Abnormal weight processing method, device and medium of laundry treating apparatus

Abstract

The application belongs to the technical field of electrical appliances, and particularly relates to a weighing abnormality processing method and device of a clothes processing equipment and a medium, which comprises the following steps: in the case of abnormal clothes weight, eccentricity detection is performed on the clothes processing equipment; the abnormal clothes weight is used to indicate that an electric signal value corresponding to the clothes weight is in an abnormal range; according to a detection result, a corresponding processing operation is performed; the processing operation comprises redistributing the clothes in the case of eccentricity abnormality, and / or performing an operation of re-determining a reason for the clothes weight abnormality in the case of clothes weight abnormality caused by non-eccentricity abnormality. The method effectively solves the weighing abnormality problem caused by uneven clothes distribution, improves the weighing accuracy, and at the same time, can timely find potential other fault problems, improves the user experience, and improves the reliability and stability of the equipment.

Description

Technical Field

[0001] This application belongs to the field of electrical technology, specifically relating to a method, equipment, and medium for handling weighing anomalies in clothing processing equipment. Background Technology

[0002] Weighing is a crucial step in the operation of modern garment processing equipment. After the garments are loaded, the equipment weighs them to determine their weight, providing basic data for subsequent washing processes, such as determining the appropriate water level, detergent dosage, and spin-drying speed.

[0003] However, garment processing equipment sometimes experiences weighing anomalies, causing the weight value to deviate from the normal range. There are various reasons for this. Currently, the usual solution is to directly adjust the weighing-related parameters to their default values ​​for garment processing. While this ensures the equipment's operation, it reduces the processing efficiency to some extent and negatively impacts the user experience. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a method, device, and medium for handling weighing anomalies in clothing processing equipment.

[0005] In a first aspect, this application provides a method for handling weighing anomalies in a garment processing device, the method comprising:

[0006] In the event of abnormal clothing weight, the clothing processing equipment is subjected to eccentricity detection; the abnormal clothing weight is used to indicate that the electrical signal value corresponding to the clothing weight is outside the normal range.

[0007] Based on the test results, corresponding processing operations are performed; the processing operations include redistributing the clothing in the case of eccentricity abnormality, and / or redetermining the cause of the clothing weight abnormality in the case of non-eccentricity abnormality.

[0008] In one possible implementation, the step of performing the corresponding processing operation based on the detection result includes:

[0009] If the detection result indicates that the first eccentricity value is abnormal, then the first processing operation is performed; the first processing operation includes performing a shaking procedure.

[0010] If the detection result indicates that the first eccentricity value is not abnormal, or if the weight of the clothing is abnormal when it is re-weighed after the first processing operation, then the clothing is re-weighed continuously to obtain multiple consecutive first electrical signal values.

[0011] Based on the abnormality of multiple first electrical signal values, a second processing operation is performed, the second processing operation including restarting the garment processing equipment and / or sending equipment fault information to the user.

[0012] In one possible implementation, performing the first processing operation includes:

[0013] Determine the corresponding jitter program parameters based on the first eccentricity value;

[0014] The shaking procedure is continuously executed, and the second eccentricity value corresponding to each execution of the shaking procedure is obtained, until the latest obtained second eccentricity value is less than or equal to the preset eccentricity value, or the number of times the shaking procedure is executed reaches the preset number.

[0015] In one possible implementation, performing the second processing operation based on the abnormality of multiple first electrical signal values ​​includes:

[0016] Determine the distribution characteristics of multiple first electrical signal values; the distribution characteristics are used to indicate the fluctuation of the multiple first electrical signals;

[0017] Based on the distribution characteristics, determine whether it is a hardware fault and execute the corresponding second processing operation.

[0018] In one possible implementation, determining whether a hardware fault exists based on the distribution characteristics and performing the corresponding second processing operation includes:

[0019] Determine whether a target distribution feature identical to the stated distribution feature exists in a preset feature database;

[0020] If so, the hardware fault type corresponding to the target distribution feature is determined; the feature database includes hardware fault types and corresponding target distribution features;

[0021] Determine whether the hardware fault type is the target hardware fault type;

[0022] If the hardware fault type is determined to be different from the target hardware fault type, a device fault information is sent to the user to indicate that the device has a hardware fault and needs to be checked and repaired.

[0023] If the hardware fault type is determined to be the target hardware fault type, or if the preset feature database does not contain a target distribution feature that is the same as the distribution feature, then the clothing processing equipment is restarted and the clothing is weighed again.

[0024] In one possible implementation, if the number of times the shaking procedure is executed reaches a preset number, or if the weight of the clothing is abnormal when re-weighing after the clothing processing equipment is restarted, or if equipment malfunction information is sent to the user, the method further includes:

[0025] Determine the user's usage habits;

[0026] Based on the user's usage habits, the value of the target parameter is determined; the target parameter is used to indicate the operating parameters related to the weight of the clothing.

[0027] In one possible implementation, determining the user's usage habits includes:

[0028] Obtain the user's historical usage data; the historical usage data includes at least one of the following: clothing processing procedure, time, clothing weight, and usage frequency of the clothing processing equipment for each use;

[0029] Based on the historical usage data, key factors related to clothing weight are identified, as well as the relationship between these key factors and clothing weight; the key factors are at least one of clothing handling procedures, time, and usage frequency.

[0030] The relationship between the user's usage habits and the key factors and clothing weight was determined.

