A dynamic anti-scale control method for a washing machine based on intelligent monitoring and self-cleaning
By employing a dynamic anti-scaling control method based on multi-dimensional intelligent monitoring and machine learning prediction, the problem of single and inefficient anti-scaling methods in washing machines has been solved. This method achieves precise and intelligent anti-scaling control, improves anti-scaling effect and energy efficiency, and extends the service life of the equipment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG XIAOYA HLDG GRP CO LTD
- Filing Date
- 2026-03-04
- Publication Date
- 2026-06-05
Smart Images
Figure CN122147656A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of washing machine control technology, specifically to a dynamic anti-scaling control method for washing machines based on intelligent monitoring and self-cleaning. Background Technology
[0002] During long-term use, calcium and magnesium ions in the water can react with detergent components to form scale, which adheres to the inner drum, heating element, and inner wall of the pipes, leading to decreased washing efficiency, increased energy consumption, and even equipment malfunction.
[0003] Current anti-scaling methods in washing machines mainly include preset fixed self-cleaning programs (such as regular high-temperature rinsing), adding anti-scaling agents, or using coated inner drums, which have the following drawbacks: 1. The monitoring methods are limited, relying solely on single parameters such as water temperature and number of washes, which cannot accurately reflect the real-time status of scale formation; 2. The scale prevention strategy is fixed and not dynamically adjusted according to water hardness and scale accumulation, resulting in insufficient scale prevention or excessive energy consumption. 3. Poor synergy between self-cleaning and scale prevention; lack of scientific basis for the timing of self-cleaning; and inability to specifically remove existing scale. 4. Without a feedback correction mechanism, the anti-scaling effect cannot be optimized in real time, and the anti-scaling efficiency decreases after long-term use.
[0004] Therefore, there is an urgent need for a scale prevention control method based on intelligent monitoring and dynamic adjustment to address the limitations of existing technologies. Summary of the Invention
[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a dynamic anti-scaling control method for washing machines based on intelligent monitoring and self-cleaning. This method solves the problems mentioned in the background section, achieving accurate monitoring, dynamic adaptation, and closed-loop optimization for anti-scaling, thereby improving anti-scaling efficiency, reducing energy consumption, and extending equipment lifespan.
[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a dynamic anti-scaling control method for washing machines based on intelligent monitoring and self-cleaning, comprising the following steps: Step 1: Construct a multi-dimensional monitoring module to collect water quality parameters, washing condition parameters, and inner drum status parameters in real time during the operation of the washing machine; Step 2: The collected parameters are preprocessed by the data processing module, the preset scale formation prediction model is input, the scale formation rate and accumulation amount are output, and the scale prevention requirement level is determined. Step 3: Dynamically match anti-scaling strategies according to the anti-scaling requirement level, including adjusting washing program parameters, controlling anti-scaling agent dosage, and activating self-cleaning mode; Step 4: During the anti-scaling and self-cleaning process, monitor the results in real time, adjust the anti-scaling strategy parameters, and form a closed-loop control.
[0007] Preferably, in step one, the multi-dimensional monitoring module includes a water quality sensor, a working condition sensor, an inner drum status sensor, and a detergent residue sensor; the water quality sensor is used to collect water hardness and turbidity, the working condition sensor is used to collect washing times, water temperature, water level, and rinsing times, the inner drum status sensor is used to collect inner drum surface roughness and adhesion amount, and the detergent residue sensor is used to collect detergent concentration in the rinsing water.
[0008] Preferably, the inner cylinder status sensor is an infrared ranging sensor, which calculates the surface roughness of the inner cylinder by detecting the change in distance between the inner cylinder surface and the sensor, with a measurement accuracy of ≤0.1μm.
[0009] Preferably, in step two, the scale formation prediction model is constructed based on a machine learning algorithm. The training set consists of historical scale formation data under different water qualities and operating conditions. The input parameters include water hardness, number of washes, water temperature, and the rate of change of surface roughness of the inner cylinder. The output parameters are the scale formation rate (unit: mg / cm²·time) and the cumulative amount (unit: mg / cm²).
[0010] Preferably, the machine learning algorithm is the random forest algorithm, and the model prediction error is ≤8%; the scale prevention requirement level is divided into three levels: low, medium and high, which correspond to the scale accumulation thresholds of 0-5mg / cm², 5-10mg / cm² and >10mg / cm², respectively.
[0011] Preferably, in step three, the dynamic matching anti-scaling strategy specifically includes: (1) Low demand level: Adjust the washing water temperature to 40-50℃, increase the number of rinses by 1, and add scale inhibitor at 80% of the standard amount; (2) Medium demand level: Start the light self-cleaning mode, including high temperature rinsing at 60-70℃ and rotating friction of the inner drum at 1200r / min. Adjust the subsequent washing water temperature to 50-60℃ and add anti-scaling agent according to the standard amount. (3) High demand level: Start the deep self-cleaning mode, including 80℃ high temperature and high pressure rinsing, special descaling agent circulation soaking for 30 minutes and inner drum ultrasonic vibration, pause regular washing, and reset the anti-scaling parameter threshold after completion.
