CN108510116B - Case and bag space planning system based on mobile terminal - Google Patents

Case and bag space planning system based on mobile terminal Download PDF

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CN108510116B
CN108510116B CN201810269291.2A CN201810269291A CN108510116B CN 108510116 B CN108510116 B CN 108510116B CN 201810269291 A CN201810269291 A CN 201810269291A CN 108510116 B CN108510116 B CN 108510116B
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mobile terminal
luggage
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江丽
程兴
宋申民
李清华
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Harbin Institute of Technology
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Abstract

The invention discloses a luggage space planning system based on a mobile terminal, which comprises: the system comprises a mobile terminal application subsystem and a server terminal subsystem, wherein the mobile terminal application subsystem comprises: the system comprises a login module, a target luggage scanning module, a scanning module of articles to be carried, a manual correction module, a space planning and display module, an article reminding module and a network module; the service terminal system includes: the article classification convolutional neural network module and the data module. The invention can effectively solve the problem of insufficient luggage space and effectively plan and use the luggage space.

Description

Case and bag space planning system based on mobile terminal
Technical Field
The invention relates to the field of intelligent life, in particular to a luggage space planning system based on a mobile terminal.
Background
In daily life, trips are frequent, and reasonable planning of limited space of the luggage before departure is a difficult problem. When people go far, people always have the problems that too many things are needed and the luggage is not enough, and the space of the luggage cannot be effectively utilized.
In view of the above-mentioned drawbacks, the inventors of the present invention have finally obtained the present invention through a long period of research and practice.
Disclosure of Invention
In order to solve the technical defects, the technical scheme adopted by the invention is to provide a luggage space planning system based on a mobile terminal, which comprises: the system comprises a mobile terminal application subsystem and a server terminal subsystem, wherein the mobile terminal application subsystem comprises: the login module is used for registering and logging in a user;
the target luggage scanning module is used for realizing scanning and identification of the internal outline of the luggage;
the scanning module of the article to be carried is used for realizing the scanning and the identification of the external outline of the article to be carried;
the manual correction module is used for realizing the functions of manually modifying, deleting and setting the number, the outline, the name and the classification of the scanned articles by a user;
the space planning and displaying module is used for displaying the optimal layout suggestion of each article in the target luggage;
the necessary article reminding module is used for storing a necessary article list of a user and giving out an reminding of articles not taken;
the network module is used for synchronizing the system information of the service terminal in real time;
the service terminal system includes:
the article classification convolutional neural network module is used for training an article classification model;
and the data module is used for storing correct data after manual correction of a user.
Preferably, the target luggage scanning module and the scanning module of the object to be carried use cameras of the mobile terminal to scan.
Preferably, the target luggage scanning module photographs the interior of the target luggage from three angles of front, left and up of each surface through a camera of the mobile terminal, and repeats until all the luggage is scanned.
Preferably, the scanning module of the articles to be carried takes pictures of a plurality of articles to be carried which are diagonally arranged from three angles of front, left and up through the camera of the mobile terminal under a consistent light color background.
Preferably, the target luggage scanning module and the scanning module of the articles to be carried both use an edge detection algorithm to detect edges.
Preferably, the edge detection algorithm is a Sobel edge detection algorithm, the Sobel edge detection algorithm calculates a gradient of the point by using gray values of pixel points of the image and pixel points around the pixel points after graying the image, and the point is considered to be an edge point if the gradient is greater than a certain preset threshold.
Preferably, the gray value calculation method includes:
and performing convolution on the original gray map by the convolution factor to respectively obtain the horizontal component and the vertical component of the gradient.
Namely:
Gx(x,y)＝[f(x+1,y-1)+2*f(x+1,y)+f(x+1,y+1)]-[f(x-1,y-1)+2*f(x-1,y)+f(x-1,y+1)]
Gy(x,y)＝[f(x-1,y-1)+2*f(x,y-1)+f(x+1,y-1)]-[f(x-1,y+1)+2*f(x,y+1)+f(x+1,y+1)]
wherein: f (a, b) represents the gray scale value of the pixel (a, b), x represents the x-th row, y represents the y-th column, f (a, b) represents the gray scale value of the pixel of the a-th row and the b-th column, Gx(x, y) represents the horizontal gradient of the pixel with coordinates (x, y).
