CN110399896B - Storage management system for clothes cabinet - Google Patents
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- CN110399896B CN110399896B CN201910257143.3A CN201910257143A CN110399896B CN 110399896 B CN110399896 B CN 110399896B CN 201910257143 A CN201910257143 A CN 201910257143A CN 110399896 B CN110399896 B CN 110399896B
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47B—TABLES; DESKS; OFFICE FURNITURE; CABINETS; DRAWERS; GENERAL DETAILS OF FURNITURE
- A47B61/00—Wardrobes
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Abstract
The invention relates to a clothes cabinet storage management system, comprising: the clothes analysis equipment is used for identifying clothes targets in the real-time equilibrium image based on a clothes imaging gray threshold range to obtain a corresponding target area, determining a plurality of geometric features corresponding to the clothes targets based on the target area, and forming the geometric features into feature vectors; the type identification device adopts a single hidden layer BP neural network with 8 inputs and 4 outputs, takes the geometric characteristics as input layer neurons, and takes the output layer as the type of the clothes season. The clothes cabinet storage management system is reliable in operation and convenient and fast to operate. Because one or more corresponding cabinets are selected from the clothes cabinet based on the clothes-dressing season type of the human body in front of the clothes cabinet and are automatically pushed, the cabinets with the property of the season can be directly provided for users.
Description
Technical Field
The invention relates to the field of clothes cabinets, in particular to a clothes cabinet storage management system.
Background
The clothes cabinet is a cabinet for storing clothes, generally takes stainless steel, solid wood, toughened glass and hardware accessories as materials, generally takes a cabinet body, a door plate, silent wheels and a door curtain as components, is internally provided with clothes hanging rods, a trousers rack, a pull basket, a disinfection lamp and other accessories, adopts the processes of punching, assembling, riveting, welding and the like, has the functions of inflaming retarding, rat proofing, seamless cockroach proofing, dust prevention, moth proofing, moisture proofing, cleanness, beauty, convenient movement and the like, and has a large-capacity intelligent disinfection wardrobe, a power-free glass cabinet, a stainless steel wardrobe, an ultraviolet disinfection clean wardrobe, a moisture-proof multifunctional wardrobe, a male and female wardrobe, a dry cockroach-proof wardrobe, a folding wardrobe and a simple wardrobe. The door bodies of the clothes cabinets are generally classified into a vertical hinged door, an inner and outer sliding doors of the cabinet and the like, and the plates used by the clothes cabinets generally comprise stainless steel, metal, wood and the like.
Disclosure of Invention
The invention has at least the following three key points:
(1) selecting one or more corresponding cabinets from the clothes cabinet based on the clothes season type of the human body in front of the clothes cabinet, and automatically pushing the selected cabinets to directly provide the cabinets with the season attribute for the user so as to facilitate clothes placement and clothes extraction of the user;
(2) selective filtering processing is carried out on the color components of the image, and meanwhile in the specific filtering processing, the intensity of the filtering processing carried out on the color matrix is determined based on the mean square error of the color matrix, so that the self-adaptive image processing is realized;
(3) and judging the overall repetition degree of the image based on the respective repetition degrees of the respective color components of the image, and providing a targeted solution for the judgment of the overall repetition degree of the image.
