Load detection method and device, clothes processing equipment and storage medium

Abstract

The invention provides a load detection method and device, clothes processing equipment and a storage medium, the clothes processing equipment comprises a barrel, an illumination module and a light sensing module, the illumination module is used for emitting illumination into the barrel, and the light sensing module is used for sensing illumination intensity; the load detection method comprises the steps that in response to a load detection instruction, an illumination module is started, and a barrel is controlled to start rotating; acquiring a plurality of illumination intensity values measured by the light sensing module; and determining feature information of the load according to the plurality of illumination intensity values, wherein the feature information comprises at least one of weight information and material information. According to the load detection method, the feature information of the load is analyzed and determined by detecting the illumination intensity value, then processing parameters or programs and the like matched with the actual condition of the load can be adjusted or generated according to the feature information of the load, and the load detection method has the advantages of being low in cost and stable in performance.

Description

Technical Field

[0001] The present application belongs to the technical field of clothing processing equipment, and in particular relates to a load detection method, device, clothing processing equipment and storage medium. Background Art

[0002] With the continuous improvement of living standards, people's demand for the intelligence of washing machines is also getting higher and higher. In addition to requiring washing machines to have good cleaning effects, they are also required to be able to perform better washing and care functions.

[0003] Existing technologies typically require adding weight sensors or employing fuzzy weighing technology to detect load weight. While using weight sensors increases the cost of the washing machine, fuzzy weighing relies on consistent motor performance and a sophisticated weighing algorithm to achieve a certain level of weight accuracy. Furthermore, detecting the material of the load typically requires deploying sensors or employing image recognition technology, significantly increasing the cost of the machine. Summary of the Invention

[0004] The embodiments of the present application provide a load detection method, device, clothing processing equipment and storage medium, which can solve the problem of how to improve the performance of clothing processing equipment in detecting the material and weight information of the internal load.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] A load detection method is applied to a clothing processing device, wherein the clothing processing device includes a drum, a lighting module, and a light sensing module, wherein the lighting module is used to emit light into the drum, and the light sensing module is used to sense the light intensity; the load detection method includes:

[0007] In response to a load detection instruction, turning on the illumination module and controlling the barrel to start rotating;

[0008] Obtaining a plurality of light intensity values ​​measured by the light sensing module;

[0009] Characteristic information of the load is determined according to the plurality of light intensity values, where the characteristic information includes at least one of weight information and material information.

[0010] In some embodiments, determining the characteristic information of the load according to the plurality of light intensity values ​​includes:

[0011] Obtaining a processing model, where the processing model is trained based on a plurality of sets of sample data, wherein the sample data includes a sample light intensity data set and sample feature information corresponding to the sample light intensity data set;

[0012] A plurality of light intensity values ​​are input into a processing model to obtain characteristic information of the load.

[0013] In some embodiments, before obtaining the processing model, the method further includes:

[0014] Performing data processing on the plurality of groups of sample data to obtain a training sample set;

[0015] Inputting the training sample set into multiple classifiers for training, and verifying the accuracy of each classifier;

[0016] According to the accuracy, a plurality of the classifiers are weightedly fused to obtain the processing model.

[0017] In some embodiments, performing weighted fusion on the plurality of classifiers according to the accuracy to obtain the processing model includes:

[0018] Sorting the plurality of classifiers from high to low according to the accuracy, and determining a preset number of the classifiers at the front as target classifiers according to the sorting result;

[0019] The plurality of target classifiers are fused according to the target weights to obtain the processing model.

[0020] In some embodiments, before fusing the plurality of target classifiers according to the target weights, the method further includes:

[0021] A corresponding weight coefficient is assigned to each target classifier according to the accuracy of the plurality of target classifiers to obtain the target weight.

[0022] In some embodiments, controlling the rotation of the barrel includes:

[0023] Controlling the cylinder to rotate based on a plurality of set speeds;

[0024] Obtaining a plurality of light intensity values ​​measured by the light sensing module includes:

[0025] Obtaining a light intensity value measured by the light sensing module when the cylinder is stationary;

[0026] The corresponding light intensity value measured by the light sensing module when the barrel rotates at each set speed is obtained.