Preferably, the gradient value of each pixel of the image is determined by:
Figure GDA0002447803080000031
wherein: g represents a gradient, GxRepresenting the horizontal gradient of a pixel with coordinate x, GyRepresenting the horizontal gradient of a pixel with coordinate y.
Preferably, the article classification convolutional neural network module performs article name and classification by using a convolutional neural network.
Preferably, the activation function of the convolutional neural network uses a ReLU activation function, where the ReLU activation function is:
Figure GDA0002447803080000032
compared with the prior art, the invention has the beneficial effects that: the invention can effectively solve the problem of insufficient luggage space, and can effectively plan and use the luggage space. 2, the invention can provide suggestions for people to go out and effectively utilize space, and the practicability and convenience of the mobile terminal application form can be greatly improved. And 3, carrying out article classification and space planning on the convolutional neural network, and providing a corresponding mobile terminal application of the intelligent luggage space planner. 4, the necessary article reminding module can complete the scanning and article classification of the list of the articles to be taken, automatically compares the necessary article list, and is a brand new intelligent reminding service. 5, the invention can solve the problem of providing a tool for optimal layout of articles in the case under the condition of limited space.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below.
FIG. 1 is a schematic diagram of the connection of modules of the present invention;
FIG. 2 is an exemplary illustration of a scanned item placement of the present invention;
FIG. 3 is an article classification convolutional neural network model of the present invention;
the figures in the drawings represent:
1-mobile terminal application subsystem 2-service terminal system 11-login module 12-target luggage scanning module 13-scanning module 14 of articles to be carried-manual correction module 15-space planning and display module 16-necessary article reminding module 17-network module 21-article classification convolution neural network module 22-data module
Detailed Description
The above and further features and advantages of the present invention are described in more detail below with reference to the accompanying drawings.
Example 1
As shown in fig. 1, the present invention is a system for planning a luggage space based on a mobile terminal, including: a mobile terminal application subsystem 1 and a service terminal system 2. The mobile terminal application subsystem 1 is used for the online downloading and planning use of the user; and the service terminal system 2 is maintained by a service team in a later period through a network.
The mobile terminal application subsystem 1 includes: the system comprises a login module 11, a target luggage scanning module 12, a to-be-carried article scanning module 13, a manual correction module 14, a space planning and display module 15, an article reminding module 16 and a network module 17.
The login module 11 is used as an authentication page of the personalized service, and is used for logging in a registration system for authentication through a user mobile terminal; the login module is used for registering through a user, transmitting user information to the service terminal system 2 for authentication and giving a use authority; and the information is encrypted and protected, so that the personal privacy is not disclosed.
And the target luggage scanning module 12 can call a camera to realize scanning and identification of the internal outline of the luggage. And under the scene of clear camera shooting pictures, shooting the interior of the target luggage from three angles of front, left and upper of each surface, repeating the shooting until all the luggage is scanned, and automatically drawing the interior contour map of the luggage by utilizing an edge detection algorithm.
The scanning module 13 of the object to be carried can use a camera to realize the external contour of the object to be carried. As shown in fig. 2, under a consistent light background, a plurality of articles to be carried, which are diagonally arranged, are photographed from three angles of "front", "left" and "top" through a camera of the mobile terminal, an external outline image of the articles is given by an edge detection algorithm, and then the names and the classifications of the articles are performed by using a convolutional neural network.
And the manual correction module 14 is used for realizing the functions of manually modifying, deleting and setting the number, the outline, the name and the classification of the scanned objects. After each scanning is finished, the user confirms whether the number, outline, name and classification of the articles are correct or not, and if the number, outline, name and classification of the articles are incorrect, the articles can be manually modified and stored.
