Methods, apparatus, electronic devices, and storage media for predicting drying time.

Abstract

This application relates to the field of dryer technology, and discloses a method for predicting drying time, comprising: determining whether at least one garment is present in the dryer; and, if garments are present in the dryer, predicting the drying time based on the moisture content of each garment, the degree of crowding of the garments, and the filter clogging coefficient. This method considers not only the influence of garment moisture content on drying time, but also the influence of garment crowding and the filter clogging coefficient, thereby enabling a more accurate prediction of drying time. This application also discloses an apparatus, electronic device, and storage medium for predicting drying time.

Description

Technical Field

[0001] This application relates to the field of dryer technology, and for example to a method, apparatus, electronic device and storage medium for predicting drying time. Background Technology

[0002] The working principle of a clothes dryer is to rotate the drum to tumble the clothes in the drum. At the same time, the dryer fan generates dry and cold air, which enters the dryer and is heated by the heater. Then it enters the rotating drum, where the moisture in the clothes inside the drum is evaporated and turned into hot and humid air. Finally, the hot and humid air loses water and cools down after being condensed by the condenser, and becomes dry and cold air again, which is then sucked away by the fan. This cycle repeats continuously.

[0003] In the process of implementing the embodiments of this disclosure, at least the following problems were found in the related art:

[0004] Current technology often determines the remaining drying time of a dryer based on the moisture content of the clothes in contact with the drying deflector within the rotating drum. The deflector is typically a pair of conductive electrodes located at the dryer's entrance. When damp clothes touch these electrodes, a preset circuit is activated. The wetter the clothes, the lower the resistance of this circuit; conversely, the drier the clothes, the higher the resistance, or even no resistance at all. Therefore, the remaining time displayed on most dryers is an estimate by the dryer itself, adjusted slightly based on the circuit resistance monitored by the deflector. During the drying process, clothes may tangle, resulting in a situation where the inside is wet and the outside is dry. When the dry clothes come into contact with the deflector during tumbling, the dryer judges that the clothes are almost dry. However, when the tangled clothes are untangled again, the remaining drying time suddenly increases. Therefore, current technology cannot accurately determine the drying time.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0007] This disclosure provides a method, apparatus, electronic device, and storage medium for predicting drying time, enabling more accurate prediction of the drying time of a dryer.

[0008] In some embodiments, the method for predicting drying time includes: determining whether at least one garment is present in the dryer; and, if garments are present in the dryer, predicting drying time based on the moisture content of each garment, the degree of crowding of the garments, and the filter clogging rate.

[0009] In some embodiments, before predicting the drying time based on the moisture content of each garment, the degree of crowding of the garments, and the clogging coefficient of the filter, the method further includes: obtaining the dry weight of each garment; obtaining the moisture content of each garment; and calculating the moisture content of each garment using the dry weight and moisture content of each garment according to a first preset algorithm.

[0010] In some embodiments, obtaining the moisture content of each garment includes: obtaining the material of each garment; obtaining the dehydration process that each garment has undergone; and performing a matching operation on a preset first database using the material of each garment and the dehydration process that each garment has undergone to obtain the moisture content of each garment; the first database stores the correspondence between material, dehydration process and moisture content.

[0011] In some embodiments, before predicting the drying time based on the moisture content of each garment, the degree of crowding of the garments, and the clogging coefficient of the filter, the method further includes: matching the volume coefficient corresponding to the material of each garment in a preset second database; multiplying the dry weight of each garment by the corresponding volume coefficient and then adding them together to obtain the total volume of the garments in the dryer; and dividing the total volume of the garments by a preset volume to obtain the degree of crowding of the garments in the dryer.

[0012] In some embodiments, before predicting the drying time based on the moisture content of each garment, the degree of crowding of the garments, and the clogging degree coefficient of the filter, the method further includes: obtaining the pressure difference across the filter while controlling the dryer to operate with an empty drum; and matching the clogging degree coefficient corresponding to the pressure difference in a preset third database.

[0013] In some embodiments, predicting drying time based on the moisture content of each garment, the degree of garment crowding, and the filter clogging coefficient includes: obtaining an initial drying time based on the moisture content; and correcting the initial drying time using the garment crowding and clogging coefficients to obtain the final drying time.

