Method for judging dirt condition in washing machine, intelligent washing method for washing machine, and washing machine

Abstract

The present application relates to a kind of cleaning machine in the dirt condition judging method, cleaning machine carries out a cleaning work, detect and obtain the motor power N in the state of washing with clean water before heating, detect and obtain the motor power data P in the cleaning state after heating, compare the change situation of P relative to N, and the fluctuation situation of motor power P with time itself, further determine the dirt condition in cleaning machine.The present application also relates to a kind of intelligent cleaning method of cleaning machine, apply the dirt condition judging method described above, and further determine the cleaning parameter in the subsequent cleaning work process of cleaning machine according to the dirt condition in cleaning.The present application also relates to the cleaning machine of the dirt condition judging method and / or intelligent cleaning method of cleaning machine in cleaning machine.The method and cleaning machine in the present application only use necessary working parameters in cleaning machine work, without needing to specially set up the equipment for collecting dirt information in cleaning machine, i.e.can accurately realize dirt condition detection, cleaning work is more intelligent, effective.

Description

Technical Field

[0001] This invention relates to a method for judging the level of dirt inside a cleaning machine, an intelligent cleaning method for a cleaning machine using this method, and a cleaning machine itself. Background Technology

[0002] To ensure cleaning effectiveness, cleaning machines typically detect the level of turbidity within the machine and adjust cleaning parameters accordingly. Current technology commonly uses turbidity sensors to detect the level of turbidity and then controls the cleaning process based on the sensor data. However, turbidity sensors and their associated data cables are expensive, and their sensitivity to different contaminants varies, making them prone to misjudgments. Furthermore, scale can easily accumulate on the turbidity sensors over time, affecting their accuracy. While cameras are sometimes used to detect contamination within the cleaning machine, they also suffer from the aforementioned problems and require significant computational resources, placing high demands on the control chip and increasing its cost. Summary of the Invention

[0003] The first technical problem to be solved by the present invention is to provide a method for judging the condition of dirt inside a cleaning machine that only uses the necessary operating parameters of the cleaning machine during operation, without the need for a special device to collect information on dirt inside the cleaning machine.

[0004] The second technical problem to be solved by the present invention is to provide an intelligent cleaning method for a cleaning machine that uses the aforementioned method for judging the condition of dirt in the cleaning machine, thereby achieving intelligent and effective cleaning.

[0005] The third technical problem to be solved by the present invention is to provide a cleaning machine that applies the aforementioned method for judging the condition of dirt in the cleaning machine and / or the intelligent cleaning method, in contrast to the prior art.

[0006] The technical solution adopted by the present invention to solve the first technical problem mentioned above is: a method for judging the condition of dirt in a cleaning machine, characterized in that: when the cleaning machine performs a cleaning operation, the motor power N under the state of rinsing with clean water before heating is detected and acquired, the motor power data P under the state of cleaning after heating is detected and acquired, the change of P relative to N and the fluctuation of the motor power P over time are compared, and then the condition of dirt in the cleaning machine is determined.

[0007] To more accurately determine the level of dirt inside the cleaning machine, the average motor power data under the clean water rinsing state before heating is calculated as the motor power N in the unheated cleaning stage.

[0008] Under the post-heating cleaning state, calculate the average motor power P0 per unit time, and simultaneously obtain the maximum motor power value Pmax and the minimum motor power value Pmin within the same unit time.

[0009] Calculate the percentage A of the difference between N and P0 relative to N, and the percentage k of the difference between Pmax and Pmin relative to N, and then determine the level of dirt in the cleaning machine based on A and k.

[0010] The technical solution adopted by the present invention to solve the second technical problem mentioned above is: an intelligent cleaning method for a cleaning machine, characterized in that: the aforementioned method for judging the condition of dirt inside the cleaning machine is applied;

[0011] The cleaning process of the cleaning machine includes a non-heated cleaning stage, a heated cleaning stage, and a non-heated cleaning stage in sequence.

[0012] When the cleaning machine performs a cleaning operation, the motor power N during the unheated cleaning stage is obtained as the motor power N under the clean water rinsing state before heating;

[0013] Real-time motor power data P during the non-heated cleaning stage is collected as the motor power data P during the heated cleaning state. The changes of P relative to N and the fluctuations of motor power P over time are compared to determine the amount of dirt inside the cleaning machine. Based on the amount of dirt inside the machine, the cleaning parameters for subsequent cleaning processes are determined.

