Dish-washing machine operation parameter determination method and device, computer equipment, computer readable storage medium and computer program product

Abstract

The invention relates to the technical field of electrical equipment, and discloses a dish-washing machine operation parameter determination method and device, computer equipment, a computer readable storage medium and a computer program product. The dish-washing machine operation parameter determination method comprises the following steps: acquiring first current information of a detergent currently used by the dish-washing machine and second current information of water currently used by the dish-washing machine; obtaining the turbidity requirement of the water after the current washing is finished and the detergent residue requirement of the water after the current washing is finished; and 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 target operation parameters of current washing. Therefore, the dynamic adjustment of the washing process can be realized, and the washing accuracy and adaptability are obviously improved; and the influence of different detergent types, concentrations and water source quality is fully considered in the process of determining the target operation parameters, so that the method has relatively high universality and adaptability.

Description

Technical Field

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

[0002] As a kitchen cleaning appliance with a long history, a dishwasher can usually replace users to complete the cumbersome dishwashing work, so it is deeply loved by consumers. When determining the washing program, traditional dishwashers generally recommend a fixed amount of detergent based on the situation of standard pollutants. Although this method can generally ensure a certain cleaning effect, it often ignores the differences in the types and concentrations of pollutants in actual use. This may lead to excessive use of detergent, which in turn causes residue problems. Summary of the Invention

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

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

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

[0006] In an alternative embodiment, the method for determining the operating parameters of the dishwasher further includes: obtaining a training data set, where the training data set includes multiple groups of data, and each group of data includes the historical operating parameters of the dishwasher during historical washing, the first historical information of the detergent used, the second historical information of the water used, the historical turbidity of the water after historical washing, and the detergent residue in the water after historical washing; training a neural network model using the training data set.

[0007] In the present invention, by using real historical washing data (including operating parameters, detergent information, water quality information, turbidity, and residue, etc.) to train the neural network model, the model can 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 alternative embodiment, the first current information includes the type of detergent used in the current washing and the dosage of the detergent used in the current washing; the first historical information includes the type of detergent used in the historical washing and the dosage of the detergent used in the historical 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 historical information includes the amount of water used in the historical washing, the temperature of the water used in the historical washing, and the hardness of the water used in the historical washing.

[0009] In the present invention, by incorporating multi-dimensional data such as detergent type, dosage, and water amount, temperature, and hardness into the input information, the system can more comprehensively perceive the current washing environment and conditions, providing a reliable data basis for accurate decision-making of subsequent operating parameters.

[0010] In an alternative embodiment, the dishwasher includes a plurality of turbidity sensors with different installation heights. The method for obtaining the historical turbidity of the water after historical washing includes: after historical washing, obtaining the detection data and installation height of each turbidity sensor respectively; obtaining the initial turbidity based on the detection data and installation height of all turbidity sensors; obtaining the water flow rate of historical washing; and correcting the initial turbidity according to the water flow rate to obtain the historical turbidity of the water after historical washing.

[0011] This is because during the washing process, the distribution of suspended particles in the water may be uneven, and it is difficult to comprehensively reflect the overall water quality condition relying solely on the turbidity sensor at a single position. By setting a plurality of turbidity sensors at different heights and comprehensively considering their detection results and installation positions, the overall turbidity level of the water after washing can be evaluated more scientifically, improving the representativeness and measurement accuracy of the data; moreover, introducing the water flow rate as a correction parameter helps to eliminate the turbidity measurement deviation caused by bubbles, making the finally obtained historical turbidity value closer to the actual situation, thereby providing high-quality data support for model training and operating parameter optimization.

