Method of determining fabric weight, washing control method, electronic device, and fabric treatment apparatus

Abstract

The application relates to the technical field of fabric treatment equipment, in particular to a fabric weight determination method, a washing control method, an electronic device and fabric treatment equipment. The fabric weight determination method comprises the following steps: acquiring a first collection parameter of a first fabric treatment cylinder and a second collection parameter of a second fabric treatment cylinder, the first collection parameter comprising the weight of a first fabric in the first fabric treatment cylinder, and the second collection parameter not comprising the weight of a second fabric in the second fabric treatment cylinder; determining washing modes of the first fabric treatment cylinder and the second fabric treatment cylinder; and determining the weight of the second fabric in the second fabric treatment cylinder based on the first collection parameter, the second collection parameter and the washing modes of the fabric treatment cylinders. In the application, the washing modes in the cylinders are matched with multiple collection parameters, so that the weight of the second fabric in the second fabric treatment cylinder can be accurately determined, the washing quality and effect of the second fabric are effectively improved, and the use experience of a user is improved.

Description

Technical Field

[0001] The present application relates to the technical field of fabric processing equipment, and in particular to a method for determining fabric weight, a washing control method, an electronic device, and a fabric processing device. Background Art

[0002] With the rapid development of social economy and the improvement of smart home ecology, fabric processing equipment such as washing machines, as intelligent home appliances, have been continuously upgraded in functions and performance to meet diversified needs and have become one of the important smart devices in modern families.

[0003] In twin-drum washing machines currently on the market, the parameters for the smaller drum are typically determined by the water level. However, these parameters can be suboptimal due to variations in fabric weight and material. Consequently, parameters such as the wash time, detergent dosage, and speed for the smaller drum, set based on an inaccurate water level, become inaccurate, leading to poor washing results or even overwashing, reducing the user experience. Summary of the Invention

[0004] In view of this, the present application provides a method for determining fabric weight, a washing control method, an electronic device and a fabric processing device to solve the problem that the existing twin-drum washing machine has poor washing effect on the fabric in the small drum, resulting in a poor user experience.

[0005] A first aspect of an embodiment of the present application provides a method for determining fabric weight, which is applied to a fabric processing device, wherein the fabric processing device includes a first fabric processing drum and a second fabric processing drum, wherein the first fabric processing drum and the second fabric processing drum are both capable of washing and / or dehydrating fabric. The method for determining fabric weight includes:

[0006] Acquire a first acquisition parameter of the first fabric processing drum and a second acquisition parameter of the second fabric processing drum, wherein the first acquisition parameter includes a weight of the first fabric in the first fabric processing drum, and the second acquisition parameter does not include a weight of the second fabric in the second fabric processing drum;

[0007] determining a wash mode for each of the first fabric treatment drum and the second fabric treatment drum;

[0008] The weight of the second fabric in the second fabric treatment drum is determined based on the first collected parameter, the second collected parameter, and the wash mode of each fabric treatment drum.

[0009] In some embodiments, the determining the weight of the second fabric in the second fabric treatment drum based on the first collected parameter, the second collected parameter, and the wash mode of each fabric treatment drum;

[0010] inputting the first collected parameter, the second collected parameter, and the washing mode of each fabric treatment drum as input features into a weight prediction model, and obtaining a weight of the second fabric in the second fabric treatment drum output by the weight prediction model;

[0011] The weight prediction model is obtained based on training data, and the training data includes historical first acquisition parameters, historical second acquisition parameters, historical washing modes of each fabric processing drum, and historical weight of the second fabric in the corresponding second fabric processing drum.

[0012] In some embodiments, the weight prediction model is deployed in a cloud server, and the fabric processing device is connected to the cloud server via a network transmission protocol.

[0013] In some embodiments, the second collected parameter includes the water level in the second fabric treatment drum;

[0014] The first acquisition parameter further includes a fabric set ratio, which is used to represent the ratio of the set of the first fabric in the first fabric treatment drum to the set of the second fabric in the second fabric treatment drum.

[0015] In some embodiments, the first fabric treatment drum has a fabric weighing function;

[0016] The second fabric processing drum does not have a fabric weighing function;

[0017] or,

[0018] The first fabric treatment drum is a large drum, and the second fabric treatment drum is a small drum.

[0019] A second aspect of an embodiment of the present application provides a washing control method, which is applied to a fabric processing device, wherein the fabric processing device includes a first fabric processing drum and a second fabric processing drum, wherein the first fabric processing drum and the second fabric processing drum can both be used to wash and / or dehydrate fabrics, and the washing control method includes:

[0020] Determining the weight of the second fabric in the second fabric processing drum using the method for determining the fabric weight as described in the first aspect;

[0021] Wash parameters for the second fabric treatment drum are optimized based on the weight of the second fabric and the wash mode of the second fabric treatment drum.

