Control method and system for putting detergent and electronic equipment

Abstract

The invention provides a control method and system for putting a detergent and electronic equipment. The method comprises the steps that a preset washing mode and the weight of clothes to be washed are input into a detergent amount preliminary prediction model for prediction, the theoretical detergent dosage is output, and the theoretical detergent dosage of a first preset proportion serves as the initial detergent adding amount; and a detergent dosage adjusting step: acquiring a real-time foam amount in the washing process, and inputting the real-time foam amount, a preset washing mode and the weight of the clothes to be washed into a detergent amount optimal judgment model for judgment to obtain a judgment result, if the judgment result indicates that the real-time detergent adding amount does not reach the optimal washing effect suitable for the to-be-washed clothes, newly adding a theoretical detergent dosage of a second preset proportion; and executing the detergent dosage adjustment step at least once until the real-time detergent addition amount reaches the optimal washing effect suitable for the clothes to be washed. And the dosage of the detergent can be accurately controlled.

Description

Technical Field

[0001] The present application relates to the field of household appliances, and in particular, to a control method for dispensing detergent, a control system for dispensing detergent, a computer-readable storage medium, and an electronic device. Background Art

[0002] As an important part of household appliances, washing machines have become an indispensable tool in modern life, greatly improving the convenience of people's daily life. With the continuous advancement of science and technology, the intelligence level of washing machines has been continuously improved, and their functions have become more and more diversified.

[0003] In the process of using a washing machine, in order to improve the washing effect, it is usually necessary to add chemical additives such as detergents and softeners. Currently, there are two main ways of dispensing in washing machines on the market: one is manual dispensing by the user, and the other is through an automatic dispensing device. The manual dispensing method is limited by factors such as the user's personal habits, experience judgment, and immediate environmental changes. It is often difficult to ensure that the amount of detergent dispensed each time can achieve the ideal washing effect. In contrast, automatic dispensing technology represents a technological innovation in the field of washing machines. Compared with manual dispensing, automatic dispensing technology can more accurately control the amount of detergent dispensed, thereby achieving better washing results.

[0004] Calculating the dosage of automatic detergent is crucial. If the dosage is insufficient, the clothes may not be cleaned thoroughly and stains may be left behind; while too much dosage may cause damage to the clothes, waste resources and pollute the environment, all of which will significantly affect the overall user experience.

[0005] At present, the main strategies adopted by washing machines for automatic detergent dosing include automatically determining the amount of detergent to be dosed based on the user's preset washing mode and the weight of the clothes. However, this method has certain limitations. Specifically, factors such as the material of the clothes, water temperature and water quality will affect the foaming effect of the detergent during the washing process, which in turn affects the accuracy of the detergent dosing. Due to the existence of these variables, the automatic dosing system still faces challenges in ensuring the accuracy of the detergent dosage. Summary of the invention

[0006] The main purpose of the present application is to provide a control method for detergent dispensing, a control system for detergent dispensing, a computer-readable storage medium and an electronic device, so as to at least solve the problem that the current automatic detergent dispensing solution still has limitations and causes the detergent dosage to be not accurate enough.

[0007] In order to achieve the above-mentioned object, according to a first aspect of the present application, a control method for adding detergent is provided, comprising: a prediction step: obtaining a preset washing mode and a weight of laundry to be washed, and inputting the preset washing mode and the weight of the laundry to a preliminary detergent dosage prediction model to predict and output a theoretical detergent dosage, and taking a first preset ratio of the theoretical detergent dosage as an initial detergent addition amount; a detergent dosage adjustment step: obtaining a real-time foam amount during washing, and inputting the real-time foam amount, the preset washing mode and the weight of the laundry to a detergent dosage optimal judgment model to obtain a judgment result, if the judgment result indicates that the real-time detergent addition amount does not reach the optimal washing effect applicable to the laundry, adding a second preset ratio of the theoretical detergent dosage; and executing the detergent dosage adjustment step at least once until the real-time detergent addition amount reaches the optimal washing effect applicable to the laundry, wherein the real-time detergent addition amount when the detergent dosage adjustment step is executed for the first time is the initial detergent addition amount.

[0008] Optionally, obtaining the real-time foam amount during the washing process includes: obtaining the frequency characteristics and the amplitude characteristics of the foam bursting sound signal; and determining the real-time foam amount in the drum according to the frequency characteristics and the amplitude characteristics of the foam bursting sound signal.

[0009] Optionally, before executing the prediction step, the method further includes: constructing a preliminary detergent dosage prediction model, wherein the preliminary detergent dosage prediction model is trained using multiple groups of training data, and any group of the training data includes: historical preset washing modes, historically weighed weights of laundry, and historical theoretical detergent dosages corresponding to the historically preset washing modes and historically weighed weights of laundry, obtained within a historical time period, wherein any of the historical theoretical detergent dosages is obtained through multiple washing experiments, the detergent dosages added in any two washing experiments are different, and the historical theoretical detergent dosage is the detergent dosage with the smallest amount of dirt after washing among the multiple detergent dosages.

