A washing parameter determination method, electronic device, and laundry treatment apparatus

Abstract

This invention relates to a method for determining washing parameters, an electronic device, and a garment processing device in the field of washing methods. The method includes: acquiring input parameters, including water inlet parameters, garment parameters, and mode parameters. The water inlet parameters include the actual water level and the number of water replenishment cycles. Based on the correspondence between the input parameters and the washing parameters, the actual washing parameters are determined. The water inlet parameters are related to sound frequency, which is generated by the tumbling of the garments during the washing drum's operation. The water level and the number of water replenishment cycles, along with other washing parameters determined by a random forest model, can more accurately determine the optimal washing time, rotation speed, temperature, and detergent dosage, significantly improving the washing effect.

Description

Technical Field

[0001] This invention relates to the field of washing method technology, and in particular to a method for determining washing parameters, an electronic device, and a garment processing device. Background Technology

[0002] In the technical field of washing methods, most washing machine parameter settings are determined by the weight of the laundry. However, due to uneven distribution of the laundry, the weighing process of the washing machine may result in a weight that does not match the actual weight of the clothes. Therefore, parameters such as water level, washing time, and detergent dosage set based on this inaccurate weight become inaccurate, leading to poor washing results or over-washing. Summary of the Invention

[0003] The technical problem this invention aims to solve is that the weighing process of a washing machine may result in a discrepancy between the weighed weight and the actual weight of the clothes due to uneven distribution of the clothes. In this case, setting washing parameters based on inaccurate weight will lead to inaccurate washing results or over-washing of the clothes. This invention provides a method for determining washing parameters, an electronic device, and a clothes processing device.

[0004] This invention aims to provide a method for determining washing parameters in a garment processing device, the garment processing device including a washing drum, the method for determining washing parameters comprising:

[0005] Obtain input parameters, including water inlet parameters, clothing parameters, and mode parameters. The water inlet parameters include the actual water level and the number of times water replenishment is performed.

[0006] Based on the correspondence between the input parameters and the washing parameters, the actual washing parameters are determined;

[0007] The water inlet parameters are related to the sound frequency;

[0008] The sound frequency is the sound frequency generated by the clothes tumbling during the operation of the washing drum.

[0009] In some embodiments, the number of water replenishment operations is determined based on the water level drop after each replenishment and the sound frequency.

[0010] In some embodiments, the method includes:

[0011] Determine the extent of the drop in water level when soaking clothes;

[0012] Determine whether to enter the water replenishment stage based on whether the drop in water level of the soaked clothes exceeds the preset value;

[0013] If the water level of the soaked clothes drops more than the preset value, the water replenishment stage is entered; otherwise, the water replenishment stage is entered based on whether the sound frequency is within the preset sound frequency range.

[0014] If the sound frequency is outside the preset sound frequency range, then the water replenishment stage is entered, and the number of times the water replenishment stage is executed is the number of times the water replenishment is executed.

[0015] In some embodiments, the water replenishment stage includes: determining the amount of water to replenish based on the drop in water level of the soaked clothes; the greater the drop in water level of the soaked clothes, the more water is replenished.

[0016] In some embodiments, the water level of the soaked clothing decreases by an amount of A;

[0017] Where A = (A1 - A2) / A1 × 100%,

[0018] A1 is the water level before the clothes are soaked after the basic water volume is injected into the washing drum.

[0019] A2 is the water level height after the basic water volume is injected into the washing drum to soak the clothes;

[0020] 10%≤A≤15%, the replenishment water volume is 1 to 1.2 times the base water injection volume;

[0021] 16%≤A≤20%, the replenishment water volume is 1.5 to 1.7 times the base water injection volume;

[0022] A > 20%, and the replenishment water volume is 2 to 2.2 times the base water volume.

[0023] In some embodiments, the water replenishment stage includes: determining the amount of water to replenish based on the deviation between the sound frequency and the preset sound frequency range, wherein the greater the deviation between the sound frequency and the preset sound frequency range, the more water is replenished.

[0024] In some embodiments, the deviation value between the sound frequency and the preset sound frequency range is B;

[0025] B = |B1-B2| / B1 × 100%,

[0026] B1 is the sound frequency value, and B2 is the maximum or minimum value in the preset sound frequency range that is closer to B1.

[0027] 5%≤B≤10%, the replenishment water volume is 0.5 to 0.7 times the base water injection volume;

[0028] 11%≤B≤15%, the replenishment water volume is 1 to 1.2 times the base water injection volume;

[0029] B > 15%, and the replenishment water volume is 1.5 to 1.7 times the base water volume.

[0030] In some embodiments, the washing parameter determination method further includes: a soaking stage;

[0031] The soaking stage is performed before determining the extent of the drop in water level for the soaked clothing.

