CN110811461B - Method and system for determining cleaning mode - Google Patents
Method and system for determining cleaning mode Download PDFInfo
- Publication number
- CN110811461B CN110811461B CN201911047990.3A CN201911047990A CN110811461B CN 110811461 B CN110811461 B CN 110811461B CN 201911047990 A CN201911047990 A CN 201911047990A CN 110811461 B CN110811461 B CN 110811461B
- Authority
- CN
- China
- Prior art keywords
- cleaning
- cloud server
- parameters
- intelligent
- cleaning device
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
Landscapes
- Washing And Drying Of Tableware (AREA)
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: the intelligent cleaning device acquires turbidity information of the tableware in the cavity in a preset acquisition cycle and sends the turbidity information to the cloud server; the cloud server acquires the environmental parameters of the intelligent cleaning device, imports the received turbidity information and the environmental parameters into a preset cleaning parameter conversion model, and calculates the cleaning parameters of the current acquisition period; if the cloud server detects that the cleaning parameters are inconsistent with the historical parameters, the cloud server sends the cleaning parameters to the intelligent cleaning device; the intelligent cleaning device adjusts the cleaning mode based on the received cleaning parameters and performs cleaning operation on the tableware in the adjusted cleaning mode. According to the intelligent cleaning device, the turbidity information of the tableware in the cavity is automatically acquired and uploaded to the cloud server, the cleaning parameters are calculated through the cloud server, the cleaning parameters are updated in real time, and the cleaning mode is adjusted, so that the accuracy of cleaning operation is guaranteed.
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, the cleaning device is gradually entering into thousands of households as one of intelligent household appliances. The cleaning device can provide convenient and comfortable tableware cleaning experience for users, and the users can select cleaning modes corresponding to different types of tableware to be cleaned in the cleaning 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 cleaning device and the cloud server;
the method for determining the cleaning mode comprises the following steps:
the intelligent cleaning device acquires turbidity information of tableware in the cavity in a preset acquisition cycle and sends the turbidity information to the cloud server;
the cloud server acquires the environmental parameters of the intelligent cleaning device, introduces the received turbidity information and the environmental parameters into a preset cleaning parameter conversion model, and calculates the cleaning parameters of the current acquisition period;
if the cloud server detects that the cleaning parameter of the current acquisition cycle is inconsistent with the cleaning parameter of the previous acquisition cycle adjacent to the current acquisition cycle, the cloud server sends the cleaning parameter of the current cycle to the intelligent cleaning device;
and the intelligent cleaning device adjusts a cleaning mode based on the received cleaning parameters and performs cleaning operation on the tableware in the adjusted cleaning mode.
A second aspect of embodiments of the present invention provides a dishwashing system, comprising: the intelligent cleaning device and the cloud server;
the intelligent cleaning device is used for acquiring turbidity information of the tableware in the cavity in a preset acquisition cycle and sending the turbidity information to the cloud server;
the cloud server is used for acquiring the environment parameters of the intelligent cleaning device, importing the received turbidity information and the environment parameters into a preset cleaning parameter conversion model, and calculating the cleaning parameters of the current acquisition period;
the cloud server is used for sending the cleaning parameters of the current acquisition cycle to the intelligent cleaning device if the cleaning parameters of the current acquisition cycle are detected to be inconsistent with the cleaning parameters of the previous acquisition cycle adjacent to the current acquisition cycle;
and the intelligent cleaning device is used for adjusting a cleaning mode based on the received cleaning parameters and executing cleaning operation on the tableware in the adjusted cleaning 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 cleaning device periodically uploads the turbidity information to the cloud server, then the cloud server calculates the cleaning parameters by combining the current environmental parameters and the turbidity information, judges whether the cleaning parameters obtained by the calculation are consistent with the historical parameters obtained by the calculation in the previous period or not, and sends the cleaning parameters to be updated if the cleaning parameters are not consistent with the historical parameters obtained by the calculation in the previous period, so that the intelligent cleaning device adjusts the cleaning mode through the cleaning parameters, executes corresponding cleaning operation based on the adjusted cleaning parameters, and achieves the purpose of updating the cleaning parameters in real time. Compared with the existing determination mode of the cleaning mode, the cleaning parameters do not need to be manually configured by a user, the cleaning mode is selected, the intelligent cleaning device automatically collects the turbidity information of the tableware in the cavity and uploads the turbidity information to the cloud server, the cleaning parameters are calculated through the cloud server and updated in real time, and the cleaning mode is adjusted, so that the accuracy of cleaning operation is ensured, the cleaning cleanliness of the tableware is improved, meanwhile, the cleaning mode is automatically configured, the user operation is reduced, and the configuration efficiency of the cleaning mode is improved.
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.
