Washing control method, device, medium and washing machine for water flow dynamic adjustment

Abstract

The application provides a washing control method and device for water flow dynamic adjustment, a medium and a washing machine. The method comprises the following steps: acquiring image data and ultrasonic monitoring data of clothes in a drum of the washing machine, modeling the clothes according to the image data and the ultrasonic monitoring data to obtain clothes distribution data; uniformly dividing an internal space of the drum to obtain a plurality of unit cells, determining a proportion of space in each unit cell that is not occupied by the clothes according to the clothes distribution data to obtain a plurality of void ratios; drawing a void ratio heat map according to the plurality of void ratios, and querying a first mapping relationship according to the void ratio heat map to obtain a plurality of target control parameters; and controlling corresponding nozzles to spray according to the target control parameters. The method solves the problem of damage to clothes caused by the lack of dynamic adjustment of the nozzles in the washing process in the prior art.

Description

Technical Field

[0001] This invention relates to the field of washing machine control technology, and more specifically, to a washing control method, apparatus, computer-readable storage medium, and washing machine with dynamic water flow adjustment. Background Technology

[0002] Traditional washing machines rely on fixed programs for water flow control, which makes it difficult to adapt to different clothing conditions, potentially leading to insufficient washing or fiber damage.

[0003] To address the aforementioned shortcomings, existing technologies propose inferring real-time water flow intensity based on vibration data of the washing machine's outer tub, and comparing the real-time value with a preset value to adjust the water flow intensity to a suitable range, thereby achieving both cleaning and garment protection.

[0004] However, the above improvements still have certain limitations. Due to the randomness of the stacking of clothes inside the drum, it affects the washing and rinsing force. The existing technology has not addressed these shortcomings, and there is still a risk of damaging the clothes. Summary of the Invention

[0005] The main objective of this application is to provide a washing control method, apparatus, computer-readable storage medium, and washing machine with dynamic water flow adjustment, so as to at least solve the problem of damage to clothes caused by the lack of dynamic adjustment of nozzles during the washing process in the prior art.

[0006] To achieve the above objectives, according to one aspect of this application, a washing control method for dynamic water flow adjustment is provided, comprising: acquiring image data and ultrasonic monitoring data of clothes inside a washing machine drum; modeling the clothes based on the image data and ultrasonic monitoring data to obtain clothes distribution data; uniformly dividing the internal space of the drum to obtain multiple cells; determining the proportion of space not occupied by clothes in each cell based on the clothes distribution data to obtain multiple void ratios; drawing a void ratio heatmap based on the multiple void ratios; querying a first mapping relationship based on the void ratio heatmap to obtain multiple target control parameters, wherein the first mapping relationship is the mapping relationship between the void ratio distribution heatmap and the control parameters corresponding to each nozzle inside the drum, and the control parameters include nozzle angle, nozzle flow rate, and total output flow rate; and controlling the corresponding nozzles to spray according to each target control parameter.

[0007] Optionally, multiple target control parameters are obtained by querying the first mapping relationship based on the porosity heatmap, including: querying the second mapping relationship based on the porosity heatmap to obtain multiple target washing parameters. The second mapping relationship is the mapping relationship between the porosity heatmap and the washing environment parameter with the highest corresponding score. The score is determined based on the degree of stain residue and fiber damage of the clothes. The washing environment parameters include water flow velocity, water flow rinsing intensity, and water flow coverage. There is a one-to-one correspondence between the target washing parameters and the cells. The third mapping relationship is the mapping relationship between the washing environment parameters and the control parameters.

[0008] Optionally, before querying the second mapping relationship based on the porosity heatmap to obtain multiple target washing parameters, the method further includes: a simulation step, using any preset clothing distribution data as boundary conditions, and nozzle angle, nozzle flow rate, and total output velocity as input variables, adjusting the input variables, and performing simulations under each input variable to obtain multiple simulated washing conditions; a first determination step, determining the water flow velocity, water flow scouring intensity, and water flow coverage corresponding to each cell based on the simulated washing conditions to obtain washing environment parameters; a calculation step, drawing the corresponding porosity heatmap based on the clothing distribution data, determining the degree of stain residue and fiber damage on the clothing based on the simulated washing conditions, and performing weighted calculations based on the degree of stain residue and fiber damage to obtain a score; a second determination step, determining the washing environment parameter corresponding to the maximum score as the washing environment parameter corresponding to the porosity heatmap; repeating the simulation step, the first determination step, the calculation step, and the second determination step at least once in sequence until the washing environment parameters corresponding to the porosity heatmaps of all preset clothing distribution data are determined; and constructing a second mapping relationship based on the correspondence between the porosity heatmap and the washing environment parameters.

[0009] Optionally, after controlling the corresponding nozzles to spray according to each target control parameter, the method further includes: monitoring the humidity at different positions of the first cell to obtain multiple first target humiditys, wherein the first cell is any cell; if the difference between the maximum and minimum values ​​of any first target humidity is greater than a first threshold, the area within a first preset distance of the humidity sensor corresponding to the first target humidity is determined as a flushing blind zone; increasing the nozzle flow rate of the nozzles in the flushing blind zone and adjusting the nozzle angle of the nozzles to align the nozzles with the flushing blind zone; and controlling the nozzles to continue spraying according to the corrected nozzle flow rate and nozzle angle.

[0010] Optionally, controlling the corresponding nozzles to spray according to each target control parameter further includes: dividing the cell into multiple sub-cells according to the nozzles in the cell; determining the void ratio corresponding to each sub-cell to obtain multiple target void ratios; reducing the total output flow rate of the nozzles corresponding to the sub-cells when the target void ratio is greater than a second threshold; and controlling the nozzles to spray according to the updated total output flow rate.

[0011] Optionally, after determining the proportion of space not occupied by clothing in each cell based on clothing distribution data and obtaining multiple void ratios, the method further includes: controlling the roller to oscillate when the void ratio corresponding to any cell is less than a third threshold, wherein the oscillation frequency of the roller is less than or equal to a fourth threshold; and recalculating the void ratio corresponding to each cell after the oscillation is completed.

[0012] Optionally, after controlling the corresponding nozzles to spray according to each target control parameter, the method further includes: for each spraying preset duration, obtaining the real-time humidity of each cell to obtain the second target humidity; determining the corresponding water flow coverage range according to the target control parameters to obtain the first target range; determining the corresponding water flow coverage range according to the second target humidity to obtain the second target range; and updating the third mapping relationship through calibration experiments when the deviation ratio between the second target range and the first target range is greater than or equal to the fifth threshold.

