Washing machine drying control method, device and drying management system

Abstract

The application provides a drying control method and device of a washing machine and a drying management system. The method comprises the following steps: obtaining the image of clothes to be dried and the mass of the clothes to be dried when the washing machine is in a drying stage, the image of clothes to be dried containing the image of all the clothes to be dried, and the mass being the sum of the mass of all the clothes to be dried; performing image recognition on the image of clothes to be dried to obtain the positive stacking height value, the unfolded area and the distribution state parameter of the clothes to be dried; inputting the mass, the positive stacking height value, the unfolded area and the distribution state into a drying parameter control model to obtain a drying parameter group, wherein the drying parameter group comprises the rotation speed of an inner drum, the rotation speed of a fan and the heating time; and controlling the washing machine to perform drying according to the drying parameter group, thereby solving the problem of poor drying quality of the washing machine in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of drying, in particular to a drying control method and device for a washing machine, a computer program product and a drying management system. BACKGROUND

[0002] In the prior art, laundry drying machines or washing machines with drying functions are one of the indispensable household appliances in modern families, which provide people with a solution for fast and uniform drying of laundry, especially in humid or cold seasons. However, the laundry drying process faces a common problem in actual application, that is, the drying effect is not ideal due to unreasonable drying time setting. This problem mainly manifests in two aspects: insufficient drying and over-drying.

[0003] Firstly, insufficient drying is usually because the drying time of the laundry drying machine is set too short, which may be due to the user's inaccurate assessment of the type and humidity of the laundry, or the laundry drying machine itself cannot complete effective drying within the preset time due to excessive load (too many clothes). As a result, the laundry cannot reach the ideal dry state, and may remain a certain humidity, which not only affects the comfort and durability of the laundry, but also may cause the laundry to mildew or produce odor.

[0004] Secondly, over-drying is a problem caused by too long drying time. When the running time of the laundry drying machine exceeds the actual need, the laundry will be affected by overheating and over-drying. In this case, the fiber structure of the laundry may be damaged, resulting in shrinkage, hardening, deformation or color fading of the laundry, reducing the quality and life of the laundry. Over-drying may also cause energy waste, increase power consumption, and cause unnecessary burden on the environment.

[0005] The drying process of a traditional washing machine usually starts after a certain shaking program after the end of the dehydration stage, and the rotation speed and overall drying process during drying are determined according to the weight of the laundry in the drum as a single reference before starting drying. In the entire macro drying process, only one parameter is determined before starting, and the washing machine will run according to this parameter until the end of the drying stage. By analyzing the process, it can be found that first, it cannot accurately determine the optimal drying parameters for the current amount and state of the laundry, and second, since it only determines the parameters once before starting, it cannot dynamically adjust the drying parameters during the drying process to set the most appropriate drying parameters in different stages of drying. SUMMARY

[0006] The main purpose of the present application is to provide a drying control method and device for a washing machine, a computer program product and a drying management system to at least solve the problem of poor drying quality of the washing machine in the prior art.

[0007] To achieve the above object, according to an aspect of the present application, a drying control method of a washing machine is provided, comprising: an acquisition step, in a case where the washing machine is in a drying phase, acquiring a to-be-dried laundry image and a laundry mass of to-be-dried laundry, the to-be-dried laundry image containing images of all the to-be-dried laundry, and the laundry mass being a sum of masses of all the to-be-dried laundry; an identification step, performing image recognition on the to-be-dried laundry image to obtain a positive stacking height value, an unfolded area and a distribution state parameter of the to-be-dried laundry, the positive stacking height value being a height of an entirety of the to-be-dried laundry in a vertical direction, the unfolded area being an area occupied by an overall contour of the to-be-dried laundry, and the distribution state parameter being a distribution proportion of the to-be-dried laundry in a plurality of equal division regions on the to-be-dried laundry image; inputting the laundry mass, the positive stacking height value, the unfolded area and the distribution state into a drying parameter control model to obtain a drying parameter group, the drying parameter group including an inner drum rotating speed, a fan rotating speed and a heating time, the drying parameter control model being obtained by training a plurality of training data, each of the training data including a historical laundry mass, a corresponding historical positive stacking height value, a corresponding historical unfolded area, a corresponding historical distribution state parameter and a corresponding historical drying parameter group; and controlling the washing machine to perform drying according to the drying parameter group.

[0008] Optionally, after the image recognition of the image of the clothes to be dried, the forward stacking height value, the unfolded area and the distribution state parameter of the clothes to be dried are obtained, the method further comprises: an analysis step of inputting the clothes quality, the forward stacking height value, the unfolded area and the distribution state into the drying parameter control model to obtain the drying parameter group, the drying parameter group comprising the clothes amount type, the cycle number, the inner drum rotating speed, the fan rotating speed and the heating time, the clothes amount type being one of multiple types from low to high, the cycle number being the number of cycle periods expected to be experienced in the drying process, the drying parameter control model being trained by multiple sets of training data, each set of training data comprising historical clothes quality, corresponding historical forward stacking height value, corresponding historical unfolded area, corresponding historical distribution state parameter and corresponding historical drying parameter group; a drying step of controlling the washing machine to dry for one cycle period according to the inner drum rotating speed, the fan rotating speed and the single-cycle heating time, the single-cycle heating time being the ratio of the heating time to the cycle number; sequentially repeating the acquisition step, the recognition step, the analysis step and the drying step at least once, and in each repeated drying step, updating the value of the drying parameter group of the current cycle period to the average value of the value of the drying parameter group of the current cycle period and the value of the drying parameter group of the last cycle period, until the current cycle number is 1 or the cycle number reaches the maximum cycle number.

[0009] Optionally, the training process of the drying parameter control model comprises: obtaining multiple drying records with qualified drying quality, the drying record comprising a historical input parameter group and a corresponding historical drying parameter group, the historical input parameter group comprising the historical clothes quality, the corresponding historical forward stacking height value, the corresponding historical unfolded area and the corresponding historical distribution state parameter; inputting each historical input parameter group into a neural network model to obtain multiple predicted drying parameter groups; adjusting the parameters of the neural network model according to the error between each predicted drying parameter group and the corresponding historical drying parameter group until all the errors are less than the corresponding predetermined threshold value, thereby obtaining the drying parameter control model.

[0010] Optionally, obtaining multiple drying records with qualified drying quality comprises: obtaining multiple original drying records with qualified drying quality; and pre-processing the original drying records to obtain the drying records, the pre-processing being used to remove noise, outliers and redundant information.

[0011] Optionally, image recognition is performed on the image of the clothes to be dried to obtain the forward stacking height value, expanded area and distribution state parameters of the clothes to be dried, including: performing image segmentation on the clothes to be dried and the washing machine in the image of the clothes to be dried to obtain the overall outline of the clothes to be dried, where the overall outline of the clothes to be dried is the overall outline of all the clothes to be dried; measuring the forward stacking height value according to the overall outline of the clothes to be dried; calculating the expanded area according to the overall outline of the clothes to be dried; dividing the image of the clothes to be dried into multiple regions; calculating the ratio of the area occupied by the overall outline of the clothes to be dried in each region to the expanded area to obtain multiple distribution state parameters.