In the embodiment of the invention, the intelligent cleaning device periodically uploads the turbidity information to the cloud server, then the cloud server calculates the cleaning parameters by combining the current environmental parameters and the turbidity information, and judging whether the cleaning parameters obtained by the calculation are consistent with the historical parameters obtained by the calculation in the previous period or not, if the cleaning parameters are not consistent with the historical parameters, sending the cleaning parameters to be updated so that the intelligent cleaning device adjusts the cleaning mode according to the cleaning parameters, and executing corresponding cleaning operation based on the adjusted cleaning parameters, thereby realizing the purpose of updating the cleaning parameters in real time, and solving the problems that the existing determining method of the cleaning mode is mainly determined by manually setting by a user or fixedly executing the default cleaning mode by a system, and the automatic adjustment can not be carried out according to the dirty degree of the tableware to be cleaned, thereby increasing the operation steps of the user and reducing the cleaning efficiency.
In the embodiment of the present invention, the main execution body of the process is a dish washing system. The tableware washing system comprises an intelligent washing device and a cloud server, wherein the intelligent washing device 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. This intelligent belt cleaning device can be intelligent dish washer and intelligent feeding bottle cleaning machine. The tableware may include: bowls, plates, tea cups, chopsticks, feeding bottles and the like. 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, the intelligent cleaning device acquires turbidity information of tableware in the cavity in a preset acquisition cycle and sends the turbidity information to the cloud server.
In this embodiment, the intelligent cleaning device is pre-configured with a collection period, which may be configured manually by a user or may be configured in a default manner by the system. Preferably, the intelligent cleaning device is provided with a plurality of different communication ports, and different communication ports are provided with acquisition cycles with different durations according to different transmission rates. When the cleaning device collects the turbidity information, the current communication port identification connected with the cloud server is judged, and a collection period with the corresponding duration is obtained based on the communication port. For example, for a mobile communication port, when the mobile communication port communicates with a cloud server through a mobile communication network, charging is often performed based on communication data volume, and at this time, the acquisition period may be long, for example, turbidity information is uploaded once in 1 minute; for the wireless communication port, the charging is performed based on the connection time, so that the acquisition period can be short, for example, turbidity information is uploaded once in 10 seconds. Preferably, the intelligent cleaning device can configure a collection period matched with each communication port according to a rated transmission rate of the communication port, and specifically, the higher the rated transmission rate is, the shorter the collection period is. The intelligent cleaning device can upload turbidity information to the cloud server in the operation process in an acquisition cycle, so that the cloud server can adjust the operation parameters in real time, the cleaning operation is adjusted, namely, the cleaning parameters are not fixed in the whole process of executing the cleaning operation, and the turbidity information in the cavity can be adjusted in real time through the cloud server, so that the accuracy of the cleaning operation is improved.
In this embodiment, the intelligent cleaning device can acquire the 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 cleaning device may further directly send the obtained original data to the cloud server, where the original data may specifically be data that may be used to represent the dirty degree of the tableware, such as an environment image in which dirt on the surface of the tableware is captured, a turbidity parameter input by a user, or a turbidity value of a water body after the tableware is pre-cleaned. The cloud server can analyze the original data after receiving the original data sent by the intelligent cleaning device, 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 cleaning device is further reduced.
Optionally, the manner of obtaining the turbidity information may specifically be: the intelligent cleaning device acquires an environment image in the cavity and extracts a contour curve of each tableware in the environment image; identifying a tableware area corresponding to each tableware and a dirty area of the tableware on the basis of the contour curve; calculating a soil ratio of the soil area and the tableware area, and generating the turbidity information according to the soil ratio of each tableware.
Optionally, the manner of obtaining the turbidity information may specifically be: the intelligent cleaning device collects an initial turbidity value before the pre-cleaning operation is executed; pre-cleaning the tableware through a water body, and detecting the comparative turbidity value of the water body after the pre-cleaning operation; and generating the turbidity information according to the initial turbidity value and the comparison turbidity value.
Optionally, in this embodiment, the intelligent cleaning device identifies a communication interface between the current cloud server 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 cleaning device 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 cleaning device may only feed back the turbidity value fed back by the turbidity sensor, that is, the turbidity information only contains the turbidity value; for a communication interface with a higher transmission rate, such as a 5G interface or a wireless communication network interface, the data size of the corresponding turbidity information is larger, and at this time, the intelligent cleaning device 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 cleaning device requests the cloud server to issue the cleaning parameters, and has a request validity period, during the request validity period, the intelligent cleaning device may upload the turbidity parameters to the cloud server, and issue the cleaning parameters through the cloud server to achieve 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 under this condition, the intelligent cleaning device performs the cleaning operation on the tableware in the cavity according to the default cleaning mode. Based on this, when the intelligent washing device 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 obtains the environmental parameters of the intelligent cleaning device, introduces the received turbidity information and the environmental parameters into a preset cleaning parameter conversion model, and calculates the cleaning parameters of the current acquisition cycle.