According to an aspect of the present invention, there is provided a wardrobe storage management system, the system comprising:
the clothes analysis equipment is connected with the balance operation equipment and used for receiving the real-time balance image, identifying clothes targets in the real-time balance image based on a clothes imaging gray threshold range to obtain corresponding target areas, and determining a plurality of geometric features corresponding to the clothes targets based on the target areas: the number of Euler holes, roundness, the number of corner points, convexity and concavity, smoothness, length-diameter ratio, compactness and main shaft angle, and forming a plurality of geometric features into a feature vector;
the type identification device is connected with the clothes analysis device, adopts a single hidden layer BP neural network with 8 inputs and 4 outputs, takes the plurality of geometric characteristics as input layer neurons, and takes the output layer as clothes season types, wherein the clothes season types comprise spring clothes, summer clothes, autumn clothes and winter clothes;
the cabinet body pushing equipment is connected with the type identification equipment and each cabinet body in the clothes cabinet and is used for receiving the clothes season type and pushing one or more cabinet bodies corresponding to the clothes season type;
the first repeatability analyzing device is used for receiving a scene capturing image obtained by capturing a scene in front of a wardrobe, acquiring each red component value of each pixel point of the scene capturing image, and outputting the red component value as a first repeatability based on the repeatability of each red component value;
the second repeatability analyzing device is used for receiving the scene capturing image, acquiring each blue component value of each pixel point of the scene capturing image, and outputting the blue component value as a second repeatability based on the repeatability of each blue component value;
and the first repeatability analyzing equipment is used for receiving the scene capture image, acquiring each green component value of each pixel point of the scene capture image, and outputting the green component value as a third repeatability based on the repeatability of each green component value.
The clothes cabinet storage management system is reliable in operation and convenient and fast to operate. Because one or more corresponding cabinets are selected from the clothes cabinet based on the clothes-dressing season type of the human body in front of the clothes cabinet and are automatically pushed, the cabinets with the property of the season can be directly provided for users.
Drawings
Embodiments of the invention will now be described with reference to the accompanying drawings, in which:
fig. 1 is a schematic structural view of a locker to which a locker storage management system according to an embodiment of the present invention is applied.
Detailed Description
Embodiments of the locker storage management system of the present invention will be described in detail with reference to the accompanying drawings.
The electronic control appears as a weak current control. Weak current generally refers to direct current circuit or audio and video line, network line and telephone line, and alternating current voltage is generally within 36V. The household appliances such as telephone, computer, signal input (cable television line) of television, audio equipment (output end line) and the like are weak current electric equipment. Strong and weak currents are generally easily distinguishable conceptually, and the main difference is the difference in application. Strong electricity is used as a power source. The weak current is used as a signal current. Voltage is not a way to distinguish between strong and weak currents.
There are two main types of weak electricity in buildings: one type is low-voltage electric energy such as national safety voltage level and control voltage, and is divided into alternating current and direct current. The alternating current is below 36V, the direct current is below 24V, such as a 24V direct current control power supply, or an emergency lighting lamp standby power supply. Another type is information sources carrying information such as voice, images, data, etc., such as telephone, television, computer information.
At present, for a user of a clothes cabinet, because clothes in all seasons are available in the cabinet, it is difficult to find clothes belonging to a current season in a large amount of clothes, not to mention that specific clothes are selected from clothes in a plurality of current seasons, and a management mechanism based on a season type is also needed for storing clothes.
In order to overcome the defects, the invention builds a clothes cabinet storage management system, and can effectively solve the corresponding technical problem.
Fig. 1 is a schematic structural view of a clothes closet according to an embodiment of the present invention.
