An APP-based remote control washing and caring robot control system

Abstract

The application belongs to the technical field of automatic clothes washing control, and specifically discloses a washing and protecting robot control system based on APP remote control, which comprises a clothes recognition technology based on deep learning, label symbol content recognition and shape style content recognition of washing clothes, identification of the new and old appearance of the washing clothes, targeted extraction of texture features, identification of the easy-to-fade blocks on the clothes, analysis of the fade risk class weight of the washing clothes, differentiation between conventional clothes (not fade) and special clothes (fade), and classification washing and airing according to the requirements of the clothes, and determination of the washing and protecting mode of the clothes to avoid the intensification of the fading of the clothes caused by the use of the powerful washing mode; temporary change confirmation of the washing mode of the washing clothes is realized through real-time reception of the remote instructions of the customers, the problem of dependence on the preset program is solved, the flexibility and individualization level of the service are improved, and the service efficiency is also improved.

Description

Technical Field

[0001] This invention belongs to the field of automatic clothing washing control technology, and relates to a washing and care robot control system based on APP remote control. Background Technology

[0002] In modern society, with the rapid development of technology and the accelerated pace of life, smart home products have gradually become an important part of people's daily lives, especially in the laundry and care industry, where consumers' demand for convenient, efficient, and intelligent laundry and care equipment is increasing. Traditional clothing washing methods can no longer meet modern people's pursuit of quality of life. Therefore, a laundry and care robot control system based on APP remote control has emerged.

[0003] Existing technologies also include solutions related to automatic laundry control. For example, Chinese patent publication number CN115618906A discloses an unmanned clothing retrieval system in an intelligent laundry room based on RFID technology. This system electrically connects a clothing export device to the output end of a clothing storage input line, and a clothing hanging device to the output end of the clothing export device. Furthermore, by incorporating a clothing storage input line, clothing export device, clothing hanging device, clothing delivery device, automatic retrieval door, PFD chip, information transmission module, information acquisition module, information analysis module, central controller, information encryption module, information operation module, information storage module, big data service platform, and data storage module, it solves the problems of long queues and high workload for laundry staff caused by traditional manual clothing collection, washing, drying, and retrieval methods.

[0004] Based on existing solutions for automatic laundry control, the following limitations are listed: 1. The laundry is not classified and identified according to special conditions (such as faded clothes and regular clothes), which makes the setting of laundry conditions inaccurate and may result in the laundry being contaminated with discoloration.

[0005] 2. The lack of a remote interaction module makes it impossible to determine temporary changes in washing conditions based on remote control commands, resulting in an overly rigid washing process. Summary of the Invention

[0006] In view of this, in order to solve the problems mentioned in the background technology, a washing and care robot control system based on APP remote control is proposed.

[0007] The objective of this invention can be achieved through the following technical solution: This invention provides a washing and care robot control system based on APP remote control. The system includes: a clothing washing condition inspection module: used to scan the outline of the clothes to be washed by the washing and care robot, and to identify the label symbol content and shape and style content of the clothes to be washed.

[0008] Storage module: Used to store the historical washing and care records of each stock of clothing, the historical texture deformation rate of each fading area, the outer shape and historical label symbols of each stock of clothing, and each historical fading area and its degree of fading of each stock of clothing.

[0009] Clothing condition identification module: Based on the outline of the laundry, the module identifies whether the laundry is new or old. New and old condition includes existing condition and newly arrived condition. When the laundry is existing condition, it is treated as existing laundry and the color fading risk assessment module is executed. When the laundry is newly arrived condition, it is treated as newly arrived laundry and the key area location module is executed.

[0010] The clothing fading risk assessment module is used to extract the outline and storage shape of existing laundry, identify each fading area and its degree of fading, and analyze the fading risk class weights of existing laundry. .

[0011] Key area localization module: Used to extract historical fading areas and their fading degrees from existing clothing, infer fading risk areas and their fading degrees from newly washed clothing, and then analyze the fading risk class weights of newly washed clothing by detecting the fabric texture features of each fading risk area. .

[0012] Washing mode control module: used to determine the weight of the color fading risk class of the washed clothes. It identifies the washing and care mode, which includes fading mode and regular mode, and receives remote instructions from customers in real time to confirm temporary changes to the washing mode.