In this embodiment, the cloud server can receive the turbidity information that each intelligent cleaning device sent to according to the washing parameter that turbidity information confirms that corresponds, and return and give the intelligent cleaning device 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 cleaning devices through different virtual interfaces simultaneously. Optionally, the cloud server may set a maximum virtual interface according to the amount of hardware resources, and if the number of the intelligent cleaning devices that currently need to request for connection is greater than the maximum virtual interface number, sequentially connect with each intelligent cleaning device 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 cleaning devices 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, high in the clouds server still can acquire intelligent belt cleaning device's environmental parameter, and this environmental parameter specifically can be temperature value, the quality of water grade of the water that is used for wasing the tableware, the clean grade of the water route of intaking of transmission water body etc..
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 and environment parameters contained in the received turbidity information into a hash function, and different turbidity parameters and/or environment parameters are used for generating different cleaning parameters, for example, the turbidity area can be used for generating a washing angle; turbidity levels can be used to generate wash duration and detergent dosage; the temperature value can determine the temperature of the water body for washing the tableware and the heating time; the water quality grade can be used for determining the dosage of the filter module for releasing the filter agent, and the like. 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 cleaning results fed back by each intelligent cleaning device in the using process, so that the accuracy of the cleaning parameter conversion model is improved.
Optionally, in this embodiment, the turbidity information includes intelligent belt cleaning device's equipment model, 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 equipment model according to this equipment model to make the calculation mode and the equipment model phase-match of washing parameter.
In S103, if the cloud server detects that the cleaning parameter of the current acquisition cycle is inconsistent with the cleaning parameter of the previous acquisition cycle adjacent to the current acquisition cycle, the cloud server sends the cleaning parameter of the current cycle to the intelligent cleaning device.
In this embodiment, because intelligent cleaning device can be with the collection cycle regularly to the high in the clouds server turbidity information of uploading, the high in the clouds server then can be with the same collection cycle output washing parameter to judge whether the washing parameter that present washing parameter and last collection cycle calculated is unanimous. If the cleaning parameters calculated at this time are consistent with the cleaning parameters calculated in the last acquisition cycle, the identification is carried out without adjusting the cleaning mode, the cleaning parameters are not required to be sent to the intelligent cleaning device, and the intelligent cleaning device can keep the original cleaning mode to continue to operate; on the contrary, if the cleaning parameter calculated this time is identified to be inconsistent with the cleaning parameter calculated in the previous acquisition cycle, it indicates that the cleaning mode of the intelligent cleaning device needs to be adjusted, and at this time, only the relevant operation of S104 is needed.
In S104, the intelligent washing device 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 cleaning parameters to the intelligent cleaning device after calculating the cleaning parameters and recognizing that the cleaning parameters are inconsistent with the cleaning parameters calculated last time. 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 cleaning device may be configured with a cleaning template, the cleaning template specifically defines an execution flow of each cleaning operation, and after the intelligent cleaning device 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.
In this embodiment, intelligence belt cleaning device can be according to the washing operation of the instant adjustment of cleaning parameter, if detect that the cleaning effect does not accord with anticipated standard cleaning effect at the abluent in-process of reality, then can optimize the adjustment to the cleaning parameter, realizes the purpose of dynamic adjustment cleaning mode, has improved the accuracy of cleaning operation.
As can be seen from the above, in the method for determining a cleaning mode provided in the embodiment of the present invention, the intelligent cleaning device periodically uploads the turbidity information to the cloud server, and then the cloud server calculates the cleaning parameters in combination with the current environmental parameters and the turbidity information, and determines whether the cleaning parameters obtained by the calculation are consistent with the historical parameters obtained by the calculation in the previous period, and if it is detected that the cleaning parameters are not consistent with the historical parameters obtained by the calculation in the previous period, the cleaning parameters to be updated are sent, so that the intelligent cleaning device adjusts the cleaning mode according to the cleaning parameters, and executes the corresponding cleaning operation based on the adjusted cleaning parameters, thereby achieving the purpose of updating the cleaning parameters in real time. Compared with the existing determination mode of the cleaning mode, the cleaning parameters do not need to be manually configured by a user, the cleaning mode is selected, the intelligent cleaning device automatically collects the turbidity information of the tableware in the cavity and uploads the turbidity information to the cloud server, the cleaning parameters are calculated through the cloud server and updated in real time, and the cleaning mode is adjusted, so that the accuracy of cleaning operation is ensured, the cleaning cleanliness of the tableware is improved, meanwhile, the cleaning mode is automatically configured, the user operation is reduced, and the configuration efficiency of the cleaning mode is improved.
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 cleaning device, each time turbidity information is received, a 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 cleaning result fed back by the intelligent cleaning device after the cleaning operation is performed based on the historical parameters.