[0031] In one possible implementation, determining the value of the target parameter based on the user's usage habits includes:

[0032] Determine the values ​​of the key factors mentioned above under the current clothing treatment;

[0033] Based on the values ​​of the key factors and the correlation, predict the weight of the clothing;

[0034] The value of the target parameter is determined based on the predicted weight of the clothing.

[0035] Secondly, this application provides a weighing anomaly handling device for a garment processing equipment, the device comprising:

[0036] The detection module is used to detect eccentricity of the clothing processing equipment when the clothing weight is abnormal; the abnormal clothing weight is used to indicate that the electrical signal value corresponding to the clothing weight is in an abnormal range.

[0037] The processing module is used to perform corresponding processing operations based on the detection results; the processing operations include redistributing the clothing in the case of eccentricity abnormality, and / or performing an operation to redetermine the cause of clothing weight abnormality in the case of clothing weight abnormality not caused by eccentricity abnormality.

[0038] In one possible implementation, the processing module is specifically used for:

[0039] If the detection result indicates that the first eccentricity value is abnormal, then the first processing operation is performed; the first processing operation includes performing a shaking procedure.

[0040] If the detection result indicates that the first eccentricity value is not abnormal, or if the weight of the clothing is abnormal when it is re-weighed after the first processing operation, then the clothing is re-weighed continuously to obtain multiple consecutive first electrical signal values.

[0041] Based on the abnormality of multiple first electrical signal values, a second processing operation is performed, the second processing operation including restarting the garment processing equipment and / or sending equipment fault information to the user.

[0042] In one possible implementation, the processing module is specifically used for:

[0043] Determine the corresponding jitter program parameters based on the first eccentricity value;

[0044] The shaking procedure is continuously executed, and the second eccentricity value corresponding to each execution of the shaking procedure is obtained, until the latest obtained second eccentricity value is less than or equal to the preset eccentricity value, or the number of times the shaking procedure is executed reaches the preset number.

[0045] In one possible implementation, the processing module is specifically used for:

[0046] Determine the distribution characteristics of multiple first electrical signal values; the distribution characteristics are used to indicate the fluctuation of the multiple first electrical signals;

[0047] Based on the distribution characteristics, determine whether it is a hardware fault and execute the corresponding second processing operation.

[0048] In one possible implementation, the processing module is specifically used for:

[0049] Determine whether a target distribution feature identical to the stated distribution feature exists in a preset feature database;

[0050] If so, then determine the hardware fault type corresponding to the target distribution feature; the feature database includes hardware fault types and corresponding target distribution features;

[0051] Determine whether the hardware fault type is the target hardware fault type;

[0052] If the hardware fault type is determined to be different from the target hardware fault type, a device fault information is sent to the user to indicate that the device has a hardware fault and needs to be checked and repaired.

[0053] If the hardware fault type is determined to be the target hardware fault type, or if the preset feature database does not contain a target distribution feature that is the same as the distribution feature, then the clothing processing equipment is restarted and the clothing is weighed again.

[0054] In one possible implementation, if the number of times the shaking procedure is executed reaches a preset number, or if the weight of the clothing is abnormal when the clothing processing equipment is re-weighed after restarting, or if equipment fault information is sent to the user, the processing module is further configured to:

[0055] Determine the user's usage habits;

[0056] Based on the user's usage habits, the value of the target parameter is determined; the target parameter is used to indicate the operating parameters related to the weight of the clothing.

[0057] In one possible implementation, the processing module is specifically used for:

[0058] Obtain the user's historical usage data; the historical usage data includes at least one of the following: clothing processing procedure, time, clothing weight, and usage frequency of the clothing processing equipment for each use;

[0059] Based on the historical usage data, key factors related to clothing weight are identified, as well as the relationship between these key factors and clothing weight; the key factors are at least one of clothing handling procedures, time, and usage frequency.

[0060] The relationship between the user's usage habits and the key factors and clothing weight was determined.

[0061] In one possible implementation, the processing module is specifically used for:

[0062] Determine the values ​​of the key factors mentioned above under the current clothing treatment;

[0063] Based on the values ​​of the key factors and the correlation, predict the weight of the clothing;

[0064] The value of the target parameter is determined based on the predicted weight of the clothing.

[0065] Thirdly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of the first aspects.

[0066] Fourthly, this application provides a garment processing device, comprising: at least one processor and a memory; wherein,

[0067] The memory stores computer-executed instructions;

[0068] The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method as described in any of the first aspects.

[0069] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, can implement the steps of the method as described in any of the first aspects.

[0070] The weighing anomaly handling method, equipment, and medium provided in this application for clothing processing equipment first perform eccentricity detection to determine whether the clothing distribution is uniform when the clothing weight is abnormal. Then, based on the detection results, corresponding processing operations are performed, including redistributing the clothing or re-determining the cause of the anomaly. This method effectively solves the weighing anomaly problem caused by uneven clothing distribution, improving weighing accuracy. Simultaneously, it can promptly detect other potential malfunctions, enhancing user experience and improving the reliability and stability of the equipment. Attached Figure Description

[0071] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0072] Figure 1 The flowchart of the weighing abnormality handling method of the clothing processing equipment provided in the embodiments of this application Figure 1 ;

[0073] Figure 2 The flowchart of the weighing abnormality handling method of the clothing processing equipment provided in the embodiments of this application Figure 2 ;

[0074] Figure 3 The flowchart of the weighing abnormality handling method of the clothing processing equipment provided in the embodiments of this application Figure 3 ;

[0075] Figure 4 A diagram of a weighing anomaly handling device for a garment processing equipment provided in an embodiment of the present invention;

[0076] Figure 5 This is a hardware schematic diagram of a clothing processing device provided in an embodiment of the present invention.

