Method for intelligently adjusting washing parameters

Abstract

The invention discloses a method for intelligently adjusting washing parameters, relates to the technical field of washing parameter adjustment, and solves the technical problem that the washing effect is affected due to the fact that the water absorption capacity of clothes materials is not considered in the prior art. Load data and standard washing condition data of the intelligent washing machine are collected through a sensor, the load data are compared with a preset weight threshold range and a preset height threshold range to obtain washing preparation data, the real-time water level and the water level time are detected in real time, and the clothes water absorption coefficient is obtained in combination with the standard washing condition data. Obtaining a clothes type according to the clothes water absorption coefficient, obtaining washing data, obtaining a clothes water absorption rate based on the water level time and the real-time water level, comparing the clothes water absorption rate with a close threshold value to obtain a type coefficient, and calculating and obtaining washing adjustment data according to the type coefficient; based on the washing data and the washing preparation data, the water absorption amount of the clothes after water adding is completed is predicted, the adjusted water inlet time is obtained through calculation, and the washing parameters are adjusted.

Description

Technical Field

[0001] The invention belongs to the field of washing parameter adjustment, and in particular relates to a method for intelligently adjusting washing parameters. Background Art

[0002] In actual operation, many users tend to put clothes directly into the washing machine and start the default washing program without adjusting the washing parameters according to the specific type or quantity of clothes; although these preset default programs have been carefully designed to provide a balanced choice for a variety of washing scenarios, their fixed parameter configurations cannot fully meet the needs of all clothes; each type of clothing has its own unique characteristics, such as water absorption, wear resistance, and sensitivity to temperature and speed, so the washing parameters that are most suitable for a certain type of clothing may be very different from other types.

[0003] The invention patent with application number CN2020115774744 discloses a washing device and its control method, device, storage medium and processor. The invention obtains the washing parameters in the barrel at different washing stages and the weighing coefficients at different washing stages, and adjusts the water inlet method at different stages based on the weighing coefficients and pressure; when obtaining the water inlet method, the invention analyzes the pressure situation in the current stage through the weighing coefficient and power, and adjusts the water inlet method according to the pressure situation. When washing different clothes, due to the different weights and tightness of clothes made of different materials, their water absorption capacity is also different. If the water inlet is adjusted directly, the water volume may be less than the set value when washing due to the water absorption of the clothes, which may affect the final washing effect.

[0004] The present invention provides a method for intelligently adjusting washing parameters to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a method for intelligently adjusting washing parameters, which is used to solve the technical problem in the prior art that the washing effect is affected due to failure to consider the water absorption capacity of the clothing material.

[0006] To achieve the above object, a first aspect of the present invention provides a method for intelligently adjusting washing parameters, comprising:

[0007] Step 1: Collect load data and standard washing condition data;

[0008] Step 2: Obtaining washing preparation data based on load data;

[0009] Step 3: Detect the real-time water level and obtain washing data in combination with standard washing condition data;

[0010] Step 4: Acquire washing adjustment data based on the washing data and the washing preparation data.

[0011] Preferably, the acquiring washing preparation data based on the load data comprises:

[0012] A1: extracting load data; wherein the load data includes the weight and height of the clothes placed in the drum;

[0013] A2: Setting a weight threshold range; wherein the weight threshold range is set according to the maximum weight that the drum can bear;

[0014] A3: Determine whether the weight of the clothes is greater than the maximum value of the weight threshold range; if yes, mark the weight status of the clothes as first-level load-bearing; if no, jump to A4;

[0015] A4: Determine whether the weight of the clothes is less than the minimum value of the weight threshold range; if yes, mark the weight status of the clothes as level three load-bearing; if no, mark the weight status of the clothes as level two load-bearing;

[0016] A5: Obtain the height status of the clothes based on the height of the clothes; match the weight status of the clothes and the height status of the clothes with the washing comparison table to obtain the washing preparation data.