In this embodiment, because each historical parameter has been sent for intelligent belt cleaning device, intelligent belt cleaning device adjusts the washing mode according to the historical parameter that receives to carry out corresponding washing operation, and after the washing operation finishes, can feed back the washing result to the 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 is a flowchart illustrating a specific implementation of the cleaning mode determining method S102 according to a third embodiment of the present invention. Referring to fig. 3, with respect to the embodiment described in fig. 1, a method S102 for determining a cleaning mode provided in this embodiment includes: s1021 to S1023 are described in detail as follows:
further, the high in the clouds server acquires intelligent belt cleaning device's environmental parameter to with receiving turbidity information and environmental parameter import preset wash parameter conversion model calculate the washing parameter of current acquisition cycle, include:
in S1021, the cloud server determines the installation location of the intelligent cleaning device according to the network address carried by the environment parameter.
In this embodiment, when the intelligent cleaning device sends the environment parameter, the data packet corresponding to the environment parameter may carry a network address. For example, adding a source address to the header of the packet. The cloud server can determine the network address of the intelligent cleaning device sending the environment parameters by analyzing the environment parameters. And determining the installation position of the intelligent cleaning device by inquiring the geographical position to which the network address belongs. Optionally, the cloud server may record the installation position of each intelligent cleaning device, after the intelligent cleaning device is installed and connected with the cloud server for the first time, the cloud server may send a position acquisition instruction to the intelligent cleaning device, at this time, the intelligent cleaning device may acquire position information through a built-in positioning module or a positioning module on a terminal of a user using the intelligent cleaning device, and return the position information to the cloud server, so that the cloud server may determine the installation position of the intelligent cleaning device.
In S1022, the cloud server queries water source information associated with the installation location, and determines a water quality level based on the water source information.
In this embodiment, the high in the clouds server can be according to this mounted position, confirms the water inlet water route that this position corresponds to confirm the relevant water source of intaking based on this water inlet water route, and inquire the water source information at this water source of intaking, for example the quality of water condition at this position and water of intaking water source, this quality of water condition can be parameters such as pH valve, hardness, impurity concentration, and confirm the quality of water grade that this water source of intaking corresponds based on this quality of water parameter.
In S1023, the cloud server queries the current weather parameters of the installation location, and encapsulates the weather parameters and the water quality level to obtain the environment parameters.
In this embodiment, the third party interface can be called to the high in the clouds server, send weather inquiry request to the server that the weather was used, obtain the weather parameter that obtains the mounted position, because different weather, not only there is the influence to the quality of water in water inlet waterway, still there is the influence to the temperature, thereby it is long when influencing the heating of the water body of intaking, the weather temperature also can cause the influence by the dissolution efficiency of sanitizer, therefore the high in the clouds server is when confirming the washing parameter, can adjust the washing parameter through the weather parameter, so that wash parameter and weather parameter phase-match. And the cloud server encapsulates the obtained weather parameters and the obtained water quality grade to generate environment parameters.
In the embodiment of the invention, the water quality grade and the weather parameters are determined and the environmental parameters are generated by acquiring the installation position of the intelligent cleaning device, so that the accuracy of cleaning parameter calculation is improved and the accurate adjustment of the cleaning mode is realized.
Fig. 4 is a flowchart illustrating a specific implementation of a cleaning mode determining method according to a fourth embodiment of the present invention. Referring to fig. 4, with respect to the embodiment described in fig. 1, the method for determining a cleaning mode provided in this embodiment further includes: s401 to S402 are specifically described as follows:
further, the cloud server acquires the environment parameters of the intelligent cleaning device, and introduces the received turbidity information and the received environment parameters into a preset cleaning parameter conversion model, and further includes, before calculating the cleaning parameters of the current acquisition cycle:
in S401, if the intelligent cleaning apparatus detects a completion instruction regarding any cleaning operation, the operation type of the operation to be performed is acquired.
In this embodiment, the intelligent cleaning device periodically sends the turbidity information to the cloud server, and when detecting the operation switching, the intelligent cleaning device also sends the turbidity information to the cloud server so as to send the cleaning parameters to be executed through the cloud server. Based on this, when the intelligent cleaning device detects a completion instruction of any currently executed cleaning operation, it indicates that the operation needs to be switched from the current cleaning operation to the operation of the next node, that is, the operation to be executed. The intelligent cleaning device can determine the cleaning operation of each cleaning operation lower link according to the cleaning flow, and determine the corresponding operation type based on the operation identification of the cleaning operation.
In S402, the intelligent cleaning device packages the operation type into the turbidity information, and sends the turbidity information to the cloud server.
In this embodiment, because the washing parameters that need be used for different washing operations are inconsistent, in order to reduce unnecessary computation and improve the accuracy of washing parameters, the intelligent washing device can encapsulate the operation type into the turbidity information, and send the turbidity information that includes the operation type to the cloud server, so that the cloud server returns about the washing parameters that match with this operation type.
In the embodiment of the invention, the turbidity information is sent to the cloud server when the operation is detected to be completed, so that the aim of controlling the cleaning parameter of each cleaning operation in real time can be fulfilled, the instantaneity of the cleaning mode determination is improved, and the response speed is increased.