[0022] In some embodiments, optimizing the wash parameters of the second fabric treatment drum based on the weight of the second fabric and the wash mode of the second fabric treatment drum includes:

[0023] The washing parameters of the second fabric treatment drum are adjusted in real time, wherein the washing parameters include at least one of washing time, detergent dosage, water level, or washing speed.

[0024] In some embodiments, the washing control method further comprises:

[0025] After each washing process is completed, the first collection parameters and washing mode of the first fabric processing drum, the second collection parameters and washing mode of the second fabric processing drum, the weight of the second fabric and the optimized washing parameters are recorded and transmitted back to the cloud server to optimize the weight prediction model.

[0026] A third aspect of an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements the method for determining the fabric weight as described in the first aspect, or implements the washing control method as described in the second aspect.

[0027] A fourth aspect of an embodiment of the present application provides a fabric processing device, which is controlled by the fabric weight determination method as described in the first aspect; or, is controlled by the washing control method as described in the second aspect; or, includes the electronic device as described in the second aspect.

[0028] Compared with the prior art, the beneficial effects of this application are mainly:

[0029] The present application discloses a method for determining fabric weight, a washing control method, an electronic device, and a fabric processing device. The method comprises: obtaining a first acquisition parameter of a first fabric processing drum and a second acquisition parameter of a second fabric processing drum, wherein the first acquisition parameter includes the weight of the first fabric in the first fabric processing drum, and the second acquisition parameter does not include the weight of the second fabric in the second fabric processing drum; determining the washing mode of each of the first and second fabric processing drums; and determining the weight of the second fabric in the second fabric processing drum based on the first acquisition parameter, the second acquisition parameter, and the washing mode of each fabric processing drum. In the present application, by utilizing multiple acquisition parameters in conjunction with the washing mode of each drum, the weight of the second fabric in the second fabric processing drum can be accurately determined, thereby effectively improving the subsequent washing quality and effect of the second fabric and enhancing the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] To more clearly illustrate the embodiments of this application or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely illustrative, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0031] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not intended to limit the conditions under which this application can be implemented, and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes should still fall within the scope of the technical contents disclosed in this application without affecting the efficacy and objectives that can be achieved by this application.

[0032] Figure 1 is a flowchart of the steps of a method for determining fabric weight according to an embodiment of the present application;

[0033] Figure 2 is a flowchart of the steps of a washing control method according to an embodiment of the present application;

[0034] Figure 3 This is a logic judgment flow chart of a washing control method according to an embodiment of the present application;

[0035] Figure 4 This is a processing flow chart of a cloud server in a washing control method according to an embodiment of the present application;

[0036] Figure 5 It is a logic diagram of a weight prediction model in a washing control method according to an embodiment of the present application. DETAILED DESCRIPTION

[0037] The following specific embodiments illustrate the implementation of this application. Those familiar with the art can easily understand the other advantages and functions of this application from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of this application, but not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0038] The terms used in the examples of this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a," "the," and "the" used in the examples of this application and the appended claims are also intended to include plural forms, and unless the context clearly indicates otherwise, "a plurality" generally includes at least two, but does not exclude the inclusion of at least one.

[0039] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0040] It should also be noted that the terms "include," "comprises," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a product or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such product or system. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the product or system comprising the element.

[0041] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, but should not be understood as limiting the present application.

[0042] like Figure 1 and combined Figures 3 to 5 As shown, an exemplary embodiment of the present application provides a method for determining fabric weight, which can be applied to a fabric processing device, wherein the fabric processing device includes a first fabric processing drum and a second fabric processing drum, and the first fabric processing drum and the second fabric processing drum can both wash and / or dehydrate fabric.

[0043] The fabric processing device may include but is not limited to a washing machine, which may include but is not limited to a washing and care machine, a washing and drying machine, etc. For example, the washing machine may be a drum washing machine or a pulsator washing machine with drying or washing and care functions, etc. Of course, the washing machine may also be other types of fully automatic washing machines.

[0044] In this example and the following examples, the fabric treatment device is described using a twin-drum washing machine as an example, and the dimensions of the first fabric treatment drum are larger than those of the second fabric treatment drum. In other words, the first fabric treatment drum is larger and the second fabric treatment drum is smaller. It should be noted that the first fabric treatment drum can also be smaller and the second fabric treatment drum can be larger. In other words, when using the fabric weight determination method of this example, the weight of the second fabric in the second fabric treatment drum can be determined when the first fabric treatment drum is larger, and the weight of the first fabric in the first fabric treatment drum can also be determined when the second fabric treatment drum is larger.

[0045] Specifically, the method for determining the weight of the fabric comprises the following steps:

[0046] Step S100: acquiring first acquisition parameters of the first fabric processing drum and second acquisition parameters of the second fabric processing drum, wherein the first acquisition parameters include the weight of the first fabric in the first fabric processing drum, and the second acquisition parameters do not include the weight of the second fabric in the second fabric processing drum.