[0014] In some embodiments, the initial drying time is corrected using the degree of clothing crowding and the filter clogging coefficient, including: matching the drying efficiency coefficient corresponding to the degree of clothing crowding in a preset fourth database; and calculating the drying time using the drying efficiency coefficient, the filter clogging coefficient, and the initial drying time according to a second preset algorithm.

[0015] In some embodiments, the apparatus for predicting drying time includes: a determining module configured to determine whether at least one garment is present in the dryer; and a predicting module configured to, when at least one garment is present in the dryer, obtain a predicted drying time based on the moisture content of each garment, the degree of crowding of the garment, and the degree of clogging of the filter.

[0016] In some embodiments, the electronic device includes a processor and a memory storing program instructions, the processor being configured to execute the method described above for predicting drying time when the program instructions are executed.

[0017] In some embodiments, the storage medium stores program instructions that, when executed, perform the method described above for predicting drying time.

[0018] The method, apparatus, electronic device, and storage medium for predicting drying time provided in this disclosure can achieve the following technical effects: by predicting the drying time based on the moisture content of each garment, the degree of crowding, and the clogging coefficient when at least one garment is present in the dryer. This considers not only the influence of garment moisture content on drying time, but also the influence of garment crowding and the filter clogging coefficient on drying time, thereby enabling more accurate prediction of drying time.

[0019] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0020] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0021] Figure 1 This is a schematic diagram of a method for predicting drying time provided in an embodiment of this disclosure;

[0022] Figure 2 This is a schematic diagram of another method for predicting drying time provided in an embodiment of this disclosure;

[0023] Figure 3 This is a schematic diagram of another method for predicting drying time provided in an embodiment of this disclosure;

[0024] Figure 4 This is a schematic diagram of another method for predicting drying time provided in an embodiment of this disclosure;

[0025] Figure 5 This is a schematic diagram of an apparatus for predicting drying time provided in an embodiment of this disclosure;

[0026] Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0027] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0028] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0029] Unless otherwise stated, the term "multiple" means two or more.

[0030] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0031] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0032] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0033] This solution applies to dryers. Dryers include, but are not limited to, heat pump dryers, condenser dryers, and washer-dryer combos. Existing technology typically estimates drying time based on the humidity of the portion of clothing in contact with the drying tray. However, drying time is also closely related to the moisture content and type of clothing in the dryer, as well as the degree of filter clogging. For example, when there are few clothes in the dryer, i.e., when the clothes are not crowded, the hot air blown out by the dryer may pass over the clothes, making them difficult to dry. Conversely, when there are many clothes in the dryer, i.e., when the clothes are crowded, many clothes may not come into contact with the hot air, which also increases drying time. Furthermore, the degree of filter clogging affects the dryer's drainage efficiency. When the filter is clogged, it also affects the rate at which water vapor is expelled from the dryer, causing some water vapor to remain inside, thus affecting the drying time. In order to more accurately predict the drying time of clothes, the embodiments of this disclosure obtain the predicted drying time based on the moisture content of each garment, the degree of crowding of the garments, and the degree of clogging of the filter. This not only considers the influence of the moisture content of the garments on the drying time, but also the influence of the degree of crowding of the garments and the degree of clogging of the filter on the drying time, thereby enabling a more accurate prediction of the drying time.

[0034] Combination Figure 1 As shown, this disclosure provides a method for predicting drying time, including:

[0035] In step S101, the electronic device determines whether there is at least one garment inside the dryer.

[0036] In step S102, when there are clothes in the dryer, the electronic device predicts the drying time based on the moisture content of each garment, the degree of crowding of the garments, and the degree of clogging of the filter.

[0037] The method for predicting drying time provided in this disclosure predicts drying time based on the moisture content of each garment, the degree of crowding, and the degree of filter clogging when clothes are present in the dryer. This method considers not only the influence of garment moisture content on drying time but also the influence of garment crowding and filter clogging, thus enabling a more accurate prediction of drying time.