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 cleaning device detects that a preset reporting trigger condition is met, turbidity information about the tableware in the cavity is sent to the cloud server, and the method comprises the following steps:
in S501, the intelligent cleaning device sends a link connection request to the cloud server through a preset default communication port.
In this embodiment, the intelligent cleaning device is provided 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 and a wireless communication port, and if the smart washing device is further configured with a communication certificate, such as a SIM card, the smart washing device may further include a mobile communication port. The intelligent cleaning device can select one from the communication ports to serve 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 cleaning device.
In S502, if the cloud server sends a link confirmation instruction based on the link connection request back within a preset effective time, the intelligent cleaning device sends the turbidity information to the cloud server through the default communication port.
In this embodiment, after the intelligent cleaning device sends 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 intelligent cleaning device does not receive the link confirmation instruction returned by the cloud server within a preset effective time, the intelligent cleaning device 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.
In this embodiment, after the intelligent cleaning device sends the link connection request, if the link confirmation instruction returned by the cloud server is not received within a preset effective time, the intelligent cleaning device recognizes that the default communication port is abnormal, and at this time, one of the standby communication ports is configured as the default communication port, and the operation of S501 is returned to be executed, 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 cleaning device detects that a preset reporting trigger condition is met, sending turbidity information about the tableware in the cavity to the cloud server, the method further includes:
in S601, the intelligent cleaning device sends a test instruction to the cloud server through each built-in available communication port.
In this embodiment, the intelligent cleaning device may further 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 cleaning device 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 cleaning apparatus 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 cleaning device may pass through the response time of each test instruction, where the response time is specifically the time from the time when the test instruction is sent to the time when the response instruction returned by the cloud server based on the test instruction is received. 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: S1024-S1026, which are specifically detailed as follows:
further, the intelligent washing device adjusts a washing mode based on the received washing parameters, and performs a washing operation on the dishes in the adjusted washing mode, including:
in S1024, the intelligent cleaning apparatus creates a plurality of cleaning templates according to the number of times of cleaning of the cleaning 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 S1025, the intelligent cleaning device determines cycle parameters corresponding to each cleaning template according to the adjustment step size and the reference parameter of the cleaning 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 S1026, 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 structure of a cleaning apparatus according to an embodiment of the present invention, where the cleaning apparatus includes units for performing the steps in the embodiment corresponding to 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 cleaning device 81 and a cloud server 82;
the intelligent cleaning device 81 is used for acquiring turbidity information of the tableware in the cavity in a preset acquisition cycle and sending the turbidity information to the cloud server;
the cloud server 82 is configured to obtain an environmental parameter of the intelligent cleaning device, import the received turbidity information and the environmental parameter into a preset cleaning parameter conversion model, and calculate a cleaning parameter of a current acquisition cycle;
the cloud server 82 is configured to send the cleaning parameters of the current acquisition cycle to the intelligent cleaning device if it is detected that the cleaning parameters of the current acquisition cycle are inconsistent with the cleaning parameters of the previous acquisition cycle adjacent to the current acquisition cycle;
and the intelligent cleaning device 81 is used for adjusting a cleaning mode based on the received cleaning parameters and executing cleaning operation on the tableware in the adjusted cleaning 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 configured to query a cleaning result fed back by the intelligent cleaning device after the cleaning operation is performed 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 cloud server 82 is specifically configured to:
the cloud server 82 is configured to determine an installation position of the intelligent cleaning device according to a network address carried by the environment parameter;
the cloud server 82 is configured to query water source information associated with the installation location, and determine a water quality grade based on the water source information;
the cloud server 82 is configured to query a weather parameter of the current installation location, and encapsulate the weather parameter and the water quality level to obtain the environment parameter.
Optionally, the intelligent cleaning device 81 is further configured to:
the intelligent cleaning device 81 is configured to, if a completion instruction about any cleaning operation is detected, obtain an operation type of an operation to be executed;
and the intelligent cleaning device 81 is used for packaging the operation type into the turbidity information and sending the turbidity information to the cloud server.
Optionally, the intelligent cleaning device 81 is specifically configured to:
the intelligent cleaning device 81 is configured to send a link connection request to the cloud server through a preset default communication port;
the intelligent cleaning device 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 effective time;
the intelligent cleaning device 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 cleaning device 81 is further configured to:
the intelligent cleaning device 81 is configured to send a test instruction to the cloud server through each built-in available communication port;
the intelligent cleaning device 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 cleaning device 81 is specifically configured to:
the intelligent cleaning device 81 is used for creating a plurality of cleaning templates according to the cleaning times of the cleaning parameters;
the intelligent cleaning device 81 is configured to determine a period parameter corresponding to each cleaning template according to the adjustment step length of the cleaning parameter and the reference parameter, and generate a cleaning script corresponding to each cleaning period;
the intelligent cleaning device 81 is configured to generate the cleaning mode based on the cleaning scripts of all the cleaning cycles.