[0012] In an alternative embodiment, before obtaining the first current information on the detergent used in the current wash of the dishwasher and the second current information on the water used in the current wash, it further includes: obtaining a user instruction; determining whether the current wash uses an interactive mode according to the user instruction; when the current wash uses the interactive mode, performing the step of obtaining the first current information on the detergent used in the current wash of the dishwasher and the second current information on the water used in 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] Thus, when the interactive mode is enabled, the system can dynamically generate the optimal operating parameters according to the real-time collected detergent and water quality information, combined with the neural network model, to achieve personalized and high-precision control; while when the interactive mode is not enabled, the system uses the verified preset parameters to ensure the stability and reliability of the washing process and avoid the uncertainty risks brought by data anomalies or model prediction deviations. For washing scenarios that require fine control (such as cleaning precious tableware, low residue requirements, etc.), users can enable the interactive mode to let the system optimize the usage of water, electricity, and detergent according to the actual situation, achieving the purpose of energy conservation and environmental protection; while in ordinary washing scenarios, the preset parameters can be used to complete the task, avoiding unnecessary resource consumption. However, when the current detergent or water quality information cannot be accurately obtained (such as sensor failure or data anomaly), the system can automatically fallback to the preset operating mode to ensure that the basic functions of the dishwasher are not affected and improve the robustness and application scope of the overall system. This mechanism constructs a multi-level control system from the "fully automatic basic mode" to the "intelligent interactive optimization mode", which is suitable for both popular household users and professional or high-end user groups with higher requirements for cleaning performance, broadening the application boundary of the product.

[0014] In an alternative embodiment, after inputting the first current information, the second current information, the turbidity requirement of water, and the detergent residue requirement into the trained neural network model to obtain the target operating parameters for the current wash, it further includes: forming a set of data with the first current information, the second current information, the target operating parameters, the turbidity requirement of water after the current wash, and the detergent residue requirement in the water after the current wash, and putting it into the training data set.

[0015] Thus, with the accumulation of more historical data and continuous training, the neural network model can continuously optimize its own performance and have 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 identify and adapt to the characteristics of different user groups or household environments, so as to provide more personalized washing solutions that meet the needs of different users.

[0016] In a second aspect, the present invention further provides a device for determining the operating parameters of a dishwasher, including a first acquisition module, a second acquisition module, and an operating parameter determination module. The first acquisition module is used to acquire the first current information of the detergent used in the current washing of the dishwasher and the second current information of the water used in the current washing. The second acquisition module is used to acquire 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. The operating parameter determination module is used to input the first current information, the second current information, the turbidity requirement of the water, and the detergent residue requirement into a trained neural network model to obtain the target operating parameters of the current washing.

[0017] In a third aspect, the present invention further provides a computer device, including a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to 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, on which computer instructions are stored. The computer instructions are used to cause a computer to execute the method for determining the operating parameters of the 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, including computer instructions, which are used to cause a computer to execute the method for determining the operating parameters of the dishwasher according to the first aspect or any corresponding embodiment thereof. 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 will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 is a flowchart of the method for determining the operating parameters of the dishwasher according to an embodiment of the present invention; Figure 2 is a flowchart of another method for determining the operating parameters of the dishwasher according to an embodiment of the present invention; Figure 3 is a flowchart of yet another method for determining the operating parameters of the dishwasher according to an embodiment of the present invention; Figure 4 is a flowchart of an example of the method for determining the operating parameters of the dishwasher according to an embodiment of the present invention; Figure 5It is a structural block diagram of a dishwasher operation parameter determination device according to an embodiment of the present invention; Figure 6 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Specific embodiments

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] According to an embodiment of the present invention, an embodiment of a method for determining dishwasher operation parameters 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 the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0024] In this embodiment, a method for determining dishwasher operation parameters is provided, which can be used for dishwashers. Figure 1 It is a flowchart of a method for determining dishwasher operation parameters according to an embodiment of the present invention. As Figure 1 shown, the process includes the following steps: Step S101: Obtain first current information on the detergent used in the current dishwasher wash and second current information on the water used in the current wash.

[0025] Specifically, the first current information includes the type of detergent used in the current wash and the amount of detergent dispensed in the current wash; the second current information includes the amount of water used in the current wash, the temperature of the water used in the current wash, and the hardness of the water used in the current wash.

[0026] Step S102: Obtain the turbidity requirement for the water after the current wash is completed and the requirement for detergent residue in the water after the current wash is completed.

[0027] Specifically, the turbidity requirement for the water refers to the control standard for the content of suspended particulate matter in the drained water after the wash is completed, which is used to reflect the cleanliness of the impurities in the water after the tableware is washed; while the requirement for detergent residue refers to the limit standard for the concentration of residual detergent in the water after the wash is completed, aiming to avoid potential impacts on human health or the environment due to excessive detergent residue. These requirements can be set according to different usage scenarios and user needs.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Specifically, the target operating parameters include but are not limited to: washing time and washing pump speed.