[0013] According to another aspect of this application, a washing control device for dynamic water flow adjustment is provided. The device includes: a first acquisition unit, used to acquire image data and ultrasonic monitoring data of clothes inside the washing machine drum, and to model the clothes based on the image data and ultrasonic monitoring data to obtain clothes distribution data; a first determination unit, used to uniformly divide the internal space of the drum to obtain multiple cells, and to determine the proportion of space not occupied by clothes in each cell based on the clothes distribution data to obtain multiple void ratios; a first processing unit, used to draw a void ratio heat map based on the multiple void ratios, and to query a first mapping relationship based on the void ratio heat map to obtain multiple target control parameters. The first mapping relationship is the mapping relationship between the void ratio distribution heat map and the control parameters corresponding to each nozzle inside the drum. The control parameters include nozzle angle, nozzle flow rate, and total output flow rate; and a first control unit, used to control the corresponding nozzles to spray according to each target control parameter.

[0014] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0015] According to another aspect of this application, a washing machine is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include methods for performing any one of them.

[0016] Applying the technical solution of this application, in the above-mentioned washing control method for dynamic water flow adjustment, firstly, image data and ultrasonic monitoring data of clothes inside the washing machine drum are acquired. Based on the image data and ultrasonic monitoring data, the clothes are modeled to obtain clothes distribution data. Then, the internal space of the drum is uniformly divided into multiple cells. Based on the clothes distribution data, the proportion of space not occupied by clothes in each cell is determined to obtain multiple porosities. Next, a porosity heatmap is drawn based on the multiple porosities, and a first mapping relationship is queried based on the porosity heatmap to obtain multiple target control parameters. The first mapping relationship is the mapping relationship between the porosity distribution heatmap and the control parameters corresponding to each nozzle inside the drum. The control parameters include nozzle angle, nozzle flow rate, and total output flow rate. Finally, the corresponding nozzles are controlled to spray according to each target control parameter. This application uses a camera and ultrasonic sensors to perform three-dimensional modeling of the clothes distribution state inside the washing machine, and draws a porosity heatmap based on the modeling data to intuitively reflect the distribution of clothes in the washing machine. Then, the nozzle control parameters are adjusted according to the porosity heatmap to achieve dynamic adjustment of the washing water flow. This solves the problem of clothing damage caused by the lack of dynamic nozzle adjustment during the washing process in the prior art. Attached Figure Description

[0017] Figure 1 A hardware block diagram of a mobile terminal for a washing control method with dynamic water flow adjustment provided in an embodiment of this application is shown.

[0018] Figure 2 A schematic flowchart of a washing control method for dynamic water flow adjustment according to an embodiment of this application is shown.

[0019] Figure 3 A structural block diagram of a washing control device for dynamic water flow regulation according to an embodiment of this application is shown.

[0020] The above figures include the following reference numerals:

[0021] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] As described in the background section, the randomness of the stacking state of clothes inside the drum affects both the washing and water rinsing force. However, the existing technology has not addressed these shortcomings, and there is still a risk of damaging clothes. To solve the problem of clothing damage caused by the lack of dynamic adjustment of the nozzles during the washing process in the existing technology, the embodiments of this application provide a washing control method, device, computer-readable storage medium, and washing machine with dynamic water flow adjustment.

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0027] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a washing control method with dynamic water flow adjustment according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0028] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the water flow dynamic adjustment washing control method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] This embodiment provides a washing control method for dynamically adjusting water flow that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than that shown here.

[0030] Figure 2 This is a flowchart of a washing control method for dynamic water flow adjustment according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0031] Step S201: Obtain image data and ultrasonic monitoring data of clothes inside the washing machine drum; model the clothes based on the image data and ultrasonic monitoring data to obtain clothes distribution data.

[0032] Specifically, the washing machine uses a built-in miniature camera to capture real-time images of the clothing distribution inside the drum, employing computer vision technology to identify the clothing's position, shape, and stacking degree. Furthermore, an ultrasonic sensor array measures the distances between different areas of the clothing, and combined with the drum's overall volume information, estimates the actual volume and distribution of the clothing. Image data and ultrasonic monitoring data are then cross-corrected to construct a three-dimensional model of the clothing distribution, yielding the aforementioned clothing distribution data.

[0033] Step S202: The internal space of the drum is evenly divided into multiple cells. Based on the clothing distribution data, the proportion of space not occupied by clothing in each cell is determined to obtain multiple void ratios.

[0034] Specifically, the internal space of the drum is evenly divided into a gridded cell matrix, and the porosity (φ) of each cell is calculated, which is the proportion of the space volume not occupied by clothes in that area, reflecting the possible situation of water flow penetration and cleaning efficiency.

[0035] The formula for calculating the porosity mentioned above is as follows:

[0036]

[0037] in, For local porosity, To account for the volume occupied by clothing in certain areas, This represents the volume of a local roller within a cell.

[0038] Step S203: Draw a porosity heat map based on multiple porosities, and query the first mapping relationship based on the porosity heat map to obtain multiple target control parameters. The first mapping relationship is the mapping relationship between the porosity distribution heat map and the control parameters corresponding to each nozzle inside the drum. The control parameters include nozzle angle, nozzle flow rate and total output flow rate.

[0039] Specifically, based on the calculated porosity data, a two-dimensional heat map of the inside of the drum is generated, where colors (such as red, yellow, and green) represent the difficulty of water flow penetration under different porosities. Specifically, red areas (φ < 0.2) in the heat map indicate densely packed clothing, making water flow difficult to penetrate; green areas (φ > 0.6) indicate sparsely distributed clothing, allowing for good water flow penetration. Using a pre-built offline CFD simulation dataset, the flow field characteristics inside the drum under different porosity distributions are mapped to the optimal control parameters of each nozzle, forming a mapping table or model, i.e., the first mapping relationship mentioned above. Based on the porosity heat map, the system queries the first mapping relationship to obtain the target control parameters for each nozzle (nozzle angle θ, flow rate qᵢ, total output velocity v).

[0040] Step S204: Control the corresponding nozzles to spray according to each target control parameter.

[0041] Specifically, adjusting the nozzle angle and flow rate directly affects the nozzle, while changing the total output flow rate is achieved by controlling the water pump power, ensuring that each nozzle sprays according to the optimal parameters.

[0042] It is understood that the above embodiments utilize multi-source sensor data (image data and ultrasonic monitoring data) to perform real-time and accurate modeling of the distribution of clothes inside the drum, calculate the porosity of each cell, and dynamically adjust the control parameters of the nozzles, including nozzle angle, nozzle flow rate and total output flow rate of the water pump, based on a pre-trained CFD operating condition library, in order to optimize water flow coverage and rinsing intensity and achieve efficient and safe washing results.

