cloud server extracts a plurality of history parameters matching the turbidity information.
In this embodiment, the cloud server can adjust the preset neural network in a training and learning manner before calculating the cleaning parameters, so as to achieve the purpose of dynamically adjusting the cleaning parameter conversion model. The specific implementation mode is as follows: the cloud server can classify the historical parameters according to different turbidity information, namely, all the historical parameters with the same turbidity information are divided into a historical parameter group. The turbidity information is identical, specifically, if the values of the respective turbidity parameters in the turbidity information are identical or the difference between the values is smaller than the floating threshold, the plurality of different turbidity information can be identified as identical turbidity information. When the cloud server responds to the adjustment request of the intelligent dishwasher, each time the turbidity information is received, the corresponding historical parameter is generated, and therefore the corresponding relation between the historical parameter and the turbidity information can be established. The historical parameter is specifically a washing parameter calculated from the responded turbidity information.
Optionally, in this embodiment, if the dish washing system is configured with a plurality of different cloud servers, the databases of the plurality of cloud servers may be shared, that is, the cloud servers may send broadcast instructions to the plurality of different cloud servers in the process of acquiring the historical parameters, in addition to extracting the historical parameters stored in the local database, so that each cloud server returns the historical parameters matched with the turbidity information.
In S202, the cloud server queries a washing result fed back by the intelligent dishwasher after the washing operation is performed based on the historical parameters.
In this embodiment, because each historical parameter has been sent for intelligent dish washer, intelligent dish washer is according to the historical parameter adjustment washing mode that receives to carry out corresponding washing operation, and after the washing operation finishes, can feed back the washing result to high in the clouds server, of course, this washing result also can wash the turbidity information of back operation. The cloud server can compare turbidity information uploaded before the cleaning operation and turbidity information after the cleaning operation, so that a cleaning result is obtained through calculation. And judging whether the variation between the two turbidity information is larger than the rated variation of the cleaning operation or not by comparing the variation between the two turbidity information, and obtaining a cleaning result according to the difference between the rated variation and the rated variation.
In S203, the cloud server calculates a difference between the cleaning result and a standard result, and determines a learning weight of the history parameter according to the difference.
In this embodiment, after obtaining the cleaning result matched with each historical parameter, the cloud server may calculate a difference between the cleaning result and the standard result. Specifically, the cleaning result may be a cleanliness grade, the standard result is a rated cleanliness, the cloud server may calculate a difference between the cleanliness grade and the rated cleanliness, and if the difference is larger, it indicates that the cleaning effect is better, the corresponding learning weight is larger; conversely, if the difference is smaller, the cleaning effect is less, and the corresponding learning weight is smaller.
In S204, the cloud server performs training and learning on a preset long-short term LSTM neural network according to all the historical parameters and the learning weights.
In this embodiment, the cleaning parameter conversion model in the cloud server is specifically a calculation model obtained based on LSTM neural network adjustment, and history parameters obtained by history calculation are used as training samples to train and learn the LSTM neural network. Specifically, learning weights of different historical parameters are determined according to a difference value between a cleaning result and a standard result, and the larger the learning weight is, the greater the learning efficiency contribution to training learning is, the higher the effect of adjusting the learning parameters in the LSTM neural network is; on the contrary, for the historical parameters with smaller learning weight, the adjustment range of the learning parameters is smaller in the training and learning process, and the purpose of posterior adjustment of the LSTM neural network can be realized by setting different learning weights, so that the accuracy of the learning operation is improved.
In S205, if the cloud server detects that the loss function of the LSTM neural network is smaller than a preset loss threshold, it recognizes that the LSTM neural network is completely trained, and recognizes the trained LSTM neural network as the cleaning parameter conversion model.
In this embodiment, the cloud server may determine whether the LSTM neural network is adjusted through a loss function, and if the loss function of the LSTM neural network is greater than or equal to a preset loss threshold, it is identified that the LSTM neural network is not adjusted, and the learning parameters in the LSTM neural network need to be adjusted; otherwise, if the loss function is smaller than the preset loss threshold, the LSTM neural network is identified to be adjusted, and the cleaning parameters can be calculated through the trained LSTM neural network, namely, the cleaning parameters are used as a cleaning parameter conversion model.
