Method for predicting structural aging of a laundry machine, electronic device and fabric cleaning apparatus

Abstract

This application discloses a method for predicting the structural aging of a washing machine, an electronic device, and a fabric cleaning device, belonging to the field of washing machine control technology. The method for predicting the structural aging of a washing machine includes: acquiring the eccentricity value, load weight value, vibration characteristic information, and rotational state information of the washing machine during operation. The vibration characteristic information characterizes the mechanical oscillation intensity and eccentricity position of the washing tub under high-speed operation, and the rotational state characteristic parameter value characterizes the stability of the washing tub's rotational rate under high-speed operation. The eccentricity value, load weight value, vibration characteristic information, and rotational state information are used as model input information and input into a washing machine structural aging prediction model to obtain a structural health status score. This achieves early warning and proactive intervention for the structural aging of the washing machine, effectively reducing the risk of tub collision caused by aging.

Description

Technical Field

[0001] This application relates to the field of washing machine control technology, and more specifically, to a method for predicting the structural aging of a washing machine, electronic equipment, and fabric cleaning equipment. Background Technology

[0002] With the continuous improvement of people's living standards and the level of economic and social development, the popularity of washing machines as an essential household appliance is increasing year by year. During the use of washing machines, the supporting structure will age after long-term operation, leading to a decrease in anti-eccentricity. When washing a large load of clothes, if the clothes are concentrated on one side, it can easily cause an imbalance of centrifugal force, resulting in violent drum impact, displacement, or even damage to the inner drum and outer shell. Currently, washing machines on the market typically rely on a passive trigger mechanism that stops washing due to excessive vibration. This method lacks the ability to anticipate the "drum impact risk" caused by structural aging. Summary of the Invention

[0003] This application provides a method for predicting the structural aging of a washing machine, an electronic device, and a fabric cleaning device, in order to at least solve the technical problem of decreased anti-eccentricity capability caused by the aging of the support structure in related technologies.

[0004] According to a first aspect of the embodiments of this application, a method for predicting structural aging of a washing machine is provided, comprising:

[0005] The eccentricity value, load weight value, vibration characteristic information and rotation state information of the washing machine are obtained during the operation phase. The vibration characteristic information is used to characterize the mechanical oscillation intensity and eccentric position of the washing tub under high-speed operation, and the rotation state characteristic parameter value is used to characterize the rotation rate stability of the washing tub under high-speed operation.

[0006] The eccentricity value, load weight value, vibration characteristic information, and rotation state information are used as model input information and input into the washing machine structural aging prediction model to obtain a structural health status score.

[0007] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, obtaining the eccentricity value, load weight value, vibration characteristic information, and rotation state information of the washing machine during operation includes:

[0008] Obtain the eccentricity value of the washing tub at low speed and the load weight value of the load inside the washing tub;

[0009] The vibration characteristics and rotational state information of the washing tub under high-speed operation are obtained. The rotational speed range of the washing tub under low-speed operation is 0-800 rpm, and the rotational speed range of the washing tub under high-speed operation is greater than or equal to 800 rpm.

[0010] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, the vibration characteristic information includes the peak amplitude value and vibration amplitude deviation value of the washing tub in the X, Y, and Z axis directions, and the rotation state information includes the angular velocity fluctuation rate of the washing tub in the X, Y, and Z axis directions.

[0011] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, obtaining the eccentricity value, load weight value, vibration characteristic information, and rotation state information of the washing machine during operation includes:

[0012] During the eccentricity detection phase, the eccentricity value and the load weight value are obtained.

[0013] Vibration characteristic information and rotation state information are acquired during the dehydration stage. The peak amplitude value and vibration amplitude deviation value are determined using vibration acceleration, and the angular velocity fluctuation rate is determined using angular velocity. Vibration acceleration and angular velocity are acquired by attitude sensors installed on the washing machine. The peak amplitude value is defined as half the difference between the maximum and minimum values ​​of the acceleration in the X-axis and Y-axis directions, and the vibration amplitude deviation value is defined as the standard deviation of vibration acceleration.