[0047] Step S200: Determine the washing modes of the first fabric treatment drum and the second fabric treatment drum.

[0048] Step S300: Determine the weight of the second fabric in the second fabric treatment drum based on the first collected parameter, the second collected parameter and the washing mode of each fabric treatment drum.

[0049] In step S100, the fabric treatment device is a twin-drum washing machine, comprising a first fabric treatment drum and a second fabric treatment drum. The first fabric treatment drum may be a main drum of the fabric treatment device, and the second fabric treatment drum may be a secondary drum of the fabric treatment device. Both the first fabric treatment drum and the second fabric treatment drum are capable of washing and / or dehydrating fabrics.

[0050] The first fabric processing drum has a fabric weighing function, and the weight of the first fabric can be directly measured using a built-in load cell. Alternatively, the weight of the first fabric can be determined based on speed data, such as current or voltage changes, from the motor driving the first fabric processing drum. The second fabric processing drum does not have a fabric weighing function, and therefore cannot directly measure the weight of the second fabric.

[0051] The first collected parameter includes the weight of the first fabric in the first fabric treatment drum, while the second collected parameter does not include the weight of the second fabric in the second fabric treatment drum. Specifically, the first collected parameter is the weight of the first fabric directly measured by a load cell built into the first fabric treatment drum, for example, 5 kg. The second collected parameter may include the water level in the second fabric treatment drum, for example, 40 cm. The second collected parameter does not include the weight of the second fabric in the second fabric treatment drum because the second fabric treatment drum does not have a fabric weighing function and cannot directly measure the weight of the second fabric.

[0052] In step S200, when determining the wash modes for the first and second fabric treatment drums, the wash modes for the first and second fabric treatment drums can be determined based on user selections. For example, the user can select a standard wash mode for the first fabric treatment drum and a delicate wash mode for the second fabric treatment drum. Different wash modes are suitable for different types of fabrics. For example, the standard wash mode is suitable for cotton fabrics, while the delicate wash mode is suitable for delicate fabrics such as silk or lace.

[0053] In step S300, the weight of the second fabric in the second fabric treatment drum can be calculated by analyzing the relationship between the first and second collected parameters and the wash modes of the fabric treatment drums. In one example, the first and second collected parameters and the wash modes of the fabric treatment drums can be input as input features into a weight prediction model, and the weight of the second fabric in the second fabric treatment drum can be output by the weight prediction model.

[0054] like Figure 3 and Figure 5 As shown, the weight prediction model is derived from training data, which includes historical first and second collected parameters, the historical wash modes of each fabric treatment drum, and the historical weight of the second fabric within the corresponding second fabric treatment drum. The weight prediction model can be a neural network model, such as a multilayer perceptron, a convolutional neural network, or a recurrent neural network. By learning from a large amount of historical data, the weight prediction model can establish a mapping relationship between the first and second collected parameters, the wash modes of each fabric treatment drum, and the weight of the second fabric, thereby accurately predicting the weight of the second fabric.

[0055] In this example, by utilizing multiple collected parameters in conjunction with the washing modes in each drum, the weight of the second fabric in the second fabric treatment drum can be accurately determined, thereby effectively improving the subsequent washing quality and effect of the second fabric and enhancing the user experience.

[0056] like Figure 1 and combined Figures 3 to 5 As shown, in some embodiments, in the process of determining the weight of the second fabric in the second fabric treatment drum using the weight of the first fabric in the first fabric treatment drum, in conjunction with the collection parameters of the second fabric treatment drum and the wash modes of the two drums, the following method can be used:

[0057] The first and second collected parameters, as well as the wash mode of each fabric treatment drum, are input into a weight prediction model as input features, and the weight prediction model outputs the weight of the second fabric in the second fabric treatment drum. The weight prediction model is derived from training data, which includes historical first and second collected parameters, historical wash modes of each fabric treatment drum, and the corresponding historical weight of the second fabric in the second fabric treatment drum.

[0058] Specifically, the weight prediction model is derived from training data, which includes historical first and second collected parameters, the historical wash modes of each fabric treatment drum, and the historical weight of the second fabric within the corresponding second fabric treatment drum. The weight prediction model can be a neural network model, such as a multilayer perceptron, a convolutional neural network, or a recurrent neural network. By learning from a large amount of historical data, the weight prediction model can establish a mapping relationship between the first and second collected parameters, the wash modes of each fabric treatment drum, and the weight of the second fabric, thereby accurately predicting the weight of the second fabric.

[0059] In one example, the training process for the weight prediction model can be as follows:

[0060] First, a large amount of training data is collected, including historical first and second collected parameters, the historical washing mode of each fabric treatment drum, and the historical weight of the second fabric in the corresponding second fabric treatment drum. The historical first collected parameters include the weight of the first fabric and the historical fabric set ratio. The historical second collected parameters include the historical water level. The historical washing mode of each fabric treatment drum includes the historical washing mode of the first fabric treatment drum and the historical washing mode of the second fabric treatment drum.