[0012] Preferably, in the deep self-cleaning mode, the water pressure for high-temperature and high-pressure rinsing is 0.3-0.5 MPa, and the ultrasonic oscillation frequency is 20-40 kHz; the special descaling agent is a citric acid-based composite descaling agent with a concentration of 5-8 g / L.
[0013] Preferably, in step four, the feedback and correction specifically involve: collecting parameters such as the surface roughness of the inner cylinder and the turbidity of the rinsing water after scale prevention and self-cleaning, comparing them with preset standard values, and if the deviation exceeds 10%, adjusting the weight coefficient of the scale formation prediction model and the scale prevention strategy parameters. The adjustment range of the scale prevention strategy parameters is water temperature fluctuation ±5℃ and scale prevention agent dosage ±10%.
[0014] (III) Beneficial Effects This invention provides a dynamic anti-scaling control method for washing machines based on intelligent monitoring and self-cleaning, which has the following beneficial effects: 1. This invention achieves precise and intelligent control of scale prevention in washing machines through multi-dimensional intelligent monitoring, machine learning prediction, dynamic anti-scaling strategy and closed-loop feedback correction. Compared with existing technologies, it significantly improves the anti-scaling effect and energy utilization, extends equipment life, and has broad application prospects.
[0015] 2. By collecting key parameters during the operation of the washing machine through a multi-dimensional intelligent monitoring module, and using a data processing module to build a scale formation prediction model, the scale prevention strategy and self-cleaning timing are dynamically adjusted to achieve closed-loop scale prevention control of "monitoring-judgment-control-feedback". Attached Figure Description
[0016] Figure 1 This is a schematic diagram of the control method flow according to Embodiment 2 of the present invention. Detailed Implementation
[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0018] Example 1: This invention provides a dynamic anti-scaling control method for washing machines based on intelligent monitoring and self-cleaning, comprising the following steps: Step 1: Construct a multi-dimensional monitoring module to collect water quality parameters, washing condition parameters, and inner drum status parameters in real time during the operation of the washing machine.
[0019] The multi-dimensional monitoring module includes a water quality sensor, a working condition sensor, an inner drum status sensor, and a detergent residue sensor. The water quality sensor is used to collect water hardness and turbidity; the working condition sensor is used to collect the number of washes, water temperature, water level, and rinsing times; the inner drum status sensor is used to collect the inner drum surface roughness and the amount of detergent residue; and the detergent residue sensor is used to collect the detergent concentration in the rinsing water. Furthermore, the inner drum status sensor uses an infrared distance measuring sensor to calculate the inner drum surface roughness by detecting changes in the distance between the inner drum surface and the sensor.
[0020] Step 2: The collected parameters are preprocessed by the data processing module, the preset scale formation prediction model is input, the scale formation rate and accumulation amount are output, and the scale prevention requirement level is determined.
[0021] The scale formation prediction model is based on a machine learning algorithm. The training set consists of historical scale formation data under different water qualities and operating conditions. The input parameters include water hardness, number of washes, water temperature, and the rate of change of the surface roughness of the inner drum. The output parameters are the scale formation rate and the cumulative amount. The machine learning algorithm is a random forest algorithm. The scale prevention requirement is divided into three levels: low, medium, and high, which correspond to the scale accumulation thresholds of 0-5 mg / cm², 5-10 mg / cm², and >10 mg / cm², respectively.
[0022] Step 3: Dynamically match anti-scaling strategies according to the anti-scaling requirement level, including adjusting washing program parameters, controlling anti-scaling agent dosage, and activating self-cleaning mode.
[0023] Specifically, the dynamic matching anti-scaling strategy is as follows: (1) Low demand level: Adjust the washing water temperature to 40-50℃, increase the number of rinses by 1, and add scale inhibitor at 80% of the standard amount; (2) Medium demand level: Start the light self-cleaning mode, including high temperature rinsing at 60-70℃ and rotating friction of the inner drum at 1200r / min. Adjust the subsequent washing water temperature to 50-60℃ and add anti-scaling agent according to the standard amount. (3) High demand level: Start the deep self-cleaning mode, including 80℃ high temperature and high pressure rinsing, special descaling agent circulation soaking for 30 minutes and inner cylinder ultrasonic vibration, pause regular washing, and reset the anti-scaling parameter threshold after completion; secondly, in the deep self-cleaning mode, the water pressure of high temperature and high pressure rinsing is 0.3-0.5MPa, and the ultrasonic vibration frequency is 20-40kHz; the special descaling agent is citric acid-based composite descaling agent with a concentration of 5-8g / L.