Therefore, the cleaning device provided by the embodiment of the invention can automatically acquire the turbidity information of the tableware in the cavity and upload the turbidity information to the cloud server without manually configuring the cleaning parameters by a user and selecting the cleaning mode, the cleaning parameters are calculated by the cloud server, the cleaning parameters are updated in real time, and the cleaning mode is adjusted, so that the accuracy of the cleaning operation is ensured, the cleanliness of the tableware is improved, meanwhile, the cleaning mode is automatically configured, the user operation is reduced, and the configuration efficiency of the cleaning mode is improved.
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 (9)
1. A method for determining a washing pattern for use in a warewashing system, the method comprising: the method for determining the cleaning mode comprises the following steps:
intelligence belt cleaning device acquires the turbidity information of tableware in the cavity with preset collection cycle, and will turbidity information send to high in the clouds server includes: identifying a communication interface between the current cloud server and the cloud server, and determining the data volume of the collected turbidity information based on the communication interface;
the high in the clouds server acquires intelligent belt cleaning device's environmental parameter to with receiving turbidity information and environmental parameter import preset wash parameter conversion model calculate the washing parameter of current acquisition cycle, include: selecting a cleaning parameter calculation model matched with the equipment model according to the equipment model of the intelligent cleaning device; the turbidity information comprises the model of the device;
if the cloud server detects that the cleaning parameter of the current acquisition cycle is inconsistent with the cleaning parameter of the previous acquisition cycle adjacent to the current acquisition cycle, the cloud server sends the cleaning parameter of the current cycle to the intelligent cleaning device;
the intelligent cleaning device adjusts a cleaning mode based on the received cleaning parameters and performs cleaning operation on the tableware in the adjusted cleaning mode;
the intelligent cleaning device adjusts a cleaning mode based on the received cleaning parameters, and performs cleaning operation on the tableware in the adjusted cleaning mode, and the method comprises the following steps:
the intelligent cleaning device creates a plurality of cleaning templates according to the cleaning times of the cleaning parameters;
the intelligent cleaning device determines the period parameters corresponding to the cleaning templates according to the adjustment step length and the reference parameters of the cleaning parameters, and generates cleaning scripts corresponding to the cleaning periods, wherein the cleaning scripts comprise: based on the cycle sequence of each cleaning cycle, adjusting a reference parameter by adjusting the step length to obtain a cleaning parameter corresponding to the current cleaning cycle, wherein the reference parameter is specifically the cleaning parameter used by the first cleaning cycle;
the intelligent cleaning device generates the cleaning mode based on the cleaning scripts of all the cleaning cycles.
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 cleaning result fed back by the intelligent cleaning device after the cleaning 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 determination method according to claim 1, wherein the cloud server obtains environmental parameters of the intelligent cleaning device, and introduces the received turbidity information and the environmental parameters into a preset cleaning parameter conversion model, and calculating the cleaning parameters of the current acquisition cycle comprises:
the cloud server determines the installation position of the intelligent cleaning device according to the network address carried by the environment parameter;
the cloud server inquires water source information related to the installation position and determines the water quality grade based on the water source information;
and the cloud server inquires weather parameters of the current installation position, and encapsulates the weather parameters and the water quality grade to obtain the environment parameters.
4. The method for determining according to claim 1, wherein the method for determining the cleaning mode further comprises:
if the intelligent cleaning device detects a finishing instruction about any cleaning operation, acquiring an operation type of an operation to be executed;
and the intelligent cleaning device packages the operation type into the turbidity information and sends the turbidity information to the cloud server.
5. The method for determining according to any one of claims 1 to 4, wherein if the intelligent cleaning device 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 cleaning device 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 cleaning device sends the turbidity information to the cloud server through the default communication port;
and if the intelligent cleaning device does not receive the link confirmation instruction returned by the cloud server within the preset effective time, setting the standby communication port as a default communication port, and returning 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 according to claim 5, wherein before the intelligent washing device detects that the preset reporting trigger condition is met, the method further comprises:
the intelligent cleaning device sends a test instruction to the cloud server through each built-in available communication port;
and the intelligent cleaning device 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. A dishwashing system, wherein the dishwashing system comprises: the intelligent cleaning device and the cloud server;
intelligence belt cleaning device for with the turbidity information of the tableware in the collection cycle of predetermineeing in the cavity, and with turbidity information send to high in the clouds server includes: identifying a communication interface between the current cloud server and the cloud server, and determining the data volume of the collected turbidity information based on the communication interface;
the high in the clouds server is used for acquireing intelligent belt cleaning device's environmental parameter to with receiving turbidity information and environmental parameter import into predetermined washing parameter conversion model, calculate the washing parameter of current acquisition cycle, include: selecting a cleaning parameter calculation model matched with the equipment model according to the equipment model of the intelligent cleaning device; the turbidity information comprises the model of the device;
the cloud server is used for sending the cleaning parameters of the current acquisition cycle to the intelligent cleaning device if the cleaning parameters of the current acquisition cycle are detected to be inconsistent with the cleaning parameters of the previous acquisition cycle adjacent to the current acquisition cycle;
the intelligent cleaning device is used for adjusting a cleaning mode based on the received cleaning parameters and executing cleaning operation on the tableware in the adjusted cleaning mode;
the intelligent cleaning device is used for adjusting a cleaning mode based on the received cleaning parameters and executing cleaning operation on the tableware in the adjusted cleaning mode, and comprises:
the intelligent cleaning device is used for creating a plurality of cleaning templates according to the cleaning times of the cleaning parameters;
the intelligent cleaning device is used for determining the period parameters corresponding to the cleaning templates according to the adjustment step length and the reference parameters of the cleaning parameters, and generating the cleaning scripts corresponding to the cleaning periods, and comprises the following steps: based on the cycle sequence of each cleaning cycle, adjusting a reference parameter by adjusting the step length to obtain a cleaning parameter corresponding to the current cleaning cycle, wherein the reference parameter is specifically the cleaning parameter used by the first cleaning cycle;
the intelligent cleaning device is used for generating the cleaning mode based on the cleaning scripts of all the cleaning cycles.
