An electric clothes drying machine intelligent control system based on Internet of Things

Abstract

The present application relates to the technical field of intelligent control of electric clothes drying machine, and discloses an intelligent control system of electric clothes drying machine based on Internet of Things, which comprises an airing horizontal rod information collection and analysis module, an airing horizontal rod loosening abnormality feedback module, a bacteria information collection and analysis module, a disinfection time length control module, a clothes information collection and analysis module, a drying information control module and a cloud database; the present application feeds back the loosening abnormality of the airing horizontal rod, controls the disinfection time length, controls the drying temperature and drying time length by combining the structural information, bacteria information and clothes information of each airing horizontal rod, improves the operation safety of the electric clothes drying machine, the sterilization and disinfection effect and the rationality of the drying temperature and drying time length setting, thereby avoiding the damage to the airing clothes caused by excessive heating, reducing the economic loss of the user, and further meeting the intelligentization and convenience demand of the user for the electric clothes drying machine.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for electric clothes drying racks, and more specifically, to an intelligent control system for an electric clothes drying rack based on the Internet of Things. Background Technology

[0002] With the improvement of people's living standards and the rapid development of Internet of Things technology, the popularity of smart homes is gradually increasing. As a part of smart homes, electric clothes drying racks need to achieve more intelligent and convenient control methods to meet users' needs. However, most existing electric clothes drying racks only have simple remote control or timer control functions, which cannot meet users' needs for intelligent and personalized control. Therefore, it is necessary to implement intelligent control for electric clothes drying racks.

[0003] The existing intelligent control methods for electric clothes drying racks still have the following problems: 1. Regarding the loosening of the drying rack, the current method mainly relies on periodic manual monitoring or only discovers the abnormality when the drying rack has fallen off. This cannot guarantee the timeliness of the discovery and repair of the loosening of the drying rack, which reduces the operational safety of the electric clothes drying rack. At the same time, the loose drying rack may cause damage or wear to the electric clothes drying rack, reducing its service life.

[0004] 2. Regarding disinfection, the impact of the types and quantities of bacteria in the air on the sterilization and disinfection demand index analysis was not considered, resulting in defects such as low accuracy in the analysis results. This reduces the accuracy of disinfection duration control and the sterilization and disinfection effect of the electric clothes dryer, thus failing to meet users' sterilization and disinfection needs.

[0005] 3. Regarding the drying process, the influence of the temperature tolerance of different types of clothes on the setting of drying temperature and time is not considered, which reduces the rationality of the drying temperature and time settings of the electric clothes dryer. As a result, it is impossible to avoid damage to the clothes caused by overheating, which increases the economic losses of users. Summary of the Invention

[0006] In view of this, in order to solve the problems mentioned in the background technology, an intelligent control system for an electric clothes drying rack based on the Internet of Things is proposed.

[0007] The objective of this invention can be achieved through the following technical solution: This invention provides an intelligent control system for an electric clothes drying rack based on the Internet of Things, including: a drying bar information acquisition and analysis module, used to acquire structural information of each drying bar in the target electric clothes drying rack and analyze the loosening abnormality index corresponding to each drying bar.

[0008] The drying rack looseness abnormality feedback module is used to issue a looseness abnormality alarm when the looseness abnormality index of a certain drying rack is greater than or equal to a set value.

[0009] The bacterial information collection and analysis module is used to collect the types and quantities of bacteria in the air in the drying area of ​​the target electric clothes dryer during each monitoring time period, and to collect the types and quantities of bacteria attached to each piece of dried clothing during each monitoring time period, and to analyze the sterilization and disinfection demand index of the dried clothing during each monitoring time period.

[0010] The disinfection duration control module is used to control the disinfection duration of the target electric clothes dryer during each monitoring time period.

[0011] The clothing information collection and analysis module is used to extract the types of clothes drying on the target electric clothes dryer and their corresponding weight when fully dry, collect the air humidity of the drying area to which the target electric clothes dryer belongs during each monitoring time period, collect the total weight of clothes drying during each monitoring time period, and analyze the drying demand index of clothes drying during each monitoring time period.

[0012] The drying information control module is used to control the drying temperature and drying time of the target electric clothes dryer during each monitoring period.