[0032] The soaking stage includes: injecting water into the washing drum to soak the clothes, and then letting them stand for a preset time after driving the washing drum to rotate forward and / or reverse.

[0033] In some embodiments, the clothing parameters include: the strength of the clothing's water absorption performance;

[0034] The method for judging the water absorption performance of the clothing is designed as follows:

[0035] The shorter the duration of the water replenishment phase, the stronger the water absorption performance of the clothing;

[0036] And / or, the more water is added during the water replenishment stage, the stronger the water absorption performance of the clothing.

[0037] In some embodiments, determining the actual washing parameters based on the correspondence between the input parameters and the washing parameters includes:

[0038] The input parameters are input into a random forest model, and the actual washing parameters are output through the random forest model. The random forest model is used to reflect the correspondence between the input parameters and the washing parameters.

[0039] In some embodiments, an electronic device is provided, the electronic device comprising:

[0040] Memory stores computer instructions;

[0041] A processor is used to invoke and execute the computer instructions to implement the above-described method for determining washing parameters.

[0042] In some embodiments, a garment processing device is provided, which uses the washing parameter determination method described above, or includes the electronic device described above.

[0043] The solution provided by this invention has the following advantages compared with the prior art:

[0044] The parameters obtained in the method include water inlet parameters, clothing parameters, and mode parameters. The water inlet parameters include the actual water level and the number of water replenishment cycles. The sound frequency is the frequency of the sound generated by the clothes tumbling during the washing drum's operation. The actual water level is monitored based on the water level frequency. Determining the actual water level and the number of water replenishment cycles based on parameters such as sound frequency is more reasonable, making the water inlet parameters more comprehensive and accurate. This improves the comprehensiveness and accuracy of the input parameters, ultimately outputting more accurate actual washing parameters, improving the washing effect, and providing more precise washing parameters for clothing. Attached Figure Description

[0045] The accompanying drawings, as part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention, but do not constitute an undue limitation of the invention. Obviously, the drawings described below are merely some embodiments, and those skilled in the art can obtain other drawings based on these drawings without creative effort. In the drawings:

[0046] Figure 1 This is one of the flowcharts illustrating the washing parameter determination method in the embodiments of the present invention;

[0047] Figure 2 This is the second flowchart of the washing parameter determination method shown in the embodiment of the present invention;

[0048] Figure 3 This is the third flowchart of the washing parameter determination method shown in the embodiment of the present invention;

[0049] Figure 4 This is the fourth flowchart of the washing parameter determination method shown in the embodiment of the present invention;

[0050] Figure 5 This is a graph illustrating the inverse relationship between water level frequency and water level height, as shown in an embodiment of the present invention.

[0051] Figure 6 This is a training graph of the random forest model shown in an embodiment of the present invention;

[0052] Figure 7 This is a curve showing the frequency of water level change over time in the mixed clothing during the water replenishment stage, as illustrated in an embodiment of the present invention.

[0053] Figure 8 This is a curve showing the frequency of water level change over time for clothing with good water absorption during the water replenishment stage, as illustrated in an embodiment of the present invention.

[0054] Figure 9 This is a curve showing the frequency of water level change over time for poorly absorbent clothing during the water replenishment stage, as illustrated in an embodiment of the present invention.

[0055] It should be noted that these accompanying drawings and textual descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art by referring to specific embodiments. Detailed Implementation

[0056] In the description of this invention, it should be noted that the terms "inner" and "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0057] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," "contact," and "communication" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0058] The weighing process of a washing machine can sometimes result in a discrepancy between the measured weight and the actual weight of the clothes due to uneven distribution of the garments. This inaccurate weight setting, which determines parameters such as water level, washing time, and detergent dosage, will also lead to inaccurate washing results or over-washing.

[0059] Based on this, the following embodiments are proposed.

[0060] Example 1:

[0061] like Figure 1 As shown, this embodiment provides a method for determining washing parameters for use in a garment processing device, the garment processing device including a washing drum, and the method for determining washing parameters includes:

[0062] Obtain input parameters, including water inlet parameters, clothing parameters, and mode parameters. The water inlet parameters include the actual water level and the number of times water replenishment is performed.

[0063] Based on the correspondence between the input parameters and the washing parameters, the actual washing parameters are determined;

[0064] The water inlet parameters are related to the sound frequency;

[0065] The sound frequency is the sound frequency generated by the clothes tumbling during the operation of the washing drum.

