Method, device, computer equipment, computer-readable storage medium, and computer program product for determining dishwasher operating parameters

Abstract

The present invention relates to the field of electrical equipment technology, and discloses a method, apparatus, computer device, computer-readable storage medium, and computer program product for determining dishwasher operating parameters. The method comprises: obtaining first current information about the detergent used in the current wash cycle and second current information about the water used in the current wash cycle; obtaining a turbidity requirement for the water after the current wash cycle and a requirement for residual detergent in the water after the current wash cycle; and inputting the first current information, the second current information, the turbidity requirement, and the residual detergent requirement into a trained neural network model to obtain target operating parameters for the current wash cycle. This allows for dynamic adjustment of the washing process, significantly improving the accuracy and adaptability of washing. Furthermore, the determination of the target operating parameters fully considers the effects of different detergent types and concentrations, as well as the quality of the water source, resulting in strong versatility and adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical equipment, and in particular to a method and device for determining operating parameters of a dishwasher, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] Dishwashers, as a long-standing kitchen cleaning appliance, often replace users with tedious dishwashing tasks, making them popular with consumers. Traditional dishwashers typically recommend a fixed amount of detergent based on standard contaminant levels when determining wash cycles. While this approach generally ensures a certain cleaning effect, it often overlooks the differences in contaminant types and concentrations in actual use. This can lead to excessive detergent use, which can cause residue problems. Summary of the Invention

[0003] In view of this, the present invention provides a method, apparatus, computer device, computer-readable storage medium and computer program product for determining operating parameters of a dishwasher, so as to dynamically achieve low residue and high cleaning effect of the dishwasher.

[0004] In a first aspect, the present invention provides a method for determining operating parameters of a dishwasher, comprising the following steps: obtaining first current information of a detergent used in a current wash of the dishwasher and second current information of water used in the current wash; obtaining a turbidity requirement of water after the current wash and a detergent residual requirement in the water after the current wash; inputting the first current information, the second current information, the water turbidity requirement, and the detergent residual requirement into a trained neural network model to obtain target operating parameters for the current wash.

[0005] The method for determining the operating parameters of a dishwasher provided by the present invention has the following beneficial effects: (1) Unlike conventional dishwashers that use a fixed detergent dosage and a unified washing program, the method obtains real-time information such as the current detergent and water quality, and combines it with the requirements for water turbidity and detergent residue after washing, and uses a neural network model to intelligently calculate the optimal operating parameters, thereby achieving dynamic adjustment of the washing process and significantly improving the accuracy and adaptability of washing; (2) The influence of different detergent types, concentrations and water source quality is fully considered in the process of determining the target operating parameters, so the method has strong versatility and adaptability, and is suitable for diverse usage environments in different regions and users.

[0006] In an optional embodiment, the method for determining the operating parameters of the dishwasher also includes: obtaining a training data set, wherein the training data set includes multiple groups of data, each group of data includes historical operating parameters of the dishwasher's historical washing, first historical information of the detergent used, second historical information of the water used, historical turbidity of the water after the historical washing, and the amount of detergent residual in the water after the historical washing; and using the training data set to train the neural network model.

[0007] The present invention trains the neural network model using real historical washing data (including operating parameters, detergent information, water quality information, turbidity and residual amount, etc.), enabling the model to learn the complex mapping relationship between different input factors and washing results, thereby improving its prediction accuracy and adaptability to new scenarios.

[0008] In an optional embodiment, the first current information includes the type of detergent used for the current wash and the amount of detergent used for the current wash; the first historical information includes the type of detergent used for the historical wash and the amount of detergent used for the historical wash; the second current information includes the amount of water used for the current wash, the temperature of the water used for the current wash, and the hardness of the water used for the current wash; the second historical information includes the amount of water used for the historical wash, the temperature of the water used for the historical wash, and the hardness of the water used for the historical wash.

[0009] The present invention incorporates multi-dimensional data such as detergent type, dosage, water volume, temperature, and hardness into input information, enabling the system to more comprehensively perceive the current washing environment and conditions, providing a reliable data basis for accurate decision-making on subsequent operating parameters.