[0014] As a further improvement, the average value of the real-time motor power data during the unheated cleaning stage is calculated as the motor power N during the unheated cleaning stage;

[0015] The average motor power P0 within a unit time period is calculated based on the real-time collected motor power P, and the maximum motor power value Pmax and the minimum motor power value Pmin within the same unit time period are obtained.

[0016] If (N-P0) / N≤A and Pmax-Pmin≤kN, then it is determined that there is relatively little dirt in the current cleaning machine, and the corresponding cleaning parameters are determined to be the set light-load cleaning parameters; where 0<A<50% and 0<k<50%.

[0017] As a further improvement, during the cleaning process, the water temperature data inside the cleaning machine is collected in real time during the heating cleaning stage. The heating time T required for the water temperature to reach the target temperature W0 is timed, and the cleaning parameters determined in the non-heating cleaning stage are adjusted based on T. Then, the subsequent cleaning work of the cleaning machine is carried out according to the adjusted cleaning parameters.

[0018] In order to have a unified judgment standard for different ambient temperatures, the heating time T is the time required for the water temperature to heat from the set temperature W1 to the target temperature W0, where W2 < W1 < W0, and W2 is the highest ambient temperature of the place where the cleaning machine is used.

[0019] As an improvement, the cleaning parameters are increased as T increases.

[0020] Preferably, the cleaning parameters of the cleaning machine include preset light-load cleaning parameters and heavy-load cleaning parameters, wherein the light-load cleaning parameters are less than the heavy-load cleaning parameters;

[0021] The time T is compared with the preset time threshold T0. The cleaning machine is controlled to perform subsequent cleaning work according to the light load cleaning parameters only when T < T0, (N - P0) / N ≤ A, and Pmax - Pmin ≤ kN. Otherwise, the subsequent cleaning work is performed according to the heavy load cleaning parameters.

[0022] The technical solution adopted by the present invention to solve the third technical problem mentioned above is: a cleaning machine, characterized in that: the aforementioned method for judging the condition of dirt in the cleaning machine and / or the aforementioned intelligent cleaning method are applied.

[0023] Compared with existing technologies, the advantages of this invention are as follows: The method for determining the level of contaminants inside the cleaning machine in this invention is based on the characteristic of the property changes of contaminants during the cleaning process, and the power changes of the motor in the cleaning machine caused by the property changes of the contaminants, thereby determining the level of contaminants inside the cleaning machine. This process only requires acquiring the necessary operating parameters of the cleaning machine, without the need for additional detection devices to detect the contaminants, resulting in low cost. Furthermore, during long-term application, there are no other factors causing errors in the detection devices, leading to better consistency and accuracy in determining the level of contaminants inside the cleaning machine. Correspondingly, the intelligent cleaning method applying this method can perform more intelligent and effective cleaning work, and the cleaning machine has a low failure rate. Cleaning machines using this method and / or the intelligent cleaning method have low-cost contaminant detection devices and highly reliable detection results. Attached Figure Description

[0024] Figure 1 This is a flowchart of the intelligent cleaning method of the cleaning machine in an embodiment of the present invention. Detailed Implementation

[0025] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0026] The cleaning machine in this invention is a type of dishwasher, comprising a base with a cleaning chamber, a motor disposed below the base, an impeller connected to the motor drive end, and a spray arm located within the cleaning chamber. The lower part of the impeller has an axial flow structure, while the upper part of the impeller has a centrifugal structure located within the spray arm. The lower part of the impeller is submerged in water and can drive water to flow axially to the upper part of the impeller. Under the centrifugal force of the impeller, the spray arm rotates and sprays cleaning water onto the dishes and utensils in the cleaning chamber.

[0027] When this washing machine is running with clean water and no detergent, the impeller experiences a stable load due to the stable properties of the water, resulting in a relatively stable motor power output. However, when the washing water comes into contact with common kitchen contaminants such as grease and egg residue on dishes, these contaminants are present in the returning water. During the washing process, the combined effects of heating and impeller agitation generate numerous bubbles, altering the properties of the washing water. Specifically, the water density decreases, and because of the continuous movement within the washing chamber, the water's properties are not uniform. This can be considered as applying a lower, more unstable load to the impeller, resulting in a significantly lower and more volatile motor power output compared to when using clean water.

