Intelligent clothes nursing machine based on Internet of Things

Abstract

The invention provides an intelligent clothes nursing machine based on the Internet of Things, and relates to the technical field of intelligent clothes nursing machines, a clothes recognition module is used for recognizing the characteristics of clothes put into the nursing machine and numbering the clothes, and a data acquisition module is used for judging the contact environment data and the wrinkle degree of the clothes; the system calculates a tidiness coefficient and an adhesion coefficient according to the environment data and the wrinkle degree of the clothes, monitors clothes protection data and an equipment state through a clothes protection detection module and sends the data and the state to a control execution module, and calculates the clothes protection cost of each piece of clothes with different clothes protection numbers according to an energy consumption detection module. A user can conveniently select the number of clothes protectors each time according to the clothes protector cost, optimal nursing parameters of the clothes are obtained through the control execution module, and parameters of a nursing strategy are formulated in combination with a neatness coefficient, an adhesion coefficient, clothes characteristics and nursing machine parameters of the clothes.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent clothing care machines, and in particular to an intelligent clothing care machine based on the Internet of Things. Background Art

[0002] With the advancement of technology and the improvement of people's living standards, traditional clothing cleaning and care methods can no longer meet the needs of modern families for convenience, efficiency and refinement. In daily life, many people face the problem of cumbersome and time-consuming clothing care and clothing damage caused by improper manual operation. Therefore, it is of great significance to improve the intelligence of clothing care machines, realize the real-time collection and sharing of clothing care machine information through the interconnection between the Internet of Things and clothing care machines, and make intelligent adjustments and optimization operations.

[0003] In the prior art, the publication number CN118727408 discloses a control method, device, equipment and medium for a clothing care machine, including determining the 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 realizing 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 only increases the plasticity of the clothes, cannot effectively remove the dust and odor of the clothes, and cannot meet the current demand for intelligence.

[0004] The above information disclosed in the Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may include 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 purpose of the present invention is to provide an intelligent clothing care machine based on the Internet of Things to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: An intelligent clothing care machine based on the Internet of Things, comprising: A clothing recognition module, which is used to establish a clothing recognition model based on a convolutional neural network, by identifying clothing features of the clothing put into the nursing machine, including style, material, fabric density, and clothing size. The styles include outerwear and innerwear, and numbering the clothing according to the clothing features; A data collection module, which is used to obtain historical weather data of the area where the user is located, determine the environmental data that the clothes are exposed to, including dust values ​​and humidity values, obtain the distance, number of steps and duration of the user's travel, determine the degree of wrinkles of the clothes, and calculate the neatness coefficient and adhesion coefficient based on the environmental data and the degree of wrinkles of the clothes; A clothing care detection module, which is installed inside the nursing machine and is used to obtain monitoring data, including a temperature sensor, a humidity sensor, a vibration sensor and a noise sensor, and is used to monitor clothing care data and device status and send them to the control execution module; An energy consumption detection module is used to collect the energy consumption of the nursing machine each time the clothes are cared for, and calculate the cost of caring for each piece of clothing at different numbers of clothes according to the energy consumption and the number of clothes, so that the user can choose the number of clothes to be cared for each time according to the cost of clothes care; The control execution module is used to obtain the best care parameters of the clothes according to the clothes characteristics of each piece of clothes number, and formulate the parameters of the care strategy in combination with the neatness coefficient, adhesion coefficient, clothes characteristics and care machine parameters of the clothes.

[0007] Furthermore, the specific steps of establishing the clothing recognition model based on convolutional neural network are: 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: in, is the input matrix, is the convolution kernel, are the coordinates of the output matrix, The convolution kernel is Row and The value of the column; Activate the layer; in, The coordinates of the output matrix of the convolutional layer output; Pooling layer; in, is the input matrix, is the maximum pooling output; Output layer; in, is the output of the pooling layer, is the result of identifying the features of clothes in the image. is the weight, 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.

[0008] Furthermore, the historical weather data includes temperature, wind speed, humidity, and air quality index; The specific steps of determining the environmental data that the clothes come into contact with are: The calculation formula of the dust value is: in, is the dust value, is the wind speed, is the dust correction coefficient, which is determined according to the material of the clothes. is the air quality index; The calculation formula of the humidity value is: in, is the humidity value, is the air humidity, is the air temperature, It is the humidity correction coefficient, which is determined according to the material of the clothes.