8. The warewashing system of claim 7, 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 cleaning result fed back by the intelligent cleaning device after the cleaning 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.
9. The warewashing system of claim 7, wherein the cloud server is specifically configured to:
the cloud server is used for determining the installation position of the intelligent cleaning device according to the network address carried by the environment parameter;
the cloud server is used for inquiring the water source information related to the installation position and determining the water quality grade based on the water source information;
and the cloud server is used for inquiring the current weather parameters of the installation position, and packaging the weather parameters and the water quality grade to obtain the environment parameters.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911047990.3A CN110811461B (en) | 2019-10-30 | 2019-10-30 | Method and system for determining cleaning mode |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911047990.3A CN110811461B (en) | 2019-10-30 | 2019-10-30 | Method and system for determining cleaning mode |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110811461A CN110811461A (en) | 2020-02-21 |
| CN110811461B true CN110811461B (en) | 2022-02-15 |
Family
ID=69551517
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911047990.3A Active CN110811461B (en) | 2019-10-30 | 2019-10-30 | Method and system for determining cleaning mode |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110811461B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113359518B (en) * | 2021-04-29 | 2023-06-13 | 青岛海尔科技有限公司 | Reminding method and device for household appliance cleaning and intelligent household appliance |
| CN115169610B (en) * | 2022-07-14 | 2023-05-09 | 中万恩科技有限公司 | Commercial dish washer diagnosis and monitoring method, device, control equipment and medium |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102377814A (en) * | 2010-08-25 | 2012-03-14 | 汎惪股份有限公司 | Remote assistance service method aiming at embedded operation system |
| CN102383286A (en) * | 2010-09-01 | 2012-03-21 | 海尔集团公司 | Intelligentized internet-of-thing washing machine |
| CN104746287A (en) * | 2015-03-19 | 2015-07-01 | 无锡小天鹅股份有限公司 | Washing machine, control method thereof and washing machine system |
| CN105986397A (en) * | 2015-02-10 | 2016-10-05 | 青岛海尔洗衣机有限公司 | Washing time control method for washing machine and washing machine |
| CN107794704A (en) * | 2016-09-07 | 2018-03-13 | 台湾松下电器股份有限公司 | Intelligent clothes washing method and system |
| CN108577730A (en) * | 2018-04-28 | 2018-09-28 | 北京金山安全软件有限公司 | Dishwasher control method, device, dishwasher and computer readable storage medium |
| CN108755001A (en) * | 2018-05-22 | 2018-11-06 | 佛山市云米电器科技有限公司 | A method of washing effect is optimized according to the water quality of washing machine region |
| CN108784585A (en) * | 2018-05-24 | 2018-11-13 | 北京金山安全软件有限公司 | Cleaning method, cleaning control method, cleaning device and server |
| CN108836226A (en) * | 2018-06-28 | 2018-11-20 | 北京金山安全软件有限公司 | Dishwasher control method, dishwasher control device, mobile terminal device and storage medium |
| CN108852239A (en) * | 2018-06-28 | 2018-11-23 | 北京金山安全软件有限公司 | Dish washing machine and modification and upgrading method thereof |
| WO2018216015A1 (en) * | 2017-05-23 | 2018-11-29 | Kwik Commerce Ltd | System and method for automated supervision of consumption and inventory of appliance consumables |
| KR20190099151A (en) * | 2019-02-01 | 2019-08-26 | 엘지전자 주식회사 | Sever communicating with dish whaher |
| CN110251034A (en) * | 2019-06-19 | 2019-09-20 | 佛山市百斯特电器科技有限公司 | A kind of control method and dish-washing machine of sterilizing operation |
| CN110367898A (en) * | 2019-07-31 | 2019-10-25 | 佛山市百斯特电器科技有限公司 | A kind of the determination method and dish-washing machine of cleaning model |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8506725B2 (en) * | 2008-02-15 | 2013-08-13 | Electrolux Home Products, Inc. | Washing appliance and associated method |
| CN108814501B (en) * | 2018-06-28 | 2020-12-01 | 北京金山安全软件有限公司 | Washing control optimization method and device, electronic equipment and storage medium |
-
2019
- 2019-10-30 CN CN201911047990.3A patent/CN110811461B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102377814A (en) * | 2010-08-25 | 2012-03-14 | 汎惪股份有限公司 | Remote assistance service method aiming at embedded operation system |
