Tableware cleaning method, device and equipment of tableware cleaning equipment and medium

Abstract

The embodiment of the invention provides a tableware cleaning method, device and equipment of tableware cleaning equipment and a medium. The method comprises the steps that target parameters of the tableware cleaning equipment, a fuzzy controller of the tableware cleaning equipment and a pre-trained washing effect prediction model are obtained; inputting the target parameters into the washing effect prediction model to obtain a predicted washing effect; inputting the target parameter into the fuzzy controller for fuzzy reasoning to obtain a fuzzy reasoning result; according to the fuzzy reasoning result, the tableware cleaning equipment is controlled to conduct cleaning, and the actual cleaning effect after cleaning is completed is obtained; and optimizing the fuzzy controller according to the predicted washing effect and the actual washing effect, so that the tableware cleaning equipment controls cleaning according to the optimized fuzzy controller. The target parameters can be obtained more accurately, the optimal washing effect can be achieved for the changed tableware parameters, resource waste is avoided, and the user experience is improved.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and in particular to a tableware cleaning method of a tableware cleaning device, a tableware cleaning device of a tableware cleaning device, a tableware cleaning device and a computer-readable storage medium. Background Art

[0002] The number of dishes is an important indicator for determining the washing program of the dishwashing equipment. At present, some dishwashing equipment on the market usually does not have an intelligent recognition module to save costs. It mainly relies on users to judge and choose the cleaning mode. However, the various cleaning modes dazzle users. Most users usually choose the standard cleaning mode to save time and effort, which will lead to a waste of water / electricity resources.

[0003] Although other tableware washing equipment is equipped with intelligent means to detect the number of tableware, it mainly uses image sensors, pressure sensors and other methods. There are technical problems such as complex control, high cost and inaccurate recognition when detecting the number of tableware through these methods. For example, the image sensor will not accurately recognize the number of tableware in the stacked state; the pressure sensor cannot obtain the size of the tableware and further the number of tableware, the location of the tableware, etc. The accuracy of obtaining tableware parameters through these methods is low, which makes it impossible for users to match the most suitable washing mode for the changing tableware, resulting in waste of water and electricity resources and affecting the user experience. Summary of the invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a tableware cleaning method of a tableware cleaning device, a tableware cleaning device of a tableware cleaning device, a tableware cleaning device and a computer-readable storage medium that overcome the above problems or at least partially solve the above problems.

[0005] In order to solve the above problems, a first aspect of an embodiment of the present invention provides a tableware cleaning method of a tableware cleaning device, the method comprising:

[0006] Obtaining target parameters of the dishwashing equipment, a fuzzy controller of the dishwashing equipment, and a pre-trained washing effect prediction model;

[0007] Inputting the target parameter into the washing effect prediction model to obtain a predicted washing effect;

[0008] Inputting the target parameter into the fuzzy controller for fuzzy reasoning to obtain a fuzzy reasoning result;

[0009] According to the fuzzy reasoning result, the dishwashing device is controlled to perform washing, and an actual washing effect after the washing is completed is obtained;

[0010] The fuzzy controller is optimized according to the predicted washing effect and the actual washing effect, so that the dishwashing device controls washing according to the optimized fuzzy controller.

[0011] Optionally, the target parameters include the number of tableware, the position of the tableware and the existing amount of water injected; the inputting the target parameters into the fuzzy controller for fuzzy reasoning to obtain the fuzzy reasoning results includes:

[0012] Inputting the target parameter into the fuzzy controller;

[0013] The fuzzy controller is used to fuzzify the number of tableware, the position of tableware and the existing water volume respectively to obtain a fuzzy set of the number of tableware, a fuzzy set of the position of tableware and a fuzzy set of the existing water volume; and fuzzy reasoning is performed based on the fuzzy set of the number of tableware, the fuzzy set of the position of tableware and the fuzzy set of the existing water volume to obtain a fuzzy set of added water volume and a fuzzy set of spray range; and the fuzzy set of added water volume and the fuzzy set of spray range are defuzzified to obtain a determined value of the added water volume and a determined value of the spray range.

[0014] Optionally, the fuzzy controller is used to fuzzify the number of tableware, the position of tableware and the existing water volume respectively to obtain a fuzzy set of the number of tableware, a fuzzy set of the position of tableware and a fuzzy set of the existing water volume; and fuzzy reasoning is performed based on the fuzzy set of the number of tableware, the fuzzy set of the position of tableware and the fuzzy set of the existing water volume to obtain a fuzzy set of added water volume and a fuzzy set of spray range, including:

[0015] The fuzzy controller is used to fuzzify the number of tableware, the position of tableware and the existing water volume according to the membership function to obtain a fuzzy set of the number of tableware, a fuzzy set of the position of tableware and a fuzzy set of the existing water volume;

[0016] Fuzzy reasoning is performed based on the tableware quantity fuzzy set, the tableware position fuzzy set, the existing water volume fuzzy set and fuzzy rules to obtain the added water volume fuzzy set and the spray range fuzzy set.

[0017] Optionally, optimizing the fuzzy controller according to the predicted washing effect and the actual washing effect includes:

[0018] According to the predicted washing effect and the actual washing effect, the fuzzy rules and the membership function of the fuzzy controller are optimized.

[0019] Optionally, it also includes:

[0020] Obtaining the actual washing effect after cleaning controlled by the optimized fuzzy controller;

[0021] The washing effect prediction model is optimized according to the actual washing effect after cleaning controlled by the optimized fuzzy controller.