[0010] Optionally, after constructing the preliminary detergent dosage prediction model, the method further includes: constructing a model for judging whether the detergent dosage is optimal, wherein the model for judging whether the detergent dosage is optimal is obtained by training with multiple groups of training data, and any group of the training data includes input parameters and output parameters obtained within a historical time period, the input parameters include historical preset washing modes, historical weighed weights of laundry, and historical foam volumes, and the output parameters are historical detergent addition amounts selected for the corresponding optimal washing effect, wherein the historical detergent addition amounts are obtained on the basis of historical theoretical detergent dosages.

[0011] Optionally, the parameters in the preset washing mode include at least one of the following: washing time, clothing material, water temperature, and set water level.

[0012] According to a second aspect of the present application, a method for controlling the addition of detergent is provided, comprising: sending a preset washing mode and the weight of the laundry to be washed to a remote server, so that the remote server performs a prediction step: obtaining a preset washing mode and the weight of the laundry, and inputting the preset washing mode and the weight of the laundry into a preliminary detergent dosage prediction model to predict and output a theoretical detergent dosage, and using a first preset ratio of the theoretical detergent dosage as an initial detergent addition amount; sending a real-time amount of foam to the remote server during washing, so that the remote server performs a detergent dosage adjustment step: during washing, the amount of foam is adjusted; ... During the process, a real-time foam amount is obtained, and the real-time foam amount, the preset washing mode and the weight of the laundry are input into a detergent dosage optimal judgment model to obtain a judgment result; if the judgment result indicates that the real-time detergent addition amount does not reach the optimal washing effect applicable to the laundry, a second preset ratio of the theoretical detergent dosage is added; and the detergent dosage adjustment step is performed at least once until the real-time detergent addition amount reaches the optimal washing effect applicable to the laundry, wherein the real-time detergent addition amount when the detergent dosage adjustment step is performed for the first time is the initial detergent addition amount.

[0013] Optionally, the process of obtaining the real-time foam amount during washing includes: obtaining a foam bursting sound signal; performing a time domain to frequency domain conversion process on the foam bursting sound signal to obtain a frequency characteristic and an amplitude characteristic of the foam bursting sound signal.

[0014] Optionally, acquiring the foam bursting sound signal includes: receiving the foam bursting sound signal collected by a plurality of sound sensors installed at multiple locations.

[0015] According to a third aspect of the present application, a control system for placing detergent is provided, comprising: a remote server, configured to execute any one of the control methods for placing detergent; a washing machine end, communicating with the remote server, configured to execute any one of the control methods for placing detergent;

[0016] According to a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the control methods for adding detergent.

[0017] According to the fifth aspect of the present application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a control method for executing any one of the detergent delivery methods.

[0018] The technical solution of the present application is applied, through the prediction step: obtaining the preset washing mode and the weight of the laundry to be washed, and inputting the preset washing mode and the weight of the laundry to the preliminary prediction model of the detergent dosage to predict and output the theoretical detergent dosage, and taking the theoretical detergent dosage of the first preset ratio as the initial detergent addition amount; the detergent dosage adjustment step: obtaining the real-time foam amount during the washing process, and inputting the real-time foam amount, the preset washing mode and the weight of the laundry to be washed into the detergent dosage optimal judgment model to obtain the judgment result, if the judgment result indicates that the real-time detergent addition amount does not reach the optimal washing effect suitable for the laundry, adding the theoretical detergent dosage of the second preset ratio; the preliminary prediction model of the detergent dosage and the optimal judgment model of the detergent dosage are applied to realize the precise control of the detergent dosage, and the preliminary prediction model of the detergent dosage considers the two factors of the preset washing mode and the weight of the laundry to be washed, and the optimal judgment model of the detergent dosage considers the preset washing mode, the weight of the laundry to be washed and the real-time foam amount, and the consideration of multiple factors realizes the precise control of the detergent dosage. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings constituting part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 A schematic flow chart of a first method for controlling the placement of detergent according to an embodiment of the present application is shown;

[0021] Figure 2 A schematic diagram of the functional steps specifically implemented by the remote server provided according to an embodiment of the present application is shown;

[0022] Figure 3 A schematic flow chart of a second method for controlling the placement of detergent according to an embodiment of the present application is shown;

[0023] Figure 4 A schematic diagram of the functional steps specifically implemented by a washing machine according to an embodiment of the present application is shown;

[0024] Figure 5 A schematic diagram of the model structure according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0026] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] As introduced in the background technology, the automatic detergent dispensing solution in the prior art still has limitations, resulting in inaccurate dosage of detergent. In order to solve the problem that the automatic detergent dispensing solution still has limitations, resulting in inaccurate dosage of detergent, the embodiments of the present application provide a detergent dispensing control method, a detergent dispensing control system, a computer-readable storage medium and an electronic device.