[0013] The cloud database is used to store the permissible length of screws not screwed into the screw holes on the drying rack, the appropriate disinfection time corresponding to each sterilization and disinfection demand index, the appropriate drying temperature and appropriate drying time corresponding to each drying demand index, the tolerance temperature corresponding to each type of clothing, and the compensation drying time corresponding to a unit reduction in drying temperature.

[0014] Specifically, the structural information includes the length of the screw not being screwed into the screw hole and the vibration frequency per unit time during the upward process when no clothes are hung to dry.

[0015] Specifically, the analysis of the loosening anomaly index corresponding to each drying bar involves the following steps: A1. Extracting from the structural information the length of the screws not being screwed into the screw holes of each drying bar in the target electric clothes dryer and the vibration frequency per unit time during the upward process when no clothes are hung, and denoting them as l. r and Where r represents the number of the drying rack, r = 1, 2, ..., g.

[0016] A2. Extract the permissible length of the un-screwed screws on the drying rack from the cloud database, and denot it as l. 许 .

[0017] A3. Calculate the loosening abnormality index χ for each drying horizontal bar. r , Where Δl and These represent the length deviation and vibration frequency of the unscrewed screw hole as set references, respectively. a1 and a2 represent the weights of the loosening anomaly assessment corresponding to the set length deviation and vibration frequency of the unscrewed screw hole, respectively.

[0018] Specifically, the analysis of the sterilization and disinfection demand index of clothes dried in the sun at each monitoring time period involves the following steps: B1. Compare the types of bacteria attached to the clothes dried in the sun at each monitoring time period. Record the same types of bacteria as a composite bacterial species, count the number of composite bacterial species, and record it as ε. i , where i represents the monitoring time period number, i = 1, 2, ..., n.

[0019] B2. Based on the number of various bacteria attached to each type of clothing during each monitoring period, calculate the number of bacteria corresponding to each comprehensive bacterial species during each monitoring period, and record it as η. ij , where j represents the number of the bacterial species, j = 1, 2, ..., m.

[0020] B3. Based on the types and quantities of bacteria in the air within the drying area of ​​the target electric clothes dryer during each monitoring time period, calculate the sterilization and disinfection requirement influencing factor λ corresponding to the airborne bacteria during each monitoring time period. i .

[0021] B4. Calculate the sterilization and disinfection demand index δ for clothes drying in each monitoring time period. i , Where ε′ and η′ represent the number of bacterial species and the number of bacteria set as references, respectively; a3 and a4 represent the weights of the proportion of the sterilization and disinfection needs assessment corresponding to the set number of bacterial species and the number of bacteria, respectively; and e represents the natural constant.

[0022] Specifically, the calculation process for the sterilization and disinfection requirement influencing factors corresponding to airborne bacteria in each monitoring time period is as follows: C1. Count the number of different types of bacteria in the air in the drying area to which the target electric clothes dryer belongs during each monitoring time period, and record it as μ. i .

[0023] C2. The total number of various bacteria in the air within the target electric clothes drying area during each monitoring time period is summed to obtain the total number of bacteria in the air within the target electric clothes drying area during each monitoring time period, and this is denoted as β. i .

[0024] C3. Calculate the influencing factor λ of sterilization and disinfection requirements for airborne bacteria in each monitoring period. i , Where μ′ and β′ represent the number of bacterial species and the total number of bacteria set as references, respectively, and a5 and a6 represent the weights of the sterilization and disinfection demand impact factor assessment corresponding to the set number of bacterial species and the total number of bacteria.

[0025] Specifically, the disinfection duration of the target electric clothes drying rack is controlled in the following manner: D1. The sterilization and disinfection demand index corresponding to the clothes drying in each monitoring time period is compared with the set reference sterilization and disinfection demand index. If the sterilization and disinfection demand index corresponding to the clothes drying in a certain monitoring time period is less than the set reference sterilization and disinfection demand index, the ultraviolet sterilization lamp of the target electric clothes drying rack is turned off.

[0026] D2. If the sterilization and disinfection demand index of the clothes drying in a certain monitoring period is greater than or equal to the set reference sterilization and disinfection demand index, it indicates that the clothes drying in that monitoring period needs to be sterilized and disinfected. The sterilization and disinfection demand index of the clothes drying in that monitoring period is compared with the appropriate disinfection duration corresponding to each sterilization and disinfection demand index stored in the cloud database to obtain the appropriate disinfection duration of the clothes drying in that monitoring period. The ultraviolet sterilization lamp of the target electric clothes drying rack is turned on, and the disinfection duration is set to the appropriate disinfection duration corresponding to the monitoring period.