The clothes cabinet storage management system shown according to the embodiment of the invention comprises:
the clothes analysis equipment is connected with the balance operation equipment and used for receiving the real-time balance image, identifying clothes targets in the real-time balance image based on a clothes imaging gray threshold range to obtain corresponding target areas, and determining a plurality of geometric features corresponding to the clothes targets based on the target areas: the number of Euler holes, roundness, the number of corner points, convexity and concavity, smoothness, length-diameter ratio, compactness and main shaft angle, and forming a plurality of geometric features into a feature vector;
the type identification device is connected with the clothes analysis device, adopts a single hidden layer BP neural network with 8 inputs and 4 outputs, takes the plurality of geometric characteristics as input layer neurons, and takes the output layer as clothes season types, wherein the clothes season types comprise spring clothes, summer clothes, autumn clothes and winter clothes;
the cabinet body pushing equipment is connected with the type identification equipment and each cabinet body in the clothes cabinet and is used for receiving the clothes season type and pushing one or more cabinet bodies corresponding to the clothes season type;
the first repeatability analyzing device is used for receiving a scene capturing image obtained by capturing a scene in front of a wardrobe, acquiring each red component value of each pixel point of the scene capturing image, and outputting the red component value as a first repeatability based on the repeatability of each red component value;
the second repeatability analyzing device is used for receiving the scene capturing image, acquiring each blue component value of each pixel point of the scene capturing image, and outputting the blue component value as a second repeatability based on the repeatability of each blue component value;
the first repeatability analyzing device is used for receiving the scene capture image, acquiring each green component value of each pixel point of the scene capture image, and outputting the green component value as a third repeatability based on the repeatability of each green component value;
a parameter identification device connected to the first, second, and third repetition degree analysis devices, respectively, for receiving the first, second, and third repetition degrees, respectively, and determining an overall repetition degree of the scene capture image based on the first, second, and third repetition degrees;
in the parameter identification device, determining the overall degree of repetition of the scene capture image based on the first, second, and third degrees of repetition comprises: the first repetition degree is in a direct proportion relation with the overall repetition degree, the second repetition degree is in a direct proportion relation with the overall repetition degree, and the third repetition degree is in a direct proportion relation with the overall repetition degree;
the geometric correction device is connected with the parameter identification device, is used for receiving the scene capture image from the first repeatability analysis device when the received numerical value of the overall repeatability is less than or equal to a preset repeatability threshold, and is also used for executing geometric correction processing on the scene capture image to obtain a corrected image;
a matrix extraction device for receiving the corrected image, performing color space conversion on the corrected image to obtain a B color matrix, an R color matrix, and a G color matrix in an RGB color space of the corrected image;
the dynamic filtering device is connected with the matrix extraction device and is used for determining the intensity of filtering processing on the R color matrix based on the mean square error of the R color matrix, determining the intensity of filtering processing on the G color matrix based on the mean square error of the G color matrix, and not performing filtering processing on the B color matrix;
the combination execution device is connected with the dynamic filtering device and is used for carrying out combination operation on the R color matrix after filtering processing, the G color matrix after filtering processing and the B color matrix without filtering processing so as to obtain a corresponding combination operation image;
the equalization operation device is connected with the combination execution device and is used for executing histogram equalization operation on the combination operation image to obtain a real-time equalization image;
wherein, in the dynamic filtering apparatus, determining the intensity of performing the filtering process on the R color matrix based on the mean square error of the R color matrix includes: the greater the mean square error of the R color matrix, the greater the intensity of the filtering process performed on the R color matrix.
Next, a detailed description will be given of a specific configuration of the locker storage management system according to the present invention.
In the locker storage management system:
in the dynamic filtering apparatus, determining the intensity of the filtering process performed on the G color matrix based on the mean square error of the G color matrix includes: the greater the mean square error of the G color matrix, the greater the intensity of the filtering process performed on the G color matrix.
The clothes cabinet storage management system can further comprise:
the first parameter acquisition equipment is connected with the equalization operation equipment and used for receiving the real-time equalization image and performing red channel mean value analysis on the real-time equalization image to obtain a first red channel mean value;
and the second parameter acquisition equipment is used for receiving the Rana image and performing red channel mean value analysis on the Rana image to obtain a second red channel mean value.
The clothes cabinet storage management system can further comprise:
the parameter comparison device is respectively connected with the first parameter acquisition device and the second parameter acquisition device, and is used for receiving the first red channel mean value and the second red channel mean value, sending a first control instruction when the first red channel mean value is greater than or equal to the second red channel mean value, sending a third control instruction when the second red channel mean value is more than three times of the first red channel mean value, and sending a second control instruction when the second red channel mean value is between three times and two times of the first red channel mean value;
the selection processing device is respectively connected with the clothes analysis device and the parameter comparison device, and is used for activating the hue modification device to perform single red hue curve lifting processing on the real-time balanced image and obtain a corresponding selection processing image to replace the real-time balanced image and send the selection processing image to the clothes analysis device when receiving the second control instruction, and is also used for activating the hue modification device to perform double red hue curve lifting processing on the real-time balanced image and obtain a corresponding selection processing image to replace the real-time balanced image and send the selection processing image to the clothes analysis device when receiving the third control instruction;
wherein the selection processing device is further configured to send the real-time equalization image to the clothing analysis device as a selection processing image to replace the real-time equalization image when receiving the first control instruction.