[0017] Preferably, the obtaining of the clothing height state based on the clothing height includes:

[0018] B1: Extract the height of clothes at each position;

[0019] B2: Calculate the average value of the height of the clothes at each position to obtain the standard height of the clothes; set the height threshold range; wherein the height threshold range is set according to the height of the drum;

[0020] B3: Determine whether the height of the clothing is greater than the maximum value of the height threshold range; if yes, mark the height status of the clothing as level one; if no, jump to B4;

[0021] B4: Determine whether the height of the clothes is less than the minimum value of the height threshold range; if yes, mark the height status of the clothes as level three; if no, mark the weight status of the clothes as level two.

[0022] Preferably, the detecting the real-time water level and acquiring the washing data in combination with the standard washing condition data comprises:

[0023] Detect the real-time water level in the drum through a liquid level gauge and record the corresponding water level time; extract standard washing condition data; wherein the standard washing condition data includes the water intake rate and standard volume of the smart washing machine;

[0024] The water inflow is obtained by combining the water level time with the corresponding water inflow rate; the current water volume is obtained by combining the real-time water level with the standard volume; the current occupied water volume is obtained by comparing the water inflow with the current water volume;

[0025] The current water volume, load data and real-time water level are combined to obtain the water absorption coefficient of the clothes. The type of clothes in the drum is obtained based on the water absorption coefficient of the clothes, and the type of clothes and the predicted water volume are integrated to obtain the washing data.

[0026] Preferably, the method of obtaining the water absorption coefficient of clothing by combining the current occupied water volume, load data and real-time water level includes:

[0027] Extract current water volume, load data and real-time water level;

[0028] The clothing water absorption coefficient YXS is calculated by the formula YXS=[ZSV×(1-YG / SW)] / YZL; wherein ZSV represents the current water volume, YG represents the height of the clothing, and YZL represents the weight of the clothing.

[0029] Preferably, obtaining the type of clothes in the drum based on the water absorption coefficient of the clothes includes:

[0030] Extract the water absorption coefficient and load data of clothes; extract the bottom area of ​​the drum from the equipment database;

[0031] The product of the weight of the clothes and the height of the clothes and the ratio of the bottom area are used as the clothing coefficient; the clothing coefficient and the clothing water absorption coefficient are used as input data to input the type recognition model to obtain the clothing type; wherein the type recognition model is constructed according to the neural network model.

[0032] Preferably, the type recognition model is constructed according to a neural network model, including:

[0033] A plurality of groups of historical clothing coefficients, historical clothing water absorption coefficients and corresponding historical clothing types are extracted from a historical database; the historical clothing coefficients, historical clothing water absorption coefficients and corresponding historical clothing types are used as training data and verification data to train a neural network model, and the trained neural network model is verified according to the verification data, and the parameters of the neural network model are adjusted according to the verification result, so as to obtain a type recognition model whose input data is the clothing coefficient and the clothing water absorption coefficient, and whose output data is the clothing type.

[0034] Preferably, the acquiring of washing adjustment data based on the washing data and the washing preparation data comprises:

[0035] Extract washing data and washing preparation data;

[0036] The type coefficient is set based on the type of clothing, and the estimated water volume YJV is calculated by the formula YJV=BTJ / (1-TG / SW);

[0037] The expected water volume is multiplied by the current water volume and then compared with the water intake to obtain the actual water volume. The difference between the expected water volume and the actual water volume is marked as the predicted water absorption. The predicted water absorption is multiplied by the type coefficient to obtain the adjusted water volume. The adjusted water volume is compared with the water intake rate to obtain the adjusted water intake time. The adjusted water volume and the adjusted water intake time are integrated to obtain the washing adjustment data.

[0038] Preferably, the setting of the category coefficient based on the clothing category includes:

[0039] Obtain the water absorption rate of clothing based on water level time and real-time water level;

[0040] Set a tight threshold and a saturation coefficient; wherein the tight threshold is set according to the tightness of different clothing materials; the saturation coefficient is set according to the difference between the tight threshold and the water absorption rate of the clothing;

[0041] Determine whether the water absorption rate of the clothing is greater than the tightness threshold; if yes, use the water absorption rate of the clothing as the type coefficient; if no, multiply the water absorption rate of the clothing by the saturation coefficient to obtain the type coefficient.

