Control method for hotel clothes protection machine nursing

Abstract

The invention provides a control method for nursing of a hotel clothes protection machine, and relates to the technical field of clothes protection machines, the space of the clothes protection machine is divided into a cleaning area and a nursing area, the weight information of clothes is acquired, the residual space ratio and the gap index of the clothes protection machine are calculated, and a clothes identification model is established. The method comprises the following steps: identifying material characteristics of clothes, including quantity, material and spinning density, and cleaning characteristics, including dust degree and wrinkle amount, obtaining accommodation information, clothes protector setting parameters and clothes characteristics of accommodation guests as preference data of the guests, and analyzing historical preference data of the guests and past default clothes protector setting parameters, so as to obtain the preference data of the guests. The optimal cleaning parameters are matched through the material features of the clothes, different cleaning parameters are calculated according to the clothes protection setting parameters and the gap index of the clothes protection machine, and the comprehensive nursing parameters are set according to the optimal nursing parameters of the clothes features, the number of the clothes and the residual space ratio.

Description

Technical Field

[0001] The invention relates to the technical field of clothing care machines, in particular to a control method for hotel clothing care machines. Background Art

[0002] With the accelerated pace of life and the pursuit of high-quality life, more and more consumers are paying attention to the convenience and efficiency of clothing care. Clothes care machine is a special device used to care, clean and dewrinkle clothes. It combines multiple functions such as steam care, drying, and deodorization to help users quickly refresh and maintain clothes without a comprehensive washing process. Since travelers stay in hotels for a short time, using a clothes care machine can quickly process clothes in a limited time, so it is particularly suitable for hotel use. How to provide a clothes care control method for hotels has become an urgent problem to be solved.

[0003] In the prior art, the publication number CN118727408 discloses a control method, device, equipment and medium for a clothing care machine, including determining a target steam amount corresponding to the acquired clothing attribute information according to the association relationship between the preset clothing attribute information and the steam amount; controlling the steam generating device in the clothing care machine to release the target steam amount to moisten the clothing to be cared for; determining the weight gain of the clothing to be cared for; determining the target drying time corresponding to the weight gain according to the association relationship between the preset weight gain and the drying time; and controlling the drying device in the clothing care machine to dry the clothing to be cared for according to the target drying time, thereby achieving the generation of the required steam amount according to the clothing attribute information and determining the target drying time according to the weight gain of the clothing to be cared for. However, this method is only for the control of steam. In actual clothing care, multiple clothing care methods are often carried out in cooperation with each other, and there is a lack of independent selection methods, which cannot meet the needs of many different users of the hotel for efficient and fine clothing care machines.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a control method for hotel clothing care machine care to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A control method for hotel clothing care machine care, the specific steps include:

[0008] Step 1: Divide the space of the clothing care machine into a cleaning area and a care area. The cleaning area is used to place clothes to be cared for, and the care area is used to place clothes that have been cleaned. Obtain the weight information of the clothes in the cleaning area, spray a certain amount of hot air into the space of the clothing care machine, and calculate the remaining space ratio and gap index of the clothing care machine according to the internal size of the clothing care machine and the time of detecting temperature changes;

[0009] Step 2: Establish a clothing recognition model, take photos of the clothes put into the cleaning area, identify the clothing feature data, and establish a data set of clothing features together with the weight of the clothes. The clothing features include material features and cleaning features. The material features include quantity, material, and textile density. The cleaning features include dustiness and wrinkle amount.

[0010] Step 3: Acquiring the accommodation information of the guest, including the length of stay, purpose of stay, accommodation price, and the clothing care setting parameters set by the customer, including clothing care mode and clothing care duration, and taking the characteristics of the clothes being cared for as the customer's preference data, and by analyzing the preference data of historical customers, taking the parameters selected by customers with the same accommodation information and clothing characteristics as the default clothing care setting parameters;

[0011] Step 4: Divide the clothes cleaning into two stages: dust removal and wrinkle removal. Obtain the optimal cleaning parameters for different clothes material characteristics in the two stages. Match them with the characteristics of the clothes to be cared for and use them as the basic cleaning parameters for each stage. Depending on whether the clothes care setting parameters and the gap index of the clothes care machine are set, different cleaning parameters are used according to different cleaning stages during cleaning.

[0012] Step 5: Control method of the maintenance area: transport the cleaned clothes to the care area, obtain the best care parameters for different clothing characteristics during care, and set basic care parameters by matching the clothing characteristics, and set comprehensive care parameters according to the number of clothes and the remaining space ratio.