[0010] In an optional embodiment, the dishwasher includes multiple turbidity sensors set at different heights, and the method for obtaining the historical turbidity of water after the historical washing is completed includes: after the historical washing is completed, obtaining the detection data and setting height of each turbidity sensor respectively; obtaining the initial turbidity based on the detection data and setting heights of all turbidity sensors; obtaining the water flow rate of the historical washing; and correcting the initial turbidity according to the water flow rate to obtain the historical turbidity of the water after the historical washing is completed.

[0011] This is because the distribution of suspended particles in the water during the washing process can be uneven, making it difficult to fully reflect the overall water quality using a single turbidity sensor. By installing multiple turbidity sensors at different heights and integrating their test results and installation locations, the overall turbidity level of the water after washing can be more scientifically assessed, improving data representativeness and measurement accuracy. Furthermore, introducing water flow velocity as a correction parameter helps eliminate turbidity measurement deviations caused by bubbles, making the final historical turbidity values ​​more realistic, thereby providing high-quality data support for model training and operational parameter optimization.

[0012] In an optional embodiment, before obtaining the first current information of the detergent used by the dishwasher for the current wash and the second current information of the water used for the current wash, it also includes: obtaining user instructions; determining whether the current wash uses the interactive mode according to the user instructions; when the current wash uses the interactive mode, executing the steps of obtaining the first current information of the detergent used by the dishwasher for the current wash and the second current information of the water used for the current wash; when the current wash does not use the interactive mode, using the preset operating parameters as the target operating parameters for the current wash.

[0013] When interactive mode is enabled, the system dynamically generates optimal operating parameters based on real-time detergent and water quality information, combined with a neural network model, for personalized, high-precision control. When interactive mode is disabled, the system uses validated preset parameters to ensure a stable and reliable washing process, avoiding uncertainty risks caused by data anomalies or model prediction bias. For washing scenarios requiring precise control (such as washing valuable tableware or requiring low residue), users can enable interactive mode to allow the system to optimize water, electricity, and detergent usage based on actual conditions, achieving energy conservation and environmental protection. For standard washing scenarios, the preset parameters are sufficient, avoiding unnecessary resource consumption. However, if accurate detergent or water quality information cannot be obtained (e.g., due to sensor failure or data anomalies), the system automatically falls back to the preset operating mode, ensuring basic dishwasher functionality remains intact and enhancing the overall system's robustness and applicability. This mechanism establishes a multi-level control system, from "fully automatic basic mode" to "intelligent interactive optimization mode," suitable for both general home users and professional or high-end users with higher cleaning performance requirements, broadening the product's application scope.

[0014] In an optional embodiment, after the first current information, the second current information, the water turbidity requirement and the detergent residual requirement are input into a trained neural network model to obtain the target operating parameters for the current washing, it also includes: the first current information, the second current information, the target operating parameters, the turbidity requirement of the water after the current washing and the detergent residual requirement in the water after the current washing are formed into a set of data and put into the training data set.

[0015] Therefore, with the accumulation of more historical data and continuous training, the neural network model can continuously optimize its own performance and possess a certain self-learning ability, so that the dishwasher can continuously improve the washing effect and resource utilization efficiency during long-term use; and because the training data set contains a variety of different washing scenarios and user usage habits, the trained model can recognize and adapt to the characteristics of different user groups or family environments, thereby providing different users with personalized washing solutions that better meet their needs.

[0016] In a second aspect, the present invention also provides a device for determining the operating parameters of a dishwasher, comprising a first acquisition module, a second acquisition module and an operating parameter determination module, wherein the first acquisition module is used to obtain first current information of the detergent used for the current washing of the dishwasher and second current information of the water used for the current washing; the second acquisition module is used to obtain the turbidity requirement of the water after the current washing is completed and the detergent residual requirement in the water after the current washing is completed; the operating parameter determination module is used to input the first current information, the second current information, the water turbidity requirement and the detergent residual requirement into a trained neural network model to obtain the target operating parameters for the current washing.

[0017] In a third aspect, the present invention further provides a computer device comprising a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to thereby execute the method for determining the operating parameters of the dishwasher according to the first aspect or any corresponding embodiment thereof.