[0042] Preferably, the method of obtaining the water absorption rate of clothing based on the water level time and the real-time water level includes:

[0043] Extract water level time and real-time water level;

[0044] The water level change graph is generated by combining the water level time and the real-time water level. The water level change rate is obtained by counting and calculating the average value of the slope in the water level change graph. The difference between the water level change rate and the water inlet rate is marked as the clothing water absorption rate.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The present invention collects load data and standard washing condition data of the intelligent washing machine through sensors, compares the load data with a preset weight threshold range and a height threshold range to obtain washing preparation data, detects the real-time water level in real time, and records the water level time, obtains the water absorption coefficient of the clothes in combination with the standard washing condition data, and obtains the type of clothes according to the water absorption coefficient of the clothes to obtain washing data, which can provide the most suitable washing settings for different types of clothes, and can effectively reduce unnecessary water consumption while ensuring water use, thereby achieving the goal of water saving; based on the washing data and the washing preparation data, the water absorption of the clothes after water addition is predicted, and the adjusted water inlet time is calculated, and the washing parameters are adjusted, thereby improving the washing efficiency, reducing unnecessary washing time, providing a more personalized washing experience, and enhancing user satisfaction.

[0047] 2. The present invention combines the real-time water level, water level time and standard washing condition data to obtain the current occupied water volume, and calculates the water absorption coefficient of the clothes. The clothing coefficient is calculated according to the load data and the bottom area of ​​the drum, and the type of clothes is obtained by inputting it into the neural network model. The water absorption rate of the clothes is obtained based on the water level time and the real-time water level, and the type coefficient is obtained by comparing it with the tight threshold. The washing adjustment data is obtained according to the type coefficient calculation. By real-time monitoring of the water level and time changes, the smart washing machine can dynamically adjust the water intake to ensure that the most appropriate amount of water is used for each washing, which not only ensures the cleaning effect but also avoids waste. The washing conditions are reasonably set to avoid damage to clothes caused by excessive washing or improper handling, thereby extending the service life of the clothes. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 The following is a flowchart of an embodiment of the present invention.

[0050] Figure 2 The figure is a complete flow chart for obtaining the weight status of clothes in one embodiment of the present invention.

[0051] Figure 3 is a complete flow chart of obtaining the height status of clothing in one embodiment of the present invention. DETAILED DESCRIPTION

[0052] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] See also Figure 1-Figure 3 The first aspect of the present invention provides a method for intelligently adjusting washing parameters, comprising:

[0054] Step 1: Collect load data and standard wash condition data.

[0055] Exemplarily, the weight of the clothes placed in the drum is collected by a gravity sensor arranged inside the drum, and the height of the clothes at different positions in the drum from the highest point of the drum is measured by an infrared detector and marked as the clothing height; the water inlet rate of the smart washing machine and the standard volume in the drum are extracted from the equipment database, and the standard volume is the maximum volume that the drum can bear.

[0056] Step 2: Obtain washing preparation data based on load data.

[0057] Exemplarily, in this embodiment, the weight threshold range is set to 2 kg to 4 kg, and it is determined whether the current weight of the clothes in the drum is greater than the maximum value of the weight threshold range. In this embodiment, the current weight of the clothes in the drum is measured to be 3 kg, and it is determined that the weight of the clothes is less than the maximum value of the weight threshold range. Then, it is further determined whether the weight of the clothes is less than the minimum value of the weight threshold range. If it is determined that the weight of the clothes is greater than the minimum value of the weight threshold range, it means that the weight of the clothes is within the weight threshold range, and the weight status of the clothes is marked as the second-level load-bearing capacity.