[0027] D3. Based on the above, the disinfection time of the target electric clothes drying rack in each monitoring period was obtained.

[0028] Specifically, the analysis of the drying demand index of clothes in each monitoring time period is as follows: E1, sum up the weights of each piece of clothing on the target electric clothes dryer when it is completely dry to obtain the total weight of the clothes when it is completely dry.

[0029] E2. Subtract the total weight of the clothes dried during each monitoring period from the total weight of the clothes when they are completely dry to obtain the moisture content of the clothes dried during each monitoring period, and record it as σ. i .

[0030] E3. Based on the air humidity of the drying area to which the target electric clothes dryer belongs during each monitoring time period, calculate the drying demand index influencing factor γ corresponding to the air humidity during each monitoring time period. i .

[0031] E4. Calculate the drying demand index for clothes in each monitoring time period. Where σ′ represents the moisture content of the reference setting.

[0032] Specifically, the control of the drying temperature and drying time of the target electric clothes dryer during each monitoring time period is as follows: F1, compare the drying demand index of the clothes in each monitoring time period with the set reference drying demand index. If the drying demand index of the clothes in a certain monitoring time period is less than the set reference drying demand index, then close the drying port of the target electric clothes dryer.

[0033] F2. If the drying demand index of clothes during a certain monitoring period is greater than or equal to the set reference drying demand index, it indicates that the clothes need to be dried during that monitoring period, and that monitoring period is recorded as the period to be controlled.

[0034] F3. Compare the drying demand index corresponding to each controllable time period of the clothes with the suitable drying temperature and suitable drying time corresponding to each drying demand index stored in the cloud database to obtain the suitable drying temperature and suitable drying time corresponding to each controllable time period of the clothes.

[0035] F4. Compare the types of clothes drying on the target electric clothes dryer with the tolerance temperatures corresponding to each type of clothes stored in the cloud database to obtain the tolerance temperature corresponding to each type of clothes drying, and extract the minimum tolerance temperature from it, and record it as W.

[0036] F5. If the suitable drying temperature for clothes during a certain controlled time period is less than or equal to the minimum tolerance temperature, the drying port of the target electric clothes dryer will be opened, and the suitable drying temperature and suitable drying time for that controlled time period will be used as the drying temperature and drying time for that controlled time period.

[0037] F6. If the suitable drying temperature for clothes during a certain controlled time period is greater than the minimum tolerable temperature, then extract the compensation drying time corresponding to the unit reduction in drying temperature from the cloud database, denoted as T. 补 The suitable drying temperature and suitable drying time corresponding to the time period to be controlled are respectively denoted as W. 适 and T 适 Open the drying vent of the target electric clothes dryer, and place W and T... 适 +(W 适 -W)*T 补 These are respectively used as the drying temperature and drying time corresponding to the time period to be controlled.

[0038] F7. In summary, the drying temperature and drying time of the target electric clothes drying rack during each monitoring period are obtained.

[0039] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention analyzes the loosening abnormality index of each drying crossbar by combining the length of the screws not screwed into the screw holes of each drying crossbar in the target electric clothes drying machine and the vibration frequency per unit time during the corresponding upward process when no clothes are hung, thus ensuring the timeliness of the discovery and repair of the loosening abnormality of the drying crossbar, improving the operational safety of the electric clothes drying machine, reducing the damage caused by the loose drying crossbar to the electric clothes drying machine, and improving the service life of the electric clothes drying machine.

[0040] (2) This invention combines the types and quantities of bacteria in the air with the types and quantities of bacteria attached to each piece of clothing to calculate the sterilization and disinfection demand influencing factors corresponding to airborne bacteria and the sterilization and disinfection demand index corresponding to clothing to each monitoring time period. This improves the accuracy of the sterilization and disinfection demand index analysis results, thereby improving the accuracy of disinfection duration control and improving the sterilization and disinfection effect of electric clothes dryers, thus meeting the sterilization and disinfection needs of users.

[0041] (3) This invention obtains the temperature tolerance of each type of clothing on the target electric clothes dryer based on the type of clothing being dried, and analyzes the drying demand index of the clothing in each monitoring time period. This results in the drying temperature and drying time of the target electric clothes dryer in each monitoring time period, which improves the rationality of the drying temperature and drying time settings of the electric clothes dryer. This avoids damage to the clothes caused by overheating and reduces the economic losses of users. Attached Figure Description

[0042] 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.