[0022] Optionally, the washing effect prediction model is trained in the following manner:

[0023] Acquire training sample parameters and actual washing effects corresponding to the training sample parameters; the sample parameters include the number of dishes, the position of the dishes, and the existing amount of water injected;

[0024] Inputting the sample parameters into a washing effect prediction model to obtain a predicted washing effect;

[0025] According to the actual washing effect corresponding to the training sample parameters and the error of the predicted washing effect, the parameters of the washing effect prediction model are adjusted to train the washing effect prediction model.

[0026] Optionally, a microwave radar is provided in the dishwashing device, and the step of obtaining target parameters of the dishwashing device includes:

[0027] Transmitting microwave signals via microwave radar;

[0028] A reflected signal after the microwave signal is reflected by the tableware is detected, and the number and position of the tableware are determined according to the reflected signal.

[0029] According to a second aspect of an embodiment of the present invention, there is provided a tableware cleaning device of a tableware cleaning equipment, the device comprising:

[0030] A parameter acquisition module, used to acquire target parameters of the dishwashing device, a fuzzy controller of the dishwashing device and a pre-trained washing effect prediction model;

[0031] A predicted effect acquisition module, used for inputting the target parameter into the washing effect prediction model to obtain a predicted washing effect;

[0032] A result acquisition module, used for inputting the target parameter into the fuzzy controller to perform fuzzy reasoning and obtain a fuzzy reasoning result;

[0033] An actual effect acquisition module, used to control the dishwashing device to perform cleaning according to the fuzzy reasoning result, and obtain the actual washing effect after the cleaning is completed;

[0034] The cleaning control module is used to optimize the fuzzy controller according to the predicted washing effect and the actual washing effect, so that the dishwashing device controls the cleaning according to the optimized fuzzy controller.

[0035] Optionally, the target parameters include the number of tableware, the position of the tableware and the existing amount of water injected; the result acquisition module includes:

[0036] A parameter input submodule, used for inputting the target parameter into the fuzzy controller;

[0037] The fuzzy processing submodule is used to fuzzify the number of tableware, the position of tableware and the existing water volume through the fuzzy controller to obtain a fuzzy set of the number of tableware, a fuzzy set of the position of tableware and a fuzzy set of the existing water volume; and to perform fuzzy reasoning based on the fuzzy set of the number of tableware, the fuzzy set of the position of tableware and the fuzzy set of the existing water volume to obtain a fuzzy set of added water volume and a fuzzy set of spray range; and to defuzzify the fuzzy set of added water volume and the fuzzy set of spray range to obtain a determined value of the added water volume and a determined value of the spray range.

[0038] Optionally, the fuzzy processing submodule includes:

[0039] A fuzzy set acquisition subunit, used to fuzzify the number of tableware, the position of tableware and the existing water volume according to the membership function through the fuzzy controller to obtain a fuzzy set of the number of tableware, a fuzzy set of the position of tableware and a fuzzy set of the existing water volume;

[0040] The fuzzy reasoning subunit is used to perform fuzzy reasoning according to the tableware quantity fuzzy set, the tableware position fuzzy set and the existing water volume fuzzy set and fuzzy rules to obtain the added water volume fuzzy set and the spray range fuzzy set.

[0041] Optionally, the cleaning control module includes:

[0042] The optimization submodule is used to optimize the fuzzy rules and membership functions of the fuzzy controller according to the predicted washing effect and the actual washing effect.

[0043] Optionally, it also includes:

[0044] An effect optimization acquisition module is used to obtain the actual washing effect after the optimized fuzzy controller is used to control the washing;

[0045] The model optimization acquisition module is used to optimize the washing effect prediction model according to the actual washing effect after the fuzzy controller after the optimization is used to control the washing.

[0046] Optionally, the washing effect prediction model is trained in the following manner:

[0047] A sample acquisition submodule, used to acquire training sample parameters and actual washing effects corresponding to the training sample parameters; the sample parameters include the number of dishes, the position of the dishes and the existing amount of water injected;

[0048] A sample input submodule, used for inputting the sample parameters into a washing effect prediction model to obtain a predicted washing effect;

[0049] The parameter adjustment submodule is used to adjust the parameters of the washing effect prediction model according to the actual washing effect corresponding to the training sample parameters and the error of the predicted washing effect, so as to train the washing effect prediction model.

[0050] Optionally, a microwave radar is provided in the dishwashing device, and the parameter acquisition module includes:

[0051] A signal transmitting submodule, used for transmitting microwave signals through a microwave radar;

[0052] The detection submodule is used to detect the reflected signal of the microwave signal after being reflected by the tableware, and determine the number and position of the tableware according to the reflected signal.

[0053] According to a third aspect of the present invention, there is provided a dishwashing cleaning device, comprising: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of a dishwashing cleaning method of the dishwashing cleaning device as described above are implemented.

[0054] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a tableware cleaning method of a tableware cleaning device as described above.