[0061] The collected training data is then preprocessed, including data cleaning, data normalization, and data augmentation. Data cleaning involves removing outliers and missing values ​​from the training data to ensure data quality. Data normalization involves converting features of different dimensions to the same dimension to facilitate model training. Data augmentation involves transforming the original data to generate more training samples and improve the model's generalization ability.

[0062] Next, choose an appropriate model structure, such as a multilayer perceptron, convolutional neural network, or recurrent neural network. Based on the characteristics of the problem and the size of the training data, determine hyperparameters such as the number of layers, the number of neurons in each layer, the activation function, and the optimization algorithm.

[0063] The model is then trained using the preprocessed training data. During the training process, the model continuously adjusts parameters to minimize the error between the predicted value and the true value, thereby learning the mapping relationship between the first and second collected parameters, the washing mode of each fabric treatment drum, and the second fabric weight.

[0064] Finally, the trained model is evaluated using validation data to test its predictive performance. If the model's predictive performance does not meet the requirements, the model's structure or hyperparameters can be adjusted and retrained until the model's predictive performance meets the requirements.

[0065] like Figure 1 and combined Figures 3 to 5As shown, in some embodiments, the first collected parameter also includes a fabric cover ratio, which represents the ratio of the number of covers of the first fabric in the first fabric treatment drum to the number of covers of the second fabric in the second fabric treatment drum. The fabric cover ratio can be set by the user before washing begins. For example, the user can set the fabric cover ratio to 2:1 if there are two covers in the first fabric treatment drum and one cover in the second fabric treatment drum. By incorporating the fabric cover ratio parameter, the weight of the second fabric can be more accurately estimated.

[0066] The second collected parameter includes the water level within the second fabric treatment drum. The water level can be measured by a water level sensor within the second fabric treatment drum. The water level is correlated with the weight of the second fabric and can be used as an auxiliary parameter to infer the weight of the second fabric.

[0067] Specifically, in practical applications, the specific process of the method for determining the weight of the fabric is as follows:

[0068] First, the user puts the first fabric into the first fabric treatment drum and the second fabric into the second fabric treatment drum, and selects or sets the fabric set ratio, for example 2:1, which means there are 2 sets of fabrics in the first fabric treatment drum and 1 set of fabrics in the second fabric treatment drum.

[0069] The user then selects a standard wash mode for the first fabric treatment drum and a delicate wash mode for the second fabric treatment drum.

[0070] Then, after washing begins, the first fabric treatment drum measures the weight of the first fabric through the built-in weighing sensor, for example, the weight of the first fabric is measured to be 5 kg. At the same time, the second fabric treatment drum measures the water level through the water level sensor, for example, the water level is measured to be 40 cm.

[0071] Finally, the weight of the first fabric, the water level, the fabric set ratio, the washing mode of the first fabric treatment drum, and the washing mode of the second fabric treatment drum are input as input features into the weight prediction model, and the weight of the second fabric in the second fabric treatment drum is obtained by the weight prediction model output. For example, the predicted weight of the second fabric is 0.5 kg.

[0072] Through the above-mentioned method for determining the fabric weight, the weight of the second fabric in the second fabric processing drum can be accurately calculated when the second fabric processing drum does not have a fabric weighing function, providing important parameters for subsequent washing control, thereby achieving more precise washing control and improving washing effects and energy utilization efficiency.

[0073] In some embodiments, the weight prediction model is deployed on a cloud server, and the fabric processing device is connected to the cloud server via a network transmission protocol. Deploying the weight prediction model on the cloud server reduces the processing burden on the fabric processing device's local controller, lowering hardware costs while leveraging the cloud's powerful computing power to accelerate data processing and analysis. The fabric processing device can establish a data connection with the cloud server via WiFi, Bluetooth, ZigBee, or other wireless communication technologies to upload and download data.

[0074] In other words, a large and diverse dataset, such as data covering different wash modes, fabric weight ranges, water levels, wash ratios, and their corresponding optimal small-tube fabric weights, is transmitted via a network transmission protocol to a remote high-performance computing server (i.e., a cloud server). This effectively reduces the processing burden on the local controller and significantly reduces hardware costs. It also leverages the powerful computing power of the cloud to accelerate data processing and analysis, improving the speed and accuracy of determining the fabric weight within the small-tube.

[0075] like Figures 1 to 5 As shown, an exemplary embodiment of the present application provides a washing control method, which is applied to a fabric processing device, wherein the fabric processing device includes a first fabric processing drum and a second fabric processing drum, both of which can be used to wash and / or dehydrate fabrics. The washing control method includes the following process:

[0076] Determining the weight of the second fabric in the second fabric processing drum using the above-mentioned method for determining the fabric weight;

[0077] Wash parameters of the second fabric treatment drum are optimized based on the weight of the second fabric and the wash mode of the second fabric treatment drum.

