Washing machine structure aging prediction method, electronic equipment and fabric cleaning equipment

Abstract

The embodiment of the invention discloses a washing machine structure aging prediction method, electronic equipment and fabric cleaning equipment, and belongs to the technical field of washing machine control. The method for predicting the aging of the structure of the washing machine comprises the steps that an eccentric value, a load weight value, vibration characteristic information and rotation state information of the washing machine in the operation stage are obtained, and the vibration characteristic information is used for representing the mechanical swing strength and the eccentric position of a washing barrel in high-speed operation; the rotation state characteristic parameter value is used for representing the rotation rate stability of the washing bucket under high-speed operation; and taking the eccentric value, the load weight value, the vibration characteristic information and the rotation state information as model input information, and inputting the model input information into a washing machine structure aging prediction model to obtain a structure health state score. Early warning and active intervention on the aging of the washing machine structure are realized, and the barrel collision risk caused by aging is effectively reduced.

Description

Technical Field

[0001] The present application relates to the technical field of washing machine control, and in particular to a method for predicting structural aging of a washing machine, an electronic device, and a fabric cleaning device. Background Art

[0002] 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 has increased year by year. During the use of washing machines, the supporting structure will age after long-term operation, resulting in a decrease in its anti-eccentricity ability. When washing a large amount of clothes, if the clothes are concentrated on one side, it is easy to cause an imbalance of centrifugal force, which can cause violent collision with the drum, deviation, and even damage to the inner drum and outer shell. The current washing machines on the market usually rely on a passive trigger mechanism caused by excessive vibration to stop washing. This method lacks the ability to predict the "drum collision risk" caused by structural aging in advance. Summary of the Invention

[0003] The embodiments of the present application provide a method for predicting structural aging of a washing machine, an electronic device, and a fabric cleaning device, so as to at least solve the technical problem in the related art of reduced anti-eccentricity capability due to aging of the supporting structure.

[0004] According to a first aspect of an embodiment of the present application, a method for predicting structural aging of a washing machine is provided, comprising: Obtaining the eccentricity value, load weight value, vibration characteristic information, and rotation state information of the washing machine during operation, wherein the vibration characteristic information is used to characterize the mechanical swing 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, 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.

[0005] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, obtaining the eccentricity value, load weight value, vibration characteristic information, and rotation state information of the washing machine during the operation phase includes: Obtaining the eccentricity value of the washing tub when running at a low speed and the load weight value of the load in the washing tub; Acquiring vibration characteristic information and rotation state information of the washing tub when running at high speed, wherein the rotation speed range of the washing tub when running at low speed is 0-800 rpm, wherein the rotation speed range of the washing tub when running at high speed is greater than or equal to 800 rpm; In combination with the first aspect, in an optional implementation of an embodiment of the present 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.

[0006] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, obtaining the eccentricity value, load weight value, vibration characteristic information, and rotation state information of the washing machine during the operation phase includes: Obtaining the eccentricity value and the load weight value during the eccentricity detection phase; Vibration characteristic information and rotation state information are obtained during the dehydration stage, where 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. The vibration acceleration and angular velocity are obtained by a posture sensor set on the washing machine. The peak amplitude value is defined as half of the difference between the maximum and minimum acceleration values ​​in the X-axis and Y-axis directions, and the vibration amplitude deviation value is defined as the standard deviation of the vibration acceleration.

[0007] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the washing machine structure aging prediction model is trained in the following manner: Obtaining target model input information including a target structural health score as a label; Input the target model input information into the initialization washing machine structure aging prediction model, and compare the target structure health status score with the output data corresponding to the washing machine structure aging prediction model; The washing machine structure aging prediction model is initialized based on the difference between the standard structural health status score and the corresponding output data of the washing machine structure aging prediction model to obtain the washing machine structure aging prediction model.