[0012] Optionally, before the washing machine enters the drying stage, the method further includes: deploying the drying parameter control model on a remote server, so that the remote server performs drying control on all washing machines of the same type.

[0013] Optionally, before the washing machine enters the drying stage, the method further includes: controlling the washing machine to execute a shaking program, wherein the shaking program is a program for controlling the washing machine to shake so that the clothes to be dried are loosened.

[0014] According to another aspect of the present application, a drying control device for a washing machine is provided, comprising: an acquisition unit for executing an acquisition step, for acquiring an image of clothes to be dried and a mass of the clothes to be dried when the washing machine is in a drying stage, wherein the image of clothes to be dried includes images of all clothes to be dried, and the mass of the clothes to be dried is the sum of the masses of all clothes to be dried; and a recognition unit for executing a recognition step, for performing image recognition on the image of clothes to be dried, and obtaining a forward stacking height value, an expanded area, and a distribution state parameter of the clothes to be dried, wherein the forward stacking height value is the vertical height of the entire body of clothes to be dried, and the expanded area is the area occupied by the overall outline of the clothes to be dried. area, the distribution state parameter is the distribution ratio of the clothes to be dried in multiple equally divided areas on the image of the clothes to be dried; the first analysis unit is used to input the clothes mass, the forward stacking height value, the expanded area and the distribution state into a drying parameter control model to obtain a drying parameter group, the drying parameter group includes the inner drum speed, the fan speed and the heating time, the drying parameter control model is trained with multiple groups of training data, each group of training data includes historical clothes mass, corresponding historical forward stacking height value, corresponding historical expanded area, corresponding historical distribution state parameter and corresponding historical drying parameter group; the first control unit is used to control the washing machine to dry according to the drying parameter group.

[0015] According to still another aspect of the present application, a computer program product is provided, comprising a computer program which, when executed by a processor, implements any of the methods described.

[0016] According to still another aspect of the present application, a drying management system is provided, comprising a plurality of same type washing machines, a remote server, one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs comprise instructions for implementing any of the methods described.

[0017] By applying the technical solution of the present application, in the drying control method of the washing machine, the image of the clothes to be dried and the clothes mass of the clothes to be dried are obtained, so that the image of the clothes to be dried is recognized to obtain the forward stacking height value, the unfolded area and the distribution state parameter of the clothes to be dried. The clothes mass, the forward stacking height value, the unfolded area and the distribution state are input into the input parameters of the drying parameter control model, and the drying parameter group including the inner drum rotating speed, the fan rotating speed and the heating time is analyzed and obtained. The washing machine can be controlled to dry according to the drying parameter group. Compared with the prior art which uses a single parameter of clothes mass to analyze the drying parameter, the clothes mass, the forward stacking height value, the unfolded area and the distribution state are used to analyze the drying parameter group suitable for the clothes to be dried from multiple dimensions, which facilitates the training of the drying parameter control model for accurately predicting the drying parameter, greatly improves the drying quality of the washing machine, and solves the problem of poor drying quality of the washing machine in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A hardware structure block diagram of a mobile terminal for performing a drying control method of a washing machine according to an embodiment of the present application is shown;

[0019] Figure 2 A flowchart of a drying control method of a washing machine according to an embodiment of the present application is shown;

[0020] Figure 3 A schematic diagram of a neural network of a drying parameter control model according to an embodiment of the present application is shown;

[0021] Figure 4 A schematic diagram of an overall profile of clothes to be dried according to an embodiment of the present application is shown;

[0022] Figure 5 A structure block diagram of a drying control device of a washing machine according to an embodiment of the present application is shown.

[0023] Among the above drawings, the following reference signs are included:

[0024] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. DETAILED DESCRIPTION

[0025] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0026] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] As introduced in the background technology, the drying quality of washing machines in the prior art is poor. To solve this technical problem, the embodiments of the present application provide a drying control method, device, computer program product and drying management system for a washing machine.

[0029] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0030] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal for a washing machine drying control method according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1The mobile terminal can further include a transmission device 106 for communication function and an input / output device 108. Those skilled in the art can understand that, Figure 1 The structure shown is only schematic and does not limit the structure of the mobile terminal. For example, the mobile terminal can include more or less components than those shown, or have a different configuration or arrangement of the components. Figure 1 The mobile terminal can include more or less components than those shown, or have a different configuration or arrangement of the components. Figure 1 The mobile terminal can include more or less components than those shown, or have a different configuration or arrangement of the components.

[0031] The memory 104 is used for storing computer programs, such as software programs of application software and modules, for example, the computer program corresponding to the drying control method of the washing machine in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implements the method described above. The memory 104 can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory remotely arranged with respect to the processor 102, and these remote memories can be connected to the mobile terminal through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used for receiving or sending data via a network. The specific examples of the network can include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC for short), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, RF for short) module, which is used for communicating with the Internet in a wireless manner.

[0032] In the embodiment, a drying control method of a washing machine running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that herein.

[0033] Figure 2 is a flowchart of the drying control method of the washing machine according to the embodiment of the present application. As Figure 2 shown, the method includes the following steps:

[0034] Step S201, an acquisition step, in the case that the washing machine is in a drying phase, acquiring a to-be-dried laundry image and a laundry mass of to-be-dried laundry, the to-be-dried laundry image containing images of all the to-be-dried laundry, and the laundry mass being a sum of masses of all the to-be-dried laundry;

[0035] Step S202, an identification step, performing image recognition on the to-be-dried laundry image to obtain a forward stacking height value, an unfolded area, and a distribution state parameter of the to-be-dried laundry, the forward stacking height value being a height of an entirety of the to-be-dried laundry in a vertical direction, the unfolded area being an area occupied by an overall contour of the to-be-dried laundry, and the distribution state parameter being a distribution proportion of the to-be-dried laundry in a plurality of equal division regions on the to-be-dried laundry image;

[0036] Step S203, inputting the laundry mass, the forward stacking height value, the unfolded area, and the distribution state into a drying parameter control model to obtain a drying parameter group, the drying parameter group including an inner drum rotating speed, a fan rotating speed, and a heating time, and the drying parameter control model being trained by a plurality of training data, each of the training data including a historical laundry mass, a corresponding historical forward stacking height value, a corresponding historical unfolded area, a corresponding historical distribution state parameter, and a corresponding historical drying parameter group;

[0037] Step S204, controlling the washing machine to perform drying according to the drying parameter group.