[0014] In conjunction with the first aspect, in an optional implementation of this application embodiment, the washing machine structural aging prediction model is trained in the following manner:

[0015] Obtain target model input information that includes the target structure health status score as annotation;

[0016] Input the target model input information into the initial washing machine structure aging prediction model, and compare the target structure health status score with the corresponding output data of the washing machine structure aging prediction model;

[0017] The washing machine structural aging prediction model is trained and initialized based on the difference between the standard structural health status score and the corresponding output data of the washing machine structural aging prediction model.

[0018] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, the washing machine structural aging prediction model includes:

[0019] The input layer contains five neurons, which correspond to the load weight, eccentricity value, peak amplitude value, vibration amplitude deviation value, and angular velocity fluctuation rate, respectively.

[0020] Hidden layers, hidden layers that contain the first hidden layer and the second hidden layer;

[0021] The output layer is a single-neuron structure used to output a structural health status score.

[0022] In conjunction with the first aspect, in an optional implementation of the embodiments of this application, the method further includes:

[0023] The structural health status score is used to determine whether there is an aging risk; if so, the operating parameters of the dehydration stage are adjusted and a user reminder mechanism is triggered.

[0024] In conjunction with the first aspect, in an optional implementation of the embodiments of this application, the method further includes:

[0025] If the structural health status score exceeds the preset threshold for a number of consecutive preset aging thresholds, then an aging risk is identified.

[0026] In conjunction with the first aspect, in an optional implementation of the embodiments of this application, the method further includes:

[0027] Equipment aging trend curves are generated based on structural health status scores.

[0028] By comparing the aging trend curve with historical data, we can predict the future aging rate.

[0029] Personalized maintenance plans can be developed based on predicted future aging rates to extend equipment lifespan. These plans may include increasing maintenance frequency, replacing critical components, or appropriately extending maintenance intervals.

[0030] In conjunction with the first aspect, in an optional implementation of this application's embodiments, after inputting the eccentricity value, load weight value, vibration characteristic information, and rotational state information as model input information into the washing machine structural aging prediction model to obtain a structural health status score, the method further includes:

[0031] When the washing cycle of the washing machine is less than the preset washing cycle threshold, the structural aging prediction model of the washing machine is corrected based on the structural health status score.

[0032] In conjunction with the first aspect, in one optional implementation of the embodiments of this application, when the washing cycle of the washing machine is less than a preset washing cycle threshold, the structural aging prediction model of the washing machine is corrected based on the structural health status score, including:

[0033] If the washing cycle of the washing machine is less than the preset washing cycle threshold, and the structural health status score output by the washing machine structural aging prediction model indicates that the washing machine has an aging risk, then the structural health status score will be marked as a false alarm sample.

[0034] The washing machine structural aging prediction model is corrected based on false alarm samples, and the judgment boundary of the washing machine structural aging prediction model is gradually corrected.

[0035] The method for predicting the structural aging of a washing machine provided in this invention first acquires the eccentricity value, load weight value, vibration characteristic information, and rotation state information of the washing machine during operation. Then, these values ​​are used as input to the washing machine structural aging prediction model to obtain a structural health status score. This process, through multi-dimensional data collection and intelligent analysis, enables early warning and proactive intervention for the structural aging of the washing machine, effectively reducing the risk of drum collisions caused by aging. Furthermore, based on the structural health status score, timely maintenance tips can be provided to users subsequently.

[0036] According to a second aspect of the embodiments of this application, a device for predicting structural aging of a washing machine is provided, comprising:

[0037] The acquisition unit is used to acquire the eccentricity value, load weight value, vibration characteristic information and rotation state information of the washing machine during the operation phase. The vibration characteristic information is used to characterize the mechanical oscillation intensity and eccentricity position of the washing tub under high-speed operation, and the rotation state characteristic parameter value is used to characterize the rotation rate stability of the washing tub under high-speed operation.