[0043] Figure 1 This is a schematic diagram of the system module structure connection of the present invention. Detailed Implementation

[0044] 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.

[0045] Please see Figure 1As shown, the present invention provides an intelligent control system for an electric clothes drying rack based on the Internet of Things, including: a drying bar information collection and analysis module, a drying bar looseness abnormality feedback module, a bacteria information collection and analysis module, a disinfection time control module, a clothing information collection and analysis module, a drying information control module, and a cloud database.

[0046] The information collection and analysis module for the drying rack is connected to the feedback module for abnormal loosening of the drying rack. The information collection and analysis module for bacteria is connected to the disinfection duration control module. The information collection and analysis module for clothing is connected to the drying information control module. All three modules—the information collection and analysis module for the drying rack, the disinfection duration control module, and the drying information control module—are connected to the cloud database.

[0047] The drying rack information acquisition and analysis module is used to collect the structural information of each drying rack in the target electric clothes drying machine and analyze the loosening abnormality index corresponding to each drying rack.

[0048] In a specific embodiment of the present invention, the structural information includes the length of the screw not screwed into the screw hole and the vibration frequency per unit time during the corresponding upward process when no clothes are hung to dry.

[0049] It should be noted that the length of the screw not screwed into the screw hole refers to the vertical distance between the center point of the top of the screw nut and the center point of the screw hole in the screw screwing plane. The length of the screw not screwed into the screw hole is determined by locating the images of each drying bar by a camera placed near the target electric clothes drying machine.

[0050] It should also be noted that when clothes of different weights are hung on the drying rack, the vibration frequency will be affected. In order to ensure the consistency of external conditions when collecting vibration frequency, this invention collects the vibration frequency per unit time during the corresponding upward process when no clothes are hung. The vibration frequency is collected by vibration sensors installed on each drying rack.

[0051] In a specific embodiment of the present invention, the analysis of the loosening abnormality index corresponding to each drying crossbar is specifically performed as follows: A1. Extract from the structural information the length of the screws not being screwed into the screw holes of each drying crossbar in the target electric clothes drying machine and the vibration frequency per unit time during the upward process when no clothes are hung, and record them as l. r and Where r represents the number of the drying rack, r = 1, 2, ..., g.

[0052] A2. Extract the permissible length of the un-screwed screws on the drying rack from the cloud database, and denot it as l. 许 .

[0053] A3. Calculate the loosening abnormality index χ for each drying horizontal bar. r , Where Δl and These represent the length deviation and vibration frequency of the unscrewed screw hole as set references, respectively. a1 and a2 represent the weights of the loosening anomaly assessment corresponding to the set length deviation and vibration frequency of the unscrewed screw hole, respectively.

[0054] The loosening abnormality feedback module for the drying rack is used to issue a loosening abnormality alarm when the loosening abnormality index of a certain drying rack is greater than or equal to a set value.

[0055] This invention analyzes the loosening anomaly index of each drying crossbar by combining the length of the screws not screwed into the screw holes of each drying crossbar in the target electric clothes drying machine with the vibration frequency per unit time during the corresponding upward process when no clothes are hung. This ensures the timeliness of the detection and repair of loose drying crossbar problems, improves the operational safety of the electric clothes drying machine, reduces the damage caused by loose drying crossbars to the electric clothes drying machine, and extends the service life of the electric clothes drying machine.

[0056] The bacterial information collection and analysis module is used to collect the types and quantities of bacteria in the air in the drying area of ​​the target electric clothes dryer during each monitoring time period, and to collect the types and quantities of bacteria attached to each piece of dried clothing during each monitoring time period, and to analyze the sterilization and disinfection demand index of the dried clothing during each monitoring time period.

[0057] It should be noted that the types and quantities of bacteria were obtained using biosensors.

[0058] In a specific embodiment of the present invention, the analysis of the sterilization and disinfection demand index of clothes dried in each monitoring time period is specifically performed as follows: B1. The types of bacteria attached to each type of clothes dried in each monitoring time period are compared with each other. The same types of bacteria are recorded as the composite bacterial types. The number of composite bacterial types is counted and recorded as ε. i , where i represents the monitoring time period number, i = 1, 2, ..., n.

