Washing equipment balance detection method and device, washing equipment and storage medium

Abstract

The invention discloses a washing equipment balance detection method and device, washing equipment and a storage medium, the washing equipment balance detection method comprises the following steps: after the washing equipment is started to run, according to a first elastic force data set of a first dynamometer in a preset time length and a second elastic force data set of a second dynamometer in the preset time length, calculating a first elastic force data set of the first dynamometer in the preset time length; determining an elastic force difference data set of the first elastic force data set and the second elastic force data set; processing the elasticity difference data set to obtain a target data set; substituting the target data set into a preset neural network model for processing to obtain an unbalance value of the washing equipment; according to the invention, the unbalance value of the washing equipment can be obtained according to the data of the first dynamometer and the second dynamometer, and the balance of the washing equipment can be accurately determined according to the unbalance value.

Description

Technical Field

[0001] This invention relates to the field of washing equipment technology, and in particular to a method, apparatus, washing equipment, and storage medium for testing the balance of washing equipment. Background Technology

[0002] Washing machines, as one of the most widely used household appliances in people's daily lives, have helped people get rid of the trouble of washing clothes and brought them great convenience. Front-loading washing machines, in particular, are widely favored by users due to their advantages such as less wear and tear during washing, better washing results, and water conservation.

[0003] In order to ensure that the washing machine can operate smoothly and quietly, it is necessary to test the balance of the washing machine. The existing balance test usually judges the balance by the degree of motor speed fluctuation. However, judging the balance of the washing machine based on the degree of motor speed fluctuation has low accuracy and can easily lead to unstable operation and drum collision. Summary of the Invention

[0004] The main objective of this invention is to provide a method, apparatus, washing equipment, and storage medium for detecting the balance of washing equipment, aiming to improve the technical problem of inaccurate balance detection of washing machines in the prior art.

[0005] An embodiment of the present invention provides a method for detecting the balance of a washing machine. The washing machine includes a body and a drum horizontally disposed inside the body. A vibration damping component is connected between the drum and the bottom of the body. A first force gauge and a second force gauge are connected between the drum and the top of the body. The first force gauge and the second force gauge are respectively connected to both sides of the drum and are symmetrically arranged about the central axis of the drum. The method for detecting the balance of the washing machine includes:

[0006] After the washing equipment is turned on, the elasticity difference dataset between the first elasticity dataset and the second elasticity dataset is determined based on the first elasticity dataset of the first force gauge within a preset time period and the second elasticity dataset of the second force gauge within the preset time period.

[0007] The elasticity difference dataset is processed to obtain the target dataset;

[0008] The target dataset is fed into a preset neural network model for processing to obtain the imbalance value of the washing equipment.

[0009] In some embodiments of the present invention, processing the elasticity difference dataset to obtain the target dataset includes:

[0010] Based on the elasticity difference dataset, a first set of feature values ​​is obtained;

[0011] The first set of feature values ​​is processed to obtain the target dataset.

[0012] In some embodiments of the present invention, obtaining the first feature value set based on the elasticity difference dataset includes:

[0013] Based on the elasticity difference dataset, determine the first maximum value, the first minimum value, the difference between the first maximum value and the first minimum value, the first average value, the first root square magnitude, the first variance, the first standard deviation, and the first RMS effective value of the elasticity difference dataset;

[0014] The first set of eigenvalues ​​includes the first maximum value, the first minimum value, the difference between the first maximum value and the first minimum value, the first average value, the first root square magnitude, the first variance, the first standard deviation, and the first RMS effective value.

[0015] In some embodiments of the present invention, processing the first feature value set to obtain the target dataset includes:

[0016] Determine the second mean and second standard deviation of the data in the first feature set;

[0017] Based on the first feature set, the second mean, and the second standard deviation, the first feature set is subjected to Z-score normalization to obtain the target dataset.

[0018] In some embodiments of the present invention, determining the elasticity difference dataset between the first elasticity dataset and the second elasticity dataset based on the first elasticity dataset of the first force gauge within a preset time period and the second elasticity dataset of the second force gauge within the preset time period includes:

[0019] Based on the first elastic force value corresponding to the first elastic force dataset and the second elastic force value corresponding to the second elastic force dataset at the same time, determine the absolute value of the difference between the first elastic force value and the second elastic force value;

[0020] Repeat the above steps to obtain the absolute value of the elasticity difference between the first elasticity dataset and the second elasticity dataset at different times of a preset duration;

[0021] The elasticity difference dataset includes the absolute values ​​of the elasticity difference between the first elasticity dataset and the second elasticity dataset at different times within a preset duration.