[0008] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, a washing machine structure aging prediction model includes: An input layer consisting of five neurons, one for each of load weight, eccentricity, peak amplitude, amplitude deviation, and angular velocity fluctuation rate; Hidden layer, the hidden layer includes the first hidden layer and the second hidden layer; The output layer is a single neuron structure, which is used to output the structural health status score.

[0009] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the method further includes: The structural health 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.

[0010] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the method further includes: If the structural health status score exceeds the preset threshold for a consecutive number of preset aging thresholds, it is determined that there is an aging risk.

[0011] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, the method further includes: Generate equipment aging trend curve based on structural health status score; Compare the aging trend curve with historical data to predict future aging rates; Develop personalized maintenance plans based on predicted future aging rates to extend equipment life. Personalized maintenance plans include increasing maintenance frequency or replacing key components, or appropriately extending maintenance intervals.

[0012] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, after 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 a 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 washing machine structure aging prediction model is modified based on the structure health status score.

[0013] In conjunction with the first aspect, in an optional implementation of the embodiment of the present application, when the washing cycle of the washing machine is less than a preset washing cycle threshold, correcting the washing machine structural aging prediction model based on the structural health status score includes: When the washing cycle of the washing machine is less than a preset washing cycle threshold, if the structural health status score output by the washing machine structural aging prediction model indicates that the washing machine has an aging risk, the structural health status score is marked as a false positive sample; The washing machine structure aging prediction model is corrected based on false positive samples, and the judgment boundary of the washing machine structure aging prediction model is gradually corrected.

[0014] In the method for predicting washing machine structural aging provided by an embodiment of the present invention, the eccentricity value, load weight value, vibration characteristic information, and rotation state information of the washing machine during operation are first obtained. These eccentricity value, load weight value, vibration characteristic information, and rotation state information are then used as model inputs and fed into a washing machine structural aging prediction model to obtain a structural health score. This process, through multi-dimensional data collection and intelligent analysis, enables early warning and proactive intervention of washing machine structural aging, effectively reducing the risk of drum collision caused by aging. Furthermore, based on the structural health score, timely maintenance reminders can be provided to users at a later stage.

[0015] According to a second aspect of an embodiment of the present application, a device for predicting structural aging of a washing machine is provided, comprising: an acquisition unit, configured to acquire an eccentricity value, a load weight value, vibration characteristic information, and rotation state information of the washing machine during operation, wherein the vibration characteristic information is used to characterize the mechanical swing 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 processing unit is used to use the eccentricity value, the load weight value, the vibration characteristic information and the rotation state information as model input information, and input them into the washing machine structure aging prediction model to obtain a structural health status score.

[0016] According to a third aspect of the embodiments of the present application, the present invention provides an electronic device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, computer instructions stored in the memory, and the processor executing the computer instructions to execute the method for predicting the structural aging of a washing machine according to the first aspect or any corresponding embodiment thereof.

[0017] According to a fourth aspect of the embodiments of the present application, an embodiment of this specification provides a computer-readable storage medium, on which computer instructions are stored. When the computer instructions are executed by a processor, the method for predicting the structural aging of a washing machine as described in any one of the above items is implemented.

[0018] According to the fifth aspect of the embodiments of the present application, the embodiments of this specification provide a computer program product or computer program, wherein the computer program product includes a computer program, and the computer program is stored in a computer-readable storage medium; the processor of the 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 the washing machine as described in any one of the above items.

[0019] According to a sixth aspect of the embodiments of the present application, the embodiments of this specification provide a fabric cleaning device, which adopts the prediction method of washing machine structure aging described in any one of the first aspects, or has the electronic device of the third aspect.

[0020] The technical effects obtained in the above-mentioned second to sixth aspects are similar to the technical effects obtained by the corresponding technical means in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 1 is a flow chart of a method for predicting washing machine structural aging provided in an embodiment of the present application; Figure 2 This is a schematic diagram of the position of the posture sensor in the prediction of washing machine structure aging provided by an embodiment of the present application; Figure 3 Schematic diagram of the structure of the structural aging prediction model provided in the embodiment of the present application; Figure 4 1 is a schematic structural diagram of a device for predicting structural aging of a washing machine provided in an embodiment of the present application; Figure 5 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] 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.