[0058] In the embodiment, the height threshold range is set to 0.5 meters to 1 meter, and it is judged whether the current height of the clothes in the drum is greater than the maximum value of the height threshold range. In this embodiment, the measured height of the clothes in the current drum is 0.6 meters, and it is judged that the height of the clothes is less than the maximum value of the height threshold range, then it is further judged whether the height of the clothes is less than the minimum value of the height threshold range. It is judged that the height of the clothes is greater than the minimum value of the height threshold range, which means that the height of the clothes is within the height threshold range, and the height status of the clothes is marked as the secondary height; the detection and analysis of the status of the clothes in the current drum can provide the most suitable washing settings for different types of clothes.

[0059] It should be noted that the height of clothes detected by the infrared detector is the height obtained by detecting downward from the drum cover, not the height of the accumulated clothes, but the difference between the drum height and the accumulated height of the clothes. It accurately controls the water intake and washing time, reduces unnecessary waste of water resources, reduces energy consumption, and greatly facilitates user use.

[0060] The weight status and height status of the clothes are matched with the washing comparison table of the smart washing machine, the corresponding detergent input amount and water input amount are prepared, and the detergent input amount and the water input amount are combined to obtain the washing preparation data.

[0061] Step 3: Detect the real-time water level and obtain washing data in combination with standard washing condition data.

[0062] Exemplarily, the washing preparation data is sent to the management module, and after receiving the start signal, detergent and water are added, the real-time water level in the drum is monitored by a liquid level gauge, and the water level time corresponding to the real-time water level is recorded.

[0063] The current water inflow rate and the water level time are calculated to obtain the current water inflow, and the current water volume SLV is calculated by the formula SLV=BTJ×(1-SW / TG); wherein BTJ represents the standard volume, SW represents the real-time water level, and TG represents the drum height of the smart washing machine.

[0064] Subtract the water intake from the current water volume to get the current occupied water volume; calculate the clothing water absorption coefficient YXS by the formula YXS=[ZSV×(1-YG / SW)] / YZL; where ZSV represents the current occupied water volume, YG represents the height of the clothing, and YZL represents the weight of the clothing.

[0065] A plurality of groups of historical clothing coefficients, historical clothing water absorption coefficients and corresponding historical clothing types are extracted from a historical database; the historical clothing coefficients, historical clothing water absorption coefficients and corresponding historical clothing types are used as training data and verification data to train a neural network model, and the trained neural network model is verified according to the verification data, and the parameters of the neural network model are adjusted according to the verification result, so as to obtain a type recognition model whose input data is the clothing coefficient and the clothing water absorption coefficient, and whose output data is the clothing type.

[0066] The bottom area of ​​the smart washing machine drum is extracted from the device database, the clothing weight is multiplied by the clothing height and then compared with the bottom area to obtain the clothing coefficient, and the clothing coefficient and the clothing water absorption coefficient are input as input data into the type recognition model. In this embodiment, the clothing type identified is silk.

[0067] Different types of clothing have different water absorption capacities. The water level change graph is obtained with the water level time as the horizontal coordinate and the real-time water level as the vertical coordinate. The average value of the slope in the water level change graph is used as the water level change rate. The difference between the water level change rate and the water inlet rate is calculated to obtain the clothing water absorption rate. The clothing water absorption rate can to a certain extent represent the tightness of the clothing material. The tighter the clothing material, the slower the water absorption, while the looser the clothing material, the faster the water absorption, and it can reach saturation in a very short time. Set a tight threshold and a saturation coefficient. Determine whether the clothing water absorption rate is greater than the tight threshold. If yes, it means that the clothing can quickly absorb water to saturation, and will not continue to absorb water after filling, resulting in a decrease in water volume. The clothing water absorption rate is used as the type coefficient. If no, it means that the clothing water absorption rate is slow, and the clothing has not yet reached saturation after filling. The clothing water absorption rate is multiplied by the saturation coefficient to obtain the type coefficient. The washing parameters can be dynamically adjusted according to the water absorption characteristics and actual status of the clothing to ensure that each piece of clothing can obtain the best cleaning effect, which is especially important for special materials or expensive clothing, avoiding damage to clothing caused by insufficient water or improper handling, and extending the service life of clothing.