[0038] The processing unit is used to input the eccentricity value, load weight value, vibration characteristic information and rotation state information as model input information into the washing machine structural aging prediction model to obtain a structural health status score.

[0039] According to a third aspect of the embodiments of this application, the present invention provides an electronic device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for predicting the structural aging of a washing machine as described in the first aspect or any corresponding embodiment.

[0040] According to a fourth aspect of the embodiments of this application, this specification provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the method for predicting the structural aging of a washing machine as described in any of the preceding claims.

[0041] According to a fifth aspect of the embodiments of this application, this specification provides a computer program product or computer program, the computer program product including a computer program stored in a computer-readable storage medium; a processor of a computer device reads the computer program from the computer-readable storage medium, and when the processor executes the computer program, it implements the method for predicting the structural aging of a washing machine as described in any of the preceding claims.

[0042] According to a sixth aspect of the embodiments of this application, this specification provides a fabric cleaning device that employs the method for predicting structural aging of a washing machine as described in any of the first aspects, or an electronic device having the third aspect.

[0043] The technical effects achieved by the second to sixth aspects mentioned above are similar to those achieved by the corresponding technical means in the first aspect, and will not be repeated here. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating the method for predicting the structural aging of a washing machine provided in an embodiment of this application.

[0045] Figure 2 This is a schematic diagram of the position of the attitude sensor in the prediction of the aging structure of the washing machine provided in the embodiments of this application;

[0046] Figure 3 This is a schematic diagram of the structural aging prediction model provided in the embodiments of this application;

[0047] Figure 4 This is a schematic diagram of the structure of the washing machine structure aging prediction device provided in the embodiments of this application;

[0048] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0049] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0050] It should be understood that "multiple" as mentioned herein refers to two or more. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. In addition, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first," "second," etc., are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and the terms "first," "second," etc., do not necessarily imply that they are different.

[0051] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0052] As mentioned in the background section, with the continuous improvement of people's living standards and the level of economic and social development, the popularity of washing machines as essential household appliances is increasing year by year. During the use of washing machines, the supporting structure (such as shock-absorbing springs and dampers) will age after long-term operation, resulting in a decrease in anti-eccentricity. When washing a large number of clothes, if the clothes are concentrated on one side, it is easy to cause an imbalance of centrifugal force, which can lead to violent drum collision, displacement, or even damage to the inner drum and outer shell. Currently, washing machines on the market usually rely on a passive trigger mechanism to stop washing due to excessive vibration. This method lacks the ability to predict the "drum collision risk" caused by structural aging.

[0053] Based on this, this application provides a method for predicting the structural aging of a washing machine. By combining attitude sensors with a structural aging prediction model, it can predict the aging phenomena of the washing machine structure in advance, and actively slow down the operation when potential risks are detected, while reminding the user to perform maintenance. (Refer to...) Figure 1 The flowchart shown is a method for predicting the structural aging of a washing machine. The method includes the following processing steps.

[0054] S101: Obtain the eccentricity value, load weight value, vibration characteristic information and rotation status information of the washing machine during operation.

[0055] In practice, during the normal operation of the washing machine, the eccentricity value, load weight value, vibration characteristic information and rotation state information are acquired. The vibration characteristic information is used to characterize the mechanical oscillation intensity and eccentric position of the washing tub under high-speed operation, and the rotation state characteristic parameter value is used to characterize the rotation rate stability of the washing tub under high-speed operation.

[0056] In one possible implementation, the eccentricity value and the load weight value inside the washing tub are acquired during the low-speed phase of the washing machine's operation, while vibration characteristic information and rotation state information are acquired during the high-speed phase. The rotational speed range of the washing tub during low-speed operation is 0-800 rpm, and the rotational speed range of the washing tub during high-speed operation is greater than or equal to 800 rpm. Of course, the above rotational speed range can also be adjusted according to actual needs.