[0028] Based on the characteristics of the changes in the properties of the cleaning water during the cleaning process, the method for judging the dirt situation inside the cleaning machine in this embodiment is as follows: When the cleaning machine performs a cleaning cycle, the motor power N under the clean water rinsing state before heating is detected and acquired. Under this condition, since there is no influence of dishwashing powder, detergent, or heating temperature on the properties of the cleaning water, substances such as oil and protein in the cleaning machine do not foam or foam very weakly under the action of the impeller. At this time, the motor power is relatively stable and basically maintained near the rated output power. In order to ensure that the calculated motor power N can more accurately reflect the actual power situation, the average value of the motor power data under the clean water rinsing state before heating is calculated based on the real-time collected power data as the motor power N in the unheated cleaning stage.

[0029] The motor power data P is acquired during the heating and cleaning process. At this point, the cleaning water in the machine has been heated, and contaminants such as oil and protein in the water have fully foamed. Consequently, the motor power decreases due to the presence of these contaminants. The more severe the contamination of the cleaning water, the lower the motor power P. Furthermore, the uneven distribution of these contaminants in the cleaning water causes fluctuations in motor power; generally, the more severe the contamination, the greater the fluctuation in motor power. By comparing the change of P relative to N, and the fluctuation of motor power P over time, the level of contamination within the cleaning machine can be determined.

[0030] In this embodiment, under the post-heating cleaning state, the average motor power P0 per unit time is calculated, and the maximum motor power value Pmax and the minimum motor power value Pmin within that unit time are obtained. The proportion A of the difference between N and P0 relative to N, and the proportion k of the difference between Pmax and Pmin relative to N are calculated, and then the level of contamination within the cleaning machine is determined based on A and k. During the equipment development phase, multiple difference proportion ranges for A and multiple fluctuation proportion ranges for k can be preset based on specific experimental conditions. Different combinations of these difference and fluctuation proportion ranges can then be used to preset corresponding contamination levels. Thus, based on the calculated combination of the difference and fluctuation proportion ranges where A and k are located, the corresponding contamination level is determined. When the cleaning machine is working, the cleaning parameters can be directly determined based on this contamination level, or the cleaning parameters can be adjusted based on the influence of other factors.

[0031] like Figure 1 As shown, the aforementioned cleaning machine can operate using the intelligent cleaning method described below, which applies the aforementioned method for judging the level of dirt inside the cleaning machine. This intelligent cleaning method is primarily suitable for cleaning processes that do not use foaming cleaning agents such as dishwashing powder and include a heated cleaning stage. Since cleaning machines typically have a pre-cleaning process before adding cleaning agents, this intelligent cleaning method is particularly suitable for this pre-cleaning process.

[0032] like Figure 1 As shown, the intelligent cleaning method of this cleaning machine is as follows.

[0033] The cleaning process of the cleaning machine includes three stages: unheated cleaning, heated cleaning, and non-heated cleaning. The unheated cleaning stage is the rinsing stage where clean water is directly introduced without heating. The heated cleaning stage is the stage where the cleaning water in the cleaning chamber is heated using a heating plate or other heating device. The non-heated cleaning stage is the cleaning stage after heating.

[0034] When the cleaning machine performs a cleaning cycle, the motor power N during the unheated cleaning stage is used as the motor power N during the pre-heating water rinsing state. Since no heating occurs during this stage, oil, protein, and other substances in the cleaning machine do not foam or foam very weakly under the action of the impeller. At this time, the motor power is relatively stable and basically maintains near the rated output power. To ensure that the calculated motor power N more accurately reflects the actual power situation, the average value of the real-time motor power data during the unheated cleaning stage is calculated as the motor power N during the unheated cleaning stage.

[0035] Real-time motor power data P during the non-heated cleaning stage is collected and used as the motor power data P during the heated cleaning state. At this point, the cleaning water in the cleaning machine has undergone the heated cleaning stage, and contaminants such as oil and protein in the water have fully foamed. Consequently, the motor power decreases due to the presence of these contaminants. The more severe the contamination of the cleaning water, the lower the motor power P. Furthermore, the uneven distribution of these contaminants in the cleaning water causes fluctuations in motor power; generally, the more severe the contamination, the greater the fluctuation in motor power. By comparing the changes in P relative to N, and the fluctuations in motor power P over time, the level of contamination within the cleaning machine is determined, and the cleaning parameters for subsequent cleaning processes are determined based on this level of contamination.