[0029] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0030] First aspect embodiment

[0031] In this embodiment, a control method for dispensing detergent running on a remote server is provided, wherein the remote server communicates with the washing machine. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] Figure 1FIG. 1 is a flow chart of a method for controlling the placement of detergent according to an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0033] Step S101, prediction step: obtaining a preset washing mode and the weight of the laundry, and inputting the preset washing mode and the weight of the laundry into a preliminary detergent dosage prediction model to predict and output a theoretical detergent dosage, and taking a first preset ratio of the theoretical detergent dosage as the initial detergent addition amount;

[0034] Among them, the parameters in the preset washing mode include at least one of the following: washing time, clothing material, water temperature, and set water level.

[0035] In some cases, the parameters in the preset washing mode may also include water quality.

[0036] The first preset ratio may be selected as 80%, 85%. Of course, the first preset ratio may be adjusted according to actual needs, but generally does not exceed 90% to leave room for subsequent adjustment processes.

[0037] Step S102, detergent dosage adjustment step: obtaining the real-time foam amount during the washing process, and inputting the real-time foam amount, the preset washing mode and the weight of the laundry into the detergent dosage optimal judgment model to obtain a judgment result, if the judgment result indicates that the real-time detergent addition amount does not reach the optimal washing effect suitable for the laundry, adding a second preset ratio of theoretical detergent dosage;

[0038] Whether the optimal washing effect is achieved can be evaluated using the following parameters:

[0039] 1) Washing ratio: Washing ratio refers to the ratio of the degree of stains removed from clothes to the initial stains during the washing process. It is one of the important indicators to measure the cleaning effect of the washing machine. The higher the washing ratio value, the more effectively the washing machine can remove stains from clothes during the washing process;

[0040] 2) Water consumption and power consumption: Water consumption and power consumption are important indicators to measure the economy and environmental protection of washing machines, and they directly affect the cost of using washing machines.

[0041] The real-time foam quantity is obtained during the washing process, including:

[0042] Acquiring frequency characteristics and amplitude characteristics of the foam bursting sound signal; specifically, the remote server receives the frequency characteristics and amplitude characteristics of the foam bursting sound signal sent by the washing machine;

[0043] The real-time foam amount in the cylinder is determined based on the frequency and amplitude characteristics of the foam bursting sound signal.

[0044] Specifically, the mapping relationship between the frequency characteristics and amplitude characteristics and the real-time foam volume is obtained through a large number of experiments.

[0045] Step S103, executing the detergent dosage adjustment step at least once until the real-time detergent addition amount reaches the optimal washing effect suitable for the laundry, wherein the real-time detergent addition amount when the detergent dosage adjustment step is executed for the first time is the initial detergent addition amount.

[0046] Additionally, before performing the prediction step, the method further comprises:

[0047] Construct a preliminary prediction model for detergent dosage,

[0048] Among them, the preliminary prediction model of detergent dosage is trained by using multiple groups of training data, and any group of training data includes: historical preset washing modes, historical weighed weights of laundry, and historical theoretical detergent dosages corresponding to historical preset washing modes and historical weighed weights of laundry, obtained within a historical time period, wherein any historical theoretical detergent dosage is obtained through multiple washing experiments, the detergent dosages added in any two washing experiments are different, and the historical theoretical detergent dosage is the detergent dosage with the smallest amount of dirt after washing among the multiple detergent dosages.

[0049] In addition, after constructing the preliminary prediction model of detergent dosage, the method further includes:

[0050] Construct a model to determine whether the detergent dosage is optimal.

[0051] Among them, the model for judging whether the detergent dosage is optimal is trained using multiple groups of training data. Any group of training data includes input parameters and output parameters obtained within a historical time period. The input parameters include historical preset washing modes, historical weighed weights of laundry, and historical foam volumes. The output parameters are historical detergent addition amounts selected for the corresponding optimal washing effect. The historical detergent addition amounts are obtained based on historical theoretical detergent dosages.

[0052] The control method for adding detergent of the present application comprises the following steps: a prediction step: obtaining a preset washing mode and a weight of laundry to be washed, and inputting the preset washing mode and the weight of laundry to a preliminary detergent dosage prediction model to predict and output a theoretical detergent dosage, and taking a theoretical detergent dosage of a first preset ratio as an initial detergent addition amount; a detergent dosage adjustment step: obtaining a real-time foam amount during washing, and inputting the real-time foam amount, the preset washing mode and the weight of laundry to a detergent dosage optimal judgment model to obtain a judgment result, and if the judgment result indicates that the real-time detergent addition amount does not reach an optimal washing effect suitable for the laundry, adding a theoretical detergent dosage of a second preset ratio; the preliminary detergent dosage prediction model and the detergent dosage optimal judgment model are applied to realize precise control of the detergent dosage, and the preliminary detergent dosage prediction model considers the two factors of the preset washing mode and the weight of laundry to be washed, and the detergent dosage optimal judgment model considers the preset washing mode, the weight of laundry to be washed and the real-time foam amount, and the consideration of multiple factors realizes precise control of the detergent dosage.