[0057] In another embodiment, the eccentricity value of the washing tub and the load weight inside the washing tub are obtained during the eccentricity detection stage, and vibration characteristic information and rotation state information are obtained during the dehydration stage.

[0058] In practice, when the washing machine reaches the eccentricity detection stage, the eccentricity value of the washing tub and the load weight inside the washing tub are recorded. The load refers to the items to be washed inside the washing tub. Since the load weight and eccentricity value are used as input data in subsequent calculations, it is not limited here whether the load weight includes the weight of water inside the washing tub.

[0059] It should be noted that the washing process of a washing machine may include more than one eccentricity detection stage. You can choose any one of the load weights and eccentricity values ​​for subsequent calculations. Preferably, you should choose the value of the eccentricity detection stage that is closest to the washing stage.

[0060] In practice, a triaxial accelerometer is installed on the top of the washing tub, and its installation position is as follows: Figure 2 As shown, the washing tub refers to the inner drum in the image. The placement of these sensors is based on their ability to accurately capture the vibration and rotational changes of the inner drum during operation. The sampling frequency of the triaxial accelerometer can be set to 1kHz to ensure the accuracy of high-frequency data acquisition. It can acquire the vibration acceleration of the washing tub in the XYZ axis directions during the spin-drying stage, and this vibration acceleration is the attitude data.

[0061] Attitude data can be used to determine the peak amplitude, vibration amplitude deviation, and angular velocity fluctuation rate in the X and Y axes. The peak amplitude and vibration amplitude deviation are the vibration characteristic information, and the angular velocity fluctuation rate is the rotation state information.

[0062] In one possible implementation, the peak amplitude value is defined as half the difference between the maximum and minimum values ​​of the acceleration in the X and Y directions, and the vibration amplitude deviation value is defined as the standard deviation of the vibration acceleration.

[0063] Vibration acceleration and angular velocity can be obtained through a triaxial accelerometer. The obtained vibration acceleration is in the format (ax, ay, az), the angular velocity is (rx, ry, rz), the peak amplitude is V, the vibration amplitude deviation is D, and the angular velocity fluctuation rate is dω / dt.

[0064] S102: The eccentricity value, load weight value, vibration characteristic information and rotation state information are used as model input information and input into the washing machine structural aging prediction model to obtain the structural health status score.

[0065] In practice, the eccentricity value, load weight value, vibration characteristic information, and rotational state information can be directly used as model input information. Alternatively, a dynamic feature parameter can be generated using the load weight, eccentricity value, peak amplitude value, vibration amplitude deviation value, and angular velocity fluctuation rate. This dynamic feature parameter, which is also the input information, is a set of five-dimensional vectors, corresponding to the peak amplitude value V, vibration amplitude deviation value D, angular velocity fluctuation rate dω / dt, load weight W, and eccentricity value B, respectively.

[0066] The dynamic feature parameters are then input into the pre-trained structural aging prediction model, and the structural health status score output by the model is obtained. The structure of this structural aging prediction model is as follows: Figure 3 As shown, the structure consists of an input layer, a hidden layer, and an output layer containing five neurons. The five neurons correspond to the load weight, eccentricity value, peak amplitude value, vibration amplitude deviation value, and angular velocity fluctuation rate, respectively. The hidden layer contains the hidden layers of the first and second hidden layers. The output layer is a single-neuron structure used to output the structural health status score, and each neuron is fully connected to the neurons in the next layer.

[0067] In one possible implementation, the first hidden layer contains 32 neurons and the second hidden layer contains 16 neurons. In another possible implementation, the first hidden layer contains 64 neurons and the second hidden layer contains 32 neurons.

[0068] When training a structural aging prediction model, the following method can be used: First, obtain target input information including the target structure's health status score as annotation. Then, input this information into the initialization model of structural aging prediction, and compare the target structure's health status score with the corresponding output data of the model. Finally, train the initialization model based on the difference between the target structure's health status score and the corresponding output data of the model, thus obtaining the structural aging prediction model. For training the model based on the difference, a loss function can be used, which can be selected as the mean squared error function. Then, the model parameters are optimized using the backpropagation algorithm. Specifically, the expression of the loss function is as follows:

[0069] ;

[0070] Where N is the number of samples and C is the number of categories. Label, It is a predicted probability.