[0043] In this embodiment, firstly, image data and ultrasonic monitoring data of clothes inside the washing machine drum are acquired. Based on the image data and ultrasonic monitoring data, the clothes are modeled to obtain clothing distribution data. Then, the internal space of the drum is uniformly divided into multiple cells. Based on the clothing distribution data, the proportion of space not occupied by clothes in each cell is determined, resulting in multiple porosity ratios. Next, a porosity heatmap is drawn based on these multiple porosity ratios, and a first mapping relationship is queried based on the porosity heatmap to obtain multiple target control parameters. The first mapping relationship is the mapping relationship between the porosity distribution heatmap and the control parameters corresponding to each nozzle inside the drum. The control parameters include nozzle angle, nozzle flow rate, and total output flow rate. Finally, the corresponding nozzles are controlled to spray according to each target control parameter. This application uses a camera and ultrasonic sensors to perform three-dimensional modeling of the clothing distribution inside the washing machine, and draws a porosity heatmap based on the modeling data to intuitively reflect the distribution of clothes in the washing machine. Then, the nozzle control parameters are adjusted according to the porosity heatmap to achieve dynamic adjustment of the washing water flow. This solves the problem of clothing damage caused by the lack of dynamic nozzle adjustment during the washing process in existing technologies.

[0044] In order to obtain the above-mentioned target control parameters, in an optional implementation, step S203 includes:

[0045] Step S2031: Query the second mapping relationship based on the porosity heatmap to obtain multiple target washing parameters. The second mapping relationship is the mapping relationship between the porosity heatmap and the washing environment parameter with the highest corresponding score. The score is determined based on the degree of stain residue and fiber damage of the clothes. The washing environment parameters include water flow rate, water flow rinsing intensity and water flow coverage. The target washing parameters correspond one-to-one with the cells.

[0046] Specifically, based on CFD simulation and experimental test data, a database is established to record the optimal washing environment parameters (water flow velocity, water flow scouring intensity, and water flow coverage) that can be achieved under different porosity distributions, i.e., the second mapping relationship mentioned above.

[0047] Understandably, for a given porosity distribution, the system will evaluate various possible water flow conditions and give a comprehensive score based on the degree of stain residue and fiber damage on the clothes. A high score means that while ensuring effective cleaning, it causes minimal damage to the clothes.

[0048] Step S2032: Query the third mapping relationship according to each target washing parameter to obtain the corresponding target control parameter. The third mapping relationship is the mapping relationship between washing environment parameters and control parameters.

[0049] Specifically, based on historical data and empirical formulas, a set of conversion rules was established from washing environment parameters to specific operating parameters of nozzles and water pumps (nozzle angle, flow rate, water pump power, etc.), namely the third mapping relationship mentioned above.

[0050] Through the above embodiments, the washing and control parameters are intelligently adjusted by analyzing the distribution of underwear in the drum in real time, ensuring washing effect while reducing fiber damage. Its core lies in using a porosity heatmap for multi-level mapping, guiding the selection of optimal washing environment parameters through a preset scoring mechanism, and then adjusting the working state of the nozzles and water pump based on these parameters to achieve precise washing control.

[0051] To construct the aforementioned second mapping relationship, in one optional implementation, before querying the second mapping relationship based on the porosity heatmap to obtain multiple target washing parameters, the method further includes:

[0052] Step S301, simulation step: take any preset clothing distribution data as boundary condition, take nozzle angle, nozzle flow rate and total output flow rate as input variables, adjust the input variables, and perform simulation under each input variable to obtain multiple simulated washing conditions;

[0053] Step S302, the first determination step, determines the water flow velocity, water flow scouring intensity and water flow coverage of each cell according to the simulated washing conditions, and obtains the washing environment parameters;

[0054] Step S303, calculation step: draw the corresponding porosity heat map based on the clothing distribution data, determine the degree of stain residue and fiber damage of the clothing based on the simulated washing conditions, and perform weighted calculation based on the degree of stain residue and fiber damage to obtain a score;

[0055] Step S304, the second determination step, is to determine the washing environment parameter corresponding to the maximum score as the washing environment parameter corresponding to the porosity heatmap;

[0056] Step S305: Repeat the simulation step, the first determination step, the calculation step, and the second determination step at least once in sequence until the washing environment parameters corresponding to the porosity heat map corresponding to all preset clothing distribution data are determined.

[0057] Specifically, a series of typical clothing stacking patterns are selected as boundary conditions to represent different porosity distributions. The nozzle angle θ, nozzle flow rate qᵢ, and total output velocity v are used as input variables for CFD simulation. By changing these parameters, multiple simulation experiments are conducted to explore washing conditions under different combinations. Each simulation, based on the current combination of input variables, outputs data on the water flow velocity, water scouring intensity, and water coverage inside the drum, forming multiple simulated washing conditions. After each simulation, washing environment parameters matching the current preset clothing distribution data are determined, including the specific water flow velocity, scouring force, and coverage. Based on actual or preset clothing distribution data, the corresponding porosity heatmap φ is calculated. The results of each simulation are analyzed to quantify the degree of stain residue and fiber damage. For example, by analyzing the impact force of the water flow on the clothing surface and the characteristics of the clothing material, the probability of fiber damage is estimated. Combining the degree of stain residue and fiber damage, a weighted algorithm is used to calculate a score for each condition. A high score means that the clothes are cleaned while minimizing damage to the clothing fibers. From the simulation results of multiple iterations, the washing environment parameters with the highest scores were selected and used as the optimal parameter configuration for the corresponding porosity heatmap.

[0058] Step S306: Construct a second mapping relationship based on the correspondence between the porosity heatmap and the washing environment parameters.

[0059] Specifically, the above steps will be repeated at least once for all preset clothing distribution data until the highest-scoring combination of washing environment parameters is found for each typical porosity distribution. All porosity heatmaps are then correlated with their corresponding high-scoring washing environment parameters to form a second mapping relationship.

[0060] Through the above embodiments, computational fluid dynamics (CFD) simulation technology is used to predict and optimize the washing effect of drum washing machines under different clothing distribution conditions. Through multiple rounds of iterative simulation, combined with porosity heatmaps and quantitative assessments of stain residue and fiber damage, a mapping relationship between efficient and safe washing environment parameters and control parameters is finally constructed.

[0061] To prevent poor washing results in some blind spots, in an optional embodiment, after controlling the corresponding nozzles to spray according to each target control parameter, the above method further includes:

[0062] Step S402: Monitor the humidity at different locations in the first cell to obtain multiple first target humidity levels. The first cell can be any cell.

[0063] Specifically, the humidity sensor installed inside the drum can monitor the humidity at different locations in the first cell (i.e., any divided unit area) and obtain a series of first target humidity values.

[0064] Step S403: If the difference between the maximum and minimum values ​​of any first target humidity is greater than the first threshold, the area within the first preset distance of the humidity sensor corresponding to the first target humidity is determined as the rinsing blind zone.