In the embodiment of the invention, by acquiring a plurality of historical parameters and cleaning results matched with the historical parameters and calculating the learning weight of each cleaning result, the learning contribution of different historical parameters to the neural network can be configured according to the cleaning effect, the training accuracy of the cleaning parameter conversion model is improved, and the calculation accuracy of the cleaning parameters is further improved.
Fig. 3 shows a flowchart of a specific implementation of the method S101 for determining a cleaning mode according to the third embodiment of the present invention. Referring to fig. 3, with respect to the embodiment described in fig. 1, a method S101 for determining a cleaning mode provided in this embodiment includes: s1011 to S1013 are specifically described as follows:
further, if the intelligent dish washer detects that a preset reporting trigger condition is met, turbidity information about tableware in the cavity is sent to the cloud server, and the method comprises the following steps:
in S1011, the intelligent dishwasher acquires an environmental image within the cavity, and extracts a contour curve of each of the dishes in the environmental image.
In this embodiment, the built-in camera module that has of intelligent dish washer, when judging the turbidity information that needs to produce, can shoot the environmental image in the cavity through controlling camera module, the shooting object of this environmental image contains the required abluent tableware, consequently can record the surface spot condition of tableware. Specifically, in order to improve the visibility of stains in the captured environment image, the intelligent dishwasher may perform a preprocessing operation when capturing the environment image, for example, perform a light supplement operation on the cavity by starting the light supplement component, and may also extend the capturing time, that is, the exposure time, or capture a plurality of environment images, and combine the plurality of environment images by using an HDR algorithm.
In this embodiment, the intelligent dishwasher may extract the contour curves included in the environment image through a contour recognition algorithm, and each closed contour curve corresponds to a dish to be cleaned, so that the contour curve of each dish can be recognized. It should be noted that the contour identification algorithm may calculate a pixel value difference between each pixel point and its adjacent pixel point, and if the pixel value difference is greater than a preset difference threshold, identify the pixel point as a contour pixel point, thereby connecting all contour pixel points to obtain a contour curve.
In S1012, the intelligent dishwasher identifies a dish region corresponding to each dish and a dirty region of the dish based on the profile curve.
In this embodiment, after the dishwasher identifies and obtains the contour curve, the dishwasher may divide the environment image according to the contour curve to obtain the tableware area corresponding to each tableware, and identify the pixel points within the preset stain pixel range as stain pixel points by identifying the pixel values of the pixel points in the tableware area, thereby identifying all the areas formed by the stain pixel points as stain areas.
In S1013, the intelligent dishwasher calculates the soil fraction of the soil area and the dish area, and generates the turbidity information according to the soil fraction of each dish.
In this embodiment, after the tableware area and the dirty area are identified, the intelligent dishwasher may calculate a ratio between the tableware area and the dirty area, identify a ratio of an area between the dirty area and the tableware area as a dirty ratio, and if the dirty ratio is higher, the corresponding turbidity level is higher, so as to determine turbidity information of the cleaning operation required to be performed this time based on the dirty ratio of all the tableware.
In the embodiment of the invention, the environmental image in the cavity is obtained, and the tableware area and the stain area are determined by the image recognition algorithm, so that the turbidity information is generated and uploaded to the cloud server, and the accuracy of the turbidity information is improved.
Fig. 4 shows a flowchart of a specific implementation of the method S101 for determining a cleaning mode according to the fourth embodiment of the present invention. Referring to fig. 4, with respect to the embodiment described in fig. 1, a method S101 for determining a cleaning mode provided in this embodiment includes: s1014 to S1016 are specifically detailed as follows:
further, if the intelligent dish washer detects that a preset reporting trigger condition is met, turbidity information about tableware in the cavity is sent to the cloud server, and the method comprises the following steps:
in S1014, the intelligent dishwasher collects an initial turbidity value before performing a pre-washing operation.