[0038] Optionally, before predicting the drying time based on the moisture content of each garment, the degree of crowding of the garments, and the clogging coefficient of the filter, the method further includes: obtaining the dry weight of each garment and obtaining the moisture content of each garment. The moisture content of each garment is calculated using the dry weight and moisture content of each garment according to a first preset algorithm.

[0039] Optionally, obtaining the moisture content of each garment includes: obtaining the material of each garment and obtaining the dehydration process each garment has undergone. The moisture content of each garment is obtained by matching the material of each garment with the dehydration process it has undergone in a preset first database; the first database stores the correspondence between material, dehydration process, and moisture content. Because the materials of each garment are different, even if the dehydration process is the same, the moisture content of each garment will be different. Obtaining the moisture content of each garment based on its material and the dehydration process it has undergone allows for a more accurate determination of the moisture content of each garment.

[0040] Optionally, the dry weight of the clothes can be obtained using the operating parameters of the washing machine motor during the weighing program.

[0041] Optionally, material information of clothing can be obtained using a program manually selected by the user or by inputting the material.

[0042] Optionally, an RFID reader (Radio Frequency Identification) can be used to read the RFID tags on each garment to obtain the dry weight and material of each garment. The dry weight refers to the weight of the garment itself.

[0043] Optionally, the spin-drying process of each garment can be obtained, including: sending a program command to the washing machine to trigger the washing machine to retrieve the most recently run spin-drying program. The spin-drying program includes spin speed and spin time.

[0044] Washing machines and dryers can communicate directly or indirectly through communication protocols to share operating parameter information.

[0045] Optionally, the moisture content of each garment can be calculated using the dry weight and moisture content of each garment according to the first preset algorithm, including: multiplying the dry weight of each garment by the moisture content of each garment to obtain the moisture content of each garment.

[0046] Optionally, before predicting drying time based on the moisture content of each garment, the degree of crowding, and the filter clogging factor, the method further includes: matching volume coefficients corresponding to the material of each garment to a preset second database; multiplying the dry weight of each garment by its corresponding volume coefficient and then summing the results to obtain the total volume of the garments in the dryer; and dividing the total volume of the garments by a preset volume to obtain the degree of crowding of the garments in the dryer. The volume coefficients corresponding to each material of the garment are used to characterize the volume of each material per unit weight. For garments of the same weight, if the materials are different, the volume of the garments will also be different. Similarly, garments of the same material but different weights will have different volumes. It is evident that the volume of garments depends on their weight and material. Therefore, by multiplying the dry weight of each garment by its corresponding volume coefficient, the volume of each garment can be obtained.

[0047] Optionally, the preset volume can be obtained by: placing the first test garments with the same material and the same moisture content but different weights into a dryer in batches for drying, obtaining the drying time corresponding to each weight of the first test garment, determining the weight with the shortest drying time as the optimal weight, multiplying the optimal weight by the volume coefficient corresponding to the first test garment to obtain the optimal volume, and determining the optimal volume as the preset volume.

[0048] Optionally, the preset volume is the capacity of the dryer.

[0049] Optionally, the degree of clogging of the filter can be further described in the following detailed description:

[0050] In a first optional embodiment, obtaining the degree of filter clogging includes: acquiring the pressure difference across the filter while the dryer is running empty. A clogging degree coefficient corresponding to the pressure difference is then matched against a preset third database. Since the dryer operates empty, no clothes affect airflow; if the filter is clogged, a pressure difference will occur across the filter, and the greater the clogging degree, the greater the pressure difference. Therefore, the clogging degree coefficient of the filter can be determined by the pressure difference across the filter.

[0051] In a second optional embodiment, the filter screen is mounted on several retractable springs. Obtaining the filter screen clogging coefficient includes: acquiring the descent amplitude of the filter screen while the dryer is running empty. A clogging coefficient corresponding to the descent amplitude is matched against a preset fifth database. Since the dryer operates empty, no clothes affect airflow. If the filter screen is clogged, a pressure difference will occur on both sides of the filter screen. Because the filter screen is mounted on retractable springs, the springs will compress due to the pressure, causing the filter screen to descend. The greater the clogging degree, the greater the descent amplitude of the filter screen. Therefore, the clogging coefficient of the filter screen can be determined by the descent amplitude. When the filter screen is not clogged, the clogging coefficient is 1. The clogging coefficient increases with the degree of filter screen clogging.