[0018] In a fourth aspect, the present invention further provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for determining operating parameters of a dishwasher according to the first aspect or any corresponding embodiment thereof.

[0019] In a fifth aspect, the present invention further provides a computer program product, comprising computer instructions for causing a computer to execute the method for determining operating parameters of a dishwasher according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 is a flow chart of a method for determining operating parameters of a dishwasher according to an embodiment of the present invention;

[0022] Figure 2 is a flow chart of another method for determining operating parameters of a dishwasher according to an embodiment of the present invention;

[0023] Figure 3 is a flow chart of a method for determining operating parameters of a dishwasher according to an embodiment of the present invention;

[0024] Figure 4 is a flowchart of an example of a method for determining operating parameters of a dishwasher according to an embodiment of the present invention;

[0025] Figure 5 is a structural block diagram of a device for determining operating parameters of a dishwasher according to an embodiment of the present invention;

[0026] Figure 6 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0028] According to an embodiment of the present invention, an embodiment of a method for determining operating parameters of a dishwasher is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] In this embodiment, a method for determining operating parameters of a dishwasher is provided, which can be used for a dishwasher. Figure 1 FIG. 1 is a flow chart of a method for determining operating parameters of a dishwasher according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0030] Step S101: obtaining first current information of detergent currently used for washing in the dishwasher and second current information of water currently used for washing.

[0031] Specifically, the first current information includes the type of detergent used for the current washing and the amount of detergent used for the current washing; the second current information includes the amount of water used for the current washing, the temperature of the water used for the current washing, and the hardness of the water used for the current washing.

[0032] Step S102: obtaining the turbidity requirement of the water after the current washing is completed and the detergent residual requirement in the water after the current washing is completed.

[0033] Specifically, the water turbidity requirement refers to the control standard for the content of suspended particulate matter in the wastewater after washing, which is used to reflect the degree of impurity cleanliness in the water after dishwashing. The detergent residual requirement refers to the limit on the concentration of residual detergent in the water after washing, which aims to avoid potential impacts on human health or the environment caused by excessive detergent residue. These requirements can be set according to different usage scenarios and user needs.

[0034] Furthermore, these turbidity and detergent residue requirements can be determined by user input, such as selecting different cleaning levels (e.g., "High Clean" or "Low Residue Mode") through the dishwasher's user interface, or automatically according to a pre-set program. For example, a dishwasher can be pre-programmed with various wash target templates, such as "Daily Wash Mode," "Infant and Toddler Products Mode," and "Energy and Water Conservation Mode." Each mode corresponds to different turbidity and residue control standards to meet diverse usage needs.

[0035] In addition, this setting method also supports dynamic adjustments based on instructions pushed by external devices or smart platforms. For example, it can be linked with a smart home system to automatically match appropriate turbidity and residual control parameters based on information such as the user's household water quality, tableware type or health preferences, thereby achieving a more intelligent and personalized washing experience.

[0036] Step S103: inputting the first current information, the second current information, the water turbidity requirement and the detergent residual requirement into the trained neural network model to obtain the target operating parameters of the current washing.

[0037] Specifically, the target operating parameters include but are not limited to: washing time and washing pump speed.

[0038] The method for determining the operating parameters of a dishwasher provided in this embodiment has the following beneficial effects: (1) Unlike conventional dishwashers that use a fixed detergent dosage and a uniform washing program, this method obtains real-time information such as the current detergent and water quality, and combines it with the requirements for water turbidity and detergent residue after washing, and uses a neural network model to intelligently calculate the optimal operating parameters, thereby achieving dynamic adjustment of the washing process and significantly improving the accuracy and adaptability of washing; (2) The influence of different detergent types, concentrations, and water source quality is fully considered in the process of determining the target operating parameters. Therefore, this method has strong versatility and adaptability and is suitable for diverse usage environments in different regions and users.