[0066] In this embodiment, the proposed method for determining washing parameters includes water inlet parameters, clothing parameters, and mode parameters. The water inlet parameters include the actual water level and the number of water replenishment cycles. The sound frequency is the frequency of the sound generated by the clothes tumbling during the washing drum's operation. The actual water level is monitored based on the water level frequency. Determining the actual water level and the number of water replenishment cycles based on parameters such as sound frequency is more reasonable, making the water inlet parameters more comprehensive and accurate. This improves the comprehensiveness and accuracy of the input parameters, ultimately outputting more accurate actual washing parameters, enhancing the washing effect, and providing more precise washing parameters for clothing.

[0067] More specifically, by acquiring the number of water replenishment cycles, the number of times the water replenishment stage is executed can be directly indicated. When clothes absorb a lot of water and the water level drops significantly during the water replenishment stage, multiple water replenishment stages are needed to ensure the clothes fully absorb water. Water replenishment stops when the actual water level stabilizes and the clothes have essentially stopped absorbing water. Therefore, acquiring the number of water replenishment cycles reflects information such as the clothes' water absorption performance and the appropriate water level, making the water intake parameters more comprehensive and accurate. Combined with acquiring the sound frequency generated by the clothes tumbling during the washing drum's operation, it can further determine whether the clothes are under reasonable water level conditions. For example, if the clothes are tangled or unevenly distributed, it may cause eccentricity, resulting in abnormal tumbling sounds within the washing drum and affecting the washing effect. By acquiring the sound frequency of the clothes tumbling within the washing drum during operation, the number of water replenishment cycles and the actual water level value can be more accurate, thereby improving the comprehensiveness and accuracy of the input parameters and ultimately outputting more accurate actual washing parameters, improving the washing effect and providing more precise washing parameters for clothes washing.

[0068] Optionally, in one implementation of this embodiment, such as Figure 2 As shown,

[0069] The number of water replenishment operations is determined based on the water level drop after each replenishment and the sound frequency.

[0070] Furthermore, the method includes:

[0071] Determine the extent of the drop in water level when soaking clothes;

[0072] Determine whether to enter the water replenishment stage based on whether the drop in water level of the soaked clothes exceeds the preset value;

[0073] If the water level of the soaked clothes drops more than the preset value, the water replenishment stage is entered; otherwise, the water replenishment stage is entered based on whether the sound frequency is within the preset sound frequency range.

[0074] If the sound frequency is outside the preset sound frequency range, then the water replenishment stage is entered, and the number of times the water replenishment stage is executed is the number of times the water replenishment is executed.

[0075] In this embodiment, the proposed washing parameter determination method monitors the water level frequency and the sound frequency of clothes tumbling inside the washing drum to determine a more reasonable water level. More specifically, it determines whether to enter the water replenishment stage based on whether the water level drop of the soaking clothes exceeds a preset value. If the water level drop exceeds the preset value, it indicates that the clothes have absorbed a lot of water during the water intake stage, resulting in a large drop in water level, and the water replenishment stage is initiated to allow the clothes to absorb water more fully. If the water level drop does not exceed the preset value, it indicates that the current water level is stable and the clothes have basically stopped absorbing water, but this is insufficient to ensure the rationality of the water level. If the clothes become tangled or unevenly distributed, it may lead to eccentricity, causing abnormal tumbling sounds inside the washing drum and affecting the washing process. The washing effect is determined by analyzing the sound frequency of the clothes tumbling inside the washing drum during operation. If the sound frequency is outside the preset range, the current water level is insufficient for the clothes to be washed in a suitable position. Therefore, the water replenishment stage is initiated to reduce tangling and promote more even distribution of the clothes, resulting in a more reasonable water level. Other washing parameters, determined by this water level and the number of water replenishments, are input into an intelligent optimization model. This model then outputs the optimal washing time, spin speed, temperature, and detergent dosage. This washing parameter determination method uses multi-dimensional feature fusion to accurately determine the state of the clothes, enabling more precise intelligent optimization of washing parameters and significantly improving washing performance. Furthermore, by continuously collecting and analyzing washing data and combining it with intelligent learning algorithms, the system can achieve dynamic performance optimization and adaptive improvement.

[0076] Intelligent optimization models can utilize random forest models to optimize and control washing machine parameters. Random forest is a machine learning algorithm based on ensemble learning, possessing nonlinear modeling capabilities, anti-overfitting properties, and high generalization ability, making it particularly suitable for complex systems with multiple inputs and outputs. During model construction, washing machine operating data is first collected, including input parameters such as water level, number of water replenishment cycles, clothes absorbency, and the user-selected mode. Simultaneously, the optimal washing time, spin speed, temperature, and detergent dosage are recorded as output parameters. After cleaning and preprocessing, the data is used for model training. Through the nonlinear fitting capabilities of the random forest model, the complex relationships between input and output parameters can be effectively uncovered. Based on the reasonable actual water level and the number of water replenishment cycles obtained from the washing parameter determination method, more reasonable washing parameters are output, improving washing performance.