[0022] In some embodiments of the present invention, the method for detecting the balance of the washing equipment further includes:

[0023] If it is determined that the imbalance value is not within the preset imbalance value range, the preset time is shortened and the imbalance value is re-determined.

[0024] In some embodiments of the present invention, the method for detecting the balance of the washing equipment further includes:

[0025] After determining three consecutive times that the imbalance value is not within the preset imbalance value range, the washing equipment is controlled to issue a maintenance alarm.

[0026] In some embodiments of the present invention, a washing equipment stability detection device is also provided, comprising:

[0027] The acquisition module is used to acquire, after the washing equipment is turned on, the first elastic force dataset of the first force gauge within a preset time period and the second elastic force dataset of the second force gauge within the preset time period;

[0028] The processing module is configured to determine an elastic force difference dataset between the first elastic force dataset and the second elastic force dataset based on the first elastic force dataset of the first force gauge within a preset time period and the second elastic force dataset of the second force gauge within the preset time period; and to process the elastic force difference dataset to obtain a target dataset.

[0029] A neural network module is used to process the target dataset to obtain the imbalance value of the washing equipment.

[0030] In some embodiments of the present invention, a washing device is also provided, including a memory, a processor, a body, and a drum horizontally disposed inside the body. A vibration damping member is connected between the drum and the bottom of the body. A first force gauge and a second force gauge are connected between the drum and the top of the body. The first force gauge and the second force gauge are respectively connected to both sides of the drum. The first force gauge and the second force gauge are symmetrically arranged about the central axis of the drum. The memory stores a computer program, and the processor is used to run the computer program in the memory to perform the steps in the above-described washing device balance detection method.

[0031] In some embodiments of the present invention, a storage medium is also provided, the storage medium storing a computer program, the computer program being executed and loaded by a processor to perform the steps in the washing equipment balance detection method described above.

[0032] The present invention provides a method, apparatus, washing equipment, and storage medium for detecting the balance of a washing machine. The method for detecting the balance of the washing machine is based on a first force gauge and a second force gauge symmetrically arranged on both sides of the drum. Based on the first elastic force dataset and the second elastic force dataset of the first force gauge and the second force gauge within a preset time period, an elastic force difference dataset is determined. Then, a target dataset is obtained based on the elastic force difference dataset. Finally, the target dataset is substituted into a preset neural network model for processing to obtain the imbalance value of the washing machine. Based on the imbalance value, the balance of the washing machine can be accurately determined. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0034] Figure 1 This is a schematic flowchart of a washing equipment balance detection method according to an embodiment of the present invention;

[0035] Figure 2 This is a schematic diagram of the structure of a washing equipment stability detection device according to an embodiment of the present invention;

[0036] Figure 3 This is a schematic diagram of the structure of a washing device according to an embodiment of the present invention;

[0037] Figure 4 This is a schematic diagram of the structure of a washing device according to an embodiment of the present invention;

[0038] Figure 5 This is a schematic diagram of the structure of a pre-built neural network model according to an embodiment of the present invention.

[0039] Reference numerals: 10, Washing equipment stability detection device; 100, Acquisition module; 200, Processing module; 300, Neural network module; 400, Machine body; 410, Drum; 421, First force gauge; 422, Second force gauge; 430, Vibration damping component; 601, Processor; 602, Memory; 603, Power supply; 604, Input unit. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0041] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0042] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0043] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0044] like Figures 1-5As shown, the present invention provides a method for testing the balance of a washing machine. The washing machine includes a body 400 and a drum 410 horizontally disposed inside the body 400. A vibration damping component 430 is connected between the drum 410 and the bottom of the body 400. A first force gauge 421 and a second force gauge 422 are connected between the drum 410 and the top of the body 400. The first force gauge 421 and the second force gauge 422 are respectively connected to both sides of the drum 410. The first force gauge 421 and the second force gauge 422 are symmetrically arranged about the central axis of the drum 410. The method for testing the balance of the washing machine includes:

[0045] S100, after the washing equipment is turned on, the elasticity difference dataset between the first elasticity dataset and the second elasticity dataset is determined based on the first elasticity dataset of the first force gauge 421 within a preset time and the second elasticity dataset of the second force gauge 422 within a preset time.