[0078] Specifically, the washing control method includes the following steps:

[0079] Step S100: acquiring first acquisition parameters of the first fabric processing drum and second acquisition parameters of the second fabric processing drum, wherein the first acquisition parameters include the weight of the first fabric in the first fabric processing drum, and the second acquisition parameters do not include the weight of the second fabric in the second fabric processing drum.

[0080] Step S200: Determine the washing modes of the first fabric treatment drum and the second fabric treatment drum.

[0081] Step S300: Determine the weight of the second fabric in the second fabric treatment drum based on the first collected parameter, the second collected parameter and the washing mode of each fabric treatment drum.

[0082] Step S400: Optimizing washing parameters of the second fabric treatment drum based on the weight of the second fabric and the washing mode of the second fabric treatment drum.

[0083] In step S100, the fabric treatment device is a twin-drum washing machine, comprising a first fabric treatment drum and a second fabric treatment drum. The first fabric treatment drum may be a main drum of the fabric treatment device, and the second fabric treatment drum may be a secondary drum of the fabric treatment device. Both the first fabric treatment drum and the second fabric treatment drum are capable of washing and / or dehydrating fabrics.

[0084] The first fabric processing drum has a fabric weighing function, and the weight of the first fabric can be directly measured using a built-in load cell. Alternatively, the weight of the first fabric can be determined based on speed data, such as current or voltage changes, from the motor driving the first fabric processing drum. The second fabric processing drum does not have a fabric weighing function, and therefore cannot directly measure the weight of the second fabric.

[0085] The first collected parameter includes the weight of the first fabric in the first fabric treatment drum, while the second collected parameter does not include the weight of the second fabric in the second fabric treatment drum. Specifically, the first collected parameter is the weight of the first fabric directly measured by a load cell built into the first fabric treatment drum, for example, 5 kg. The second collected parameter may include the water level in the second fabric treatment drum, for example, 40 cm. The second collected parameter does not include the weight of the second fabric in the second fabric treatment drum because the second fabric treatment drum does not have a fabric weighing function and cannot directly measure the weight of the second fabric.

[0086] In step S200, when determining the wash modes for the first and second fabric treatment drums, the wash modes for the first and second fabric treatment drums can be determined based on user selections. For example, the user can select a standard wash mode for the first fabric treatment drum and a delicate wash mode for the second fabric treatment drum. Different wash modes are suitable for different types of fabrics. For example, the standard wash mode is suitable for cotton fabrics, while the delicate wash mode is suitable for delicate fabrics such as silk or lace.

[0087] In step S300, the weight of the second fabric in the second fabric treatment drum can be calculated by analyzing the relationship between the first and second collected parameters and the wash modes of the fabric treatment drums. In one example, the first and second collected parameters and the wash modes of the fabric treatment drums can be input as input features into a weight prediction model, and the weight of the second fabric in the second fabric treatment drum can be output by the weight prediction model.

[0088] like Figure 3 and Figure 5As shown, the weight prediction model is derived from training data, which includes historical first and second collected parameters, the historical wash modes of each fabric treatment drum, and the historical weight of the second fabric within the corresponding second fabric treatment drum. The weight prediction model can be a neural network model, such as a multilayer perceptron, a convolutional neural network, or a recurrent neural network. By learning from a large amount of historical data, the weight prediction model can establish a mapping relationship between the first and second collected parameters, the wash modes of each fabric treatment drum, and the weight of the second fabric, thereby accurately predicting the weight of the second fabric.

[0089] In step S400, during the process of optimizing the washing parameters of the second fabric treatment drum, the washing parameters of the second fabric treatment drum may be adjusted in real time, wherein the washing parameters include at least one of washing time, detergent dosage, water level or washing speed.

[0090] For example, when the second fabric is lighter, the washing time, the amount of detergent used, the water level, or the washing speed can be reduced to save energy and water resources and reduce wear on the fabric. When the second fabric is heavier, the washing time, the amount of detergent used, the water level, or the washing speed can be increased to ensure a good washing effect.

[0091] Specifically, a preset washing parameter table can be queried based on the weight of the second fabric and the washing mode of the second fabric treatment drum to obtain corresponding washing parameters. The washing parameter table stores optimal washing parameters for different fabric weights and different washing modes, which are obtained through extensive experiments and data analysis.

[0092] For example, when the weight of the second fabric is 0.5 kg and the washing mode of the second fabric treatment drum is the gentle washing mode, the corresponding washing parameters can be queried from the washing parameter table: washing time is 15 minutes, detergent dosage is 10 ml, water level height is 20 cm, and washing speed is 400 rpm.

[0093] When the weight of the second fabric is 1 kg and the washing mode of the second fabric treatment drum is the standard washing mode, the corresponding washing parameters can be obtained from the washing parameter table: washing time is 25 minutes, detergent dosage is 20 ml, water level height is 25 cm, and washing speed is 600 rpm.

