Washing machine
By using sensor components in the washing machine to collect the temperature of washing water and adjust the heating time in combination with machine learning models, the problem of fluctuations in washing water temperature is solved, and the cleaning effect and energy utilization efficiency are improved.
Patent Information
- Application Number
- CN202311222171.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-09-21
- Publication Date
- 2025-03-21
AI Technical Summary
The heating time of the existing washing machine during the heating stage during the washing procedure is fixed, resulting in fluctuations in the temperature of the washing water, which may not be able to reach the set temperature, affecting the cleaning effect.
By setting the sensor component in the washing machine to collect the temperature value of the washing water, and using the controller to input the inlet water temperature value and washing setting information to the target machine learning model, the target heating time is obtained and the heating stage duration in the washing program is adjusted.
Ensure that the washing water can be effectively heated to the set temperature during the heating stage, improve cleaning effect, reduce energy waste in the washing machine, and improve user satisfaction.
Smart Images
Figure CN119663603A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of washing machine control, and in particular to a washing machine. Background Art
[0002] As people's living standards continue to improve, washing clothes with a washing machine has become a common way of cleaning clothes. In the related art, in order to make the washing machine wash the clothes to be washed better, the clothes to be washed are usually washed with heated washing water. However, since the duration of each washing stage in the washing program for washing the clothes to be washed is fixed, while the temperature of the washing water fluctuates, if the temperature of the washing water is too low, it may cause the temperature value of the washing water to still not be heated to the temperature value required by the washing program after the heating stage in the washing program is completed, resulting in a decrease in the washing ability of the washing machine to wash the clothes. Summary of the invention
[0003] In order to solve the above technical problems, an embodiment of the present application provides a washing machine.
[0004] According to one aspect of an embodiment of the present application, a washing machine is provided, comprising: a housing; an inner drum and a sensor assembly arranged in the housing; the sensor assembly is used to collect the temperature value of washing water flowing in from a water inlet opened on the housing, and is electrically connected to a controller in the housing, and the controller is configured to perform the following steps: obtaining an inlet water temperature value and washing setting information; the washing setting information includes information on a washing program for washing clothes to be washed in the inner drum; inputting the inlet water temperature value and the washing setting information into a target machine learning model to obtain a target heating time; and adjusting the heating time corresponding to the heating stage in the washing program based on the target heating time.
[0005] In the above embodiment, the washing machine can obtain the water inlet temperature value and the washing setting information based on the controller, and then input the obtained water inlet temperature value and washing setting information into the target machine learning model through the controller to obtain the target heating time, so that the controller adjusts the heating time corresponding to the heating stage in the washing program according to the target heating time. On the one hand, when the heating time corresponding to the heating stage is too long, the overall washing time can be reduced to avoid wasting resources of the washing machine. On the other hand, it can ensure that after the heating stage is completed, the washing water can be heated from the water inlet temperature value to the set temperature value corresponding to the washing setting information in the washing program, so that the cleaning effect of the washing machine is not easily weakened, thereby improving the user's satisfaction with the use of the washing machine.
[0006] In some embodiments, the controller is also configured to perform the following steps: obtaining multiple historical water inlet temperature values recorded by the sensor component from a historical water inlet temperature table; calculating the average temperature value between the multiple historical water inlet temperature values and the current water inlet temperature value; inputting the average temperature value as the water inlet temperature value and the washing setting information into the target machine learning model to obtain an optimized heating time; and adjusting the target heating time based on the optimized heating time.
[0007] In the above embodiment, when the temperature value of the washing water produces a large abnormal fluctuation or the sensor component has an abnormality when collecting or transmitting the temperature value, the controller can correct the temperature abnormality by using the average temperature value between multiple historical water inlet temperature values and the current water inlet temperature value, and then output the corrected optimized heating time through the target machine learning model. Finally, the target heating time is adjusted based on the optimized heating time, thereby further ensuring that the washing water is heated to the set temperature value after the heating stage is completed, and at the same time, preventing the washing machine from wasting too much energy during the heating stage.
[0008] In some embodiments, the controller is further configured to perform the following steps: in the process of obtaining the multiple historical water inlet temperature values from the historical water inlet temperature table, filtering out the water inlet temperature values whose recording time does not exceed a preset time from the historical water inlet temperature table as historical water inlet temperature values; or, selecting a preset number of water inlet temperature values from the historical water inlet temperature table in sequence according to the recording time as historical water inlet temperature values.
[0009] In the above embodiment, the controller ensures that the historical inlet water temperature values obtained from the historical inlet water temperature table are consistent with the temperature change range of the washing water at the current washing time by screening the inlet water temperature values whose recording time does not exceed the preset time from the historical inlet water temperature table or selecting a preset number of inlet water temperature values in sequence according to the recording time as the historical inlet water temperature values, so as to avoid causing excessive deviation between the average temperature value calculated from the multiple historical inlet water temperature values and the current inlet water temperature value.