[0046] The first elasticity dataset includes the elasticity values ​​of the first force gauge 421 at various times within the preset duration; the second elasticity dataset includes the elasticity values ​​of the second force gauge 422 at various times within the preset duration; the preset duration is a duration that can be adjusted in the controller of the washing equipment according to the actual situation, or multiple different durations can be pre-stored in the controller of the washing equipment as preset durations, and then a suitable duration can be selected as the preset duration according to the actual situation.

[0047] The elasticity difference dataset is a collection of the differences between the elasticity values ​​of the first force gauge 421 and the second force gauge 422 within a preset time period, which can reflect the tilt of the roller 410.

[0048] S200, the elasticity difference dataset is processed to obtain the target dataset.

[0049] The controller of the washing equipment contains a pre-stored program for processing elasticity difference data. The target dataset can be obtained by processing the elasticity difference data according to the program.

[0050] S300: Substitute the target dataset into the preset neural network model to obtain the imbalance value of the washing equipment.

[0051] The controller of the washing equipment has a pre-stored neural network model, which is used to process the target dataset to obtain the imbalance value of the washing equipment. After obtaining the target dataset, it is only necessary to substitute it into the pre-stored neural network model to obtain the imbalance value of the washing equipment at the preset time, so as to determine the balance of the washing equipment based on the imbalance value.

[0052] The pre-built neural network model includes a three-layer network structure: an input layer, a hidden layer, and an output layer. The target dataset is input into the input layer, the hidden layer performs calculations on the target dataset, and finally the output layer outputs the result, i.e., the output layer outputs the imbalance value. The pre-built neural network model is trained using the gradient descent algorithm to obtain the optimal threshold neural network model.

[0053] Understandably, the washing equipment balance detection method is based on the first force gauge 421 and the second force gauge 422 symmetrically arranged on both sides of the drum 410. According to the first elastic force dataset and the second elastic force dataset of the first force gauge 421 and the second force gauge 422 within a preset time period, the elastic force difference dataset is determined. Then, the target dataset is obtained according to the elastic force difference dataset. Finally, the target dataset is substituted into the preset neural network model for processing to obtain the imbalance value of the washing equipment. According to the imbalance value, the balance of the washing equipment can be accurately determined.

[0054] In some embodiments, S200, the elasticity difference dataset is processed to obtain the target dataset, including:

[0055] S210, based on the elasticity difference dataset, obtain the first set of feature values.

[0056] The first feature set is a set of multiple data obtained by processing the elasticity difference dataset according to a preset program.

[0057] S220, process the first set of feature values ​​to obtain the target dataset.

[0058] The target dataset is a collection of multiple data obtained by processing the data in the first feature value set according to a preset procedure.

[0059] In some embodiments, S210, a first set of feature values ​​is obtained based on the elasticity difference dataset, including:

[0060] Based on the elasticity difference dataset, determine the first maximum value, the first minimum value, the difference between the first maximum value and the first minimum value, the first average value, the first root square magnitude, the first variance, the first standard deviation, and the first RMS effective value of the elasticity difference dataset.

[0061] The elastic force difference dataset includes: ΔF1, ΔF2, ΔF3, ... ΔF N .

[0062] Among them, the first maximum value is ΔF1, ΔF2, ΔF3, ... ΔF N The maximum value in the range, i.e., the first maximum value, is A1 = max(ΔF). N The first minimum value is ΔF1, ΔF2, ΔF3, ..., ΔF NThe minimum value, that is, the first minimum value, is A2 = min(ΔF). N ).

[0063] The difference between the first maximum value and the first minimum value is A3, where A3 = ΔF. max -ΔF min .

[0064] Among them, the first average value is

[0065] Wherein, the first root amplitude is

[0066] Wherein, the first variance is

[0067] The first standard deviation is:

[0068] The first RMS valid value is:

[0069] The first set of eigenvalues ​​includes the first maximum value, the first minimum value, the difference between the first maximum value and the first minimum value, the first average value, the first root square magnitude, the first variance, the first standard deviation, and the first RMS effective value.

[0070] That is, the first set of eigenvalues ​​includes A1, A2, A3, A4, A5, A6, A7, and A8.

[0071] Among them, RMS (Root Mean Square) is the effective value.

[0072] In some embodiments, S220, the first feature set is processed to obtain the target dataset, including:

[0073] S221, Determine the second mean and second standard deviation of the data in the first set of eigenvalues.