[0053] In this patent, a sound sensor is used to determine the real-time foam amount while washing, and whether the current detergent dosage has reached the target value is determined based on the real-time foam amount. If the target value has not been reached, detergent continues to be added. This can achieve dynamic adjustment of the detergent without affecting the overall washing process.

[0054] See Figure 2 ,The remote server collects the preset washing mode and the weight of the laundry to be washed, and inputs the preliminary prediction model of the detergent dosage, and obtains the theoretical detergent dosage;

[0055] The remote server collects the preset washing mode, the weight of the laundry, the frequency characteristics and the amplitude characteristics, inputs them into the optimal detergent dosage judgment model, and obtains the result of whether it is necessary to add more detergent. In other words, the preset washing mode, the weight of the laundry, the frequency characteristics and the amplitude characteristics can be input into the optimal detergent dosage judgment model to obtain the result of whether it is necessary to add more detergent.

[0056] Second aspect embodiment

[0057] In this embodiment, a control method for adding detergent running on the washing machine end is provided, wherein a remote server communicates with the washing machine end. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0058] Figure 3FIG. 1 is a flow chart of a method for controlling the placement of detergent according to an embodiment of the present application. Figure 3 As shown, the method comprises the following steps:

[0059] Step S301, sending the preset washing mode and the weight of the laundry to be washed to the remote server, so that the remote server performs a prediction step: obtaining the preset washing mode and the weight of the laundry, and inputting the preset washing mode and the weight of the laundry to a preliminary detergent dosage prediction model to predict and output a theoretical detergent dosage, and using a first preset ratio of the theoretical detergent dosage as the initial detergent addition amount;

[0060] Step S302, during the washing process, the real-time foam amount is sent to the remote server, so that the remote server executes a detergent dosage adjustment step: during the washing process, the real-time foam amount is obtained, and the real-time foam amount, the preset washing mode and the weight of the laundry are input into a detergent dosage optimal judgment model to obtain a judgment result, if the judgment result indicates that the real-time detergent addition amount does not reach the optimal washing effect applicable to the laundry, the theoretical detergent dosage of the second preset proportion is added; and the detergent dosage adjustment step is executed at least once until the real-time detergent addition amount reaches the optimal washing effect applicable to the laundry, wherein the real-time detergent addition amount when the detergent dosage adjustment step is executed for the first time is the initial detergent addition amount.

[0061] Among them, the process of obtaining the real-time foam amount during the washing process includes: obtaining a foam bursting sound signal; performing a time domain to frequency domain conversion process on the foam bursting sound signal to obtain the frequency characteristics and amplitude characteristics of the foam bursting sound signal.

[0062] Wherein, obtaining the foam bursting sound signal includes: receiving the foam bursting sound signal collected by a plurality of sound sensors installed at multiple positions.

[0063] Specifically, the sound sensor uses a piezoelectric ceramic hydrophone as a sound sensor with a sensitivity of ≥-166.5dBVre:1V / μPascal (ie, when the sound pressure changes by 1 microPascal, the output voltage of the sensor changes by 1 volt), and an operating frequency range of 20Hz-20KHz.

[0064] The sound sensor is installed between the inner drum and the outer drum and fixed at the upper position of the outer drum. This is because in the washing process of the washing machine, the clothes will be soaked in water and at the bottom in the initial state, and the foam will float on the water surface and have a certain contact with the clothes. When the washing machine rotates for washing, the rotation of the inner drum will throw the clothes and foam up, and the foam will burst when it is thrown up. At this time, collecting the frequency and amplitude of the foam bursting can minimize the influence of water flow, clothing friction, etc.

[0065] The control method for detergent dosage of the present embodiment applies a preliminary detergent dosage prediction model and an optimal detergent dosage judgment model to achieve precise control of detergent dosage. The preliminary detergent dosage prediction model takes into account two factors: the preset washing mode and the weight of the laundry to be washed. The optimal detergent dosage judgment model takes into account the preset washing mode, the weight of the laundry to be washed, and the real-time foam amount. The consideration of multiple factors enables precise control of detergent dosage.

[0066] See Figure 4 ,The main function of the washing machine is to input relevant ,information into the corresponding model, respond to the output of the ,model, and process the signals collected by the sound sensor.

[0067] The preliminary prediction model of the detergent dosage and the optimal determination model of the detergent dosage in the embodiment of the present application can be adopted Figure 5 The neural network model in .

[0068] The embodiments of the present application have advantages over solutions that only determine the amount of detergent once before washing, have high requirements for the accuracy of the predicted results of the detergent amount, and need to exhaust all influencing factors in the washing process. They have advantages over solutions that can more accurately control the total amount of detergent added, but require multiple washing and waiting processes after each washing, which not only increases the overall washing time, but also increases the risk of wear and tear of clothes during long-term washing.