[0023] It should be understood that the "plurality" mentioned herein refers to two or more. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in order to facilitate a clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0024] In addition, the terms "comprises" and "having" and any variations thereof are intended to cover a non-exclusive inclusion. For example, a process, method, system, product or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, product or apparatus.

[0025] As mentioned in the background technology, 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 has increased year by year. During the use of washing machines, the supporting structures (such as shock-absorbing springs and dampers) will age after long-term operation, resulting in a decrease in the ability to resist eccentricity. When washing a large amount of clothes, if the clothes are concentrated on one side, it is easy to cause an imbalance of centrifugal force, which can cause violent collision with the drum, deviation, and even damage to the inner drum and outer shell. The washing machines currently on the market usually rely on a passive trigger mechanism caused by excessive vibration to stop washing. This method lacks the ability to predict the "drum collision risk" caused by structural aging in advance.

[0026] Based on this, the embodiment of the present application provides a method for predicting the structural aging of a washing machine. By combining the posture sensor with the structural aging prediction model analysis, it is possible to predict the structural aging phenomenon of the washing machine in advance, and actively slow down the operation when potential risks are detected, while reminding the user to perform maintenance. Figure 1 The flowchart of the method for predicting the structural aging of a washing machine is shown, and the method includes the following processing procedures.

[0027] S101: Acquire the eccentricity value, load weight value, vibration characteristic information and rotation state information of the washing machine during operation.

[0028] In specific implementation, when the washing machine is operating normally, the eccentricity value, load weight value, vibration characteristic information and rotation state information are obtained, wherein the vibration characteristic information is used to characterize the mechanical swing 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.

[0029] In one possible implementation, the eccentricity value and the weight of the load in the washing tub are obtained during the low-speed phase of the washing machine, and the vibration characteristic information and rotation state information are obtained during the high-speed phase of the washing machine. The speed range of the washing tub during low-speed operation is 0-800 rpm, and the speed range of the washing tub during high-speed operation is greater than or equal to 800 rpm. Of course, the above speed ranges can also be adjusted according to actual needs.

[0030] In another embodiment, the eccentricity value of the washing tub and the weight of the load in the washing tub are acquired in the eccentricity detection stage, and the vibration characteristic information and the rotation state information are acquired in the dehydration stage.

[0031] In specific implementation, when the washing machine reaches the eccentricity detection stage, the eccentricity value of the washing tub and the load weight in the washing tub are recorded. The load refers to the objects to be washed in the washing tub. Since the load weight and the eccentricity value are used as input data in subsequent calculations, it is not limited here whether the load weight includes the weight of the water in the washing tub.

[0032] It should be noted that the washing process of the washing machine may include more than one eccentricity detection stage. You can choose any load weight and eccentricity value for subsequent calculation. Preferably, choose the value of the eccentricity detection stage closest to the washing stage. In specific implementation, a three-axis acceleration sensor is set 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 location of these sensors is based on their ability to accurately capture the vibration and rotational state 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 obtain the vibration acceleration of the washing tub in the X, Y, and Z axes during the spin cycle. This vibration acceleration is the posture data. The posture data can be used to determine the peak amplitude values, vibration amplitude deviation values, and angular velocity fluctuation rates in the X-axis and Y-axis directions. The peak amplitude values ​​and vibration amplitude deviation values ​​are the vibration characteristic information, and the angular velocity fluctuation rate is the rotation state information.

[0033] In one possible implementation, the peak amplitude value is defined as half of the difference between the maximum and minimum accelerations in the X-axis and Y-axis directions, and the vibration amplitude deviation value is defined as the standard deviation of the vibration acceleration.