[0077] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0079] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein.

[0080] In this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0081] In modern garment processing equipment, weighing is a crucial step in the washing process. However, weighing anomalies occur frequently, which can be caused by various reasons such as uneven garment distribution, sensor malfunction, and circuit problems. The traditional solution is usually to adjust the weighing-related parameters to their default values ​​to ensure normal equipment operation. However, while this approach guarantees basic functionality, it can reduce garment processing efficiency and negatively impact the user experience.

[0082] To address the problems in existing technologies, this application provides a method for handling weighing anomalies in a garment processing device. The method determines the cause of the weighing anomaly by acquiring the eccentricity value of the garment processing device. If the eccentricity value is abnormal, a shaking procedure is executed to redistribute the garments, aiming to resolve the weighing anomaly. If the eccentricity value is not abnormal, or if the weighing remains abnormal after executing the shaking procedure, multiple electrical signal values ​​are acquired through continuous weighing. Further analysis of these electrical signal values ​​is conducted to determine if there are any hardware malfunctions or other problems, and corresponding measures are taken, such as restarting the device or sending fault information to the user.

[0083] This method effectively solves the weighing anomaly caused by uneven clothing distribution by acquiring the eccentricity value and executing a shaking procedure, thus improving weighing accuracy. Simultaneously, by continuously weighing and analyzing anomalies in electrical signal values, potential hardware faults can be detected and addressed promptly. Furthermore, this method minimizes the impact on clothing processing results while ensuring normal equipment operation, enhancing the user experience. By automatically detecting and handling weighing anomalies, the intelligence level of the clothing processing equipment is enhanced, reducing user intervention requirements and improving equipment reliability and stability.

[0084] The technical solutions of this application and how they solve the aforementioned technical problems are described in detail below with specific embodiments. These specific embodiments may exist independently or in combination with each other. Identical or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0085] This embodiment provides a method for handling weighing abnormalities in clothing processing equipment. Figure 1 The flowchart of the weighing abnormality handling method of the clothing processing equipment provided in the embodiments of this application Figure 1 The method includes:

[0086] S101. In the event of abnormal clothing weight, perform eccentricity detection on the clothing processing equipment.

[0087] The abnormal weight of the clothing is used to indicate that the electrical signal value corresponding to the weight of the clothing is outside the normal range.

[0088] In this step, abnormal garment weight may be due to uneven garment distribution. Eccentricity is an important indicator of how evenly garments are distributed within the washing machine. Eccentricity detection allows for a preliminary assessment of garment distribution uniformity, providing a basis for subsequent processing. Garment weight detection can be performed after the user loads the garments into the washing machine, or at other stages requiring weighing. After detection, it's necessary to determine if the garment weight is abnormal. Abnormal garment weight indicates that the detected electrical signal value significantly exceeds the normal range. For example, if the normal electrical signal range is 10-100, a detected signal of 5 or 150 is outside the normal range, even if the actual garment weight is within the normal range. It's important to note that during the weighing process, the electrical signal is a form of conversion of physical quantities (such as pressure or weight) into measurable electrical signals. These signals can be expressed as voltage or current.

[0089] For example, eccentricity detection can be achieved using accelerometers or vibration sensors installed inside the washing machine. These sensors detect the vibration of the drum or impeller during rotation and convert the vibration data into electrical signals, which are then processed to obtain the eccentricity value. The eccentricity value is typically calculated by analyzing the frequency and amplitude of the vibration data collected by the sensors, using mathematical methods such as Fourier transform to determine the distribution of the clothing. The magnitude of the eccentricity value is directly related to the uniformity of the clothing distribution; a larger eccentricity value indicates a more uneven distribution of clothing, which may lead to abnormal weighing.

[0090] S102. Based on the detection results, perform the corresponding processing operations.

[0091] The processing operations include redistributing the clothing in the event of eccentricity abnormality, and / or performing an operation to redetermine the cause of the clothing weight abnormality in the event of clothing weight abnormality not caused by eccentricity abnormality.

[0092] In this step, weighing abnormalities in the garment processing equipment can be caused by a variety of complex factors. Determining whether the abnormality is due to eccentricity in this step can quickly narrow down the problem area. For example, if eccentricity is detected, the problem of uneven garment distribution can be addressed directly; if it is not eccentricity, then garment distribution factors can be ruled out, and other causes can be investigated, such as sensor malfunctions or circuit problems. This avoids blindly checking all possible factors and improves the efficiency of problem diagnosis.

[0093] Different causes of anomalies require different handling measures. For eccentric anomalies, redistributing the clothing is an effective solution, ensuring even distribution of the garments within the drum and guaranteeing weighing accuracy. However, for non-eccentric anomalies, blindly redistributing the clothing will not solve the problem; appropriate handling strategies must be adopted based on the specific cause, such as repairing or replacing the sensor, or inspecting and repairing the circuitry. By differentiating between different anomalies, the most suitable handling method can be selected for each situation, optimizing the processing flow, reducing unnecessary operations, and improving the operating efficiency and reliability of the equipment.

[0094] For example, when an abnormality in eccentricity is detected, a redistribution of the clothing can be performed to eliminate the abnormality. Redistribution of clothing refers to the operation of evenly distributing the clothing within the drum after detecting the abnormality. Redistribution methods include, but are not limited to, adjusting the drum's rotation speed and direction, and using mechanical devices to agitate it. For instance, the drum can initially rotate at a lower speed, allowing the clothing to gradually distribute evenly towards the drum wall under centrifugal force; then the rotation speed can be gradually increased to further adjust the distribution of the clothing. During the redistribution process, multiple checks and adjustments may be necessary until the clothing is evenly distributed and the abnormality is eliminated.