[0065] Specifically, the system uses the collected humidity data to calculate the difference between the maximum and minimum humidity values ​​within a cell, and determines whether this difference exceeds a preset first threshold. This threshold is used to identify the existence of rinsing blind spots. If the difference exceeds the first threshold, the system considers a rinsing blind spot to exist, i.e., the area with the lowest humidity. The exact location of this blind spot within the drum can be determined using the location information from the humidity sensor.

[0066] Step S404: Increase the nozzle flow rate of the nozzle in the flushing blind zone and adjust the nozzle angle so that the nozzle is aligned with the flushing blind zone.

[0067] Specifically, after identifying the flushing blind spot, the system automatically increases the nozzle flow rate qᵢ for that area and fine-tunes the nozzle angle θ to ensure that the water flow can accurately and powerfully impact the blind spot, increasing the water's penetration and coverage.

[0068] Step S405: Control the nozzle to continue spraying based on the corrected nozzle flow rate and nozzle angle.

[0069] Specifically, based on the adjusted flow rate and angle, the system recalculates the spray control strategy to ensure that the water flow can effectively cover and rinse blind spots.

[0070] Through the above embodiments, by monitoring the humidity changes of each cell within the drum in real time, potential rinsing blind spots during the washing process can be intelligently detected and eliminated, ensuring that all areas of clothing receive a uniform and thorough rinse. The implementation of this technology relies on high-precision monitoring by humidity sensors and the ability to dynamically adjust nozzle angles and flow rates based on the monitoring results to achieve effective rinsing of localized areas.

[0071] To prevent damage to clothing, in an optional embodiment, step S204 further includes:

[0072] Step S2041: Divide the cell into multiple sub-cells based on the nozzle in the cell;

[0073] Specifically, in addition to the original uniform division, this solution further subdivides the cell containing the nozzle into multiple smaller sub-cells, which facilitates more precise control of the water flow.

[0074] Step S2042: Determine the void ratio corresponding to each sub-cell to obtain multiple target void ratios;

[0075] Specifically, the void ratio φ of each sub-cell is calculated in real time, and φ reflects the proportion of space in that area that is not occupied by clothing.

[0076] Step S2043: If the target porosity is greater than the second threshold, reduce the total output flow rate of the nozzle corresponding to the sub-cell.

[0077] Specifically, when the target porosity is greater than or equal to the second threshold (0.8), the total output flow rate of the corresponding nozzle is reduced to prevent excessive local impact from damaging the clothing.

[0078] Step S2044: Control the nozzles to spray according to the updated total output flow rate.

[0079] Specifically, based on the adjusted flow rate, the system recalculates the spray control strategy to ensure appropriate water flow.

[0080] Through the above embodiments, more refined water flow control is achieved based on the porosity of sub-cells, aiming to avoid excessive water flow in areas where clothes are sparsely distributed, thereby reducing damage to clothes while ensuring washing effect.

[0081] To improve washing performance, in one optional implementation, after determining the proportion of unoccupied space in each cell based on clothing distribution data and obtaining multiple void ratios, the method further includes:

[0082] Step S501: When the void ratio of any cell is less than the third threshold, control the roller to oscillate, wherein the oscillation frequency of the roller is less than or equal to the fourth threshold.

[0083] Specifically, after generating the porosity heatmap, if any cell has a porosity φ lower than the third threshold (e.g., the third threshold is set to 0.2), it indicates that the clothes are piled too tightly, hindering water penetration. The drum vibration mechanism is then activated, using a built-in vibration motor or adjusting the drum rotation mode to make the drum vibrate at a frequency less than or equal to the fourth threshold. The fourth threshold (e.g., set to 5 Hz) ensures that the drum vibration is not too intense, avoiding damage to clothing fibers or the washing machine structure.

[0084] Step S502: After the oscillation is completed, recalculate the void ratio corresponding to each cell.

[0085] Specifically, after the drum oscillates, the system recalculates the porosity of each cell. The oscillation improves the previously tightly packed distribution of clothing, potentially raising the porosity to a level more conducive to water penetration.

[0086] Through the above embodiments, it is clear that the distribution of clothes inside the drum of a drum washing machine directly affects water penetration and washing efficiency. When clothes are too densely packed, i.e., the porosity of a certain cell is below the third threshold, water cannot fully penetrate the clothes, resulting in poor washing performance. This solution introduces a drum oscillation mechanism to improve clothes distribution and increase porosity while ensuring that the clothes fibers are not damaged, thereby optimizing water coverage and cleaning efficiency during the washing process. The oscillation frequency is designed to be below the fourth threshold to prevent adverse effects on the clothes or the washing machine structure.

[0087] To ensure the accuracy of the aforementioned target control parameters, after controlling the corresponding nozzles to spray according to each target control parameter, the method further includes:

[0088] Step S601: After a preset spraying time, obtain the real-time humidity of each cell to get the second target humidity;

[0089] Specifically, during the spraying process of the drum washing machine, for each preset spraying time (e.g., every 2 minutes), the humidity sensor measures and records the real-time humidity of each cell to obtain the second target humidity H2.

[0090] Step S602: Determine the corresponding water flow coverage range according to the target control parameters to obtain the first target range; determine the corresponding water flow coverage range according to the second target humidity to obtain the second target range.

[0091] Specifically, based on the current target control parameters, the system can calculate the theoretically achievable water flow coverage area, i.e., the first target range C1. Based on the distribution of the second target humidity H2, the system can estimate the actual achievable water flow coverage area, i.e., the second target range C2.

[0092] Step S603: If the deviation ratio between the second target range and the first target range is greater than or equal to the fifth threshold, update the third mapping relationship through calibration experiments.

[0093] Specifically, the system calculates the deviation ratio ΔC between the second target range C2 and the first target range C1 to assess the degree of matching between the actual humidity distribution and the theoretical coverage area. When the deviation ratio ΔC is greater than or equal to the fifth threshold (assumed to be 10%), the system considers that there is a significant mismatch between the current nozzle control parameters and the actual water flow distribution, and a calibration experiment is required to update the third mapping relationship.

[0094] Through the above embodiments, by monitoring the real-time humidity during the spraying process and comparing the preset water flow coverage range with the actual humidity coverage, the nozzle control parameters are dynamically adjusted to achieve the optimal water flow distribution effect. When a large deviation is found between the actual humidity coverage range and the preset range, i.e., the deviation ratio is greater than or equal to the fifth threshold, the system will initiate a calibration experiment to update the third mapping relationship, i.e., the mapping table between the nozzle control parameters and the water flow coverage range, to more accurately guide subsequent spraying control.

[0095] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0096] This application also provides a washing control device for dynamic water flow adjustment. It should be noted that the washing control device for dynamic water flow adjustment in this application can be used to execute the washing control method for dynamic water flow adjustment provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0097] The following describes the washing control device with dynamic water flow adjustment provided in the embodiments of this application.