[0034] Vibration acceleration and angular velocity can be obtained through the three-axis accelerometer. The obtained vibration acceleration format is (ax,ay,az), the angular velocity is (rx,ry,rz), ​​the peak amplitude value is V, the vibration amplitude deviation value is D, and the angular velocity fluctuation rate is dω / dt.

[0035] S102: 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 a washing machine structural aging prediction model to obtain a structural health status score.

[0036] In practice, the eccentricity, load weight, vibration characteristics, and rotational state information can be directly used as model inputs. Alternatively, dynamic characteristic parameters can be generated using the load weight, eccentricity, peak amplitude, vibration amplitude deviation, and angular velocity fluctuation. These dynamic characteristic parameters, or input information, are a set of five-dimensional vectors corresponding to the peak amplitude V, vibration amplitude deviation D, angular velocity fluctuation dω / dt, load weight W, and eccentricity B.

[0037] Then the dynamic characteristic parameters are input into the pre-trained structural aging prediction model, and the structural health status score of the model output is obtained. The structure of the structural aging prediction model is as follows: Figure 3As shown, it contains an input layer, a hidden layer and an output layer of five neurons, where 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 includes the hidden layer of the first hidden layer and the second hidden layer; the output layer is a single neuron structure, which is used to output the structural health status score, and each neuron is fully connected to the neurons in the next layer.

[0038] In one possible implementation, the first hidden layer includes 32 neurons and the second hidden layer includes 16 neurons. In another possible implementation, the first hidden layer includes 64 neurons and the second hidden layer includes 32 neurons.

[0039] When training the structural aging prediction model, the training can be performed in the following manner: first, the target input information including the target structural health status score as a label is obtained, and then the input information is input into the initialized structural aging prediction model, and the target structural health status score is compared with the output data corresponding to the structural aging prediction model. Finally, the initialized structural aging prediction model is trained based on the difference between the target structural health status score and the output data corresponding to the structural aging prediction model to obtain the structural aging prediction model. For the difference-based model training, a loss function can be used for training. The loss function can be selected as the mean square error function, and the model parameters are optimized through the back propagation algorithm. Specifically, the expression of the loss function is as follows: ; Where N is the number of samples, C is the number of categories, Label, is the predicted probability.

[0040] To accommodate the impact of varying 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 score. Given the potential for subtle differences in individual user environments, this method also proposes an online fine-tuning method based on early user usage data to further improve the accuracy and personalized adaptability of aging detection. Specifically, when the washing machine's wash cycle is less than a preset wash cycle threshold, the structural aging prediction model is corrected based on the structural health score. Specifically, if the structural health score output by the structural aging prediction model indicates an aging risk when the washing machine's wash cycle is less than a preset wash cycle threshold, for example, if the output health score is less than 0.6, the structural health score is marked as a false positive. The structural aging prediction model is then corrected based on these false positives, gradually adjusting the judgment boundary of the structural aging prediction model. The wash cycle threshold can be customized; in this disclosed embodiment, 100 wash cycles are used as an example. Within the first 100 wash cycles, if the model predicts an aging risk but the actual device is normal, the prediction is marked as a false positive. Based on false positive samples, the model parameters are adjusted through the back-propagation algorithm, gradually correcting the model's judgment boundary. When the cumulative number of washes reaches 100, the model parameters are frozen and enter inference mode, which is only used for health status prediction.

[0041] When the structural health score exceeds a preset threshold for three consecutive dehydration stages, for example, 0.9, the washing machine's support structure is judged to be at risk of aging. At this point, the washing machine's display will display a "support structure aging risk" prompt to alert the user to potential risks. At the same time, the operating parameters of the dehydration stage are automatically adjusted, the speed is reduced by one gear, and the maximum acceleration setting is reduced to reduce the impact load on the support structure. In addition, an equipment status report can be sent to the after-sales service system to prompt the arrangement of professional maintenance. The remote communication module is connected to the after-sales service system via a wireless network. The equipment status report includes the structural health score, aging trend curve, and historical data comparison results.