[0074] The first feature set is a collection of multiple processed data. For example, the first feature set includes A1, A2, A3, A4, A5, A6, A7, and A8.

[0075] That is, the second average is The second standard deviation is

[0076] S222, Based on the first set of eigenvalues, the second mean, and the second standard deviation, perform Z-score normalization on the first set of eigenvalues ​​to obtain the target dataset.

[0077] Specifically, Z-score normalization of the first feature set requires performing Z-score normalization on each data point in the first feature set.

[0078] Z-score, or Z-score, is the standard score.

[0079] For example, the target data set can be obtained by processing the first feature value set in the above embodiment. The target data set includes B1, B2, B3, B4, B5, B6, B7, and B8.

[0080] Normalizing A1 yields the following result:

[0081] Similarly,

[0082] In some embodiments, S100, determining the elastic force difference dataset between the first elastic force dataset and the second elastic force dataset based on the first elastic force dataset of the first force gauge within a preset time period and the second elastic force dataset of the second force gauge within a preset time period includes:

[0083] S110, based on the first elastic force value corresponding to the first elastic force dataset and the second elastic force value corresponding to the second elastic force dataset at the same time, determine the absolute value of the difference between the first elastic force value and the second elastic force value.

[0084] The first elastic force value is F. A The second elastic force value is F B That is, ΔF i =|F Ai -F Bi |

[0085] Among them, F Ai Let F be the elastic force value of the first force gauge at time i. Bi Let be the elastic force value of the second force gauge at time i.

[0086] S120, Repeat the above steps to obtain the absolute value of the elasticity difference between the first elasticity dataset and the second elasticity dataset at different times of a preset duration.

[0087] Repeating the above steps yields ΔF1, ΔF2, ΔF3, ... ΔF. N .

[0088] Among them, ΔF can be determined by a fixed time interval. i This allows for the construction of an elastic force difference dataset by determining the absolute values ​​of multiple elastic force differences; alternatively, it allows for the determination of ΔF at each moment based on a preset duration. i To construct an elasticity difference dataset.

[0089] The elasticity difference dataset includes the absolute values ​​of the elasticity difference between the first elasticity dataset and the second elasticity dataset at different times within a preset duration.

[0090] Understandably, the elasticity difference dataset is constructed by calculating the absolute value of the elasticity difference at different times between the first elasticity dataset and the second elasticity dataset.

[0091] In some embodiments, the method for detecting the balance of a washing machine further includes:

[0092] S400: If the unbalance value is determined to be outside the preset unbalance value range, the preset time is shortened and the unbalance value is re-determined.

[0093] The preset imbalance value range is a range that exists in the washing equipment controller. When the imbalance value is within this range, the drum sway of the washing equipment can be considered to be within the normal range. Therefore, when the imbalance value is not within the preset imbalance value range, the imbalance value needs to be determined again to avoid misjudgment caused by other interference.

[0094] When the imbalance value is determined to be outside the preset imbalance value range, shortening the preset time can improve the accuracy of imbalance value determination and avoid other interferences that could lead to excessive errors in imbalance value determination.

[0095] In some embodiments, the method for detecting the balance of a washing machine further includes:

[0096] S500 will issue a maintenance alarm for the washing equipment after three consecutive determinations that the imbalance value is not within the preset imbalance value range.

[0097] If the imbalance value is determined to be outside the imbalance range three times in a row, the influence of other factors on the determination of the imbalance value can be basically ruled out. Therefore, it can be determined that there is a problem with the vibration damping component of the washing equipment, and it needs to be repaired.

[0098] In some embodiments, the present invention also provides a washing equipment stability detection device 10, including an acquisition module 100, a processing module 200, and a neural network module 300. The acquisition module 100 is used to acquire, after the washing equipment is turned on, a first elastic force dataset from a first force gauge within a preset time period and a second elastic force dataset from a second force gauge within a preset time period. The processing module 200 is used to determine an elastic force difference dataset between the first elastic force dataset and the second elastic force dataset based on the first elastic force dataset from the first force gauge within the preset time period and the second elastic force dataset from the second force gauge within the preset time period. The processing module 200 is used to process the elastic force difference dataset to obtain a target dataset. The neural network module 300 is used to process the target dataset to obtain the imbalance value of the washing equipment.