[0039] In this embodiment, a method for determining operating parameters of a dishwasher is provided, which can be used for a dishwasher. Figure 2 FIG. 1 is a flow chart of another method for determining operating parameters of a dishwasher according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0040] Step S201: Obtain a training dataset, where the training dataset includes multiple sets of data, each set of data including historical operating parameters of the dishwasher's historical washes, first historical information about the detergent used, second historical information about the water used, historical turbidity of the water after each wash, and the amount of detergent remaining in the water after each wash. Specifically, the first historical information includes the type of detergent used and the amount of detergent used in each wash; the second historical information includes the amount of water used, the temperature of the water used, and the hardness of the water used.

[0041] This is because, in actual use, a dishwasher's cleaning performance (turbidity: RE01) and residual laundry residue (residual concentration: RE02) are largely determined by the following factors: detergent dosage C1, water temperature T1, wash time t1, water volume L1, wash pump speed w1, detergent type Ty-n, and water hardness H. Based on the above analysis, data can be collected in the format [C1, T1, t1, L1, W1, Ty-n, H], [RE01, RE02].

[0042] In an optional embodiment, the dishwasher includes multiple turbidity sensors set at different heights, and the method for obtaining the historical turbidity of water after the historical washing is completed includes: after the historical washing is completed, obtaining the detection data and setting height of each turbidity sensor respectively; obtaining the initial turbidity based on the detection data and setting heights of all turbidity sensors; obtaining the water flow rate of the historical washing; and correcting the initial turbidity according to the water flow rate to obtain the historical turbidity of the water after the historical washing is completed.

[0043] This is because suspended particles in the water can be unevenly distributed during the washing process. Furthermore, turbidity sensors typically use light scattering, making them susceptible to influences unrelated to contaminants, such as bubbles. Relying on a single turbidity sensor alone cannot fully reflect the overall water quality. By installing multiple turbidity sensors at different heights and integrating their test results and installation locations, we can more scientifically assess the overall turbidity level of the water after washing, improving data representativeness and measurement accuracy.

[0044] Specifically, 2 or 3 turbidity sensors can be arranged in the direction of gravity, such as at the bottom of a water cup, at the inlet and outlet of a washing pump, etc. Ca, Cb, and Cc are the collected data of the three sensors, and Ha, Hb, and Hc are the heights of the sensors relative to the lowest point of the water cup.

[0045] For example, the initial turbidity can be calculated by the following formula.

[0046]

[0047] Among them, m0, a, k, and b are all constants and can be evaluated using collected data.

[0048] In addition, the presence of a large number of tiny bubbles increases the light scattering intensity, seriously affecting the sensing accuracy. The initial turbidity can be corrected using the following formula:

[0049]

[0050] in, Indicates water flow velocity, which is related to driving pressure and flow channel.

[0051] By introducing water flow velocity as a correction parameter, it helps to eliminate the turbidity measurement deviation caused by bubbles, making the final historical turbidity value closer to the actual situation, thereby providing high-quality data support for model training and operation parameter optimization.

[0052] Step S202: Train the neural network model using the training data set.

[0053] Specifically, [C1, T1, t1, L1, W1, Ty-n, H] can be used as input, and [RE01, RE02] as the corresponding actual output. The model calculates and outputs [Cal01, Cal02] (Cal01: turbidity calculated by the model, Cal02: residual concentration calculated by the model). During training, the model parameters are continuously adjusted to ensure that the two values ​​[ABS (Cal01 - RE01) and ABS (Cal02 - RE01)] reach the minimum or set value. Model training stops when the difference meets the required value.

[0054] This embodiment trains the neural network model using real historical washing data (including operating parameters, detergent information, water quality information, turbidity, and residual amount, etc.), enabling the model to learn the complex mapping relationship between different input factors and washing results, thereby improving its prediction accuracy and adaptability to new scenarios.

[0055] Step S203: Acquire first current information of the detergent currently used for washing in the dishwasher and second current information of the water currently used for washing.

[0056] Step S204: obtaining the turbidity requirement of the water after the current washing and the detergent residual requirement in the water after the current washing.

[0057] Step S205: input the first current information, the second current information, the water turbidity requirement and the detergent residual requirement into the trained neural network model to obtain the target operating parameters of the current washing.

[0058] The method for determining dishwasher operating parameters provided in this embodiment can achieve dynamic adjustment of the washing process, significantly improving the accuracy and adaptability of washing; and the influence of different detergent types, concentrations, and water source quality are fully considered in the process of determining the target operating parameters. Therefore, it has strong versatility and adaptability and is suitable for diverse usage environments in different regions and users.