[0036] In this embodiment, the specific analysis method for the change of P relative to N and the fluctuation of motor power P over time is as follows: The average motor power P0 within a unit of time is calculated based on the real-time collected motor power P. This unit of time is specifically set as needed; for example, if the unit of time is 1 minute, the average value P0 of the motor power data collected within one minute from the current moment, including the current moment, is calculated. Simultaneously, the maximum motor power value Pmax and the minimum motor power value Pmin within this unit of time are obtained.

[0037] If (N-P0) / N ≤ A and Pmax-Pmin ≤ kN, it is determined that there is relatively little contaminant in the cleaning machine, and the corresponding cleaning parameters are set as the light-load cleaning parameters; where 0 < A < 50%, 0 < k < 50%, and in this embodiment, A = 10%, k = 10%. That is, (N-P0) / N ≤ 10% indicates that there are relatively few contaminants in the cleaning water, and therefore, due to the small amount of foaming caused by the contaminants, the average motor power P0 does not change much compared to the motor power N when rinsing with unheated water in the unheated cleaning stage. At the same time, Pmax-Pmin ≤ 10%N indicates that the motor power fluctuation is small in the non-heated cleaning stage, which further confirms that there are relatively few contaminants in the cleaning water. Thus, it can be basically determined that there are relatively few contaminants in the cleaning machine. For cleaning machines with simple control programs, the cleaning parameters can be directly determined based on this result, and then the cleaning machine can be controlled to perform subsequent cleaning work according to the cleaning parameters.

[0038] However, in this embodiment, during the heating and cleaning process, it is necessary to further determine the number of tableware inside the cleaning machine. Specifically, during each cleaning cycle, the water temperature data inside the cleaning machine is collected in real time during the heating and cleaning phase. The heating time T required to heat the water to the target temperature W0 is timed, and the cleaning parameters determined during the non-heating and cleaning phase are adjusted based on T. Then, subsequent cleaning operations are performed based on the adjusted cleaning parameters. The more tableware inside the cleaning machine, the greater the heat exchange required, and the longer the heating time T is needed to achieve the same target temperature.

[0039] However, different inlet water temperatures also affect the heating time. Ambient temperature is a crucial factor influencing inlet water temperature. To establish a unified standard for different ambient temperatures, the heating time T is the time required for the water to heat from the set temperature W1 to the target temperature W0, where W2 < W1 < W0, and W2 is the highest ambient temperature at the location where the cleaning machine is used. For example, in environments with varying temperatures throughout the year, the inlet water temperature can range from below -30°C to above 30°C. Typically, W1 can be set to around 33°C, so setting W1 to 35°C better standardizes the heating time. The target temperature W0 usually needs to reach above 40°C to ensure effective cleaning.

[0040] As T increases, it indicates a larger number of dishes placed inside the washing machine. Therefore, the washing parameters should be increased accordingly, based on the conditions (N-P0) / N≤A and Pmax-Pmin≤kN. Depending on the situation, specific rules for increasing the washing parameters can be set to ultimately determine the washing parameters of the washing machine.

[0041] In this embodiment, the simplest method for determining cleaning parameters is adopted. Specifically, the cleaning parameters of the cleaning machine include preset light-load cleaning parameters and heavy-load cleaning parameters, with the light-load cleaning parameters being less than the heavy-load cleaning parameters. Depending on the specific product of the cleaning machine, the cleaning parameters may include at least one of the following: motor speed, water flow rate, heating temperature, and cleaning time.

[0042] The time T is compared with the preset time threshold T0. The cleaning machine is controlled to perform subsequent cleaning work according to the light load cleaning parameters only when T < T0, (N - P0) / N ≤ A, and Pmax - Pmin ≤ kN. Otherwise, the subsequent cleaning work is performed according to the heavy load cleaning parameters.