[0068] The expected water volume YJV is calculated using the formula YJV=BTJ / (1-TG / SW), the expected water volume is multiplied by the current water volume and then compared with the water intake to obtain the actual water volume, the difference between the expected water volume and the actual water volume is marked as the predicted water absorption, the predicted water absorption is multiplied by the type coefficient to obtain the adjusted water volume, the adjusted water volume is compared with the water intake rate to obtain the adjusted water intake time, the adjusted water volume and the adjusted water intake time are integrated to obtain the washing adjustment data, the water intake time is adjusted according to the washing adjustment data to ensure that the water volume can complete the cleaning work, the water intake volume can be dynamically adjusted to ensure that the most appropriate amount of water is used for each wash, which not only ensures the cleaning effect but also avoids waste, and provides detailed washing process information, such as water level change curve, expected completion time, etc., so that users have a clearer understanding of the washing progress and enhance their sense of trust.

[0069] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0070] Working principle of the present invention:

[0071] The present invention collects load data and standard washing condition data of an intelligent washing machine through sensors, compares the load data with a preset weight threshold range and a height threshold range to obtain washing preparation data, detects the real-time water level in real time, and records the water level time, and obtains the water absorption coefficient of the clothes in combination with the standard washing condition data, and obtains the type of clothes according to the water absorption coefficient of the clothes to obtain washing data; obtains the current water volume by combining the real-time water level, the water level time and the standard washing condition data, and calculates the water absorption coefficient of the clothes, calculates the clothing coefficient according to the load data and the bottom area of ​​the drum, and inputs it into a neural network model to obtain the type of clothes, obtains the water absorption rate of the clothes based on the water level time and the real-time water level, and compares it with the tight threshold to obtain the type coefficient, and obtains the washing adjustment data according to the type coefficient calculation; predicts the water absorption amount of the clothes after the water addition is completed based on the washing data and the washing preparation data, calculates the adjusted water inlet time, and adjusts the washing parameters.

[0072] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for intelligently adjusting washing parameters, characterized in that: include: Step 1: Collect load data and standard washing condition data; Step 2: Obtaining washing preparation data based on load data; Step 3: Detect the real-time water level and obtain washing data in combination with standard washing condition data; Step 4: Acquire washing adjustment data based on the washing data and the washing preparation data.

2. The method for intelligently adjusting washing parameters according to claim 1, characterized in that: The obtaining of washing preparation data based on the load data comprises: A1: extracting load data; wherein the load data includes the weight and height of the clothes placed in the drum; A2: Setting a weight threshold range; wherein the weight threshold range is set according to the maximum weight that the drum can bear; A3: Determine whether the weight of the clothes is greater than the maximum value of the weight threshold range; if yes, mark the weight status of the clothes as first-level load-bearing; if no, jump to A4; A4: Determine whether the weight of the clothes is less than the minimum value of the weight threshold range; if yes, mark the weight status of the clothes as level three load-bearing; if no, mark the weight status of the clothes as level two load-bearing; A5: Obtain the height status of the clothes based on the height of the clothes; match the weight status of the clothes and the height status of the clothes with the washing comparison table to obtain the washing preparation data.

3. The method for intelligently adjusting washing parameters according to claim 2, characterized in that: The obtaining of the clothing height state based on the clothing height includes: B1: Extract the height of clothes at each position; B2: Calculate the average value of the height of the clothes at each position to obtain the standard height of the clothes; set the height threshold range; wherein the height threshold range is set according to the height of the drum; B3: Determine whether the height of the clothing is greater than the maximum value of the height threshold range; if yes, mark the height status of the clothing as level one; if no, jump to B4; B4: Determine whether the height of the clothes is less than the minimum value of the height threshold range; if yes, mark the height status of the clothes as level three; if no, mark the weight status of the clothes as level two.