[0024] Step 4: During the anti-scaling and self-cleaning process, monitor the results in real time, adjust the anti-scaling strategy parameters, and form a closed-loop control.
[0025] Specifically, the feedback and correction are as follows: collect parameters of inner cylinder surface roughness and turbidity of rinsing water after anti-scaling and self-cleaning, and compare them with preset standard values. If the deviation exceeds 10%, adjust the weight coefficient of the scale formation prediction model and the anti-scaling strategy parameters. The adjustment range of the anti-scaling strategy parameters is water temperature fluctuation ±5℃ and anti-scaling agent dosage ±10%.
[0026] Example 2: like Figure 1 As shown, this embodiment of the invention provides a dynamic anti-scaling control method for washing machines based on intelligent monitoring and self-cleaning, including the following steps: Step 1: Construct a multi-dimensional monitoring module: Deploy water quality sensors, operating condition sensors, inner drum status sensors, and detergent residue sensors to collect parameters such as water hardness (0-500mg / L), water temperature (20-80℃), number of washes, inner drum surface roughness (accuracy ≤0.1μm), rinse water turbidity, and detergent concentration, with a sampling frequency of 1 time / minute.
[0027] Step 2: Establish a scale formation prediction model: A prediction model is built based on the random forest algorithm. The model uses historical monitoring data (covering scale formation data under different water quality and operating conditions) as the training set, inputs multi-dimensional monitoring parameters, and outputs the scale formation rate (mg / cm²·time) and cumulative amount (mg / cm²). The model prediction error is ≤8%.
[0028] Step 3: Determine the scale prevention requirement level: Set a threshold for scale accumulation and divide it into three demand levels: low (0-5 mg / cm²), medium (5-10 mg / cm²), and high (>10 mg / cm²). The data processing module automatically determines the current level based on the prediction results.
[0029] Step 4: Implement a dynamic anti-scaling strategy: (1) Low level: Optimize the washing program, adjust the water temperature to 40-50℃, increase the number of rinses by 1, and add 80% of the standard amount of anti-scaling agent to avoid excessive consumption; (2) Medium level: Start mild self-cleaning, rinse at 60-70℃ high temperature, rotate the inner drum at 1200r / min, and adjust the subsequent washing water temperature to 50-60℃ simultaneously. Add anti-scaling agent according to the standard amount. (3) High level: Start deep self-cleaning, pause regular washing, perform 80℃ high temperature and high pressure (0.3-0.5MPa) rinsing with 5-8g / L citric acid-based descaling agent for 30min and 20-40kHz ultrasonic vibration, and reset the anti-scaling parameter threshold after completion.
[0030] Step 5: Correct the closed-loop feedback: Collect the surface roughness of the inner cylinder and the turbidity of the rinsing water after anti-scaling and self-cleaning, and compare them with the preset standard values (roughness ≤ 0.5 μm, turbidity ≤ 5 NTU). When the deviation exceeds 10%, dynamically adjust the prediction model weight coefficient and anti-scaling strategy parameters (water temperature ± 5℃, anti-scaling agent ± 10%).
[0031] In summary, this invention achieves precise and intelligent control of scale prevention in washing machines through multi-dimensional intelligent monitoring, machine learning prediction, dynamic anti-scaling strategies, and closed-loop feedback correction. Compared with existing technologies, it significantly improves the anti-scaling effect and energy utilization, extends equipment life, and has broad application prospects.
[0032] Example 3: This third embodiment, based on the second embodiment, implements a scenario with low scale prevention requirements. Specifically: 1. Monitoring data: water hardness 150mg / L, washing times 5 times, water temperature 35℃, inner cylinder surface roughness 0.3μm, rinsing water turbidity 3NTU; 2. Predicted results: Scale formation rate is 0.8 mg / cm²·time, and cumulative amount is 2.4 mg / cm², which is judged as low demand level; 3. Implementation strategy: Adjust the washing water temperature to 45℃, increase the number of rinsing cycles from 2 to 3, and add 80% (i.e., 8g) of the standard amount of anti-scaling agent (10g / cycle). 4. Feedback Correction: After cleaning, the inner cylinder roughness is 0.28μm and the turbidity is 2NTU, which meets the standard and no parameter adjustment is required.
[0033] Example 4: This fourth embodiment, based on the second embodiment, implements a scenario with a high anti-scaling requirement. Specifically: 1. Monitoring data: water hardness 400mg / L, washing times 20 times, water temperature 60℃, inner cylinder surface roughness 1.2μm, rinsing water turbidity 12NTU; 2. Predicted results: Scale formation rate is 2.5 mg / cm²·time, and cumulative amount is 12 mg / cm², which is judged as high demand level; 3. Execution strategy: Initiate deep self-cleaning, rinse at 80℃ high temperature and high pressure (0.4MPa) for 10 minutes, inject 6g / L citric acid-based descaling agent and circulate for 30 minutes, and ultrasonically vibrate at 25kHz for 15 minutes. After completion, the roughness of the inner cylinder is reduced to 0.4μm. 4. Feedback Correction: After cleaning, the turbidity was 4 NTU. The weighting coefficient of water hardness in the prediction model was adjusted by +0.1. The concentration of the high-grade anti-scaling agent was subsequently adjusted to 7 g / L.