[0094] When the weight of the second fabric is 1.5 kg and the washing mode of the second fabric treatment drum is the strong washing mode, the corresponding washing parameters can be obtained from the washing parameter table: washing time is 35 minutes, detergent dosage is 30 ml, water level height is 30 cm, and washing speed is 800 rpm.

[0095] By optimizing the washing parameters according to the weight of the second fabric and the washing mode of the second fabric treatment drum, more precise washing control can be achieved, the washing effect and energy utilization efficiency can be improved, and the service life of the fabric can be extended.

[0096] In this example, by utilizing multiple collected parameters in conjunction with the washing modes in each drum, the weight of the second fabric in the second fabric treatment drum can be accurately determined. Then, based on the weight of the second fabric and the washing mode of the second fabric in the second fabric treatment drum, the subsequent washing of the second fabric is optimized to achieve more precise washing control, improve washing effects and energy utilization efficiency, thereby effectively improving the subsequent washing quality and effects of the second fabric and enhancing the user experience.

[0097] like Figures 1 to 5 As shown, in some embodiments, the washing control method further includes: after each washing process is completed, recording the first collection parameters and washing mode of the first fabric processing drum, the second collection parameters and washing mode of the second fabric processing drum, the weight of the second fabric and the optimized washing parameters in this washing process and transmitting them back to the cloud server to optimize the weight prediction model.

[0098] Specifically, after each wash cycle, the fabric processing device records various data from the wash cycle, including the first collected parameter of the first fabric processing drum, the second collected parameter of the second fabric processing drum, the fabric set ratio, the wash mode of the first fabric processing drum, the wash mode of the second fabric processing drum, the weight of the first fabric, the weight of the second fabric, and the optimized wash parameters. The fabric processing device then transmits this data back to the cloud server via a network transmission protocol.

[0099] After receiving this data, the cloud server adds it to the training dataset and uses the updated training dataset to retrain the weight prediction model, continuously optimizing its performance. This allows the weight prediction model to continuously learn from new data, adapting to different user habits and fabric types, and improving its prediction accuracy and generalization capabilities.

[0100] An exemplary embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a method for determining fabric weight as described in any of the above embodiments, or implements a washing control method as described in any of the above embodiments.

[0101] In this embodiment, the electronic device may be a control unit of the fabric processing device, or an external device communicatively connected to the fabric processing device, such as a smart phone, a tablet computer, or a personal computer.

[0102] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may include non-volatile random access memory. The processor may be a central processing unit, a microprocessor, or other data processing chip, and is configured to execute the computer program stored in the memory.

[0103] An exemplary embodiment of the present application provides a fabric processing device, which is controlled by the method for determining the fabric weight of any of the above embodiments; or, is controlled by the washing control method of any of the above embodiments; or, includes the electronic device described in Example 4.

[0104] In this embodiment, the fabric treatment device is a twin-drum washing machine, comprising a first fabric treatment drum and a second fabric treatment drum. The first fabric treatment drum may be a main drum of the washing machine, and the second fabric treatment drum may be a secondary drum of the washing machine. Both the first and second fabric treatment drums are capable of washing and / or dehydrating fabrics. The first fabric treatment drum has a fabric weighing function, allowing the weight of the first fabric to be directly measured using a built-in load cell. The second fabric treatment drum does not have a fabric weighing function and cannot directly measure the weight of the second fabric.

[0105] The fabric processing device further includes a control unit, which may adopt a control system in the prior art and will not be described in detail here.

[0106] The fabric processing device also includes a communication module for establishing a data connection with a cloud server to upload and download data. The communication module can support WiFi, Bluetooth, ZigBee or other wireless communication technologies, or can support wired communication technologies such as Ethernet.

[0107] The fabric treatment device also includes a user interface for receiving user input and displaying information. The user interface may include a touch screen, buttons, indicator lights, a buzzer, etc. The user can use the user interface to set the fabric set ratio, select a washing mode, etc.

[0108] The fabric processing device further includes various sensors, such as a weighing sensor, a water level sensor, a temperature sensor, etc. The weighing sensor is used to measure the weight of the first fabric, the water level sensor is used to measure the water level in the second fabric processing drum, and the temperature sensor is used to measure the water temperature.

[0109] The fabric treatment equipment also includes actuators, such as a water valve, a detergent dosing device, a motor, etc. The water valve is used to control water inlet and outlet, the detergent dosing device is used to control the dosing of detergent, and the motor is used to drive the fabric treatment drum to rotate.

