A method for intelligently adjusting washing parameters

Abstract

The present invention discloses a method for intelligently adjusting washing parameters, relates to the technical field of washing parameter adjustment, and solves the technical problem in the prior art that the washing effect is affected because the water absorption capacity of the clothing material is not taken into consideration; the present invention collects load data and standard washing condition data of the intelligent washing machine through a sensor, 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 and water level time in real time, combines the standard washing condition data to obtain the water absorption coefficient of the clothing, and obtains the type of clothing according to the water absorption coefficient of the clothing to obtain washing data, obtains the water absorption rate of the clothing based on the water level time and the real-time water level, and compares it with the tight threshold to obtain the type coefficient, calculates and obtains washing adjustment data according to the type coefficient, predicts the water absorption amount of the clothing after water addition is completed based on the washing data and the washing preparation data, calculates and obtains the adjusted water inlet time, and adjusts the washing parameters.

Description

Technical Field

[0001] The present 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 practice, 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. Therefore, the washing parameters that are most suitable for a certain type of clothing may be very different from those for 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 obtains 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 of 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 during the final 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 to solve the technical problem in the prior art that the washing effect is affected due to the failure to consider the water absorption capacity of the clothing material.

[0006] To achieve the above objectives, 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 the standard washing condition data;

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

[0011] Preferably, obtaining washing preparation data based on load data includes:

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

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

[0014] A3: Determine whether the weight of the clothing is greater than the maximum value of the weight threshold range; if so, mark the clothing weight status as level 1 load-bearing; if not, 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 3 load-bearing; if no, mark the weight status of the clothes as level 2 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 washing preparation data.

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

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

[0019] B2: Calculate the average 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 clothing height status as level one; if no, jump to B4;

[0021] B4: Determine whether the height of the clothing is less than the minimum value of the height threshold range; if yes, mark the clothing height status as level three; if not, mark the clothing weight status as level two.

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

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

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

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

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

[0032] Preferably, the category recognition model is constructed based on a neural network model, including:

[0033] Several 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 results 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 obtaining of washing adjustment data based on the washing data and the washing preparation data includes:

[0035] Extract washing data and washing preparation data;

[0036] Set the type coefficient based on the type of clothing and calculate the expected water volume YJV using the formula YJV = BTJ / (1-TG / SW);

[0037] Multiply the expected water volume by the current water volume and compare it with the water intake to obtain the actual water volume. Mark the difference between the expected water volume and the actual water volume as the predicted water absorption. Multiply the predicted water absorption by the type coefficient to obtain the adjusted water volume. Compare the adjusted water volume with the water intake rate to obtain the adjusted water intake time. Integrate the adjusted water volume and the adjusted water intake time 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 the tightness threshold and saturation coefficient; the tightness threshold is set according to the tightness of different clothing materials; the saturation coefficient is set according to the difference between the tightness 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 not, 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 average value of the slope in the water level change graph is counted and calculated to obtain the water level change rate. The difference between the water level change rate and the water inflow rate is marked as the water absorption rate of the clothing.

[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 smart washing machine through sensors, compares the load data with preset weight threshold ranges and height threshold ranges to obtain washing preparation data, detects the real-time water level, and records the water level time. In combination with the standard washing condition data, the water absorption coefficient of the clothes is obtained, and the type of clothes is obtained based on the water absorption coefficient of the clothes to obtain washing data. The most suitable washing settings can be provided for different types of clothes, which can effectively reduce unnecessary water consumption while ensuring water use and achieve water-saving goals; based on the washing data and 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 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 water volume, and calculates the water absorption coefficient of the clothes. The clothing coefficient is calculated based on the load data and the bottom area of ​​the drum, and input into the neural network model to obtain the type of clothes. The water absorption rate of the clothes is obtained based on the water level time and the real-time water level, and compared with the tight threshold to obtain the type coefficient. The washing adjustment data is calculated based on the type coefficient. By real-time monitoring of water level and time changes, the smart washing machine can dynamically adjust the water intake to ensure that the optimal amount of water is used for each wash, which not only ensures the cleaning effect but also avoids waste. Reasonable setting of washing conditions can avoid damage to clothes caused by excessive washing or improper handling, thereby extending the service life of 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0049] Figure 1 This is a workflow diagram in one embodiment of the present invention.

[0050] Figure 2 FIG. 4 is a complete flow chart for obtaining the weight status of clothing in one embodiment of the present invention.

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

[0052] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

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

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

[0055] For example, the weight of the clothes placed inside 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] For example, in this embodiment, the weight threshold range is set to 2 kg to 4 kg, and it is judged 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 judged that the weight of the clothes is less than the maximum value of the weight threshold range. Then, it is further judged whether the weight of the clothes is less than the minimum value of the weight threshold range. It is judged that the weight of the clothes is greater than the minimum value of the weight threshold range, which 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.

[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 second level 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, and also reduces energy consumption, greatly facilitating 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 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 the 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 the liquid level gauge, and the water level time corresponding to the real-time water level is recorded.

[0063] The current water inflow is calculated by combining the water inflow rate and the water level time, and the current water volume SLV is calculated by the formula SLV = BTJ × (1-SW / TG); where 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 using 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] Several 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 results 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 is identified as silk.