[0013] Furthermore, the calculation formula for calculating the remaining space ratio and the gap index of the clothing care machine is:

[0014] The calculation formula of the remaining space ratio of the clothing care machine is:

[0015]

[0016] Wherein, Vs is the remaining space ratio of the clothes care machine, Ly, Hy, and Wy are the length, width, and height of the inside of the clothes care machine, Vw(it) is the volume of hot air sprayed at time it, and Ts1 is the time for detecting temperature changes;

[0017] The calculation formula of the gap index of the clothing care machine is:

[0018]

[0019] Wherein, Kq is the gap index of the clothing care machine, and Ts is the time for detecting temperature change when there is no clothes inside the clothing care machine.

[0020] Furthermore, the clothing recognition model is based on a CNN neural network, including the establishment of the model and the training of the model;

[0021] The establishment of the model specifically includes a convolutional layer, an activation layer, a pooling layer and an output layer;

[0022] Convolutional layer: extracts features from the bounding box and label of the input image through convolution operation, and the calculation formula is:

[0023]

[0024] Where I is the input matrix, K is the convolution kernel, (xc, yc) is the coordinate of the output matrix, and K(ic, jc) is the value of the convolution kernel in the icth row and jcth column;

[0025] Activate the layer;

[0026] ReLU(xz)=max(0,xz)

[0027] Among them, xz is the coordinate of the output matrix of the convolutional layer output;

[0028] Pooling layer;

[0029] MaxPooling(xd,yd)=max{I(xc+ic,jc+jc)|ic,jc∈(3,3)}

[0030] Where I is the input matrix, MaxPooling(xd,yd) is the maximum pooling output;

[0031] Output layer;

[0032] y=W*xm+b

[0033] Among them, xm is the output of the pooling layer, y is the result of identifying the features of clothes in the image, W is the weight, and b is the bias parameter;

[0034] The model is trained by taking images containing clothing features as the input of the model and clothing feature data as the output of the model.

[0035] Furthermore, the clothing protection modes include soft, standard, and enhanced, and the soft, standard, and enhanced modes are calculated by the difference in clothing protection mode ratios, and the specific calculation formula is:

[0036] Standard*(1+α1)=Enhancement

[0037] Soft*(1+α1)=Standard

[0038] Among them, α1 is the difference in the mode ratio of clothing protection;

[0039] The clothing care duration includes fast, regular and fine, and the fast, regular and fine are calculated by the difference in the duration of clothing care. The specific calculation formula is:

[0040] Normal*(1+α2)=fine

[0041] Fast*(1+α2)=Regular

[0042] Among them, α2 is the difference in the duration ratio of clothing protection.

[0043] Furthermore, the specific method of analyzing the historical customer preference data and selecting the parameters selected by customers with the same accommodation information and clothing characteristics as the default clothing setting parameters is as follows:

[0044] According to the same accommodation information and clothing features, the preference data in the historical data is counted, and through the statistics of the preference settings, the parameters with the most selected clothing care mode and clothing care duration are used as the default clothing care setting parameters.

[0045] Furthermore, the basic cleaning parameters of each stage include basic dust removal air output power, basic dust removal shaking power, and basic dust removal time in the dust removal stage, and basic wrinkle removal steam size, basic wrinkle removal shaking power, and basic wrinkle removal time in the wrinkle removal stage.

[0046] Furthermore, the cleaning parameters of the dust removal stage include dust removal air output power, dust removal shaking power, and dust removal time;

[0047] The calculation formula of the wind output power is:

[0048]

[0049] Among them, Vcf is the dust removal air output power, Vcb is the dust removal basic wind speed power, Qh is the dust degree of clothes, Kq is the gap index of the clothes care machine, α1 is the mode ratio difference of clothes care, β1 is the default clothes care mode ratio, A is whether the clothes care mode is selected, -1≤α1≤1;

[0050] The time calculation formula for removing dust is:

[0051]

[0052] Among them, Tcc is the dust removal time, Tcb is the basic dust removal time, Qh is the dust degree of clothes, Kq is the gap index of the clothes care machine, α2 is the proportional difference of the clothes care time, β2 is the default clothes care time ratio, A is whether the clothes care mode is selected, -1≤α2≤1;

[0053] The calculation formula of the dust removal shaking power is:

[0054]

[0055] Among them, Wcd is the dust removal shaking power, Wcb is the dust removal basic shaking power, Qh is the dust degree of clothes, Kq is the gap index of the clothing care machine, α1 is the mode ratio difference of clothing care, β1 is the default clothing care mode ratio, A is whether the clothing care mode is selected, -1≤α1≤1.