[0095] For example, determining whether an abnormal weight of clothing is caused by a non-eccentricity anomaly can be done if the eccentricity detection result shows no abnormality; or if the eccentricity anomaly is detected, the clothing is redistributed to eliminate the anomaly, and the clothing weight is still abnormal if this still occurs. When the abnormal weight is not caused by a eccentricity anomaly, it is necessary to re-determine the cause of the abnormal weight. These operations include, but are not limited to, checking the performance indicators of the weighing sensor, such as sensitivity, accuracy, and linearity; checking whether the weighing circuit connections are loose, short-circuited, or open-circuited, and whether there is electromagnetic interference; and checking whether there are errors in the weighing software algorithm.

[0096] For example, the step of performing corresponding processing operations based on the detection results includes:

[0097] If the detection result indicates that the first eccentricity value is abnormal, then the first processing operation is performed; the first processing operation includes performing a shaking procedure.

[0098] If the detection result indicates that the first eccentricity value is not abnormal, or if the weight of the clothing is abnormal when it is re-weighed after the first processing operation, then the clothing is re-weighed continuously to obtain multiple consecutive first electrical signal values.

[0099] Based on the abnormality of multiple first electrical signal values, a second processing operation is performed, the second processing operation including restarting the garment processing equipment and / or sending equipment fault information to the user.

[0100] In the example above, an abnormal eccentricity value usually indicates severely uneven distribution of clothing, which can be a significant cause of weighing anomalies. By determining whether the eccentricity value is abnormal, the root cause of the problem can be quickly identified, allowing for appropriate corrective measures. If the eccentricity value exceeds a threshold, it indicates severely uneven clothing distribution. In this case, executing a shaking procedure can redistribute the clothing, making it more even and resolving the weighing anomaly. If the eccentricity value is not abnormal, it indicates relatively even clothing distribution, and the weighing anomaly may be caused by other reasons, such as sensor malfunction or circuit problems. This example effectively solves weighing anomalies caused by uneven clothing distribution, improving weighing accuracy. Simultaneously, it allows for the timely detection and handling of other potential faults.

[0101] It should be noted that after the shaking procedure restores the initial eccentricity value to normal, a re-weighing is necessary to confirm whether the weight is back to normal. If the weight is normal, the problem is resolved; if it remains abnormal, further analysis is needed. Re-weighing determines whether the eccentricity issue caused the abnormal weight. If the re-weighing restores the weight to normal, it indicates that the eccentricity was the cause of the abnormal weight; otherwise, it indicates that the eccentricity was not the cause. If the weight of the clothing remains abnormal after re-weighing, the problem may not be caused by the eccentricity value, and further analysis of other causes is required.

[0102] Considering that some faults may manifest in the characteristics of continuous electrical signal values, analyzing the numerical or distribution characteristics of multiple initial electrical signal values ​​can more accurately identify potential hardware faults and other problems. For example, excessive fluctuations or specific patterns in electrical signal values ​​may indicate a particular type of problem with the sensor or circuitry. Furthermore, single measurements can be affected by random factors, leading to misjudgments. Continuously acquiring multiple electrical signal values ​​can reduce the impact of random errors and improve diagnostic reliability. Multiple electrical signal values ​​can be obtained through continuous weighing; the distribution patterns of these values ​​can provide information about the causes of faults. Continuous weighing refers to repeatedly measuring the weight of clothing using a weighing sensor over a period of time, obtaining a set of continuous weight data. For example, weighing can be performed every second until 10 weighings are completed. Based on this set of continuous weight data, possible causes of faults can be determined. For instance, if a set of continuous weight data contains individual values ​​with large jumps, it may indicate a problem such as poor hardware contact.

[0103] For example, statistical characteristics such as the mean, variance, and peak value of these first signal values ​​can be calculated, and / or the distribution of the first electrical signal values ​​can be analyzed, such as whether they exhibit a specific pattern or trend. By analyzing the distribution characteristics of the electrical signal values, possible fault modes can be identified. For example, if the fluctuation range of the electrical signal values ​​is too large, it may indicate a specific type of problem with the sensor or circuit. Alternatively, the extracted features can be compared with preset thresholds to determine whether they exceed the normal range. Machine learning algorithms (such as support vector machines, artificial neural networks, etc.) can also be used to classify the extracted features to determine the fault type. If a hardware fault is diagnosed, the device can be restarted to restore normal operation, and a device fault information can be sent to the user, prompting the user to repair or replace the device.

[0104] For example, performing the first processing operation includes:

[0105] Determine the corresponding jitter program parameters based on the first eccentricity value;

[0106] The shaking procedure is continuously executed, and the second eccentricity value corresponding to each execution of the shaking procedure is obtained, until the latest obtained second eccentricity value is less than or equal to the preset eccentricity value, or the number of times the shaking procedure is executed reaches the preset number.

[0107] The above example dynamically adjusts the parameters of the shaking program based on the eccentricity value, enabling the adoption of appropriate shaking strategies for different degrees of eccentricity and improving the uniformity of clothing distribution. By continuously executing the shaking program and monitoring the eccentricity value in real time, the clothing distribution can be gradually optimized until the eccentricity value returns to normal or a preset number of times is reached. This dynamic adjustment and continuous optimization method not only improves the uniformity of clothing distribution but also reduces weighing errors caused by eccentricity, ensuring the accuracy of weighing results. Furthermore, by setting a preset number of times, unnecessary shaking operations can be avoided, saving time and energy. The preset number of times can be determined based on experiments or experience; for example, the preset number of times could be 3.