The clothes cabinet storage management system can further comprise:
the hue modification device is used for performing single-time or double-time red hue curve lifting processing on the input image in an activated state and stopping performing single-time or double-time red hue curve lifting processing on the input image in an inactivated state;
the hue modification device is realized by a digital processing chip, a Harvard structure with separated programs and data is adopted in the digital processing chip, a hardware multiplier is arranged in the digital processing chip, and various digital processing control instructions are provided by adopting pipeline operation to realize various digital signal processing algorithms respectively.
The clothes cabinet storage management method according to the embodiment of the invention comprises the following steps:
using a clothes analysis device connected with a balance operation device for receiving a real-time balance image, identifying clothes targets in the real-time balance image based on a clothes imaging gray threshold range to obtain corresponding target areas, and determining a plurality of geometric features corresponding to the clothes targets based on the target areas: the number of Euler holes, roundness, the number of corner points, convexity and concavity, smoothness, length-diameter ratio, compactness and main shaft angle, and forming a plurality of geometric features into a feature vector;
using a type identification device, connecting with the clothes analysis device, adopting a single hidden layer BP neural network with 8 inputs and 4 outputs, taking the plurality of geometric features as input layer neurons, and taking the output layer as clothes season types, wherein the clothes season types comprise spring clothes, summer clothes, autumn clothes and winter clothes;
the using cabinet body pushing equipment is connected with the type identification equipment and each cabinet body in the clothes cabinet and is used for receiving the clothes season type and pushing one or more cabinet bodies corresponding to the clothes season type;
using a first repeatability analysis device for receiving a scene capture image obtained by capturing a scene in front of a wardrobe, obtaining each red component value of each pixel point of the scene capture image, and outputting as a first repeatability based on the repeatability of each red component value;
using a second repetition degree analyzing device for receiving the scene capturing image, acquiring each blue component value of each pixel point of the scene capturing image, and outputting as a second repetition degree based on a repetition degree of each blue component value;
using a first repeatability analyzing device for receiving the scene capturing image, acquiring each green component value of each pixel point of the scene capturing image, and outputting as a third repeatability based on the repeatability of each green component value;
a parameter identification device connected to the first, second, and third repetition degree analysis devices, respectively, for receiving the first, second, and third repetition degrees, respectively, and determining an overall repetition degree of the scene capture image based on the first, second, and third repetition degrees;
in the parameter identification device, determining the overall degree of repetition of the scene capture image based on the first, second, and third degrees of repetition comprises: the first repetition degree is in a direct proportion relation with the overall repetition degree, the second repetition degree is in a direct proportion relation with the overall repetition degree, and the third repetition degree is in a direct proportion relation with the overall repetition degree;
using a geometric correction device connected to the parameter identification device, for receiving a scene capture image from the first repetition degree analysis device when the received value of the overall repetition degree is less than or equal to a preset repetition degree threshold value, and for performing geometric correction processing on the scene capture image to obtain a corrected image;
using a matrix extraction device for receiving the corrected image, performing color space conversion on the corrected image to obtain a B color matrix, an R color matrix, and a G color matrix in an RGB color space of the corrected image;
using a dynamic filtering device, connected to the matrix extraction device, for determining an intensity of performing a filtering process on the R color matrix based on a mean square error of the R color matrix, determining an intensity of performing a filtering process on the G color matrix based on a mean square error of the G color matrix, and performing no filtering process on the B color matrix;
using a combination execution device connected with the dynamic filtering device and used for carrying out combination operation on the R color matrix after filtering processing, the G color matrix after filtering processing and the B color matrix without filtering processing so as to obtain a corresponding combination operation image;
the using equalization operation device is connected with the combination execution device and is used for executing histogram equalization operation on the combination operation image to obtain a real-time equalization image;
wherein, in the dynamic filtering apparatus, determining the intensity of performing the filtering process on the R color matrix based on the mean square error of the R color matrix includes: the greater the mean square error of the R color matrix, the greater the intensity of the filtering process performed on the R color matrix.