In this embodiment, dispose the water storage part in the intelligence dish washer, this water storage part can link to each other with the water route of intaking, and the water intaking valve in the water route of intaking opens the back, and in the water storage part can be carried through the water route of intaking to the water, intelligence dish washer can treat through the water that stores in the water storage part and wash the tableware execution washing operation. Because the water quality conditions of the water bodies conveyed by different water inlet waterways are different and the water quality conditions at different times can be changed, in order to determine that the pollution degree of the water bodies subjected to pre-cleaning is caused by kitchen residues on the surfaces of tableware or due to the influence of the water quality of the water bodies, the initial turbidity value of the water bodies for cleaning the tableware needs to be determined before the turbidity level of the tableware to be cleaned is determined.
It should be noted that the intelligent dishwasher may be provided with a turbidity sensor in the water storage part, and the initial turbidity value of the water body before the pre-washing operation is performed is determined by the turbidity sensor. The turbidity sensor can be a turbidity sensor based on a voltage signal, the turbidity sensor is provided with two electrodes, when detection is needed, a preset voltage value is loaded on the two electrodes in an internal loop, and a certain amount of ions exist because a water body is not distilled water, so that certain electric conductivity exists, and if the turbidity degree of the water body is higher, the electric conductivity is lower; conversely, the lower the degree of contamination of the water body, the higher the conductivity. Accordingly, the turbidity sensor may collect current values generated by the two electrodes based on the preset voltage and determine an initial turbidity value of the water body based on the current values.
In S1045, the intelligent dishwasher performs a pre-washing operation on the dishware through a water body, and detects a comparative turbidity value of the water body after the pre-washing operation.
In this embodiment, intelligent dishwasher is before carrying out formal washing operation, owing to select the washing mode of matching according to the dirty degree of tableware, consequently need treat that the washing tableware carries out the washing operation in advance, and the washing operation in advance specifically is to spray the water of cleaning in the tableware surface with preset water velocity to collect the water after this spray rinsing operation, confirm the dirty degree of tableware through the dirty degree of the water after the spray rinsing operation. Preferably, the water flow rate of the pre-washing operation may be greater than that of the formal washing operation, because more kitchen residues may be adhered to the surface of the tableware during the pre-washing operation, and because the temperature is low, the kitchen residues are adhered to the surface of the tableware, and if the water flow rate is low and the water temperature is low, the kitchen residues cannot be washed from the surface of the tableware, thereby reducing the accuracy of the turbidity level identification.
In this embodiment, intelligence dish washer can treat through the water in the water storage part and wash the tableware and carry out the operation of wasing in advance to produce the water after wasing in advance, dish washer can acquire the turbidity value of wasing operation back water in advance through turbidity sensor equally, and the turbidity value is compared to the aforesaid promptly, can confirm the dirty degree of wasing the tableware according to the difference degree of comparing between turbidity value and the initial turbidity value.
In S1046, the intelligent dishwasher generates the turbidity information according to the initial turbidity value and the comparative turbidity value.
In this embodiment, the intelligent dishwasher may import the identified initial turbidity value and the comparative turbidity value into a preset turbidity level conversion function, thereby identifying the turbidity level of the dishes to be washed and using the turbidity level as turbidity information. If the turbidity level is higher, the more kitchen residues on the surface of the tableware to be cleaned are indicated; conversely, a lower turbidity level indicates less kitchen residue on the surface of the dishes to be cleaned. Specifically, the turbidity level transfer function may be:
wherein, ClearStast is the initial turbidity value; CompareDust is the comparative turbidity value, BaseDust is the reference turbidity parameter; BaseLv is a reference turbidity level; DustLv is the turbidity level.
In the embodiment of the invention, the tableware is subjected to pre-cleaning operation, and the turbidity information of the tableware is determined according to the turbidity degree of the water body obtained by the pre-cleaning operation, so that the accuracy of the turbidity information can be improved, and the accuracy of the cleaning parameters is improved.