[0009] Furthermore, the judgment logic of the wrinkle degree is as follows: the user's walking speed is calculated by the number of steps in a period of time, and compared with the walking speed threshold to judge the intensity of the travel, and the wrinkle degree of the clothes is calculated according to the distance exceeding the walking speed threshold and the distance not exceeding the walking speed threshold. The specific calculation formula is as follows; The calculation formula of the walking speed is: in, is the pace, For the The number of steps in a time period, For the The duration of the time period; when When walking, it is judged as vigorous walking; in, is the pace threshold; The calculation formula of the wrinkle degree is: in, The wrinkle degree of the clothes, is the pace exceeding the pace threshold, is the total number of time periods exceeding the pace threshold, For distances exceeding the pace threshold, is the total number of time periods that did not exceed the pace threshold, is the distance that does not exceed the pace threshold. is the pace that does not exceed the pace threshold.

[0010] Furthermore, the calculation formula of the neatness coefficient of the clothes is: in, is the neatness coefficient, The wrinkle degree of the clothes, is the dust value; The calculation formula of the adhesion coefficient of the clothes is: in, is the adhesion coefficient, The wrinkle degree of the clothes, is the humidity value.

[0011] Furthermore, the nursing machine parameters include vibration motor power, steam size; The optimal care parameters include optimal care parameters for deodorization, optimal care parameters for dust removal, and optimal care temperature and humidity; The care strategy described includes dust removal, odor removal, and environmental maintenance.

[0012] Furthermore, the dust removal care strategy is: by calculating the dust removal strength of each piece of clothing, according to the number of clothes in the care machine, the area of ​​clothes, and the power of the vibration motor, the dust removal time is calculated; in, For dust removal strength, is the neatness coefficient, Optimal care parameters for dust removal of clothes, is the clothing style adhesion value, which is 0.8 for outerwear and 0.2 for innerwear; The dust removal time calculation formula is: in, For dust removal time, For dust removal strength, For nursing care The size of the garment, the density of the fabric, is the number of clothes in the care machine, It is the vibration motor power of the nursing machine.

[0013] Furthermore, the specific strategy of deodorization is: by calculating the deodorization strength of each piece of clothing, according to the number of clothes in the care machine, the area of ​​clothes, and the steam power, the deodorization time is calculated; The deodorization strength calculation formula is: in, For deodorizing effect, is the adhesion coefficient, The best care parameters for deodorizing clothes, is the clothing style adhesion value, which is 0.8 for outerwear and 0.2 for innerwear; The deodorization time calculation formula is: in, For deodorization time, For deodorizing effect, For nursing care The size of the garment, the density of the fabric, is the number of clothes in the care machine, Steam size for nursing machine.

[0014] Furthermore, the calculation method of the nursing temperature value is: in, To care for temperature, is the maximum value of the optimal nursing parameter for temperature in clothing care, It is the minimum value of the optimal nursing parameter for temperature in clothing care; The calculation method of the nursing humidity value is: in, To care for humidity, It is the maximum value of the best care parameter of humidity in clothing care. It is the minimum value of the optimal care parameter for humidity in clothing care.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: the clothing characteristics of the clothing put into the care machine are identified through the clothing identification module, the clothing is numbered, the environmental data and wrinkle degree of the clothing contact are judged according to the data acquisition module, and the neatness coefficient and the adhesion coefficient are calculated according to the environmental data and the wrinkle degree of the clothing, the clothing care data and the equipment status are monitored by the clothing care detection module and sent to the control execution module, the clothing care cost of each piece of clothing at different clothing care quantities is calculated according to the energy consumption detection module, so that the user can choose the number of clothing care each time according to the clothing care cost, the optimal clothing care parameters are obtained through the control execution module, and the parameters of the care strategy are formulated in combination with the neatness coefficient, adhesion coefficient, clothing characteristics and care machine parameters of the clothing; The present invention identifies the characteristics of clothes put into the care machine through the clothes recognition module, obtains the best care parameters of the clothes according to the characteristics of the clothes, can improve the accuracy and efficiency of clothes care, can realize the personalization, precision and intelligence of clothes care, and is used to judge the environmental data and wrinkle degree of the clothes according to the data acquisition module, and calculates the neatness coefficient and adhesion coefficient according to the environmental data and the wrinkle degree of the clothes. By obtaining the exposure of the clothes in different environments, the pollution of the clothes by the pollutants of the clothes can be quantified, and the care plan can be adjusted according to different environmental conditions. The clothes care cost of each piece of clothes at different clothes care quantities is calculated according to the energy consumption detection module, which is convenient for users to choose the number of clothes care each time according to the clothes care cost. Users can remotely control and monitor through smart devices without going to the site in person to perform complex settings and operations, and can save energy consumption at the same time, making consumers' daily life more convenient and environmentally friendly. The best care parameters of clothes are obtained by controlling the execution module, and the parameters of the care strategy are formulated in combination with the neatness coefficient, adhesion coefficient, clothes characteristics, and care machine parameters of the clothes, so as to bring people a new clothing care experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the system structure of the production platform in the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] 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.