[0055] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0056] The embodiment of the present invention provides a tableware cleaning method, device, equipment and storage medium of a tableware cleaning device, the method comprising: obtaining the target parameters of the tableware cleaning device, the fuzzy controller of the tableware cleaning device and a pre-trained washing effect prediction model; inputting the target parameters into the washing effect prediction model to obtain a predicted washing effect; inputting the target parameters into the fuzzy controller for fuzzy reasoning to obtain a fuzzy reasoning result; according to the fuzzy reasoning result, controlling the tableware cleaning device to clean and obtain the actual washing effect after cleaning; according to the predicted washing effect and the actual washing effect, optimizing the fuzzy controller so that the tableware cleaning device controls cleaning according to the optimized fuzzy controller. The target parameters can be obtained more accurately, and the reasoning results can be obtained through the fuzzy controller. The best washing effect can be achieved for the changing tableware parameters, thus avoiding resource waste and improving user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a flowchart of the steps of a tableware cleaning method of a tableware cleaning device provided by an embodiment of the present invention;

[0058] Figure 2 is a flowchart of steps of another tableware cleaning method of a tableware cleaning device provided by an embodiment of the present invention;

[0059] Figure 3 is a schematic diagram of a detection process of a tableware cleaning method of a tableware cleaning device provided by an embodiment of the present invention;

[0060] Figure 4 is a schematic diagram of a fuzzy controller of a tableware cleaning method of a tableware cleaning device provided by an embodiment of the present invention;

[0061] Figure 5 It is a structural block diagram of a tableware cleaning device of a tableware cleaning device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0063] The existing method of obtaining tableware parameters has low accuracy, which makes it impossible for users to match the most appropriate washing mode for the changing tableware, resulting in waste of water and electricity resources and affecting user experience.

[0064] One of the core concepts of the embodiments of the present invention is that the present invention can obtain the predicted washing effect corresponding to the target parameters of the dishwashing equipment by pre-training a washing effect prediction model and inputting the acquired target parameters of the dishwashing equipment into the washing effect prediction model. At the same time, the target parameters are input into the fuzzy controller for fuzzy reasoning to obtain the fuzzy reasoning results, and the dishwashing equipment is controlled to wash the dishes according to the fuzzy reasoning results to obtain the actual washing effect after the washing is completed. The fuzzy controller is optimized according to the predicted washing effect and the actual washing effect, and the dishwashing equipment is controlled to complete the washing of the dishes according to the optimized fuzzy controller. The fuzzy reasoning results can be provided more accurately according to the changes in the target parameters, thereby controlling the dishwashing equipment to achieve the best washing effect, avoiding waste of resources, and improving user experience.

[0065] Reference Figure 1 , shows a flowchart of the steps of a tableware cleaning method provided by a tableware cleaning device according to an embodiment of the present invention, and the method may specifically include the following steps:

[0066] Step 101, obtaining target parameters of the dishwashing equipment, a fuzzy controller of the dishwashing equipment and a pre-trained washing effect prediction model;

[0067] The tableware washing equipment is a device for automatically washing tableware, and generally includes a dishwasher, a tableware sterilizing machine, etc. The present invention does not limit this.

[0068] The target parameters of the dishwashing machine include the number of dishes, the position information of the dishes and the existing amount of water that has been injected into the dishwashing machine.

[0069] Fuzzy controller is a control system based on fuzzy logic. It controls complex systems by processing fuzzy input and output.

[0070] The washing effect prediction model is a model used to predict the cleanliness of dishes during the washing process. This model can be applied to a variety of household appliances such as washing machines and dishwashers, as well as industrial washing equipment. By predicting the washing effect, washing parameters can be optimized, washing efficiency can be improved, and resources can be saved.

[0071] In this embodiment, the target parameters generally include: the number of dishes, the location information of the dishes and the current amount of water that has been injected into the dishwasher. These parameters can be obtained by detecting the dishes and the amount of water through the detection device of the dishwasher.

[0072] Fuzzy controllers are usually used to deal with uncertainty and ambiguity problems in dishwashers. For example, washing parameters are adjusted according to the number of dishes, the location of dishes, etc., and the parameters of the fuzzy controller are obtained through the control system of the dishwasher.

[0073] The washing effect prediction model is usually a machine learning model used to predict the washing effect of dishes after washing. The model parameters can be obtained directly from a pre-trained model file or through a communication interface with the dishwasher.

[0074] Step 102, inputting the target parameter into the washing effect prediction model to obtain a predicted washing effect;

[0075] Predicting washing effects is a complex process involving multiple variables and factors. Through reasonable data collection, data preprocessing, model selection and training, model evaluation, and model optimization and deployment, an effective prediction model can be constructed to achieve intelligent washing process control, improve washing efficiency, save resources, and ensure washing quality.

[0076] In this embodiment, the acquired target parameters are converted into the input format required by the washing effect prediction model. Usually, the model input is a feature vector. The prepared input feature vector is input into the washing effect prediction model, and the model will output the predicted washing effect.

[0077] Step 103, inputting the target parameter into the fuzzy controller for fuzzy reasoning to obtain a fuzzy reasoning result;

[0078] Fuzzy inference is the core part of fuzzy logic system, which derives fuzzy output by processing fuzzy input and fuzzy rules. Fuzzy inference usually includes the following steps: fuzzification, rule evaluation, inference and defuzzification.

[0079] The result of fuzzy inference is a fuzzy output, which usually needs to be converted into a precise control signal through a defuzzification process.

[0080] In this embodiment, target parameters are obtained from the dishwasher and converted into the input format required by the fuzzy controller. Usually, the input of the fuzzy controller is some fuzzy variables. The prepared input variables are input into the fuzzy controller. The fuzzy controller will perform reasoning according to predefined fuzzy rules and output the fuzzy reasoning results.