Fig. 5 shows a flowchart of a specific implementation of S101 of a method for determining a cleaning mode according to a fifth embodiment of the present invention. Referring to fig. 5, with respect to the embodiment described in fig. 1 to 4, the method S101 for determining a cleaning mode provided in this embodiment includes: s501 to S503 are specifically detailed as follows:
further, if the intelligent dish washer detects that a preset reporting trigger condition is met, turbidity information about tableware in the cavity is sent to the cloud server, and the method comprises the following steps:
in S501, the intelligent dishwasher sends a link connection request to the cloud server through a preset default communication port.
In this embodiment, the intelligent dishwasher is configured with a plurality of different communication ports, and the different communication ports correspond to different communication modes. For example, the communication port includes a wired communication port, a wireless communication port, and a mobile communication port if the intelligent dishwasher is also configured with communication credentials, such as a SIM card. The intelligent dish washing machine can select one from the communication ports as a default communication port, and when the turbidity information is sent, a link connection request is sent to the cloud server through the default communication port so as to establish communication connection between the cloud server and the intelligent dish washing machine.
In S502, if the cloud server returns a link confirmation instruction based on the link connection request within a preset effective time, the intelligent dishwasher sends the turbidity information to the cloud server through the default communication port.
In this embodiment, after sending the link connection request, if a link confirmation instruction returned by the cloud server is received within the valid time, it is recognized that the default communication port can normally receive and send data, and thus, the turbidity information can be sent to the cloud server through the default communication port.
In S503, if the smart dishwasher does not receive the link confirmation instruction returned by the cloud server within a preset effective time, the smart dishwasher sets a standby communication port as a default communication port, and returns to execute the operation of sending a link connection request to the cloud server through the preset default communication port.
In this embodiment, after sending the link connection request, if the link confirmation instruction returned by the cloud server is not received within a preset effective time, the intelligent dishwasher recognizes that the default communication port is abnormal, at this time, configures one of the standby communication ports as the default communication port, and returns to perform the operation of S501 above, so as to determine whether the newly configured default communication port is valid until the communication connection with the cloud server can be established.
In the embodiment of the invention, the success rate of the transmission operation of the turbidity information can be improved by configuring the default communication port and carrying out the test operation of the validity of the communication port before the turbidity information is transmitted, and the transmission mode is automatically switched when the transmission is abnormal.
Fig. 6 is a flowchart illustrating a specific implementation of a cleaning mode determining method according to a sixth embodiment of the present invention. Referring to fig. 6, with respect to the embodiment shown in fig. 5, the method for determining a cleaning mode according to this embodiment further includes, before S101: s601 to S602 are specifically described as follows:
further, before the intelligent dishwasher detects that a preset reporting trigger condition is met, sends turbidity information about tableware in the cavity to the cloud server, the method further includes:
in S601, the intelligent dishwasher sends a test instruction to the cloud server through each built-in available communication port.
In this embodiment, the intelligent dishwasher may select a communication port with the best communication effect as the default communication port in an automatic identification manner, in addition to configuring the default communication port in the default manner. Therefore, the intelligent dishwasher can respectively send the test instruction to the cloud server according to each available communication port, and the time required for receiving and sending data through the communication port can be calculated by sending the test instruction.
In S602, the intelligent dishwasher counts response time of each test instruction, and sets the available communication port corresponding to the test instruction with the shortest response time as the default communication port.
In this embodiment, the intelligent dishwasher may pass a response time of each test instruction, where the response time is specifically a time from sending the test instruction to receiving a response instruction returned by the cloud server based on the test instruction. And then sorting according to the magnitude of each response time, and selecting the available communication port used by the test instruction with the shortest response time as a default communication port.
In the embodiment of the invention, the purpose of automatically configuring the default communication port is realized by carrying out instruction test operation on each available communication port, so that the operation steps of a user are reduced, and the configuration efficiency is improved.
Fig. 7 is a flowchart illustrating a specific implementation of the cleaning mode determining method S102 according to a seventh embodiment of the present invention. Referring to fig. 7, with respect to the embodiment described in fig. 1 to 4, the method for determining the cleaning mode provided in this embodiment includes, at S102: s1021 to S1023 are described in detail as follows:
further, the intelligent dishwasher adjusting a washing mode based on the received washing parameters and performing a washing operation on the dishes in the adjusted washing mode includes:
in S1021, the intelligent dishwasher creates a plurality of washing templates according to the number of washing times of the washing parameters.