[0038] In the drying control method of the washing machine, the to-be-dried laundry image and the laundry mass of the to-be-dried laundry are acquired, the to-be-dried laundry image is recognized to obtain the forward stacking height value, the unfolded area, and the distribution state parameter of the to-be-dried laundry, the laundry mass, the forward stacking height value, the unfolded area, and the distribution state are input into input parameters of the drying parameter control model, and a drying parameter group including the inner drum rotating speed, the fan rotating speed, and the heating time is analyzed and obtained, so that the washing machine can be controlled to perform drying according to the drying parameter group. Compared with the prior art which uses a single parameter of the laundry mass to analyze the drying parameter, the laundry mass, the forward stacking height value, the unfolded area, and the distribution state are used to analyze the drying parameter group suitable for the to-be-dried laundry from multiple dimensions, a drying parameter control model with high prediction accuracy of drying parameters is trained, the drying quality of the washing machine is greatly improved, and the problem of poor drying quality of the washing machine in the prior art is solved.

[0039] In order to further improve the drying quality, in an optional implementation, after the to-be-dried laundry image is recognized to obtain the forward stacking height value, the unfolded area, and the distribution state parameter of the to-be-dried laundry, the method further includes:

[0040] Step S301, an analysis step, inputting the clothing mass, the forward stacking height value, the expanded area, and the distribution state into the drying parameter control model to obtain the drying parameter group, the drying parameter group including the clothing load type, the number of cycles, the inner drum speed, the fan speed, and the heating time, the clothing load type being one of multiple types of clothing loads ranging from low to high, the number of cycles being the number of cycles expected to be experienced in the drying process, the drying parameter control model being trained using multiple sets of the training data, each set of the training data including historical clothing mass, corresponding historical forward stacking height values, corresponding historical expanded area, corresponding historical distribution state parameters, and corresponding historical drying parameter group;

[0041] Step S302, a drying step, controlling the washing machine to dry for one cycle according to the inner drum speed, the fan speed, and a single-cycle heating time, where the single-cycle heating time is a ratio of the heating time to the number of cycles;

[0042] Step S303, repeat the above-mentioned acquisition step, the above-mentioned identification step, the above-mentioned analysis step and the above-mentioned drying step in sequence at least once, and in each repetition of the above-mentioned drying step, update the value of the above-mentioned drying parameter group of the current above-mentioned cycle period to the average value of the above-mentioned drying parameter group of the current above-mentioned cycle period and the value of the above-mentioned drying parameter group of the previous above-mentioned cycle period, until the current above-mentioned number of cycles is 1 or the number of cycles reaches the maximum number of cycles.

[0043] In the above embodiment, if Figure 3 As shown, the above-mentioned clothing mass g, the above-mentioned forward stacking height value h, the above-mentioned expanded area s and the above-mentioned distribution state θ are input into the above-mentioned drying parameter control model to obtain the above-mentioned drying parameter group, which includes the clothing amount type N, the number of cycles p, the above-mentioned inner drum speed V 内 、The above fan speed V 风 Based on the four parameters passed in and the heating time S, the prediction model predicts the n types corresponding to the current amount of clothes through multi-layer neuron calculations. After determining the type of clothes, the corresponding drying process is divided into the corresponding P cycles, and the specific operating values ​​of the three parameters of the inner drum speed, the wind speed of the fan, and the heating time of the heating mechanism during each drying cycle are determined. An example of the prediction result is shown in Table 1. After taking a photo of the clothes to be dried in the drum for the first time, the model predicts that the current amount of clothes is large and belongs to the i-th type, and the number of subsequent drying cycles is set to p. The inner drum speed V in each drying cycle is set to 0. 内 , wind speed V of the fan 风, the heating time S of the heating mechanism is respectively set as a corresponding preset value. Meanwhile, in order to further optimize the entire drying process and achieve the purpose of dynamic adjustment, in the prediction result, the clothes in the barrel are photographed and predicted after each cycle, and the parameter value group obtained this time is compared with the value predicted for the first time before starting drying, and the median value of the two is selected as the parameter value for the next cycle. Therefore, in order to ensure that the washing machine performs drying work in the best state every cycle, the prediction is performed again after the end of each subsequent cycle, and the optimal program is calculated to execute.

[0044] Table 1

[0045]

[0046] In order to ensure to improve the drying quality, in an optional implementation, the training process of the above drying parameter control model includes:

[0047] In step S401, a plurality of drying records with qualified drying quality are obtained, the drying record includes a historical input parameter group and a corresponding historical drying parameter group, the historical input parameter group includes the historical clothes quality, the corresponding historical forward stacking height value, the corresponding historical spreading area, and the corresponding historical distribution state parameter;

[0048] In step S402, each historical input parameter group is input into a neural network model to obtain a plurality of predicted drying parameter groups;

[0049] In step S403, the parameters of the neural network model are adjusted according to the error between each predicted drying parameter group and the corresponding historical drying parameter group, until all the errors are less than a corresponding predetermined threshold value, and the drying parameter control model is obtained.

[0050] In the above implementation, the historical running data of the washing machine is collected, including the forward stacking height value h of the clothes in the barrel, the spreading area s, the clothes quality g, the distribution state parameter θ of the current clothes in the barrel, and the subsequent program execution flow corresponding to the above related parameters (including: the number of cycles determined by different parameters when the drying process is divided into multiple cycles, the specific running values of the inner barrel speed, the fan speed, and the heating time of the heating mechanism in each cycle). The forward stacking height value h of the clothes in the barrel, the spreading area s, the clothes quality g, and the distribution state parameter θ of the current clothes in the barrel are regarded as a data point. Then, the collected data is cleaned and preprocessed to remove noise, outliers and redundant information, and finally the data is labeled to clearly indicate the subsequent execution flow corresponding to each data point. A machine learning model suitable for sequence prediction or classification is selected, a multi-layer feedforward neural network (BP neural network) model is selected, and the labeled data is used to train the model, and the model parameters are adjusted to optimize the prediction performance.

[0051] To obtain the training data, in an optional embodiment, the step S401 comprises:

[0052] Step S4011, obtaining a plurality of original drying records of qualified drying quality;

[0053] Step S4012, preprocessing the original drying records to obtain the drying records, and the preprocessing is used to remove noise, outliers and redundant information.

[0054] In the above embodiment, the stacking height h, the spread area s, the mass g in the drum and the distribution state parameter θ of the current clothes in the drum are regarded as a data point. Then the collected data is cleaned and preprocessed to remove noise, outliers and redundant information, and finally the data is labeled to clearly indicate the subsequent execution process corresponding to each data point.

[0055] To calculate the input parameters, in an optional embodiment, the step S202 comprises:

[0056] Step S2021, image segmentation of the to-be-dried clothes and the washing machine in the to-be-dried clothes image to obtain a to-be-dried clothes overall contour, and the to-be-dried clothes overall contour is the contour of the whole composed of all the to-be-dried clothes;

[0057] Step S2022, measuring the forward stacking height value according to the to-be-dried clothes overall contour;

[0058] Step S2023, calculating the spread area according to the to-be-dried clothes overall contour;

[0059] Step S2024, equally dividing the to-be-dried clothes image into a plurality of regions;

[0060] Step S2025, calculating the ratio of the area occupied by the to-be-dried clothes overall contour in each region to the spread area to obtain a plurality of distribution state parameters.