[0071] To adapt to the impact of different installation environments and individual manufacturing differences on washing machine performance, and to facilitate model training, the model can be trained on a personal computer. The trained structural aging prediction model is then placed in the washing machine to output a structural health status score. Considering the subtle differences in the usage environment of each actual user, this method also proposes an online fine-tuning method based on early user data to further improve the accuracy and personalized adaptability of aging detection. Specifically, when the washing cycle of the washing machine is less than a preset washing cycle threshold, the structural aging prediction model is corrected based on the structural health status score. Specifically, when the washing cycle of the washing machine is less than the preset washing cycle threshold, if the structural health status score output by the structural aging prediction model indicates that the washing machine has an aging risk (e.g., the output health status score is below 0.6), the structural health status score is marked as a false alarm sample. The structural aging prediction model is then corrected based on the false alarm sample, gradually correcting the judgment boundary of the structural aging prediction model. The washing cycle threshold can be set according to needs; in this embodiment, 100 cycles are used as an example. Within the first 100 washing cycles, if the model predicts an aging risk but the actual equipment is normal, the prediction is marked as a false alarm sample. Based on false positive samples, the model parameters are adjusted using the backpropagation algorithm to gradually correct the model's judgment boundaries. Once the cumulative number of washes reaches 100, the model parameters are frozen, and the model enters inference mode, used only for health status prediction.

[0072] When the structural health score exceeds a preset threshold (e.g., 0.9) for three consecutive spin cycles, the washing machine's support structure is deemed to be at risk of aging. At this point, the washing machine's display screen will show a "Support Structure Aging Risk" warning, alerting the user to the potential risk. Simultaneously, the operating parameters for the spin cycle will be automatically adjusted, reducing the spin speed by one level and decreasing the maximum acceleration setting to lessen the impact load on the support structure. Furthermore, an equipment status report can be sent to the after-sales service system, prompting for professional repair. The remote communication module connects to the after-sales service system via a wireless network, and the equipment status report includes the structural health score, aging trend curve, and historical data comparison results.

[0073] To further enhance the refined management of equipment maintenance, an aging trend curve can be generated based on the structural health status score. This curve is created by plotting the structural health status score for each washing cycle as a time series, visually displaying the aging process of the equipment. By comparing the aging trend curve with historical data, the future aging rate can be predicted. For example, for equipment with a rapid aging rate, it is recommended that users increase maintenance frequency or replace key components; while for equipment with a slow aging rate, the maintenance interval can be appropriately extended. This data-driven approach enables personalized management of equipment maintenance, significantly extending the equipment's lifespan.

[0074] In this embodiment, the method for predicting the structural aging of a washing machine provided by the present invention first acquires the eccentricity value of the washing tub and the load weight inside the washing tub during the eccentricity detection stage. Then, it acquires the attitude data during the operation of the washing machine and extracts vibration characteristic information and rotational state information based on the attitude data. Subsequently, based on the vibration characteristics, rotational state information, eccentricity value, and load weight, it generates dynamic feature parameters for the current operating cycle. Finally, it inputs the dynamic feature parameters into a pre-trained structural aging prediction model and outputs a structural health status score. This process, through multi-dimensional data collection and intelligent analysis, achieves early warning and proactive intervention for the structural aging of the washing machine, effectively reducing the risk of tub collisions caused by aging. Furthermore, based on the structural health status score, it can provide users with timely maintenance prompts in the future.

[0075] In the above embodiments of this application, the descriptions of each embodiment have their own emphasis. Parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. The steps illustrated in the related flowcharts can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be performed in a different order than that shown here. In other words, the order of steps described in the foregoing embodiments is merely an example. Reasonable adjustments to the order of steps based on the content of the embodiments of this application are also within the protection scope of the embodiments of this application.