[0081] Step 104, controlling the dishwashing device to perform washing according to the fuzzy reasoning result, and obtaining an actual washing effect after the washing is completed;

[0082] In this embodiment, a fuzzy inference result is obtained from the fuzzy controller, and according to the fuzzy inference result, the parameters of the dishwasher are set, and the washing process is started. After the washing is completed, the actual washing effect is obtained.

[0083] Step 105: optimizing the fuzzy controller according to the predicted washing effect and the actual washing effect, so that the dishwashing device controls washing according to the optimized fuzzy controller.

[0084] In this embodiment, the prediction result of the washing effect prediction model and the actual washing effect after washing are completed are obtained, the error between the predicted washing effect and the actual washing effect is calculated, and the parameters or rules of the fuzzy controller are updated according to the error to optimize the fuzzy controller. The optimized fuzzy controller is used to control the dishwasher for washing.

[0085] Reference Figure 2 , shows a flowchart of the steps of another tableware cleaning method provided by another tableware cleaning device according to an embodiment of the present invention, and the method may specifically include the following steps:

[0086] Step 201, obtaining target parameters of the dishwashing device, a fuzzy controller of the dishwashing device and a pre-trained washing effect prediction model;

[0087] In this embodiment, the target parameters generally include: the number of tableware, tableware location information, and the existing amount of water that has been injected into the tableware washing device. These parameters can be obtained by detecting the tableware and the amount of water through the detection device of the tableware washing device. The fuzzy controller is generally used to deal with uncertainty and fuzziness problems in the tableware washing device. For example, the washing parameters are adjusted according to the number of tableware, tableware location information, etc., and the parameters of the fuzzy controller are obtained through the control system of the tableware washing device. The washing effect prediction model is generally a machine learning model used to predict the washing effect of the tableware after washing. The pre-trained model file can be directly obtained, or the parameters of the model can be obtained through the communication interface with the tableware washing device.

[0088] In some embodiments, a microwave radar is provided in the dishwashing device; step 201 includes the following sub-steps:

[0089] Sub-step S11, transmitting a microwave signal through a microwave radar;

[0090] Microwave radar is a radar system that uses electromagnetic waves in the microwave frequency band for detection and measurement. Microwave radar detects targets by emitting microwave signals and receiving signals reflected from the target. Its basic working principle includes: the radar system emits microwave signals, and after the microwave signals encounter the target, part of the signal is reflected back by the target. The radar system receives the reflected signal and can determine the target's location, distance and other information by processing the received signal.

[0091] In this embodiment, the microwave radar emits a microwave signal of a specific frequency. When the microwave signal contacts the tableware, tableware of different materials and shapes will reflect with different intensities, thereby analyzing the quantity and location information of the tableware.

[0092] Sub-step S12, detecting a reflected signal after the microwave signal is reflected by the tableware, and determining the number and position of the tableware according to the reflected signal.

[0093] In this embodiment, the microwave radar system transmits microwave signals and receives microwave signals reflected from tableware. The number and location of tableware can be determined by analyzing the reflected signals. Targets in the reflected signals are detected by signal processing algorithms (such as CFAR detection, constant false alarm rate detection, etc.). The detected targets are classified to determine whether they are tableware, and the specific location of each tableware is determined based on the distance, speed and angle information.

[0094] In some embodiments, step 201 further includes the following sub-steps:

[0095] Sub-step S21, obtaining training sample parameters and actual washing effects corresponding to the training sample parameters; the sample parameters include the number of dishes, the position of the dishes and the existing amount of water injected;

[0096] In this embodiment, the number and position of dishes can be determined by sending microwave signals by microwave radar, and the existing amount of water injected can be measured by the detection device in the dishwasher. The washing effect data corresponding to different numbers, positions and initial water inflow of dishes are collected, and different numbers, positions and existing amounts of water injected are used as training samples.

[0097] Sub-step S22, inputting the sample parameters into a washing effect prediction model to obtain a predicted washing effect;

[0098] The washing effect prediction model includes a neural network model, which is used to learn and adjust the parameters in the fuzzy controller. The neural network model can be set as a simple feedforward neural network, such as a BP neural network, including an input layer, a hidden layer, and an output layer, wherein the input layer is the number, position, and initial water volume of the dishes, and the hidden layer can be designed according to the actual situation. The number of hidden layers and the number of neurons can be determined using empirical rules or cross-validation, and the output layer is the predicted washing effect. This embodiment of the present invention is not limited to this.

[0099] In this embodiment, the number of dishes and the initial water volume in the obtained training sample are normalized and pre-processed to facilitate input into the washing effect prediction model. The sample parameters are input into the washing effect prediction model to obtain the predicted washing effect.

[0100] Sub-step S23, adjusting the parameters of the washing effect prediction model according to the actual washing effect corresponding to the training sample parameters and the error of the predicted washing effect, so as to train the washing effect prediction model.

[0101] In this embodiment, the training sample parameters and the corresponding actual washing effect are input into the washing prediction model to obtain the predicted washing effect. The washing effect prediction model is trained using the training sample, and the network weights are adjusted by the back propagation algorithm to output the error between the actual washing effect and the model parameters. The model parameters are adjusted or different algorithms are selected according to the obtained error, and the washing effect prediction model is trained to improve the prediction accuracy of the model.

[0102] Step 202, inputting the target parameter into the washing effect prediction model to obtain a predicted washing effect;

[0103] In this embodiment, the acquired target parameters are converted into the input format required by the washing effect prediction model. Usually, the model input is a feature vector. The prepared input feature vector is input into the washing effect prediction model, and the model will output the predicted washing effect.