[0061] In the above embodiment, the first camera is installed in a suitable area directly in front of the washing machine to capture the front of the washing machine's inner drum. Computer vision-based semantic segmentation technology is used to create a dataset by labeling a large number of images. This dataset is trained in the MobileU-net model of a convolutional neural network (CNN). The resulting model can accurately recognize real-time captured images and achieve precise segmentation of the separate regions between the clothing and the washing machine. Therefore, after the pre-processing stage of dehydration is completed, the first camera captures an image of the front of the drum, which is sent to a remote server. A segmentation algorithm is used to separate the clothing from the drum wall, ultimately measuring the forward stack height h of the clothing at the current stage. The second camera is installed in the upper area directly in front of the washing machine. After the pre-processing stage is completed, it captures an image of the washing machine's inner drum from an oblique downward angle. Through image segmentation, the area s of the clothing within the inner drum wall is identified. It is worth noting that when the clothes in the drum have the same stacking height h and unfolding area s, the state of the clothes in the inner drum cannot be uniquely determined, and different clothing distribution states will also have different effects on the subsequent drying control process; therefore, in addition to the stacking height and unfolding area, another variable needs to be introduced to describe the current distribution state of the clothes in the inner drum. Therefore, the clothing distribution state parameter θ is introduced in this patent. In addition to obtaining the area parameter through the second camera, the distribution state parameter θ of the clothes in the drum needs to be calculated using semantic segmentation technology. When calculating the distribution state parameter, if Figure 4 As shown in the figure, the shaded part representing the clothes is divided into four parts by drawing two cross-cutting lines with the center of the entire picture as the midpoint. The clothes are divided into four parts. By calculating the number of pixels in the four parts and the percentage of clothing pixels in all pixels in the entire quadrant, a set of data within 100 is obtained (for example, the four quadrants are (20, 25, 10, 5) respectively). Finally, the values ​​of the four quadrants are input into the prediction network according to (θ1, θ2, θ3, θ4).

[0062] In order to calculate the input parameters, in an optional embodiment, before the washing machine enters the drying stage, the method further includes:

[0063] Step S501: deploy the drying parameter control model on a remote server, so that the remote server performs drying control on all washing machines of the same type.

[0064] In the above embodiment, the trained prediction model (drying parameter control model) is deployed on a remote server, allowing the remote server to perform drying control for all similar washing machines. After the pre-processing stage, the clothes in the drum are photographed using two cameras at the front. After obtaining the photos, the photos are transmitted to the remote server. Relying on the high performance of the remote server, the photos can be quickly processed. After receiving the photos, the remote server inputs them into the semantic segmentation model. The segmentation processing of the model determines the stacking height h, expansion area s, and distribution state parameter θ of the clothes in the washing machine drum in the current photo. The total mass g of the clothes in the drum is calculated locally using the weighing module in the washing machine body. At this point, the four parameters of stacking height h, expansion area s, distribution state parameter θ, and total mass g of the clothes in the drum are obtained and set as a parameter group. The total mass g of the clothes in the drum is then sent to the remote server for subsequent processing. After receiving the parameter g, the remote server inputs the other three parameters obtained by itself into the prediction model. The prediction model predicts the n types corresponding to the current amount of clothing based on the four parameters passed in through multi-layer neuron calculations. After determining the type of clothing, the corresponding drying process is divided into a corresponding P number of cycles, and the specific operating values ​​of the three parameters during drying in each cycle are determined: the inner drum speed, the fan speed, and the heating time of the heating mechanism.

[0065] In order to ensure that the clothes to be dried are loose, in an optional embodiment, before the washing machine enters the drying stage, the method further includes:

[0066] Step S601, controlling the washing machine to execute a shaking program, wherein the shaking program is a program for controlling the washing machine to shake so as to loosen the clothes to be dried.

[0067] In the above embodiment, after the dehydration stage is completed and before the drying stage begins, due to the high-speed dehydration, the clothes are in a state of being close to the drum wall, which is not conducive to the subsequent drying process. In order to ensure that the clothes to be dried are as loose as possible, a pre-drying shaking process needs to be performed. At the same time, in order to ensure the accuracy of the subsequent prediction results, a relatively unified reference standard needs to be established before the drying begins to reduce the influence of other irrelevant or redundant features. Therefore, a pre-processing process needs to be introduced. For example, the washing machine is rotated forward and backward at a lower specific speed for a certain period of time so that the clothes in the drum are uniformly processed to facilitate the prediction of the subsequent processing process of different amounts of clothes.

[0068] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0069] The embodiment of the present application also provides a drying control device for a washing machine. It should be noted that the drying control device for the washing machine in the embodiment of the present application can be used to execute the drying control method for the washing machine provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred embodiments, and the details that have been explained will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceivable.

[0070] The following is an introduction to the drying control device of the washing machine provided in the embodiment of the present application.

[0071] Figure 5 FIG. 1 is a structural block diagram of a drying control device for a washing machine according to an embodiment of the present application. Figure 5 As shown, the device includes:

[0072] an acquiring unit 10 configured to execute an acquiring step, when the washing machine is in a drying stage, acquiring an image of the laundry to be dried and a mass of the laundry to be dried, wherein the image of the laundry to be dried includes images of all the laundry to be dried, and the mass of the laundry is the sum of the masses of all the laundry to be dried;

[0073] a recognition unit 20 configured to perform a recognition step, performing image recognition on the image of the laundry to be dried, and obtaining a forward stacking height value, an expanded area, and a distribution state parameter of the laundry to be dried, wherein the forward stacking height value is the vertical height of the entire laundry to be dried; the expanded area is the area occupied by the overall outline of the laundry to be dried; and the distribution state parameter is the distribution ratio of the laundry to be dried in a plurality of equally divided regions on the image of the laundry to be dried;

[0074] a first analyzing unit 30 for inputting the clothing mass, the forward stacking height, the unfolded area, and the distribution state into a drying parameter control model to obtain a drying parameter set, wherein the drying parameter set includes an inner drum speed, a fan speed, and a heating time, wherein the drying parameter control model is trained with multiple sets of training data, each set of training data including a historical clothing mass, a corresponding historical forward stacking height, a corresponding historical unfolded area, a corresponding historical distribution state parameter, and a corresponding historical drying parameter set;

[0075] The first control unit 40 is used to control the washing machine to dry according to the drying parameter group.