[0098] Figure 3 This is a structural block diagram of a washing control device for dynamic water flow adjustment according to an embodiment of this application. Figure 3 As shown, the device includes:

[0099] The first acquisition unit 10 is used to acquire image data and ultrasonic monitoring data of clothes inside the washing machine drum, and to model the clothes based on the image data and ultrasonic monitoring data to obtain clothes distribution data.

[0100] Specifically, the washing machine uses a built-in miniature camera to capture real-time images of the clothing distribution inside the drum, employing computer vision technology to identify the clothing's position, shape, and stacking degree. Furthermore, an ultrasonic sensor array measures the distances between different areas of the clothing, and combined with the drum's overall volume information, estimates the actual volume and distribution of the clothing. Image data and ultrasonic monitoring data are then cross-corrected to construct a three-dimensional model of the clothing distribution, yielding the aforementioned clothing distribution data.

[0101] The first determining unit 20 is used to uniformly divide the internal space of the drum into multiple cells, and determine the proportion of space not occupied by clothes in each cell according to the clothing distribution data to obtain multiple void ratios.

[0102] Specifically, the internal space of the drum is evenly divided into a gridded cell matrix, and the porosity (φ) of each cell is calculated, which is the proportion of the space volume not occupied by clothes in that area, reflecting the possible situation of water flow penetration and cleaning efficiency.

[0103] The formula for calculating the porosity mentioned above is as follows:

[0104]

[0105] in, For local porosity, To account for the volume occupied by clothing in certain areas, This represents the volume of a local roller within a cell.

[0106] The first processing unit 30 is used to draw a porosity heat map based on multiple porosities, and query a first mapping relationship based on the porosity heat map to obtain multiple target control parameters. The first mapping relationship is the mapping relationship between the porosity distribution heat map and the control parameters corresponding to each nozzle inside the drum. The control parameters include nozzle angle, nozzle flow rate and total output flow rate.

[0107] Specifically, based on the calculated porosity data, a two-dimensional heat map of the inside of the drum is generated, where colors (such as red, yellow, and green) represent the difficulty of water flow penetration under different porosities. Specifically, red areas (φ < 0.2) in the heat map indicate densely packed clothing, making water flow difficult to penetrate; green areas (φ > 0.6) indicate sparsely distributed clothing, allowing for good water flow penetration. Using a pre-built offline CFD simulation dataset, the flow field characteristics inside the drum under different porosity distributions are mapped to the optimal control parameters of each nozzle, forming a mapping table or model, i.e., the first mapping relationship mentioned above. Based on the porosity heat map, the system queries the first mapping relationship to obtain the target control parameters for each nozzle (nozzle angle θ, flow rate qᵢ, total output velocity v).

[0108] The first control unit 40 is used to control the corresponding nozzles to spray according to each target control parameter.

[0109] Specifically, adjusting the nozzle angle and flow rate directly affects the nozzle, while changing the total output flow rate is achieved by controlling the water pump power, ensuring that each nozzle sprays according to the optimal parameters.

[0110] It is understood that the above embodiments utilize multi-source sensor data (image data and ultrasonic monitoring data) to perform real-time and accurate modeling of the distribution of clothes inside the drum, calculate the porosity of each cell, and dynamically adjust the control parameters of the nozzles, including nozzle angle, nozzle flow rate and total output flow rate of the water pump, based on a pre-trained CFD operating condition library, in order to optimize water flow coverage and rinsing intensity and achieve efficient and safe washing results.

[0111] In this embodiment, the first acquisition unit acquires image data and ultrasonic monitoring data of the clothes inside the washing machine drum, and models the clothes based on the image data and ultrasonic monitoring data to obtain clothes distribution data. The first determination unit uniformly divides the internal space of the drum into multiple cells, and determines the proportion of space not occupied by clothes in each cell based on the clothes distribution data to obtain multiple porosities. The first processing unit draws a porosity heatmap based on the multiple porosities, and queries a first mapping relationship based on the porosity heatmap to obtain multiple target control parameters. The first mapping relationship is the mapping relationship between the porosity distribution heatmap and the control parameters corresponding to each nozzle inside the drum. The control parameters include nozzle angle, nozzle flow rate, and total output flow rate. The first control unit controls the corresponding nozzle to spray according to each target control parameter. This application uses a camera and ultrasonic sensor to perform three-dimensional modeling of the distribution state of clothes inside the washing machine, and draws a porosity heatmap based on the modeling data to intuitively reflect the distribution of clothes in the washing machine. Then, the nozzle control parameters are adjusted according to the porosity heatmap to achieve dynamic adjustment of the washing water flow. This solves the problem of lack of dynamic adjustment of nozzles during the washing process in the prior art, which leads to damage to clothes.

[0112] In order to obtain the aforementioned target control parameters, in one optional embodiment, the first processing unit includes:

[0113] The first query module is used to query the second mapping relationship based on the porosity heatmap to obtain multiple target washing parameters. The second mapping relationship is the mapping relationship between the porosity heatmap and the washing environment parameter with the highest corresponding score. The score is determined based on the degree of stain residue and fiber damage of the clothes. The washing environment parameters include water flow rate, water flow rinsing intensity and water flow coverage. There is a one-to-one correspondence between the target washing parameters and the cells.

[0114] Specifically, based on CFD simulation and experimental test data, a database is established to record the optimal washing environment parameters (water flow velocity, water flow scouring intensity, and water flow coverage) that can be achieved under different porosity distributions, i.e., the second mapping relationship mentioned above.

[0115] Understandably, for a given porosity distribution, the system will evaluate various possible water flow conditions and give a comprehensive score based on the degree of stain residue and fiber damage on the clothes. A high score means that while ensuring effective cleaning, it causes minimal damage to the clothes.

[0116] The second query module is used to query the third mapping relationship according to each target washing parameter to obtain the corresponding target control parameters. The third mapping relationship is the mapping relationship between washing environment parameters and control parameters.

[0117] Specifically, based on historical data and empirical formulas, a set of conversion rules was established from washing environment parameters to specific operating parameters of nozzles and water pumps (nozzle angle, flow rate, water pump power, etc.), namely the third mapping relationship mentioned above.

[0118] Through the above embodiments, the washing and control parameters are intelligently adjusted by analyzing the distribution of underwear in the drum in real time, ensuring washing effect while reducing fiber damage. Its core lies in using a porosity heatmap for multi-level mapping, guiding the selection of optimal washing environment parameters through a preset scoring mechanism, and then adjusting the working state of the nozzles and water pump based on these parameters to achieve precise washing control.