[0027] A load detection device is applied to a clothes processing device, wherein the clothes processing device includes a drum, a lighting module, and a light sensing module. The lighting module is used to emit light into the interior of the drum, and the light sensing module is used to sense the light intensity. The load detection device includes:

[0028] a control module, configured to turn on the illumination module and control the rotation of the barrel in response to a load detection instruction;

[0029] An acquisition module, used to acquire a plurality of light intensity values ​​measured by the light sensing module;

[0030] The processing module determines characteristic information of the load according to the plurality of light intensity values, where the characteristic information includes at least one of weight information and material information.

[0031] A clothes processing device, comprising:

[0032] Cylinder;

[0033] A lighting module, used for emitting light into the interior of the cylinder;

[0034] Light sensing module, used to sense light intensity;

[0035] A processor, connected to the illumination module and the light sensing module, configured to:

[0036] In response to a load detection instruction, turning on the illumination module and controlling the rotation of the barrel;

[0037] Obtaining a plurality of light intensity values ​​measured by the light sensing module;

[0038] Characteristic information of the load is determined according to the plurality of light intensity values, where the characteristic information includes at least one of weight information and material information.

[0039] In some embodiments, the illumination module and the light sensing module are respectively disposed on opposite sides of the barrel.

[0040] A storage medium stores a computer program, which executes the load detection method when the computer program is running.

[0041] The load detection method, device, clothing processing equipment, and storage medium provided in the embodiments of the present application illuminate the interior of the drum and detect the light intensity value to analyze and determine the characteristic information of the load. Based on the characteristic information of the load, processing parameters or programs adapted to the actual load conditions can be adjusted or generated to achieve better processing results. Furthermore, compared to existing load detection methods, the load detection method of the present application has the advantages of low cost and stable performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0043] In order to more completely understand the present application and its beneficial effects, the following description will be given in conjunction with the accompanying drawings. In the following description, the same reference numerals represent the same parts.

[0044] Figure 1 A flow chart of the load detection method provided in an embodiment of the present application.

[0045] Figure 2 A method for establishing a processing model is provided in an embodiment of the present application.

[0046] Figure 3 Another flow chart of the load detection method provided in an embodiment of the present application.

[0047] Figure 4 A schematic structural diagram of a load detection device provided in an embodiment of the present application.

[0048] Figure 5 This is a schematic structural diagram of the clothing processing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0050] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present application, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0051] “A and / or B” includes the following three combinations: A only, B only, and a combination of A and B.

[0052] The use of "suitable for" or "configured to" in this application is intended to be open and inclusive language, and does not exclude devices that are adapted or configured to perform additional tasks or steps. In addition, the use of "based on" is intended to be open and inclusive, as a process, step, calculation, or other action that is "based on" one or more stated conditions or values ​​may, in practice, be based on additional conditions or values ​​beyond those stated.

[0053] In this application, the word "exemplary" is used to mean "serving as an example, illustration, or illustration." Any embodiment described in this application as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is given to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that one of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0054] The embodiment of the present application provides a load detection method, which is applied to a clothing processing device. It should be noted that the clothing processing device can be a washing machine, a shoe washing machine, a washer-dryer, a clothes dryer and other devices, and the load is the clothing, shoes and other items to be processed in the clothing processing device. Figure 5As shown, the clothes processing device 400 includes a drum 410, a lighting module 420 and a light sensing module 430. The lighting module 420 is used to emit light into the drum 410, and the light sensing module 430 is used to sense the light intensity. Figure 1 , Figure 1 A flow chart of a load detection method provided in an embodiment of the present application. The load detection method includes the following steps S101 to S103:

[0055] Step S101: In response to a load detection instruction, the illumination module is turned on and the cylinder is controlled to start rotating;

[0056] It should be noted that before setting up and processing a load, the laundry processing device needs to detect load information, such as weight and material information, to select processing parameters that are appropriate for the actual load conditions, thereby achieving better processing results. The load detection instruction can be manually input by the user or automatically issued based on a pre-set load detection step in the laundry processing program. The laundry processing program can be manually selected by the user, such as actively selecting a standard wash program. Alternatively, the laundry processing program can be generated by the laundry processing device after intelligent load identification.

[0057] The illumination module 420 is used to illuminate the interior of the barrel 410. The load inside the barrel 410 reflects and transmits the light, allowing the light sensing module 430 to sense the corresponding light intensity. It is understood that when the material and / or weight of the load vary, the load will reflect and transmit light differently, resulting in different light intensities detected by the light sensing module 430.