[0059] In this embodiment, a method for determining operating parameters of a dishwasher is provided, which can be used for a dishwasher. Figure 3 FIG. 1 is a flow chart of a method for determining operating parameters of a dishwasher according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0060] Step S301: Obtain user instructions.

[0061] Step S302: Determine whether the current washing mode is in interactive mode according to the user instruction; if the current washing mode is in interactive mode, proceed to step S304; otherwise, proceed to step S303;

[0062] Step S303: using the preset operating parameters as target operating parameters for the current washing.

[0063] Step S304: obtaining first current information of the detergent currently used for washing in the dishwasher and second current information of the water currently used for washing.

[0064] Step S305: Obtain the turbidity requirement of the water after the current washing is completed and the detergent residual requirement in the water after the current washing is completed.

[0065] Step S306: input the first current information, the second current information, the water turbidity requirement, and the detergent residual requirement into the trained neural network model to obtain the target operating parameters of the current washing.

[0066] Step S307: The first current information, the second current information, the target operating parameters, the turbidity requirement of the water after the current wash, and the detergent residue requirement in the water after the current wash are formed into a set of data and put into a training data set.

[0067] With the accumulation of more historical data and continuous training, the neural network model can continuously optimize its own performance and possess a certain self-learning ability, so that the dishwasher can continuously improve the washing effect and resource utilization efficiency during long-term use; and because the training data set contains a variety of different washing scenarios and user usage habits, the trained model can recognize and adapt to the characteristics of different user groups or family environments, thereby providing different users with personalized washing solutions that better meet their needs.

[0068] In order to explain the method for determining the operating parameters of the dishwasher in this embodiment in more detail, a specific example is given. Figure 4 As shown, the following steps are included:

[0069] First, a neural network model is trained through variable identification and data collection to obtain multi-dimensional input-output relationships. The trained model can then be used to determine the optimal operating parameters for the dishwasher and preset these optimal operating parameters (also referred to as "optimal parameters"). When the user selects interactive mode, the dishwasher obtains first current information about the detergent used in the current wash and second current information about the water used in the current wash. The required turbidity of the water after the current wash and the required detergent residue in the water after the current wash are obtained. The first current information, the second current information, the required turbidity of the water after the current wash, and the required detergent residue are input into the trained neural network model to obtain the target operating parameters for the current wash. After the current wash is completed, the first current information, the second current information, the target operating parameters, the required turbidity of the water after the current wash, and the required detergent residue in the water after the current wash are combined into a set of data and added to the training dataset. This allows the neural network model to be continuously optimized based on the user's specific usage data. The optimized neural network model can then be used to obtain new optimal operating parameters that match the user's specific usage. Furthermore, the new optimal operating parameters can be used to update the threshold optimal operating parameters in the dishwasher. When the user uses the interactive mode, the manufacturer's operating parameters, that is, the optimal operating parameters preset in the dishwasher, can be used.

[0070] When interactive mode is enabled, the system dynamically generates optimal operating parameters based on real-time detergent and water quality information, combined with a neural network model, for personalized, high-precision control. When interactive mode is disabled, the system uses validated preset parameters to ensure a stable and reliable wash process, avoiding the risk of uncertainty caused by data anomalies or model prediction bias. For wash scenarios requiring precise control (such as washing valuable tableware or requiring low residue), users can enable interactive mode to allow the system to optimize water, electricity, and detergent usage based on actual conditions, achieving energy conservation and environmental protection. For standard wash scenarios, the preset parameters are sufficient, avoiding unnecessary resource consumption. However, if accurate detergent or water quality information cannot be obtained (e.g., due to sensor failure or data anomalies), the system automatically falls back to the preset operating mode, ensuring basic dishwasher functionality remains intact and enhancing overall system robustness and applicability. This mechanism establishes a multi-level control system, from "fully automatic basic mode" to "intelligent interactive optimization mode," suitable for both general home users and professional or high-end users with higher cleaning performance requirements, broadening the product's application scope.