[0013] Drying condition recognition module: This module detects the external environment through the washing robot and the degree of water draining of the washed clothes. Combined with remote instructions from the customer, it determines the drying conditions for the washed clothes.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention is based on deep learning clothing recognition technology, which recognizes the label symbol content and shape and style content of the washed clothes, identifies the new and old condition of the washed clothes, extracts texture features in a targeted manner, identifies easily faded areas on the clothes, analyzes the fading risk class weight of the washed clothes, distinguishes between regular clothes (which will not fade) and special clothes (which will fade), and then classifies and washes and dries them according to the requirements of the clothes. At the same time, it determines the washing and care mode of the clothes to avoid the use of strong washing mode which will aggravate the fading of the clothes. For example, for clothes with easily faded prints or dyed areas, a gentle cold water washing and gentle stirring mode can effectively reduce the loss of dye and maintain the original color brightness of the clothes. This is especially important for some clothes with special colors or patterns (such as retro denim clothes, art printed tops, etc.), which can extend the beautiful service life of the clothes.

[0015] (2) By receiving remote instructions from customers in real time, the present invention can temporarily change and confirm the washing mode of laundry, which solves the problem of dependence on preset programs. This not only improves the flexibility and personalization of the service, but also helps to improve service efficiency. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the system module connections of the present invention.

[0018] Figure 2 This is a schematic diagram of the implementation steps of the clothing newness and wear recognition module of the present invention.

[0019] Figure 3 This is a display diagram showing the corresponding discrimination of the final washing and care mode in this invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1As shown, the present invention provides a washing and care robot control system based on APP remote control. The system includes: a clothing washing condition inspection module, a storage module, a clothing newness and oldness recognition module, a clothing fading risk assessment module, a key area positioning module, a washing mode control module, and a drying condition recognition module.

[0022] The clothing washing condition inspection module is connected to the clothing newness and wear identification module. The clothing newness and wear identification module is connected to the clothing fading risk assessment module and the key area positioning module, respectively. Then, the clothing fading risk assessment module and the key area positioning module are integrated and connected to the washing mode control module. The washing mode control module is connected to the drying condition identification module. The storage module is connected to the clothing newness and wear identification module, the clothing fading risk assessment module, and the key area positioning module, respectively.

[0023] The garment washing condition inspection module is used to scan the outline of the garments to be washed by the washing robot, and to identify the label symbols and shape and style of the garments. The label symbols include washing instructions and fabric composition symbols, and the shape and style include color grayscale and complex pattern features (color levels and mixing degree, and tightness of color and pattern combination).

[0024] The fabric composition description symbols include material performance characteristics (the fabric's water resistance, softness, and wrinkle resistance) and material type (cotton, silk). The washing instructions include drying mode (drying tolerance temperature and wind resistance intensity) and ironing mode (ironing tolerance temperature).

[0025] For example, the color levels and blending degree are obtained by identifying the ratio between the total number of different color gray levels in the pattern and the number of preset reference gray levels. The color binding tightness is determined by identifying its binding method (printing method, embroidery method), specifically: obtaining the corresponding preset pattern texture features of the printing method and embroidery method, comparing them with the pattern texture of the laundry garment through image texture detection technology, matching the binding method to which the pattern texture of the laundry garment belongs, and then comparing it with the corresponding preset color binding tightness of each binding method to match the color binding tightness that matches the binding method to which the pattern texture of the laundry garment belongs.

[0026] The color grayscale refers to the maximum grayscale value of different colors in the pattern.

[0027] The storage module is used to store the historical washing and care records of each stock of clothing, the corresponding historical texture deformation rate of each fading area, the outer shape and historical label symbols of each stock of clothing, and each historical fading area and its degree of fading of each stock of clothing.

[0028] The historical washing and care records include the number of historical washes and the operating conditions (temperature, time) for each wash and care session.

[0029] Please see Figure 2 As shown, the clothing condition identification module is used to identify the condition of the laundry based on its outline. The condition includes existing stock and new arrivals. When the condition of the laundry is existing stock, the laundry is treated as existing laundry and the clothing fading risk assessment module is executed. When the condition of the laundry is new arrivals, the laundry is treated as new arrivals and the key area positioning module is executed.