[0058] Optionally, the clothing processing device 400 also includes a rotating shaft 440 and a motor (not shown). The drum 410 is suitable for being connected to the motor of the clothing processing device through the rotating shaft 440. The motor is suitable for driving the drum 410 to rotate through the rotating shaft 440. The rotation speed of the drum 410 depends on the rotation speed of the motor.

[0059] Step S102: obtaining a plurality of light intensity values ​​measured by the light sensing module;

[0060] It should be noted that after the illumination module 420 is turned on, the light sensing module 430 detects the light intensity multiple times to obtain multiple light intensity values. For example, the light sensing module 430 detects the light intensity once every preset time interval. Obtaining the multiple light intensity values ​​measured by the light sensing module can be to obtain multiple light intensity values ​​that meet set conditions from the multiple obtained light intensity values. For example, from the multiple light intensity values, the light intensity values ​​measured when the rotation speed of the drum reaches a first preset rotation speed, a second preset rotation speed, and a third preset rotation speed, respectively, can be obtained.

[0061] Step S103: determining characteristic information of the load according to the plurality of light intensity values, where the characteristic information includes at least one of weight information and material information.

[0062] It should be noted that the characteristic information includes at least one of weight information and material information, indicating that the characteristic information may include weight information and / or material information, and may also include humidity information, dirtiness information, or other information related to the load. Because different load materials and / or weights reflect and transmit light differently, the light intensity detected by the light sensor module 430 varies. Therefore, the material and / or weight of the load can be determined based on the measured light intensity.

[0063] The load detection method provided in the embodiments of this application illuminates the interior of the cylinder and detects the light intensity value to analyze and determine the characteristic information of the load. Based on the characteristic information of the load, it can then adjust or generate processing parameters or programs that are adapted to the actual load conditions to achieve better processing results. Furthermore, compared to existing load detection methods, the load detection method of this application has the advantages of low cost and stable performance.

[0064] Regarding how to determine characteristic information of a load based on a plurality of light intensity values, the present application provides an embodiment, that is, determining characteristic information of a load based on a plurality of light intensity values ​​includes:

[0065] Obtaining a processing model, where the processing model is trained based on a plurality of sets of sample data, the sample data including a sample light intensity data set and sample feature information corresponding to the sample light intensity data set;

[0066] Several light intensity values ​​are input into the processing model to obtain characteristic information of the load.

[0067] It should be noted that the sample data can be obtained by testers through testing. The data in the sample data should ensure accuracy and the data volume should be sufficient to ensure the calculation accuracy of the processing model. In some embodiments, before the processing model is obtained through training with the sample data, the original sample data can also be subjected to a series of data processing operations such as preprocessing and imbalance classification processing. On the one hand, this can prevent the classification model from failing due to data problems, and on the other hand, it can accelerate the training of the classification model, improve the accuracy of the algorithm, and improve the adaptability of the classification algorithm model to imbalanced data sets.

[0068] For example, N groups of sample collection experiments are conducted on several prototypes, and the load weight of each group of experiments is M i , the materials are T i The light sensor module measures the light intensity value E0 when the inner barrel speed is 0, n1 and n2 i 、E1 iand E2 i (where i = 1, 2, ..., N), and thus N sets of sample data are obtained. Optionally, N sets of feature vectors [E0 i ,E1 i ,E2 i ] and label vector [M i ,T i ](where: i=1,2,……,N), and normalize the eigenvector to eliminate the bias.

[0069] Optionally, see Figure 2 , Figure 2 A method for establishing a processing model provided in an embodiment of the present application. To improve the accuracy of the processing model, before obtaining the processing model, the method further includes:

[0070] Perform data processing on several groups of sample data to obtain a training sample set;

[0071] Input the training sample set into multiple classifiers for training and verify the accuracy of each classifier;

[0072] According to the accuracy, multiple classifiers are weighted and fused to obtain the processing model.

[0073] In this way, the processing model is constructed through a voting method weighted by category accuracy, which can comprehensively consider the inconsistent results of different single classifiers in classifying different types of fault information, and can reflect the advantages of different single classifiers, thereby further improving the accuracy and stability of the training model in effective classification and prediction.

[0074] The multiple classifiers may be any combination of one or more of a K-nearest neighbor classifier, a decision tree classifier, a support vector machine classifier, a naive Bayes classifier, or a random forest classifier.

[0075] In some embodiments, before obtaining the processing model through sample data training, a series of data processing operations such as preprocessing and imbalance category processing can be performed on the original sample data. On the one hand, this can prevent the classification model from failing to work due to data reasons. On the other hand, it can accelerate the training of the classification model, improve the accuracy of the algorithm, and improve the adaptability of the classification algorithm model to imbalanced data sets.