[0019] Example

[0020] See also Figure 1 , the present invention provides a technical solution: An intelligent clothing care machine based on the Internet of Things includes a clothing recognition module, a data acquisition module, a clothing care detection module, an energy consumption detection module and a control execution module, wherein: The clothing recognition module is used to establish a clothing recognition model based on a convolutional neural network, by identifying clothing features of the clothes placed in the care machine, including style, material, fabric density, and clothing size. The styles include outer wear and inner wear, and the clothes are numbered according to the clothing features.

[0021] CNN, or convolutional neural network, is a deep learning model specifically designed to process data with grid structures, 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.

[0022] 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.

[0023] First, use a camera to take pictures of the clothes to be cared for. Each image should include the entire clothing or its significant parts, 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 cleaning 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 the consistency of the image size of the input model, which helps to improve the efficiency of the training model. The processed images form a training image set for training the clothing recognition model.

[0024] In this embodiment, the specific steps of establishing a clothing recognition model based on a convolutional neural network are: 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: in, is the input matrix, is the convolution kernel, are the coordinates of the output matrix, The convolution kernel is Row and The value of the column; Activate the layer; in, The coordinates of the output matrix of the convolutional layer output; Pooling layer; in, is the input matrix, is the maximum pooling output; Output layer; in, is the output of the pooling layer, is the result of identifying the features of clothes in the image. is the weight, 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.

[0025] Clothes are divided into outerwear and innerwear. As outerwear is usually exposed to the environment, it is more likely to be stained with dust and pollutants, while innerwear is relatively less affected by external pollution. In contrast, innerwear is worn close to the body and is more likely to be stained with more sweat and produce odor. Based on this difference, precise care can be carried out to improve cleaning efficiency and save resources. The frequency and method of washing innerwear and outerwear are differentiated, which can reduce the wear of clothing fibers caused by unnecessary washing, thereby extending the service life of clothing.

[0026] The data acquisition module is used to obtain historical weather data of the user's area, determine the environmental data that the clothes are in contact with, including dust values ​​and humidity values, obtain the user's travel distance, number of steps and duration, determine the degree of wrinkles on the clothes, and calculate the neatness coefficient and adhesion coefficient based on the environmental data and the degree of wrinkles on the clothes.

[0027] High temperatures may cause users to sweat more, which in turn may contaminate the part of clothing that comes into contact with the skin. Warm and humid environments are more likely to cause sweat stains and odors. Higher wind speeds may carry more dust in the air, causing outerwear to be stained with more dust. High humidity increases the water absorption of clothing, making it easy to absorb dirt and particles in the air, which in turn increases dust deposition. Higher air pollution levels mean more particulate matter, dust, and smoke, which will adhere to outerwear, making it easier for clothing to get dirty.

[0028] In this embodiment, the historical weather data includes temperature, wind speed, humidity, and air quality index; The specific steps of determining the environmental data that the clothes come into contact with are: The calculation formula of the dust value is: in, is the dust value, is the wind speed, is the dust correction coefficient, which is determined according to the material of the clothes. The air quality index.

[0029] The dust value reflects the degree of dust that clothes are contaminated with in the environment. The higher the dust value, the more dust the clothes are contaminated with.

[0030] The calculation formula of the humidity value is: in, is the humidity value, is the air humidity, is the air temperature, It is the humidity correction coefficient, which is determined according to the material of the clothes.

[0031] The dust value reflects the degree of moisture absorbed by the clothes in the environment. The higher the humidity value, the more moisture the clothes absorb.

[0032] In this embodiment, the judgment logic of the wrinkle degree is as follows: the user's walking speed is calculated by the number of steps in a period of time, and compared with the walking speed threshold to judge the intensity of the travel, and the wrinkle degree of the clothes is calculated according to the distance exceeding the walking speed threshold and the distance not exceeding the walking speed threshold. The specific calculation formula is as follows; The calculation formula of the walking speed is: in, is the pace, For the The number of steps in a time period, For the The duration of the time period; when When walking, it is judged as vigorous walking; in, is the pace threshold; The calculation formula of the wrinkle degree is: in, The wrinkle degree of the clothes, is the pace exceeding the pace threshold, is the total number of time periods exceeding the pace threshold, For distances exceeding the pace threshold, is the total number of time periods that did not exceed the pace threshold, is the distance that does not exceed the pace threshold. is the pace that does not exceed the pace threshold.

[0033] The wrinkle degree of clothes reflects the wrinkles caused by intense movement when traveling. Textile materials are prone to wrinkles because they lack the chemical structure to adapt and recover from deformation. When the body moves, the constantly changing posture during the movement causes the clothes to be subjected to forces in different directions. The surface of the clothes is unevenly stressed, resulting in stretching and compression. Especially at the joints, such as the knees and elbows, repeated bending and stretching make these areas prone to wrinkles.

[0034] In this embodiment, the calculation formula of the neatness coefficient of the clothes is: in, is the neatness coefficient, The wrinkle degree of the clothes, is the dust value; The neatness coefficient reflects the cleanliness of clothes. The more wrinkles and dust the clothes have, the more difficult it is to clean.

[0035] The calculation formula of the adhesion coefficient of the clothes is: in, is the adhesion coefficient, The wrinkle degree of the clothes, is the humidity value; The cleanliness coefficient reflects the degree of moisture and odor on clothes. The more wrinkles, moisture and odor the clothes have, the more difficult it is to clean.

[0036] The clothing care detection module is installed inside the nursing machine and is used to obtain monitoring data. It includes a temperature sensor, a humidity sensor, a vibration sensor and a noise sensor, which are used to monitor clothing care data and device status and send them to the control execution module.

[0037] Temperature sensors ensure that the temperature of the clothes is within the appropriate range during the care process to prevent the clothes from being damaged by overheating. In addition, different types of fabrics require different temperatures to achieve the best care effect. The right humidity helps to remove wrinkles in clothes and also prevents mold growth caused by excessive humidity. The data from the humidity sensor can help regulate the degree of drying of clothes to ensure that the clothes are not too dry or too wet. Excessive vibration may mean that the equipment is not operating properly or the clothes are not positioned properly in the drum. Vibration data is used to determine the operating status of the equipment and can help prevent functional failure or equipment damage. Abnormal noise may indicate that the equipment is malfunctioning, such as worn parts or improper installation. By monitoring noise changes, the system can provide early warning and remind users to perform maintenance. By collecting and analyzing data in real time, users can remotely control and monitor through smart devices, without having to go to the site in person for complex settings and operations, and can make timely adjustments to environmental changes, such as adjusting temperature and humidity, to optimize the clothing treatment effect.

[0038] The energy consumption detection module is used to collect the energy consumption of the care machine each time the clothes are cared for, and calculate the clothing care cost of each piece of clothing at different clothing care quantities based on the energy consumption and the clothing quantity, so that the user can choose the clothing care quantity each time based on the clothing care cost.

[0039] By combining energy consumption data with the number of clothes processed, the system can calculate the care cost of each piece of clothing, helping users to clearly understand the energy consumption and economic cost of each operation, and providing a reference for choosing the most economical and efficient usage mode.

[0040] In this embodiment, the steps for calculating the clothing care cost are: , For the The average cost per clothing care session, For the The number of clothes in the second clothing care, For the Energy consumption during the second clothing care; , The number of clothes is The average stable cost of For the The number of clothing care is The average cost of clothing care, The number of clothes is The number of times, is a positive integer; , The number of clothes to be cared for when the cost of caring for each piece of clothing is the lowest.

[0041] The control execution module is used to obtain the best care parameters of the clothes according to the clothes characteristics of each piece of clothes number, and formulate the parameters of the care strategy in combination with the neatness coefficient, adhesion coefficient, clothes characteristics and care machine parameters of the clothes.

[0042] In this embodiment, the nursing machine parameters include the vibration motor power and the steam size; The optimal care parameters include the optimal care parameters for deodorization, the optimal care parameters for dust removal, and the optimal care temperature and humidity.

[0043] Develop care strategies including dust removal, odor removal, and environmental maintenance.

[0044] In this embodiment, the dust removal care strategy is: calculate the dust removal strength of each piece of clothing through the neatness coefficient and the best care parameters for clothes dust removal, and calculate the dust removal time according to the number of clothes in the care machine, the area of ​​clothes, and the power of the vibration motor; in, For dust removal strength, is the neatness coefficient, Optimal care parameters for dust removal of clothes, is the clothing style adhesion value, which is 0.8 for outerwear and 0.2 for innerwear; The dust removal time calculation formula is: in, For dust removal time, For dust removal strength, For nursing care The size of the garment, the density of the fabric, is the number of clothes in the care machine, It is the vibration motor power of the nursing machine.

[0045] The dust removal intensity reflects the ability required to remove the dust accumulated on a piece of clothing. When you want to judge the time required for dust removal based on the power of the nursing machine's vibration motor, you also need to consider the number of clothes and the size of each piece of clothing, and make comprehensive considerations. During dust removal, the motor needs to be shaken to make the dust on the clothes fall off, and at the same time, keep blowing to take away the fallen dust.

[0046] In this embodiment, the specific strategy of deodorization is: by calculating the deodorization strength of each piece of clothing, according to the number of clothes in the care machine, the area of ​​clothes, and the steam power, the deodorization time is calculated; The deodorization strength calculation formula is: in, For deodorizing effect, is the adhesion coefficient, The best care parameters for deodorizing clothes, is the clothing style adhesion value, which is 0.8 for outerwear and 0.2 for innerwear; The deodorization time calculation formula is: in, For deodorization time, For deodorizing effect, For nursing care The size of the garment, the density of the fabric, is the number of clothes in the care machine, Steam size for nursing machine.

[0047] The deodorization strength reflects the ability required to remove the accumulated odor and sweat stains on a piece of clothing. When you want to judge the time required for deodorization based on the size of the steam in the care machine, you also need to consider the number of clothes and the size of each piece of clothing, and make comprehensive considerations. When deodorizing, steam needs to be sprayed continuously to soften the folds on the clothes, and the odor molecules are carried away by the steam. At the same time, keep blowing to take away the steam carrying the odor and keep the air circulating inside the clothing care machine.

[0048] In this embodiment, the calculation method of the nursing temperature value is: in, To care for temperature, is the maximum value of the optimal nursing parameter for temperature in clothing care, It is the minimum value of the optimal nursing parameter for temperature in clothing care; The calculation method of the nursing humidity value is: in, To care for humidity, It is the maximum value of the best care parameter of humidity in clothing care. It is the minimum value of the optimal care parameter for humidity in clothing care.

[0049] 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.

[0050] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using 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.

[0051] 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.

[0052] 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. An intelligent clothing care machine based on the Internet of Things, characterized in that: include: A clothing recognition module, which is used to establish a clothing recognition model based on a convolutional neural network, by identifying clothing features of the clothing put into the nursing machine, including style, material, fabric density, and clothing size. The styles include outerwear and innerwear, and numbering the clothing according to the clothing features; A data collection module, which is used to obtain historical weather data of the area where the user is located, determine the environmental data that the clothes are exposed to, including dust values ​​and humidity values, obtain the distance, number of steps and duration of the user's travel, determine the degree of wrinkles of the clothes, and calculate the neatness coefficient and adhesion coefficient based on the environmental data and the degree of wrinkles of the clothes; A clothing care detection module, which is installed inside the nursing machine and is used to obtain monitoring data, including a temperature sensor, a humidity sensor, a vibration sensor and a noise sensor, and is used to monitor clothing care data and device status and send them to the control execution module; An energy consumption detection module is used to collect the energy consumption of the nursing machine each time the clothes are cared for, and calculate the cost of caring for each piece of clothing at different numbers of clothes according to the energy consumption and the number of clothes, so that the user can choose the number of clothes to be cared for each time according to the cost of clothes care; The control execution module is used to obtain the best care parameters of the clothes according to the clothes characteristics of each piece of clothes number, and formulate the parameters of the care strategy in combination with the neatness coefficient, adhesion coefficient, clothes characteristics and care machine parameters of the clothes.