[0030] In a preferred embodiment, the method for identifying the new and old condition of laundry is as follows: extract the outer shape of each stock of laundry from the storage module of the laundry robot, compare it with the outer shape of the laundry, if the outer shape of a certain stock of laundry in the storage module matches the outer shape of the laundry, then the stock of laundry in the storage module is recorded as old clothing, and the historical label symbol of the old clothing is obtained from the storage module, compared with the label symbol of the laundry, to identify the newness of the laundry label symbol, and then compared with a preset label newness threshold. When it exceeds the preset label newness threshold, the new and old condition of the laundry is recorded as stock condition.

[0031] Specifically, the method for identifying the newness of the laundry label symbol is as follows: Preset texture contours are obtained for each fuzzy feature of the clothing label symbol. Each fuzzy feature includes wrinkles, stripes, and font symbols. Image recognition technology is used to identify the quantized texture contour values ​​of each fuzzy feature of the historical label symbol for older clothing. The quantized texture contour values ​​of each fuzzy feature include, for example, the number of wrinkles and stripes, and the grayscale value of the font symbols. Similarly, the quantized texture contour values ​​of each fuzzy feature of the laundry label symbol are extracted, and the newness of the laundry label symbol is calculated by comparison. An exemplary calculation formula is as follows: In the formula These represent the number of pleats associated with the laundry label symbol and the grayscale of the font symbol, respectively. These respectively indicate the number of pleats / stripes associated with the historical label symbols of vintage clothing and the grayscale of the font symbol. These represent the number of wrinkles and stripes, the corresponding preset reference difference in the grayscale of the font symbol, and e represents the natural constant. Because the labels of old clothes are repeatedly rubbed and rubbed during the washing process, the texture and outline of the labels gradually become blurred. Therefore, the fewer the number of wrinkles and stripes on the label and the higher the grayscale of the font symbol, the more obviously the label is new.

[0032] The purpose of identifying the newness of the laundry label symbols is to eliminate identification errors for new clothes of the same style.

[0033] If the outer shape of each stored garment in the storage module does not match the outer shape of the laundry garment, then the new and old condition of the laundry garment is recorded as the new condition.

[0034] The clothing fading risk assessment module is used to extract the outline storage shape of existing laundry, identify each fading area and its degree of fading, and analyze the fading risk class weights of existing laundry. .

[0035] In a preferred embodiment, the identification of each faded area and its degree of fading of the existing laundry includes: marking the position of each mark on the outline of the clothing to obtain the position of each mark on the outline of the clothing, and delineating the corresponding area of ​​each mark position with a specified area.

[0036] Based on image recognition algorithms, the color coordinates of the clothes at each marked position in the outline storage of the existing laundry are extracted. The color coordinates of the clothes at each marked position in the outline storage of the laundry are obtained. The difference value of the color coordinates of the clothes at each marked position in the outline storage of the existing laundry is obtained by comparison. Then, it is compared with a preset reference color coordinate difference threshold. The corresponding blocks at each marked position whose color coordinate difference value exceeds the preset reference color coordinate difference threshold are selected and recorded as the faded blocks of the existing laundry.

[0037] Obtain the color coordinate difference value of each faded area of ​​the existing laundry, and compare it with the preset reference color coordinate difference threshold to obtain the degree of fading of each faded area of ​​the existing laundry.

[0038] In a further preferred embodiment, the analysis of the fading risk class weights of the existing laundry includes: recording the degree of fading in each fading area of ​​the existing laundry as... , This indicates the number of each faded area in the existing stock of washed clothes. .

[0039] The fabric texture deformation rate of each fading area in the existing stock of laundry is detected. Then, the historical texture deformation rates of each fading area in the existing stock of laundry are selected from the historical texture deformation rates of each fading area in the storage module. The ratio of these historical rates yields the washing damage weight of each fading area in the existing stock of laundry. .