[0110] Taking the example of a large first fabric processing drum and a small second fabric processing drum, this example provides a dual-drum intelligent control logic based on the fabric processing equipment. This control logic can be divided into three stages: parameter collection, model training, and model application. The model in this example is a weight prediction model. The dual-drum intelligent control logic can include three stages: parameter collection, model training, and model application. The specific processes of each stage are as follows:

[0111] Parameter collection stage:

[0112] The fabric weight g1 of the large drum, the water level h of the small drum, the set ratio a1:a2 selected by the user, and the mode X selected by the user (mode X1 selected for the large drum and mode X2 selected for the small drum) are all key parameters that determine the fabric weight g2 of the small drum. Therefore, the above parameters are covered in the parameter collection stage.

[0113] Based on daily washing habits, users typically wash fabrics in batches by set. Even if the fabrics are not all washed in the same cycle, the set ratio can be scientifically inferred to effectively determine the number of fabric sets in the small drum. Before the wash begins, the user is prompted to select the set ratio. For example, if the large drum holds two sets of clothes and the small drum holds one set of underwear, the set ratio is 2:1. The user's selected large drum mode X1 and small drum mode X2 are recorded. After the wash begins, the large drum is weighed to determine the fabric weight g1. The water level sensor in the small drum detects the water level h in the small drum.

[0114] Fabrics of different materials have different weight characteristics, and these weight differences can be quantified using statistical analysis. The weight ranges for common fabric materials are shown in Table 1. The model first calculates the number of fabric sets n1 within the large drum based on the fabric weight g1 of the large drum and the user-selected large drum mode X1 (which corresponds to a specific type of fabric material). Subsequently, the model infers the number of fabric sets n2 within the small drum based on the user-selected set ratio a1:a2 and the number of fabric sets n1 within the large drum. Furthermore, the model infers the fabric weight g2 within the small drum through learning and calculation, taking into account the user-selected small drum mode X2 (used to determine the material type of the small drum), the water level h within the small drum (which serves as an auxiliary parameter to infer the fabric material), and the number of fabric sets n2 within the small drum.

[0115]

[0116] Table 1

[0117] Model training phase:

[0118] Model Description: The model's inputs are the fabric weight g1 of the large drum, the water level h of the small drum, the user's preferred set ratio a1:a2, and the user-selected mode X (mode X1 for the large drum and mode X2 for the small drum). The output is the fabric weight g2 of the small drum. This is a multiple-input, single-output problem, so this patent uses a neural network model to implement its functionality.

[0119] A large and diverse data set covering different wash modes, fabric weight ranges, water levels, wash ratios, and their corresponding optimal small-tube fabric weights is transmitted to a remote high-performance computing server via a network transmission protocol. This effectively reduces the processing burden on the local controller, significantly lowering hardware costs while leveraging the powerful computing power of the cloud to accelerate data processing and analysis.

[0120] A machine learning model is deployed on a cloud server. This model is trained using parameters such as the collected data on the fabric weight g1 of the large drum, the water level h of the small drum, the user's preferred set ratio a1:a2, and the user's selected mode X (mode X1 for the large drum and mode X2 for the small drum) as input features. The core goal of model training is to establish a mapping relationship between the fabric weight of the large drum, the water level h of the small drum, the set ratio, the user-selected mode, and the fabric weight of the small drum.

[0121] The fabric weight of the large drum, the water level of the small drum, the set ratio selected by the user, the mode selected by the user and the fabric weight of the small drum used in the model training process are all derived from the parameter collection stage of the early design. This stage involves a large number of repetitive experiments and data recording, providing a solid data foundation for subsequent model training.

[0122] For example, during this wash cycle, cotton fabrics are placed in the large drum and lace underwear is placed in the small drum. The user-set wash ratio is a1:a2 = 1:1. The standard wash mode X1 selected for the large drum identifies the material characteristics of the cotton fabrics, while the lace wash mode X2 selected for the small drum identifies the material characteristics of the lace underwear. After the wash cycle begins, the control unit weighs the large drum and measures the total weight of the fabrics in the large drum as g1 = 1400g. It also measures the water level in the small drum as h. Based on the weight of a single piece of cotton fabric of 350g, the control unit calculates the number of fabrics in the large drum as n1 = 4 pieces from the weighing data g1 = 1400g. Based on the user-set wash ratio a1:a2 = 1:1 and the number of fabrics in the large drum (n1 = 4 pieces, two sets of fabrics), the control unit calculates the number of fabrics in the small drum as n2 = 2 pieces. Based on the empirical value that a single piece of lace underwear weighs 100g, the total weight of the fabrics in the small drum should be g2 = 200g.

[0123] Measuring the water level h in the small drum for additional verification confirmed that the water level h matched the inferred fabric weight g2 = 200g, with no significant deviation. The control unit ultimately confirmed the fabric weight in the small drum to be g2 = 200g, confirming the correct inference. This process continuously improves the model's prediction accuracy and generalization capabilities, providing strong support for the practical application of the dual-drum intelligent control unit.