4. The method for intelligently adjusting washing parameters according to claim 1, characterized in that: The detecting of the real-time water level and obtaining washing data in combination with the standard washing condition data includes: Detect the real-time water level in the drum through a liquid level gauge and record the corresponding water level time; extract standard washing condition data; wherein the standard washing condition data includes the water intake rate and standard volume of the smart washing machine; The water inflow is obtained by combining the water level time with the corresponding water inflow rate; the current water volume is obtained by combining the real-time water level with the standard volume; the current occupied water volume is obtained by comparing the water inflow with the current water volume; The current water volume, load data and real-time water level are combined to obtain the water absorption coefficient of the clothes. The type of clothes in the drum is obtained based on the water absorption coefficient of the clothes, and the type of clothes and the predicted water volume are integrated to obtain the washing data.

5. The method for intelligently adjusting washing parameters according to claim 4, characterized in that: The method of combining the current occupied water volume, load data and real-time water level to obtain the water absorption coefficient of clothing includes: Extract current water volume, load data and real-time water level; The clothing water absorption coefficient YXS is calculated by the formula YXS=[ZSV×(1-YG / SW)] / YZL; wherein ZSV represents the current water volume, YG represents the height of the clothing, and YZL represents the weight of the clothing.

6. The method for intelligently adjusting washing parameters according to claim 4, characterized in that: The method of obtaining the type of clothes in the drum based on the water absorption coefficient of the clothes includes: Extract the water absorption coefficient and load data of clothes; extract the bottom area of ​​the drum from the equipment database; The product of the weight of the clothes and the height of the clothes and the ratio of the bottom area are used as the clothing coefficient; the clothing coefficient and the clothing water absorption coefficient are used as input data to input the type recognition model to obtain the clothing type; wherein the type recognition model is constructed according to the neural network model.

7. A method for intelligently adjusting washing parameters according to claim 6, characterized in that: The type recognition model is constructed according to a neural network model, including: A plurality of groups of historical clothing coefficients, historical clothing water absorption coefficients and corresponding historical clothing types are extracted from a historical database; the historical clothing coefficients, historical clothing water absorption coefficients and corresponding historical clothing types are used as training data and verification data to train a neural network model, and the trained neural network model is verified according to the verification data, and the parameters of the neural network model are adjusted according to the verification result, so as to obtain a type recognition model whose input data is the clothing coefficient and the clothing water absorption coefficient, and whose output data is the clothing type.

8. The method for intelligently adjusting washing parameters according to claim 1, characterized in that: The step of acquiring washing adjustment data based on washing data and washing preparation data includes: Extract washing data and washing preparation data; The type coefficient is set based on the type of clothing, and the estimated water volume YJV is calculated by the formula YJV=BTJ / (1-TG / SW); The expected water volume is multiplied by the current water volume and then compared with the water intake to obtain the actual water volume. The difference between the expected water volume and the actual water volume is marked as the predicted water absorption. The predicted water absorption is multiplied by the type coefficient to obtain the adjusted water volume. The adjusted water volume is compared with the water intake rate to obtain the adjusted water intake time. The adjusted water volume and the adjusted water intake time are integrated to obtain the washing adjustment data.

9. The method for intelligently adjusting washing parameters according to claim 8, characterized in that: The setting of the category coefficient based on the clothing category includes: Obtain the water absorption rate of clothing based on water level time and real-time water level; Set a tight threshold and a saturation coefficient; wherein the tight threshold is set according to the tightness of different clothing materials; the saturation coefficient is set according to the difference between the tight threshold and the water absorption rate of the clothing; Determine whether the water absorption rate of the clothing is greater than the tightness threshold; if yes, use the water absorption rate of the clothing as the type coefficient; if no, multiply the water absorption rate of the clothing by the saturation coefficient to obtain the type coefficient.

10. The method for intelligently adjusting washing parameters according to claim 9, characterized in that: The method of obtaining the water absorption rate of clothing based on the water level time and the real-time water level includes: Extract water level time and real-time water level; The water level change graph is generated by combining the water level time and the real-time water level. The water level change rate is obtained by counting and calculating the average value of the slope in the water level change graph. The difference between the water level change rate and the water inlet rate is marked as the clothing water absorption rate.

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