[0040] The fabric texture deformation rate refers to the stretching, twisting, and other texture deformations that occur in some fabrics after washing. The detection method involves using a high-precision camera to capture images of the fabric texture of existing laundry before washing under fixed lighting conditions, shooting angle, and distance. After washing, the fabric texture image is captured again under the same conditions. Professional image analysis software is then used to compare and analyze the two images. For example, the texture deformation rate is determined by calculating the changes in the angle of the texture lines and the proportion of change in the line spacing. If the original texture lines are horizontal and tilted at a certain angle after washing, the ratio of this angle change to the original angle can be considered as part of the deformation rate. Similarly, for the texture spacing, the ratio of the change in spacing before and after washing to the original spacing is calculated. Combining these factors yields a quantified texture deformation rate.

[0041] Extract fabric composition information from the labels of existing laundry garments to identify the fabric's water resistance. Softness and anti-wrinkle The material damage weighting formula for each unit of fading area in washed clothing is used for assessment. This is equivalent to obtaining the material damage weight of each fading area in the existing stock of washed clothes. ,in These represent the preset reference water resistance, reference softness, and reference wrinkle resistance, respectively.

[0042] By using the historical washing records stored in the washing robot's storage module, the historical number of washes for the existing laundry can be retrieved. Furthermore, the weights of color fading risk categories for existing laundry items were analyzed. , This indicates the total number of color-faded areas in the existing stock of washed clothes.

[0043] The key area localization module is used to extract historical fading areas and their fading degrees from each existing garment, inferring fading risk areas and their fading degrees from newly washed garments, and then analyzing the fading risk class weights of the newly washed garments by detecting the fabric texture features of each fading risk area. .

[0044] In a preferred embodiment, the step of inferring the fading risk areas and their fading degree of newly washed clothes includes: extracting the historical fading areas and their fading degree of each existing garment from the storage module, comparing them to obtain a historical fading area set for the existing garments, extracting the fading degree of each historical fading area in each existing garment, and then using the position elements of each historical fading area in the historical fading area set as row elements, with the row element numbered as follows. The degree of fading in each stock of clothing is used as the column element, and the column element number is... Construct a block matrix of historical fading levels of existing clothing. ,in These represent the degree of fading of the first historical fading block in the first and second stock items of clothing, respectively. These represent the degree of fading of the second historical fading block within the first and second stock items of clothing, respectively. This indicates the degree of fading of the i-th historical fading block within the j-th stock of clothing.

[0045] The historical blocks in the historical fading degree block matrix that do not contain any 0 elements are selected and denoted as the fading risk blocks for newly washed clothes.

[0046] Specifically, the historical fading block set refers to the set of blocks in all existing clothing items that have shown signs of fading. When a certain historical fading block does not exist in a certain stock of clothing, the degree of fading of that historical fading block in that stock of clothing is element 0.

[0047] The corresponding column elements of each fading risk area of ​​newly washed clothes are statistically analyzed, and their average values ​​are calculated to obtain the fading degree of each fading risk area.

[0048] In a further preferred embodiment, the analysis of the fading risk category weights of newly washed clothes includes: marking each fading risk area in the outline of the newly washed clothes; and similarly evaluating the material damage weights of each fading area of ​​the newly washed clothes based on the material damage weight evaluation method for each fading area of ​​the existing washed clothes. , This indicates the number of each fading risk area. .

[0049] The degree of fading in each fading risk area is recorded as follows: This is equivalent to the material wear compensation factor for each fading risk area of ​​newly washed clothes.

[0050] The purpose of evaluating the material wear compensation factor for each fading risk area of ​​the newly washed clothes is to eliminate the deviation in fabric fading caused by user behavior due to the shape of the clothes.

[0051] Extract the color grayscale and complex pattern characteristics of each fading risk area from the shape and style of the newly washed garments, and assess the potential fading rate of each fading risk area. Furthermore, the weights of the color fading risk categories for newly washed clothes were analyzed. , This indicates the total number of newly added laundry fading risk zones.

[0052] Specifically, the method for assessing the potential fading ratio of each fading risk area in newly washed clothes is as follows: The color grayscale of each fading risk area is denoted as... The color level, mixing degree, and color bonding tightness of the complex pattern features in each fading risk area are recorded as follows: Assess the potential color fading rate of each color-risk area in newly washed clothes. ,in is the preset reference color grayscale, and e is the natural constant.

[0053] The washing mode control module is used to determine the weight of the color fading risk class of the washed clothes. It identifies the washing and care mode, which includes fading mode and regular mode, and receives remote instructions from customers in real time to confirm temporary changes to the washing mode.