[0069] Third aspect embodiment

[0070] This embodiment relates to a remote server, including:

[0071] A prediction unit, used for the prediction step: obtaining a preset washing mode and the weight of the laundry, and inputting the preset washing mode and the weight of the laundry into a preliminary detergent dosage prediction model to predict and output a theoretical detergent dosage, and using a first preset ratio of the theoretical detergent dosage as an initial detergent addition amount;

[0072] The adjustment unit is used for the detergent dosage adjustment step: obtaining the real-time foam amount during the washing process, and inputting the real-time foam amount, the preset washing mode and the weight of the laundry into the detergent dosage optimal judgment model to obtain a judgment result, if the judgment result indicates that the real-time detergent addition amount does not reach the optimal washing effect suitable for the laundry, adding the theoretical detergent dosage of a second preset ratio;

[0073] The execution unit is used to execute the above-mentioned detergent dosage adjustment step at least once until the above-mentioned real-time detergent addition amount reaches the optimal washing effect suitable for the above-mentioned laundry, wherein the above-mentioned real-time detergent addition amount when the above-mentioned detergent dosage adjustment step is executed for the first time is the above-mentioned initial detergent addition amount.

[0074] The adjustment unit includes an acquisition module, which is used to obtain the real-time foam amount during the washing process, wherein the acquisition module includes an acquisition submodule and a determination submodule, the acquisition submodule is used to obtain the frequency characteristics and amplitude characteristics of the foam bursting sound signal; the determination submodule is used to determine the above-mentioned real-time foam amount in the drum according to the above-mentioned frequency characteristics and the above-mentioned amplitude characteristics of the above-mentioned foam bursting sound signal.

[0075] The remote server also includes a first construction unit, which is used to construct the above-mentioned preliminary detergent dosage prediction model before executing the above-mentioned prediction step, wherein the above-mentioned preliminary detergent dosage prediction model is obtained by training with multiple groups of training data, and any group of the above-mentioned training data includes: historical preset washing modes, historically weighed weights of laundry to be washed, and historical theoretical detergent dosages corresponding to the above-mentioned historical preset washing modes and historically weighed weights of laundry to be washed, which are obtained within a historical time period, wherein any of the above-mentioned historical theoretical detergent dosages is obtained through multiple washing experiments, and the detergent dosages added in any two washing experiments are different. The above-mentioned historical theoretical detergent dosage is the detergent dosage with the smallest amount of dirt after washing among the multiple detergent dosages.

[0076] The remote server also includes a second construction unit, which is used to construct a model for judging whether the above-mentioned detergent dosage is optimal after constructing the above-mentioned preliminary prediction model for the detergent dosage, wherein the above-mentioned model for judging whether the above-mentioned detergent dosage is optimal is obtained by training with multiple groups of training data, and any group of the above-mentioned training data includes input parameters and output parameters obtained within a historical time period, and the above-mentioned input parameters include historical preset washing modes, historical weighed weights of laundry, and historical foam volumes, and the above-mentioned output parameters are historical detergent addition amounts selected for the corresponding optimal washing effect, wherein the above-mentioned historical detergent addition amounts are obtained on the basis of historical theoretical detergent dosages.

[0077] Optionally, the parameters in the above-mentioned preset washing mode include at least one of the following: washing time, clothing material, water temperature, and set water level.

[0078] Fourth aspect embodiment

[0079] This embodiment relates to a washing machine end, comprising:

[0080] A first sending unit is used to send the preset washing mode and the weight of the laundry to the remote server, so that the remote server performs a prediction step: obtaining the preset washing mode and the weight of the laundry, and inputting the preset washing mode and the weight of the laundry into a preliminary detergent dosage prediction model to predict and output a theoretical detergent dosage, and using a first preset ratio of the theoretical detergent dosage as an initial detergent addition amount;

[0081] The second sending unit is used to send the real-time foam amount to the remote server during the washing process, so that the remote server performs a detergent dosage adjustment step: obtaining the real-time foam amount during the washing process, and inputting the real-time foam amount, the preset washing mode and the weight of the laundry into a detergent dosage optimal judgment model to obtain a judgment result, if the judgment result indicates that the real-time detergent addition amount does not reach the optimal washing effect applicable to the laundry, adding the theoretical detergent dosage of a second preset proportion; and performing the detergent dosage adjustment step at least once until the real-time detergent addition amount reaches the optimal washing effect applicable to the laundry, wherein the real-time detergent addition amount when the detergent dosage adjustment step is performed for the first time is the initial detergent addition amount.

[0082] The washing machine end also includes an acquisition unit and a processing unit. The acquisition unit is used to acquire the foam bursting sound signal; the processing unit is used to perform time domain to frequency domain conversion processing on the foam bursting sound signal to obtain the frequency characteristics and amplitude characteristics of the foam bursting sound signal.

[0083] Fifth aspect embodiment

[0084] This embodiment provides a control system for dispensing detergent, including: a remote server for executing the control method for dispensing detergent in the first embodiment; and a washing machine end, communicating with the remote server for the control method for dispensing detergent in the second embodiment.

[0085] Remote server specific execution Figure 2 The steps shown are specifically performed by the washing machine end Figure 4 In the steps shown, the entire system is divided into three stages: parameter acquisition stage, model training stage and model application stage.