[0076] The above examples illustrate the method embodiments according to this application. The present invention also provides a method and apparatus for predicting the structural aging of a washing machine. Figure 4 This is a schematic diagram of a device for predicting structural aging of a washing machine according to an embodiment of the present invention. (Refer to...) Figure 4 The washing machine structural aging prediction device 700 includes the following modules.

[0077] The acquisition unit 701 is used to acquire the eccentricity value, load weight value, vibration characteristic information and rotation state information of the washing machine during the operation phase. The vibration characteristic information is used to characterize the mechanical oscillation intensity and eccentricity position of the washing tub under high-speed operation, and the rotation state characteristic parameter value is used to characterize the rotation rate stability of the washing tub under high-speed operation.

[0078] The processing unit 702 is used to input the eccentricity value, load weight value, vibration characteristic information and rotation state information as model input information into the washing machine structure aging prediction model to obtain a structural health status score.

[0079] The above describes the device embodiments of this application. For detailed descriptions of data, terms, nouns, specific execution processes of steps, technical problems and effects, alternative methods and combinations, please refer to the description in the method embodiments, which will not be repeated here.

[0080] This application also provides a computer program product including computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods for predicting the structural aging of a washing machine according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0081] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this specification. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages.

[0082] This application also provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor of the steps in the method for predicting the structural aging of a washing machine according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0083] This application embodiment also provides an electronic device, including a memory and a processor. The memory stores a method for predicting the structural aging of a washing machine, and the processor is used to employ the aforementioned method for predicting the structural aging of a washing machine when executing the method for predicting the structural aging of a washing machine.

[0084] Specifically, such as Figure 5As shown, the electronic device includes a processor 100, at least one communication bus 200, a user interface 300, at least one external communication interface 400, and a memory 500. The communication bus 200 is configured to enable communication between these components. The user interface 300 may include a display screen, and the external communication interface 400 may include standard wired and wireless interfaces. The memory 500 stores a method for predicting the structural aging of a washing machine. The processor 100 is used to employ the aforementioned method when executing the method for predicting the structural aging of the washing machine stored in the memory 500.

[0085] The descriptions of the above computer program products, computer-readable storage media, and electronic devices are similar to those of the above method embodiments, and have similar beneficial effects. For any technical details not disclosed in the computer program products, computer-readable storage media, and electronic devices of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0086] The sequence numbers or order of description of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0087] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0088] 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0089] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0090] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital versatile disc (DVD)), or a semiconductor medium (e.g., solid state disk (SSD)). It is worth noting that the computer-readable storage medium mentioned in the embodiments of this application can be a non-volatile storage medium; in other words, it can be a non-transient storage medium.

[0091] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in the embodiments of this application are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the scene data of the current frame in the 3D virtual scene involved in the embodiments of this application, the client's device information, and the scene interaction information are all obtained with full authorization.

[0092] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for predicting structural aging in a washing machine, characterized in that, The method includes: The eccentricity value, load weight value, vibration characteristic information and rotation state information of the washing machine are obtained during the operation phase. The vibration characteristic information is used to characterize the mechanical oscillation intensity and eccentric position of the washing tub under high-speed operation, and the rotation state characteristic parameter value is used to characterize the rotation rate stability of the washing tub under high-speed operation. The eccentricity value, the load weight value, the vibration characteristic information, and the rotation state information are used as model input information and input into the washing machine structure aging prediction model to obtain a structural health status score. The acquisition of the eccentricity value, load weight value, vibration characteristic information, and rotation state information of the washing machine during operation includes: Obtain the eccentricity value of the washing tub under low-speed operation and the load weight value of the load inside the washing tub; Obtain the vibration characteristics and rotational state information of the washing tub under high-speed operation; or The acquisition of the eccentricity value, load weight value, vibration characteristic information, and rotation state information of the washing machine during operation includes: The eccentricity value and the load weight value are obtained during the eccentricity detection phase. The vibration characteristic information and the rotational state information are acquired during the dehydration stage.