[0067] Different types of clothing have different water absorption capacities. A water level change graph is generated with water level time as the horizontal axis and real-time water level as the vertical axis. 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 inflow rate is calculated to obtain the clothing water absorption rate. The clothing water absorption rate can, to a certain extent, indicate the tightness of the clothing material. The tighter the clothing material, the slower it absorbs water, while the looser the clothing material, the faster it absorbs water and can reach saturation in a very short time. A tightness threshold and a saturation coefficient are set. The clothing water absorption rate is then determined to be greater than the tightness threshold. If so, 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 not, it means that the clothing water absorption rate is slow and 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 achieve the best cleaning effect. This is especially important for special materials or valuable clothing, avoiding damage to clothing caused by insufficient water or improper handling, and extending the service life of the 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. Detailed washing process information is provided, such as the water level change curve, the expected completion time, etc., so that users have a clearer understanding of the washing progress and enhance their sense of trust.

[0069] Some of the data in the above formula are calculated by removing the dimensions and taking their numerical values. The formula is a formula that is 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 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, and obtains the water absorption coefficient of the clothes in combination with the standard washing condition data, and obtains the type of clothes based on the water absorption coefficient of the clothes to obtain washing data; the current water volume is obtained by combining the real-time water level, the water level time and the standard washing condition data, and the water absorption coefficient of the clothes is calculated, the clothing coefficient is calculated according to the load data and the bottom area of ​​the drum, and the clothing type is input into a 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 compared with the tight threshold to obtain the type coefficient, and the washing adjustment data is calculated according to the type coefficient; the water absorption amount of the clothes after the water addition is completed is predicted based on the washing data and the washing preparation data, and the adjusted water inlet time is calculated to adjust the washing parameters.

[0072] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. 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 the standard washing condition data; Step 4: Obtaining washing adjustment data based on the washing data and the washing preparation data; The acquiring of washing preparation data based on the load data includes: A1: Extracting load data, wherein the load data includes the weight and height of the clothes placed in the drum; A2: Set the weight threshold range; the weight threshold range is set according to the maximum weight that the drum can bear; A3: Determine whether the weight of the clothing is greater than the maximum value of the weight threshold range; if so, mark the clothing weight status as level 1 load-bearing; if not, 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 3 load-bearing; if no, mark the weight status of the clothes as level 2 load-bearing; A5: Obtaining the height status of the clothes based on the height of the clothes; matching the weight status of the clothes and the height status of the clothes with the washing comparison table to obtain washing preparation data; 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 inflow 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 determine the water absorption coefficient of the clothes. The type of clothes in the drum is determined based on the water absorption coefficient, and the type of clothes is integrated with the predicted water volume to obtain the washing data. The obtaining of washing adjustment data based on the washing data and the washing preparation data includes: Extract washing data and washing preparation data; Set the type coefficient based on the type of clothing and calculate the expected water volume YJV using the formula YJV=BTJ / (1-TG / SW). BTJ represents the standard volume, SW represents the real-time water level, and TG represents the drum height of the smart washing machine. Multiply the expected water volume by the current water volume and compare it with the water intake to obtain the actual water volume. Mark the difference between the expected water volume and the actual water volume as the predicted water absorption. Multiply the predicted water absorption by the type coefficient to obtain the adjusted water volume. Compare the adjusted water volume with the water intake rate to obtain the adjusted water intake time. Integrate the adjusted water volume and the adjusted water intake time to obtain the washing adjustment data.

2. The method for intelligently adjusting washing parameters according to claim 1, characterized in that: The obtaining of the clothing height status based on the clothing height includes: B1: Extract the height of clothing at each position; B2: Calculate the average 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 clothing height status as level one; if no, jump to B4; B4: Determine whether the height of the clothing is less than the minimum value of the height threshold range; if yes, mark the clothing height status as level three; if not, mark the clothing weight status as level two.

3. The method for intelligently adjusting washing parameters according to claim 1, 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 using the formula YXS=[ZSV×(1-YG / SW)] / YZL; where ZSV represents the current water volume, YG represents the height of the clothing, and YZL represents the weight of the clothing.

4. The method for intelligently adjusting washing parameters according to claim 1, 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 the clothes; extract the bottom area of ​​the drum from the equipment database; The product of the weight of the clothing and the height of the clothing and the ratio of the bottom area are used as the clothing coefficient; the clothing coefficient and the clothing water absorption coefficient are input as input data into the type recognition model to obtain the clothing type; wherein the type recognition model is constructed based on the neural network model.

5. The method for intelligently adjusting washing parameters according to claim 4, characterized in that: The category recognition model is constructed based on a neural network model, including: Several 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 results 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.

6. The method for intelligently adjusting washing parameters according to claim 5, 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 the tightness threshold and saturation coefficient; the tightness threshold is set according to the tightness of different clothing materials; the saturation coefficient is set according to the difference between the tightness 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 not, multiply the water absorption rate of the clothing by the saturation coefficient to obtain the type coefficient.

7. The method for intelligently adjusting washing parameters according to claim 6, 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 average value of the slope in the water level change graph is counted and calculated to obtain the water level change rate. The difference between the water level change rate and the water inflow rate is marked as the water absorption rate of the clothing.

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