[0052] Furthermore, before placing the clothes to be dried into the dryer, obtain the degree of clogging coefficient.

[0053] Optionally, the drying time can be predicted based on the moisture content of each garment, the degree of crowding, and the degree of blockage. This includes: obtaining the initial drying time based on the moisture content, and correcting the initial drying time using the degree of crowding and the degree of blockage to obtain a more accurate drying time.

[0054] Furthermore, the initial drying time for each moisture content is described in detail below:

[0055] In a first optional embodiment, obtaining the initial drying time based on the moisture content includes: dividing the moisture content of each garment by a preset baseline moisture content to obtain the multiple of the moisture content of each garment relative to the baseline moisture content; multiplying the multiple of the moisture content of each garment relative to the baseline moisture content by a preset time and the drying multiple corresponding to each material of the garment to obtain the estimated drying time for each material of the garment; and determining the sum of the estimated drying times as the initial drying time.

[0056] In a second optional embodiment, obtaining the initial drying time based on the moisture content includes: dividing the moisture content of each garment by a preset baseline moisture content to obtain the multiple of the moisture content of each garment relative to the baseline moisture content; multiplying the multiple of the moisture content of each garment relative to the baseline moisture content by a preset time and the drying multiple corresponding to each material of the garment to obtain the estimated drying time for each material of the garment; and determining the largest estimated drying time as the initial drying time.

[0057] Furthermore, the preset time is obtained as follows: the drying time of the second test garment is determined as the preset time. The moisture content of the second test garment is a baseline moisture content, for example, 2 kg. The moisture content of the second test garment is a preset standard moisture content, for example, 80%. The material of the second test garment is a preset standard material, for example, cotton. And the drying time of the second test garment is the time required for the second test garment to be completely dried by the dryer.

[0058] Furthermore, the drying ratio for each material of clothing was obtained as follows: the drying time of the third test garments of different materials was divided by a preset time to obtain the drying ratio for each material. The moisture content of the third test garments was the baseline moisture content, and the moisture content rate of the third test garments was a preset moisture content rate. The material of the third test garments was, for example, linen, silk, synthetic fiber, or wool.

[0059] Optionally, the initial drying time can be corrected using the degree of clothing crowding and clogging. This includes: matching a drying efficiency coefficient corresponding to the degree of clothing crowding in a preset fourth database, and calculating the drying time using the drying efficiency coefficient, filter clogging coefficient, and initial drying time according to a second preset algorithm. This allows for a more accurate prediction of drying time.

[0060] Furthermore, the optimal crowding level is obtained by dividing the optimal volume by a preset volume. When the crowding level is at the optimal level, the drying efficiency coefficient corresponding to the crowding level is the minimum drying efficiency coefficient, for example, 1. The larger the absolute value of the difference between the crowding level and the optimal crowding level, the larger the drying efficiency coefficient corresponding to the crowding level, indicating that the clothes are more difficult to dry.

[0061] Optionally, the drying time is calculated using the drying efficiency coefficient, the filter clogging coefficient, and the initial drying time according to the second preset algorithm, including: determining the drying time as the product of the drying efficiency coefficient, the filter clogging coefficient, and the initial drying time.

[0062] Optionally, after predicting the drying time based on the moisture content of each garment, the degree of crowding of the garments, and the degree of filter blockage, the method further includes: sending the difference between the predicted drying time and the current running time of the dryer to the display module through the dryer's main control module, triggering the display module to display the remaining drying time.

[0063] In some embodiments, the main control module of the dryer determines the remaining drying time of the dryer as the difference between the predicted drying time and the current running time of the dryer.

[0064] Combination Figure 2 As shown, this disclosure provides a method for predicting drying time, including:

[0065] In step S201, the electronic device determines whether there is at least one garment inside the dryer.

[0066] In step S202, when there is at least one garment in the dryer, the electronic device obtains the moisture content of each garment, the filter clogging coefficient, and the degree of crowding of the garment.

[0067] In step S203, the electronic device predicts the drying time based on the moisture content of each garment, the degree of crowding, and the degree of blockage.