[0108] For example, the step of performing a second processing operation based on the abnormality of multiple first electrical signal values ​​includes:

[0109] Determine the distribution characteristics of multiple first electrical signal values; the distribution characteristics are used to indicate the fluctuation of the multiple first electrical signals;

[0110] Based on the distribution characteristics, determine whether it is a hardware fault and execute the corresponding second processing operation.

[0111] The above example analyzes the distribution characteristics of multiple electrical signal values ​​to determine the presence of a hardware fault and performs corresponding processing operations based on the judgment results. Analyzing the fluctuation of electrical signals refers to the characteristics of the signal values' changes over a period of time, including but not limited to their amplitude, frequency, and trend. By analyzing these characteristics, the stability of the electrical signals and potential fault modes can be determined. Distribution characteristics can be quantified using parameters such as mean, variance, range, standard deviation, or fluctuation frequency to characterize the fluctuation features. A mapping relationship between characteristics and hardware faults can be preset. After analyzing the current distribution characteristics, a comparison is made with the preset characteristics to confirm whether they are the same or similar. If they are the same or similar, a hardware fault can be identified. By analyzing the distribution characteristics of multiple electrical signal values, potential hardware faults can be identified more accurately, reducing false positives and false negatives, thereby resolving problems or issuing maintenance notifications in a timely manner.

[0112] The weighing anomaly handling method for the clothing processing equipment provided in this embodiment first performs an eccentricity detection to determine whether the clothing distribution is uniform when the clothing weight is abnormal. Then, based on the detection results, corresponding processing operations are performed, including redistributing the clothing or re-determining the cause of the anomaly. This method effectively solves the weighing anomaly problem caused by uneven clothing distribution, improving weighing accuracy. Simultaneously, it can promptly detect other potential faults, enhancing the user experience and improving the reliability and stability of the equipment.

[0113] This embodiment provides a method for handling weighing abnormalities in clothing processing equipment. Figure 2 The flowchart of the weighing abnormality handling method of the clothing processing equipment provided in the embodiments of this application Figure 2 .like Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, the process of performing the corresponding second processing operation is described in detail. The method includes:

[0114] S201. If the first eccentricity value is not abnormal, or if the weight of the clothing is abnormal when it is re-weighed after the first processing operation, then the clothing is re-weighed continuously to obtain multiple consecutive first electrical signal values.

[0115] This step is the same as step S103, and will not be repeated here.

[0116] S202. Determine the distribution characteristics of the plurality of first electrical signal values; the distribution characteristics are used to indicate the fluctuation of the plurality of first electrical signals.

[0117] S203. Determine whether there is a target distribution feature in the preset feature database that is the same as the distribution feature.

[0118] S204. If not, control the clothing processing equipment to restart and re-weigh the clothing.

[0119] It should be noted that by analyzing the distribution characteristics of multiple electrical signal values, we can understand the fluctuations in the electrical signals and thus determine whether any anomalies exist. Distribution characteristics can reflect the stability and trends of electrical signals, helping to identify potential hardware faults or other problems. For example, if the fluctuation range of electrical signal values ​​is too large, it may indicate noise interference or other problems in the sensor or circuit; if the electrical signal values ​​show a continuous upward or downward trend, it may indicate sensor drift or other problems. By extracting these characteristics, we can gain a more comprehensive understanding of the changes in electrical signals, providing a basis for subsequent fault diagnosis. Exemplary distribution characteristics include, but are not limited to, the following forms: mean, median, mode, etc., reflecting central tendency; variance, standard deviation, range, interquartile range, etc., reflecting dispersion; skewness, kurtosis, etc., reflecting the shape of the distribution; and other features: maximum value, minimum value, quartiles, etc. These forms can be used individually or in combination, without limitation.

[0120] By comparing the extracted distribution features with a pre-defined feature database, the existence of known fault modes can be quickly identified. The feature database stores distribution features corresponding to various known faults; matching determines whether the current distribution features match a known fault feature. For example, a matching threshold can be set; when the matching degree exceeds the threshold, the same target distribution feature is considered to exist. For instance, if the current distribution features match a certain fault feature in the database with a degree exceeding the threshold, it indicates a possible hardware fault of that type, requiring further processing. The feature database stores distribution features corresponding to various hardware faults, which can be obtained through extensive experimentation and historical data accumulation.

[0121] If the extracted distribution features do not find a match in the feature database, the current anomaly is likely caused by a temporary problem or an unknown reason. Restarting the device can attempt to restore it to normal operation. Restarting is a common fault recovery method that can clear temporary faults or software problems in the device. For example, if the device's control board experiences a brief freeze or communication failure, restarting can restore its normal function. Re-weighing can verify whether the problem has been resolved. If the problem persists, further analysis and processing are required.

[0122] S205. If so, determine the hardware fault type corresponding to the target distribution feature; the feature database includes hardware fault types and corresponding target distribution features.

[0123] S206. Determine whether the hardware fault type is the target hardware fault type.

[0124] It's important to note that if the extracted distribution features find a match in the feature database, it indicates that the current anomaly is likely caused by a known hardware failure. By identifying the hardware failure type corresponding to the target distribution features, the problem can be quickly located, and appropriate handling measures can be taken. The feature database stores various hardware failure types and their corresponding distribution features. Through matching, the hardware failure type corresponding to the current distribution features can be quickly determined. Further, by determining whether the hardware failure type is the target hardware failure type, it can be decided whether specific handling measures are needed. The target hardware failure type refers to those failure types that can be recovered by restarting the device or other simple operations. For example, some hardware failures may be caused by software problems or temporary interference, which can be recovered by restarting. By determining whether the failure type is the target hardware failure type, it can be decided whether a restart operation or other handling measures are required.