[0104] Step 203, inputting the target parameter into the fuzzy controller for fuzzy reasoning to obtain a fuzzy reasoning result;

[0105] In this embodiment, target parameters are obtained from the dishwasher and converted into the input format required by the fuzzy controller. Usually, the input of the fuzzy controller is some fuzzy variables. The prepared input variables are input into the fuzzy controller. The fuzzy controller will perform reasoning according to predefined fuzzy rules and output the fuzzy reasoning results.

[0106] In some embodiments, the target parameters include the number of dishes, the position of the dishes, and the existing amount of water injected; the step 203 includes the following sub-steps:

[0107] Sub-step S31, inputting the target parameter into the fuzzy controller;

[0108] In this embodiment, target parameters are obtained, which are usually the number of dishes, the position of the dishes and the existing amount of water injected, and these parameters are used as inputs of the fuzzy controller.

[0109] In sub-step S32, the fuzzy controller is used to fuzzify the number of tableware, the position of tableware and the existing water volume respectively to obtain a fuzzy set of the number of tableware, a fuzzy set of the position of tableware and a fuzzy set of the existing water volume; and fuzzy reasoning is performed based on the fuzzy set of the number of tableware, the fuzzy set of the position of tableware and the fuzzy set of the existing water volume to obtain a fuzzy set of added water volume and a fuzzy set of spray range; and the fuzzy set of added water volume and the fuzzy set of spray range are defuzzified to obtain a determined value of the added water volume and a determined value of the spray range.

[0110] The target parameters will be used as the input of the fuzzy controller. Fuzzification of the target parameters is the process of converting the clear target parameters into fuzzy sets. Fuzzy sets use fuzzy linguistic variables (such as "few", "medium", "many") to describe the input parameters. Define fuzzy linguistic variables for each target input parameter. For example, the number of cutlery can be defined as "few", "medium", "many".

[0111] A membership function is defined for each fuzzy linguistic variable to describe the degree to which the input parameter belongs to each fuzzy set. Common membership functions include triangular, trapezoidal, and Gaussian functions. The target parameter is mapped to the corresponding fuzzy set and the membership is calculated. The shape and parameters of the membership function can also be adjusted to more accurately reflect the fuzziness of the input variable.

[0112] The fuzzy rule base is the core of the fuzzy controller, which is used to describe the relationship between the input parameters and the output control signal. For example: If the number of dishes is less dishes and the existing water volume is less water volume, then the amount of water to be added is to add a medium amount of water. According to the input parameters and the rules in the rule base, the fuzzy set of the output control signal is calculated.

[0113] Defuzzification is the process of converting a fuzzy output set into a clear control signal. Through the weighted average method, defuzzification can clearly output the determined values ​​of the amount of water to be added and the spray range. After defuzzification, a clear control signal is obtained for the actual washing process.

[0114] In this embodiment, the number of tableware, the position of tableware and the amount of water available are fuzzified by the fuzzy controller. The number of tableware is defined as a fuzzy linguistic variable, such as "few", "medium", and "a lot". A membership function is defined for each fuzzy linguistic variable to describe the degree to which the number of tableware belongs to each fuzzy set. The number of tableware is mapped to the corresponding fuzzy set, and the membership degree is calculated to obtain the fuzzy set of the number of tableware.

[0115] The tableware positions are defined as fuzzy linguistic variables, such as "centralized" and "dispersed". A membership function is defined for each tableware position fuzzy linguistic variable to describe the degree to which the tableware position belongs to each fuzzy set. The tableware positions are mapped to the corresponding fuzzy sets, and the membership degrees are calculated to obtain the tableware position fuzzy sets.

[0116] The existing water volume is fuzzified and fuzzy linguistic variables of the existing water volume are defined, such as "very little", "medium", and "a lot". The membership function is defined for the fuzzy linguistic variables of the existing water volume to describe the degree to which the existing water volume belongs to each fuzzy set, the existing water volume is mapped to the corresponding fuzzy set, and the membership degree is calculated to obtain the fuzzy set of the existing water volume.

[0117] Fuzzy reasoning is performed based on the fuzzy sets of the number of dishes, the location of dishes, and the existing water volume to obtain the fuzzy set of added water volume and the fuzzy set of spray range. Fuzzy rules are defined to describe the relationship between input parameters and output control signals. The fuzzy set of the output control signal is calculated based on the input target parameters and fuzzy rules through the reasoning mechanism to obtain the fuzzy set of added water volume and the fuzzy set of spray range.

[0118] Defuzzification is the process of converting the fuzzy output set into a clear control signal. The weighted average method is used to calculate the clear value of the added water amount and the clear value of the spray range to obtain the determined value of the added water amount and the determined value of the spray range.

[0119] In some embodiments, the sub-step S32 includes the following sub-steps:

[0120] The fuzzy controller is used to fuzzify the number of tableware, the position of tableware and the existing water volume according to the membership function to obtain a fuzzy set of the number of tableware, a fuzzy set of the position of tableware and a fuzzy set of the existing water volume;

[0121] Fuzzy reasoning is performed based on the tableware quantity fuzzy set, the tableware position fuzzy set, the existing water volume fuzzy set and fuzzy rules to obtain the added water volume fuzzy set and the spray range fuzzy set.