[0068] The method for predicting drying time provided in this disclosure involves acquiring the moisture content of each garment, the degree of crowding within the dryer, and the degree of filter clogging. The drying time is then predicted based on these factors. This approach considers not only the impact of garment moisture content on drying time but also the effects of garment crowding and filter clogging, resulting in a more accurate prediction of drying time. Furthermore, the remaining drying time is calculated by the dryer's main control module and sent to the display panel for display, allowing the user to obtain a more accurate estimate of the remaining runtime.

[0069] Combination Figure 3 As shown, this disclosure provides a method for predicting drying time, including:

[0070] In step S301, the electronic device obtains the moisture content of each garment, the clogging coefficient of the filter, and the crowding level of the garment.

[0071] In step S302, the electronic device determines whether there is at least one garment inside the dryer.

[0072] In step S303, when there is at least one garment in the dryer, the electronic device predicts the drying time based on the moisture content of each garment, the degree of crowding, and the degree of blockage.

[0073] In step S304, the electronic device sends the difference between the predicted drying time and the current drying time to the display panel of the dryer, triggering the display panel to display the remaining drying time.

[0074] The method for predicting drying time provided in this disclosure involves acquiring the moisture content of each garment, the degree of crowding within the dryer, and the degree of filter clogging. The drying time is then predicted based on these factors. This approach considers not only the impact of garment moisture content on drying time but also the effects of garment crowding and filter clogging, resulting in a more accurate prediction of drying time. Furthermore, the remaining drying time is calculated by the dryer's main control module and sent to the display panel for display, allowing the user to obtain a more accurate estimate of the remaining runtime.

[0075] Obviously, the remaining drying time displayed on the display panel decreases as the actual drying time increases. When the actual drying time is equal to the predicted drying time, the remaining drying time displayed on the display panel is 0.

[0076] Optionally, after predicting the drying time based on the moisture content, crowding, and blockage of each garment, the process also includes: triggering the dryer to dry the garments according to the drying time.

[0077] Combination Figure 4 As shown, this disclosure provides a method for predicting drying time, including:

[0078] In step S401, the electronic device determines whether there is at least one garment inside the dryer.

[0079] In step S402, when there are clothes in the dryer, the electronic device calculates the predicted drying time by using the moisture content of each garment, the degree of crowding and blockage of the garments.

[0080] In step S403, the electronic device triggers the dryer to dry the clothes according to the predicted drying time.

[0081] The method for predicting drying time provided in this disclosure calculates the drying time based on the moisture content of each garment, the degree of crowding, and the degree of filter blockage, assuming at least one garment is present in the dryer. This considers not only the influence of garment moisture content on drying time but also the influence of garment crowding and filter blockage, thus enabling more accurate time prediction. Furthermore, by triggering the dryer to dry the garments according to the specified drying time, the possibility that the garments are not actually dried after the dryer has started drying according to the specified time can be reduced.

[0082] In some embodiments, the dryer includes a main control module, and the washing machine includes an RFID reader. When the washing machine is running a spin-drying program, it sends the spin-drying program, the material of each garment, and the dry weight to the main control module. The main control module then controls the dryer to run idle to obtain a filter clogging coefficient. After the washing machine completes its spin-drying program, the user removes the spun-dry garments from the washing machine and places them into the dryer's rotating drum. The main control module matches the moisture content corresponding to each material and spin-drying program in a preset first database. It then multiplies the moisture content of each garment by its dry weight to obtain the moisture content of each garment. The main control module multiplies the ratio of the moisture content of each garment to a baseline moisture content by a preset time and the corresponding drying factor for each garment to obtain the estimated drying time for each garment. The sum of these estimated drying times is determined as the initial drying time. Simultaneously, the main control module matches the volume coefficients corresponding to each material in the preset second database. Then, it multiplies the dry weight of each garment by the corresponding volume coefficient to obtain the volume of each garment, and sums the volumes of all garments to determine the total volume of the garments. The ratio between the total volume of the garments and the preset volume is determined as the garment crowding level. Based on the garment crowding level, a matching operation is performed in the fourth database to obtain the drying efficiency coefficient. Finally, the main control module multiplies the drying efficiency coefficient, the filter clogging level coefficient, and the initial drying time to obtain the drying time. This approach considers not only the influence of garment moisture content on drying time but also the influence of garment crowding level and filter clogging level, thus enabling a more accurate prediction of drying time.