[0077] In this embodiment, the sound frequency of the clothes tumbling inside the washing drum is obtained by a sound sensor. The sound sensor is installed between the inner and outer drums of the washing drum and fixed to the clothes agitation area near the middle of the outer drum to capture the sound frequency of the clothes tumbling after being agitated. When the sound frequency of the clothes tumbling inside the washing drum is obtained, the drive motor of the clothes handling equipment runs at the normal washing speed. The sound sensor collects the sound frequency of the clothes tumbling after being agitated during the operation of the washing drum. Based on whether the sound frequency is within the preset sound frequency range, it is determined whether the water level of the clothes at this time meets the optimal water level for washing.

[0078] Water level frequency can reflect water level height. That is, by monitoring the water level frequency and comparing its changes over a certain period, the magnitude of the water level drop can be obtained. The relationship between water level frequency and water level height is as follows: Figure 5 As shown, the water level frequency value obtained by the water level sensor is inversely proportional to the water level height. Figure 5 In the diagram, the horizontal axis represents the water level frequency value output by the water level sensor, and the vertical axis represents the actual water level height. When the actual water level height increases, the frequency value output by the water level sensor decreases accordingly, and vice versa. This inverse relationship can be expressed mathematically as: F = k / H. Here, F is the output frequency of the water level sensor, H is the water level height, and k is a constant proportionality factor related to the size and height design of the washing machine drum. In this embodiment, the magnitude of the water level drop can be obtained by monitoring the water level frequency value using the water level sensor.

[0079] Optionally, in one implementation of this embodiment, such as Figure 3 As shown,

[0080] The water replenishment stage includes: determining the amount of water to replenish based on the drop in water level of the soaked clothes; the greater the drop in water level of the soaked clothes, the more water is replenished.

[0081] Furthermore, the water level drop of the soaked clothes is A;

[0082] Where A = (A1 - A2) / A1 × 100%,

[0083] A1 is the water level before the clothes are soaked after the basic water volume is injected into the washing drum.

[0084] A2 is the water level height after the basic water volume is injected into the washing drum to soak the clothes;

[0085] 10%≤A≤15%, the replenishment water volume is 1 to 1.2 times the base water injection volume;

[0086] 16%≤A≤20%, the replenishment water volume is 1.5 to 1.7 times the base water injection volume;

[0087] A > 20%, and the replenishment water volume is 2 to 2.2 times the base water volume.

[0088] The water replenishment stage includes: determining the amount of water to replenish based on the deviation between the sound frequency and the preset sound frequency range; the greater the deviation between the sound frequency and the preset sound frequency range, the more water is replenished.

[0089] The deviation value between the sound frequency and the preset sound frequency range is B;

[0090] B = |B1-B2| / B1 × 100%,

[0091] B1 is the sound frequency value, and B2 is the maximum or minimum value in the preset sound frequency range that is closer to B1.

[0092] 5%≤B≤10%, the replenishment water volume is 0.5 to 0.7 times the base water injection volume;

[0093] 11%≤B≤15%, the replenishment water volume is 1 to 1.2 times the base water injection volume;

[0094] B > 15%, and the replenishment water volume is 1.5 to 1.7 times the base water volume.

[0095] In this embodiment, if the water level of the soaked clothes drops by more than the preset value, the water replenishment stage is entered; or if the sound frequency is outside the preset sound frequency range, the water replenishment stage is entered.

[0096] During the water replenishment phase, an appropriate amount of water is added, and the operation of driving the washing drum to rotate clockwise twice and counterclockwise twice is repeated, followed by a 1-minute resting period. During this process, the system continuously monitors water level changes: if the water level drop does not exceed 10%, the sound frequency is detected again. If the sound frequency is within the preset sound frequency range, the current actual water level h is recorded as a reference value. If the water level drop exceeds 10% or the sound frequency is outside the preset sound frequency range, the system continues the water replenishment operation until both the actual water level and sound frequency meet the requirements. At this point, the actual water level h is recorded as a reference value to provide a basis for optimizing subsequent washing programs.

[0097] During the water replenishment stage, the amount of water to be replenished is determined based on the magnitude of the drop in water level of the soaked clothing.

[0098] When the water level drops by 10% to 15%, the clothes are considered to have absorbed little water, and αL of water should be added first. When the water level drops by 16% to 20%, the clothes are considered to have absorbed moderate water, and 1.5αL of water should be added. When the water level drops by more than 20%, the clothes are considered to have absorbed a lot of water, and 2αL of water should be added. After adding water once, let the clothes soak thoroughly again, and then judge whether to add water again based on the water level change, until the water level meets the requirements.

[0099] During the water replenishment phase, the amount of water replenishment is determined based on the deviation between the sound frequency and the preset sound frequency range.