[0076] In the drying control device of the above-mentioned washing machine, by obtaining the image of the clothes to be dried and the clothes mass of the clothes to be dried, image recognition is performed on the image of the clothes to be dried, and the forward stacking height value, unfolding area and distribution state parameters of the above-mentioned clothes to be dried are obtained. The clothes mass, forward stacking height value, unfolding area and distribution state are input into the input parameters of the drying parameter control model, and the drying parameter group is obtained by analysis, including the inner drum speed, the fan speed and the heating time. The above-mentioned washing machine can be controlled to dry according to the drying parameter group. Compared with the prior art that uses a single parameter of clothes mass to analyze the drying parameters, the present application uses clothes mass, forward stacking height value, unfolding area and distribution state to analyze the drying parameter group suitable for the clothes to be dried from multiple dimensions, so as to facilitate the training of a drying parameter control model that accurately predicts the drying parameters, thereby greatly improving the drying quality of the washing machine and solving the problem of poor drying quality of the washing machine in the prior art.

[0077] In order to further improve the drying quality, in an optional embodiment, the above device further includes:

[0078] a second analysis unit for executing an analysis step, wherein after performing image recognition on the image of the clothes to be dried and obtaining a forward stacking height value, an expanded area, and a distribution state parameter of the clothes to be dried, the clothes mass, the forward stacking height value, the expanded area, and the distribution state are input into the drying parameter control model to obtain the drying parameter group, wherein the drying parameter group includes a clothes quantity type, a number of cycles, a speed of the inner drum, a speed of the fan, and a heating time, wherein the clothes quantity type is one of multiple types of clothes quantity ranging from low to high, the number of cycles is the number of cycles expected to be experienced in the drying process, and the drying parameter control model is obtained by training with multiple sets of the training data, wherein each set of the training data includes a historical clothes mass, a corresponding historical forward stacking height value, a corresponding historical expanded area, a corresponding historical distribution state parameter, and a corresponding historical drying parameter group;

[0079] a second control unit, configured to execute a drying step, controlling the washing machine to dry for one cycle according to the inner drum speed, the fan speed, and a single-cycle heating time, wherein the single-cycle heating time is a ratio of the heating time to the number of cycles;

[0080] A repeating unit is used to repeat the above-mentioned acquisition step, the above-mentioned identification step, the above-mentioned analysis step and the above-mentioned drying step in sequence at least once, and in each repetition of the above-mentioned drying step, update the value of the above-mentioned drying parameter group of the current above-mentioned cycle period to the average value of the above-mentioned drying parameter group of the current above-mentioned cycle period and the value of the above-mentioned drying parameter group of the previous above-mentioned cycle period, until the current number of cycles is 1 or the number of cycles reaches the maximum number of cycles.

[0081] In the above embodiment, if Figure 3 As shown, the above-mentioned clothing mass g, the above-mentioned forward stacking height value h, the above-mentioned expanded area s and the above-mentioned distribution state θ are input into the above-mentioned drying parameter control model to obtain the above-mentioned drying parameter group, which includes the clothing amount type N, the number of cycles p, the above-mentioned inner drum speed V 内 、The above fan speed V 风 Based on the four parameters passed in and the heating time S, the prediction model predicts the n types corresponding to the current amount of clothes through multi-layer neuron calculations. After determining the type of clothes, the corresponding drying process is divided into the corresponding P cycles, and the specific operating values ​​of the three parameters of the inner drum speed, the wind speed of the fan, and the heating time of the heating mechanism during each drying cycle are determined. An example of the prediction result is shown in Table 1. After taking a photo of the clothes to be dried in the drum for the first time, the model predicts that the current amount of clothes is large and belongs to the i-th type, and the number of subsequent drying cycles is set to p. The inner drum speed V in each drying cycle is set to 0. 内 , wind speed V of the fan 风 , and the heating time S of the heating mechanism are set to corresponding preset values. Furthermore, to further optimize the entire drying process and achieve dynamic adjustment, the predicted results capture the laundry in the tub after each cycle and predict its parameters. The resulting parameter set for the next cycle is compared with the initial prediction before the start of drying, and the median of the two is selected as the parameter value for the next run. Therefore, to ensure that the washing machine performs optimally with each cycle, predictions are performed again after each subsequent cycle to calculate the optimal program for execution.

[0082] In order to ensure improved drying quality, in an optional embodiment, the apparatus includes a training unit, and the training unit includes:

[0083] an acquisition module, configured to acquire a plurality of drying records with satisfactory drying quality, the drying records comprising a historical input parameter group and a corresponding historical drying parameter group, the historical input parameter group comprising the historical clothing mass, the corresponding historical forward stacking height value, the corresponding historical unfolding area, and the corresponding historical distribution state parameter;

[0084] A training module, for inputting each of the above historical input parameter groups into a neural network model to obtain a plurality of predicted drying parameter groups;

[0085] The adjustment module is used to adjust the parameters of the neural network model according to the errors between each of the predicted drying parameter groups and the corresponding historical drying parameter groups until all of the errors are less than the corresponding predetermined thresholds, thereby obtaining the drying parameter control model.

[0086] In the above embodiment, the historical operation data of the washing machine is collected, including the forward stacking height value h of the clothes in the drum, the spread area s, the clothes mass g, the distribution state parameter θ of the current clothes in the drum, and the subsequent program execution flow corresponding to the above related parameters (including: when the drying process is divided into multiple cycles, the cycle number determined by different parameters, the specific operation values of the inner drum speed, the fan speed, and the heating time of the heating mechanism in each cycle). The forward stacking height value h of the clothes in the drum, the spread area s, the clothes mass g, and the distribution state parameter θ of the current clothes in the drum are regarded as a data point. Then, the collected data is cleaned and preprocessed to remove noise, outliers, and redundant information, and finally the data is labeled to clearly indicate the subsequent execution flow corresponding to each data point. A machine learning model suitable for sequence prediction or classification is selected, and a multi-layer feedforward neural network (BP neural network) model is selected. The labeled data is used to train the model, and the model parameters are adjusted to optimize the prediction performance.

[0087] To obtain the training data, in an optional embodiment, the obtaining module comprises:

[0088] An obtaining submodule is configured to obtain a plurality of original drying records of qualified drying quality.

[0089] A processing submodule is configured to pre-process the original drying records to obtain the drying records, and the pre-processing is used to remove noise, outliers, and redundant information.

[0090] In the above embodiment, the stacking height h of the clothes in the drum, the spread area s, the mass g in the drum, and the distribution state parameter θ of the current clothes in the drum are regarded as a data point. Then, the collected data is cleaned and preprocessed to remove noise, outliers, and redundant information, and finally the data is labeled to clearly indicate the subsequent execution flow corresponding to each data point.

[0091] To calculate the input parameters, in an optional embodiment, the recognition unit comprises:

[0092] A segmentation module is configured to perform image segmentation on the to-be-dried clothes in the to-be-dried clothes image and the washing machine to obtain a to-be-dried clothes overall contour, wherein the to-be-dried clothes overall contour is the contour of the whole formed by all the to-be-dried clothes.

[0093] A measurement module is configured to measure the forward stacking height value according to the to-be-dried clothes overall contour.

[0094] A first calculation module is configured to calculate the spread area according to the to-be-dried clothes overall contour.