In this embodiment, the cleaning parameters include the number of cleaning cycles, that is, the tableware in the cavity needs to be cleaned in a plurality of cleaning cycles, where the number of the cleaning cycles matches the number of the cleaning cycles, and each cleaning cycle corresponds to one cleaning template.
In S1022, the intelligent dishwasher determines the cycle parameters corresponding to each of the cleaning templates according to the adjustment step size of the cleaning parameters and the reference parameters, and generates the cleaning script corresponding to each cleaning cycle.
In this embodiment, the washing parameters are also configured with a reference parameter and an adjustment step, and as the number of times of washing is increased, the turbidity on the surface of the tableware is reduced, and at this time, part of the washing parameters may be reduced appropriately, such as the dosage of detergent and the amount of washing water. The reference parameters are specifically cleaning parameters used by the first cleaning cycle, and based on the cycle sequence of each cleaning cycle, the reference parameters are adjusted by adjusting the step length to obtain the cleaning parameters corresponding to the current cleaning cycle, and a cleaning script related to the cleaning cycle is generated.
In S1023, the cleaning pattern is generated based on the cleaning scripts of all the cleaning cycles.
In this embodiment, based on the cleaning scripts corresponding to each cleaning cycle, the cleaning scripts are sequentially combined to obtain the cleaning mode.
In the embodiment of the invention, different cleaning scripts are configured for different cleaning cycles, so that different cleaning parameters of different cleaning cycles are realized, the cleaning cleanliness is ensured, the resource consumption can be reduced, and the purpose of environmental protection is achieved.
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 invention.
Fig. 8 is a block diagram illustrating a dishwasher according to an embodiment of the present invention, which includes units for performing steps of the embodiment of fig. 1. Please refer to fig. 1 and fig. 1 for the corresponding description of the embodiment. For convenience of explanation, only the portions related to the present embodiment are shown.
Referring to fig. 8, the intelligent household appliance system includes an intelligent dishwasher 81 and a cloud server 82;
the intelligent dishwasher 81 is configured to send turbidity information about tableware in the cavity to the cloud server 82 if it is detected that a preset reporting trigger condition is met;
the cloud server 82 is configured to import the received turbidity information into a preset washing parameter conversion model, calculate a washing parameter corresponding to the turbidity information, and send the washing parameter to the intelligent dishwasher 81;
the intelligent dishwasher 81 is configured to adjust a washing mode based on the received washing parameters, and perform a washing operation on the dishes in the adjusted washing mode.
Optionally, the cloud server 82 is further configured to:
the cloud server 82 is configured to extract a plurality of historical parameters matched with the turbidity information;
the cloud server 82 is used for inquiring a washing result fed back by the intelligent dishwasher after the washing operation is executed based on the historical parameters;
the cloud server 82 is configured to calculate a difference between the cleaning result and a standard result, and determine a learning weight of the historical parameter according to the difference;
the cloud server 82 is configured to train and learn a preset long-term and short-term LSTM neural network according to all the historical parameters and the learning weights;
the cloud server 82 is configured to recognize that the LSTM neural network is trained completely if it is detected that the loss function of the LSTM neural network is smaller than a preset loss threshold, and recognize the trained LSTM neural network as the cleaning parameter conversion model.
Optionally, the intelligent dishwasher 81 is specifically configured to:
the intelligent dishwasher 81 is configured to obtain an environment image in the cavity, and extract a contour curve of each tableware in the environment image;
the intelligent dishwasher 81 is used for identifying a tableware area corresponding to each tableware and a stain area of the tableware based on the contour curve;
the intelligent dishwasher 81 is configured to calculate a soil ratio of the soil area and the tableware area, and generate the turbidity information according to the soil ratio of each tableware.
Optionally, the intelligent dishwasher 81 is specifically configured to:
the intelligent dishwasher 81 is used for collecting an initial turbidity value before the pre-washing operation is executed;
the intelligent dishwasher 81 is used for pre-cleaning the tableware through a water body and detecting the comparative turbidity value of the water body after the pre-cleaning operation;
the intelligent dishwasher 81 is configured to generate the turbidity information according to the initial turbidity value and the comparative turbidity value.