[0125] S207. If not, send equipment fault information to the user to indicate that the equipment has a hardware fault and needs to be inspected and repaired.

[0126] S208. If so, proceed to step S204.

[0127] It should be noted that if the identified hardware fault type is not the target hardware fault type, it means that the fault cannot be recovered by a simple restart or other operation, and user inspection and repair are required. Sending fault information to the user can promptly notify the user of the equipment problem and avoid affecting subsequent use. The equipment fault information can include the fault type and suggested repair measures to provide the user with direction for handling. The equipment fault information can be sent to the user via the display screen, prompt sound, mobile device push, or other means. If the identified hardware fault type is the target hardware fault type, it means that the fault can be recovered by restarting the equipment or other simple operations. By executing step S204 (restarting the equipment and reweighing), the normal operation of the equipment can be restored.

[0128] The weighing anomaly handling method for the garment processing equipment provided in this embodiment acquires multiple electrical signal values ​​by continuously re-weighing when a weighing anomaly occurs. These values ​​are then analyzed for their distribution characteristics and compared with a preset feature database to determine if a hardware fault exists and to take corresponding corrective measures. This method effectively reduces false positives and false negatives, enhancing the reliability and stability of the equipment. Accurate fault diagnosis promptly notifies users to repair or replace the equipment, improving the user experience. Furthermore, it optimizes the fault handling process, distinguishing between target and non-target hardware fault types, thus improving processing efficiency and targeting.

[0129] This embodiment provides a method for handling weighing abnormalities in clothing processing equipment. Figure 3The flowchart of the weighing abnormality handling method of the clothing processing equipment provided in the embodiments of this application Figure 3 .like Figure 3 As shown, if the number of times the shaking procedure is executed reaches a preset number, or if the weight of the clothing is abnormal when re-weighing after the clothing processing equipment is restarted, or if equipment fault information is sent to the user, the method further includes:

[0130] S301. Determine the user's usage habits.

[0131] It's important to note that when the issue of abnormal clothing weight cannot be identified or resolved, user behavior data during clothing processing can be collected to better understand user needs and preferences, ensuring the normal operation of the processing system. This data can reveal user patterns in different scenarios, providing a basis for subsequent adjustments to target parameters. By analyzing these habits, the clothing processing equipment can dynamically adjust operating parameters, improving efficiency and user experience while reducing manual intervention. User habits refer to specific behavioral patterns or regularities exhibited by users related to clothing weight when using the equipment. These habits can be determined by analyzing historical data items that may be related to clothing weight, such as usage frequency, usage time, and usage scenarios. Identifying user habits provides a basis for accurately predicting user needs, or more specifically, the current weight of clothing the user needs to process.

[0132] S302. Based on the user's usage habits, determine the value of the target parameter; the target parameter is used to indicate the operating parameters related to the weight of the clothing.

[0133] In this step, determining the target parameter values ​​based on user habits ensures that, even without access to the actual weight of the clothing, the operating parameters, determined by user habits, can best meet the user's current needs, thereby improving equipment efficiency and user experience. Adjusting the target parameters reduces manual intervention by the user and avoids poor processing results caused by setting default values, making the device more intelligent.

[0134] Target parameters refer to operating parameters related to the weight of the clothing; that is, operating parameters that need to be indirectly determined by the specific weight of the clothing. These parameters directly affect the operating performance of the equipment. Target parameters include, but are not limited to, water level, detergent dosage, dehydration speed, and running time. They are specifically determined based on the functions and actual needs of the clothing processing equipment and are not limited here.

[0135] For example, determining the user's usage habits includes:

[0136] Obtain the user's historical usage data; the historical usage data includes at least one of the following: clothing processing procedure, time, clothing weight, and usage frequency of the clothing processing equipment for each use;

[0137] Based on the historical usage data, key factors related to clothing weight are identified, as well as the relationship between these key factors and clothing weight; the key factors are at least one of clothing handling procedures, time, and usage frequency.

[0138] The relationship between the user's usage habits and the key factors and clothing weight was determined.

[0139] It should be noted that users may exhibit patterns related to clothing weight in certain aspects. For example, different washing programs typically correspond to different types and quantities of clothing, which may be correlated with clothing weight; users may put in different amounts of clothing at different times, such as doing laundry all at once on weekends; winter clothing tends to be heavier than summer clothing; users who wash frequently may do so with smaller loads per wash, while less frequent users may do so with larger loads per wash, and so on. The above example uses historical user data to analyze key factors related to clothing weight and their correlations, thereby determining user habits.

[0140] User history data represents records generated when a user uses the garment processing device, including but not limited to the garment processing procedure, time, garment weight, and frequency of device use for each use. Key factors refer to those extracted from history data that are correlated with garment weight. Key factors are at least one of the following: garment processing procedure, time, and frequency of use. The correlation indicates the relationship between key factors and garment weight; for example, garments are typically heavier when the user uses the device at night, and lighter when using the quick wash program.

[0141] For example, based on users' historical usage data, data groups can be created for each data item and clothing weight. These could include data groups for clothing handling procedures and corresponding clothing weights, time and clothing weight, and usage frequency and clothing weight. Correlation analysis is then performed on each data group to identify the data items in the group with the strongest correlation as key factors. For instance, the linear correlation between data items and clothing weight can be calculated using the Pearson correlation coefficient. Mutual information, regression analysis, and other methods can also be used to identify relevant key factors and their corresponding relationships.