[0059] B2. Based on the number of various bacteria attached to each type of clothing during each monitoring period, calculate the number of bacteria corresponding to each comprehensive bacterial species during each monitoring period, and record it as η. ij , where j represents the number of the bacterial species, j = 1, 2, ..., m.

[0060] B3. Based on the types and quantities of bacteria in the air within the drying area of ​​the target electric clothes dryer during each monitoring time period, calculate the sterilization and disinfection requirement influencing factor λ corresponding to the airborne bacteria during each monitoring time period. i .

[0061] In a specific embodiment of the present invention, the calculation process for the sterilization and disinfection requirement influencing factors corresponding to airborne bacteria in each monitoring time period is as follows: C1. Count the number of different types of bacteria in the air in the drying area to which the target electric clothes dryer belongs in each monitoring time period, and record it as μ. i .

[0062] C2. The total number of various bacteria in the air within the target electric clothes drying area during each monitoring time period is summed to obtain the total number of bacteria in the air within the target electric clothes drying area during each monitoring time period, and this is denoted as β. i .

[0063] C3. Calculate the influencing factor λ of sterilization and disinfection requirements for airborne bacteria in each monitoring period. i , Where μ′ and β′ represent the number of bacterial species and the total number of bacteria set as references, respectively, and a5 and a6 represent the weights of the sterilization and disinfection demand impact factor assessment corresponding to the set number of bacterial species and the total number of bacteria.

[0064] B4. Calculate the sterilization and disinfection demand index δ for clothes drying in each monitoring time period. i , Where ε′ and η′ represent the number of bacterial species and the number of bacteria set as references, respectively; a3 and a4 represent the weights of the proportion of the sterilization and disinfection needs assessment corresponding to the set number of bacterial species and the number of bacteria, respectively; and e represents the natural constant.

[0065] The disinfection duration control module is used to control the disinfection duration of the target electric clothes dryer during each monitoring time period.

[0066] In a specific embodiment of the present invention, the disinfection duration of the target electric clothes drying rack during each monitoring time period is controlled in the following manner: D1. The sterilization and disinfection demand index corresponding to the clothes drying during each monitoring time period is compared with the set reference sterilization and disinfection demand index. If the sterilization and disinfection demand index corresponding to the clothes drying during a certain monitoring time period is less than the set reference sterilization and disinfection demand index, the ultraviolet sterilization lamp of the target electric clothes drying rack is turned off.

[0067] D2. If the sterilization and disinfection demand index of the clothes drying in a certain monitoring period is greater than or equal to the set reference sterilization and disinfection demand index, it indicates that the clothes drying in that monitoring period needs to be sterilized and disinfected. The sterilization and disinfection demand index of the clothes drying in that monitoring period is compared with the appropriate disinfection duration corresponding to each sterilization and disinfection demand index stored in the cloud database to obtain the appropriate disinfection duration of the clothes drying in that monitoring period. The ultraviolet sterilization lamp of the target electric clothes drying rack is turned on, and the disinfection duration is set to the appropriate disinfection duration corresponding to the monitoring period.

[0068] D3. Based on the above, the disinfection time of the target electric clothes drying rack in each monitoring period was obtained.

[0069] This invention combines the types and quantities of bacteria in the air with the types and quantities of bacteria attached to each piece of clothing to calculate the sterilization and disinfection demand influencing factors corresponding to airborne bacteria and the sterilization and disinfection demand index corresponding to clothing to each monitoring time period. This improves the accuracy of the sterilization and disinfection demand index analysis results, thereby improving the accuracy of disinfection duration control and enhancing the sterilization and disinfection effect of electric clothes dryers, thus meeting the sterilization and disinfection needs of users.

[0070] The clothing information collection and analysis module is used to extract the types of clothes drying on the target electric clothes dryer and their corresponding weights when fully dry, collect the air humidity of the drying area to which the target electric clothes dryer belongs during each monitoring time period, collect the total weight of clothes drying during each monitoring time period, and analyze the drying demand index of clothes drying during each monitoring time period.

[0071] It should be noted that the types of clothes to be dried are obtained by extracting them from the name tags of the clothes. In a specific embodiment of the present invention, the types of clothes include, but are not limited to, cotton, linen, leather, chemical fiber, flax and bamboo fiber.