[0119] To construct the aforementioned second mapping relationship, in one optional embodiment, the apparatus further includes:

[0120] The simulation unit is used to perform simulation steps before obtaining multiple target washing parameters by querying the second mapping relationship based on the porosity heatmap. It takes any preset clothing distribution data as the boundary condition, and the nozzle angle, nozzle flow rate and total output flow rate as input variables. It adjusts the input variables and performs simulations under each input variable to obtain multiple simulated washing conditions.

[0121] The second determining unit is used to execute the first determining step, and to determine the water flow velocity, water flow scouring intensity and water flow coverage of each cell according to the simulated washing conditions, so as to obtain the washing environment parameters.

[0122] The calculation unit is used to perform calculation steps, draw the corresponding porosity heat map based on the clothing distribution data, determine the degree of stain residue and fiber damage of the clothing based on the simulated washing conditions, and perform weighted calculation based on the degree of stain residue and fiber damage to obtain a score;

[0123] The third determining unit is used to execute the second determining step, and to determine the washing environment parameter corresponding to the maximum score as the washing environment parameter corresponding to the porosity heatmap.

[0124] The repeating unit is used to repeat the simulation step, the first determination step, the calculation step, and the second determination step at least once in sequence until the washing environment parameters corresponding to the porosity heatmap of all preset clothing distribution data are determined.

[0125] Specifically, a series of typical clothing stacking patterns are selected as boundary conditions to represent different porosity distributions. The nozzle angle θ, nozzle flow rate qᵢ, and total output velocity v are used as input variables for CFD simulation. By changing these parameters, multiple simulation experiments are conducted to explore washing conditions under different combinations. Each simulation, based on the current combination of input variables, outputs data on the water flow velocity, water scouring intensity, and water coverage inside the drum, forming multiple simulated washing conditions. After each simulation, washing environment parameters matching the current preset clothing distribution data are determined, including the specific water flow velocity, scouring force, and coverage. Based on actual or preset clothing distribution data, the corresponding porosity heatmap φ is calculated. The results of each simulation are analyzed to quantify the degree of stain residue and fiber damage. For example, by analyzing the impact force of the water flow on the clothing surface and the characteristics of the clothing material, the probability of fiber damage is estimated. Combining the degree of stain residue and fiber damage, a weighted algorithm is used to calculate a score for each condition. A high score means that the clothes are cleaned while minimizing damage to the clothing fibers. From the simulation results of multiple iterations, the washing environment parameters with the highest scores were selected and used as the optimal parameter configuration for the corresponding porosity heatmap.

[0126] The construction unit is used to construct a second mapping relationship based on the correspondence between the porosity heatmap and the washing environment parameters.

[0127] Specifically, the above steps will be repeated at least once for all preset clothing distribution data until the highest-scoring combination of washing environment parameters is found for each typical porosity distribution. All porosity heatmaps are then correlated with their corresponding high-scoring washing environment parameters to form a second mapping relationship.

[0128] Through the above embodiments, computational fluid dynamics (CFD) simulation technology is used to predict and optimize the washing effect of drum washing machines under different clothing distribution conditions. Through multiple rounds of iterative simulation, combined with porosity heatmaps and quantitative assessments of stain residue and fiber damage, a mapping relationship between efficient and safe washing environment parameters and control parameters is finally constructed.

[0129] To prevent poor washing performance in some blind spots, in an optional embodiment, the above-mentioned device further includes:

[0130] The second acquisition unit is used to monitor the humidity at different positions of the first cell after controlling the corresponding nozzles to spray according to the target control parameters, and obtain multiple first target humiditys. The first cell can be any cell.

[0131] Specifically, the humidity sensor installed inside the drum can monitor the humidity at different locations in the first cell (i.e., any divided unit area) and obtain a series of first target humidity values.

[0132] The fourth determining unit is used to determine the area within a first preset distance of the humidity sensor corresponding to the first target humidity as the rinsing blind zone when the difference between the maximum and minimum values ​​of any first target humidity is greater than a first threshold.

[0133] Specifically, the system uses the collected humidity data to calculate the difference between the maximum and minimum humidity values ​​within a cell, and determines whether this difference exceeds a preset first threshold. This threshold is used to identify the existence of rinsing blind spots. If the difference exceeds the first threshold, the system considers a rinsing blind spot to exist, i.e., the area with the lowest humidity. The exact location of this blind spot within the drum can be determined using the location information from the humidity sensor.

[0134] The second processing unit is used to increase the nozzle flow rate of the nozzles in the flushing blind zone and adjust the nozzle angle of the nozzles so that the nozzles are aligned with the flushing blind zone.

[0135] Specifically, after identifying the flushing blind spot, the system automatically increases the nozzle flow rate qᵢ for that area and fine-tunes the nozzle angle θ to ensure that the water flow can accurately and powerfully impact the blind spot, increasing the water's penetration and coverage.

[0136] The second control unit is used to control the nozzle to continue spraying based on the corrected nozzle flow rate and nozzle angle.

[0137] Specifically, based on the adjusted flow rate and angle, the system recalculates the spray control strategy to ensure that the water flow can effectively cover and rinse blind spots.

[0138] Through the above embodiments, by monitoring the humidity changes of each cell within the drum in real time, potential rinsing blind spots during the washing process can be intelligently detected and eliminated, ensuring that all areas of clothing receive a uniform and thorough rinse. The implementation of this technology relies on high-precision monitoring by humidity sensors and the ability to dynamically adjust nozzle angles and flow rates based on the monitoring results to achieve effective rinsing of localized areas.

[0139] To prevent damage to clothing, in one optional embodiment, the first control unit further includes:

[0140] The processing module is used to divide a cell into multiple sub-cells based on the nozzles in the cell;

[0141] Specifically, in addition to the original uniform division, this solution further subdivides the cell containing the nozzle into multiple smaller sub-cells, which facilitates more precise control of the water flow.

[0142] The determination module is used to determine the void ratio corresponding to each sub-cell, thereby obtaining multiple target void ratios;

[0143] Specifically, the void ratio φ of each sub-cell is calculated in real time, and φ reflects the proportion of space in that area that is not occupied by clothing.

[0144] The adjustment module is used to reduce the total output flow rate of the nozzles corresponding to the sub-cell when the target void ratio is greater than the second threshold.

[0145] Specifically, when the target porosity is greater than or equal to the second threshold (0.8), the total output flow rate of the corresponding nozzle is reduced to prevent excessive local impact from damaging the clothing.

[0146] The control module is used to control the nozzles to spray according to the updated total output flow rate.

[0147] Specifically, based on the adjusted flow rate, the system recalculates the spray control strategy to ensure appropriate water flow.

[0148] Through the above embodiments, more refined water flow control is achieved based on the porosity of sub-cells, aiming to avoid excessive water flow in areas where clothes are sparsely distributed, thereby reducing damage to clothes while ensuring washing effect.