[0032] 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.

[0033] 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: Step S201: Obtain a training data set, where the training data set includes multiple groups of data, and each group of data includes the historical operating parameters of the dishwasher's historical washing, the first historical information of the detergent used, the second historical information of the water used, the historical turbidity of the water after the historical washing is completed, and the detergent residue in the water after the historical washing is completed. Specifically, the first historical information includes the type of detergent used in the historical washing and the dosage of the detergent used in the historical washing; the second historical information includes the amount of water used in the historical washing, the temperature of the water used in the historical washing, and the hardness of the water used in the historical washing.

[0034] This is because, during actual use, the washing effect (turbidity: RE01) and washing residue (residual concentration: RE02) of the dishwasher are basically jointly determined by the following factors: detergent dosage C1, water temperature T1, washing duration t1, water volume L1, washing pump speed w1, detergent type Ty-n, and water hardness H. Based on the above analysis, data can be collected in the format of [C1, T1, t1, L1, W1, Ty-n, H], [RE01, RE02].

[0035] In an alternative embodiment, the dishwasher includes multiple turbidity sensors with different installation heights. The method for obtaining the historical turbidity of the water after the historical washing is completed includes: after the historical washing is completed, obtain the detection data and installation height of each turbidity sensor respectively; obtain the initial turbidity based on the detection data and installation height of all turbidity sensors; obtain the water flow rate of the historical washing; and correct the initial turbidity according to the water flow rate to obtain the historical turbidity of the water after the historical washing is completed.

[0036] This is because the distribution of suspended particles in the water during the washing process may be uneven, and at the same time, turbidity sensors usually use the principle of light scattering for detection and are easily affected by factors unrelated to pollutants such as bubbles; it is difficult to comprehensively reflect the overall water quality situation relying solely on a turbidity sensor at a single position. By setting multiple turbidity sensors at different heights and comprehensively considering their detection results and installation positions, the overall turbidity level of the water after washing can be evaluated more scientifically, improving the representativeness and measurement accuracy of the data.

[0037] Specifically, 2 or 3 turbidity sensors can be arranged in the gravity direction, such as at the bottom of the water cup, the inlet and outlet of the washing pump, etc. Ca, Cb, and Cc are the acquisition data of the three sensors respectively, and Ha, Hb, and Hc are the heights of the sensors relative to the lowest point of the water cup.

[0038] Exemplarily, the initial turbidity can be calculated by the following formula.

[0039]

[0040] Where m0, a, k, and b are all constants and can be evaluated using the acquisition data.

[0041] In addition, the presence of a large number of microbubbles increases the light scattering intensity, seriously affecting the sensing accuracy. The following formula can be used to correct the initial turbidity:

[0042] where represents the water flow velocity, which is related to the driving pressure and the flow channel.

[0043] By introducing the water flow velocity as a correction parameter, it helps to eliminate the turbidity measurement deviation caused by bubbles, making the finally obtained historical turbidity value closer to the actual situation, thereby providing high-quality data support for model training and operation parameter optimization.

[0044] Step S202: Train the neural network model using the training data set.

[0045] Specifically, [C1, T1, t1, L1, W1, Ty-n, H] can be used as the input values, and [RE01, RE02] are used as the corresponding actual output values. After the model calculation, [Cal01, Cal02] are output (Cal01: the turbidity calculated by the model, Cal02: the residual concentration calculated by the model). During the training process, the model parameters are continuously adjusted to make the two values of [ABS(Cal01 - RE01), ABS(Cal02 - RE01)] reach the minimum value or the set value. When the difference meets the requirements, the model training is stopped.

[0046] In this embodiment, by using real historical washing data (including operation parameters, detergent information, water quality information, turbidity, and residue, etc.) to train the neural network model, the model can learn the complex mapping relationship between different input factors and washing results, thereby improving its prediction accuracy and adaptability to new scenarios.

[0047] Step S203: Obtain the first current information of the detergent currently used in the dishwasher and the second current information of the water currently used for washing.

[0048] Step S204: 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.

[0049] Step S205: Input the first current information, the second current information, the turbidity requirement of the water, and the detergent residue requirement into the trained neural network model to obtain the target operation parameters for the current washing.