[0100] When 5% ≤ B ≤ 10%, the clothing is considered slightly dehydrated, and 0.5αL of water is added; when 11% ≤ B ≤ 15%, the clothing is considered moderately dehydrated, and αL of water is added; when B exceeds 15%, the clothing is considered significantly dehydrated, and 1.5αL of water is added. The sound frequency detection is repeated until the sound frequency falls within the preset sound frequency range, and the final number of water additions, n, is recorded. Then, the parameter setting stage begins.

[0101] Furthermore, the method for determining washing parameters also includes: a soaking stage;

[0102] The soaking stage is performed before determining the extent of the drop in water level for the soaked clothing.

[0103] The soaking stage includes: injecting water into the washing drum to soak the clothes, and then letting them stand for a preset time after driving the washing drum to rotate forward and / or reverse.

[0104] In this embodiment, after the user places the clothes to be washed into the washing drum, the water inlet stage begins. The control system of the clothing processing equipment injects a base water volume of αL into the washing drum through a preset base water injection mode, where α is 8% of the full drum volume. The drive motor then drives the washing drum to rotate forward twice and reverse twice, followed by a 1-minute settling period to ensure the internal state of the washing drum stabilizes and allows the clothes sufficient time to absorb water. During this process, the system continuously monitors the water level change: if the water level drop does not exceed 10% of the base water level (i.e., the preset drop value is 10%), for example, A1... =20cm, A2=19cm, then A=(20-19) / 20×100%=5%, which does not exceed the preset amplitude value of 10%, so it enters the sound frequency detection stage. Based on whether the sound frequency of the clothes tumbling in the washing drum during operation is within the preset sound frequency range, it is determined whether to enter the water replenishment stage. If the sound frequency is outside the preset sound frequency range, then the water replenishment stage is entered. Otherwise, if the sound frequency is within the preset sound frequency range, then the current actual water level height h is recorded as a preliminary reference value. The washing parameters are determined based on the actual water level height and the number of water replenishment executions in the water replenishment stage.

[0105] Alternatively, in one implementation of this embodiment,

[0106] The clothing parameters include: the strength of the clothing's water absorption performance;

[0107] The method for judging the water absorption performance of the clothing is designed as follows:

[0108] The shorter the duration of the water replenishment phase, the stronger the water absorption performance of the clothing;

[0109] And / or, the more water is added during the water replenishment stage, the stronger the water absorption performance of the clothing.

[0110] Furthermore, determining the actual washing parameters based on the correspondence between the input parameters and the washing parameters includes:

[0111] The input parameters are input into a random forest model, and the actual washing parameters are output through the random forest model. The random forest model is used to reflect the correspondence between the input parameters and the washing parameters.

[0112] In this embodiment, adding input parameters such as the absorbency of the clothes and the washing mode can further provide a basis for calculation for the random forest model, making its output washing parameters more accurate.

[0113] The system records key parameters such as the user-selected washing mode, actual water level h, the absorbency of the clothes, and the number of times water is replenished n, and then inputs these parameters into the random forest model.

[0114] like Figure 6 As shown, during model training, the Random Forest algorithm constructs multiple decision trees by randomly selecting samples and features, and then votes or averages the predictions of each tree to obtain the final predicted output parameters. During training, model parameters (such as the number of trees and tree depth) are optimized through cross-validation to ensure the model's generalization ability and prediction accuracy. Furthermore, the Random Forest model can also evaluate the importance of input parameters, revealing the degree to which each parameter contributes to the output results.

[0115] In this embodiment, the method for judging the water absorption performance of the clothing is designed as follows: the shorter the duration of the water replenishment stage, the stronger the water absorption performance of the clothing; and / or, the more water replenished during the water replenishment stage, the stronger the water absorption performance of the clothing. This allows for accurate determination of the water absorption performance of the clothing.

[0116] like Figures 7 to 9 The following examples demonstrate how mixed clothing can be made while maintaining a consistent weight. Figure 7 Clothing with good water absorption properties Figure 8 ) and clothing with poor water absorption ( Figure 9 Table 1 details the water level frequency changes during the water intake and replenishment phases for these three types of clothing, as well as the final water level frequency after replenishment. The data shows that during replenishment, clothing with poor absorbency exhibits longer replenishment times and more persistent water level fluctuations, with minimal fluctuations once the water level stabilizes. Clothing with good absorbency shows shorter replenishment times and more stable water level changes, with the largest fluctuations once the water level stabilizes. Mixed clothing falls between these two categories, showing shorter replenishment times, intermittent water level fluctuations, and larger fluctuations once the water level stabilizes. These characteristics provide a valid basis for judging the absorbency of different types of clothing.