[0056] Furthermore, the cleaning parameters in the wrinkle removal stage include wrinkle removal shaking power, wrinkle removal steam volume, and wrinkle removal time;

[0057] The calculation formula of the wrinkle removal jitter power is:

[0058]

[0059] Where, Wzd is the anti-wrinkle shaking power, Wzb is the anti-wrinkle basic shaking power, Qz is the wrinkle degree of clothes, Kq is the gap index of the clothes care machine, α1 is the mode ratio difference of clothes care, β1 is the default clothes care mode ratio, A is whether the clothes care mode is selected, -1≤α1≤1;

[0060] The calculation formula of the wrinkle removal steam size is:

[0061]

[0062] Among them, Pzz is the size of wrinkle removal steam, Pzb is the size of wrinkle removal basic steam, Qz is the wrinkle degree of clothes, Kq is the gap index of the clothes care machine, α1 is the mode ratio difference of clothes care, β1 is the default clothes care mode ratio, A is whether the clothes care mode is selected, -1≤α1≤1;

[0063] The calculation formula for the wrinkle removal time is:

[0064]

[0065] Among them, Tpz is the wrinkle removal time, Tzb is the basic wrinkle removal time, Qz is the wrinkle degree of clothes, Kq is the gap index of the clothing care machine, α1 is the mode ratio difference of clothing care, β2 is the default clothing care time ratio, A is whether the clothing care mode is selected, -1≤α2≤1.

[0066] Furthermore, the basic nursing parameters include basic temperature, basic humidity and basic nursing duration;

[0067] The comprehensive nursing parameters include comprehensive temperature, comprehensive humidity and comprehensive nursing duration;

[0068] The calculation formula of the comprehensive temperature is:

[0069]

[0070] Among them, Hwen is the comprehensive temperature, Ny is the number of clothes, HW iy is the basic temperature of the iy-th piece of clothing care, Vs is the remaining space ratio of the clothing care machine, 0 <Vs≤1;

[0071] The calculation formula of the comprehensive humidity is:

[0072]

[0073] Among them, Hshi is the comprehensive humidity, Ny is the number of clothes, HS is is the basic humidity of the is-th piece of clothing care, Vs is the remaining space ratio of the clothing care machine, 0 <Vs≤1;

[0074] The calculation formula for the comprehensive nursing time is:

[0075]

[0076] Among them, Tshi is the comprehensive care time, Ny is the number of clothes, HSih is the basic care time for the ihth piece of clothing, Vs is the remaining space ratio of the clothing care machine, 0 <Vs≤1。

[0077] Compared with the prior art, the beneficial effects of the present invention are that the space of a clothing care machine is divided into a cleaning area and a care area, the weight information of clothes is obtained, the remaining space ratio and the gap index of the clothing care machine are calculated, a clothing recognition model is established, the material characteristics of clothes including quantity, material, and textile density are identified, and the cleaning characteristics including dustiness and wrinkle amount are identified, the accommodation information of the guests staying at the hotel, clothing care setting parameters and clothing characteristics are obtained as the customer's preference data, and by analyzing the historical customer preference data, the default clothing care setting parameters in the past are used to match the best cleaning parameters through the material characteristics of the clothes, different cleaning parameters are calculated according to the clothing care setting parameters and the gap index of the clothing care machine, and the comprehensive care parameters are set according to the best care parameters of the clothing characteristics and the number of clothes and the remaining space ratio.

[0078] The present invention is based on; establishing a clothing recognition model to recognize clothes put into a clothing care machine. The clothing recognition model can quickly and accurately recognize the type of clothes, provide accurate care plans according to the type of clothes, obtain the accommodation information, clothing care setting parameters and clothing characteristics of the guests, and use them as the customer's preference data to effectively improve the accuracy of the clothing care plan. The different stages of clothing care are controlled respectively through the real-time remaining space ratio and gap index, clothing care setting parameters, cleaning characteristics and material characteristics of the clothing care machine. Not only does it achieve the mutual coordination of multiple clothing care methods and improve the efficiency and effect of clothing care, it also performs clothing care from multiple aspects and angles, improves the flexibility of clothing care, and effectively meets the needs of many different users in the hotel. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0080] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0081] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0082] Example:

[0083] See also Figure 1 , the present invention provides a technical solution:

[0084] A control method for hotel clothing care machine care, the specific steps include:

[0085] Step 1: Divide the space of the clothing care machine into a cleaning area and a care area. The cleaning area is used to place clothes to be cared for, and the care area is used to place clothes that have been cleaned. Obtain the weight information of the clothes in the cleaning area, and spray a certain amount of hot air into the space of the clothing care machine. Calculate the remaining space ratio and gap index of the clothing care machine based on the internal size of the clothing care machine and the time for detecting temperature changes.

[0086] By separating the cleaning and care processes, the laundry machine can clean the next piece of clothing while caring for one piece of clothing. This parallel processing method greatly shortens the overall processing time of a single piece of clothing and improves the overall operating efficiency. By performing different functions in this partitioned manner, the efficiency of clothing care is greatly improved. There is no need to wait for the clothing to be cared for before the next piece of clothing can be put in. Therefore, it is particularly suitable for hotel users who are in a hurry.

[0087] In this embodiment, the calculation formula for calculating the remaining space ratio and the gap index of the clothing care machine is:

[0088] The calculation formula of the remaining space ratio of the clothing care machine is:

[0089]

[0090] Among them, Vs is the remaining space ratio of the clothing care machine, Ly, Hy, and Wy are the length, width, and height of the interior of the clothing care machine, Vw(it) is the volume of hot air sprayed at time it, and Ts1 is the time for detecting temperature changes.

[0091] The remaining space ratio refers to the ratio of the remaining space inside the clothes care machine to the overall space of the clothes care machine. The larger the remaining space ratio, the smaller the space used for placing clothes inside and the larger the unused space. When caring for clothes, the temperature and humidity can be adjusted appropriately so that the temperature and humidity can quickly reach the surface of the clothes and care for the clothes. In the control method of clothes care by combining the characteristics of clothes, the number of clothes and the remaining space ratio, a decision-making basis is provided from the perspective of space.

[0092] The calculation formula of the gap index of the clothing care machine is:

[0093]

[0094] Wherein, Kq is the gap index of the clothing care machine, and Ts is the time for detecting temperature change when there is no clothes inside the clothing care machine.

[0095] The gap index reflects the size of the gaps between the clothes placed inside the clothing care machine. The larger the gap index, the more crowded the clothes placed inside. When cleaning clothes, the power of shaking power, air output power and steam size needs to be increased appropriately to penetrate the crowded gaps and reach the entire piece of clothing, thereby playing a role in comprehensive cleaning of the clothes. In setting the cleaning parameters in combination with the material characteristics of the clothes, the basic cleaning parameters of each stage and the gap index, the gap index provides a decision-making basis for the setting of the cleaning parameters in space.

[0096] Step 2: Establish a clothing recognition model, take photos of the clothes placed in the cleaning area, identify the clothing feature data, and establish a data set of clothing features together with the clothing weight. The clothing features include material features and cleaning features. The material features include quantity, material, and textile density. The cleaning features include dustiness and wrinkle amount.

[0097] CNN, or convolutional neural network, is a deep learning model specifically designed to process grid-structured data, such as image data. CNN plays a particularly prominent role in image processing because its unique architecture can effectively capture spatial and local information in images, enabling it to perform well in image recognition tasks.

[0098] CNN has a convolutional layer that can learn local features in an image. It is very effective in identifying complex patterns and materials. The convolution operation of CNN enables the parameters learned by the model to be shared across the entire image space, which greatly reduces the number of model parameters and improves training efficiency and the generalization ability of the model.

[0099] First, use a camera to take pictures of the clothes to be cared for. Each image should include the entire clothing or a significant part of it, ensuring the diversity of shooting data at different angles and lighting conditions. The collected images are classified and labeled, including material features such as cotton, polyester, silk, etc., and clean features such as dust level and wrinkle amount. The labeling of these features requires expert knowledge or standard equipment measurement. The images are standardized such as scaling and cropping to ensure that the image size of the input model is consistent, which helps to improve the efficiency of the training model. The processed images form a training image set for training the clothing recognition model.

[0100] In this embodiment, the clothing recognition model is based on a CNN neural network, including the establishment of a model and the training of the model;

[0101] The establishment of the model specifically includes a convolutional layer, an activation layer, a pooling layer and an output layer;

[0102] Convolutional layer: extracts features from the bounding box and label of the input image through convolution operation, and the calculation formula is:

[0103]

[0104] Where I is the input matrix, K is the convolution kernel, (xc, yc) is the coordinate of the output matrix, and K(ic, jc) is the value of the convolution kernel in the icth row and jcth column;

[0105] Activate the layer;

[0106] ReLU(xz)=max(0,xz)

[0107] Among them, xz is the coordinate of the output matrix of the convolutional layer output;

[0108] Pooling layer;

[0109] MaxPooling(xd,yd)=max{I(xc+ic,yc+jc)|ic,jc∈(3,3)}

[0110] Where I is the input matrix, MaxPooling(xd,yd) is the maximum pooling output;

[0111] Output layer;

[0112] y=W*xm+b

[0113] Among them, xm is the output of the pooling layer, y is the result of identifying the features of clothes in the image, W is the weight, and b is the bias parameter;

[0114] The model is trained by taking images containing clothing features as the input of the model and clothing feature data as the output of the model.

[0115] Step 3: Acquire the accommodation information of the guest, including the length of stay, purpose of stay, accommodation price, and clothing care setting parameters set by the customer, including clothing care mode and clothing care duration, and use the characteristics of the clothes being cared for as the customer's preference data. By analyzing the preference data of historical customers, the parameters selected by customers with the same accommodation information and clothing characteristics are used as the default clothing care setting parameters.

[0116] By analyzing accommodation information and the user's clothing care setting parameters, the customer's habits and preferences can be inferred, and services that better meet their needs can be provided. For example, customers on business trips may prefer fast and efficient clothing care, while customers on vacation may value comfortable and exquisite care. When enough preference data is accumulated, the hotel can customize the clothing care experience according to different guest categories. For example, long-term guests may want a different clothing care plan from short-term guests.

[0117] In this embodiment, the clothing protection modes include soft, standard, and enhanced, and the soft, standard, and enhanced modes are calculated by the difference in clothing protection mode ratios. The specific calculation formula is:

[0118] Standard*(1+α1)=Enhancement

[0119] Soft*(1+α1)=Standard

[0120] Among them, α1 is the mode ratio difference of clothing protection.

[0121] In this embodiment, the clothing care duration includes fast, regular, and fine, and the fast, regular, and fine are calculated by the difference in the duration ratio of clothing care. The specific calculation formula is:

[0122] Normal*(1+αβ2)=fine

[0123] Fast*(1+α2)=Normal

[0124] Among them, α2 is the difference in the duration ratio of clothing protection.

[0125] In this embodiment, the specific method of analyzing the historical customer preference data and using the parameters selected by customers with the same accommodation information and clothing characteristics as the default clothing setting parameters is as follows:

[0126] According to the same accommodation information and clothing features, the preference data in the historical data is counted, and through the statistics of the preference settings, the parameters with the most selected clothing care mode and clothing care duration are used as the default clothing care setting parameters.

[0127] Step 4: Divide clothing cleaning into two stages: removing dust and removing wrinkles. Obtain the optimal cleaning parameters for the two stages based on the material characteristics of different clothes. Match them with the characteristics of the clothes to be cared for and use them as the basic cleaning parameters for each stage. Depending on whether the clothing care setting parameters and the gap index between the clothing care machine are set, different cleaning parameters are used according to different cleaning stages during cleaning.

[0128] Clothes cleaning is divided into two stages: dust removal and wrinkle removal. In each stage, the optimal cleaning parameters are determined according to the material characteristics of the clothes, so that clothes care can be more refined. Since dust mainly accumulates on the surface of clothes, there is no need to apply steam to the clothes during the dust cleaning process. It can usually be removed through air flow or slight vibration. Wrinkles on clothes need to be processed through heat, steam or slight stretching, which usually requires relatively high temperature and humidity conditions. Therefore, different treatment methods are needed for wrinkles.

[0129] Clothes made of different materials have different requirements for cleaning methods. By obtaining material characteristics, the best cleaning parameters for each material can be accurately matched to ensure that each piece of clothing receives the most suitable care. Using cleaning parameters suitable for material characteristics avoids over-treatment of clothing, thereby reducing the risk of wear, deformation or damage. This is especially important for some delicate fabrics such as silk or wool.

[0130] Different stages of clothing care are controlled respectively through the real-time remaining space ratio and gap index of the clothing care machine, clothing care setting parameters, cleaning characteristics and material characteristics of clothes. This not only realizes the mutual coordination of multiple clothing care methods, improves the efficiency and effect of clothing care, but also carries out clothing care from multiple aspects and angles, improves the flexibility of clothing care, and effectively meets the needs of many different users in the hotel.

[0131] In this embodiment, the basic cleaning parameters of each stage include the basic dust removal air output power, basic dust removal shaking power, and basic dust removal time in the dust removal stage, and the basic wrinkle removal steam size, basic wrinkle removal shaking power, and basic wrinkle removal time in the wrinkle removal stage.

[0132] In this embodiment, the cleaning parameters of the dust removal stage include dust removal air output power, dust removal shaking power, and dust removal time;

[0133] The calculation formula of the wind output power is:

[0134]

[0135] Among them, Vcf is the dust removal air output power, Vcb is the dust removal basic wind speed power, Qh is the dust degree of clothes, Kq is the gap index of the clothes care machine, α1 is the mode ratio difference of clothes care, β1 is the default clothes care mode ratio, A is whether the clothes care mode is selected, -1≤α1≤1.

[0136] The time calculation formula for removing dust is:

[0137]

[0138] Among them, Tcc is the dust removal time, Tcb is the basic dust removal time, Qh is the dust degree of clothes, Kq is the gap index of the clothing care machine, α2 is the proportional difference of the clothing care time, β2 is the default clothing care time ratio, A is whether the clothing care mode is selected, -1≤α2≤1.

[0139] The calculation formula of the dust removal shaking power is:

[0140]

[0141] Among them, Wcd is the dust removal shaking power, Wcb is the dust removal basic shaking power, Qh is the dust degree of clothes, Kq is the gap index of the clothing care machine, α1 is the mode ratio difference of clothing care, β1 is the default clothing care mode ratio, A is whether the clothing care mode is selected, -1≤α1≤1.

[0142] In this embodiment, the cleaning parameters in the wrinkle removal stage include wrinkle removal shaking power, wrinkle removal steam volume, and wrinkle removal time;

[0143] The calculation formula of the wrinkle removal jitter power is:

[0144]

[0145] Where, Wzd is the anti-wrinkle shaking power, Wzb is the anti-wrinkle basic shaking power, Qz is the amount of clothing wrinkles, Kq is the gap index of the clothing care machine, α1 is the mode ratio difference of clothing care, β1 is the default clothing care mode ratio, A is whether the clothing care mode is selected, -1≤α1≤1;

[0146] The calculation formula of the wrinkle removal steam size is:

[0147]

[0148] Among them, Pzz is the size of wrinkle removal steam, Pzb is the size of wrinkle removal basic steam, Qz is the amount of clothing wrinkles, Kq is the gap index of the clothing care machine, α1 is the mode ratio difference of clothing care, β1 is the default clothing care mode ratio, A is whether the clothing care mode is selected, -1≤α1≤1;

[0149] The calculation formula for the wrinkle removal time is:

[0150]

[0151] Among them, Tpz is the wrinkle removal time, Tzb is the basic wrinkle removal time, Qz is the amount of wrinkles on the clothes, Kq is the gap index of the clothing care machine, α1 is the mode ratio difference of the clothing care, β2 is the default clothing care time ratio, A is whether the clothing care mode is selected, -1≤α2≤1.

[0152] Step 5: Control method of the maintenance area: transport the cleaned clothes to the care area, obtain the best care parameters for different clothing characteristics during care, and set basic care parameters by matching the clothing characteristics, and set comprehensive care parameters according to the number of clothes and the remaining space ratio.

[0153] When caring for clothes made of different materials, different care methods are also required. For example, silk and cotton clothes have completely different requirements for temperature and humidity. By obtaining these characteristics, care parameters can be customized to ensure that each piece of clothing receives precise care. The optimal care parameters can significantly improve the care effect, including sterilization, deodorization, etc., and ultimately keep the clothes in the best condition, which is very necessary for guests staying at the hotel.

[0154] When considering the care parameters, the effectiveness of the equipment is closely related to the number of clothes processed and the available space. The parameter settings based on quantity and space ensure the optimal efficiency in the use of resources such as steam, water, electricity, etc., to avoid waste, and also avoid excessive damage to clothes. Whether it is a single piece of clothing or multiple pieces of clothing, this parameter adjustment can balance the equipment's processing capacity and processing load, ensuring that the care effect of each piece of clothing is consistent, because in the process of clothing care, only considering the material of the clothes, not the volume of the clothes, often leads to over-care or under-care.

[0155] Through dynamic adjustment and personalized settings, the clothing care machine can adapt to the diverse clothing care needs and improve the efficiency and accuracy of clothing care. Through scientific and reasonable parameter settings, it can not only meet the strict requirements of high-end hotels on the quality of customer clothing care, but also achieve maximum savings in resource utilization.

[0156] In this embodiment, the basic nursing parameters include basic temperature, basic humidity and basic nursing duration;

[0157] The comprehensive nursing parameters include comprehensive temperature, comprehensive humidity and comprehensive nursing duration;

[0158] The calculation formula of the comprehensive temperature is:

[0159]

[0160] Among them, Hwen is the comprehensive temperature, Ny is the number of clothes, HWi y is the basic temperature of the iy-th piece of clothing care, Vs is the remaining space ratio of the clothing care machine, 0 <Vs≤1;

[0161] The calculation formula of the comprehensive humidity is:

[0162]

[0163] Among them, Hshi is the comprehensive humidity, Ny is the number of clothes, HSis is the basic humidity of the is-th piece of clothing, Vs is the remaining space ratio of the clothing care machine, 0 <Vs≤1;

[0164] The calculation formula for the comprehensive nursing time is:

[0165]

[0166] Among them, Tshi is the comprehensive care time, Ny is the number of clothes, HSih is the basic care time for the ihth piece of clothing, Vs is the remaining space ratio of the clothing care machine, 0 <Vs≤1。

[0167] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0168] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0169] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0170] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A control method for hotel clothing care machine care, characterized in that: The specific steps include: Step 1: Divide the space of the clothing care machine into a cleaning area and a care area. The cleaning area is used to place clothes to be cared for, and the care area is used to place clothes that have been cleaned. Obtain the weight information of the clothes in the cleaning area, spray a certain amount of hot air into the space of the clothing care machine, and calculate the remaining space ratio and gap index of the clothing care machine according to the internal size of the clothing care machine and the time of detecting temperature changes; Step 2: Establish a clothing recognition model, take photos of the clothes put into the cleaning area, identify the clothing feature data, and establish a data set of clothing features together with the weight of the clothes. The clothing features include material features and cleaning features. The material features include quantity, material, and textile density. The cleaning features include dustiness and wrinkle amount. Step 3: Acquiring the accommodation information of the guest, including the length of stay, purpose of stay, accommodation price, and the clothing care setting parameters set by the customer, including clothing care mode and clothing care duration, and taking the characteristics of the clothes being cared for as the customer's preference data, and by analyzing the preference data of historical customers, taking the parameters selected by customers with the same accommodation information and clothing characteristics as the default clothing care setting parameters; Step 4: Divide the clothes cleaning into two stages: dust removal and wrinkle removal. Obtain the optimal cleaning parameters for different clothes material characteristics in the two stages. Match them with the characteristics of the clothes to be cared for and use them as the basic cleaning parameters for each stage. Depending on whether the clothes care setting parameters and the gap index of the clothes care machine are set, different cleaning parameters are used according to different cleaning stages during cleaning. Step 5: Control method of the maintenance area: transport the cleaned clothes to the care area, obtain the best care parameters for different clothing characteristics during care, and set basic care parameters by matching the clothing characteristics, and set comprehensive care parameters according to the number of clothes and the remaining space ratio.

2. A control method for hotel clothing care machine care according to claim 1, characterized in that: The calculation formula for calculating the remaining space ratio and the gap index of the clothing care machine is: The calculation formula of the remaining space ratio of the clothing care machine is: Wherein, Vs is the remaining space ratio of the clothes care machine, Ly, Hy, and Wy are the length, width, and height of the inside of the clothes care machine, Vw(it) is the volume of hot air sprayed at time it, and Ts1 is the time for detecting temperature changes; The calculation formula of the gap index of the clothing care machine is: Wherein, Kq is the gap index of the clothing care machine, and Ts is the time for detecting temperature change when there is no clothes inside the clothing care machine.

3. A control method for hotel clothing care machine care according to claim 1, characterized in that: The clothing recognition model is based on a CNN neural network, including model establishment and model training; The establishment of the model specifically includes a convolutional layer, an activation layer, a pooling layer and an output layer; Convolutional layer: extracts features from the bounding box and label of the input image through convolution operation, and the calculation formula is: Where I is the input matrix, K is the convolution kernel, (xc, yc) is the coordinate of the output matrix, and K(ic, jc) is the value of the convolution kernel in the icth row and jcth column; Activate the layer; ReLU(xz)=max(0,xz) Among them, xz is the coordinate of the output matrix of the convolutional layer output; Pooling layer; MaxPooling(xd,yd)=max{I(xc+ic,yc+jc)|ic,jc∈(3,3)} Where I is the input matrix, MaxPooling(xd,yd) is the maximum pooling output; Output layer; y=W*xm+b Among them, xm is the output of the pooling layer, y is the result of identifying the features of clothes in the image, W is the weight, and b is the bias parameter; The model is trained by taking images containing clothing features as the input of the model and clothing feature data as the output of the model.

4. A control method for hotel clothing care machine care according to claim 1, characterized in that: The clothing care modes include soft, standard, and enhanced, and the soft, standard, and enhanced modes are calculated by the difference in clothing care mode ratios. The specific calculation formula is: Standard*(1+α1)=Enhancement Soft*(1+α1)=Standard Among them, α1 is the difference in the mode ratio of clothing protection; The clothing care duration includes fast, regular and fine, and the fast, regular and fine are calculated by the difference in the duration of clothing care. The specific calculation formula is: Normal*(1+α2)=fine Fast*(1+α2)=Normal Among them, α2 is the difference in the duration ratio of clothing protection.