[0149] To improve the washing effect, in one optional embodiment, the above-mentioned device further includes:

[0150] The third control unit is used to determine the proportion of space not occupied by clothing in each cell based on clothing distribution data and obtain multiple void ratios. If the void ratio of any cell is less than the third threshold, the control unit controls the roller to oscillate. The oscillation frequency of the roller is less than or equal to the fourth threshold.

[0151] Specifically, after generating the porosity heatmap, if any cell has a porosity φ lower than the third threshold (e.g., the third threshold is set to 0.2), it indicates that the clothes are piled too tightly, hindering water penetration. The drum vibration mechanism is then activated, using a built-in vibration motor or adjusting the drum rotation mode to make the drum vibrate at a frequency less than or equal to the fourth threshold. The fourth threshold (e.g., set to 5 Hz) ensures that the drum vibration is not too intense, avoiding damage to clothing fibers or the washing machine structure.

[0152] The update cell is used to recalculate the porosity of each cell after the oscillation is complete.

[0153] Specifically, after the drum oscillates, the system recalculates the porosity of each cell. The oscillation improves the previously tightly packed distribution of clothing, potentially raising the porosity to a level more conducive to water penetration.

[0154] Through the above embodiments, it is clear that the distribution of clothes inside the drum of a drum washing machine directly affects water penetration and washing efficiency. When clothes are too densely packed, i.e., the porosity of a certain cell is below the third threshold, water cannot fully penetrate the clothes, resulting in poor washing performance. This solution introduces a drum oscillation mechanism to improve clothes distribution and increase porosity while ensuring that the clothes fibers are not damaged, thereby optimizing water coverage and cleaning efficiency during the washing process. The oscillation frequency is designed to be below the fourth threshold to prevent adverse effects on the clothes or the washing machine structure.

[0155] To ensure the accuracy of the aforementioned target control parameters, the device further includes:

[0156] The third acquisition unit is used to acquire the real-time humidity of each cell after controlling the corresponding nozzles to spray according to each target control parameter, and to obtain the second target humidity after each preset spray duration.

[0157] Specifically, during the spraying process of the drum washing machine, for each preset spraying time (e.g., every 2 minutes), the humidity sensor measures and records the real-time humidity of each cell to obtain the second target humidity H2.

[0158] The fifth determining unit is used to determine the corresponding water flow coverage range based on the target control parameters to obtain the first target range, and to determine the corresponding water flow coverage range based on the second target humidity to obtain the second target range.

[0159] Specifically, based on the current target control parameters, the system can calculate the theoretically achievable water flow coverage area, i.e., the first target range C1. Based on the distribution of the second target humidity H2, the system can estimate the actual achievable water flow coverage area, i.e., the second target range C2.

[0160] The correction unit is used to update the third mapping relationship through calibration experiments when the deviation ratio between the second target range and the first target range is greater than or equal to the fifth threshold.

[0161] Specifically, the system calculates the deviation ratio ΔC between the second target range C2 and the first target range C1 to assess the degree of matching between the actual humidity distribution and the theoretical coverage area. When the deviation ratio ΔC is greater than or equal to the fifth threshold (assumed to be 10%), the system considers that there is a significant mismatch between the current nozzle control parameters and the actual water flow distribution, and a calibration experiment is required to update the third mapping relationship.

[0162] Through the above embodiments, by monitoring the real-time humidity during the spraying process and comparing the preset water flow coverage range with the actual humidity coverage, the nozzle control parameters are dynamically adjusted to achieve the optimal water flow distribution effect. When a large deviation is found between the actual humidity coverage range and the preset range, i.e., the deviation ratio is greater than or equal to the fifth threshold, the system will initiate a calibration experiment to update the third mapping relationship, i.e., the mapping table between the nozzle control parameters and the water flow coverage range, to more accurately guide subsequent spraying control.

[0163] The aforementioned water flow dynamic adjustment washing control device includes a processor and a memory. The first acquisition unit, the first determination unit, the first processing unit, and the first control unit are all stored as program units in the memory. The processor executes the program units stored in the memory to achieve the corresponding functions. All of the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0164] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can reduce clothing damage.

[0165] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0166] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the washing control method of dynamically adjusting water flow.

[0167] This invention provides a processor for running a program, wherein the program executes the washing control method for dynamic water flow adjustment.

[0168] This invention provides a washing machine, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements at least the washing control step of dynamically adjusting the water flow.

[0169] This application also provides a computer program product that, when executed on a data processing device, is adapted to perform a program that initializes a washing control with at least dynamic water flow regulation.

[0170] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0171] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0172] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0173] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0174] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0175] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0176] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0177] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0178] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0179] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0180] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0181] 1) The washing control method for dynamic water flow adjustment in this application first acquires image data and ultrasonic monitoring data of clothes inside the washing machine drum. Based on the image data and ultrasonic monitoring data, the clothes are modeled to obtain clothes distribution data. Then, the internal space of the drum is uniformly divided into multiple cells. Based on the clothes distribution data, the proportion of space not occupied by clothes in each cell is determined to obtain multiple porosities. Next, a porosity heatmap is drawn based on the multiple porosities, and a first mapping relationship is queried based on the porosity heatmap to obtain multiple target control parameters. The first mapping relationship is the mapping relationship between the porosity distribution heatmap and the control parameters corresponding to each nozzle inside the drum. The control parameters include nozzle angle, nozzle flow rate, and total output flow velocity. Finally, the corresponding nozzles are controlled to spray according to each target control parameter. This application uses a camera and ultrasonic sensors to perform three-dimensional modeling of the clothes distribution state inside the washing machine, and draws a porosity heatmap based on the modeling data to intuitively reflect the distribution of clothes in the washing machine. Then, the nozzle control parameters are adjusted according to the porosity heatmap to achieve dynamic adjustment of the washing water flow. This solves the problem of clothing damage caused by the lack of dynamic nozzle adjustment during the washing process in the prior art.

[0182] 2) The washing control device for dynamic water flow adjustment of this application includes: a first acquisition unit acquiring image data and ultrasonic monitoring data of clothes inside the washing machine drum, and modeling the clothes based on the image data and ultrasonic monitoring data to obtain clothes distribution data; a first determination unit uniformly dividing the internal space of the drum into multiple cells, and determining the proportion of space not occupied by clothes in each cell based on the clothes distribution data to obtain multiple porosities; a first processing unit drawing a porosity heat map based on the multiple porosities, and querying a first mapping relationship based on the porosity heat map to obtain multiple target control parameters. The first mapping relationship is the mapping relationship between the porosity distribution heat map and the control parameters corresponding to each nozzle inside the drum. The control parameters include nozzle angle, nozzle flow rate, and total output flow rate; a first control unit controlling the corresponding nozzle to spray according to each target control parameter. This application uses a camera and ultrasonic sensor to perform three-dimensional modeling of the distribution state of clothes inside the washing machine, and draws a porosity heat map based on the modeling data to intuitively reflect the distribution of clothes in the washing machine. Then, it adjusts the nozzle control parameters based on the porosity heat map to achieve dynamic adjustment of the washing water flow. This solves the problem of lack of dynamic adjustment of nozzles during the washing process in the prior art, which leads to damage to clothes.