[0054] In a preferred embodiment, the specific content of the washing and care mode determination is as follows: the fading risk class weight of the washed clothes is compared with the preset fading risk class weight threshold value. When the fading risk class weight of the washed clothes exceeds the preset fading risk class weight threshold value, the washing and care mode of the washed clothes is controlled as the fading mode, otherwise it is controlled as the normal mode.

[0055] The difference between the fading mode and the regular mode lies in the different washing parameters (drum speed, spin speed, and soaking time) of the washing equipment.

[0056] This invention utilizes deep learning-based clothing recognition technology to identify label symbols and shape / style information on laundry, determine the condition of the clothing (new or old), extract texture features, and identify easily fading areas. It analyzes the fading risk weights of different clothing categories to distinguish between regular (non-fading) and special (fading) garments, then categorizes them for washing and drying according to their specific requirements. Simultaneously, it determines the appropriate washing and care mode to avoid using harsh washing methods that could exacerbate fading. For example, for clothing with easily fading prints or dyed areas, a gentle cold-water wash with gentle agitation effectively reduces dye loss and maintains the original color vibrancy. This is particularly important for clothing with special colors or patterns (such as vintage denim or artistically printed tops), extending their aesthetic lifespan.

[0057] Please see Figure 3As shown, in a further preferred embodiment, the content of the temporary change confirmation of the washing mode includes: identifying the customer's remote command category, including stain level requirement command, clothing care command, and quick wash command, comparing it with the corresponding washing and care setting mode of each remote command category, obtaining the washing and care setting mode that matches the customer's remote command category, recording it as the control mode for washing clothes, and controlling the corresponding washing parameters of the control mode for washing clothes.

[0058] The washing and care settings include Level 1, Level 2, and Level 3 modes, and their corresponding washing parameters are controlled as follows: Level 1 mode controls the drum speed to be... The secondary mode controls the drum speed to be Three-level mode control of drum speed is , .

[0059] The washing parameters of the control mode and washing and care mode of the laundry are matched with the preset washing parameter ranges of each washing and care safety level. The washing and care safety level of the control mode and washing and care mode of the laundry is identified and compared. If the washing and care safety level of the control mode of the laundry is lower than that of the washing and care mode of the washing and care mode, a washing and care risk warning is sent to the customer and the customer's remote command type is executed and confirmed. Otherwise, the control mode of the laundry is used as the final washing and care mode.

[0060] The drying condition recognition module is used to detect the external environment through the washing and care robot, as well as the degree of water draining of the washed clothes, and to determine the drying conditions of the washed clothes in combination with remote instructions from the customer.

[0061] In a preferred embodiment, detecting the degree of water draining of the washed clothes includes: detecting the moisture content of the washed clothes using sensors when the clothes are being dried by the washing robot. And obtain the water resistance rating of the fabric to which the washed garment is made. Calculate the degree of water draining from the washed clothes. .

[0062] In a further preferred embodiment, determining the drying conditions for the washed clothes includes: retrieving the washing boundary time of the quick wash command in the customer's remote instructions, detecting the moisture content of the washed clothes at the washing boundary time through the washing robot, comparing it with a preset benchmark moisture content, and controlling the washing robot to perform a quick drying operation on the washed clothes when the moisture content is higher than the preset benchmark moisture content.

[0063] The washing boundary time refers to the time period during which the customer requests to collect their clothes.

[0064] The washing and care robot uses environmental monitoring equipment installed inside to detect the external environment, including air humidity. Sunlight intensity Combined with the moisture content of the washed clothes at the washing boundary time To evaluate the deodorizing function of laundry detergents ,in These represent the preset reference air humidity and reference solar radiation intensity, respectively. These represent the preset influence weights of air humidity and sunlight intensity, which are then used to determine the deodorization operation command for washing clothes. In the formula This indicates that a deodorization operation command has been executed. This indicates that the deodorization operation instruction will not be executed. This indicates the preset deodorization function's expected threshold value.

[0065] This invention solves the problem of relying on preset programs by receiving remote instructions from customers in real time and temporarily changing the washing mode of laundry. This not only improves the flexibility and personalization of the service, but also helps to improve service efficiency.