[0042] To further enhance the refined management of equipment maintenance, an equipment aging trend curve can be generated based on the structural health score. This aging trend curve plots the structural health score for each wash cycle as a time series, visually demonstrating the equipment's aging process. Comparing the aging trend curve with historical data can predict future aging rates. For example, for equipment with a faster aging rate, users are advised to increase maintenance frequency or replace key components; for equipment with a slower aging rate, maintenance intervals can be appropriately extended. This process enables personalized management of equipment maintenance through a data-driven approach, significantly extending the equipment's service life.

[0043] In this embodiment, the method for predicting washing machine structural aging provided by the present invention first obtains the eccentricity value of the washing tub and the load weight of the load in the washing tub during the eccentricity detection phase. Then, the posture data of the washing machine during operation is obtained, and vibration characteristic information and rotation state information are extracted based on the posture data. Dynamic characteristic parameters for the current operation cycle are then generated based on the vibration characteristics, rotation state information, eccentricity value, and load weight. Finally, the dynamic characteristic parameters are input into a pre-trained structural aging prediction model to output a structural health status score. This process, through multi-dimensional data collection and intelligent analysis, enables early warning and proactive intervention of washing machine structural aging, effectively reducing the risk of tub collision caused by aging. Furthermore, based on the structural health status score, timely maintenance prompts can be provided to users in the future.

[0044] In the above embodiments of the present application, the descriptions of the various embodiments have their own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The steps shown in the relevant flow charts can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flow charts, in some cases, the steps shown or described can be executed in an order different from that shown here. In other words, the order of steps described in the foregoing embodiments is only an example, and reasonable adjustment of the order of steps based on the content of the embodiments of the present application is also within the scope of protection of the embodiments of the present application.

[0045] The above is an illustration of the method embodiment according to the present application. The embodiment of the present invention further provides a method and device for predicting structural aging of a washing machine. Figure 4 Schematic diagram of a method and device for predicting structural aging of a washing machine according to an embodiment of the present invention. Figure 4 The device 700 for predicting structural aging of a washing machine includes the following modules.

[0046] An acquisition unit 701 is configured to acquire an eccentricity value, a load weight value, vibration characteristic information, and rotation state information of the washing machine during operation, wherein the vibration characteristic information is used to characterize the mechanical swing 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; The processing unit 702 is configured to use the eccentricity value, the load weight value, the vibration characteristic information, and the rotation state information as model input information, and input the information into a washing machine structure aging prediction model to obtain a structural health status score.

[0047] The above describes the device embodiments of the present application. For detailed descriptions of the specific execution processes of data, terms, nouns, steps, technical issues and effects, alternative methods and combinations, please refer to the descriptions in the method embodiments, which will not be repeated here.

[0048] An embodiment of the present application also provides a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method for predicting washing machine structural aging according to various embodiments of this specification described in the above "Exemplary Method" section of this specification.

[0049] The computer program product can be written in any combination of one or more programming languages ​​to write program codes for executing the operations of the embodiments of this specification, and the programming languages ​​include object-oriented programming languages ​​such as Java, C++, etc., and also include conventional procedural programming languages ​​such as "C" language or similar programming languages.

[0050] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, and the computer program is used by a processor to execute the steps of the method for predicting washing machine structural aging according to various embodiments of the present specification as described in the "Exemplary Method" section above.

[0051] An embodiment of the present application also provides an electronic device, including a memory and a processor, wherein the memory stores a method for predicting the structural aging of a washing machine, and the processor is configured to adopt the above-described method for predicting the structural aging of a washing machine when executing the method.

[0052] 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 a standard wired interface and a wireless interface. The memory 500 stores a method for predicting the structural aging of a washing machine. The processor 100 is configured to employ the aforementioned method when executing the method for predicting the structural aging of a washing machine stored in the memory 500.