[0122] The membership function is used to describe the degree to which an input value belongs to a fuzzy set. The value of the membership function is usually between 0 and 1, indicating the degree to which the input value belongs to the fuzzy set. Common membership functions include triangle, trapezoid, Gaussian, etc. For example: if the input number of cutlery is 10 and the defined number of cutlery is very few, then the corresponding membership is 0.2; when the defined number of cutlery is medium, the corresponding membership is 0.8; when the defined number of cutlery is a lot, the corresponding membership is 0.1, and the output fuzzy set of the number of cutlery is (very few: 0.2, medium: 0.8, a lot: 0.1).

[0123] The initial fuzzy rules of the fuzzy controller can be constructed based on experience. Subsequently, the fuzzy rules and membership function parameters can be adjusted according to the training results of the washing effect prediction model. The fuzzy sets of the number of tableware, the position fuzzy sets and the water volume fuzzy sets are inferred through the adjusted fuzzy rules to obtain the fuzzy sets that need to adjust the water volume and spray range. Subsequently, the weighted average method is used to defuzzify the fuzzy sets to obtain the determined values ​​of the amount of water and the spray range that need to be added, and the working parameters are adjusted to perform the cleaning work of the dishwasher.

[0124] In this embodiment, a fuzzy linguistic variable and a corresponding membership function are defined for each target parameter. The membership function describes the degree to which the input parameter belongs to each fuzzy set, the target parameter is mapped to the corresponding fuzzy set, and the membership is calculated to obtain the fuzzy set of the number of tableware, the fuzzy set of the tableware position, and the fuzzy set of the existing water volume.

[0125] Fuzzy rules are defined to describe the relationship between input parameters and output control signals. Fuzzy reasoning mechanism is used to calculate the fuzzy set of output control signals according to input parameters and fuzzy rules, and the fuzzy set of added water volume and spray range is obtained.

[0126] Step 204, according to the fuzzy reasoning result, controlling the dishwashing device to perform washing, and obtaining an actual washing effect after the washing is completed;

[0127] In this embodiment, a fuzzy inference result is obtained from the fuzzy controller, and according to the fuzzy inference result, the parameters of the dishwasher are set, and the washing process is started. After the washing is completed, the actual washing effect is obtained.

[0128] Step 205: optimizing the fuzzy controller according to the predicted washing effect and the actual washing effect, so that the dishwashing device controls washing according to the optimized fuzzy controller.

[0129] In this embodiment, the actual washing effect is finally fed back to the washing effect prediction model for further learning and parameter adjustment. The washing effect prediction model can adaptively adjust the fuzzy rules and membership function parameters of the fuzzy controller to improve the washing effect.

[0130] Obtain the prediction results of the washing effect prediction model and the actual washing effect after washing is completed, calculate the error between the predicted washing effect and the actual washing effect, update the parameters or rules of the fuzzy controller according to the error to optimize the fuzzy controller, and use the optimized fuzzy controller to control the dishwasher for washing.

[0131] In some embodiments, step 205 includes the following sub-steps:

[0132] Sub-step S42, optimizing the fuzzy rules and membership function of the fuzzy controller according to the predicted washing effect and the actual washing effect;

[0133] In this embodiment, a large amount of training data is obtained, including target parameters (number of dishes, position of dishes, existing water volume) and corresponding actual washing effects. The current fuzzy controller is used to predict the training data, and the error between the predicted washing effect and the actual washing effect is calculated. The error distribution is analyzed to find out the main cause of the error. According to the error analysis results, the fuzzy rule base is optimized to adjust the output of the existing rules to make it more consistent with the actual washing effect.

[0134] According to the error analysis results, the membership function can be optimized to adjust the shape of the membership function to make it more consistent with the distribution of the input parameters, or to adjust the range of the membership function to make it more consistent with the actual value of the input parameters. Use the optimized fuzzy rules and membership functions to retrain the fuzzy controller, fuzzify the input parameters through the new membership function, use the new fuzzy rules for fuzzy reasoning, and calculate the fuzzy set of the output control signal. Use the validation set or cross-validation method to evaluate the performance of the optimized fuzzy controller.

[0135] Step 206, obtaining the actual washing effect after washing is controlled by the optimized fuzzy controller;

[0136] In this embodiment, a target parameter sample is obtained, and the obtained target parameter sample is input into the dishwasher, and the dishwasher is controlled to wash using the optimized fuzzy controller to obtain the actual washing effect after the washing is completed.

[0137] Step 207, optimizing the washing effect prediction model according to the actual washing effect after cleaning controlled by the optimized fuzzy controller.

[0138] The actual washing effect obtained by controlling the washing with the optimized fuzzy controller will be fed back to the washing effect prediction model to calculate the error between the predicted washing effect and the actual washing effect. The model performance is evaluated based on the error to obtain the evaluation result. According to the model evaluation result, the model structure and parameters are adjusted. According to the actual washing effect data, the model parameters and structure are automatically adjusted. According to the feedback and data analysis results, the washing effect prediction model is continuously optimized to improve the prediction accuracy.

[0139] Reference Figure 3 , showing a schematic diagram of a detection process of a tableware cleaning method of a tableware cleaning device provided by an embodiment of the present invention;

[0140] When the dishwashing equipment starts cleaning, a microwave signal is emitted by a microwave radar to obtain information on the number and location of the dishes. The existing water volume is detected by a detection device in the dishwashing equipment. The number and location of the dishes and the existing water volume are used as training samples and input into a washing effect prediction model. The washing effect prediction model is trained and the predicted washing effect is output.