2. The intelligent clothing care machine based on the Internet of Things according to claim 1, characterized in that: The specific steps of establishing a clothing recognition model based on a convolutional neural network are: 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: in, is the input matrix, is the convolution kernel, are the coordinates of the output matrix, The convolution kernel is Row and The value of the column; Activate the layer; in, The coordinates of the output matrix of the convolutional layer output; Pooling layer; in, is the input matrix, is the maximum pooling output; Output layer; in, is the output of the pooling layer, is the result of identifying the features of clothes in the image. is the weight, 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.

3. The intelligent clothing care machine based on the Internet of Things according to claim 1 is characterized in that: The historical weather data includes temperature, wind speed, humidity, and air quality index; The specific steps of determining the environmental data that the clothes come into contact with are: The calculation formula of the dust value is: in, is the dust value, is the wind speed, is the dust correction coefficient, which is determined according to the material of the clothes. is the air quality index; The calculation formula of the humidity value is: in, is the humidity value, is the air humidity, is the air temperature, It is the humidity correction coefficient, which is determined according to the material of the clothes.

4. The intelligent clothing care machine based on the Internet of Things according to claim 3 is characterized in that: The logic for judging the wrinkle degree is as follows: the user's walking speed is calculated by the number of steps in a period of time, and compared with the walking speed threshold to judge the intensity of the trip, and the wrinkle degree of the clothes is calculated according to the distance that exceeds the walking speed threshold and the distance that does not exceed the walking speed threshold. The specific calculation formula is as follows; The calculation formula of the walking speed is: in, is the pace, For the The number of steps in a time period, For the The duration of the time period; when When walking, it is judged as vigorous walking; in, is the pace threshold; The calculation formula of the wrinkle degree is: in, The wrinkle degree of the clothes, is the pace exceeding the pace threshold, is the total number of time periods exceeding the pace threshold, For distances exceeding the pace threshold, is the total number of time periods that did not exceed the pace threshold, For the distance that does not exceed the pace threshold, is the pace that does not exceed the pace threshold.

5. The intelligent clothing care machine based on the Internet of Things according to claim 4 is characterized in that: The calculation formula of the neatness coefficient of the clothes is: in, is the neatness coefficient, The wrinkle degree of the clothes, is the dust value; The calculation formula of the adhesion coefficient of the clothes is: in, is the adhesion coefficient, The wrinkle degree of the clothes, is the humidity value.

6. The intelligent clothing care machine based on the Internet of Things according to claim 5 is characterized in that: The parameters of the nursing machine include the power of the vibration motor and the size of the steam; The optimal care parameters include optimal care parameters for deodorization, optimal care parameters for dust removal, and optimal care temperature and humidity; The care strategy described includes dust removal, odor removal, and environmental maintenance.

7. The intelligent clothing care machine based on the Internet of Things according to claim 6 is characterized by: The dust removal care strategy is: by calculating the dust removal strength of each piece of clothing, according to the number of clothes in the care machine, the area of ​​clothes, and the power of the vibration motor, the dust removal time is calculated; in, For dust removal strength, is the neatness coefficient, Optimal care parameters for dust removal of clothes, is the clothing style adhesion value, which is 0.8 for outerwear and 0.2 for innerwear; The dust removal time calculation formula is: in, For dust removal time, For dust removal strength, For nursing care The size of the garment, the density of the fabric, is the number of clothes in the care machine, It is the vibration motor power of the nursing machine.

8. The intelligent clothing care machine based on the Internet of Things according to claim 6, characterized in that: The specific strategy of deodorization is: by calculating the deodorization strength of each piece of clothing, according to the number of clothes in the care machine, the area of ​​clothes, and the steam power, the deodorization time is calculated; The deodorization strength calculation formula is: in, For deodorizing effect, is the adhesion coefficient, The best care parameters for deodorizing clothes, is the clothing style adhesion value, which is 0.8 for outerwear and 0.2 for innerwear; The deodorization time calculation formula is: in, For deodorization time, For deodorizing effect, For nursing care The size of the garment, the density of the fabric, is the number of clothes in the care machine, Steam size for nursing machine.

9. The intelligent clothing care machine based on the Internet of Things according to claim 6, characterized in that: The calculation method of the nursing temperature value is: in, To care for temperature, is the maximum value of the optimal nursing parameter for temperature in clothing care, It is the minimum value of the optimal nursing parameter for temperature in clothing care; The calculation method of the nursing humidity value is: in, To care for humidity, It is the maximum value of the best care parameter of humidity in clothing care. It is the minimum value of the optimal care parameter for humidity in clothing care.

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