Optionally, the intelligent dishwasher 81 is specifically configured to:
the intelligent dishwasher 81 is configured to send a link connection request to the cloud server through a preset default communication port;
the intelligent dishwasher 81 is configured to send the turbidity information to the cloud server through the default communication port if a link confirmation instruction returned by the cloud server based on the link connection request is within a preset valid time;
the intelligent dishwasher 81 is configured to set the standby communication port as a default communication port if the link confirmation instruction returned by the cloud server is not received within a preset effective time, and return to execute the operation of sending the link connection request to the cloud server through the preset default communication port.
Optionally, the intelligent dishwasher 81 is further configured to:
the intelligent dishwasher 81 is configured to send a test instruction to the cloud server through each built-in available communication port;
the intelligent dishwasher 81 is configured to count response time of each test instruction, and set the available communication port corresponding to the test instruction with the shortest response time as the default communication port.
Optionally, the intelligent dishwasher 81 is specifically configured to:
the intelligent dishwasher 81 is used for creating a plurality of cleaning templates according to the cleaning times of the cleaning parameters;
the intelligent dishwasher 81 is used for determining the period parameters corresponding to the cleaning templates according to the adjustment step length of the cleaning parameters and the reference parameters, and generating the cleaning scripts corresponding to the cleaning periods;
the intelligent dishwasher 81 is configured to generate the washing pattern based on the washing scripts of all the washing cycles.
Therefore, the dishwasher provided by the embodiment of the invention can automatically determine the cleaning parameters and adjust the cleaning mode through the cleaning parameters by interaction between the intelligent dishwasher and the cloud server without manually setting the cleaning mode by a user, so that the determination efficiency of the cleaning mode is improved, and the operation of the user is reduced. On the other hand, the calculation operation of the cleaning parameters is processed by the cloud server, so that the calculation pressure of the intelligent dish washing machine is reduced, the demand of the calculation capacity of the intelligent dish washing machine is reduced, and the counterfeiting cost of the intelligent dish washing machine is reduced.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will 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 invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for determining a washing pattern for use in a warewashing system, the method comprising: the intelligent dishwasher and the cloud server, the method for determining the washing mode comprises the following steps:
if the intelligent dish washing machine detects that a preset reporting trigger condition is met, sending turbidity information about tableware in the cavity to the cloud server;
the cloud server imports the received turbidity information into a preset cleaning parameter conversion model, calculates cleaning parameters corresponding to the turbidity information and sends the cleaning parameters to the intelligent dish washing machine;
and the intelligent dishwasher adjusts a washing mode based on the received washing parameters and performs washing operation on the tableware in the adjusted washing mode.
2. The method according to claim 1, wherein before the cloud server importing the received turbidity information into a preset washing parameter conversion model and calculating the washing parameters corresponding to the turbidity information, the method further comprises:
the cloud server extracts a plurality of historical parameters matched with the turbidity information;
the cloud server inquires a washing result fed back by the intelligent dishwasher after the washing operation is executed based on the historical parameters;
the cloud server calculates a difference value between the cleaning result and a standard result, and determines the learning weight of the historical parameter according to the difference value;
the cloud server trains and learns the preset long-term and short-term LSTM neural network according to all the historical parameters and the learning weight;
and if the cloud server detects that the loss function of the LSTM neural network is smaller than a preset loss threshold value, recognizing that the LSTM neural network is trained completely, and recognizing the trained LSTM neural network as the cleaning parameter conversion model.
3. The method for determining according to claim 1, wherein if the intelligent dishwasher detects that a preset reporting trigger condition is met, sending turbidity information about the dishes in the cavity to the cloud server includes:
the intelligent dishwasher acquires an environment image in the cavity and extracts a contour curve of each tableware in the environment image;
the intelligent dishwasher identifies a tableware area corresponding to each tableware and a stain area of the tableware based on the contour curve;
the intelligent dishwasher calculates the stain proportion of the stain area and the tableware area, and generates the turbidity information according to the stain proportion of each tableware.