[0141] By inputting the obtained parameters such as the number of dishes, the location of dishes, the existing water volume and the predicted washing effect into the fuzzy controller, the determined values ​​of the amount of water to be added and the spray range can be obtained. The dishwashing equipment can be controlled to wash according to the determined values ​​of the amount of water to be added and the spray range to obtain the actual washing effect. The actual washing effect can be fed back to the washing effect prediction model to further optimize the model and the fuzzy controller.

[0142] Reference Figure 4 , showing a schematic diagram of a fuzzy controller of a tableware cleaning method of a tableware cleaning device provided by an embodiment of the present invention;

[0143] A fuzzy controller is a control system based on fuzzy logic, which achieves control by fuzzifying input variables, applying fuzzy rules for reasoning, and defuzzifying output variables.

[0144] The parameters such as the number of cutlery, the position of cutlery, and the existing water volume will be used as the input of the fuzzy controller, and the input parameters will be fuzzified. The clear target parameters will be converted into fuzzy sets. The fuzzy sets use fuzzy linguistic variables (such as "few", "medium", "a lot") to describe the input parameters, and the fuzzy sets of the number of cutlery, the position of cutlery, and the existing water volume will be obtained.

[0145] Use fuzzy reasoning mechanism to calculate the fuzzy set of output control signal according to input parameters and fuzzy rules, and get the fuzzy set of added water volume and spray range.

[0146] Defuzzification is the process of converting a fuzzy output set into a clear control signal. By defuzzifying the fuzzy set of water addition and the fuzzy set of spray range by weighted average method, the determined values ​​of the required water addition and spray range can be clearly output.

[0147] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0148] Reference Figure 5 , shows a structural block diagram of a tableware cleaning device of a tableware cleaning device provided by an embodiment of the present invention, which may specifically include the following modules:

[0149] A parameter acquisition module 301 is used to acquire target parameters of the dishwashing device, a fuzzy controller of the dishwashing device and a pre-trained washing effect prediction model;

[0150] A predicted effect acquisition module 302 is used to input the target parameter into the washing effect prediction model to obtain a predicted washing effect;

[0151] A result acquisition module 303 is used to input the target parameter into the fuzzy controller for fuzzy reasoning to obtain a fuzzy reasoning result;

[0152] The actual effect acquisition module 304 is used to control the dishwashing device to perform washing according to the fuzzy reasoning result, and obtain the actual washing effect after the washing is completed;

[0153] The cleaning control module 305 is used to optimize the fuzzy controller according to the predicted washing effect and the actual washing effect, so that the dishwashing device controls the cleaning according to the optimized fuzzy controller.

[0154] Optionally, the target parameters include the number of tableware, the position of the tableware and the existing amount of water injected; the result acquisition module 303 includes:

[0155] A parameter input submodule, used for inputting the target parameter into the fuzzy controller;

[0156] The fuzzy processing submodule is used to fuzzify the number of tableware, the position of tableware and the existing water volume through the fuzzy controller to obtain a fuzzy set of the number of tableware, a fuzzy set of the position of tableware and a fuzzy set of the existing water volume; and to perform fuzzy reasoning based on the fuzzy set of the number of tableware, the fuzzy set of the position of tableware and the fuzzy set of the existing water volume to obtain a fuzzy set of added water volume and a fuzzy set of spray range; and to defuzzify the fuzzy set of added water volume and the fuzzy set of spray range to obtain a determined value of the added water volume and a determined value of the spray range.

[0157] Optionally, the fuzzy processing submodule includes:

[0158] A fuzzy set acquisition subunit, used to fuzzify the number of tableware, the position of tableware and the existing water volume according to the membership function through the fuzzy controller to obtain a fuzzy set of the number of tableware, a fuzzy set of the position of tableware and a fuzzy set of the existing water volume;

[0159] The fuzzy reasoning subunit is used to perform fuzzy reasoning according to the tableware quantity fuzzy set, the tableware position fuzzy set and the existing water volume fuzzy set and fuzzy rules to obtain the added water volume fuzzy set and the spray range fuzzy set.

[0160] Optionally, the cleaning control module 305 includes:

[0161] The optimization submodule is used to optimize the fuzzy rules and membership functions of the fuzzy controller according to the predicted washing effect and the actual washing effect.

[0162] Optionally, it also includes:

[0163] An effect optimization acquisition module is used to obtain the actual washing effect after the optimized fuzzy controller is used to control the washing;

[0164] The model optimization acquisition module is used to optimize the washing effect prediction model according to the actual washing effect after the fuzzy controller after the optimization is used to control the washing.

[0165] Optionally, the washing effect prediction model is trained in the following manner:

[0166] A sample acquisition submodule, used to acquire training sample parameters and actual washing effects corresponding to the training sample parameters; the sample parameters include the number of dishes, the position of the dishes and the existing amount of water injected;

[0167] A sample input submodule, used for inputting the sample parameters into a washing effect prediction model to obtain a predicted washing effect;

[0168] The parameter adjustment submodule is used to adjust the parameters of the washing effect prediction model according to the actual washing effect corresponding to the training sample parameters and the error of the predicted washing effect, so as to train the washing effect prediction model.

[0169] Optionally, a microwave radar is provided in the dishwashing device, and the parameter acquisition module 301 includes:

[0170] A signal transmitting submodule, used for transmitting microwave signals through a microwave radar;

[0171] The detection submodule is used to detect the reflected signal of the microwave signal after being reflected by the tableware, and determine the number and position of the tableware according to the reflected signal.