[0095] A division module is configured to equally divide the to-be-dried clothes image into a plurality of regions.

[0096] The second calculation module is configured to calculate a ratio of an area of the overall profile of the laundry to be dried in each of the regions to the unfolded area, to obtain a plurality of distribution state parameters.

[0097] In the above embodiment, the first camera is installed in a proper area in front of the washing machine and is used to capture the front of the inner drum of the washing machine. Based on the semantic segmentation technology of computer vision, a large number of pictures are labeled, and the labeled pictures are made into a data set. The data set is trained in the MobileU-net model of the convolutional neural network (CNN), and the model obtained by the training can accurately identify the real-time captured images, so as to realize the accurate segmentation of the respective independent areas of the laundry and the washing machine. Therefore, after the front processing stage is completed, the first camera is used to capture the front image of the inner drum, the obtained image is sent to a remote server, and the laundry and the drum wall are segmented by a segmentation algorithm, so as to finally measure the forward stacking height value h of the laundry in the current stage. The second camera is installed in an upper area in front of the washing machine. After the program of the front processing stage is executed, the second camera captures the picture of the inner drum of the washing machine in a downward direction. Through image segmentation, the unfolded area s of the laundry in the inner drum wall of the washing machine is identified. It is worth noting that when the laundry in the drum has the same stacking height h and unfolded area s, the state of the laundry in the inner drum cannot be uniquely determined, and different distribution states of the laundry will have different effects on the subsequent drying control process. In addition to the stacking height and the unfolded area, a variable is needed to describe the distribution state of the current laundry in the inner drum. Therefore, in the present patent, the laundry distribution state parameter θ is introduced. In addition to obtaining the area parameter by the second camera, the semantic segmentation technology is also used to calculate the distribution state parameter θ of the laundry in the drum. In calculating the distribution state parameter, as shown in the figure, the shadow part representing the laundry is divided into four parts by drawing two cross-sectional division lines with the center of the entire picture as the midpoint. A group of data within 100 (for example, according to the four quadrants, (20, 25, 10, 5)) is obtained by calculating the number of pixel points in the four parts and calculating the percentage of the laundry pixel points in all pixel points in the entire quadrant. Finally, the values of the four quadrants are input into the prediction network according to (θ1, θ2, θ3, θ4). Figure 4

[0098] In order to calculate the input parameters, in an optional embodiment, the apparatus further comprises:

[0099] The deployment unit is configured to deploy the drying parameter control model on a remote server before the washing machine enters the drying stage, so that the remote server controls the drying of all washing machines of the same type.

[0100] ​In the above embodiment, the trained prediction model (drying parameter control model) is deployed on a remote server, which can control the drying of all similar washing machines on the remote server. After the pre-processing stage, the clothes in the drum are photographed by the two front cameras, and the photos are transmitted to the remote server. The remote server can quickly process the photos due to its high performance. The remote server inputs the photos into the semantic segmentation model to obtain the stacking height h, spreading area s, and distribution state parameter θ of the clothes in the washing machine. The total mass g of the clothes in the drum is calculated by the weighing module in the washing machine. Thus, the four parameters of stacking height h, spreading area s, distribution state parameter θ, and total mass g of the clothes in the drum are obtained, and the four parameters are set as a parameter group. Then, the total mass g of the clothes in the drum is also sent to the remote server for subsequent processing. After receiving the parameter g, the remote server inputs the other three parameters obtained by itself into the prediction model. The prediction model predicts the n types corresponding to the current amount of clothes according to the four parameters inputted by the multi-layer neurons. After determining the type of the amount of clothes, the corresponding drying process is divided into P cycles, and the specific operating values of the inner drum speed, fan speed, and heating time in each cycle are determined.

[0101] To ensure that the clothes to be dried are loose, an optional embodiment includes:

[0102] A third control unit is configured to control the washing machine to perform a shaking program before the washing machine enters the drying stage. The shaking program is a program for controlling the washing machine to shake to loosen the clothes to be dried.

[0103] In the above embodiment, after the dehydration stage is completed and before the drying stage starts, the clothes are in a state of clinging to the drum wall, which is not conducive to the subsequent drying process. To ensure that the clothes to be dried are as loose as possible, a shaking program before drying is needed. To ensure the accuracy of the prediction result, a uniform reference standard is needed before the drying starts to reduce the influence of other irrelevant or redundant features. Therefore, a pre-processing procedure is needed. For example, the washing machine is rotated forward and backward at a specific low speed for a certain period of time to uniformly process the clothes in the drum to facilitate the subsequent processing process prediction of different amounts of clothes.

[0104] The drying control device of the washing machine comprises a processor and a memory, and the acquisition unit, the identification unit, the first analysis unit and the first control unit are all stored in the memory as program units, and the corresponding functions are realized by executing the program units stored in the memory by the processor. The above-mentioned modules are located in the same processor; or, the above-mentioned modules are respectively located in different processors in any combination.

[0105] The processor comprises a core, and the corresponding program unit is called from the memory by the core. One or more cores can be provided, and the problem of poor drying quality of the washing machine in the prior art can be solved by adjusting the core parameters.

[0106] The memory can include a non-permanent memory in a computer readable medium, a random access memory (RAM) and / or a non-volatile memory such as a read-only memory (ROM) or a flash memory (flash RAM), and the memory comprises at least one memory chip.

[0107] The embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium comprises a stored program, wherein the program controls a device where the computer readable storage medium is located to execute the drying control method of the washing machine when the program runs.

[0108] Specifically, the drying control method of the washing machine comprises:

[0109] In step S201, the step of acquiring is performed, and in the case that the washing machine is in a drying stage, the image of the clothes to be dried and the mass of the clothes to be dried are acquired, the image of the clothes to be dried comprises the images of all the clothes to be dried, and the mass of the clothes to be dried is the sum of the masses of all the clothes to be dried;

[0110] In step S202, the step of identifying is performed, and the image of the clothes to be dried is subjected to image recognition to obtain the forward stacking height value, the unfolded area and the distribution state parameter of the clothes to be dried, the forward stacking height value is the height of the whole in the vertical direction formed by the clothes to be dried, the unfolded area is the area occupied by the whole outline of the clothes to be dried, and the distribution state parameter is the distribution proportion of the clothes to be dried in a plurality of equal division regions in the image of the clothes to be dried;

[0111] In step S203, the mass of the clothes, the forward stacking height value, the unfolded area and the distribution state are input into a drying parameter control model to obtain a drying parameter group, the drying parameter group comprises the rotation speed of the inner drum, the rotation speed of the fan and the heating time, the drying parameter control model is obtained by training a plurality of training data, and each set of the training data comprises a historical mass of clothes, a corresponding historical forward stacking height value, a corresponding historical unfolded area, a corresponding historical distribution state parameter and a corresponding historical drying parameter group.

[0112] Step S204: controlling the washing machine to dry according to the drying parameter group.