[0071] This embodiment also provides a device for determining dishwasher operating parameters. This device is used to implement the aforementioned embodiments and preferred implementations, and details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented using software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0072] This embodiment provides a device for determining operating parameters of a dishwasher, such as Figure 5 As shown, including:

[0073] The first acquisition module 501 is configured to acquire first current information of a detergent currently used for washing in the dishwasher and second current information of water currently used for washing.

[0074] The second acquisition module 502 is used to obtain the turbidity requirement of the water after the current washing is completed and the detergent residue requirement in the water after the current washing is completed.

[0075] The operation parameter determination module 503 is used to input the first current information, the second current information, the water turbidity requirement and the detergent residue requirement into the trained neural network model to obtain the target operation parameters of the current washing.

[0076] In some optional embodiments, the device for determining dishwasher operating parameters further includes a training module. The training module is specifically configured to: obtain a training data set, wherein the training data set includes multiple sets of data, each set of data including historical operating parameters of the dishwasher during historical washes, first historical information about detergent used, second historical information about water used, historical turbidity of water after historical washes, and the amount of detergent remaining in the water after historical washes; and train a neural network model using the training data set.

[0077] In some optional embodiments, the first current information includes the type of detergent used for the current wash and the amount of detergent used for the current wash; the first historical information includes the type of detergent used for the historical wash and the amount of detergent used for the historical wash; the second current information includes the amount of water used for the current wash, the temperature of the water used for the current wash, and the hardness of the water used for the current wash; the second historical information includes the amount of water used for the historical wash, the temperature of the water used for the historical wash, and the hardness of the water used for the historical wash.

[0078] In some optional embodiments, the dishwasher includes multiple turbidity sensors set at different heights, and the training module is specifically used to: obtain the detection data and setting height of each turbidity sensor after the historical washing is completed; obtain the initial turbidity based on the detection data and setting heights of all turbidity sensors; obtain the water flow rate of the historical washing; correct the initial turbidity according to the water flow rate to obtain the historical turbidity of the water after the historical washing is completed.

[0079] In some optional embodiments, the device for determining dishwasher operating parameters further includes a preprocessing module. Prior to obtaining first current information about the detergent used in the current wash cycle and second current information about the water used in the current wash cycle, the preprocessing module is further configured to: obtain a user instruction; determine, based on the user instruction, whether the current wash cycle utilizes an interactive mode; if the current wash cycle utilizes the interactive mode, execute the steps of obtaining the first current information about the detergent used in the current wash cycle and the second current information about the water used in the current wash cycle; and if the current wash cycle utilizes no interactive mode, use the preset operating parameters as target operating parameters for the current wash cycle.

[0080] In some optional embodiments, after the first current information, the second current information, the water turbidity requirement, and the detergent residual requirement are input into the trained neural network model to obtain the target operating parameters for the current washing, the training module is also used to: form the first current information, the second current information, the target operating parameters, the turbidity requirement for the water after the current washing, and the detergent residual requirement in the water after the current washing into a set of data, and put it into the training data set.

[0081] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0082] The device for determining the operating parameters of the dishwasher in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0083] The embodiment of the present invention also provides a computer device having the above Figure 5 The device for determining the operating parameters of the dishwasher is shown.

[0084] See also Figure 6 , Figure 6 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 6As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0085] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0086] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0087] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0088] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0089] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 6 The bus connection is taken as an example.

[0090] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device. Examples include a touch screen, keypad, mouse, trackpad, touchpad, pointing stick, one or more mouse buttons, trackball, joystick, etc. The output device 40 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibration motors). Such display devices include, but are not limited to, liquid crystal displays, light emitting diodes, monitors, and plasma displays. In some optional embodiments, the display device may be a touch screen.