[0099] In some embodiments, the present invention also provides a washing device, which may include components such as a processor 601 with one or more processing cores, a memory 602 with one or more computer-readable storage media, a power supply 603, and an input unit 604; the washing device also includes a body 400 and a drum 410 horizontally disposed inside the body 400, a vibration damping member 430 connecting the drum 410 and the bottom of the body 400, and a first force gauge 421 and a second force gauge 422 connecting the drum 410 and the top of the body 400, the first force gauge 421 and the second force gauge 422 being respectively connected to both sides of the drum 410, and the first force gauge 421 and the second force gauge 422 being symmetrically arranged about the central axis of the drum 410. Those skilled in the art will understand that the above-described structure of the washing device does not constitute a limitation on the washing device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0100] The processor 601 is the controller of the washing machine, connecting various parts of the washing machine through various interfaces and lines. It executes software programs and / or modules stored in the memory 602, and calls data stored in the memory 602, to perform various functions and process data, thereby providing overall monitoring of the washing machine. Optionally, the processor 601 may include one or more processing cores; preferably, the processor 601 may integrate an application processor 601 and a modem processor 601, wherein the application processor 601 mainly handles the operating system, user interface, and computer programs, while the modem processor 601 mainly handles wireless communication. It is understood that the modem processor 601 may also not be integrated into the processor 601.

[0101] The memory 602 can be used to store software programs and modules. The processor 601 executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, computer programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the server, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide the processor 601 with access to the memory.

[0102] The washing equipment also includes a power supply 603 that supplies power to the various components. Preferably, the power supply 603 can be logically connected to the processor 601 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply may also include one or more DC or AC power sources, a recharging system, a power fault detection circuit, a power converter or inverter, a power status indicator, or any other components.

[0103] The washing device may also include an input unit 604, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0104] Although not shown, the washing device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 601 in the washing device loads the executable files corresponding to the processes of one or more computer programs into the memory 602 according to the following instructions, and the processor 601 runs the computer programs stored in the memory 602 to perform the following steps:

[0105] After the washing equipment is turned on, the elasticity difference dataset between the first elasticity dataset and the second elasticity dataset is determined based on the first elasticity dataset of the first force gauge within a preset time and the second elasticity dataset of the second force gauge within a preset time.

[0106] The target dataset is obtained by processing the elasticity difference dataset;

[0107] The target dataset is fed into a pre-set neural network model for processing to obtain the imbalance value of the washing equipment.

[0108] By performing the above steps, based on the first and second force gauges symmetrically arranged on both sides of the drum, the elasticity difference dataset is determined according to the first elasticity dataset and the second elasticity dataset of the first and second force gauges within a preset time period; then the target dataset is obtained according to the elasticity difference dataset; finally, the target dataset is substituted into the preset neural network model for processing to obtain the imbalance value of the washing equipment. According to the imbalance value, the balance of the washing equipment can be accurately determined.

[0109] Those skilled in the art will understand that all or part of the steps in any of the methods in the above embodiments can be performed by a computer program or by a computer program controlling related hardware. The computer program can be stored in a computer-readable storage medium and loaded and executed by the processor 601.

[0110] In some embodiments, the present invention also provides a storage medium storing a computer program, which is loaded by a processor to perform the following steps;

[0111] After the washing equipment is turned on, the elasticity difference dataset between the first elasticity dataset and the second elasticity dataset is determined based on the first elasticity dataset of the first force gauge within a preset time and the second elasticity dataset of the second force gauge within a preset time.

[0112] The target dataset is obtained by processing the elasticity difference dataset;

[0113] The target dataset is fed into a pre-set neural network model for processing to obtain the imbalance value of the washing equipment.

[0114] By performing the above steps, based on the first and second force gauges symmetrically arranged on both sides of the drum, the elasticity difference dataset is determined according to the first elasticity dataset and the second elasticity dataset of the first and second force gauges within a preset time period; then the target dataset is obtained according to the elasticity difference dataset; finally, the target dataset is substituted into the preset neural network model for processing to obtain the imbalance value of the washing equipment. According to the imbalance value, the balance of the washing equipment can be accurately determined.

[0115] It will be understood by those skilled in the art that any references to memory, storage, database, or other media used in the embodiments provided by this invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0116] Since the computer program stored in the storage medium can execute the steps in the washing equipment balance detection method in any embodiment of the present invention, the beneficial effects that the washing equipment balance detection method in any embodiment of the present invention can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0117] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0118] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the detailed descriptions of other embodiments above, which will not be repeated here.