[0113] An embodiment of the present invention provides a processor, which is used to run a program, wherein the drying control method of the washing machine is executed when the program is run.

[0114] Specifically, the drying control method of the washing machine includes:

[0115] Step S201, an acquisition step, in which, when the washing machine is in a drying stage, an image of clothes to be dried and a mass of the clothes to be dried are acquired, wherein the image of clothes to be dried includes images of all the clothes to be dried, and the mass of the clothes is the sum of the masses of all the clothes to be dried;

[0116] Step S202, a recognition step, performing image recognition on the image of the clothes to be dried to obtain a forward stacking height value, an expanded area, and a distribution state parameter of the clothes to be dried, wherein the forward stacking height value is the vertical height of the entire clothes to be dried, the expanded area is the area occupied by the overall outline of the clothes to be dried, and the distribution state parameter is the distribution ratio of the clothes to be dried in multiple equally divided areas on the image of the clothes to be dried;

[0117] Step S203: Inputting the clothing mass, the forward stacking height, the unfolded area, and the distribution state into a drying parameter control model to obtain a drying parameter set, wherein the drying parameter set includes an inner drum speed, a fan speed, and a heating time. The drying parameter control model is trained using multiple sets of training data, each set of training data including a historical clothing mass, a corresponding historical forward stacking height, a corresponding historical unfolded area, a corresponding historical distribution state parameter, and a corresponding historical drying parameter set.

[0118] Step S204: controlling the washing machine to dry according to the drying parameter group.

[0119] An embodiment of the present invention provides a drying management system, including multiple washing machines of the same type, a remote server, a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, at least the following steps are performed:

[0120] Step S201, an acquisition step, in which, when the washing machine is in a drying stage, an image of clothes to be dried and a mass of the clothes to be dried are acquired, wherein the image of clothes to be dried includes images of all the clothes to be dried, and the mass of the clothes is the sum of the masses of all the clothes to be dried;

[0121] Step S202, a recognition step, image recognition is performed on the above-mentioned laundry image to be dried to obtain a forward stacking height value, an unfolded area and a distribution state parameter of the above-mentioned laundry to be dried, the forward stacking height value is the height of the whole composed of the above-mentioned laundry to be dried in the vertical direction, the unfolded area is the area occupied by the overall contour of the above-mentioned laundry to be dried, and the distribution state parameter is the distribution proportion of the above-mentioned laundry to be dried in a plurality of equal division regions on the above-mentioned laundry image to be dried.

[0122] Step S203, inputting the above-mentioned laundry quality, the above-mentioned forward stacking height value, the above-mentioned unfolded area and the above-mentioned distribution state into a drying parameter control model to obtain a drying parameter group, the drying parameter group includes the inner drum rotating speed, the fan rotating speed and the heating time, the drying parameter control model is trained by a plurality of groups of training data, and each group of the training data includes the historical laundry quality, the corresponding historical forward stacking height value, the corresponding historical unfolded area, the corresponding historical distribution state parameter and the corresponding historical drying parameter group.

[0123] Step S204, controlling the above-mentioned washing machine to perform drying according to the above-mentioned drying parameter group.

[0124] The application also provides a computer program product adapted to execute the program of at least the following method steps when executed on a data processing device:

[0125] Step S201, an acquisition step, in the case that the washing machine is in a drying phase, acquiring a laundry image to be dried and a laundry quality of the laundry to be dried, the laundry image to be dried contains the images of all the laundry to be dried, and the laundry quality is the sum of the qualities of all the laundry to be dried;

[0126] Step S202, a recognition step, image recognition is performed on the above-mentioned laundry image to be dried to obtain a forward stacking height value, an unfolded area and a distribution state parameter of the above-mentioned laundry to be dried, the forward stacking height value is the height of the whole composed of the above-mentioned laundry to be dried in the vertical direction, the unfolded area is the area occupied by the overall contour of the above-mentioned laundry to be dried, and the distribution state parameter is the distribution proportion of the above-mentioned laundry to be dried in a plurality of equal division regions on the above-mentioned laundry image to be dried.

[0127] Step S203, inputting the above-mentioned laundry quality, the above-mentioned forward stacking height value, the above-mentioned unfolded area and the above-mentioned distribution state into a drying parameter control model to obtain a drying parameter group, the drying parameter group includes the inner drum rotating speed, the fan rotating speed and the heating time, the drying parameter control model is trained by a plurality of groups of training data, and each group of the training data includes the historical laundry quality, the corresponding historical forward stacking height value, the corresponding historical unfolded area, the corresponding historical distribution state parameter and the corresponding historical drying parameter group.

[0128] Step S204, the drying is performed according to the above drying parameter set.

[0129] It is apparent that those skilled in the art shall understand that the above-mentioned modules or steps of the present application can be realized by general computing devices, which can be centralized on a single computing device or distributed on a network composed of multiple computing devices, which can be realized by program codes executable by the computing devices, so that they can be stored in storage devices and executed by the computing devices, and in some cases, the steps shown or described can be executed in different order, or they can be respectively manufactured into individual integrated circuit modules, or multiple modules or steps among them can be manufactured into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.

[0130] Those skilled in the art should clearly understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product in the form of being implemented on one or more computer usable storage media containing computer usable program codes (including but not limited to disk storage, CD-ROM, optical storage, etc.).

[0131] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The devices that realize the functions specified in one or more flows and / or blocks.

[0132] These computer program instructions can also be stored in a computer readable storage medium that can guide the computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a manufactured product including instruction devices that implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The devices that realize the functions specified in one or more flows and / or blocks.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0134] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0135] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0136] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0137] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0138] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0139] 1) In the drying control method of the washing machine of the present application, by obtaining the image of the clothes to be dried and the clothes mass of the clothes to be dried, image recognition is performed on the image of the clothes to be dried, and the forward stacking height value, unfolding area and distribution state parameters of the above-mentioned clothes to be dried are obtained. The clothes mass, forward stacking height value, unfolding area and distribution state are input into the input parameters of the drying parameter control model, and a drying parameter group is obtained by analysis, including the inner drum speed, the fan speed and the heating time. The above-mentioned washing machine can be controlled to dry according to the drying parameter group. Compared with the prior art that uses a single parameter of clothes mass to analyze the drying parameters, the present application uses clothes mass, forward stacking height value, unfolding area and distribution state to analyze the drying parameter group suitable for the clothes to be dried from multiple dimensions, which facilitates the training of a drying parameter control model that accurately predicts the drying parameters, greatly improves the drying quality of the washing machine, and solves the problem of poor drying quality of the washing machine in the prior art.