[0066] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A control system for a washing and care robot based on remote control via an app, characterized in that, The system includes: Clothing washing condition inspection module: used to scan the outline of the laundry by the washing robot, and to identify the label symbol content and shape and style content of the laundry. Storage module: Used to store the historical washing and care records of each stock of clothing, the historical texture deformation rate of each faded area, the outer shape and historical label symbols of each stock of clothing, and the historical faded areas and their degree of fading of each stock of clothing. Clothing condition identification module: Based on the outline of the laundry, this module identifies whether the laundry is new or old. New and old condition includes existing stock and newly arrived stock. When the laundry is in the existing stock condition, it is treated as existing laundry and the color fading risk assessment module is executed. When the laundry is in the newly arrived stock condition, it is treated as newly arrived laundry and the key area location module is executed. The clothing fading risk assessment module is used to extract the outline and storage shape of existing laundry, identify each fading area and its degree of fading, and analyze the fading risk class weights of existing laundry. ; The analysis of the fading risk weights of existing laundry includes: assigning the degree of fading to each fading area of ​​the existing laundry as follows: , This indicates the number of each faded area in the existing stock of washed clothes. The fabric texture deformation rate of each fading area in the existing stock of laundry is detected, and the corresponding historical texture deformation rates of each fading area in the existing stock of laundry are selected from the historical texture deformation rates of each fading area in the existing stock of laundry in the storage module. The ratio is used to obtain the washing damage weight of each fading area in the existing stock of laundry. Extract fabric composition information from the labels of existing laundry garments to identify the fabric's water resistance. Softness and anti-wrinkle properties The material damage weighting formula for each unit of fading area in washed clothing is used for assessment. This is equivalent to obtaining the material damage weight of each fading area in the existing stock of washed clothes. ,in These represent the preset reference water resistance, reference softness, and reference wrinkle resistance; the historical washing records of the existing laundry are retrieved from the robot's storage module. Furthermore, the weights of color fading risk categories for existing laundry items were analyzed. , This indicates the total number of color-faded areas in the existing stock of washed clothes; Key area localization module: Used to extract historical fading areas and their fading degrees from existing clothing, infer fading risk areas and their fading degrees from newly washed clothing, and then analyze the fading risk class weights of newly washed clothing by detecting the fabric texture features of each fading risk area. ; Washing mode control module: used to determine the weight of the color fading risk class of the washed clothes. It determines the washing and care mode, which includes fading mode and regular mode, and receives remote instructions from customers in real time to confirm temporary changes to the washing mode. Drying condition recognition module: This module is used to detect the external environment through the washing and care robot and to detect the degree of water draining of the washed clothes to determine the drying conditions for the washed clothes.

2. The APP-based remote control system for a washing and care robot according to claim 1, characterized in that, The method for identifying the new and old condition of laundry is as follows: the outer shape of each stock of laundry is extracted from the storage module of the laundry robot and compared with the outer shape of the laundry. If the outer shape of a stock of laundry in the storage module matches the outer shape of the laundry, the stock of laundry in the storage module is recorded as old. The historical label symbol of the old clothing is obtained from the storage module and compared with the label symbol of the laundry to identify the newness of the laundry label symbol. Then, it is compared with a preset label newness threshold. When it exceeds the preset label newness threshold, the new and old condition of the laundry is recorded as stock condition. If the outer shape of each stored garment in the storage module does not match the outer shape of the laundry garment, then the new and old condition of the laundry garment is recorded as the new condition.

3. The APP-based remote control system for a washing and care robot according to claim 1, characterized in that, The identification of each faded area and its degree of fading of existing laundry includes: marking the position of each mark on the outer shape of the clothing to obtain the position of each mark on the outer shape of the clothing, and delineating the corresponding area of ​​each mark position with a specified area. Based on image recognition algorithms, the color coordinate difference between the outer shape of existing laundry and the corresponding blocks of each marked position in the outer shape storage is identified. Then, the corresponding blocks of each marked position whose color coordinate difference exceeds the preset reference color coordinate difference threshold are selected and recorded as each faded block of existing laundry. Obtain the color coordinate difference value of each faded area of ​​the existing laundry, and compare it with the preset reference color coordinate difference threshold to obtain the degree of fading of each faded area of ​​the existing laundry.