[0050] The method for determining the operating parameters of the dishwasher provided in this embodiment can achieve dynamic adjustment of the washing process, significantly improving the accuracy and adaptability of washing; moreover, the influence of different detergent types, concentrations, and water source quality is fully considered during the determination of the target operating parameters, so it has strong versatility and adaptability and is suitable for the diverse usage environments of different regions and users.

[0051] In this embodiment, a method for determining the operating parameters of a dishwasher is provided, which can be used for a dishwasher. Figure 3 It is a flowchart of another method for determining the operating parameters of a dishwasher according to an embodiment of the present invention. As Figure 3 shown, this process includes the following steps: Step S301: Obtain a user instruction.

[0052] Step S302: Determine whether the current washing uses the interactive mode according to the user instruction; when the current washing uses the interactive mode, proceed to step S304; otherwise, proceed to step S303; Step S303: Use the preset operating parameters as the target operating parameters for the current washing.

[0053] Step S304: Obtain the first current information of the detergent used in the current washing of the dishwasher and the second current information of the water used in the current washing.

[0054] Step S305: 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.

[0055] Step S306: Input the first current information, the second current information, the turbidity requirement of the water, and the detergent residue requirement into the trained neural network model to obtain the target operating parameters for the current washing.

[0056] Step S307: Combine 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 residue requirement in the water after the current washing is completed into a set of data and put it into the training data set.

[0057] With the accumulation of more historical data and continuous training, the neural network model can continuously optimize its own performance and have a certain self-learning ability, so that the dishwasher can continuously improve the washing effect and resource utilization efficiency during long-term use; moreover, since the training data set contains a variety of different washing scenarios and user usage habits, the trained model can identify and adapt to the characteristics of different user groups or family environments, so as to provide personalized washing solutions that better meet the needs of different users.

[0058] To illustrate the method for determining the operating parameters of the dishwasher in this embodiment in more detail, a specific example is given. As Figure 4As shown, it includes the following steps: First, through variable identification and data collection, the neural network model can be trained to obtain the relationship between multi-dimensional inputs and outputs. Furthermore, the trained model can be used to determine the optimal operating parameters of the dishwasher, and the optimal operating parameters (which can also be simply referred to as the optimal parameters) can be preset into the dishwasher. When the user selects the interactive mode, the first current information of the detergent used in the current wash of the dishwasher and the second current information of the water used in the current wash are obtained; the turbidity requirement of the water after the current wash and the detergent residue requirement in the water after the current wash are obtained; the first current information, the second current information, the turbidity requirement of the water, and the detergent residue requirement 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 turbidity requirement of the water after the current wash, and the detergent residue requirement in the water after the current wash are combined into a set of data and put into the training dataset. Thus, the neural network model can be continuously optimized according to the specific usage data of the user. Further, new optimal operating parameters that match the specific usage of the user can be obtained through the optimized neural network model; even further, the optimal operating parameters of the thresholds in the dishwasher can be updated using the new optimal operating parameters. When the user uses the interactive mode, the operating parameters of the manufacturer, that is, the optimal operating parameters preset into the dishwasher, can be used.

[0059] When the interactive mode is enabled, the system can dynamically generate optimal operating parameters based on the real-time collected detergent and water quality information in combination with the neural network model, achieving personalized and high-precision control; while when the interactive mode is not enabled, the system uses the verified preset parameters to ensure the stability and reliability of the washing process and avoid the uncertainty risks brought by data anomalies or model prediction deviations. For washing scenarios that require fine control (such as cleaning precious tableware, low residue requirements, etc.), the user can enable the interactive mode to let the system optimize the usage amounts of water, electricity, and detergent according to the actual situation, achieving the purpose of energy conservation and environmental protection; while in ordinary washing scenarios, the preset parameters can be used to complete the task, avoiding unnecessary resource consumption. However, when the current detergent or water quality information cannot be accurately obtained (such as sensor failures or data anomalies), the system can automatically fallback to the preset operating mode to ensure that the basic functions of the dishwasher are not affected, enhancing the robustness and application scope of the overall system. This mechanism constructs a multi-level control system from the "fully automatic basic mode" to the "intelligent interactive optimization mode", which is suitable for both the general household users and can also meet the professional or high-end user groups with higher requirements for cleaning performance, broadening the application boundary of the product.