[0086] Parameter collection stage:

[0087] When the amount of detergent is constant, the length of washing time, the characteristics of the clothing material, the water temperature, the total weight of the clothing, and the set amount of water are all key variables that affect the foaming effect and foam stability of the detergent during the washing process. Therefore, these key parameters must be covered in the parameter collection stage. First, the washing mode of the washing machine (that is, the preset washing mode) is recorded during parameter collection. The washing mode includes the preset washing time, clothing material, temperature, and set water level (the water level data directly represents the amount of water required during the washing process). During the weighing stage of the washing process, the built-in weighing system of the washing machine or external auxiliary equipment is used to weigh the clothes placed in the drum to obtain the weight of the clothes. And after washing is completed, the amount of detergent used for this wash is recorded.

[0088] In a washing machine, the generation of foam is closely related to the vibration and propagation of sound. When foam is generated inside the washing machine, the bursting and flow of the foam will produce some specific sounds. These sound signals can be captured by sound sensors and converted into electrical signals. The frequency of the sound of foam bursting is related to many factors such as the size of the foam, the material, and the way it bursts. Generally speaking, the frequency of sound generated when smaller bubbles burst is relatively high, which may be around several thousand hertz (kHz). For example, when some soap bubbles with a diameter of several millimeters burst, the sound frequency may be between 2-5kHz. The amplitude reflects the loudness of the sound. The amplitude of the sound of foam bursting is also affected by many factors. From an energy perspective, less energy is released when small bubbles burst, and the amplitude of the sound is also smaller. For example, the amplitude of the sound when a small soap bubble bursts may be between 0.01-0.1 Pascal (Pa) (where 0.1 Pascal sound pressure is approximately equivalent to 74 decibels of sound pressure level).

[0089] The principle of the sound sensor detecting the amount of foam: (1) Capturing the sound signal: The sound sensor has a built-in sound-sensitive capacitive electret microphone that can capture the sound signals generated by the bursting and flow of foam inside the washing machine. (2) Signal conversion and transmission: The captured sound signal vibrates through the electret film in the microphone, causing the capacitance to change, thereby generating a small voltage that changes accordingly. This voltage is then converted into an electrical signal and transmitted to the processing system. (3) Signal processing and analysis: After the processing system receives the electrical signal, it analyzes and processes it through appropriate algorithms. Since different amounts of foam will cause changes in the frequency, amplitude and other characteristics of the sound signal, the processing system can determine the current amount of foam in the washing machine based on these characteristics. Table 1 shows the relationship between the amount of foam and the frequency and amplitude.

[0090] Table 1 Relationship between foam volume, frequency and amplitude

[0091]

[0092] During the rinsing stage of the washing process, a sound sensor is used to capture the specific sound signals generated in the inner drum of the washing machine with high sensitivity. The collected sound signals are then processed through FFT transformation, an efficient digital signal processing technology, to convert the time domain signal into a frequency domain representation, thereby accurately analyzing the characteristic frequency and amplitude information corresponding to the current amount of foam. This process not only realizes real-time monitoring of the foam state, but also provides a basis for the intelligent adjustment of subsequent washing programs.

[0093] FFT transform is actually fast discrete Fourier transform. The Fourier principle shows that any continuously measured time series or signal can be expressed as an infinite superposition of sine wave signals of different frequencies. The Fourier transform algorithm created based on this principle uses the original signal directly measured to calculate the frequency, amplitude and phase of different sine wave signals in the signal in an accumulative manner.

[0094] Model training phase:

[0095] Model description: The model training stage mainly trains two models: the preliminary prediction model for detergent dosage and the model for determining whether the detergent dosage is optimal. For the preliminary prediction model for detergent dosage, its input items are washing mode (washing time, clothing material, water temperature, set water level), clothing weight, and the output is the predicted detergent dosage corresponding to the current amount of clothing; while the model for determining whether the detergent dosage is optimal uses the washing mode (washing time, clothing material, water temperature, set water level), clothing weight, and its current foam content range (i.e., specific frequency and amplitude) as network input items, and the output item is whether the current foam content range is the optimal range for detergent usage; although the above two models have different input and output items, they both belong to prediction problems, so a feedforward neural network (BP) is used in this patent to implement its functions.

[0096] 2. Send a massive and diverse data set, covering the optimal detergent dosage and foam content range (i.e. specific frequency and amplitude) under different washing modes, clothing weight ranges and their corresponding optimal washing effects, to a remote high-performance computing server through a network transmission protocol. This strategy effectively reduces the processing burden of the local controller, significantly reduces hardware costs, and accelerates the data processing and analysis process by leveraging the powerful computing power of the cloud.

[0097] 3. On the server side, deploy a machine learning model, which uses the collected washing mode, clothing weight, and the corresponding optimal detergent dosage (preliminary detergent dosage prediction model), the frequency and amplitude of the optimal foam volume range (detergent dosage optimal judgment model) parameters as input features to train the model. The core goal of model training is to establish a mapping relationship between washing mode, clothing load and optimal detergent dosage, and the frequency and amplitude of the optimal foam volume range.