[0124] Model application phase:

[0125] As shown in Table 2 (parameter comparison chart) below, before the start of this wash cycle, the user selected wash mode X1 for the large drum and X2 for the small drum, with a wash ratio of a1:a2. After the wash cycle begins, the large drum fabric weight g1 and the small drum water level h are obtained. X1, X2, g1, h, and a1:a2 are input into the model (i.e., the weight prediction model). The model then learns and calculates the small drum fabric weight g2. It should be noted that the "a," "b," and "c" in Table 2's "small drum fabric weight (g)" represent the weight of the corresponding material in the wash mode.

[0126] The current washing parameters are optimized based on the small drum fabric weight g2 and the small drum washing mode X2. After the washing is completed, the a1:a2, g1, h, X, g2 of this washing will be recorded and input into the model for model optimization.

[0127]

[0128] Table 2

[0129] In the above example, by utilizing multiple collected parameters in conjunction with the washing modes in each drum, the weight of the second fabric in the second fabric treatment drum can be accurately determined, thereby effectively improving the subsequent washing quality and effect of the second fabric, and enhancing the user experience. At the same time, it can also effectively avoid the problem of insufficient or excessive washing, thereby achieving the goal of precise fabric care and energy saving and environmental protection.

[0130] The serial numbers in the embodiments of this application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0131] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0133] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining fabric weight, applied to a fabric processing device, wherein the fabric processing device comprises a first fabric processing drum and a second fabric processing drum, wherein the first fabric processing drum and the second fabric processing drum are both capable of washing and / or dehydrating fabric, wherein: The method for determining the weight of the fabric comprises: Acquiring first acquisition parameters of the first fabric treatment drum, wherein the first acquisition parameters also include a fabric set ratio, which is used to represent the ratio of the set number of the first fabric in the first fabric treatment drum to the set number of the second fabric in the second fabric treatment drum; and obtaining second acquisition parameters of the second fabric treatment drum, wherein the first acquisition parameters include the weight of the first fabric in the first fabric treatment drum, the second acquisition parameters do not include the weight of the second fabric in the second fabric treatment drum, and the second acquisition parameters include the water level in the second fabric treatment drum; determining a wash mode for each of the first fabric treatment drum and the second fabric treatment drum; The weight of the second fabric in the second fabric treatment drum is determined based on the first collected parameter, the second collected parameter, and the wash mode of each fabric treatment drum.

2. The method for determining fabric weight according to claim 1, wherein: determining the weight of the second fabric in the second fabric treatment drum based on the first collected parameter, the second collected parameter and the washing mode of each fabric treatment drum; inputting the first collected parameter, the second collected parameter, and the washing mode of each fabric treatment drum as input features into a weight prediction model, and obtaining a weight of the second fabric in the second fabric treatment drum output by the weight prediction model; The weight prediction model is obtained based on training data, and the training data includes historical first acquisition parameters, historical second acquisition parameters, historical washing modes of each fabric processing drum, and historical weight of the second fabric in the corresponding second fabric processing drum.

3. The method for determining fabric weight according to claim 2, wherein: The weight prediction model is deployed in a cloud server, and the fabric processing device is connected to the cloud server via a network transmission protocol.

4. The method for determining fabric weight according to claim 1, wherein: The first fabric processing drum has a fabric weighing function; The second fabric processing drum does not have a fabric weighing function; or, The first fabric treatment drum is a large drum, and the second fabric treatment drum is a small drum.

5. A washing control method, applied to a fabric processing device, wherein the fabric processing device comprises a first fabric processing drum and a second fabric processing drum, wherein the first fabric processing drum and the second fabric processing drum can both be used to wash and / or dehydrate fabrics, wherein: The washing control method comprises: Determining the weight of the second fabric in the second fabric treatment drum using the method for determining the fabric weight according to any one of claims 1 to 4; Wash parameters for the second fabric treatment drum are optimized based on the weight of the second fabric and the wash mode of the second fabric treatment drum.

6. The washing control method according to claim 5, characterized in that: The optimizing the washing parameters of the second fabric treatment drum based on the weight of the second fabric and the washing mode of the second fabric treatment drum comprises: The washing parameters of the second fabric treatment drum are adjusted in real time, wherein the washing parameters include at least one of washing time, detergent dosage, water level, or washing speed.

7. The washing control method according to claim 5, characterized in that: The washing control method further comprises: After each washing process is completed, the first collection parameters and washing mode of the first fabric processing drum, the second collection parameters and washing mode of the second fabric processing drum, the weight of the second fabric and the optimized washing parameters are recorded and transmitted back to the cloud server to optimize the weight prediction model.

8. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for determining the fabric weight according to any one of claims 1 to 4 is implemented, or the washing control method according to any one of claims 5 to 7 is implemented.

9. A fabric processing device, characterized in that: The method for determining the weight of the fabric according to any one of claims 1 to 4 is used for control; Alternatively, the washing control method according to any one of claims 5 to 7 is used for control; Or, including the electronic device according to claim 8.

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