[0119] The above description is merely an optional embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the application concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for detecting the balance of a washing machine, characterized in that: The washing device comprises a body and a drum horizontally arranged inside the body, a vibration damping member is connected between the drum and the bottom of the body, a first dynamometer and a second dynamometer are connected between the drum and the top of the body, the first dynamometer and the second dynamometer are respectively connected to both sides of the drum, the first dynamometer and the second dynamometer are symmetrically arranged about the central axis of the drum, and the balance detection method of the washing device comprises: After the washing device is started, determining an elastic force difference data set between the first elastic force data set and the second elastic force data set according to a first elastic force data set of the first dynamometer within a preset time period and a second elastic force data set of the second dynamometer within the preset time period; Processing the elastic force difference data set to obtain a target data set; Substitute the target data set into a preset neural network model for processing to obtain an imbalance value of the washing equipment.

2. The method for detecting the balance of washing equipment according to claim 1, characterized in that: The step of processing the elastic force difference data set to obtain a target data set includes: Obtaining a first eigenvalue set according to the elastic force difference data set; The first feature value set is processed to obtain a target data set.

3. The method for detecting the balance of washing equipment according to claim 2, characterized in that: The step of obtaining a first eigenvalue set according to the elastic force difference data set includes: Determine, according to the elastic force difference data set, a first maximum value, a first minimum value, a difference between the first maximum value and the first minimum value, a first average value, a first square root amplitude, a first variance, a first standard deviation, and a first RMS effective value of the elastic force difference data set; The first eigenvalue set includes the first maximum value, the first minimum value, the difference between the first maximum value and the first minimum value, the first average value, the first root amplitude, the first variance, the first standard deviation, and the first RMS effective value.

4. The method for detecting the balance of washing equipment according to claim 3, characterized in that: The processing of the first feature value set to obtain a target data set includes: Determine a second mean value and a second standard deviation of the data in the first eigenvalue set; According to the first eigenvalue set, the second mean value, and the second standard deviation, Z-score normalization processing is performed on the first eigenvalue set to obtain the target data set.

5. The method for detecting the balance of washing equipment according to claim 3, characterized in that: The step of determining an elastic force difference data set between the first elastic force data set and the second elastic force data set according to a first elastic force data set of the first dynamometer within a preset time period and a second elastic force data set of the second dynamometer within the preset time period comprises: determining an absolute value of a difference between the first elastic force value and the second elastic force value according to a first elastic force value corresponding to the first elastic force data set and a second elastic force value corresponding to the second elastic force data set at the same time; Repeat the above steps to obtain the absolute value of the elastic force difference between the first elastic force data set and the second elastic force data set at different moments in the preset time period; The elastic force difference data set includes absolute values ​​of elastic force differences between the first elastic force data set and the second elastic force data set at different moments in a preset time period.

6. The method for detecting the balance of washing equipment according to claim 1, characterized in that: The washing equipment balance detection method further includes: If it is determined that the imbalance value is not within the preset imbalance value range, the preset time duration is shortened and the imbalance value is re-determined.

7. The method for detecting balance of washing equipment according to claim 6, characterized in that: The washing equipment balance detection method further includes: After determining three times in a row that the imbalance value is not within the preset imbalance value range, the washing device is controlled to issue a maintenance alarm.

8. A washing equipment stability detection device, characterized in that: include: an acquisition module, configured to acquire a first elastic force data set of the first dynamometer within a preset time period and a second elastic force data set of the second dynamometer within the preset time period after the washing device is turned on; a processing module, configured to determine an elastic force difference data set between the first elastic force data set and the second elastic force data set according to a first elastic force data set of the first dynamometer within a preset time period and a second elastic force data set of the second dynamometer within the preset time period; and for processing the elastic force difference data set to obtain a target data set; The neural network module is used to process the target data set to obtain the imbalance value of the washing equipment.

9. A washing device, characterized in that: The invention comprises a memory, a processor, a body and a drum horizontally arranged inside the body, a vibration damping member is connected between the drum and the bottom of the body, a first dynamometer and a second dynamometer are connected between the drum and the top of the body, the first dynamometer and the second dynamometer are respectively connected to two sides of the drum, the first dynamometer and the second dynamometer are symmetrically arranged about the central axis of the drum, the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the steps in the washing equipment balance detection method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a computer program, and the computer program is loaded and executed by the processor to execute the steps in the washing equipment balance detection method according to any one of claims 1 to 7.

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