[0142] The above examples demonstrate that even in cases of abnormal clothing weight, the system can accurately predict the user's current needs, providing a solid basis for determining appropriate operating parameters. This ensures the normal operation of clothing processing while effectively guaranteeing the processing results and enhancing the user experience.

[0143] For example, determining the value of the target parameter based on the user's usage habits includes:

[0144] Determine the values ​​of the key factors mentioned above under the current clothing treatment;

[0145] Based on the values ​​of the key factors and the correlation, predict the weight of the clothing;

[0146] The value of the target parameter is determined based on the predicted weight of the clothing.

[0147] It should be noted that the above example dynamically predicts the weight of clothes in the current clothing handling scenario by analyzing user habits, and adjusts the values ​​of target parameters based on the prediction results. The values ​​of key factors represent the specific values ​​of key factors related to the weight of clothes in the current clothing handling scenario. For example, if the key factors are the clothing handling program and time, the values ​​might be the cotton clothing washing program and 8 PM. Further, based on historical usage data and the correlation between key factors, the weight of clothes in the current scenario is predicted. For example, based on the correlation, when using the cotton clothing washing program at 8 PM, the weight of clothes is typically 5 kg. Finally, based on the predicted weight of clothes, the specific values ​​of the operating parameters related to the weight of clothes are determined. For example, based on a predicted weight of 5 kg of clothes, the water level is determined to be medium-high, the detergent dosage to be medium, and the spin-drying speed to be high.

[0148] The weighing anomaly handling method provided in this embodiment of the clothing processing equipment obtains historical user data, analyzes key factors related to clothing weight and their correlations, and determines user usage habits. Based on these usage habits, it determines the values ​​of key factors in the current clothing processing scenario, predicts clothing weight, and dynamically adjusts the values ​​of target parameters according to the prediction results. This method improves the intelligence level of the equipment, enabling it to automatically adjust operating parameters according to the user's actual usage habits even in cases of abnormal clothing weighing, ensuring clothing processing effectiveness and reducing resource waste.

[0149] This embodiment also provides a weighing anomaly handling device for clothing processing equipment. Figure 4 This is a diagram of a weighing anomaly handling device for a garment processing equipment provided in an embodiment of the present invention, as shown below. Figure 4 As shown, the weighing anomaly handling device 40 includes:

[0150] The detection module 401 is used to perform eccentricity detection on the clothing processing equipment when the clothing weight is abnormal; the abnormal clothing weight is used to indicate that the electrical signal value corresponding to the clothing weight is in an abnormal range.

[0151] The processing module 402 is used to perform corresponding processing operations based on the detection results; the processing operations include redistributing the clothing in the case of eccentricity abnormality, and / or performing an operation to redetermine the cause of clothing weight abnormality in the case of clothing weight abnormality not caused by eccentricity abnormality.

[0152] In one possible implementation, the processing module 402 is specifically used for:

[0153] If the detection result indicates that the first eccentricity value is abnormal, then the first processing operation is performed; the first processing operation includes performing a shaking procedure.

[0154] If the detection result indicates that the first eccentricity value is not abnormal, or if the weight of the clothing is abnormal when it is re-weighed after the first processing operation, then the clothing is re-weighed continuously to obtain multiple consecutive first electrical signal values.

[0155] Based on the abnormality of multiple first electrical signal values, a second processing operation is performed, the second processing operation including restarting the garment processing equipment and / or sending equipment fault information to the user.

[0156] In one possible implementation, the processing module 402 is specifically used for:

[0157] Determine the corresponding jitter program parameters based on the first eccentricity value;

[0158] The shaking procedure is continuously executed, and the second eccentricity value corresponding to each execution of the shaking procedure is obtained, until the latest obtained second eccentricity value is less than or equal to the preset eccentricity value, or the number of times the shaking procedure is executed reaches the preset number.

[0159] In one possible implementation, the processing module 402 is specifically used for:

[0160] Determine the distribution characteristics of multiple first electrical signal values; the distribution characteristics are used to indicate the fluctuation of the multiple first electrical signals;

[0161] Based on the distribution characteristics, determine whether it is a hardware fault and execute the corresponding second processing operation.

[0162] In one possible implementation, the processing module 402 is specifically used for:

[0163] Determine whether a target distribution feature identical to the stated distribution feature exists in a preset feature database;

[0164] If so, the hardware fault type corresponding to the target distribution feature is determined; the feature database includes hardware fault types and corresponding target distribution features;

[0165] Determine whether the hardware fault type is the target hardware fault type;

[0166] If the hardware fault type is determined to be different from the target hardware fault type, a device fault information is sent to the user to indicate that the device has a hardware fault and needs to be checked and repaired.

[0167] If the hardware fault type is determined to be the target hardware fault type, or if the preset feature database does not contain a target distribution feature that is the same as the distribution feature, then the clothing processing equipment is restarted and the clothing is weighed again.

[0168] In one possible implementation, if the number of times the shaking procedure is executed reaches a preset number, or if the weight of the clothing is abnormal when the clothing processing equipment is re-weighed after restarting, or if equipment fault information is sent to the user, the processing module 402 is further configured to:

[0169] Determine the user's usage habits;

[0170] Based on the user's usage habits, the value of the target parameter is determined; the target parameter is used to indicate the operating parameters related to the weight of the clothing.