[0060] In this embodiment, a device for determining the operating parameters of a dishwasher is also provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated here. As used hereinafter, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0061] This embodiment provides a device for determining the operating parameters of a dishwasher. As Figure 5 shown, it includes: A first acquisition module 501, configured to acquire 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.

[0062] A second acquisition module 502, configured to acquire the turbidity requirement of the water after the current washing ends and the detergent residue requirement in the water after the current washing ends.

[0063] An operating parameter determination module 503, configured to input the first current information, the second current information, the turbidity requirement of the water, and the detergent residue requirement into a trained neural network model to obtain the target operating parameters of the current washing.

[0064] In some optional implementation manners, the device for determining the operating parameters of the dishwasher further includes a training module. The training module is specifically configured to: acquire a training data set, where the training data set includes multiple groups of data, and each group of data includes the historical operating parameters of the dishwasher's historical washing, the first historical information of the detergent used, the second historical information of the water used, the historical turbidity of the water after the historical washing ends, and the detergent residue amount in the water after the historical washing ends; use the training data set to train the neural network model.

[0065] In some optional implementation manners, the first current information includes the type of the detergent used in the current washing and the dosage of the detergent used in the current washing; the first historical information includes the type of the detergent used in the historical washing and the dosage of the detergent used in the historical washing; the second current information includes the water volume of the 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 historical information includes the water volume of the water used in the historical washing, the temperature of the water used in the historical washing, and the hardness of the water used in the historical washing.

[0066] In some optional implementation manners, the dishwasher includes multiple turbidity sensors with different installation heights. The training module is specifically configured to: after the historical washing ends, respectively acquire the detection data and installation height of each turbidity sensor; obtain the initial turbidity based on the detection data and installation height of all turbidity sensors; acquire 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 ends.

[0067] In some alternative embodiments, the device for determining the operating parameters of the dishwasher further includes a preprocessing module. Before obtaining the first current information on the detergent used in the current wash of the dishwasher and the second current information on the water used in the current wash, the preprocessing module is further configured to: obtain a user instruction; determine whether the current wash uses an interactive mode according to the user instruction; when the current wash uses the interactive mode, perform the step of obtaining the first current information on the detergent used in the current wash of the dishwasher and the second current information on the water used in the current wash; when the current wash does not use the interactive mode, use the preset operating parameters as the target operating parameters for the current wash.

[0068] In some alternative embodiments, after inputting the first current information, the second current information, the turbidity requirement of the water, and the detergent residue requirement into the trained neural network model to obtain the target operating parameters for the current wash, the training module is further configured to: form a set of data from 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, and put it into the training data set.

[0069] The further functional descriptions of the above-mentioned various modules and units are the same as those in the corresponding foregoing embodiments, and will not be elaborated herein.

[0070] The device for determining the operating parameters of the dishwasher in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0071] This embodiment of the present invention further provides a computer device having the Figure 5 shown device for determining the operating parameters of the dishwasher.

[0072] Please refer to Figure 6 , Figure 6 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As shown in Figure 6As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as an array of servers, a set of blade servers, or a multi-processor system). Figure 6 Taking one processor 10 as an example in

[0073] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field-programmable gate array, a generic array logic, or any combination thereof.

[0074] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiments.

[0075] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device. In addition, the memory 20 can include high-speed random access memory and can also include non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0076] The memory 20 can include volatile memory, such as random access memory; the memory can also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memory.

[0077] 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 can be connected through a bus or other means.Figure 6 Take the bus connection as an example.

[0078] The input device 30 can receive input digital or character information, and generate key signal inputs related to the user settings and function controls of the computer device, such as touch screens, keypads, mice, trackpads, touchpads, joysticks, one or more mouse buttons, trackballs, joysticks, etc. The output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors), etc. The above display devices include, but are not limited to, liquid crystal displays, light emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touch screen.