[0117] Table 1. Time and water level frequency changes during the water ingress and replenishment stages of different garments.

[0118]

[0119] To optimize the system's accuracy in identifying the water absorption performance of clothing, a training dataset based on multiple types of clothing was established. Specifically, various clothing samples of the same weight but different materials and structures were selected and placed in a washing machine. Real-time data on the frequency changes in water level during the water intake and replenishment phases were collected, along with the time parameters for each phase. After preprocessing and feature extraction, this data was input into a random forest model, where the random forest algorithm was used to model and train the water level frequency change features. Through learning and optimization with a large amount of sample data, the model gradually grasps the water level frequency change patterns of different clothing types during the water intake and replenishment phases, thereby significantly improving the accuracy and precision of identifying the water absorption performance of clothing.

[0120] During the sound frequency detection phase, the degree of eccentricity generated during the washing process varies due to differences in the weight and material of the clothing. This eccentricity causes variations in the noise level of the washing machine, and the noise level corresponding to the optimal water level differs for each type of clothing, as shown in Table 2. Therefore, by monitoring the sound frequency of the clothes tumbling inside the washing drum in real time, it is possible to effectively determine whether the current water level is within a reasonable range, thereby ensuring the smoothness of the washing process and optimizing the washing effect.

[0121] During model training, washing parameter determination methods were used to obtain data on actual water level heights under different washing conditions. This allowed the random forest model to comprehensively capture and quantify the correspondence between water level heights and sound frequencies under different conditions. Through this relationship, the random forest model could accurately establish the mapping relationship between water level heights and noise frequencies, providing a solid data foundation for subsequent model training. This ensured that the model could more accurately identify and optimize water level settings for different types of clothing.

[0122] Table 2. Sound frequencies corresponding to different types and weights of clothing.

[0123]

[0124] Model application phase:

[0125] In the model application phase, the random forest model uses real-time input parameters such as actual water level, number of water replenishments, absorbency of clothes, and user-selected washing mode to perform inference calculations and output the optimal washing time, spin speed, temperature, and detergent dosage. This method can dynamically adjust washing parameters according to actual working conditions, significantly improving washing efficiency and effect while reducing energy and detergent consumption.

[0126] For example: The laundry equipment is turned on, and the user selects wash type X. First, an initial water volume of αL is added to allow the clothes to fully absorb water. If the water level drops by 12%, a replenishment phase begins, adding another αL of water until the clothes are fully absorbed. If the water level drops by less than 10%, a sound frequency detection phase begins. At this point, the sound frequency is outside the preset sound frequency range, differing from the maximum value within the preset range by 8%. Another 0.5αL of water is added. When the sound frequency is detected again and falls within the preset range, the actual water level h and the number of replenishment cycles n are recorded. The process then proceeds to the parameter setting phase. The parameters—water level h, number of replenishment cycles n, weak absorbency of the clothes, and the user-selected wash type X—are input into a model (random forest) to obtain the optimal washing parameters for this wash: washing time t, spin speed r, temperature T, and detergent dosage β. The washing process then continues.

[0127] Table 3. Parameter Comparison Example Table

[0128]

[0129] The washing parameter determination method in this embodiment is based on the judgment of water level frequency and sound frequency. First, the optimal water level is determined by the water level frequency changes during the water intake and replenishment phases, and by identifying the sound frequency range. Second, the water absorption performance of the clothing, i.e., the water absorption rate, is obtained based on the water level change frequency during the replenishment phase. Finally, parameters such as water level height, replenishment frequency, and water absorption rate are input into the model to obtain the optimal washing time, spin speed, temperature, and detergent dosage. The washing parameters determined by this method are more targeted to the type of clothing being washed, improving the washing effect and achieving a more precise and optimized washing result. This not only effectively improves cleaning efficiency but also better protects the clothing.

[0130] Example 2

[0131] This embodiment provides an electronic device, which includes:

[0132] Memory stores computer instructions;

[0133] The processor is used to invoke and execute computer instructions to implement the washing parameter determination method in Embodiment 1.