2. The method according to claim 1, characterized in that, The washing tub operates at low speeds within a speed range of 0-800 rpm, while operating at high speeds within a speed range of 800 rpm or greater.

3. The method according to claim 1, characterized in that, The vibration characteristic information includes the peak amplitude value and vibration amplitude deviation value of the washing tub in the X, Y, and Z axis directions, and the rotation state information includes the angular velocity fluctuation rate of the washing tub in the X, Y, and Z axis directions.

4. The method according to claim 3, characterized in that, The peak amplitude value and the vibration amplitude deviation value are determined using vibration acceleration, the angular velocity fluctuation rate is determined using angular velocity, the vibration acceleration and the angular velocity are obtained by an attitude sensor installed on the washing machine, the peak amplitude value is defined as half the difference between the maximum and minimum values ​​of the acceleration in the X-axis and Y-axis directions, and the vibration amplitude deviation value is defined as the standard deviation of the vibration acceleration.

5. The method according to any one of claims 1-4, characterized in that, The washing machine structural aging prediction model is trained in the following way: Obtain target model input information that includes the target structure health status score as annotation; The target model input information is input into the initialization washing machine structure aging prediction model, and the target structure health status score is compared with the output data corresponding to the washing machine structure aging prediction model. The initial washing machine structural aging prediction model is trained based on the difference between the standard structural health status score and the corresponding output data of the washing machine structural aging prediction model, thus obtaining the washing machine structural aging prediction model.

6. The method according to claim 3, characterized in that, The washing machine structural aging prediction model includes: An input layer containing five neurons, wherein the five neurons correspond to the load weight, the eccentricity value, the peak amplitude value, the vibration amplitude deviation value, and the angular velocity fluctuation rate, respectively. Hidden layer, wherein the hidden layer comprises a first hidden layer and a second hidden layer; The output layer is a single-neuron structure used to output a structural health status score.

7. The method according to claim 1, characterized in that, The method further includes: The structural health status score is used to determine whether there is an aging risk; if so, the operating parameters of the dehydration stage are adjusted and a user reminder mechanism is triggered.

8. The method according to claim 7, characterized in that, The method further includes: If the structural health status score exceeds the preset threshold for a number of consecutive preset aging thresholds, then an aging risk is identified.

9. The method according to claim 8, characterized in that, The method further includes: A device aging trend curve is generated based on the structural health status score. By comparing the aging trend curve with historical data, the future aging rate can be predicted. Personalized maintenance plans are developed based on predicted future aging rates to extend equipment lifespan. These personalized maintenance plans may include increasing maintenance frequency, replacing critical components, or appropriately extending maintenance intervals.

10. The method according to claim 1, characterized in that, After inputting the eccentricity value, the load weight value, the vibration characteristic information, and the rotation state information as model input information into the washing machine structural aging prediction model to obtain a structural health status score, the method further includes: When the washing cycle of the washing machine is less than a preset washing cycle threshold, the structural aging prediction model of the washing machine is corrected based on the structural health status score.

11. The method according to claim 10, characterized in that, When the washing cycle of the washing machine is less than a preset washing cycle threshold, the structural aging prediction model of the washing machine is corrected based on the structural health status score, including: If the washing cycle of the washing machine is less than a preset washing cycle threshold, and the structural health status score output by the washing machine structural aging prediction model indicates that the washing machine has an aging risk, then the structural health status score is marked as a false alarm sample. The washing machine structural aging prediction model is corrected based on the false alarm samples, and the judgment boundary of the washing machine structural aging prediction model is gradually corrected.

12. An electronic device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for predicting structural aging of a washing machine as described in any one of claims 1 to 11.

13. A fabric cleaning device, characterized in that, It employs the method for predicting structural aging of a washing machine as described in any one of claims 1 to 11, or has the electronic device described in claim 12.

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