[0076] Exemplarily, performing data processing on several groups of sample data to obtain a training sample set may include:

[0077] Identifying several groups of sample data to determine missing samples; processing the missing samples to obtain an initial training set; or, standardizing several groups of sample data to obtain an initial training set; or, performing correlation analysis on several groups of sample data to determine the correlation between each group of sample data in the several groups of sample data, and screening samples based on the correlation between each group of sample data in the several groups of sample data to obtain an initial training set;

[0078] The initial training set is divided into a training sample set and a test sample set.

[0079] In the above implementation process, during the preprocessing of several groups of sample data: based on the complexity of several groups of sample data, relevant processing is performed on the missing data in several groups of sample data to eliminate the impact of missing data, or several groups of sample data are standardized, or correlation analysis is performed on the original data sample set to eliminate attribute features and redundant features that have weak correlation with the sample data, and select the data attributes that are most conducive to the construction of the classification model, which can improve the success rate of the processing model construction, accelerate the training of the processing model, and improve the accuracy of the weighted fusion classification model.

[0080] For example, the initial training set may be divided into a random portion, such as 4 / 5 of the sample data in the initial training set, as a training sample set, and the remaining 1 / 5 of the sample data as a test sample set. Verifying the accuracy of each classifier may include inputting the test sample set into each trained classifier to verify the accuracy of each classifier.

[0081] In some embodiments, performing weighted fusion on multiple classifiers based on accuracy to obtain a processing model includes:

[0082] Sort the multiple classifiers according to their accuracy from high to low, and determine the classifiers with a preset number of positions at the front as target classifiers according to the sorting results;

[0083] Multiple target classifiers are fused according to the target weights to obtain a processing model.

[0084] Optionally, the multiple classifiers include 6 different classifiers, and the 6 classifiers are sorted from high to low according to the accuracy, and the top 3 classifiers are determined as target classifiers according to the sorting results; the 3 target classifiers are fused according to the target weight to obtain a processing model.

[0085] Regarding how to determine appropriate target weights, the present application provides a possible implementation method, that is, before fusing multiple target classifiers according to the target weights, the following steps are further included:

[0086] According to the accuracy of multiple target classifiers, a corresponding weight coefficient is assigned to each target classifier to obtain the target weight.

[0087] For example, there are three target classifiers, named P1, P2, and P3 respectively, and the corresponding prediction results of the three algorithms of P1, P2, and P3 are [M i , T i ](i=1,2,3). Among them, the accuracy of P1 is greater than the accuracy of P2, and the accuracy of P2 is greater than the accuracy of P3. According to the accuracy of P1, P2, and P3, P1 is assigned a weight coefficient g1, P2 is assigned a weight coefficient g2, and P3 is assigned a weight coefficient g3. Among them, g1>g2>g3, and g1>g2+g3. The prediction results of the final processing model are as follows:

[0088] M=g1M1+g2M2+g3M3,

[0089] Where M is the load weight and T is the load material.

[0090] For example, the accuracy of a target classifier is positively correlated with its corresponding weight coefficient, i.e., the higher the accuracy of a target classifier, the higher the weight coefficient. For example, if the accuracy rates of three target classifiers are 95%, 90%, and 85%, respectively, the weight coefficient assigned to the target classifier with an accuracy of 95% is 3, the weight coefficient assigned to the target classifier with an accuracy of 90% is 2, and the weight coefficient assigned to the target classifier with an accuracy of 85% is 1.

[0091] The embodiment of determining the characteristic information of the load based on a plurality of light intensity values ​​provided in this application can accurately calculate the characteristic information of the load through an algorithm. Compared with existing load detection methods, the load detection method of this application has the advantages of low cost and stable performance.

[0092] Regarding how to obtain the light intensity value, optionally, controlling the rotation of the barrel includes:

[0093] Controlling the cylinder to rotate based on a number of set speeds;

[0094] Obtaining several light intensity values ​​measured by the light sensor module includes:

[0095] Obtaining the light intensity value measured by the light sensing module when the cylinder is stationary;

[0096] Obtain the corresponding light intensity value measured by the light sensing module when the barrel rotates at each set speed.