[0098] 4. The optimal detergent dosage, optimal washing effect foam frequency and amplitude parameters used in the model training process are all derived from the parameter collection stage of the previous design. This stage involves a large number of repetitive experiments and data recording, aiming to capture and quantify the subtle changes in washing effects and foam characteristics under different conditions, and provide a solid data foundation for subsequent model training. For example, 20ml of detergent is used to wash 1kg of clothes, mode selection X, and when the washing machine runs to the rinsing stage, the sound sensor is used to identify the signal when the inner drum generates foam, and its frequency F and amplitude A are obtained by conversion. Finally, after the washing stage is completed, check whether the clothes are washed clean. If not, increase the detergent dosage and repeat the above experiment until the clothes are washed clean. After the clothes are washed, all experimental parameters and experimental results are recorded. The model is trained by collecting a large amount of similar experimental data and inputting the data into the prediction model. Through this process, the prediction accuracy and generalization ability of the model can be continuously improved, providing strong support for the practical application of the intelligent detergent delivery system.

[0099] 3. Model application stage:

[0100] 1. First, the current washing program selected by the user and the actual amount of clothes are used as the initial input conditions to import the detergent intelligent delivery control system. First, before washing starts, the current washing mode and clothes weight are sent to the remote server to obtain the theoretical optimal dosage through the preliminary prediction model of the detergent dosage in the server, and this value * 80% is used as the initial detergent addition amount; secondly, after the washing process starts and a certain period of washing, the detergent dosage judgment model is entered. At this stage, the sound sensor is used to capture the values ​​of the foam frequency and amplitude in the inner drum in real time, and it is passed to the pre-trained model together with the current washing mode and clothes weight. The model quickly analyzes the input data to determine whether the current detergent dosage has achieved the optimal washing effect for the current clothing load. If the model determines that the current detergent dosage is not optimal, the system will automatically trigger the adjustment mechanism to increase the detergent dosage in time, and then judge again whether the increased detergent dosage has achieved the optimal washing effect until the best washing effect is achieved. The parameter comparison example table is shown in Table 2.

[0101] Table 2 Parameter comparison example table

[0102]

[0103] 2. Application example: The weight of the clothes washed this time is β, the washing type selected by the user is X, and the predicted amount of detergent added for the first time is R. Then 0.8R is used as the initial amount of detergent added. After the washing process starts, the foam frequency recognized by the sensor for the first time is F1, and the amplitude is A1. β, X, F1, and A1 are input into the system, and the system calculates whether the current amount of detergent has reached the optimal amount. If the optimal amount of detergent has not been reached, the system automatically adds a small amount of detergent, and the sensor recognizes the frequency F2 and the amplitude A2 again, and inputs it into the system together with β and X. The system repeats the above operations until it calculates that the current amount of detergent has reached the optimal amount, then the detergent is added and the washing process continues.

[0104] The prediction model is constructed through C++ or Python code, and its network architecture is shown in the following figure: Figure 5 As shown, the neural network architecture used in the model is BP neural network.

[0105] The two models are sequential, and the output of the first model (preliminary prediction of detergent dosage R) needs to be part of the input of the second model (adding detergent starting from 80% R).

[0106] Before the prediction model is used, it is necessary to train the model. The model training process is as follows: After the model is built, the preliminary prediction model of the detergent dosage is trained in the laboratory. In the experiment, five parameter groups (recorded as θ1) are collected for each experiment, including the washing time of the washing machine, the material of the clothes, the water temperature, the set water level, and the weight of the clothes. At the same time, different amounts of detergent are added artificially to observe the degree of cleanliness of the clothes corresponding to different amounts of detergent. The detergent dosage corresponding to the cleanest washing is selected and recorded. The above data (including the five parameters θ1 and the detergent dosage corresponding to the cleanest washing) are fed as input parameters to the built prediction model to train the model. The trained model can infer the corresponding predicted detergent dosage R after inputting the five parameters represented by θ.

[0107] In order to eliminate the influence of actual washing, in the second model, detergent is added gradually starting from 80% R. When training the model for determining whether the amount of detergent is optimal, six parameters θ2 are recorded, including washing time, clothing material, water temperature, set water level, clothing weight, and foam content range (i.e., the frequency and amplitude of foam bursting in the barrel after adding detergent). At the same time, all input parameters when the clothing reaches the optimal washing effect are recorded. All the above data (including the six parameters θ2 corresponding to the optimal washing effect) are used as input parameters to train the model. The trained model can infer whether the optimal washing effect has been achieved after inputting the six parameters represented by θ2, and thus stop adding detergent.

[0108] In addition, the above model training phase needs to last for at least three months of experimental period and obtain as much relevant data as possible.

[0109] Sixth aspect embodiment

[0110] This embodiment provides an electronic device, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing any one of the above-mentioned control methods for dispensing detergent.

[0111] Seventh aspect embodiment

[0112] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the control method for adding detergent.