[0091] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0092] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0093] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for determining operating parameters of a dishwasher, characterized in that: include: Obtaining first current information of a detergent currently used in a washing machine and second current information of water currently used in the washing machine, wherein the first current information includes the type of the detergent currently used in the washing machine and the amount of the detergent currently used in the washing machine; and the second current information includes the amount of water currently used in the washing machine, the temperature of the water currently used in the washing machine, and the hardness of the water currently used in the washing machine; Obtaining the turbidity requirement of the water after the current washing is completed and the detergent residual requirement in the water after the current washing is completed; Inputting the first current information, the second current information, the water turbidity requirement, and the detergent residual requirement into a trained neural network model to obtain target operating parameters for the current washing; Also includes: Obtaining a training data set, wherein the training data set includes multiple sets of data, each set of data including historical operating parameters of the dishwasher's historical washes, first historical information of detergent used, second historical information of water used, historical turbidity of water after the completion of the historical washes, and detergent residue in the water after the completion of the historical washes; wherein the first historical information includes the type of detergent used in the historical washes and the amount of detergent used in the historical washes; and the second historical information includes the amount of water used in the historical washes, the temperature of the water used in the historical washes, and the hardness of the water used in the historical washes; Training the neural network model using the training data set; The dishwasher includes a plurality of turbidity sensors arranged at different heights, and the method for obtaining the historical turbidity of water after the historical washing is completed includes: After the historical washing is completed, the detection data and setting height of each turbidity sensor are respectively obtained; Obtain initial turbidity based on the detection data of all turbidity sensors and the set height; Obtaining the water flow rate of the historical washing; The initial turbidity is corrected according to the water flow rate to obtain the historical turbidity of the water after the historical washing is completed.

2. The method according to claim 1, characterized in that Before obtaining the first current information of the detergent currently used for washing in the dishwasher and the second current information of the water currently used for washing, the method further includes: Get user instructions; Determining whether the current washing process uses an interactive mode according to the user instruction; When the interactive mode is used in the current washing, the step of acquiring first current information of the detergent used in the current washing of the dishwasher and second current information of the water used in the current washing is performed; When the interactive mode is not used in the current washing, the preset operating parameters are used as the target operating parameters of the current washing.

3. The method according to claim 1, characterized in that After inputting the first current information, the second current information, the water turbidity requirement, and the detergent residue requirement into a trained neural network model to obtain the target operating parameters for the current washing, the method further includes: The first current information, the second current information, the target operating parameters, the turbidity requirement of the water after the current washing is completed, and the detergent residual requirement in the water after the current washing is completed are formed into a set of data and put into the training data set.

4. A device for determining operating parameters of a dishwasher, characterized in that: include: A first acquisition module is used to acquire first current information of a detergent currently used for washing in the dishwasher and second current information of water currently used for washing; The first current information includes the type of detergent used in the current washing and the amount of detergent used in the current washing; the second current information includes the amount of water used in the current washing, the temperature of the water used in the current washing, and the hardness of the water used in the current washing; The second acquisition module is used to obtain the turbidity requirement of the water after the current washing and the detergent residual requirement in the water after the current washing; an operating parameter determination module, configured to input the first current information, the second current information, the water turbidity requirement, and the detergent residue requirement into a trained neural network model to obtain target operating parameters for the current wash; Also includes training modules; The training module is specifically configured to: obtain a training data set, wherein the training data set includes multiple sets of data, each set of data including historical operating parameters of the dishwasher during historical washes, first historical information of detergent used, second historical information of water used, historical turbidity of water after the completion of the historical washes, and detergent residue in the water after the completion of the historical washes; wherein the first historical information includes the type of detergent used during the historical washes and the amount of detergent used during the historical washes; and wherein the second historical information includes the amount of water used during the historical washes, the temperature of the water used during the historical washes, and the hardness of the water used during the historical washes; and train the neural network model using the training data set; The dishwasher includes a plurality of turbidity sensors set at different heights, and the training module is specifically configured to: after the historical washing is completed, obtain the detection data and the setting height of each turbidity sensor; and obtain the initial turbidity based on the detection data and the setting heights of all turbidity sensors; Obtaining the water flow rate of the historical washing; The initial turbidity is corrected according to the water flow rate to obtain the historical turbidity of the water after the historical washing is completed.

5. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for determining the operating parameters of the dishwasher according to any one of claims 1 to 3 by executing the computer instructions.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for determining the operating parameters of the dishwasher according to any one of claims 1 to 3.

7. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for determining operating parameters of a dishwasher according to any one of claims 1 to 3.

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