Next, the specific steps of the locker storage management method of the present invention will be further described.
The clothes cabinet storage management method comprises the following steps:
in the dynamic filtering apparatus, determining the intensity of the filtering process performed on the G color matrix based on the mean square error of the G color matrix includes: the greater the mean square error of the G color matrix, the greater the intensity of the filtering process performed on the G color matrix.
The clothes cabinet storage management method may further include:
using a first parameter obtaining device, connected to the equalization operation device, for receiving the real-time equalization image, and performing red channel mean value analysis on the real-time equalization image to obtain a first red channel mean value;
using a second parameter acquisition device for receiving the lena map, performing red channel mean analysis on the lena map to obtain a second red channel mean.
The clothes cabinet storage management method may further include:
the using parameter comparison device is respectively connected with the first parameter acquisition device and the second parameter acquisition device and is used for receiving the first red channel mean value and the second red channel mean value, sending a first control instruction when the first red channel mean value is larger than or equal to the second red channel mean value, sending a third control instruction when the second red channel mean value is more than three times of the first red channel mean value, and sending a second control instruction when the second red channel mean value is between three times and two times of the first red channel mean value;
the using selection processing device is respectively connected with the clothes analysis device and the parameter comparison device, and is used for activating the hue modification device to perform single red hue curve lifting processing on the real-time equalized image and obtain a corresponding selection processing image to replace the real-time equalized image and send the selection processing image to the clothes analysis device when receiving the second control instruction, and is also used for activating the hue modification device to perform double red hue curve lifting processing on the real-time equalized image and obtain a corresponding selection processing image to replace the real-time equalized image and send the selection processing image to the clothes analysis device when receiving the third control instruction;
wherein the selection processing device is further configured to send the real-time equalization image to the clothing analysis device as a selection processing image to replace the real-time equalization image when receiving the first control instruction.
The clothes cabinet storage management method may further include:
using hue modification equipment for performing single-time or double-time red hue curve lifting processing on the input image in an activated state and stopping performing single-time or double-time red hue curve lifting processing on the input image in an inactivated state;
the hue modification device is realized by a digital processing chip, a Harvard structure with separated programs and data is adopted in the digital processing chip, a hardware multiplier is arranged in the digital processing chip, and various digital processing control instructions are provided by adopting pipeline operation to realize various digital signal processing algorithms respectively.
In addition, the laundry analyzing apparatus is implemented using a GPU. The GPU is a display chip capable of supporting T & L (Transform and Lighting) from hardware, and since T & L is an important part in 3D rendering, it is used to calculate the 3D position of a polygon and process dynamic ray effects, also referred to as "geometric processing". A good T & L unit, which can provide fine 3D objects and high-level light special effects; however, in most PCs, most of the operations of T & L are handled by the CPU (that is, software T & L), and because the CPU has many tasks and performs non-3D graphics processing such as memory management and input response in addition to T & L, the performance is greatly reduced during actual operations, and the CPU generally waits for CPU data, and the operation speed of the CPU is far from the requirement of a complicated three-dimensional game. Even if the operating frequency of the CPU exceeds 1GHz or more, it is not much helpful to him, because it is a problem in the design of the PC itself, and has no great relation to the speed of the CPU.
Finally, it should be noted that each functional device in the embodiments of the present invention may be integrated into one processing device, or each device may exist alone physically, or two or more devices may be integrated into one device.