4. The APP-based remote control system for a washing and care robot according to claim 1, characterized in that, The method for predicting the fading risk areas and their fading levels of newly washed clothes includes: extracting historical fading areas and their fading levels from the storage module, comparing them to obtain a set of historical fading areas for the existing clothes, extracting their fading levels in each piece of clothing, and then using the positional elements of each historical fading area in the historical fading area set as row elements, with the row element numbered as follows. The degree of fading in each stock of clothing is used as the column element, and the column element number is... Construct a block matrix of historical fading levels of existing clothing. ,in These represent the degree of fading of the first historical fading block in the first and second stock items of clothing, respectively. These represent the degree of fading of the second historical fading block within the first and second stock items of clothing, respectively. This indicates the degree of fading of the i-th historical fading block within the j-th stock of clothing. Filter out the historical blocks in the historical fading degree block matrix where no row element is 0, and record them as the fading risk blocks of newly washed clothes. The corresponding column elements of each fading risk area of ​​newly washed clothes are statistically analyzed, and their average values ​​are calculated to obtain the fading degree of each fading risk area.

5. The APP-based remote control system for a washing and care robot according to claim 1, characterized in that, The analysis of the fading risk weights of newly washed clothes includes: marking each fading risk area in the outline of the newly washed clothes; and similarly evaluating the material damage weights of each fading area in the newly washed clothes based on the material damage weight assessment method for each fading area of ​​the existing washed clothes. , This indicates the number of each fading risk area. ; The degree of fading in each fading risk area is recorded as follows: This is equivalent to the material wear compensation factor for each fading risk area of ​​newly washed clothes; Extract the color grayscale and complex pattern characteristics of each fading risk area from the shape and style of the newly washed garments, and assess the potential fading rate of each fading risk area. Furthermore, the weights of the color fading risk categories for newly washed clothes were analyzed. , This indicates the total number of newly added laundry fading risk zones.

6. The APP-based remote control system for a washing and care robot according to claim 1, characterized in that, The specific content of the washing and care mode determination is as follows: the weight of the fading risk category of the washed clothes is compared with the preset fading risk category weight threshold. When the fading risk category weight of the washed clothes exceeds the preset fading risk category weight threshold, the washing and care mode of the washed clothes is controlled as the fading mode, otherwise it is controlled as the normal mode.

7. The APP-based remote control system for a washing and care robot according to claim 1, characterized in that, The content of the temporary change confirmation of the washing mode includes: identifying the customer's remote command category, including stain level requirement command, clothing care command, and quick wash command, comparing it with the corresponding washing and care setting mode of each remote command category, obtaining the washing and care setting mode that matches the customer's remote command category, recording it as the control mode for washing clothes, and controlling the corresponding washing parameters of the control mode for washing clothes. The system obtains the control mode and the corresponding washing safety level of the washing parameters of the washing and care mode for the laundry, and then compares them. If the washing safety level of the control mode is lower than that of the washing mode, a washing risk warning is sent to the customer, and the customer's remote command type is executed and confirmed. Otherwise, the control mode of the laundry is used as the final washing and care mode.

8. The APP-based remote control system for a washing and care robot according to claim 1, characterized in that, The detection of the degree of water draining from the washed clothes includes: using sensors to detect the moisture content of the washed clothes when the washing robot is used to dry them. And obtain the water resistance rating of the fabric to which the washed garment is made. Calculate the degree of water draining from the washed clothes. .

9. A washing and care robot control system based on APP remote control according to claim 7, characterized in that, The process of determining the drying conditions for laundry includes: retrieving the washing boundary time of the quick wash command in the customer's remote instructions; detecting the moisture content of the laundry at the washing boundary time using a washing robot; comparing it with a preset benchmark moisture content; and controlling the washing robot to perform a quick drying operation on the laundry when the moisture content is higher than the preset benchmark moisture content. The robot uses environmental monitoring equipment installed inside to detect the external environment and assess the desired deodorizing function of the laundry. This allows the system to determine if a deodorizing operation is required for washing clothes. In the formula This indicates that a deodorization operation command has been executed. This indicates that the deodorization operation instruction will not be executed. This indicates the preset deodorization function's expected threshold value.

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