And a space planning and displaying module 15 for displaying the optimal layout suggestion of the target luggage for each article. And respectively providing two space planning results by using a dynamic planning algorithm and a greedy algorithm. For situations where the total space is insufficient, a recommendation is given to discard the item list.
And the necessary article reminding module 16 is used for storing a necessary article list of the user and giving an reminding of the articles which are not taken. After the manual correction module is completed, an automatic matching algorithm is applied to automatically compare the scanned articles with the necessary article list, and if the unscanned articles exist, a prompt is given.
And the network module 17 is used for synchronizing the model of the convolutional neural network of the service terminal system in real time.
The service terminal system 2 comprises an article classification convolutional neural network module 21 and a data module 22.
The article classification convolutional neural network module 21 is used for training an article classification model.
And the data module 22 is used for storing correct data after manual correction by a user and is also used for correcting the article classification convolutional neural network module.
The user interacts with the mobile terminal application subsystem 1, a service team maintains the service terminal system 1, and the mobile terminal application subsystem 1 and the service terminal system 2 communicate through a network. In order to achieve the best scanning effect and complete the scanning of a plurality of articles by using the target luggage scanning module 12 of the mobile terminal application subsystem 1 and the scanning module 13 of the articles to be carried, the articles to be scanned are put according to fig. 2, and are photographed from the front surface, the side surface and the upper surface of the articles respectively, and after edge detection is performed, the articles are classified by using the neural network module 21 trained by the service terminal system 2.
The classification result is corrected manually by the user, and the corrected result is transmitted to the data module 22 of the service terminal system 2 to be stored. The service subsystem 2 will modify the neural network using the corrective data in the data module 22.
The planning algorithm of the articles in the luggage adopts a dynamic planning method and a greedy algorithm, and the results calculated by the two methods are displayed and selected by the user.
The dynamic programming algorithm is to decompose the problem into a plurality of sub-problems (division stages) according to the spatial characteristics, and to represent various objective conditions in which the problem develops into each stage by using different states (determining states and state variables). And determining a decision method and a state transition equation according to the relation between the states of two adjacent stages (determining a decision and writing out the state transition equation). The optimal value is calculated in a memorialization manner (memo method) from bottom to top or from top to bottom. And constructing an optimal solution of the spatial characteristics according to the information obtained when the optimal value is calculated.
The greedy algorithm takes into account the importance and volume of each item. The weight of each article is defined as importance/volume, and then the articles are sequentially loaded into the case according to the weight sequence of the articles.
Matching of the necessary item list. Before each packing, the user manually confirms the list of necessary articles, and the mark of each article on the list is initialized to False. After each article is scanned, the name of the article can be obtained, and the program searches and judges whether the article is in the necessary article list, if so, the mark of the necessary article is set to True, otherwise, the next article is searched until the scanned article list is judged to be complete. And if the mark of the necessary item list is False at the end of the scanning, giving a prompt to the user.
The invention can provide a tool for optimal layout of articles in a case under the condition of limited space.
Example 2
The difference between the present embodiment and the above embodiments is that the target luggage scanning module 12 can call a camera to scan and identify the internal outline of the luggage. And under the scene of clear camera shooting pictures, shooting the interior of the target luggage from three angles of front, left and upper of each surface, repeating the shooting until all the luggage is scanned, and automatically drawing the interior contour map of the luggage by utilizing an edge detection algorithm.
The scanning module 13 of the object to be carried can use a camera to realize the external contour of the object to be carried. As shown in fig. 2, under a consistent light background, a plurality of articles to be carried, which are diagonally arranged, are photographed from three angles of "front", "left" and "top" through a camera of the mobile terminal, an external outline image of the articles is given by an edge detection algorithm, and then the names and the classifications of the articles are performed by using a convolutional neural network.
The target bag scanning module 12 and the scanning module 13 of the object to be carried both use the edge detection algorithm to detect the edge.
The invention adopts a Sobel edge detection algorithm: after graying the image, calculating the gradient of the point by utilizing the gray values of the pixel points and the surrounding pixel points.