[0134] This electronic device monitors the water level frequency and the sound frequency of clothes tumbling inside the washing drum to determine a more reasonable water level. More specifically, it determines whether to enter the water replenishment stage based on whether the water level drop exceeds a preset value. If the water level drop exceeds the preset value, it indicates that the clothes have absorbed a lot of water during the water intake stage, resulting in a significant drop in water level. In this case, the water replenishment stage is initiated to allow the clothes to absorb water more fully. If the water level drop does not exceed the preset value, it indicates that the current water level is stable and the clothes have essentially stopped absorbing water. However, this does not guarantee a reasonable water level. If the clothes are tangled or unevenly distributed, it may cause eccentricity, leading to abnormal tumbling sounds inside the washing drum and affecting the washing effect. During the operation of the washing drum, the system determines whether to enter the water replenishment stage by detecting whether the sound frequency of the clothes tumbling inside the drum is within a preset sound frequency range. If the sound frequency is outside the preset range, it indicates that the current water level is still insufficient for the clothes to be washed in a suitable position within the drum. Therefore, in this state, the water replenishment stage is initiated. By adding water to the drum, the tangling of clothes is reduced, promoting a more even distribution of clothes in the water, thus making the actual water level more reasonable. Other washing parameters determined by this water level and the number of water replenishments are input into an intelligent optimization model. For example, this reasonable actual water level and the number of water replenishments are input into the model, which then outputs the optimal washing time, spin speed, temperature, and detergent dosage. This washing parameter determination method accurately identifies the state of the clothes through multi-dimensional feature fusion, enabling more accurate intelligent optimization of washing parameters and significantly improving washing performance. Furthermore, by continuously collecting and analyzing washing data, combined with intelligent learning algorithms, the system can achieve dynamic performance optimization and adaptive improvement.

[0135] Example 3

[0136] This embodiment provides a garment processing device that uses the washing parameter determination method in Embodiment 1, or includes the electronic equipment in Embodiment 2.

[0137] This clothing processing equipment monitors the water level frequency and the sound frequency of clothes tumbling inside the washing drum to determine a more reasonable water level. More specifically, it determines whether to enter the water replenishment stage based on whether the water level drop exceeds a preset value. If the water level drop exceeds the preset value, it indicates that the clothes have absorbed a lot of water during the water intake stage, resulting in a significant drop in water level. In this case, the equipment enters the water replenishment stage to allow the clothes to absorb water more fully. If the water level drop does not exceed the preset value, it indicates that the current water level is stable and the clothes have essentially stopped absorbing water. However, this does not guarantee a reasonable water level. If the clothes are tangled or unevenly distributed, it may cause eccentricity, leading to abnormal tumbling sounds inside the washing drum and affecting the washing effect. The system determines whether to enter the water replenishment stage based on whether the sound frequency of the clothes tumbling inside the washing drum falls within a preset sound frequency range during operation. If the sound frequency is outside the preset range, it indicates that the current water level is insufficient for the clothes to be washed in a suitable position within the drum. Therefore, in this state, the water replenishment stage is initiated. By adding water to the drum, the tangling of clothes is reduced, promoting a more even distribution of clothes in the water and thus achieving a more reasonable actual water level. Other washing parameters determined by this water level and the number of water replenishments are input into an intelligent optimization model. For example, this reasonable actual water level and the number of water replenishments are then input into the model, which outputs the optimal washing time, spin speed, temperature, and detergent dosage. This washing parameter determination method accurately identifies the state of the clothes through multi-dimensional feature fusion, enabling more accurate intelligent optimization of washing parameters and significantly improving washing performance. Furthermore, by continuously collecting and analyzing washing data, combined with intelligent learning algorithms, the system can achieve dynamic performance optimization and adaptive improvement.

[0138] In summary, the ingenious design of the method for determining washing parameters lies in:

[0139] By monitoring the water level frequency and the sound frequency of clothes tumbling inside the washing drum, a more reasonable water level is determined. The water level drop is adjusted based on whether it exceeds a preset value to determine whether to initiate a water replenishment phase. If the drop exceeds the preset value, it indicates that the clothes have absorbed a significant amount of water during the initial water intake phase, and a water replenishment phase is initiated to ensure the clothes absorb water more fully. If the drop does not exceed the preset value, the water level is stable, and the clothes have largely stopped absorbing water. However, this does not guarantee a proper water level; if clothes are tangled or unevenly distributed, it may cause eccentricity and abnormal tumbling sounds inside the washing drum. This affects the washing effect. By combining the sound frequency of the clothes tumbling inside the washing drum during operation with a preset sound frequency range, the system determines whether to enter the water replenishment stage. If the sound frequency is outside the preset range, it indicates that the current water level is still insufficient for the clothes to be washed in a suitable position within the drum. Therefore, in this state, the water replenishment stage is initiated. By adding water to the drum, the tangling of clothes is reduced, promoting a more even distribution of clothes in the water, thus making the actual water level more reasonable. Using this water level and the number of water replenishments during the replenishment stage, along with other washing parameters determined by a random forest model, the optimal washing time, spin speed, temperature, and detergent dosage can be more accurately determined. This washing parameter determination method uses multi-dimensional feature fusion to accurately identify the state of the clothes, enabling more accurate intelligent optimization of washing parameters and significantly improving the washing effect.