5. The control method for hotel clothing care machine according to claim 1, characterized in that: The specific method of analyzing the historical customer preference data and using the parameters selected by customers with the same accommodation information and clothing characteristics as the default clothing setting parameters is as follows: According to the same accommodation information and clothing features, the preference data in the historical data is counted, and through the statistics of the preference settings, the parameters with the most selected clothing care mode and clothing care duration are used as the default clothing care setting parameters.

6. A control method for hotel clothing care machine care according to claim 1, characterized in that: The basic cleaning parameters of each stage include the basic dust removal air output power, basic dust removal shaking power, and basic dust removal time in the dust removal stage, and the basic wrinkle removal steam size, basic wrinkle removal shaking power, and basic wrinkle removal time in the wrinkle removal stage.

7. A control method for hotel clothing care machine care according to claim 6, characterized in that: The cleaning parameters of the dust removal stage include dust removal air output power, dust removal shaking power, and dust removal time; The calculation formula of the wind output power is: Among them, Vcf is the dust removal air output power, Vcb is the dust removal basic wind speed power, Qh is the dust degree of clothes, Kq is the gap index of the clothes care machine, α1 is the mode ratio difference of clothes care, β1 is the default clothes care mode ratio, A is whether the clothes care mode is selected, -1≤α1≤1; The time calculation formula for removing dust is: Among them, Tcc is the dust removal time, Tcb is the basic dust removal time, Qh is the dust degree of clothes, Kq is the gap index of the clothes care machine, α2 is the proportional difference of the clothes care time, β2 is the default clothes care time ratio, A is whether the clothes care mode is selected, -1≤α2≤1; The calculation formula of the dust removal shaking power is: Among them, Wcd is the dust removal shaking power, Wcb is the dust removal basic shaking power, Qh is the dust degree of clothes, Kq is the gap index of the clothing care machine, α1 is the mode ratio difference of clothing care, β1 is the default clothing care mode ratio, A is whether the clothing care mode is selected, -1≤α1≤1.

8. A control method for hotel clothing care machine care according to claim 6, characterized in that: The cleaning parameters in the wrinkle removal stage include wrinkle removal shaking power, wrinkle removal steam volume, and wrinkle removal time; The calculation formula of the wrinkle removal jitter power is: Where, Wzd is the anti-wrinkle shaking power, Wzb is the anti-wrinkle basic shaking power, Qz is the wrinkle degree of clothes, Kq is the gap index of the clothes care machine, α1 is the mode ratio difference of clothes care, β1 is the default clothes care mode ratio, A is whether the clothes care mode is selected, -1≤α1≤1; The calculation formula of the wrinkle removal steam size is: Among them, Pzz is the size of wrinkle removal steam, Pzb is the size of wrinkle removal basic steam, Qz is the wrinkle degree of clothes, Kq is the gap index of the clothes care machine, α1 is the mode ratio difference of clothes care, β1 is the default clothes care mode ratio, A is whether the clothes care mode is selected, -1≤α1≤1; The calculation formula for the wrinkle removal time is: Among them, Tpz is the wrinkle removal time, Tzb is the basic wrinkle removal time, Qz is the wrinkle degree of clothes, Kq is the gap index of the clothing care machine, α1 is the mode ratio difference of clothing care, β2 is the default clothing care time ratio, A is whether the clothing care mode is selected, -1≤α2≤1.

9. A control method for hotel clothing care machine care according to claim 1, characterized in that: The basic nursing parameters include basic temperature, basic humidity and basic nursing duration; The comprehensive nursing parameters include comprehensive temperature, comprehensive humidity and comprehensive nursing duration; The calculation formula of the comprehensive temperature is: Among them, Hwen is the comprehensive temperature, Ny is the number of clothes, is the basic temperature of the iy-th piece of clothing care, Vs is the remaining space ratio of the clothing care machine, 0 <Vs≤1; The calculation formula of the comprehensive humidity is: Among them, Hshi is the comprehensive humidity, Ny is the number of clothes, HSi s为 The basic humidity of the is-th piece of clothing care, Vs is the remaining space ratio of the clothing care machine, 0 <Vs≤1; The calculation formula for the comprehensive nursing time is: Among them, Tshi is the comprehensive care time, Ny is the number of clothes, HS ih is the basic care time for the ihth piece of clothing, Vs is the remaining space ratio of the clothing care machine, 0 <Vs≤1。

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