[0079] The embodiments of the present invention also provide a computer-readable storage medium. The methods according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the methods described herein can be stored as 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 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 types of memories. 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, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0080] A part of the present invention can be applied as a computer program product, such as computer program instructions, which when executed by a computer, can call or provide the methods and / or technical solutions according to the present invention through the operation of the computer. Those skilled in the art should be able to understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are 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. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0081] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for determining the operating parameters of a dishwasher, characterized in that Comprising: Obtaining first current information of the detergent used in the current wash of the dishwasher and second current information of the water used in the current wash, where the first current information includes the type of the detergent used in the current wash and the dosage of the detergent used in the current wash; the second current information includes the water volume of the water used in the current wash, the temperature of the water used in the current wash, and the hardness of the water used in the current wash; Obtaining the turbidity requirement of the water after the current wash ends and the detergent residue requirement in the water after the current wash ends; Inputting the first current information, the second current information, the turbidity requirement of the water, and the detergent residue requirement into a trained neural network model to obtain the target operating parameters of the current wash; Further comprising: Obtaining a training data set, where the training data set includes multiple groups of data, and each group of data includes the historical operating parameters of the dishwasher's historical wash, first historical information of the detergent used, second historical information of the water used, the historical turbidity of the water after the historical wash ends, and the detergent residue amount in the water after the historical wash ends; where the first historical information includes the type of the detergent used in the historical wash and the dosage of the detergent used in the historical wash; the second historical information includes the water volume of the water used in the historical wash, the temperature of the water used in the historical wash, and the hardness of the water used in the historical wash; Training the neural network model using the training data set.

2. The method according to claim 1, characterized in that The dishwasher includes multiple turbidity sensors with different installation heights, and the method for obtaining the historical turbidity of the water after the historical wash ends includes: After the historical wash ends, respectively obtaining the detection data and installation height of each turbidity sensor; Obtaining an initial turbidity based on the detection data and installation height of all turbidity sensors; Obtaining the water flow rate of the historical wash; Correcting the initial turbidity according to the water flow rate to obtain the historical turbidity of the water after the historical wash ends.

3. The method according to claim 1, wherein Before obtaining the first current information of the detergent used in the current wash of the dishwasher and the second current information of the water used in the current wash, further comprising: Obtaining a user instruction; Judging whether the current wash uses an interactive mode according to the user instruction; When the current wash uses the interactive mode, performing the step of obtaining the first current information of the detergent used in the current wash of the dishwasher and the second current information of the water used in the current wash; When the current wash does not use the interactive mode, using the preset operating parameters as the target operating parameters of the current wash.

4. The method according to claim 1, wherein After inputting the first current information, the second current information, the turbidity requirement of the water, and the detergent residue requirement into a trained neural network model to obtain the target operating parameters of the current wash, further comprising: Forming a group of data with the first current information, the second current information, the target operating parameters, the turbidity requirement of the water after the current wash ends, and the detergent residue requirement in the water after the current wash ends, and putting it into the training data set.

5. A device for determining the operating parameters of a dishwasher, characterized in that, Comprising: A first acquisition module, configured to acquire 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; wherein the first current information includes the type of the detergent used in the current washing and the dosage of the detergent used in the current washing; the second current information includes the water volume of the 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; A second acquisition module, configured to acquire 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; An operating parameter determination module, configured to input the first current information, the second current information, the turbidity requirement of the water, and the detergent residue requirement into a trained neural network model to obtain the target operating parameters of the current washing; It further includes a training module; The training module is specifically configured to: acquire a training data set, where the training data set includes multiple groups of data, and each group of data includes the historical operating parameters of the dishwasher's historical washing, first historical information of the detergent used, second historical information of the water used, the historical turbidity of the water after the historical washing is completed, and the detergent residue amount in the water after the historical washing is completed; wherein the first historical information includes the type of the detergent used in the historical washing and the dosage of the detergent used in the historical washing; the second historical information includes the water volume of the water used in the historical washing, the temperature of the water used in the historical washing, and the hardness of the water used in the historical washing; and use the training data set to train the neural network model.

6. A computer device, characterized in that, Comprising: A memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the method for determining the operating parameters of the dishwasher according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the method for determining the operating parameters of the dishwasher according to any one of claims 1 to 4.

8. A computer program product, characterized in that, Comprising computer instructions, and the computer instructions are used to cause a computer to execute the method for determining the operating parameters of the dishwasher according to any one of claims 1 to 4.

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