[0034] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A dynamic anti-scaling control method for washing machines based on intelligent monitoring and self-cleaning, characterized in that: Includes the following steps: Step 1: Construct a multi-dimensional monitoring module to collect water quality parameters, washing condition parameters, and inner drum status parameters in real time during the operation of the washing machine; Step 2: The collected parameters are preprocessed by the data processing module, the preset scale formation prediction model is input, the scale formation rate and accumulation amount are output, and the scale prevention requirement level is determined. Step 3: Dynamically match anti-scaling strategies according to the anti-scaling requirement level, including adjusting washing program parameters, controlling anti-scaling agent dosage, and activating self-cleaning mode; Step 4: During the anti-scaling and self-cleaning process, monitor the results in real time, adjust the anti-scaling strategy parameters, and form a closed-loop control.
2. The dynamic anti-scaling control method for washing machines based on intelligent monitoring and self-cleaning according to claim 1, characterized in that: In step one, the multi-dimensional monitoring module includes a water quality sensor, a working condition sensor, an inner drum status sensor, and a detergent residue sensor. The water quality sensor is used to collect water hardness and turbidity, the working condition sensor is used to collect the number of washes, water temperature, water level, and number of rinses, the inner drum status sensor is used to collect the surface roughness and amount of residue on the inner drum, and the detergent residue sensor is used to collect the detergent concentration in the rinse water.
3. The dynamic anti-scaling control method for washing machines based on intelligent monitoring and self-cleaning according to claim 2, characterized in that: The inner cylinder status sensor is an infrared ranging sensor, which calculates the surface roughness of the inner cylinder by detecting the change in distance between the inner cylinder surface and the sensor.
4. The dynamic anti-scaling control method for washing machines based on intelligent monitoring and self-cleaning according to claim 1, characterized in that: In step two, the scale formation prediction model is constructed based on a machine learning algorithm. The training set consists of historical scale formation data under different water qualities and operating conditions. The input parameters include water hardness, number of washes, water temperature, and the rate of change of the surface roughness of the inner cylinder. The output parameters are the scale formation rate and the cumulative amount.
5. The dynamic anti-scaling control method for washing machines based on intelligent monitoring and self-cleaning according to claim 4, characterized in that: The machine learning algorithm is the random forest algorithm, and the scale prevention requirement is divided into three levels: low, medium and high, which correspond to the scale accumulation thresholds of 0-5 mg / cm², 5-10 mg / cm² and >10 mg / cm², respectively.
6. The dynamic anti-scaling control method for washing machines based on intelligent monitoring and self-cleaning according to claim 1, characterized in that: In step three, the dynamic matching anti-scaling strategy specifically includes: (1) Low demand level: Adjust the washing water temperature to 40-50℃, increase the number of rinses by 1, and add scale inhibitor at 80% of the standard amount; (2) Medium demand level: Start the light self-cleaning mode, including high temperature rinsing at 60-70℃ and rotating friction of the inner drum at 1200r / min. Adjust the subsequent washing water temperature to 50-60℃ and add anti-scaling agent according to the standard amount. (3) High demand level: Start the deep self-cleaning mode, including 80℃ high temperature and high pressure rinsing, special descaling agent circulation soaking for 30 minutes and inner drum ultrasonic vibration, pause regular washing, and reset the anti-scaling parameter threshold after completion.
7. The dynamic anti-scaling control method for washing machines based on intelligent monitoring and self-cleaning according to claim 6, characterized in that: In the deep self-cleaning mode, the water pressure for high-temperature and high-pressure rinsing is 0.3-0.5MPa, and the ultrasonic oscillation frequency is 20-40kHz; the special descaling agent is a citric acid-based composite descaling agent with a concentration of 5-8g / L.
8. The dynamic anti-scaling control method for washing machines based on intelligent monitoring and self-cleaning according to claim 1, characterized in that: In step four, the feedback and correction specifically involve: collecting parameters such as the surface roughness of the inner cylinder and the turbidity of the rinsing water after scale prevention and self-cleaning, and comparing them with preset standard values. If the deviation exceeds 10%, the weight coefficient of the scale formation prediction model and the scale prevention strategy parameters are adjusted. The adjustment range of the scale prevention strategy parameters is water temperature fluctuation ±5℃ and scale prevention agent dosage ±10%.