The Sobel convolution factor is:
Figure GDA0002447803080000071
and performing convolution on the original gray scale map by the convolution factor to respectively obtain a horizontal component and a vertical component of the gradient. Namely:
Gx(x,y)＝[f(x+1,y-1)+2*f(x+1,y)+f(x+1,y+1)]-[f(x-1,y-1)+2*f(x-1,y)+f(x-1,y+1)]
Gy(x,y)＝[f(x-1,y-1)+2*f(x,y-1)+f(x+1,y-1)]-[f(x-1,y+1)+2*f(x,y+1)+f(x+1,y+1)]
where f (a, b) represents the gray scale value of the pixel (a, b) point.
The gradient value of each pixel of the image is then determined by:
Figure GDA0002447803080000081
x represents the x-th row, y represents the y-th column, f (a, b) represents the gray scale values of the pixels of the a-th row and the b-th column, G represents the gradient, G represents the gray scale valuex(x, y) represents the horizontal gradient of the pixel with coordinates (x, y).
It is calculated that if the gradient G is greater than some preset threshold, the point (x, y) is considered to be an edge point.
The Soble edge detection algorithm is simple and convenient, the efficiency is high in practical application, and the speed of the whole system can be improved.
Example 3
The difference between this embodiment and the above embodiment is that the scanning module 13 of the object to be carried can use a camera to realize the external contour of the object to be carried. As shown in fig. 2, under a consistent light background, a plurality of articles to be carried, which are diagonally arranged, are photographed from three angles of "front", "left" and "top" through a camera of the mobile terminal, an external outline image of the articles is given by an edge detection algorithm, and then the names and the classifications of the articles are performed by using a convolutional neural network.
As shown in fig. 3, the convolutional neural network for item classification gives a concrete structure of the neural network used for item classification. The neural network has a total of 12 convolutional layers, each convolutional layer uses a convolutional kernel with the size of 3 x 3, pool/2 represents the convolutional layer and then a pooling layer is used for pooling by adopting a max-pooling mode. The number of convolution kernels per layer is given in turn by the figure. The activation function uses the ReLU function.
The specific form of the ReLU activation function is:
Figure GDA0002447803080000082
the neural network is trained in a back propagation mode, the min-batch selection is 256, and the step length is 10 e-3.
After the scanning is finished, the scanning image of the article is used as the input of the neural network, then the neural network carries out calculation, and the name of each article is given in the output.
Example 4
The present embodiment is different from the above embodiments in that the mobile terminal of the present invention may include a mobile phone, a tablet computer, and a device with a camera and on which the present system can be installed. The system is fully utilized by the conventional equipment, and the problem of insufficient space of a case of a user can be reasonably solved.
Example 5
The difference between the embodiment and the embodiment is that the invention can also be used for planning a series of limited spaces such as space layout in an express box. The limited space is reasonably planned for a long time, and the good habit of a user to the space planning can be improved.
The foregoing is merely a preferred embodiment of the invention, which is intended to be illustrative and not limiting. It will be understood by those skilled in the art that various changes, modifications and equivalents may be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (10)

1. A luggage space planning system based on a mobile terminal is characterized by comprising: the system comprises a mobile terminal application subsystem and a server terminal subsystem, wherein the mobile terminal application subsystem comprises: the login module is used for registering and logging in a user;
the target luggage scanning module is used for realizing scanning and identification of the internal outline of the luggage;
the scanning module of the article to be carried is used for realizing the scanning and the identification of the external outline of the article to be carried;
the manual correction module is used for realizing the functions of manually modifying, deleting and setting the number, the outline, the name and the classification of the scanned articles by a user;
the space planning and displaying module is used for displaying the optimal layout suggestion of each article in the target luggage;
the necessary article reminding module is used for storing a necessary article list of a user and giving out an reminding of articles not taken;
the network module is used for synchronizing the system information of the service terminal in real time;
the service terminal system includes:
the article classification convolutional neural network module is used for training an article classification model;
and the data module is used for storing correct data after manual correction of a user.