The functions, if implemented in the form of software-enabled devices and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (2)
1. A wardrobe storage management system, the system comprising:
the clothes analysis equipment is connected with the balance operation equipment and used for receiving the real-time balance image, identifying clothes targets in the real-time balance image based on a clothes imaging gray threshold range to obtain corresponding target areas, and determining a plurality of geometric features corresponding to the clothes targets based on the target areas: the number of Euler holes, roundness, the number of corner points, convexity and concavity, smoothness, length-diameter ratio, compactness and main shaft angle, and forming a plurality of geometric features into a feature vector;
the type identification device is connected with the clothes analysis device, adopts a single hidden layer BP neural network with 8 inputs and 4 outputs, takes the plurality of geometric characteristics as input layer neurons, and takes the output layer as clothes season types, wherein the clothes season types comprise spring clothes, summer clothes, autumn clothes and winter clothes;
the cabinet body pushing equipment is connected with the type identification equipment and each cabinet body in the clothes cabinet and is used for receiving the clothes season type and pushing one or more cabinet bodies corresponding to the clothes season type;
the first repeatability analyzing device is used for receiving a scene capturing image obtained by capturing a scene in front of a wardrobe, acquiring each red component value of each pixel point of the scene capturing image, and outputting the red component value as a first repeatability based on the repeatability of each red component value;
the second repeatability analyzing device is used for receiving the scene capturing image, acquiring each blue component value of each pixel point of the scene capturing image, and outputting the blue component value as a second repeatability based on the repeatability of each blue component value;
the third repeatability analyzing device is used for receiving the scene capture image, acquiring each green component value of each pixel point of the scene capture image, and outputting the green component value as a third repeatability based on the repeatability of each green component value;
a parameter identification device connected to the first, second, and third repetition degree analysis devices, respectively, for receiving the first, second, and third repetition degrees, respectively, and determining an overall repetition degree of the scene capture image based on the first, second, and third repetition degrees;
in the parameter identification device, determining the overall degree of repetition of the scene capture image based on the first, second, and third degrees of repetition comprises: the first repetition degree is in a direct proportion relation with the overall repetition degree, the second repetition degree is in a direct proportion relation with the overall repetition degree, and the third repetition degree is in a direct proportion relation with the overall repetition degree;
the geometric correction device is connected with the parameter identification device, is used for receiving the scene capture image from the first repeatability analysis device when the received numerical value of the overall repeatability is less than or equal to a preset repeatability threshold, and is also used for executing geometric correction processing on the scene capture image to obtain a corrected image;
a matrix extraction device for receiving the corrected image, performing color space conversion on the corrected image to obtain a B color matrix, an R color matrix, and a G color matrix in an RGB color space of the corrected image;
the dynamic filtering device is connected with the matrix extraction device and is used for determining the intensity of filtering processing on the R color matrix based on the mean square error of the R color matrix, determining the intensity of filtering processing on the G color matrix based on the mean square error of the G color matrix, and not performing filtering processing on the B color matrix;
the combination execution device is connected with the dynamic filtering device and is used for carrying out combination operation on the R color matrix after filtering processing, the G color matrix after filtering processing and the B color matrix without filtering processing so as to obtain a corresponding combination operation image;
the equalization operation device is connected with the combination execution device and is used for executing histogram equalization operation on the combination operation image to obtain a real-time equalization image;
wherein, in the dynamic filtering apparatus, determining the intensity of performing the filtering process on the R color matrix based on the mean square error of the R color matrix includes: the greater the mean square error of the R color matrix, the greater the intensity of the filtering process performed on the R color matrix.
2. The cabinet storage management system according to claim 1, wherein:
in the dynamic filtering apparatus, determining the intensity of the filtering process performed on the G color matrix based on the mean square error of the G color matrix includes: the greater the mean square error of the G color matrix, the greater the intensity of the filtering process performed on the G color matrix.
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