[0083] Combination Figure 5 As shown, this disclosure provides an apparatus 500 for predicting drying time, including a determining module 501 and a predicting module 502. The determining module 501 is configured to determine whether at least one garment is present in the dryer. The predicting module 502 is configured to predict the drying time based on the moisture content of each garment, the degree of crowding of the garments, and the clogging degree coefficient of the filter when at least one garment is present in the dryer.

[0084] The apparatus for predicting drying time provided in this disclosure obtains the predicted drying time based on the moisture content of each garment, the degree of crowding of the garments, and the degree of filter clogging, provided that at least one garment is present in the dryer. This approach considers not only the influence of garment moisture content on drying time but also the influence of garment crowding and filter clogging, thereby enabling a more accurate prediction of drying time.

[0085] Optionally, the device for predicting drying time further includes a first acquisition module configured to acquire the dry weight of each garment and the moisture content of each garment. The moisture content of each garment is calculated using the dry weight and moisture content of each garment according to a first preset algorithm.

[0086] Optionally, the moisture content of each garment is obtained, including: obtaining the material of each garment and obtaining the dehydration process that each garment has undergone. The moisture content of each garment is obtained by matching the material of each garment and the dehydration process that each garment has undergone in a preset first database; the first database stores the correspondence between material, dehydration process and moisture content.

[0087] Optionally, the device for predicting drying time further includes a second acquisition module, configured to match volume coefficients corresponding to the material of each garment in a preset second database, multiply the dry weight of each garment by its corresponding volume coefficient, and then sum the results to obtain the total volume of the garments in the dryer. The total volume of the garments is then divided by a preset volume, and the degree of crowding of the garments in the dryer is calculated using a formula.

[0088] Optionally, the device for predicting drying time further includes a third acquisition module, configured to acquire the pressure difference across the filter screen while controlling the dryer to operate with an empty drum. A clogging degree coefficient corresponding to the pressure difference is matched against a preset third database. Here, operating the dryer with an empty drum indicates that the drying fan is running. The rotating drum may or may not rotate.

[0089] Optionally, the prediction module is configured to predict drying time based on the moisture content of each garment, the degree of garment crowding, and the degree of filter clogging in the following manner: obtain the initial drying time based on the moisture content, correct the initial drying time using the garment crowding and filter clogging coefficients, and obtain the final drying time.

[0090] Optionally, the initial drying time can be corrected by using the degree of clothing crowding and clogging, including: matching the drying efficiency coefficient corresponding to the degree of clothing crowding in a preset fourth database, and calculating the final drying time using the drying efficiency coefficient, the filter clogging coefficient, and the initial drying time according to a second preset algorithm.

[0091] Combination Figure 6 As shown, this disclosure provides an electronic device 600, including a processor 601 and a memory 602. Optionally, the device may further include a communication interface 603 and a bus 604. The processor 601, communication interface 603, and memory 602 can communicate with each other via the bus 604. The communication interface 603 can be used for information transmission. The processor 601 can call logical instructions in the memory 602 to execute the method for predicting drying time described in the above embodiment.

[0092] Using the electronic device provided in this disclosure, the drying time can be predicted based on the moisture content of each garment, the degree of crowding, and the degree of filter clogging when at least one garment is present in the dryer. This considers not only the influence of garment moisture content on drying time, but also the influence of garment crowding and filter clogging, thereby enabling a more accurate prediction of drying time.

[0093] In some embodiments, the electronic device is a device for issuing control commands to control the dryer, such as a computer, a server, or the main control module of the dryer.

[0094] Furthermore, the logic instructions in the aforementioned memory 602 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0095] The memory 602, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 601 executes functional applications and data processing by running the program instructions / modules stored in the memory 602, that is, it implements the method for predicting drying time in the above embodiments.

[0096] The memory 602 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 602 may include high-speed random access memory and may also include non-volatile memory.

[0097] This disclosure provides a storage medium storing program instructions that, when executed, perform the method described above for predicting drying time.