[0140] 2) In the drying control device of the washing machine of the present application, by obtaining the image of the clothes to be dried and the clothes mass of the clothes to be dried, image recognition is performed on the image of the clothes to be dried, and the forward stacking height value, unfolding area and distribution state parameters of the above-mentioned clothes to be dried are obtained. The clothes mass, forward stacking height value, unfolding area and distribution state are input into the input parameters of the drying parameter control model, and the drying parameter group is obtained by analysis, including the inner drum speed, the fan speed and the heating time. The above-mentioned washing machine can be controlled to dry according to the drying parameter group. Compared with the prior art that uses a single parameter of clothes mass to analyze the drying parameters, the present application uses clothes mass, forward stacking height value, unfolding area and distribution state to analyze the drying parameter group suitable for the clothes to be dried from multiple dimensions, which facilitates the training of a drying parameter control model that accurately predicts the drying parameters, greatly improves the drying quality of the washing machine, and solves the problem of poor drying quality of the washing machine in the prior art.

[0141] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A drying control method for a washing machine, characterized in that: include: an acquiring step of acquiring, when the washing machine is in a drying stage, an image of the laundry to be dried and a mass of the laundry to be dried, wherein the image of the laundry to be dried includes images of all the laundry to be dried, and the mass of the laundry is the sum of the masses of all the laundry to be dried; a recognition step of performing image recognition on the image of the laundry to be dried to obtain a forward stacking height value, an expanded area, and a distribution state parameter of the laundry to be dried, wherein the forward stacking height value is the vertical height of the entire laundry to be dried; the expanded area is the area occupied by the overall outline of the laundry to be dried; and the distribution state parameter is the distribution ratio of the laundry to be dried in multiple equally divided areas on the image of the laundry to be dried; Inputting the clothing mass, the forward stacking height value, the unfolded area, and the distribution state parameter into a drying parameter control model to obtain a drying parameter group, wherein the drying parameter group includes an inner drum speed, a fan speed, and a heating time, and the drying parameter control model is trained with multiple sets of training data, each set of training data including a historical clothing mass, a corresponding historical forward stacking height value, a corresponding historical unfolded area, a corresponding historical distribution state parameter, and a corresponding historical drying parameter group; The washing machine is controlled to perform drying according to the drying parameter group.

2. The method according to claim 1, characterized in that After performing image recognition on the image of the clothes to be dried to obtain the forward stacking height value, the spread area, and the distribution state parameters of the clothes to be dried, the method further includes: an analysis step, inputting the clothing mass, the forward stacking height value, the expanded area, and the distribution state parameter into the drying parameter control model to obtain the drying parameter group, the drying parameter group including a clothing load type, a number of cycles, the inner drum speed, the fan speed, and the heating time, the clothing load type being one of multiple types of clothing loads ranging from low to high, the number of cycles being the number of cycles expected to be experienced in the drying process, the drying parameter control model being trained using multiple sets of training data, each set of training data including historical clothing mass, corresponding historical forward stacking height values, corresponding historical expanded area, corresponding historical distribution state parameter, and corresponding historical drying parameter group; a drying step, controlling the washing machine to dry for one cycle according to the inner drum speed, the fan speed, and a single-cycle heating time, wherein the single-cycle heating time is a ratio of the heating time to the number of cycles; Repeat the acquisition step, the identification step, the analysis step and the drying step in sequence at least once, and in each repeated drying step, update the value of the drying parameter group of the current cycle to the average value of the drying parameter group of the current cycle and the value of the drying parameter group of the previous cycle, until the current number of cycles is 1 or the number of cycles reaches the maximum number of cycles.

3. The method according to claim 2, characterized in that The training process of the drying parameter control model includes: Acquire multiple drying records with qualified drying quality, the drying records comprising a historical input parameter group and a corresponding historical drying parameter group, the historical input parameter group comprising the historical clothing mass, the corresponding historical forward stacking height value, the corresponding historical unfolding area, and the corresponding historical distribution state parameter; Inputting each of the historical input parameter groups into a neural network model to obtain a plurality of predicted drying parameter groups; The parameters of the neural network model are adjusted according to the errors between each predicted drying parameter group and the corresponding historical drying parameter group until all the errors are smaller than the corresponding predetermined thresholds, thereby obtaining the drying parameter control model.

4. The method according to claim 3, characterized in that Obtain multiple drying records with qualified drying quality, including: Obtain multiple original drying records with qualified drying quality; The original drying record is preprocessed to obtain the drying record, wherein the preprocessing is used to remove noise, abnormal values ​​and redundant information.

5. The method according to claim 1, wherein Performing image recognition on the image of the clothes to be dried to obtain the forward stacking height value, the unfolded area, and the distribution state parameters of the clothes to be dried, including: performing image segmentation on the clothes to be dried and the washing machine in the clothes to be dried image to obtain an overall outline of the clothes to be dried, where the overall outline of the clothes to be dried is an overall outline of all the clothes to be dried; Measuring the forward stacking height value according to the overall outline of the clothes to be dried; Calculating the expanded area according to the overall outline of the clothes to be dried; Dividing the image of the clothes to be dried into a plurality of equal areas; The ratio of the area occupied by the overall outline of the clothes to be dried in each of the regions to the expanded area is calculated to obtain a plurality of distribution state parameters.

6. The method according to any one of claims 1 to 5, characterized in that Before the washing machine enters the drying stage, the method further comprises: The drying parameter control model is deployed on a remote server, so that the remote server performs drying control on all washing machines of the same type.

7. The method according to any one of claims 1 to 5, characterized in that Before the washing machine enters the drying stage, the method further comprises: The washing machine is controlled to execute a shaking program, wherein the shaking program is a program for controlling the washing machine to shake so that the clothes to be dried are loosened.

8. A drying control device for a washing machine, characterized in that: include: an acquiring unit, configured to execute an acquiring step, when the washing machine is in a drying stage, acquiring an image of the laundry to be dried and a mass of the laundry to be dried, wherein the image of the laundry to be dried includes images of all the laundry to be dried, and the mass of the laundry is the sum of the masses of all the laundry to be dried; a recognition unit, configured to execute a recognition step, perform image recognition on the image of the laundry to be dried, and obtain a forward stacking height value, an expanded area, and a distribution state parameter of the laundry to be dried, wherein the forward stacking height value is the vertical height of the entire laundry to be dried; the expanded area is the area occupied by the overall outline of the laundry to be dried; and the distribution state parameter is the distribution ratio of the laundry to be dried in a plurality of equally divided regions on the image of the laundry to be dried; a first analyzing unit, configured to input the clothing mass, the forward stacking height value, the unfolded area, and the distribution state parameter into a drying parameter control model to obtain a drying parameter group, the drying parameter group including an inner drum speed, a fan speed, and a heating time, the drying parameter control model being trained with multiple sets of training data, each set of training data including a historical clothing mass, a corresponding historical forward stacking height value, a corresponding historical unfolded area, a corresponding historical distribution state parameter, and a corresponding historical drying parameter group; The first control unit is used to control the washing machine to dry according to the drying parameter group.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A drying management system, characterized in that: include: A plurality of washing machines of the same type, a remote server, one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing any one of claims 1 to 7.

Images