[0113] An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein the control method for adding detergent is executed when the program is run.

[0114] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program in a control method for initializing the addition of detergent.

[0115] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0116] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0117] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing 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.

[0118] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture 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.

[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing 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.

[0120] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0121] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0122] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0123] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0124] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for controlling the placement of detergent, characterized in that: include: Prediction step: obtaining a preset washing mode and the weight of the laundry, and inputting the preset washing mode and the weight of the laundry into a preliminary detergent dosage prediction model to predict and output a theoretical detergent dosage, and using a first preset ratio of the theoretical detergent dosage as an initial detergent addition amount; Detergent dosage adjustment step: obtaining a real-time foam amount during washing, and inputting the real-time foam amount, the preset washing mode and the weight of the laundry into a detergent dosage optimal judgment model to obtain a judgment result; if the judgment result indicates that the real-time detergent addition amount does not achieve the optimal washing effect suitable for the laundry, adding a second preset ratio of the theoretical detergent dosage; The detergent dosage adjustment step is performed at least once until the real-time detergent addition amount reaches an optimal washing effect suitable for the laundry, wherein the real-time detergent addition amount when the detergent dosage adjustment step is performed for the first time is the initial detergent addition amount.

2. The method according to claim 1, characterized in that Get real-time foam levels during washing, including: Obtaining frequency characteristics and amplitude characteristics of the foam bursting sound signal; The real-time foam amount in the cylinder is determined according to the frequency characteristic and the amplitude characteristic of the foam bursting sound signal.

3. The method according to claim 1, characterized in that Before performing the prediction step, the method further includes: Constructing the preliminary prediction model of the detergent dosage, Among them, the preliminary detergent dosage prediction model is trained using multiple groups of training data, and any group of the training data includes: historical preset washing modes, historical weighed weights of laundry, and historical theoretical detergent dosages corresponding to the historical preset washing modes and historical weighed weights of laundry, obtained within a historical time period, wherein any of the historical theoretical detergent dosages is obtained through multiple washing experiments, the detergent dosages added in any two washing experiments are different, and the historical theoretical detergent dosage is the detergent dosage with the smallest amount of dirt after washing among the multiple detergent dosages.

4. The method according to claim 3, characterized in that After constructing the preliminary detergent dosage prediction model, the method further includes: Constructing a model for determining whether the detergent dosage is optimal, The model for determining whether the detergent dosage is optimal is trained using multiple sets of training data, wherein any set of training data includes input parameters and output parameters obtained within a historical time period, wherein the input parameters include historical preset washing modes, historical weighed weights of laundry, and historical foam volumes, and the output parameters are historical detergent addition amounts selected for the corresponding optimal washing effect, wherein the historical detergent addition amounts are obtained on the basis of historical theoretical detergent dosages.

5. The method according to any one of claims 1 to 4, characterized in that The parameters in the preset washing mode include at least one of the following: Washing time, clothing material, water temperature, set water level.

6. A method for controlling the placement of detergent, characterized in that: include: The preset washing mode and the weight of the laundry are sent to a remote server, so that the remote server performs a prediction step: obtaining the preset washing mode and the weight of the laundry, and inputting the preset washing mode and the weight of the laundry into a preliminary detergent dosage prediction model to predict and output a theoretical detergent dosage, and using a first preset ratio of the theoretical detergent dosage as an initial detergent addition amount; During the washing process, the real-time foam amount is sent to a remote server so that the remote server performs a detergent dosage adjustment step: during the washing process, the real-time foam amount is obtained, and the real-time foam amount, the preset washing mode and the weight of the laundry are input into a detergent dosage optimal judgment model to obtain a judgment result; if the judgment result indicates that the real-time detergent addition amount does not reach the optimal washing effect applicable to the laundry, the theoretical detergent dosage of a second preset proportion is added; and the detergent dosage adjustment step is performed at least once until the real-time detergent addition amount reaches the optimal washing effect applicable to the laundry, wherein the real-time detergent addition amount when the detergent dosage adjustment step is performed for the first time is the initial detergent addition amount.

7. The method according to claim 6, characterized in that The process of obtaining real-time foam volume during washing includes: Acquire the sound signal of bubble bursting; The foam bursting sound signal is converted from the time domain to the frequency domain to obtain the frequency characteristics and amplitude characteristics of the foam bursting sound signal.

8. The method according to claim 7, characterized in that Acquire bubble bursting sound signals, including: The foam bursting sound signal collected by a plurality of sound sensors installed at multiple locations is received.

9. A control system for dispensing detergent, characterized in that: include: A remote server, used to execute the control method for dispensing detergent according to any one of claims 1 to 5; The washing machine end communicates with the remote server and is used to execute the control method for adding detergent as described in any one of claims 6 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for controlling the addition of detergent as described in any one of claims 1 to 5 or any one of claims 6 to 8.

11. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a control method for executing the detergent delivery method described in any one of claims 1 to 5 or any one of claims 6 to 8.

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