[0072] It should also be noted that the weight of each piece of clothing when it is completely dry is obtained by using a weight sensor when each piece of clothing is completely dry, the air humidity is obtained by a humidity sensor placed in the respective drying area, and the total weight of the clothes is obtained by a weight sensor placed on the drying bar.

[0073] In a specific embodiment of the present invention, the analysis of the drying demand index of clothes in each monitoring time period is specifically as follows: E1, the weights of each piece of clothes on the target electric clothes dryer when they are completely dry are summed up to obtain the total weight of the clothes when they are completely dry.

[0074] E2. Subtract the total weight of the clothes dried during each monitoring period from the total weight of the clothes when they are completely dry to obtain the moisture content of the clothes dried during each monitoring period, and record it as σ. i .

[0075] E3. Based on the air humidity of the drying area to which the target electric clothes dryer belongs during each monitoring time period, calculate the drying demand index influencing factor γ corresponding to the air humidity during each monitoring time period. i .

[0076] It should be noted that the specific calculation process for the drying demand index influencing factor corresponding to the air humidity in each monitoring time period is as follows: G1, the air humidity of the drying area to which the target electric clothes dryer belongs in each monitoring time period is denoted as ρ. i .

[0077] G2. Calculate the drying demand index influencing factor γ corresponding to air humidity in each monitoring period. i , Where ρ′ represents the set reference air humidity.

[0078] E4. Calculate the drying demand index for clothes in each monitoring time period. Where σ′ represents the moisture content of the reference setting.

[0079] The drying information control module is used to control the drying temperature and drying time of the target electric clothes dryer during each monitoring time period.

[0080] In a specific embodiment of the present invention, the control of the drying temperature and drying time of the target electric clothes dryer during each monitoring time period is specifically controlled as follows: F1, compare the drying demand index of the clothes to be dried during each monitoring time period with the set reference drying demand index. If the drying demand index of the clothes to be dried during a certain monitoring time period is less than the set reference drying demand index, then close the drying port of the target electric clothes dryer.

[0081] F2. If the drying demand index of clothes during a certain monitoring period is greater than or equal to the set reference drying demand index, it indicates that the clothes need to be dried during that monitoring period, and that monitoring period is recorded as the period to be controlled.

[0082] F3. Compare the drying demand index corresponding to each controllable time period of the clothes with the suitable drying temperature and suitable drying time corresponding to each drying demand index stored in the cloud database to obtain the suitable drying temperature and suitable drying time corresponding to each controllable time period of the clothes.

[0083] F4. Compare the types of clothes drying on the target electric clothes dryer with the tolerance temperatures corresponding to each type of clothes stored in the cloud database to obtain the tolerance temperature corresponding to each type of clothes drying, and extract the minimum tolerance temperature from it, and record it as W.

[0084] F5. If the suitable drying temperature for clothes during a certain controlled time period is less than or equal to the minimum tolerance temperature, the drying port of the target electric clothes dryer will be opened, and the suitable drying temperature and suitable drying time for that controlled time period will be used as the drying temperature and drying time for that controlled time period.

[0085] F6. If the suitable drying temperature for clothes during a certain controlled time period is greater than the minimum tolerable temperature, then extract the compensation drying time corresponding to the unit reduction in drying temperature from the cloud database, denoted as T. 补 The suitable drying temperature and suitable drying time corresponding to the time period to be controlled are respectively denoted as W. 适 and T 适 Open the drying vent of the target electric clothes dryer, and place W and T... 适 +(W 适 -W)*T 补 These are respectively used as the drying temperature and drying time corresponding to the time period to be controlled.

[0086] F7. In summary, the drying temperature and drying time of the target electric clothes drying rack during each monitoring period are obtained.

[0087] This invention improves the rationality of the drying temperature and drying time settings of the electric clothes dryer by obtaining the temperature tolerance of each type of clothing on the target electric clothes dryer and analyzing the drying demand index of the clothing in each monitoring time period.

[0088] The cloud database is used to store the permissible length of the screws on the drying rack that are not screwed into the screw holes, the appropriate disinfection time corresponding to each sterilization and disinfection demand index, the appropriate drying temperature and appropriate drying time corresponding to each drying demand index, the tolerance temperature corresponding to each type of clothing, and the compensation drying time corresponding to a unit reduction in drying temperature.