[0010] In some embodiments, the controller is also configured to perform the following steps: determining an expected heating time based on the inlet water temperature value and the washing setting information; wherein the expected heating time represents the time taken for the heater in the washing machine to heat the inlet water from the inlet water temperature value to the set temperature value in the washing setting information; if the expected heating time exceeds the target heating time, the expected heating time is used as the target heating time.
[0011] In the above embodiment, the controller further ensures that the washing water is heated to the set temperature value after the heating stage is completed through the relationship between the estimated heating time and the target heating time.
[0012] In some embodiments, the controller is further configured to perform the following steps: during the running of the washing program, obtaining the remaining duration of the heating stage corresponding to the target heating duration; calculating the expected heating temperature value after heating the washing water in the washing machine based on the remaining duration; if the expected heating temperature value does not reach the set temperature value in the washing setting information, increasing the target heating time.
[0013] In the above embodiment, the controller further determines whether the washing water can be heated to the set temperature value after the heating stage is completed by calculating the expected heating temperature value during the running of the washing program. If it is determined to be no, the target heating time is increased to ensure that the washing water can be further heated during the heating stage.
[0014] In some embodiments, the controller is further configured to perform the following steps: in the process of increasing the target heating time, determining the extended time according to the difference between the expected heating temperature value and the set temperature value; if it is determined that the washing program belongs to the target washing program, then increasing the target heating time corresponding to the heating stage in the washing program by the extended time, and reducing the main wash time corresponding to the main wash stage in the washing program by the extended time.
[0015] In the above embodiment, after the controller increases the target heating time by an extended time, it can achieve the purpose of ensuring that the washing water is heated to the set temperature value while avoiding extending the time too long, which leads to energy waste during the execution of the washing program.
[0016] In some embodiments, the sensor assembly is also used to collect the load weight value of the clothes to be washed in the inner drum; the controller is also configured to perform the following steps: in the process of obtaining the water inlet temperature value and the washing setting information, the load weight value is obtained, and the water inlet temperature value, the load weight value and the washing setting information are input into the target machine learning model to obtain the target heating time.
[0017] In the above embodiment, the controller further ensures that after the heating stage adjusted based on the target heating time is completed, the washing water is heated to the set temperature value according to the acquired load weight value, and it is not easy to cause waste of resources.
[0018] In some embodiments, the controller is further configured to perform the following steps: determining the washing program in response to a washing signal; wherein the washing signal is generated by a user triggering a multimedia device on the box; determining the washing time of the washing program based on an average temperature value corresponding to multiple historical water inlet temperature values obtained from a historical water inlet temperature table, and sending the washing time to the multimedia device for display.
[0019] In the above embodiment, the washing time of the washing program viewed by the user on the multimedia device is more closely matched with the current temperature of the washing water and is more accurate, thereby improving the user's satisfaction with the use of the washing machine.
[0020] In some embodiments, the training process of the target machine learning model includes the following steps: constructing an initial machine learning model, and inputting a test water inlet temperature value and a test washing setting information into the initial machine learning model to obtain an initial heating time; adjusting the initial machine learning model based on the difference between the test heating time corresponding to the water inlet temperature value and the test washing setting information and the initial heating time to obtain a trained machine learning model, and using the trained machine learning model as the target machine learning model.
[0021] In some embodiments, the training process of the target machine learning model also includes the following steps: inputting the water inlet temperature value and the washing setting information into the trained machine learning model to obtain an initial heating time; adjusting the trained machine learning model according to the difference between the initial heating time and the target heating time adjusted based on the optimized heating time, the expected heating time or the extended time to obtain an updated machine learning model, and using the updated machine learning model as the target machine learning model.
[0022] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:
[0024] Figure 1 It is a structural schematic diagram of a washing machine shown in an exemplary embodiment of the present application.
[0025] Figure 2 It is a schematic diagram of the structure of a water inlet temperature sensor in a sensor assembly shown as an exemplary embodiment of the present application.
[0026] Figure 3 It is a schematic diagram of the structure of a weight sensor in a sensor assembly shown as an exemplary embodiment of the present application.
[0027] Figure 4It is a schematic diagram of the structure of a multimedia device in a washing machine shown in an exemplary embodiment of the present application.
[0028] Figure 5 This is a flow chart of a method for controlling a washing machine provided in an embodiment of the present application. DETAILED DESCRIPTION
[0029] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.
[0030] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.
[0031] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.
[0032] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.
[0033] It should be noted that the "multiple" mentioned in this article refers to two or more. "And / or" describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.
[0034] Figure 1 Schematic diagram of a washing machine provided by an exemplary embodiment of the present application. Figure 1 As shown, the washing machine 10 comprises a housing 11, an inner drum 12, a sensor assembly and a controller. Each part will be introduced one by one below.
[0035] The box body 11 is provided with a water inlet 13 for receiving washing water for washing the laundry and a drain outlet 14 for discharging wastewater after washing the laundry.
[0036] The inner drum 12 is disposed in the box body 11 , and the inner drum 12 is used to contain clothes to be washed.
[0037] The sensor assembly is disposed in the box body 11 , and is used to collect the temperature value of the washing water flowing in from the water inlet 13 opened on the box body 11 .
[0038] In one example, refer to Figure 2 As shown, the sensor assembly includes a water inlet temperature sensor 15, which is arranged on the side wall of the water inlet to collect the water inlet temperature value of the washing water flowing in from the water inlet through the water inlet temperature sensor 15.
[0039] The water inlet temperature sensor may be a temperature sensor such as a thermocouple, a thermistor, a platinum resistance detector (RTD), or a temperature IC.
[0040] The controller is disposed in the housing 11 and is electrically connected to the sensor assembly. The controller is also configured to perform the following steps:
[0041] Get the water inlet temperature value and washing setting information;
[0042] Input the water inlet temperature value and washing setting information into the target machine learning model to obtain the target heating time;
[0043] Adjust the heating time corresponding to the heating phase in the washing program based on the target heating time.
[0044] The water inlet temperature value is the temperature value of the washing water collected by the sensor component. The washing setting information includes information about the washing program for washing the clothes to be washed in the inner drum. The washing program is the washing method preset by the manufacturer of the washing machine for the washing machine. Different washing methods include different washing stages and different washing parameters. The washing stages include but are not limited to the heating stage, the main washing stage and the rinsing stage. The washing parameters include but are not limited to the set temperature value and the set water level value.
[0045] In the above process, the controller obtains the water inlet temperature value by electrically connecting with the sensor component to obtain the temperature value of the washing water flowing in from the water inlet collected by the sensor component as the water inlet temperature value.
[0046] The controller may obtain the washing setting information by first determining the washing program selected by the user, and then generating corresponding washing setting information according to the information of the selected washing program.
[0047] In an embodiment of the present application, after obtaining the inlet water temperature value and the washing setting information, the controller can input the inlet water temperature value and the washing setting information into the target machine learning model to obtain the target heating time, that is, the target machine learning model is used to predict the heating time required for the washing machine to heat the washing water from the inlet water temperature value to the temperature value set in the washing setting information based on the inlet water temperature value and the washing setting information.
[0048] In one example, if the input water inlet temperature value is 5°C and the set temperature value in the washing setting information is 50°C, the target machine learning model will obtain a target heating time of 20 minutes. In another example, compared with the previous example, the input water inlet temperature value and the set temperature value in the washing setting information remain unchanged, but the set water level value in the washing setting information is adjusted from 6 liters to 8 liters, then the target machine learning model will obtain a target heating time of 25 minutes. In other words, the target machine learning model also takes into account the effect of the set water level value in the washing setting information on the heating time required to heat the washing water from the inlet water temperature value to the set temperature value.
[0049] In order to make the target heating time predicted by the target machine learning model more accurate, the training process of the target machine learning model includes the following steps:
[0050] An initial machine learning model is constructed, and the test water inlet temperature value and the test washing setting information are input into the initial machine learning model to obtain an initial heating time;
[0051] The initial machine learning model is adjusted based on the difference between the test heating time and the initial heating time corresponding to the test water inlet temperature value and the test washing setting information to obtain a trained machine learning model, and the trained machine learning model is used as the target machine learning model.
[0052] Through the above steps for training the target machine learning model, the target machine learning model can be optimized according to the benchmark data obtained by the developer in advance, that is, the initial machine learning model is adjusted and trained based on the test washing background information, the test washing setting information and the corresponding test heating time to improve the prediction accuracy of the trained machine learning model for the heating time.
[0053] In an embodiment of the present application, after obtaining the target heating time through the target machine learning model, the heating time corresponding to the heating stage in the washing program can be adjusted based on the target heating time to ensure that the temperature value of the washing water in the washing machine can rise to the required temperature value after the heating stage is completed, that is, the washing water is heated from the inlet temperature value to the set temperature value in the washing setting information.
[0054] Among them, the method of adjusting the heating time corresponding to the heating stage in the washing program based on the target heating time can be by first calculating the difference between the heating time corresponding to the heating stage in the washing program and the target heating time, and then adjusting the heating time corresponding to the heating stage according to the difference. Specifically, if the difference is a positive number, the heating time corresponding to the heating stage is shortened by the time corresponding to the difference to improve the washing efficiency of the washing machine and reduce the overall washing time; if the difference is a negative number, the heating time corresponding to the heating stage is extended by the time corresponding to the difference to ensure that the washing water is heated from the inlet water temperature value to the set temperature value in the washing setting information, so that the cleaning effect of the washing machine is not easily weakened.
[0055] Through the above implementation, the washing machine can obtain the water inlet temperature value and washing setting information based on the controller, and then input the obtained water inlet temperature value and washing setting information into the target machine learning model through the controller to obtain the target heating time, so that the controller adjusts the heating time corresponding to the heating stage in the washing program according to the target heating time. On the one hand, when the heating time corresponding to the heating stage is too long, the overall washing time can be reduced to avoid wasting resources of the washing machine. On the other hand, it can ensure that after the heating stage is completed, the washing water can be heated from the water inlet temperature value to the set temperature value corresponding to the washing setting information in the washing program, so that the cleaning effect of the washing machine is not easily weakened, thereby improving the user's satisfaction with the use of the washing machine.
[0056] In another exemplary embodiment, the controller is further configured to perform the following steps:
[0057] Acquire multiple historical inlet water temperature values recorded by the sensor component from the historical inlet water temperature table;
[0058] Calculate the average temperature value between multiple historical water inlet temperature values and the current water inlet temperature value;
[0059] The average temperature value is used as the water inlet temperature value and the washing setting information to input into the target machine learning model to obtain the optimized heating time;
[0060] The target heating time is adjusted based on the optimized heating time.
[0061] The sensor component records each temperature value of the washing water collected in a preset table to generate a historical water inlet temperature table based on the table. The historical water inlet temperature value represents the temperature value of the washing water flowing into the water inlet collected by the sensor component before.
[0062] In the above process, the method of obtaining multiple historical inlet water temperature values from the historical inlet water temperature table can be flexibly set as needed. In one example, the inlet water temperature values whose recording time does not exceed the preset time can be screened from the historical inlet water temperature table as historical inlet water temperature values to ensure that the historical inlet water temperature values obtained from the historical inlet water temperature table are consistent with the temperature change range of the washing water during the current washing time, and are not likely to cause deviations in the average temperature value between the subsequently calculated multiple historical inlet water temperature values and the current inlet water temperature value. The preset time can be adjusted according to the actual situation of the washing machine, and is not limited here.
[0063] For example, if the preset time is 72 hours, the inlet water temperature values whose time difference between the recording time and the current time does not exceed 72 hours are selected from the historical inlet water temperature table as the historical inlet water temperature values.
[0064] In another example, a preset number of water inlet temperature values can be selected from the historical water inlet temperature table in sequence according to the recording time as the historical water inlet temperature values, so that the number of historical water inlet temperature values is limited by the preset number on the premise of screening out the historical water inlet temperature values that meet the temperature change interval of the washing water in the current time period from the historical water inlet temperature table, thereby reducing the amount of resources occupied in the subsequent calculation of the average temperature value between multiple historical water inlet temperature values and the current water inlet temperature value. The preset number can also be adjusted according to the actual situation of the washing machine.
[0065] For example, if the preset number of times is 2, the water inlet temperature values of the two most recent records are selected from the historical water inlet temperature table according to the recording time as the historical water inlet temperature value.
[0066] In addition, considering that the corresponding temperature value of washing water changes more significantly every time the season changes, the controller can also determine the corresponding target quarterly time zone through the current washing time when obtaining multiple historical inlet water temperature values from the historical inlet water temperature table, and then filter out the inlet water temperature values with recording time in the target quarterly time zone from the historical inlet water temperature table as the historical inlet water temperature values, thereby further ensuring that the historical temperature values obtained are consistent with the temperature change range of the washing water at the current washing time.
[0067] In an embodiment of the present application, after obtaining multiple historical inlet water temperature values from the historical inlet water temperature table, the average temperature value between the multiple historical inlet water temperature values and the current inlet water temperature value can be calculated to eliminate the influence of temperature fluctuations of washing water on the inlet water temperature value. At the same time, it also avoids the situation where a large deviation occurs between the inlet water temperature value obtained by the controller and the actual inlet water temperature value due to an abnormality in the sensor component when collecting the temperature value of the washing water or transmitting the inlet water temperature value.
[0068] Taking into account the temperature fluctuations of washing water itself and the abnormalities that may occur when the sensor component collects or transmits temperature values, therefore, in the above process, after obtaining the target heating time, the controller can input the average temperature value as the inlet temperature value and the washing setting information into the target machine learning model to obtain the optimized heating time. That is, when the temperature value of the washing water produces a large abnormal fluctuation or the sensor component has an abnormality when collecting or transmitting the temperature value, the temperature abnormality is corrected by the average temperature value between multiple historical inlet temperature values and the current inlet temperature value, and then the corrected heating time is output through the target machine learning model, that is, the optimized heating time. Finally, the target heating time is adjusted based on the optimized heating time, so that after the heating time corresponding to the heating stage is corrected to the adjusted target heating time, it is further ensured that the washing water is heated to the set temperature value after the heating stage is completed. At the same time, it is further ensured that too much energy is not wasted during the execution of the heating stage.
[0069] In another exemplary embodiment, the controller is further configured to perform the following steps:
[0070] Determine the estimated heating time based on the water inlet temperature value and the wash setting information;
[0071] The target heating duration is adjusted based on the estimated heating duration.
[0072] In an embodiment of the present application, after the controller obtains the target heating time, it can also determine the expected heating time based on the inlet water temperature value and the washing setting information, wherein the expected heating time represents the time taken by the heater in the washing machine to heat the inlet water from the inlet water temperature value to the set temperature value in the washing setting information; specifically, the expected heating time can be determined by the specific heat capacity of water, the temperature difference between the inlet water temperature value and the set temperature value, the set water level value in the washing setting information, and the corresponding output power of the heating module of the washing machine.
[0073] For example, the specific heat capacity of water is 4.2×103J / (kg·℃). If the corresponding output power of the heating module of the washing machine is 1000W, the temperature difference between the current water inlet temperature and the set temperature is 45℃, and the set water level is 8 liters, then the corresponding expected heating time is the product of the specific heat capacity of water and the set water level divided by the power corresponding to the heating module, thereby obtaining the time required for the heating module of the washing machine to raise the temperature of the washing water by 1℃. Finally, multiply the above time by the temperature difference between the current water inlet temperature and the set temperature to obtain the expected heating time, which is 25 minutes and 12 seconds.
[0074] In addition, in the process of determining the estimated heating time based on the inlet water temperature value and the washing setting information, you can also first obtain the average temperature value between the current inlet water temperature value and multiple historical inlet water temperature values in the historical inlet water temperature table, and then use the average temperature value as the inlet water temperature value and the washing setting information to determine the estimated heating time, so as to eliminate abnormal temperature fluctuations that occur when the sensor component collects the temperature value of the washing water.
[0075] In the above process, after the controller determines the expected heating time, if it is determined that the expected heating time exceeds the target heating time, the expected heating time will be used as the target heating time to further ensure that the washing water is heated to the set temperature value after the heating stage is completed.
[0076] Referring to the above example, when the temperature difference between the current inlet water temperature value and the set temperature value is 45°C and the set water level value is 8 liters, the target heating time output by the target machine learning model is 25 minutes, but the estimated heating time is 25 minutes and 12 seconds. In other words, it takes at least 25 minutes and 12 seconds to heat the washing water at the current water level to the set temperature value. Therefore, when it is determined that the estimated heating time exceeds the target heating time, the estimated heating time is used as the target heating time to avoid the problem that the washing water cannot be heated to the set temperature value.
[0077] In another exemplary embodiment, the controller is further configured to perform the following steps:
[0078] During the washing process, the remaining time of the heating phase corresponding to the target heating time is obtained;
[0079] Calculate the expected heating temperature value of the washing water in the washing machine after heating according to the remaining time;
[0080] If the estimated heating temperature value does not reach the set temperature value in the washing setting information, the target heating time is increased.
[0081] In an embodiment of the present application, after the controller adjusts the heating time corresponding to the heating stage to the target heating time and starts running the washing program, it can obtain the remaining time of the heating stage corresponding to the target heating time, that is, the remaining time of the heating stage in the washing program. Specifically, the remaining time can be obtained by calculating the difference between the target heating time and the time currently spent on executing the heating stage, and the difference represents the remaining time of the heating stage.
[0082] In the above process, after the controller obtains the remaining time of the heating phase, it can calculate the expected heating temperature value of the washing water in the washing machine after heating according to the remaining time to determine the temperature of the washing water after the heating phase is completed.
[0083] Among them, the method of calculating the expected heating temperature value of the washing water in the washing machine after heating according to the remaining time, in one example, the temperature value to be raised of the washing water can be calculated according to the remaining time, and then the current temperature value of the washing water in the washing machine is obtained, and the sum of the temperature value to be raised and the current temperature value is used as the expected heating temperature value. In order to calculate the temperature value to be raised according to the remaining time, the time required for the washing water to rise by 1°C can be obtained by first using the specific heat capacity of water, the set water level value and the corresponding output power of the heating module, and then the remaining time is divided by the time required for the washing water to rise by 1°C to obtain the temperature value to be raised.
[0084] In an embodiment of the present application, after the controller calculates the expected heating temperature value, if it is determined that the expected heating temperature value does not reach the set temperature value in the washing setting information, it indicates that after the heating phase is completed, the washing water cannot be heated to the preset set temperature value, and the target heating time is increased so that the washing water can be further heated during the heating phase.
[0085] The method of increasing the target heating time can be flexibly set as needed. In one example, the target heating time can be increased according to the preset extension time. In another example, the extension time can be determined according to the difference between the expected heating temperature value and the set temperature value, so that after the target heating time is increased by the extension time, the purpose of ensuring that the washing water is heated to the set temperature value can be achieved while avoiding the problem of wasting energy during the heating phase due to excessive extension time.
[0086] In addition, the controller can also perform the following steps in the process of increasing the extension time:
[0087] If it is determined that the washing program belongs to the target washing program, the target heating time corresponding to the heating phase in the washing program is increased by the extended time, and the main wash time corresponding to the main wash phase in the washing program is reduced by the extended time.
[0088] It should be noted that the target washing program is a washing program in which the main washing phase requires a shorter duration.
[0089] Among them, the method of determining whether a washing program belongs to the target washing program can be flexibly set as needed. In one example, labels for indicating whether a washing program belongs to the target washing program or not can be set in advance for different washing programs, so that the label can be used to determine whether the current washing program belongs to the target washing program. In another example, it can be determined whether the priority corresponding to the main wash stage in the washing degree does not exceed the target priority. If the judgment is yes, the washing program is characterized as the target washing program. That is to say, the main wash stage in different washing programs is preset with a corresponding priority for determining whether the washing program belongs to the target washing program.
[0090] In the above process, if the controller determines that the washing program belongs to the target washing program, it means that the time required for the main wash stage in the current washing program is relatively short, so the target heating time corresponding to the heating stage in the washing program is increased and extended, and the main wash time corresponding to the main wash stage in the washing program is reduced and extended. While increasing the execution time of the heating stage, the washing time of the main wash stage in the washing program is shortened in a reasonable manner, thereby achieving the purpose of increasing the target heating time corresponding to the heating stage without extending the total time of the washing program.
[0091] In another exemplary embodiment, the sensor assembly is also used to collect the load weight value of the clothes to be washed in the inner drum. Figure 3 As shown, the sensor assembly further includes a weight sensor 16 disposed in the housing 11 , so as to collect the load weight value of the laundry to be washed in the inner drum 12 through the weight sensor 16 .
[0092] Additionally, the controller is configured to perform the following steps:
[0093] In the process of obtaining the water inlet temperature value and the washing setting information, obtaining the load weight value;
[0094] The inlet water temperature value, load weight value, and washing setting information are input into the target machine learning model to obtain the target heating time.
[0095] Considering that the heavier the clothes to be washed in the inner drum, the larger the volume occupied by the clothes to be washed in the inner drum, the amount of washing water required to reach the set water level value is reduced accordingly. At the same time, the specific heat capacity corresponding to the clothes to be washed is usually smaller than the specific heat capacity of the washing water. That is to say, when heating the washing water with the same set water level value, the greater the load weight value corresponding to the clothes to be washed in the inner drum, the shorter the time required to heat the washing water to the set temperature value.
[0096] In an embodiment of the present application, the controller can also obtain the load weight value during the process of obtaining the water inlet temperature value and the washing setting information, and input the obtained water inlet temperature value, load weight value and washing setting information into the target machine learning model, so that the target machine learning model can obtain the target heating time according to the water inlet temperature value, load weight value and washing setting information output, further ensuring that after the heating stage adjusted based on the target heating time is completed, the washing water is heated to the set temperature value, and resources are not easily wasted.
[0097] In the process of obtaining the load weight value, the controller can first control the washing machine to drain the washing water in the inner drum to a preset empty drum water level, and then obtain the load weight value of the clothes to be washed in the inner drum collected by the sensor component to improve the accuracy of the load weight value collected by the sensor component.
[0098] In addition, in order to further ensure that the washing machine does not waste resources during the washing process, the sensor component can also be used to collect fabric information corresponding to the clothes to be washed in the inner drum. After the controller obtains the fabric information through the electrical connection between the sensor component, it can determine the corresponding target fabric specific heat capacity based on the fabric information, and then synchronously input the target fabric specific heat capacity, the water inlet temperature value, the load weight value and the washing setting information into the target machine learning model to obtain the target heating time.
[0099] In another exemplary embodiment, considering that the target heating time obtained by the target machine learning model according to the water inlet temperature value and the washing setting information may be adjusted during the above steps, that is, the target heating time is adjusted after the optimized heating time, the estimated heating time or the extended heating time is obtained, the training process of the target machine learning model further includes the following steps:
[0100] Input the water inlet temperature value and washing setting information into the trained machine learning model to obtain the initial heating time;
[0101] The trained machine learning model is adjusted according to the difference between the initial heating time and the target heating time adjusted based on the optimized heating time, the expected heating time or the extended time to obtain an updated machine learning model, and the updated machine learning model is used as the target machine learning model.
[0102] Among them, the trained machine learning model is the current target machine learning model, and the updated machine learning model is the target machine learning model to be updated.
[0103] In addition, the training process of the above-mentioned target machine learning model can also be implemented through a data cloud platform that is communicatively connected to the controller. Specifically, the controller sends the inlet water temperature value, washing setting information, and the target heating time adjusted based on the optimized heating time, the expected heating time, or the extended time to the data cloud platform based on the communication connection, so that the data cloud platform adjusts the pre-stored trained machine learning model to obtain an updated machine learning model. The controller then receives the updated machine learning model sent from the data cloud platform as the target machine learning model based on the communication connection.
[0104] Since the data cloud platform can connect to the controllers of multiple washing machines at the same time, the data cloud platform can achieve a faster update speed based on the water inlet temperature values, washing setting information and adjusted target heating time sent by multiple washing machines, thereby making the target heating time output by the updated machine learning model received by the controller in the washing machine more accurate.
[0105] In another exemplary embodiment, the controller is further configured to perform the following steps:
[0106] determining a wash program in response to a wash signal;
[0107] The washing time of the washing program is determined based on an average temperature value corresponding to a plurality of historical water inlet temperature values obtained from the historical water inlet temperature table, and the washing time is sent to the multimedia device for display.
[0108] It should be noted that, in order to facilitate users to know the washing time of the washing machine, the manufacturer of the washing machine usually sets a multimedia device 17 on the washing machine 10 for displaying the washing time corresponding to the washing program for users to check. For specific setting methods, refer to Figure 4 shown.
[0109] In an embodiment of the present application, a washing program is determined in response to a washing signal, wherein the washing signal is triggered by a user through a multimedia device on the cabinet. That is, the multimedia device can also be used to provide an operation panel for the user to select different washing programs in the washing machine.
[0110] Due to the different inlet water temperature values, the time required to heat the washing water from the inlet water temperature value to the set temperature value corresponding to the washing setting information in the washing program is also different, that is, the washing time corresponding to the washing program under different inlet water temperature conditions is also different. Therefore, in the above process, after determining the washing program, the controller can determine the washing time of the washing program based on the average temperature value corresponding to multiple historical inlet water temperature values obtained from the historical inlet water temperature table, and send the washing time to the multimedia device for display, so that the washing time of the washing program retrieved by the user from the multimedia device is more matched with the current temperature of the washing water and more accurate, thereby improving the user's satisfaction with the use of the washing machine.
[0111] Figure 5 A control method for a washing machine provided in an embodiment of the present application is combined with Figure 1-Figure 4 The structural diagram of the washing machine in FIG. 1 , the control method of the clothes dryer includes the following steps S01 to S17:
[0112] Step S01, determining a washing program in response to a washing signal.
[0113] The washing signal is triggered by the user through the multimedia device on the box.
[0114] Step S02, determining the washing time corresponding to the washing program, and sending the washing time to the multimedia device for display.
[0115] Among them, the method of determining the washing time corresponding to the washing program can refer to the above embodiment and will not be repeated here.
[0116] Step S03, obtaining the water inlet temperature value, load weight value and washing setting information.
[0117] Among them, the water inlet temperature value is the temperature value of the washing water flowing into the water inlet of the box, the load weight value is the weight value of the clothes to be washed in the inner drum of the box, and the washing setting information is the information of the washing program selected by the user for washing the clothes to be washed in the inner drum.
[0118] Step S04, input the water inlet temperature value, load weight value and washing setting information into the target machine learning model to obtain the target heating time.
[0119] After executing step S04, in order to further adjust the target heating time obtained by the target machine learning model, steps S05 to S07 can be executed.
[0120] Step S05, calculating an average temperature value between a plurality of historical inlet water temperature values obtained from the historical inlet water temperature table and the current inlet water temperature value.
[0121] Step S06, input the average temperature value, load weight value and washing setting information into the target machine learning model to obtain the optimized heating time.
[0122] Step S07, adjusting the target heating time based on the optimized heating time.
[0123] Since the average temperature value can correct the abnormal fluctuations in the current temperature value of the washing water and the abnormal fluctuations that occur when the sensor components collect or transmit temperature values, the target machine learning model adjusts the target heating time based on the optimized heating time corrected by the average temperature value output, so as to ensure that the washing water can be heated to the set temperature value in the washing setting information after the heating time corresponding to the heating stage in the washing program is subsequently adjusted to the target heating time.
[0124] In addition, after executing step S04, steps S08 to S10 may be executed to adjust the target heating time.
[0125] Step S08, determining the estimated heating time according to the water inlet temperature value and the washing setting information.
[0126] Among them, the method of determining the estimated heating time according to the water inlet temperature value and the washing setting information can refer to the above embodiment and will not be repeated here.
[0127] Step S09, determining whether the estimated heating time exceeds the target heating time.
[0128] Considering that the estimated heating time represents the shortest time required to heat the washing water from the current temperature value to the temperature value set in the washing setting information, in step S09, if the judgment is yes, step S10 is executed to adjust the target heating time based on the estimated heating time. On the contrary, it represents that after the heating stage is executed according to the target heating time, the temperature value of the heated washing water is sufficient to reach the set temperature value, and there is no need to adjust the target heating time.
[0129] In addition, after executing step S07, the above steps S08-S10 can also be executed to adjust the target heating time to ensure that the target heating time adjusted based on the optimized heating time reaches the shortest time required to heat the washing water from the current temperature value to the temperature value set in the washing setting information.
[0130] Step S11, adjusting the heating time corresponding to the heating stage in the washing program according to the target heating time.
[0131] Step S12, calculating the expected heating temperature value of the washing water according to the remaining time of the heating stage.
[0132] Step S13, determining whether the estimated heating temperature value reaches the set temperature value.
[0133] In step S13, if the judgment is yes, it indicates that after the heating stage is executed according to the target heating time, the temperature value of the washing water can reach the set temperature value in the washing setting information, and there is no need to adjust the execution time of the heating stage. On the contrary, it indicates that after the heating stage is executed according to the target heating time, the temperature value of the washing water cannot reach the set temperature value in the washing setting information, and it is necessary to further execute steps S14-S16 to adjust the execution time of the heating stage.
[0134] Step S14, determining whether the washing program belongs to the target washing program.
[0135] In step S14, if the judgment is yes, it indicates that the duration required for the main wash phase in the washing program is short, then step S15 is executed to extend the target heating duration corresponding to the heating phase in the washing program and shorten the main wash duration of the main wash phase in the washing program, so as to increase the target heating duration corresponding to the heating phase without extending the total time of the washing program. If the judgment is no, step S16 is executed to directly extend the target heating duration corresponding to the heating phase in the washing program to ensure that the temperature of the washing water reaches the set temperature value after the heating phase is completed.
[0136] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.
[0137] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.
Claims
1. A washing machine, characterized in that: include: Box; An inner cylinder and a sensor assembly disposed in the housing; The sensor assembly is used to collect the temperature value of the washing water flowing in from the water inlet opened on the box body, and is electrically connected to the controller in the box body, and the controller is configured to perform the following steps: Acquiring a water inlet temperature value and washing setting information; the washing setting information includes information of a washing program for washing the clothes to be washed in the inner drum; Inputting the water inlet temperature value and the washing setting information into a target machine learning model to obtain a target heating time; The heating duration corresponding to the heating stage in the washing program is adjusted based on the target heating duration.
2. The washing machine according to claim 1, characterized in that: The controller is also configured to perform the following steps: Acquire multiple historical water inlet temperature values recorded by the sensor component from a historical water inlet temperature table; Calculating an average temperature value between the multiple historical water inlet temperature values and the current water inlet temperature value; Inputting the average temperature value as the water inlet temperature value and the washing setting information into the target machine learning model to obtain an optimized heating time; The target heating duration is adjusted based on the optimized heating duration.
3. The washing machine according to claim 2, characterized in that: The controller is also configured to perform the following steps: In the process of obtaining the plurality of historical water inlet temperature values from the historical water inlet temperature table, water inlet temperature values whose recording time does not exceed a preset time are selected from the historical water inlet temperature table as historical water inlet temperature values; or, A preset number of water inlet temperature values are selected from the historical water inlet temperature table in sequence according to the recording time as historical water inlet temperature values.
4. The washing machine according to claim 1, characterized in that: The controller is also configured to perform the following steps: Determine the estimated heating time according to the inlet water temperature value and the washing setting information; wherein the estimated heating time represents the time taken by the heater in the washing machine to heat the inlet water from the inlet water temperature value to the set temperature value in the washing setting information; If the estimated heating time exceeds the target heating time, the estimated heating time is used as the target heating time.
5. The washing machine according to claim 1, characterized in that: The controller is also configured to perform the following steps: During the running of the washing program, obtaining the remaining duration of the heating phase corresponding to the target heating duration; Calculating an expected heating temperature value of the washing water in the washing machine after heating according to the remaining time; If the estimated heating temperature value does not reach the set temperature value in the washing setting information, the target heating time is increased.
6. The washing machine according to claim 5, characterized in that: The controller is also configured to perform the following steps: In the process of increasing the target heating time, determining the extended time according to the difference between the estimated heating temperature value and the set temperature value; If it is determined that the washing program belongs to the target washing program, the target heating time corresponding to the heating phase in the washing program is increased by the extended time, and the main wash time corresponding to the main wash phase in the washing program is reduced by the extended time.
7. The washing machine according to claim 1, characterized in that: The sensor assembly is also used to collect the load weight value of the clothes to be washed in the inner drum; The controller is also configured to perform the following steps: In the process of obtaining the water inlet temperature value and the washing setting information, obtaining the load weight value, The water inlet temperature value, the load weight value and the washing setting information are input into the target machine learning model to obtain the target heating time.
8. The washing machine according to claim 1, characterized in that: The controller is also configured to perform the following steps: Determining the washing program in response to a washing signal; wherein the washing signal is triggered and generated by a user through a multimedia device on the box; The washing time of the washing program is determined based on an average temperature value corresponding to a plurality of historical water inlet temperature values obtained from a historical water inlet temperature table, and the washing time is sent to the multimedia device for display.
9. The washing machine according to claim 1, characterized in that: The training process of the target machine learning model includes the following steps: Constructing an initial machine learning model, and inputting the test water inlet temperature value and the test washing setting information into the initial machine learning model to obtain an initial heating time; The initial machine learning model is adjusted based on the difference between the test heating time corresponding to the water inlet temperature value and the test washing setting information and the initial heating time to obtain a trained machine learning model, and the trained machine learning model is used as the target machine learning model.
10. The washing machine according to claim 9, characterized in that: The training process of the target machine learning model also includes the following steps: Inputting the water inlet temperature value and the washing setting information into the trained machine learning model to obtain an initial heating time; The trained machine learning model is adjusted according to the difference between the initial heating time and the target heating time adjusted based on the optimized heating time, the expected heating time or the extended time to obtain an updated machine learning model, and the updated machine learning model is used as the target machine learning model.