[0140] It can be further understood that in this disclosure, "many" refers to two or more, and other quantifiers are similar. "And / or" describes the relationship between related objects, indicating that three relationships can exist; for example, A and / or B can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. The singular forms "a," "the," and "the" are also intended to include the plural forms unless the context clearly indicates otherwise.

[0141] It is further understood that the terms "first," "second," etc., are used to describe various types of information, but this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another, and do not indicate a specific order or degree of importance. In fact, the expressions "first," "second," etc., are completely interchangeable. For example, without departing from the scope of this disclosure, first information can also be referred to as second information, and similarly, second information can also be referred to as first information.

[0142] It is further understood that although operations are described in a specific order in the accompanying drawings in the embodiments of this disclosure, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all of the shown operations to be performed to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0143] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0144] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for determining washing parameters, used in a garment processing device, the garment processing device comprising a washing drum, characterized in that, The method for determining the washing parameters includes: Obtain input parameters, including water inlet parameters, clothing parameters, and mode parameters. The water inlet parameters include the actual water level and the number of times water replenishment is performed. Based on the correspondence between the input parameters and the washing parameters, the actual washing parameters are determined; The water inlet parameters are related to the sound frequency; The sound frequency is the sound frequency generated by the clothes tumbling during the operation of the washing drum; The number of water replenishment operations is determined based on the water level drop after each replenishment and the sound frequency. The method includes: Determine the extent of the drop in water level when soaking clothes; Determine whether to enter the water replenishment stage based on whether the drop in water level of the soaked clothes exceeds the preset value; If the water level of the soaked clothes drops more than the preset value, the water replenishment stage is entered; otherwise, the water replenishment stage is entered based on whether the sound frequency is within the preset sound frequency range. If the sound frequency is outside the preset sound frequency range, then the water replenishment stage is entered, and the number of times the water replenishment stage is executed is the number of times the water replenishment is executed.

2. The method for determining washing parameters according to claim 1, The hydration phase includes: The amount of water to be added is determined based on the drop in water level of the soaked clothes. The greater the drop in water level, the more water needs to be added.

3. The method for determining washing parameters according to claim 2, The water level drop during the soaking of the clothes is A; in, A = (A1 - A2) / A1 × 100%, A1 is the water level before the clothes are soaked after the basic water volume is injected into the washing drum. A2 is the water level height after the basic water volume is injected into the washing drum to soak the clothes; 10%≤A≤15%, the replenishment water volume is 1 to 1.2 times the base water injection volume; 16%≤A≤20%, the replenishment water volume is 1.5 to 1.7 times the base water injection volume; A > 20%, and the replenishment water volume is 2 to 2.2 times the base water volume.

4. The method for determining washing parameters according to claim 3, The hydration phase includes: The amount of water to be replenished is determined based on the deviation between the sound frequency and the preset sound frequency range. The greater the deviation between the sound frequency and the preset sound frequency range, the more water is replenished.

5. The method for determining washing parameters according to claim 4, The deviation value between the sound frequency and the preset sound frequency range is B; B = |B1-B2| / B1 × 100%, B1 is the value of the sound frequency, and B2 is the maximum or minimum value in the preset sound frequency range that is closer to B1; 5%≤B≤10%, the replenishment water volume is 0.5 to 0.7 times the base water injection volume; 11%≤B≤15%, the replenishment water volume is 1 to 1.2 times the base water injection volume; B > 15%, and the replenishment water volume is 1.5 to 1.7 times the base water volume.

6. The method for determining washing parameters according to claim 1, characterized in that, The method for determining washing parameters also includes: a soaking stage; The soaking stage is performed before determining the extent of the drop in water level for the soaked clothing. The soaking stage includes: injecting water into the washing drum to soak the clothes, and then letting them stand for a preset time after driving the washing drum to rotate forward and / or reverse.

7. The method for determining washing parameters according to any one of claims 1-6, characterized in that, The clothing parameters include: the strength of the clothing's water absorption performance; The method for judging the water absorption performance of the clothing is designed as follows: The shorter the duration of the water replenishment phase, the stronger the water absorption performance of the clothing; And / or, the more water is added during the water replenishment stage, the stronger the water absorption performance of the clothing.

8. The method for determining washing parameters according to claim 1, characterized in that, The step of determining the actual washing parameters based on the correspondence between the input parameters and the washing parameters includes: The input parameters are input into a random forest model, and the actual washing parameters are output through the random forest model. The random forest model is used to reflect the correspondence between the input parameters and the washing parameters.

9. An electronic device, characterized in that, The electronic device includes: Memory stores computer instructions; A processor for invoking and executing the computer instructions to implement the washing parameter determination method as described in any one of claims 1-8.

10. A garment processing device, characterized in that, The garment processing device uses the washing parameter determination method as described in any one of claims 1-8, or includes the electronic device as described in claim 9.

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