[0089] 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. An intelligent control system for an electric clothes drying rack based on the Internet of Things, characterized in that, include: The data collection and analysis module for the clothes drying rack is used to collect the structural information of each clothes drying rack in the target electric clothes drying machine and analyze the loosening abnormality index of each clothes drying rack. The drying rack looseness abnormality feedback module is used to issue a looseness abnormality alarm when the looseness abnormality index of a certain drying rack is greater than or equal to a set value; The bacterial information collection and analysis module is used to collect the types and quantities of bacteria in the air in the drying area of ​​the target electric clothes dryer during each monitoring time period, and to collect the types and quantities of bacteria attached to each piece of dried clothing during each monitoring time period, and to analyze the sterilization and disinfection demand index of dried clothing during each monitoring time period. The disinfection duration control module is used to control the disinfection duration of the target electric clothes dryer during each monitoring time period; The clothing information collection and analysis module is used to extract the types of clothes drying on the target electric clothes dryer and their corresponding weight when fully dry, collect the air humidity of the drying area to which the target electric clothes dryer belongs during each monitoring time period, collect the total weight of clothes drying during each monitoring time period, and analyze the drying demand index of clothes drying during each monitoring time period. The drying information control module is used to control the drying temperature and drying time of the target electric clothes dryer during each monitoring time period; The cloud database is used to store the permissible length of the screws not being screwed into the screw holes of the drying rack, the appropriate disinfection time corresponding to each sterilization and disinfection demand index, the appropriate drying temperature and appropriate drying time corresponding to each drying demand index, the tolerance temperature corresponding to each type of clothing, and the compensation drying time corresponding to the unit reduction of drying temperature. The structural information includes the length of the screw not being screwed into the screw hole and the vibration frequency per unit time during the upward process when no clothes are hung to dry. The analysis of the loosening abnormality index corresponding to each drying rack is as follows: A1. Extract from the structural information the length of the un-screwed sections of each horizontal bar of the target electric clothes drying rack and the vibration frequency per unit time during the upward movement when no clothes are hung on them, and record them as follows: and ,in, This indicates the number of the drying rack. ; A2. Extract the permissible length of the un-screwed screws on the drying rack from the cloud database, and record it as... ; A3. Calculate the loosening abnormality index corresponding to each drying horizontal bar. , ,in, and These represent the length deviation and vibration frequency of the un-screwed hole, respectively, as set as a reference. and These represent the weighting of the loosening anomaly assessment percentage corresponding to the set length deviation of the unscrewed screw hole and the vibration frequency, respectively.

2. The intelligent control system for an electric clothes drying rack based on the Internet of Things according to claim 1, characterized in that: The analysis of the sterilization and disinfection demand index for clothes drying in different monitoring time periods is as follows: B1. Compare the bacterial species attached to each type of dried clothing during each monitoring period. Record the same bacterial species as a composite bacterial species, count the total number of composite bacterial species, and record it as... ,in, Indicates the number of the monitoring period. ; B2. Based on the number of various bacteria attached to each type of clothing during each monitoring period, calculate the number of bacteria corresponding to each comprehensive bacterial species during each monitoring period, and record it as follows: ,in, The number indicating the number of bacterial species. ; B3. Based on the types and quantities of bacteria in the air within the drying area of ​​the target electric clothes dryer during each monitoring time period, calculate the sterilization and disinfection requirements influencing factors for airborne bacteria during each monitoring time period. ; B4. Calculate the sterilization and disinfection demand index for clothes drying in each monitoring time period. , ,in, and These represent the combined number of bacterial species and the total number of bacteria used as a reference, respectively. and These represent the weighted proportions of the overall bacterial species and bacterial quantity in the assessment of sterilization and disinfection needs, respectively. Represents the natural constant.

3. The intelligent control system for an electric clothes drying rack based on the Internet of Things according to claim 2, characterized in that: The calculation process for the sterilization and disinfection requirement influencing factors corresponding to airborne bacteria in each monitoring time period is as follows: C1. Count the number of different types of bacteria in the air within the drying area of ​​the target electric clothes drying rack during each monitoring time period, and record them as follows: ; C2. The total number of various bacteria in the air within the target electric clothes drying rack's drying area during each monitoring time period is summed to obtain the total number of bacteria in the air within the target electric clothes drying rack's drying area during each monitoring time period, and this total number is recorded as follows: ; C3. Calculate the influencing factors of sterilization and disinfection requirements for airborne bacteria during each monitoring period. , ,in, and These represent the number of bacterial species and the total number of bacteria used as a reference, respectively. and These represent the weighting of the factors influencing the sterilization and disinfection requirements, corresponding to the number of bacterial species and the total number of bacteria, respectively.