| CN102383286A (en) * | 2010-09-01 | 2012-03-21 | 海尔集团公司 | Intelligentized internet-of-thing washing machine |
| CN105986397A (en) * | 2015-02-10 | 2016-10-05 | 青岛海尔洗衣机有限公司 | Washing time control method for washing machine and washing machine |
| CN104746287A (en) * | 2015-03-19 | 2015-07-01 | 无锡小天鹅股份有限公司 | Washing machine, control method thereof and washing machine system |
| CN107794704A (en) * | 2016-09-07 | 2018-03-13 | 台湾松下电器股份有限公司 | Intelligent clothes washing method and system |
| WO2018216015A1 (en) * | 2017-05-23 | 2018-11-29 | Kwik Commerce Ltd | System and method for automated supervision of consumption and inventory of appliance consumables |
| CN108577730A (en) * | 2018-04-28 | 2018-09-28 | 北京金山安全软件有限公司 | Dishwasher control method, device, dishwasher and computer readable storage medium |
| CN108755001A (en) * | 2018-05-22 | 2018-11-06 | 佛山市云米电器科技有限公司 | A method of washing effect is optimized according to the water quality of washing machine region |
| CN108784585A (en) * | 2018-05-24 | 2018-11-13 | 北京金山安全软件有限公司 | Cleaning method, cleaning control method, cleaning device and server |
| CN108836226A (en) * | 2018-06-28 | 2018-11-20 | 北京金山安全软件有限公司 | Dishwasher control method, dishwasher control device, mobile terminal device and storage medium |
| CN108852239A (en) * | 2018-06-28 | 2018-11-23 | 北京金山安全软件有限公司 | Dish washing machine and modification and upgrading method thereof |
| KR20190099151A (en) * | 2019-02-01 | 2019-08-26 | 엘지전자 주식회사 | Sever communicating with dish whaher |
| CN110251034A (en) * | 2019-06-19 | 2019-09-20 | 佛山市百斯特电器科技有限公司 | A kind of control method and dish-washing machine of sterilizing operation |
| CN110367898A (en) * | 2019-07-31 | 2019-10-25 | 佛山市百斯特电器科技有限公司 | A kind of the determination method and dish-washing machine of cleaning model |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110811461A (en) | 2020-02-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110811460B (en) | Method and system for determining cleaning mode | |
| CN110420000B (en) | Method for determining cleaning mode and dish-washing machine | |
| CN108814501B (en) | Washing control optimization method and device, electronic equipment and storage medium | |
| CN110811461B (en) | Method and system for determining cleaning mode | |
| CN110367898B (en) | Method for determining cleaning mode and dish-washing machine | |
| US11812912B2 (en) | Method for the dosing of cleaning agents | |
| WO2016145715A1 (en) | Washing machine and control method therefor and washing machine system | |
| WO2016145714A1 (en) | Washing machine and control method therefor and washing machine system | |
| US10326610B2 (en) | Apparatus and method for determining a schedule of an appliance | |
| WO2016145713A1 (en) | Washing machine and control method therefor and washing machine system | |
| CN110840349A (en) | Method and system for generating placement schematic diagram | |
| CN103488691A (en) | Task scheduling device and task scheduling method | |
| CN104698309B (en) | The method and system that a kind of power information obtains | |
| CN104092775A (en) | Intelligent household electrical appliance self-learning method and system | |
| CN106052029B (en) | It is a kind of for the control method of dehumidification system, device and dehumidification system | |
| CN110448250B (en) | Tableware storing and taking method and system | |
| WO2019218873A1 (en) | Water quality map generation method and control method using water quality map | |
| CN108826701A (en) | A kind of intelligent control method and device of storage-type electric water heater | |
| WO2016176864A1 (en) | Method and device for controlling reserved charging of electric vehicle | |
| CN106899656B (en) | Apparatus control method and device | |
| CN105468594A (en) | Method and system for optimizing data collection and server | |
| CN110255724A (en) | A kind of method and apparatus of regulating water quality | |
| CN109799420A (en) | A kind of the internet of things functional detection system and detection method of internet of things home appliance | |
| CN104896752B (en) | Computing Method of The Heating Time, server, Hot water units and hot-water heating system in advance | |
| CN115399697A (en) | Self-cleaning method and device for cleaning equipment, storage medium and electronic device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |