A method for intelligently judging the lint accumulation in a clothes dryer and the clothes dryer

Abstract

This invention discloses an intelligent method for judging lint accumulation in a clothes dryer and a clothes dryer in general. The intelligent method for judging lint accumulation is as follows: The clothes dryer acquires the weight and moisture content of the clothes. A preset neural network learning module matches the drying process time threshold and energy consumption threshold based on the weight, moisture content, and user-selected drying parameters of the clothes. The time threshold includes a first time threshold, and the energy consumption threshold includes a first energy consumption threshold. After the drying process starts, the clothes dryer continuously acquires the running time and running energy consumption of the drying process, which are defined as a first parameter and a second parameter, respectively. These parameters are compared with the first time threshold and the first energy consumption threshold, respectively. If the first parameter is greater than the first time threshold, or the second parameter is greater than the first energy consumption threshold, it is judged that there is a lot of lint accumulation, and the user is prompted to clean the lint. The above method can make a more accurate judgment on the lint accumulation in the clothes dryer, improving the intelligence level of the clothes dryer.

Description

Technical Field

[0001] This invention belongs to the field of clothes dryers, and specifically relates to an intelligent method for judging the accumulation of lint in clothes dryers and a clothes dryer. Background Technology

[0002] As a household appliance that can quickly dry clothes, clothes dryers allow people to wear dry and comfortable clothes even in humid weather, while reducing the space required for drying clothes and improving people's quality of life. As a result, clothes dryers are becoming increasingly popular among users.

[0003] At the same time, with the application of dryers, users have put forward higher requirements for drying speed, drying uniformity and energy consumption. In order to improve the drying effect and reduce safety hazards, existing dryers are equipped with filter devices to filter lint. However, the continuous accumulation of lint will cause air duct blockage, thereby reducing drying efficiency and increasing the energy consumption of dryers.

[0004] To address this issue, Chinese invention patent application CN201210163162.8 discloses a control method for determining clogging of the lint filter in a heat pump dryer. Specifically, it detects the temperature difference between the inlet and outlet air under stable operating conditions of the heat pump system to determine whether the lint filter is clogged, thus prompting the user to clean the lint filter. Although the above solution can determine the clogging status of the lint filter based on the temperature difference between the inlet and outlet air, in actual use, the temperature difference between the inlet and outlet air is also affected by residual condensate in the circulating air duct. Furthermore, when the heat pump system is operating stably, the degree of lint clogging has a relatively small impact on the temperature of the inlet and outlet air, which can easily lead to misjudgment.

[0005] In response, Chinese invention patent application number CN201810863254.4 discloses a method for judging the blockage of a lint filter. Specifically, an automatic weighing device is installed on the lint filter, and the degree of blockage of the lint filter is judged based on the change in the weight of the lint filter. The above solution can obtain the lint accumulation situation relatively directly. However, the lint filter is usually fixed in the air duct. Therefore, the weight of the lint filter measured is often different from the actual increase in weight, which may lead to the inability to accurately judge the degree of lint accumulation.

[0006] In view of this, the present invention is proposed. Summary of the Invention

[0007] One of the objectives of this invention is to provide an intelligent method for judging the accumulation of lint in a clothes dryer, addressing the problems in the prior art. This method uses a neural network learning module to match the time and energy consumption thresholds required for drying clothes based on the weight, moisture content, and drying parameters of the clothes. The actual time and energy consumed in the drying process are then compared with these thresholds to determine the accumulation of lint.

[0008] Another objective of this invention is to provide a dryer that uses the above-mentioned intelligent lint accumulation judgment method, which can accurately judge the lint accumulation and prompt the user to clean it, thereby avoiding excessive lint accumulation from affecting the drying speed and increasing drying energy consumption.

[0009] To achieve the above objectives, the first aspect of the present invention provides an intelligent method for judging the accumulation of lint in a clothes dryer, comprising the following steps:

[0010] S1. Obtain the weight and moisture content of the clothing;

[0011] S2. The preset neural network learning module matches the time threshold and energy consumption threshold of the drying process based on the weight of the clothes, the moisture content and the drying parameters.

[0012] S3. Start the drying process, obtain the running time of the drying process in real time and define it as the first parameter, and obtain the running energy consumption of the drying process in real time and define it as the second parameter.

[0013] S4. Compare the first parameter and the second parameter with the time threshold and the energy consumption threshold respectively, and determine the accumulation of wire chips based on the comparison results.

[0014] In the above-mentioned judgment method, the judgment on the accumulation of lint can be realized in the dryer itself or in other smart terminals that are connected to the dryer. The drying parameters can be user-set drying parameters or drying parameters matched according to the weight and moisture content of the clothes.

[0015] In the above scheme, the neural network learning module matches the time and energy required to dry clothes under normal circumstances based on the weight, moisture content, and drying parameters of the clothes. These are used as time thresholds and energy consumption thresholds, respectively. These are compared with the actual running time and energy consumption during the drying process. The comparison results determine the accumulation of lint, enabling a more accurate assessment of the degree of lint accumulation. Furthermore, in step S2, the neural network learning module communicates with the cloud to learn the relationship between the weight, moisture content, and drying parameters of the clothes stored in the cloud and the running energy consumption and running time of the drying process.

[0016] The neural network learning module communicates with the cloud and learns from the weight, moisture content, drying parameters, and corresponding running time and energy consumption of the clothes stored in the cloud. Based on the weight, moisture content, and drying parameters of the clothes to be dried, it matches the corresponding time and energy consumption thresholds required for normal drying. When the running time and energy consumption of the drying process are compared with the corresponding time and energy consumption thresholds, it can make a more accurate judgment on the lint accumulation of the dryer, thus improving the user experience.

[0017] Furthermore, the time threshold includes a first time threshold, and the energy consumption threshold includes a first energy consumption threshold;

[0018] In step S4, if the first parameter is less than or equal to the first time threshold and the second parameter is less than or equal to the first energy consumption threshold, the drying process continues and returns to step S3.

[0019] In the above scheme, the first parameter is less than or equal to the first time threshold, and the second parameter is less than or equal to the first energy consumption threshold, indicating that the dryer is in a normal drying process. It is determined that the lint accumulation is small at this time and has not had a significant impact on the energy consumption of the drying process. Therefore, the drying process continues and returns to step S3 to continue to obtain the first and second parameters.

[0020] Furthermore, in step S4, if the first parameter is greater than the first time threshold, or the second parameter is greater than the first energy consumption threshold, the drying process continues, and the process proceeds to step S5.

[0021] Further, step S5 is: issuing an alarm to prompt the user to clean up the lint.

[0022] In the above scheme, when the first parameter is greater than the first time threshold or the second parameter is greater than the first energy consumption threshold, it indicates that the lint blockage is relatively serious, which leads to increased energy consumption and extended running time of the drying process. However, at this time, the drying efficiency of the dryer is relatively small, and the dryer can still complete the drying smoothly. Therefore, it is not necessary to stop the drying process. It is only necessary to issue an alarm to inform the user that there is a lot of lint accumulation and prompt the user to clean it.

[0023] Furthermore, the time threshold also includes a second time threshold, which is greater than the first time threshold;

[0024] The energy consumption threshold also includes a second energy consumption threshold, which is greater than the first energy consumption threshold;

[0025] In step S4, if the first parameter is greater than the second time threshold, or the second parameter is greater than the second energy consumption threshold, proceed to step S6.

[0026] Furthermore, step S6 is: pause the drying process, issue an alarm to prompt the user to clean up the lint, and resume the drying process after cleaning is completed.

[0027] In the above scheme, when the first parameter is greater than the first time threshold, it continues to be compared with the second time threshold, or when the second parameter is greater than the first energy consumption threshold, it continues to be compared with the second energy consumption threshold. If the first parameter is greater than the second time threshold, or the second parameter is greater than the second energy consumption threshold, it indicates that there is a lot of lint accumulated and it has a significant impact on the drying efficiency of the dryer. Even if drying continues, it will only further increase the energy consumption of the dryer and cannot completely dry the clothes. At this time, the drying process is paused, and the dryer or the mobile terminal connected to the dryer sends an alarm to the user to remind the user to clean the lint. After the user completes the cleaning, the dryer continues the unfinished drying process. This improves the intelligence level of the dryer, avoids the dryer from working continuously when it is severely blocked, which would lead to increased energy consumption, and also avoids the dryer from being in a severely blocked state for a long time, which would cause the internal temperature of the dryer to rise and create safety hazards.

[0028] Furthermore, the ratio of the first time threshold to the second time threshold ranges from (1:1.3) to (1:2);

[0029] The ratio of the first energy consumption threshold to the second energy consumption threshold is in the range of (1:1.3)-(1:2).

[0030] Preferably, the ratio of the first time threshold to the second time threshold is 1:1.3; the ratio of the first energy consumption threshold to the second energy consumption threshold is 1:1.3.

[0031] In the above scheme, the ratio of the first time threshold to the second time threshold, and the ratio of the first energy consumption threshold to the second energy consumption threshold, are relatively optimized ratio ranges obtained by technicians based on extensive research. Within this range, the accumulation of lint inside the dryer can be judged earlier, and the user can be promptly reminded to clean it. This avoids the dryer running for a long time without drying the clothes, which would lead to increased energy consumption and improve the user experience.

[0032] Furthermore, the drying parameters in the above scheme include at least the heating power, air supply power, and target moisture content.

[0033] A second aspect of the present invention provides a dryer employing the above-described intelligent method for judging the accumulation of lint in a dryer.

[0034] Specifically, clothes dryers include:

[0035] A humidity sensor is used to detect the moisture content of clothing.

[0036] A weight sensor allows users to detect changes in the weight of the clothes in the dryer drum and obtain the weight of the clothes.

[0037] The neural network learning module, pre-installed inside the dryer, communicates with the cloud to learn the relationship between the weight, moisture content, and drying parameters of the clothes stored in the cloud and the operating energy consumption and running time of the dryer. It also matches the corresponding time threshold and energy consumption threshold according to the weight, moisture content, and drying parameters of the clothes in the dryer.

[0038] Preferably, the neural network learning module is integrated into the dryer's computer board.

[0039] In the above scheme, the humidity sensor and weight sensor respectively acquire the moisture content and weight of the clothes and send them to the computer board. The computer board sends the moisture content, weight and user-input drying parameters to the neural network learning module. The neural network learning module matches the corresponding time threshold and energy consumption threshold and sends them to the computer board. During the drying process, the computer board compares the acquired first and second parameters with the time threshold and energy consumption threshold. Based on the comparison results, it determines the lint accumulation inside the dryer and controls the dryer to perform the corresponding subsequent actions.

[0040] The beneficial effects of this invention are as follows:

[0041] The neural network learning module learns the relationship between clothing weight, moisture content, drying parameters, and the energy consumption and running time of the drying process from the cloud. It then matches corresponding time and energy thresholds based on the actual weight, moisture content, and drying parameters of the clothes inside the dryer. The computer board compares the actual running time and energy consumption of the drying process with these thresholds, respectively, and judges the accumulation of lint based on the comparison results. This allows for a more accurate assessment of the degree of lint accumulation. The time thresholds include a first time threshold and a second time threshold, and the energy thresholds include a first energy consumption threshold and a second energy consumption threshold. This allows the system to determine whether accumulated lint is clogging the air duct based on drying time and energy consumption, and further assess the severity of the blockage. If the blockage is severe and the clothes cannot be dried smoothly, the drying process is paused until the lint is cleared, and then resumes. This avoids the dryer running continuously without completing the drying process, which would lead to a significant increase in energy consumption, and also prevents the dryer from operating in a blocked state for extended periods, thus avoiding potential safety hazards. Attached Figure Description

[0042] Figure 1 This is a flowchart of the intelligent method for judging the accumulation of lint in a clothes dryer according to the present invention.

[0043] Figure 2 This is a flowchart of the first method for intelligently judging the accumulation of lint in a clothes dryer according to the present invention.

[0044] Figure 3 This is a second flowchart of the intelligent judgment method for lint accumulation in a clothes dryer according to the present invention. Detailed Implementation

[0045] Exemplary embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. Those skilled in the art will understand that the following embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0046] In the description of this invention, unless otherwise explicitly specified and limited, the terms "set" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0047] In the description of this invention, it should be noted that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0048] This invention provides an intelligent method for judging the accumulation of lint in a clothes dryer, such as... Figure 1 As shown, it includes the following steps:

[0049] S1. Obtain the weight and moisture content of the clothing;

[0050] S2. The preset neural network learning module matches the time threshold and energy consumption threshold of the drying process based on the weight of the clothes, the moisture content and the drying parameters.

[0051] S3. Start the drying process, obtain the running time of the drying process in real time and define it as the first parameter, and obtain the running energy consumption of the drying process in real time and define it as the second parameter.

[0052] S4. Compare the first parameter and the second parameter with the time threshold and the energy consumption threshold respectively, and determine the accumulation of wire chips based on the comparison results.

[0053] The neural network learning module is connected to the cloud and learns the relationship between the weight, moisture content, and drying parameters of the clothes stored in the cloud and the running energy consumption and running time of the drying process. Then, based on the weight, moisture content, and drying parameters of the clothes obtained in step S1, it matches the corresponding time threshold and energy consumption threshold, and then compares them with the actual time and energy consumed in the drying process. Based on the comparison results, it judges the accumulation of lint, which can more accurately judge the degree of lint accumulation.

[0054] Furthermore, the present invention will be described and illustrated in more detail by way of specific embodiments.

[0055] Example 1

[0056] As an embodiment of the present invention, this embodiment provides an intelligent method for judging the accumulation of lint in a clothes dryer.

[0057] In this embodiment, the neural network learning module is preset inside the dryer, and the drying parameters are the preset drying parameters within the drying mode corresponding to the drying mode selected by the user.

[0058] Furthermore, the process of the intelligent judgment method for lint accumulation in dryers is as follows: Figure 2 As shown, after the user puts the clothes into the dryer, they select a drying mode on the control panel. The dryer obtains the weight and moisture content of the clothes. Then, the neural network learning module matches the corresponding time threshold and energy consumption threshold based on the weight and moisture content of the clothes obtained by the dryer and the preset drying parameters corresponding to the drying mode selected by the user. The time threshold represents the time required to dry the clothes when the dryer is not clogged by lint, and the energy consumption threshold represents the energy consumed by the dryer to dry the clothes when it is not clogged by lint.

[0059] After the drying process starts, the dryer obtains the running time and energy consumption of the drying process in real time, which are defined as the first parameter and the second parameter, respectively. The computer board compares the first parameter and the second parameter with the time threshold and the energy consumption threshold, respectively, to determine whether lint has caused blockage in the dryer, and can accurately know the lint accumulation.

[0060] Furthermore, the time threshold includes a first time threshold, and the energy consumption threshold includes a first energy consumption threshold. The computer board compares the first parameter with the first time threshold and the second parameter with the first energy consumption threshold. If the first parameter is less than or equal to the first time threshold and the second parameter is less than or equal to the first energy consumption threshold, it indicates that the drying process is in progress. The computer board then checks whether the clothes are dry. If not, the drying process continues, and the computer board continues to acquire drying time and drying energy consumption to obtain new first and second parameters. These parameters are then compared with the first and second time thresholds again, and the process is repeated.

[0061] Furthermore, when the first parameter is greater than the first time threshold, or the second parameter is greater than the first energy consumption threshold, the drying time or drying energy consumption exceeds the normal drying time or energy consumption matched by the neural network learning module. It can be determined that the dryer has accumulated a lot of lint, causing blockage and thus reducing the drying efficiency. At this time, step S5 is entered, and the dryer issues an alarm to prompt the user to clean the lint.

[0062] In the above solution, the dryer can issue an alarm immediately when it detects a large accumulation of lint inside the dryer, or it can issue an alarm to the user after the drying process is complete.

[0063] Furthermore, the dryer communicates with the user's mobile terminal, which has a program installed to receive the dryer's operating status. When the dryer determines that there is a lot of lint accumulated, it prompts the user to clean the lint through the mobile terminal.

[0064] Furthermore, the drying parameters in the above scheme include at least the heating power, air supply power, and target moisture content.

[0065] The target moisture content is the moisture content of the clothes when drying is complete.

[0066] Furthermore, users can select a drying mode through the dryer's control panel or a mobile terminal connected to the dryer. The computer board then obtains the drying parameters corresponding to the drying mode and sends these parameters, along with the weight and moisture content of the clothes, to the neural network learning module.

[0067] Furthermore, the condition for the dryer to end the drying process is that the actual moisture content of the clothes is detected to be less than or equal to the target moisture content.

[0068] Example 2

[0069] As another embodiment of the present invention, this embodiment is further improved based on Embodiment 1, as follows:

[0070] In this embodiment, the process of the intelligent judgment method for lint accumulation in the dryer is as follows: Figure 3 As shown, the time threshold also includes a second time threshold, and the energy consumption threshold also includes a second energy consumption threshold. The second time threshold is greater than the first time threshold, and the second energy consumption threshold is greater than the first energy consumption threshold.

[0071] When the first parameter is greater than the first time threshold, the first parameter is compared with the second time threshold. Or when the second parameter is greater than the first energy consumption threshold, the second parameter is compared with the second energy consumption threshold. If the first parameter is less than or equal to the second time threshold, or the second parameter is less than or equal to the second energy consumption threshold, the dryer will only issue an alarm to the user and continue the drying process. At the same time, it will continue to obtain the drying time and drying energy consumption, and repeat the above process until the drying is finished.

[0072] If the first parameter is detected to be greater than the second time threshold or the second parameter is greater than the second energy consumption threshold before the drying process ends, it indicates that the lint blockage is relatively serious. At this time, continuing the drying process can only prolong the drying time and increase the drying energy consumption, but cannot completely dry the clothes. Therefore, based on safety and energy saving considerations, step S6 is entered to pause the drying process. The dryer issues an alarm to remind the user to clean the lint. After cleaning, the drying process continues.

[0073] Alternatively, as an alternative to the above solution, a mobile terminal connected to the dryer can send an alarm to the user, prompting them to clean up the lint.

[0074] In the above solution, after cleaning up the lint, the user can control the dryer to continue the drying process through the control panel of the dryer or a mobile terminal.

[0075] Alternatively, as an alternative to the above solution, the dryer can be equipped with a cleaning device for cleaning lint from the lint filter. After the computer board pauses the drying process, it controls the cleaning device to run and clean the accumulated lint. After cleaning is completed, the computer board controls the dryer to continue the drying process.

[0076] In the above solution, when the dryer detects severe lint blockage inside, it automatically activates the cleaning device to remove the lint, thus preventing users from being unable to clean in time and extending the drying cycle. This improves the user experience and the level of intelligence of the dryer.

[0077] Furthermore, after the computer board pauses the drying process, the dryer sends an alarm to the user through a mobile terminal that is connected to the dryer. The user can then choose whether to perform cleaning via the mobile terminal. If so, the computer board controls the cleaning device to run.

[0078] If not, the drying process will be paused and will resume once the user has finished cleaning.

[0079] Furthermore, in the above scheme, the ratio of the first time threshold to the second time threshold is in the range of (1:1.3) to (1:2); the ratio of the first energy consumption threshold to the second energy consumption threshold is in the range of (1:1.3) to (1:2).

[0080] The above ratio range is a relatively optimized range obtained by technicians based on a large amount of research. Within this range, the dryer can promptly determine the accumulation of lint based on drying time and energy consumption, and then promptly remind the user to clean it or perform automatic cleaning. This avoids the dryer running for a long time without drying the clothes, which would lead to a significant increase in energy consumption, and also avoids the safety hazards caused by prolonged drying, thus improving the user experience.

[0081] Example 3

[0082] As another embodiment of the present invention, this embodiment provides an intelligent judgment method for the accumulation of lint in a clothes dryer, which is the same as that in embodiment one. The difference is that in this embodiment, the neural network learning module is located in a mobile terminal.

[0083] Specifically, the dryer sends the weight and moisture content of the clothes to a mobile terminal that is connected to the dryer. The mobile terminal matches drying parameters based on the weight and moisture content of the clothes. The neural network learning module in the mobile terminal matches time thresholds and energy consumption thresholds for the drying process based on the weight, moisture content and drying parameters of the clothes. The mobile terminal sends the time thresholds and energy consumption thresholds to the dryer, and the dryer executes the drying process according to the drying parameters.

[0084] Alternatively, as an alternative to the above solution, the user can set drying parameters based on the weight and moisture content of the clothes received by the mobile terminal, and the neural network learning module can match the time threshold and energy consumption threshold of the drying process based on the weight, moisture content and drying parameters of the clothes.

[0085] Furthermore, the dryer acquires the running time and energy consumption of the drying process in real time, which are defined as the first parameter and the second parameter, respectively. Then, the first parameter and the second parameter are compared to determine the lint accumulation inside the dryer. If the first parameter is less than the first time threshold and the second parameter is less than the first energy consumption threshold, the running time and energy consumption are acquired again, and the above judgment steps are repeated.

[0086] If the first parameter is greater than the first time threshold, or the second parameter is greater than the first energy consumption threshold, the dryer determines that there is a lot of lint accumulated and sends the judgment result to the mobile terminal. After receiving the judgment result, the mobile terminal prompts the user to take action, and the user can choose to continue drying or perform a cleaning step.

[0087] In the above solution, the cleaning step involves the dryer driving a cleaning device to automatically clean up the lint.

[0088] Alternatively, as an alternative to the above scheme, if the first parameter is greater than the first time threshold, or the second parameter is greater than the first energy consumption threshold, the dryer will send the comparison result to the mobile terminal. The mobile terminal will analyze the comparison result, determine the lint accumulation, and prompt the user to take action.

[0089] Example 4

[0090] As another embodiment of the present invention, this embodiment provides an intelligent method for judging the accumulation of lint in a dryer, which is the same as that in embodiment one. The difference is that in this embodiment, the dryer is connected to the cloud for communication.

[0091] Specifically, the dryer obtains drying parameters from the cloud that match the weight and moisture content of the clothes. Then, a neural network learning module matches the energy consumption and energy consumption threshold of the drying process based on the weight, moisture content, and drying parameters. The dryer then executes the drying process according to the drying parameters. This invention also provides a dryer with the above-mentioned intelligent method for judging lint accumulation, specifically:

[0092] Example 5

[0093] As another embodiment of the present invention, this embodiment provides a dryer having an intelligent method for judging the accumulation of lint as described in Embodiment 2.

[0094] In this embodiment, the clothes dryer includes,

[0095] A humidity sensor is used to detect the moisture content of clothing.

[0096] A weight sensor allows users to detect changes in the weight of the clothes in the dryer drum and obtain the weight of the clothes.

[0097] The neural network learning module, pre-installed inside the dryer, communicates with the cloud to learn the relationship between the weight, moisture content, and drying parameters of the clothes stored in the cloud and the operating energy consumption and running time of the dryer. It also matches the corresponding time threshold and energy consumption threshold according to the weight, moisture content, and drying parameters of the clothes in the dryer.

[0098] Preferably, the neural network learning module is integrated into the dryer's computer board.

[0099] In the above scheme, the humidity sensor and weight sensor respectively acquire the moisture content and weight of the clothes and send them to the computer board. The computer board sends the moisture content, weight and user-input drying parameters to the neural network learning module. The neural network learning module matches the corresponding time threshold and energy consumption threshold and sends them to the computer board. During the drying process, the computer board compares the acquired first and second parameters with the time threshold and energy consumption threshold. Based on the comparison results, it determines the lint accumulation inside the dryer and controls the dryer to perform the corresponding subsequent actions.

[0100] Example 6

[0101] As another embodiment of the present invention, this embodiment provides a clothes dryer with an intelligent judgment method for lint accumulation as described in Embodiment 3. The structure of the clothes dryer is the same as that in Embodiment 4, except that the neural network learning module is preset in the mobile terminal.

[0102] Specifically, the humidity sensor and weight sensor acquire the moisture content and weight of the clothes, respectively. The dryer sends the moisture content and weight to the mobile terminal. The mobile terminal matches the drying parameters automatically based on the moisture content and weight of the clothes, or the user sets the drying parameters based on the moisture content and weight of the clothes. The neural network learning module matches the corresponding time threshold and energy consumption threshold and sends them to the computer board.

[0103] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-described technical content to create equivalent embodiments without departing from the scope of the present invention. The implementation schemes in the above embodiments can also be further combined or replaced. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for intelligently judging the accumulation of lint in a clothes dryer, characterized in that, Includes the following steps: S1. Obtain the weight and moisture content of the clothing; S2. The preset neural network learning module matches the time threshold and energy consumption threshold of the drying process based on the weight of the clothes, the moisture content and the drying parameters. S3. Start the drying process, obtain the running time of the drying process in real time and define it as the first parameter, and obtain the running energy consumption of the drying process in real time and define it as the second parameter. S4. Compare the first parameter and the second parameter with the time threshold and the energy consumption threshold respectively, and determine the chip accumulation based on the comparison results. The dryer ends the drying process when the actual moisture content of the clothes is detected to be less than or equal to the target moisture content; where the target moisture content is the moisture content of the clothes when the drying process is completed. In step S4, if the first parameter is greater than the first time threshold or the second parameter is greater than the first energy consumption threshold, proceed to step S5; if the first parameter is greater than the second time threshold or the second parameter is greater than the second energy consumption threshold, proceed to step S6. Step S5 is: issue an alarm to prompt the user to clean up the lint; Step S6 is: pause the drying process, issue an alarm to prompt the user to clean up the lint, and resume the drying process after cleaning is completed; The ratio of the first time threshold to the second time threshold is in the range of (1:1.3) to (1:2); the ratio of the first energy consumption threshold to the second energy consumption threshold is in the range of (1:1.3) to (1:2).

2. The intelligent method for judging the accumulation of lint in a clothes dryer according to claim 1, characterized in that, In step S2, the neural network learning module communicates with the cloud to learn the relationship between the weight, moisture content, and drying parameters of the clothes stored in the cloud and the operating energy consumption and running time of the dryer.

3. The intelligent method for judging the accumulation of lint in a clothes dryer according to claim 1, characterized in that, In step S4, if the first parameter is less than or equal to the first time threshold, or the second parameter is less than or equal to the first energy consumption threshold, return to step S3.

4. The intelligent method for judging the accumulation of lint in a clothes dryer according to any one of claims 1-3, characterized in that, The drying parameters include heating power, air supply power, and target moisture content.

5. A clothes dryer, characterized in that, The intelligent method for judging the accumulation of lint in a dryer as described in any one of claims 1-4 is adopted.

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