4. The intelligent control system for an electric clothes drying rack based on the Internet of Things according to claim 1, characterized in that: The disinfection duration of the target electric clothes drying rack is controlled within each monitoring time period. The specific control method is as follows: D1. Compare the sterilization and disinfection demand index of the clothes drying in each monitoring time period with the set reference sterilization and disinfection demand index. If the sterilization and disinfection demand index of the clothes drying in a certain monitoring time period is less than the set reference sterilization and disinfection demand index, then turn off the ultraviolet sterilization lamp of the target electric clothes drying machine. D2. If the sterilization and disinfection demand index of the clothes drying in a certain monitoring period is greater than or equal to the set reference sterilization and disinfection demand index, it indicates that the clothes drying in that monitoring period needs to be sterilized and disinfected. The sterilization and disinfection demand index of the clothes drying in that monitoring period is compared with the appropriate disinfection duration corresponding to each sterilization and disinfection demand index stored in the cloud database to obtain the appropriate disinfection duration of the clothes drying in that monitoring period. The ultraviolet sterilization lamp of the target electric clothes drying rack is turned on, and the disinfection duration is set to the appropriate disinfection duration corresponding to the monitoring period. D3. Based on the above, the disinfection time of the target electric clothes drying rack in each monitoring period was obtained.

5. The intelligent control system for an electric clothes drying rack based on the Internet of Things according to claim 1, characterized in that: The analysis of the drying demand index for clothes drying in different monitoring time periods is as follows: E1. Add up the weights of all the clothes on the target electric clothes dryer when they are completely dry to get the total weight of the clothes when they are completely dry. E2. Subtract the total weight of the clothes dried during each monitoring period from the total weight of the clothes when they are completely dry to obtain the moisture content of the clothes dried during each monitoring period, and record it as follows: ; E3. Based on the air humidity of the drying area to which the target electric clothes dryer belongs during each monitoring time period, calculate the drying demand index influencing factor corresponding to the air humidity during each monitoring time period. ; E4. Calculate the drying demand index for clothes in each monitoring time period. , ,in, This indicates the moisture content used as a reference.

6. The intelligent control system for an electric clothes drying rack based on the Internet of Things according to claim 1, characterized in that: The drying temperature and drying time of the target electric clothes drying machine are controlled during each monitoring time period. The specific control method is as follows: F1. Compare the drying demand index of the clothes in each monitoring time period with the set reference drying demand index. If the drying demand index of the clothes in a certain monitoring time period is less than the set reference drying demand index, then close the drying port of the target electric clothes dryer. F2. If the drying demand index of clothes during a certain monitoring period is greater than or equal to the set reference drying demand index, it indicates that the clothes need to be dried during that monitoring period, and that monitoring period is recorded as the period to be controlled. F3. Compare the drying demand index corresponding to each control period of the clothes to be dried with the suitable drying temperature and suitable drying time corresponding to each drying demand index stored in the cloud database to obtain the suitable drying temperature and suitable drying time corresponding to each control period of the clothes to be dried. F4. Compare the types of clothes drying on the target electric clothes dryer with the temperature tolerance of each type of clothing stored in the cloud database to obtain the temperature tolerance of each type of clothing. Extract the minimum temperature tolerance and record it as . ; F5. If the suitable drying temperature of the clothes during a certain controlled time period is less than or equal to the minimum tolerable temperature, the drying port of the target electric clothes dryer will be opened, and the suitable drying temperature and suitable drying time during the controlled time period will be used as the drying temperature and drying time during the controlled time period. F6. If the suitable drying temperature for clothes during a certain controlled time period is greater than the minimum tolerable temperature, then extract the compensatory drying time corresponding to the unit reduction in drying temperature from the cloud database, denoted as... The suitable drying temperature and suitable drying time corresponding to the time period to be controlled are respectively recorded as follows: and Open the drying port of the target electric clothes dryer and... and These are respectively used as the drying temperature and drying time corresponding to the time period to be controlled; F7. In summary, the drying temperature and drying time of the target electric clothes drying rack during each monitoring period are obtained.

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