2. A mobile terminal-based luggage space planning system according to claim 1, wherein the target luggage scanning module and the scanning module of the articles to be carried are scanned by using a camera of the intelligent terminal.
3. A luggage space planning system based on a mobile terminal as claimed in claim 2, wherein the target luggage scanning module takes pictures of the interior of the target luggage from three angles of "front", "left" and "up" of each surface through the camera of the intelligent terminal, and repeats until all the luggage is scanned.
4. A luggage space planning system based on a mobile terminal as claimed in claim 2, wherein the scanning module of the articles to be carried takes pictures of a plurality of articles to be carried which are arranged in a diagonal line from three angles of "front", "left" and "up" through the camera of the intelligent terminal under the consistent light color background.
5. A mobile terminal-based luggage space planning system according to claim 3 or 4, wherein said target luggage scanning module and said scanning module of the article to be carried both use edge detection algorithm to detect edges.
6. A luggage space planning system based on a mobile terminal according to claim 5, wherein the edge detection algorithm is a Sobel edge detection algorithm, the Sobel edge detection algorithm calculates the gradient of the point by using the gray values of the pixel points of the image and the surrounding pixel points after graying the image, and the point is considered to be an edge point if the gradient is greater than a certain preset threshold.
7. A luggage space planning system based on a mobile terminal according to claim 6, wherein the gray value is calculated by the following method:
convolving the original gray map by a convolution factor to respectively obtain a horizontal component and a vertical component of the gradient, namely:
Gx(x,y)＝[f(x+1,y-1)+2*f(x+1,y)+f(x+1,y+1)]-[f(x-1,y-1)+2*f(x-1,y)+f(x-1,y+1)]
Gy(x,y)＝[f(x-1,y-1)+2*f(x,y-1)+f(x+1,y-1)]-[f(x-1,y+1)+2*f(x,y+1)+f(x+1,y+1)]
wherein: f (a, b) represents the gray scale value of the pixel (a, b), x represents the x-th row, y represents the y-th column, f (a, b) represents the gray scale value of the pixel of the a-th row and the b-th column, Gx(x, y) represents the horizontal gradient of the pixel with coordinates (x, y).
8. A mobile terminal-based luggage space planning system according to claim 7, wherein the gradient value of each pixel of said image is obtained by the following formula:
Figure FDA0002447803070000021
wherein: g represents a gradient, GxRepresenting the horizontal gradient of a pixel with coordinate x, GyRepresenting the horizontal gradient of a pixel with coordinate y.
9. A mobile terminal-based luggage space planning system according to claim 1, wherein said article classification convolutional neural network module utilizes convolutional neural network for article naming and classification.
10. A mobile terminal-based luggage space planning system according to claim 9, wherein the activation function of said convolutional neural network uses a ReLU activation function, said ReLU activation function being:
Figure FDA0002447803070000031
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101140639A (en) * 2006-09-06 2008-03-12 张斌 Method for arranging at most article in finite dimension three dimensions
CN106485268A (en) * 2016-09-27 2017-03-08 东软集团股份有限公司 A kind of image-recognizing method and device
CN107526991A (en) * 2016-06-20 2017-12-29 青岛海尔智能技术研发有限公司 A kind of cold storage plant deposits object storage management system and cold storage plant
KR20180026962A (en) * 2016-09-05 2018-03-14 인천국제공항공사 Method for separating image of adjacent baggage of baggage position control system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101140639A (en) * 2006-09-06 2008-03-12 张斌 Method for arranging at most article in finite dimension three dimensions
CN107526991A (en) * 2016-06-20 2017-12-29 青岛海尔智能技术研发有限公司 A kind of cold storage plant deposits object storage management system and cold storage plant
KR20180026962A (en) * 2016-09-05 2018-03-14 인천국제공항공사 Method for separating image of adjacent baggage of baggage position control system
CN106485268A (en) * 2016-09-27 2017-03-08 东软集团股份有限公司 A kind of image-recognizing method and device

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