[0053] The descriptions of the computer program product, computer-readable storage medium, and electronic device described above are similar to the descriptions of the method embodiments described above and have similar beneficial effects as the method embodiments. For technical details not disclosed in the computer program product, computer-readable storage medium, and electronic device of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0054] The sequence of the serial numbers or introduction of the embodiments of this application is for description only and does not represent the superiority or inferiority of the embodiments.

[0055] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0056] The units described as separate components may or may not be physically separate, and 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 may be selected according to actual needs to achieve the purpose of the present embodiment.

[0057] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0058] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of 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 the present 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 computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, or a magnetic tape), an optical medium (e.g., a digital versatile disc (DVD)), or a semiconductor medium (e.g., a solid state disk (SSD)). It is worth noting that the computer-readable storage medium mentioned in the embodiments of the present application may be a non-volatile storage medium, in other words, a non-transient storage medium. 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 relevant 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 three-dimensional virtual scene, the client's device information, and the scene interaction information involved in the embodiments of this application are all obtained with full authorization.

[0059] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for predicting structural aging of a washing machine, characterized in that: The method comprises: Obtaining an eccentricity value, a load weight value, vibration characteristic information, and rotation state information of the washing machine during operation, wherein the vibration characteristic information is used to characterize the mechanical swing 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 a washing machine structural aging prediction model to obtain a structural health status score.

2. The method according to claim 1, characterized in that The obtaining of the eccentricity value, load weight value, vibration characteristic information, and rotation state information of the washing machine during the operation phase includes: Obtaining an eccentricity value of the washing tub when running at a low speed and a load weight value of the load in the washing tub; Vibration characteristic information and rotation state information of the washing tub at high speed operation are obtained, wherein the rotation speed range of the washing tub at low speed operation is 0-800 rpm, and the rotation speed range of the washing tub at high speed operation is greater than or equal to 800 rpm.

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

4. The method according to claim 3, characterized in that The obtaining of the eccentricity value, load weight value, vibration characteristic information, and rotation state information of the washing machine during the operation phase includes: Acquiring the eccentricity value and the load weight value in an eccentricity detection stage; The vibration characteristic information and the rotation state information are obtained during the dehydration stage, wherein 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 a posture sensor provided on the washing machine, the peak amplitude value is defined as half of the difference between the maximum value and the minimum value 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 to 4, characterized in that The washing machine structure aging prediction model is trained in the following way: Obtaining target model input information including a target structural health score as a label; Inputting the target model input information into an initialized washing machine structure aging prediction model, and comparing the target structure health status score with the output data corresponding to the washing machine structure aging prediction model; The initialized washing machine structure 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 structure aging prediction model to obtain the washing machine structure aging prediction model.

6. The method according to claim 3, characterized in that The washing machine structure aging prediction model includes: an input layer comprising 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 layers, the hidden layers comprising a first hidden layer and a second hidden layer; The output layer is a single neuron structure and is used to output a structural health status score.

7. The method according to claim 1, characterized in that The method further comprises: Whether there is an aging risk is determined based on the structural health status score; 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 comprises: If the structural health status score exceeds the preset threshold for a consecutive number of preset aging thresholds, it is determined that there is an aging risk.

9. The method according to claim 8, characterized in that The method further comprises: generating an equipment aging trend curve based on the structural health score; Comparing the aging trend curve with historical data to predict future aging rates; Develop personalized maintenance plans based on predicted future aging rates to extend equipment life, where the personalized maintenance plans include increasing maintenance frequency or replacing key 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 a 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 washing machine structure aging prediction model is modified based on the structure 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 washing machine structure aging prediction model is modified based on the structure health status score, including: When the washing cycle of the washing machine is less than a preset washing cycle threshold, if the structural health status score output by the washing machine structural aging prediction model indicates that the washing machine has an aging risk, marking the structural health status score as a false positive sample; The washing machine structure aging prediction model is modified based on the false positive samples, and the judgment boundary of the washing machine structure aging prediction model is gradually corrected.

12. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for predicting structural aging of a washing machine according to any one of claims 1 to 11 by executing the computer instructions.

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

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