[0097] For example, see Figure 3 , Figure 3Another flow chart of the load detection method provided in an embodiment of the present application. The load detection method includes the following steps S201-S204:

[0098] Step S201: In response to a load detection instruction, when the cylinder is stationary, obtaining a light intensity value E0;

[0099] Step S202: When the cylinder is controlled to accelerate to a rotation speed n1, a light intensity value E1 is obtained;

[0100] Step S203: When the cylinder is controlled to accelerate to a rotation speed n2, a light intensity value E2 is obtained;

[0101] Step S204: Input E0, E1, and E2 into the processing model to obtain the weight and material of the load.

[0102] The load detection method provided in the embodiments of this application illuminates the interior of the cylinder and inputs the light intensity value into a processing model to obtain characteristic information of the load. This characteristic information can then be used to adjust or generate processing parameters or programs that are adapted to the actual load conditions, thereby achieving better processing results. Furthermore, compared to existing load detection methods, the load detection method of this application has the advantages of low cost and stable performance.

[0103] The present application also provides a load detection device, which is applied to a clothes processing device, such as Figure 5 The clothes processing device 400 includes a drum 410, a lighting module 420 and a light sensing module 430. The lighting module 420 is used to emit light into the drum 410, and the light sensing module 430 is used to sense the light intensity. Figure 4 , Figure 4 The load detection device 300 includes a control module 310 , an acquisition module 320 , and a processing module 330 .

[0104] Among them, the control module 310 is used to respond to the load detection instruction, turn on the lighting module, and control the rotation of the cylinder; the acquisition module 320 is used to obtain several light intensity values ​​measured by the light sensing module; the processing module 330 determines the characteristic information of the load based on the several light intensity values, and the characteristic information includes at least one of weight information and material information.

[0105] The load detection device provided in the embodiments of the present application obtains a plurality of light intensity values ​​measured by a light sensing module and inputs these light intensity values ​​into a processing model to derive characteristic information of the load. Based on this characteristic information, the device can then adjust or generate processing parameters or programs that are adapted to the actual load conditions, thereby achieving a better processing effect. Furthermore, compared to existing load detection methods, the load detection method of the present application has the advantages of low cost and stable performance.

[0106] The present application also provides a clothes processing device, for example, see Figure 5 , Figure 5 Schematic diagram of the structure of the clothes processing device provided in an embodiment of the present application. The clothes processing device 400 includes a drum 410, a lighting module 420, a light sensing module 430 and a processor 450.

[0107] Among them, the lighting module 420 is used to emit light into the inside of the barrel 410, the light sensing module 430 is used to sense the light intensity, and the processor 450 is connected to the lighting module 420 and the light sensing module 430, and is used to: respond to the load detection instruction, turn on the lighting module 420, and control the rotation of the barrel 410; obtain several light intensity values ​​measured by the light sensing module 430; determine the characteristic information of the load based on the several light intensity values, the characteristic information including at least one of weight information and material information.

[0108] In some embodiments, the illumination module 420 includes a plurality of light sources arranged in an array. Optionally, the illumination module 420 is disposed on the side of the door of the clothing processing device facing the cylinder 410, with the plurality of light sources evenly distributed at the opening of the cylinder 410 and emitting light toward the interior of the cylinder 410. Alternatively, the illumination module 420 can also be disposed on the sidewall of the cylinder 410, with the plurality of light sources evenly distributed along the depth direction of the cylinder 410 and emitting light toward the interior of the cylinder 410. In this way, the uniformity of illumination in each area within the cylinder 410 can be improved, which is conducive to improving the accuracy of load information feature detection. In some embodiments, the illumination module 420 is also reused as the downlight of the clothing processing device 400.

[0109] Optionally, the illumination module 420 and the light sensing module 430 are respectively disposed on opposite sides of the barrel 410 . In other embodiments, the illumination module 420 and the light sensing module 430 may also be disposed on the same side of the barrel 410 .

[0110] The clothing processing device 400 also includes a rotating shaft 440 and a motor (not shown). The drum 410 is suitable for being connected to the motor of the clothing processing device through the rotating shaft 440. The motor is suitable for driving the drum 410 to rotate through the rotating shaft 440. The rotation speed of the drum 410 depends on the rotation speed of the motor.

[0111] The laundry processing device 400 provided in the embodiment of the present application illuminates the interior of the drum 410 and detects the light intensity value to analyze and determine the characteristic information of the load. Based on the characteristic information of the load, it can then adjust or generate processing parameters or programs that are adapted to the actual load conditions to achieve better processing results. Furthermore, compared to existing load detection methods, the load detection method of the present application has the advantages of low cost and stable performance.