[0171] In one possible implementation, the processing module 402 is specifically used for:

[0172] Obtain the user's historical usage data; the historical usage data includes at least one of the following: clothing processing procedure, time, clothing weight, and usage frequency of the clothing processing equipment for each use;

[0173] Based on the historical usage data, key factors related to clothing weight are identified, as well as the relationship between these key factors and clothing weight; the key factors are at least one of clothing handling procedures, time, and usage frequency.

[0174] The relationship between the user's usage habits and the key factors and clothing weight was determined.

[0175] In one possible implementation, the processing module 402 is specifically used for:

[0176] Determine the values ​​of the key factors mentioned above under the current clothing treatment;

[0177] Based on the values ​​of the key factors and the correlation, predict the weight of the clothing;

[0178] The value of the target parameter is determined based on the predicted weight of the clothing.

[0179] The weighing anomaly handling device provided in this embodiment can execute the weighing anomaly handling method of the clothing processing equipment provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0180] Figure 5 This is a hardware schematic diagram of a garment processing device provided in an embodiment of the present invention. Figure 5 As shown, the garment processing device 50 provided in this embodiment includes at least one processor 501 and a memory 502. The device 50 also includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0181] In a specific implementation, at least one processor 501 executes computer execution instructions stored in the memory 502, causing at least one processor 501 to perform the above method.

[0182] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0183] In the above Figure 5 In the illustrated embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0184] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0185] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0186] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the method described above.

[0187] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0188] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0189] The division of units described herein is merely a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0190] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0191] In addition, the functional units in the various embodiments of the present invention 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.

[0192] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0193] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0194] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for handling weighing anomalies in a garment processing device, characterized in that, The method includes: In the event of abnormal clothing weight, the clothing processing equipment is subjected to eccentricity detection; the abnormal clothing weight is used to indicate that the electrical signal value corresponding to the clothing weight is outside the normal range. Based on the test results, corresponding processing operations are performed; the processing operations include redistributing the clothing in the case of eccentricity abnormality, and / or redetermining the cause of the clothing weight abnormality in the case of non-eccentricity abnormality.

2. The method according to claim 1, characterized in that, The step of performing corresponding processing operations based on the detection results includes: If the detection result indicates that the first eccentricity value is abnormal, then the first processing operation is performed; the first processing operation includes performing a shaking procedure. If the detection result indicates that the first eccentricity value is not abnormal, or if the weight of the clothing is abnormal when it is re-weighed after the first processing operation, then the clothing is re-weighed continuously to obtain multiple consecutive first electrical signal values. Based on the abnormality of multiple first electrical signal values, a second processing operation is performed, the second processing operation including restarting the garment processing equipment and / or sending equipment fault information to the user.

3. The method according to claim 2, characterized in that, The execution of the first processing operation includes: Determine the corresponding jitter program parameters based on the first eccentricity value; The shaking procedure is continuously executed, and the second eccentricity value corresponding to each execution of the shaking procedure is obtained, until the latest obtained second eccentricity value is less than or equal to the preset eccentricity value, or the number of times the shaking procedure is executed reaches the preset number.

4. The method according to claim 2, characterized in that, The step of performing a second processing operation based on the abnormality of multiple first electrical signal values ​​includes: Determine the distribution characteristics of multiple first electrical signal values; the distribution characteristics are used to indicate the fluctuation of the multiple first electrical signals; Based on the distribution characteristics, determine whether it is a hardware fault and execute the corresponding second processing operation.

5. The method according to claim 4, characterized in that, The step of determining whether a hardware fault exists based on the distribution characteristics and executing the corresponding second processing operation includes: Determine whether a target distribution feature identical to the stated distribution feature exists in a preset feature database; If so, then determine the hardware fault type corresponding to the target distribution feature; the feature database includes hardware fault types and corresponding target distribution features; Determine whether the hardware fault type is the target hardware fault type; If the hardware fault type is determined to be different from the target hardware fault type, a device fault information is sent to the user to indicate that the device has a hardware fault and needs to be checked and repaired. If the hardware fault type is determined to be the target hardware fault type, or if the preset feature database does not contain a target distribution feature that is the same as the distribution feature, then the clothing processing equipment is restarted and the clothing is weighed again.

6. The method according to any one of claims 1-5, characterized in that, If the number of times the shaking procedure is executed reaches a preset number, or if the weight of the clothing is abnormal when the clothing processing equipment is re-weighed after restarting, or if equipment malfunction information is sent to the user, the method further includes: Determine the user's usage habits; Based on the user's usage habits, the value of the target parameter is determined; the target parameter is used to indicate the operating parameters related to the weight of the clothing.

7. The method according to claim 6, characterized in that, Determining the user's usage habits includes: Obtain the user's historical usage data; the historical usage data includes at least one of the following: clothing processing procedure, time, clothing weight, and usage frequency of the clothing processing equipment for each use; Based on the historical usage data, key factors related to clothing weight are identified, as well as the relationship between these key factors and clothing weight; the key factors are at least one of clothing handling procedures, time, and usage frequency. The relationship between the user's usage habits and the key factors and clothing weight was determined.

8. The method according to claim 7, characterized in that, Determining the value of the target parameter based on the user's usage habits includes: Determine the values ​​of the key factors mentioned above under the current clothing treatment; Based on the values ​​of the key factors and the correlation, predict the weight of the clothing; The value of the target parameter is determined based on the predicted weight of the clothing.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-8.

10. A garment processing device, characterized in that, include: At least one processor and memory; wherein, The memory stores computer-executed instructions; The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method as described in any one of claims 1-8.

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