[0183] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A washing control method for dynamic water flow adjustment, characterized in that, include: Image data and ultrasonic monitoring data of clothes inside the washing machine drum are acquired, and the clothes are modeled based on the image data and ultrasonic monitoring data to obtain clothes distribution data; The internal space of the roller is evenly divided into multiple cells. The proportion of space not occupied by clothing in each cell is determined based on the clothing distribution data to obtain multiple void ratios. A porosity heatmap is plotted based on multiple porosities, and a first mapping relationship is queried based on the porosity heatmap to obtain multiple target control parameters. The first mapping relationship is the mapping relationship between the porosity heatmap and the control parameters corresponding to each nozzle inside the drum. The control parameters include nozzle angle, nozzle flow rate and total output flow rate. The corresponding nozzles are controlled to spray according to the target control parameters; Based on the porosity heatmap, a first mapping relationship is queried to obtain multiple target control parameters, including: Based on the porosity heatmap, a second mapping relationship is queried to obtain multiple target washing parameters. The second mapping relationship is the mapping relationship between the porosity heatmap and the washing environment parameter with the highest corresponding score. The score is determined based on the degree of stain residue and fiber damage of the clothes. The washing environment parameters include water flow velocity, water flow rinsing intensity and water flow coverage. The target washing parameters correspond one-to-one with the cells. Based on each of the target washing parameters, the third mapping relationship is queried to obtain the corresponding target control parameters. The third mapping relationship is the mapping relationship between the washing environment parameters and the control parameters.

2. The method according to claim 1, characterized in that, Before obtaining multiple target washing parameters by querying the second mapping relationship based on the porosity heatmap, the method further includes: The simulation steps involve using any preset clothing distribution data as boundary conditions, and the nozzle angle, nozzle flow rate, and total output flow rate as input variables. The input variables are adjusted, and simulations are performed under each input variable to obtain multiple simulated washing conditions. The first determination step involves determining the water flow velocity, water flow scouring intensity, and water flow coverage area corresponding to each cell based on the simulated washing conditions, thereby obtaining the washing environment parameters. The calculation steps are as follows: draw the corresponding porosity heatmap based on the clothing distribution data; determine the degree of stain residue and fiber damage of the clothing based on the simulated washing conditions; and perform a weighted calculation based on the degree of stain residue and fiber damage to obtain the score. The second determination step is to determine the washing environment parameter corresponding to the maximum score as the washing environment parameter corresponding to the porosity heatmap. Repeat the simulation step, the first determination step, the calculation step, and the second determination step at least once in sequence until the washing environment parameters corresponding to the porosity heatmap corresponding to all the preset clothing distribution data are determined. The second mapping relationship is constructed based on the correspondence between the porosity heatmap and the washing environment parameters.

3. The method according to claim 1, characterized in that, After controlling the corresponding nozzles to spray according to the target control parameters, the method further includes: The humidity at different locations in the first cell is monitored to obtain multiple first target humidity levels, where the first cell can be any one of the cells. If the difference between the maximum and minimum values ​​of any of the first target humidity values ​​is greater than the first threshold, the area within a first preset distance of the humidity sensor corresponding to the first target humidity is determined as the rinsing blind zone. Increase the nozzle flow rate of the nozzle in the flushing blind zone, and adjust the nozzle angle of the nozzle to align the nozzle with the flushing blind zone; The nozzle is controlled to continue spraying based on the corrected nozzle flow rate and the nozzle angle.

4. The method according to claim 1, characterized in that, Controlling the corresponding nozzles to spray according to each of the target control parameters further includes: The cell is divided into multiple sub-cells based on the nozzle in the cell; Determine the void ratio corresponding to each sub-cell to obtain multiple target void ratios; If the target porosity is greater than the second threshold, reduce the total output flow rate of the nozzle corresponding to the sub-cell. The nozzles are controlled to spray according to the updated total output flow rate.

5. The method according to claim 1, characterized in that, After determining the proportion of space not occupied by clothing in each cell based on the clothing distribution data, and obtaining multiple void ratios, the method further includes: If the porosity of any cell is less than a third threshold, the roller is controlled to oscillate, wherein the oscillation frequency of the roller is less than or equal to a fourth threshold. Once the oscillation is complete, the porosity corresponding to each cell is recalculated.

6. The method according to claim 1, characterized in that, After controlling the corresponding nozzles to spray according to the target control parameters, the method further includes: For each preset spray duration, the real-time humidity of each cell is obtained to determine the second target humidity. The water flow coverage range is determined according to the target control parameters to obtain the first target range, and the water flow coverage range is determined according to the second target humidity to obtain the second target range. If the deviation ratio between the second target range and the first target range is greater than or equal to the fifth threshold, the third mapping relationship is updated through a calibration experiment.

7. A washing control device for dynamic water flow adjustment, characterized in that, The device includes: The first acquisition unit is used to acquire image data and ultrasonic monitoring data of clothes inside the washing machine drum, and to model the clothes based on the image data and ultrasonic monitoring data to obtain clothes distribution data. The first determining unit is used to uniformly divide the internal space of the roller to obtain multiple cell units, and to determine the proportion of space not occupied by clothing in each cell unit according to the clothing distribution data to obtain multiple void ratios. The first processing unit is configured to draw a porosity heatmap based on the multiple porosities, and query a first mapping relationship based on the porosity heatmap to obtain multiple target control parameters. The first mapping relationship is the mapping relationship between the porosity heatmap and the control parameters corresponding to each nozzle inside the drum. The control parameters include nozzle angle, nozzle flow rate and total output flow rate. The first control unit is used to control the corresponding nozzle to spray according to each of the target control parameters; The first processing unit includes: The first query module is used to query the second mapping relationship based on the porosity heatmap to obtain multiple target washing parameters. The second mapping relationship is the mapping relationship between the porosity heatmap and the washing environment parameter with the highest corresponding score. The score is determined based on the degree of stain residue and fiber damage of the clothes. The washing environment parameter includes water flow velocity, water flow rinsing intensity and water flow coverage. The target washing parameters correspond one-to-one with the cell. The second query module is used to query the third mapping relationship according to each of the target washing parameters to obtain the corresponding target control parameters. The third mapping relationship is the mapping relationship between the washing environment parameters and the control parameters.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 6.

9. A washing machine, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising methods for performing any one of claims 1 to 6.

Images