[0112] An embodiment of the present application further provides a storage medium having a computer program stored thereon, which executes the load detection method in any embodiment when the computer program is run.

[0113] Exemplarily, the above-mentioned computer-readable storage media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes, etc.), optical disks (e.g., CDs (Compact Disks), DVDs (Digital Versatile Disks), etc.), smart cards, and flash memory devices (e.g., EPROMs (Erasable Programmable Read-Only Memory), cards, sticks, or key drives, etc.). The various computer-readable storage media described in the embodiments of the present application may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0114] The load detection method, device, clothing processing equipment and storage medium provided in the embodiments of the present application are introduced in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A load detection method, applied to a clothing processing device, characterized in that: The clothing processing device comprises a drum, a lighting module and a light sensing module, wherein the lighting module is used to emit light into the drum, and the light sensing module is used to sense light intensity; the load detection method comprises: In response to a load detection instruction, the illumination module is turned on, and the barrel is controlled to start rotating; Obtaining a plurality of light intensity values ​​measured by the light sensing module; Characteristic information of the load is determined according to a plurality of the light intensity values, wherein the characteristic information includes at least one of weight information and material information.

2. The load detection method according to claim 1, characterized in that: The determining of the characteristic information of the load according to the plurality of light intensity values ​​comprises: Acquire a processing model, wherein the processing model is obtained by training based on a plurality of groups of sample data, wherein the sample data includes a sample light intensity data group and sample feature information corresponding to the sample light intensity data group; A plurality of the light intensity values ​​are input into a processing model to obtain characteristic information of the load.

3. The load detection method according to claim 2, characterized in that: Before getting the processing model, also include: Performing data processing on a plurality of groups of sample data to obtain a training sample set; Inputting the training sample set into multiple classifiers for training, and verifying the accuracy of each of the classifiers; According to the accuracy rate, a plurality of the classifiers are weightedly fused to obtain the processing model.

4. The load detection method according to claim 3, characterized in that: The step of performing weighted fusion on the plurality of classifiers according to the accuracy rate to obtain the processing model comprises: Sorting the plurality of classifiers from high to low according to the accuracy, and determining a preset number of the classifiers at the front as target classifiers according to the sorting result; The plurality of target classifiers are fused according to the target weights to obtain the processing model.

5. The load detection method according to claim 4, characterized in that: Before fusing the plurality of target classifiers according to the target weights, the method further comprises: A corresponding weight coefficient is assigned to each target classifier according to the accuracy of the plurality of target classifiers to obtain the target weight.

6. The load detection method according to any one of claims 1 to 5, characterized in that: The controlling the rotation of the cylinder comprises: Controlling the cylinder to rotate based on a plurality of set speeds; Acquiring a plurality of light intensity values ​​measured by the light sensing module includes: Obtaining the light intensity value measured by the light sensing module when the cylinder is stationary; The corresponding light intensity value measured by the light sensing module when the barrel rotates at each set rotation speed is obtained.

7. A load detection device, applied to a clothes processing device, characterized in that: The clothing processing device comprises a drum, a lighting module and a light sensing module, wherein the lighting module is used to emit light into the drum, and the light sensing module is used to sense the light intensity; the load detection device comprises: A control module, used for turning on the illumination module and controlling the rotation of the barrel in response to a load detection instruction; An acquisition module, used to acquire a plurality of light intensity values ​​measured by the light sensing module; The processing module determines characteristic information of the load according to the plurality of light intensity values, wherein the characteristic information includes at least one of weight information and material information.

8. A clothes processing device, characterized in that: include: Cylinder; A lighting module, used for emitting light into the interior of the cylinder; Light sensing module, used to sense light intensity; A processor is connected to the illumination module and the light sensing module, and is used to: In response to a load detection instruction, turning on the illumination module and controlling the rotation of the barrel; Obtaining a plurality of light intensity values ​​measured by the light sensing module; Characteristic information of the load is determined according to a plurality of the light intensity values, wherein the characteristic information includes at least one of weight information and material information.

9. The clothes processing device according to claim 8, characterized in that: The illumination module and the light sensing module are respectively arranged on two opposite sides of the barrel.

10. A storage medium, characterized in that: A computer program is stored thereon, and when the computer program is run, the load detection method according to any one of claims 1 to 6 is executed.

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