4. The method for determining according to claim 1, wherein if the intelligent dishwasher detects that a preset reporting trigger condition is met, sending turbidity information about the dishes in the cavity to the cloud server includes:
the intelligent dishwasher collects an initial turbidity value before performing a pre-washing operation;
the intelligent dishwasher carries out pre-cleaning operation on the tableware through a water body, and detects the comparative turbidity value of the water body after the pre-cleaning operation;
the intelligent dishwasher generates the turbidity information according to the initial turbidity value and the comparison turbidity value.
5. The method for determining according to any one of claims 1 to 4, wherein if the intelligent dishwasher detects that a preset reporting trigger condition is met, sending turbidity information about the dishes in the cavity to the cloud server includes:
the intelligent dishwasher sends a link connection request to the cloud server through a preset default communication port;
if the cloud server returns a link confirmation instruction based on the link connection request within a preset effective time, the intelligent dishwasher sends the turbidity information to the cloud server through the default communication port;
and if the link confirmation instruction returned by the cloud server is not received within the preset effective time, the intelligent dishwasher sets the standby communication port as a default communication port and returns to execute the operation of sending the link connection request to the cloud server through the preset default communication port.
6. The method for determining the turbidity of the dishware in the cavity of the intelligent dishwasher, before the intelligent dishwasher detects that a preset reporting trigger condition is met, sending turbidity information about the dishware in the cavity to the cloud server, further comprising:
the intelligent dishwasher sends a test instruction to the cloud server through each built-in available communication port;
and the intelligent dishwasher counts the response time of each test instruction, and sets the available communication port corresponding to the test instruction with the shortest response time as the default communication port.
7. The method of any one of claims 1 to 4, wherein the intelligent dishwasher adjusts a washing mode based on the received washing parameters and performs a washing operation on the dishes in the adjusted washing mode, including:
the intelligent dishwasher creates a plurality of cleaning templates according to the cleaning times of the cleaning parameters;
the intelligent dishwasher determines the period parameters corresponding to the cleaning templates according to the adjustment step length of the cleaning parameters and the reference parameters, and generates the cleaning scripts corresponding to the cleaning periods;
the intelligent dishwasher generates the washing pattern based on the washing script of all the washing cycles.
8. A dishwashing system, wherein the dishwashing system comprises: the intelligent dish washing machine comprises an intelligent dish washing machine and a cloud server;
the intelligent dish washing machine is used for sending turbidity information about tableware in the cavity to the cloud server if the preset reporting trigger condition is met;
the cloud server is used for importing the received turbidity information into a preset washing parameter conversion model, calculating washing parameters corresponding to the turbidity information and sending the washing parameters to the intelligent dish washing machine;
and the intelligent dishwasher is used for adjusting a washing mode based on the received washing parameters and executing washing operation on the tableware in the adjusted washing mode.
9. The warewashing system of claim 8, wherein the cloud server is further configured to:
the cloud server is used for extracting a plurality of historical parameters matched with the turbidity information;
the cloud server is used for inquiring a washing result fed back by the intelligent dishwasher after the washing operation is executed based on the historical parameters;
the cloud server is used for calculating a difference value between the cleaning result and a standard result and determining the learning weight of the historical parameter according to the difference value;
the cloud server is used for training and learning a preset long-term and short-term LSTM neural network according to all the historical parameters and the learning weight;
and the cloud server is used for recognizing that the LSTM neural network is trained completely and recognizing the trained LSTM neural network as the cleaning parameter conversion model if the loss function of the LSTM neural network is detected to be smaller than a preset loss threshold value.
10. Dishwashing system according to claim 7, wherein the intelligent dishwasher is specifically configured to:
the intelligent dishwasher is used for acquiring an environment image in the cavity and extracting a contour curve of each tableware in the environment image;
the intelligent dishwasher is used for identifying a tableware area corresponding to each tableware and a stain area of the tableware based on the contour curve;
the intelligent dishwasher is used for calculating the stain proportion of the stain area and the tableware area and generating the turbidity information according to the stain proportion of each tableware.
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