[0098] The aforementioned computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0099] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, including: a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and other media capable of storing program code; it can also be a transient storage medium.

[0100] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0101] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0102] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0103] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for predicting drying time, characterized in that, include: Determine if there is at least one garment inside the dryer; When there are clothes in the dryer, the drying time is predicted based on the moisture content of each garment, the degree of crowding of the garments, and the clogging coefficient of the filter. Before predicting drying time based on the moisture content of each garment, the density of the garments, and the filter clogging factor, the following also applies: The volume coefficients corresponding to the materials of each garment are matched against the pre-set second database. The total volume of clothes in the dryer is obtained by multiplying the dry weight of each garment by its corresponding volume factor and then adding them together. The degree of crowding of clothes inside the dryer is obtained by dividing the total volume of the clothes by a preset volume. The preset volume is obtained by the following method: first test garments with the same material and the same moisture content but different weights are placed in a dryer in batches for drying, the drying time corresponding to each weight of the first test garment is obtained, the weight with the shortest drying time is determined as the optimal weight, the optimal weight is obtained by multiplying the optimal weight by the volume coefficient corresponding to the first test garment, and the optimal volume is determined as the preset volume.

2. The method according to claim 1, characterized in that, Before predicting drying time based on the moisture content of each garment, the density of the garments, and the filter clogging factor, the following also applies: Obtain the dry weight of each garment; Obtain the moisture content of each garment; The moisture content of each garment is calculated based on the dry weight and moisture content of each garment using the first preset algorithm.

3. The method according to claim 2, characterized in that, Obtain the moisture content of each garment, including: Obtain the material of each garment; Obtain the dehydration process that each garment has undergone; The moisture content of each garment is obtained by matching the garment's material and the dehydration process it has undergone with a preset first database. The first database stores the correspondence between material, dehydration process, and moisture content.

4. The method according to claim 1, characterized in that, Before predicting drying time based on the moisture content of each garment, the density of the garments, and the filter clogging factor, the following also applies: Under the condition of controlling the dryer to run empty, obtain the pressure difference on both sides of the filter screen; Match the blockage degree coefficient corresponding to the pressure difference in the preset third database.

5. The method according to claim 1, characterized in that, The drying time is predicted based on the moisture content of each garment, the density of the garments, and the degree of clogging of the filter, including: The initial drying time is obtained based on the moisture content. The initial drying time is corrected by using the coefficients of clothing crowding and blockage to obtain the actual drying time.

6. The method according to claim 5, characterized in that, The initial drying time is corrected using a coefficient based on the degree of clothing crowding and blockage, including: Match the drying efficiency coefficient corresponding to the degree of clothing crowding in the preset fourth database; The drying time is calculated based on the second preset algorithm using the drying efficiency coefficient, the filter clogging coefficient, and the initial drying time.

7. An apparatus for predicting drying time, characterized in that, include: The determination module is configured to determine whether at least one garment is present inside the dryer; The prediction module is configured to predict drying time based on the moisture content of each garment, the degree of crowding of the garments, and the filter clogging coefficient when there is at least one garment in the dryer. Before predicting drying time based on the moisture content of each garment, the density of the garments, and the filter clogging factor, the following also applies: The volume coefficients corresponding to the materials of each garment are matched against the pre-set second database. The total volume of clothes in the dryer is obtained by multiplying the dry weight of each garment by its corresponding volume factor and then adding them together. The degree of crowding of clothes inside the dryer is obtained by dividing the total volume of the clothes by a preset volume. The preset volume is obtained by the following method: first test garments with the same material and the same moisture content but different weights are placed in a dryer in batches for drying, the drying time corresponding to each weight of the first test garment is obtained, the weight with the shortest drying time is determined as the optimal weight, the optimal weight is obtained by multiplying the optimal weight by the volume coefficient corresponding to the first test garment, and the optimal volume is determined as the preset volume.

8. An electronic device comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to, when executing the program instructions, perform the method for predicting drying time as described in any one of claims 1 to 6.

9. A storage medium storing program instructions, characterized in that, When the program instructions are executed, they perform the method for predicting drying time as described in any one of claims 1 to 6.

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