[0043] The intelligent cleaning method of this cleaning machine is based on the characteristics of the changes in the properties of dirt during the cleaning process. The power changes of the motor in the cleaning machine caused by the changes in the properties of dirt are then used to determine the dirt situation inside the cleaning machine. In this process, only the necessary operating parameters of the cleaning machine need to be obtained. There is no need to set up additional detection devices to detect the dirt situation. The cost is low, and in long-term application, there are no other factors that may cause errors in the detection devices. The judgment of the dirt situation inside the cleaning machine is more consistent and accurate.

Claims

1. A method for judging the condition of dirt inside a cleaning machine, characterized in that: When the cleaning machine performs a cleaning operation, it detects and acquires the motor power N under the clean water rinsing state before heating, and detects and acquires the motor power data P under the cleaning state after heating. It compares the change of P relative to N, as well as the fluctuation of the motor power P over time, and thus determines the amount of dirt in the cleaning machine. The average value of the motor power data under the clean water rinsing state before heating is used as the motor power N in the unheated cleaning stage; Under the post-heating cleaning state, calculate the average motor power P0 per unit time, and simultaneously obtain the maximum motor power value Pmax and the minimum motor power value Pmin corresponding to that unit time. Calculate the percentage A of the difference between N and P0 relative to N, and the percentage k of the difference between Pmax and Pmin relative to N, and then determine the level of dirt in the cleaning machine based on A and k.

2. An intelligent cleaning method for a cleaning machine, characterized in that: Apply the method for judging the condition of dirt inside the cleaning machine as described in claim 1; The cleaning process of the cleaning machine includes a non-heated cleaning stage, a heated cleaning stage, and a non-heated cleaning stage in sequence. When the cleaning machine performs a cleaning operation, the motor power N during the unheated cleaning stage is obtained as the motor power N under the clean water rinsing state before heating; Real-time motor power data P during the non-heated cleaning stage is collected as the motor power data P during the heated cleaning state. The changes of P relative to N and the fluctuations of motor power P over time are compared to determine the amount of dirt inside the cleaning machine. Based on the amount of dirt inside the machine, the cleaning parameters for subsequent cleaning processes are determined.

3. The intelligent cleaning method for the cleaning machine according to claim 2, characterized in that: The average real-time motor power data during the unheated cleaning stage is calculated as the motor power N during the unheated cleaning stage; The average motor power P0 per unit time is calculated based on the real-time motor power P collected in real time, and the maximum motor power value Pmax and the minimum motor power value Pmin corresponding to this unit time are obtained at the same time. If (N-P0) / N≤A and Pmax-Pmin≤kN, then it is determined that there is relatively little dirt in the current cleaning machine, and the corresponding cleaning parameters are determined to be the set light-load cleaning parameters; where 0<A<50% and 0<k<50%.

4. The intelligent cleaning method for the cleaning machine according to claim 3, characterized in that: When the cleaning machine performs a cleaning operation, it collects the water temperature data in the cleaning machine in real time during the heating cleaning stage, and times the heating time T required to heat the water to the target temperature W0. Based on T, the cleaning parameters determined in the non-heating cleaning stage are adjusted, and then the subsequent cleaning work of the cleaning machine is carried out according to the adjusted cleaning parameters.

5. The intelligent cleaning method for the cleaning machine according to claim 4, characterized in that: The heating time T is the time required for the water temperature to rise from the set temperature W1 to the target temperature W0, where W2 < W1 < W0, and W2 is the highest ambient temperature of the location where the cleaning machine is used.

6. The intelligent cleaning method for the cleaning machine according to claim 5, characterized in that: Increase the cleaning parameters as T increases.

7. The intelligent cleaning method for the cleaning machine according to claim 5, characterized in that: The cleaning parameters of the cleaning machine include preset light-load cleaning parameters and heavy-load cleaning parameters, with the light-load cleaning parameters being less than the heavy-load cleaning parameters. The time T is compared with the preset time threshold T0. The cleaning machine is controlled to perform subsequent cleaning work according to the light load cleaning parameters only when T < T0, (N - P0) / N ≤ A, and Pmax - Pmin ≤ kN. Otherwise, the subsequent cleaning work is performed according to the heavy load cleaning parameters.

8. A cleaning machine, characterized in that: The method for judging the condition of dirt in the cleaning machine as described in claim 1 and / or the intelligent cleaning method for the cleaning machine as described in any one of claims 2 to 7 are applied.

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