[0172] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0173] The embodiment of the present invention further provides a tableware cleaning device, comprising:

[0174] It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, the various processes of the tableware cleaning method embodiment of the above-mentioned tableware cleaning device are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0175] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of an embodiment of a tableware cleaning method of the above-mentioned tableware cleaning device is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0176] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0177] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0178] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0179] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0180] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0181] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0182] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0183] The tableware cleaning method of a tableware cleaning device and a tableware cleaning device of a tableware cleaning device provided by the present invention are introduced in detail above. Specific examples are used herein to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A tableware cleaning method using a tableware cleaning device, characterized in that: The method comprises: Obtaining target parameters of the dishwashing equipment, a fuzzy controller of the dishwashing equipment, and a pre-trained washing effect prediction model; Inputting the target parameter into the washing effect prediction model to obtain a predicted washing effect; Inputting the target parameter into the fuzzy controller for fuzzy reasoning to obtain a fuzzy reasoning result; According to the fuzzy reasoning result, the dishwashing device is controlled to perform washing, and an actual washing effect after the washing is completed is obtained; The fuzzy controller is optimized according to the predicted washing effect and the actual washing effect, so that the dishwashing device controls washing according to the optimized fuzzy controller.

2. The method according to claim 1, characterized in that The target parameters include the number of dishes, the location of the dishes and the existing amount of water that has been injected; The step of inputting the target parameter into the fuzzy controller for fuzzy reasoning to obtain a fuzzy reasoning result comprises: Inputting the target parameter into the fuzzy controller; The fuzzy controller is used to fuzzify the number of tableware, the position of tableware and the existing water volume respectively to obtain a fuzzy set of the number of tableware, a fuzzy set of the position of tableware and a fuzzy set of the existing water volume; and fuzzy reasoning is performed based on the fuzzy set of the number of tableware, the fuzzy set of the position of tableware and the fuzzy set of the existing water volume to obtain a fuzzy set of added water volume and a fuzzy set of spray range; and the fuzzy set of added water volume and the fuzzy set of spray range are defuzzified to obtain a determined value of the added water volume and a determined value of the spray range.

3. The method according to claim 2, characterized in that The fuzzy controller is used to fuzzify the number of tableware, the position of tableware and the existing water volume to obtain a fuzzy set of the number of tableware, a fuzzy set of the position of tableware and a fuzzy set of the existing water volume; Fuzzy reasoning is performed based on the tableware quantity fuzzy set, the tableware position fuzzy set and the existing water volume fuzzy set to obtain the added water volume fuzzy set and the spray range fuzzy set, including: The fuzzy controller is used to fuzzify the number of tableware, the position of tableware and the existing water volume according to the membership function to obtain a fuzzy set of the number of tableware, a fuzzy set of the position of tableware and a fuzzy set of the existing water volume; Fuzzy reasoning is performed based on the tableware quantity fuzzy set, the tableware position fuzzy set, the existing water volume fuzzy set and fuzzy rules to obtain the added water volume fuzzy set and the spray range fuzzy set.

4. The method according to claim 1, characterized in that: The step of optimizing the fuzzy controller according to the predicted washing effect and the actual washing effect comprises: According to the predicted washing effect and the actual washing effect, the fuzzy rules and the membership function of the fuzzy controller are optimized.

5. The method according to claim 1, characterized in that Also includes: Obtaining the actual washing effect after cleaning controlled by the optimized fuzzy controller; The washing effect prediction model is optimized according to the actual washing effect after cleaning controlled by the optimized fuzzy controller.

6. The method according to claim 1, characterized in that The washing effect prediction model is trained in the following way: Acquire training sample parameters and actual washing effects corresponding to the training sample parameters; the sample parameters include the number of dishes, the position of the dishes, and the existing amount of water injected; Inputting the sample parameters into a washing effect prediction model to obtain a predicted washing effect; According to the actual washing effect corresponding to the training sample parameters and the error of the predicted washing effect, the parameters of the washing effect prediction model are adjusted to train the washing effect prediction model.

7. The method according to claim 1, characterized in that The dishwashing cleaning device is provided with a microwave radar, and the step of obtaining target parameters of the dishwashing cleaning device includes: Transmitting microwave signals via microwave radar; A reflected signal after the microwave signal is reflected by the tableware is detected, and the number and position of the tableware are determined according to the reflected signal.

8. A tableware cleaning device of a tableware cleaning equipment, characterized in that: The device comprises: A parameter acquisition module, used to acquire target parameters of the dishwashing device, a fuzzy controller of the dishwashing device and a pre-trained washing effect prediction model; A predicted effect acquisition module, used for inputting the target parameter into the washing effect prediction model to obtain a predicted washing effect; A result acquisition module, used for inputting the target parameter into the fuzzy controller to perform fuzzy reasoning and obtain a fuzzy reasoning result; An actual effect acquisition module, used to control the dishwashing device to perform cleaning according to the fuzzy reasoning result, and obtain the actual washing effect after the cleaning is completed; The cleaning control module is used to optimize the fuzzy controller according to the predicted washing effect and the actual washing effect, so that the dishwashing device controls the cleaning according to the optimized fuzzy controller.

9. A dishwashing device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of a tableware cleaning method of a tableware cleaning device as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a tableware cleaning method of a tableware cleaning device according to any one of claims 1 to 7 are implemented.

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