CN108510116A - A kind of luggage space planning system based on mobile terminal - Google Patents
A kind of luggage space planning system based on mobile terminal Download PDFInfo
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- 238000013439 planning Methods 0.000 title claims abstract description 29
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 17
- 238000003708 edge detection Methods 0.000 claims description 17
- 238000013528 artificial neural network Methods 0.000 claims description 9
- 230000004913 activation Effects 0.000 claims description 8
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- 230000004048 modification Effects 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
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Abstract
The present invention discloses a kind of luggage space planning system based on mobile terminal, including：Mobile terminal application subsystem and service terminal system, the mobile terminal application subsystem include：Log-in module, the scan module of target luggage wait for the scan module of belongings, manual rectification module, space planning and display module, must be with article reminding module, network module；The service terminals system includes：Taxonomy of goods convolutional neural networks module, data module.The present invention can effective solution luggage insufficient space the problem of, to luggage space carry out effectively plan and use.
Description
Technical field
The present invention relates to wisdom sphere of life, and in particular to a kind of luggage space planning system based on mobile terminal.
Background technology
In daily life, trip is frequent, and it is one to the confined space of luggage make rational planning for before setting out
Problem.And people can always encounter that thing is too many when long journey, the inadequate problem of luggage, some reasons are luggage
Space cannot efficiently use.
In view of drawbacks described above, creator of the present invention obtains the present invention finally by prolonged research and practice.
Invention content
To solve above-mentioned technological deficiency, the technical solution adopted by the present invention is, provides a kind of case based on mobile terminal
Packet space planning system, including：Mobile terminal application subsystem and service terminal system, the mobile terminal application subsystem include：
Log-in module for user's registration and logs in；
The scan module of target luggage, for realizing the scanning recognition of the in-profile of luggage；
The scan module for waiting for belongings, for realizing the exterior contour of belongings is waited for；
Manual rectification module, for realizing user's manual modification to the number of scanned item, profile, title, classification, deleting
It removes, set-up function；
Space planning and display module, to show the optimal location suggestion in target luggage of each article；
With list of articles and not band article reminding must must be provided for store user with article reminding module；
Network module, for service terminals system information described in real-time synchronization；
The service terminals system includes：
Taxonomy of goods convolutional neural networks module, for training taxonomy of goods model；
Data module, for storing the correct data after user corrects manually.
Preferably, the scan module of the target luggage and waiting for what the scan modules of belongings was carried using mobile terminal
Camera is scanned.
Preferably, the scan module of the target luggage by the mobile terminal camera to the inside of target luggage from
" preceding " " left side " "upper" three angle in each face is taken pictures, and is repeated up to scanning and is completed all luggages.
Preferably, the scan module for waiting for belongings is imaged under consistent light background by the mobile terminal
Head takes pictures to the multiple articles to be carried arranged at oblique line from " preceding " " left side " "upper" three angle.
Preferably, the target luggage scan module and item scan module use edge detection algorithm to detect edge.
Preferably, the edge detection algorithm is Sobel edge detection algorithms, the Sobel edge detection algorithms pass through
After carrying out gray processing to image, the point is calculated using the pixel of described image and the gray value of surrounding pixel
Gradient is calculated, if gradient is more than a certain predetermined threshold value, then it is assumed that the point is marginal point.
Preferably, the computational methods of the gray value are：
Make convolution by warp factor and former gray-scale map, respectively obtains the horizontal component and vertical component of gradient.I.e.：
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) indicates that the gray value that pixel (a, b) is put, x represent xth row, and y represents y row, and f (a, b) represents the
The gray value of the pixel of a rows, b row, Gx(x, y) represents coordinate as the horizontal gradient of the pixel of (x, y).
Preferably, the Grad of each pixel of described image then by：
Wherein：G represents gradient, GxCoordinate is represented as the horizontal gradient of the pixel of x, GyCoordinate is represented as the level of the pixel of y
Gradient.
Preferably, the convolutional neural networks module of the taxonomy of goods carries out Item Title using convolutional neural networks and divides
Class.
Preferably, the activation primitive of the convolutional neural networks, using ReLU activation primitives, the ReLU activation primitives are：
Compared with the prior art the beneficial effects of the present invention are：1, the present invention being capable of effective solution luggage insufficient space
The problem of, luggage space effectively plan and use.2, the present invention can give effective profit of the trip and space of people
With provide suggestion, and by mobile terminal apply in the form of will greatly improve its practicability and convenience.3, the present invention is by convolution
Neural network carries out taxonomy of goods and space planning, and the mobile terminal for providing corresponding intelligent luggage space planning device is answered
With.4, of the invention must can be completed the inventory with article by scanner uni taxonomy of goods with article reminding module, automatic to compare
It is a kind of completely new intelligent reminding service to necessary list of articles.5, the present invention can solve under limited space conditions, provide object
A kind of tool of optimal location of the product in luggage.
Description of the drawings
It is required in being described below to embodiment in order to illustrate more clearly of the technical solution in various embodiments of the present invention
The attached drawing used is briefly described.
Fig. 1 is each module connection diagram of the present invention；
Fig. 2 is that the band scanned item of the present invention puts exemplary plot；
Fig. 3 is the taxonomy of goods convolutional neural networks model of the present invention；
Digital representation in figure：
The mobile terminals 1- application subsystem 2- service terminals system 11- log-in module 12- target luggage scan modules 13-
Wait for that the manual rectification module 15- space plannings display module 16- of the scan module 14- of belongings must be with article reminding module
17- network module 21- taxonomy of goods convolutional neural networks module 22- data modules
Specific implementation mode
Below in conjunction with attached drawing, the forgoing and additional technical features and advantages are described in more detail.
Embodiment 1
As shown in Figure 1, the present invention is a kind of luggage space planning system based on mobile terminal, including：It applies mobile terminal
Subsystem 1 and service terminal system 2.Mobile terminal application subsystem 1 carries professional etiquette of going forward side by side into line for user and draws use up and down；
Service terminals system 2 carries out later maintenance by service team by network.
Mobile terminal application subsystem 1 includes：Log-in module 11, the scan module 12 of target luggage, waits for sweeping for belongings
Module 13 is retouched, manual rectification module 14, space planning display module 15 must be with article reminding module 16, network module 17.
Wherein, log-in module 11 log in Accreditation System as the certification page of personalized service by customer mobile terminal
Certification is used；User information is sent to service terminals system 2 and is authenticated by log-in module by the registration of user,
Give access right；And protection is encrypted to its information, it is ensured that individual privacy is not revealed.
Target luggage scan module 12, can call camera, to realize luggage in-profile scanning recognition.It is taking the photograph
As under picture clearly scene, taking pictures from " preceding " " left side " "upper" three angle in each face to the inside of target luggage, weight
It is multiple to complete all luggages until scanning, and draw luggage in-profile figure automatically using edge detection algorithm.
The scan module 13 for waiting for belongings, can call camera, to realize the exterior contour for waiting for belongings.Such as figure
Shown in 2, under consistent light background, by mobile terminal camera to multiple articles to be carried for being arranged at oblique line from " preceding "
The angle of " left side " "upper" three is taken pictures, and the exterior contour figure of article is provided by edge detection algorithm, then utilizes convolutional Neural
Network carries out Item Title and classification.
Manual rectification module 14, to the number of scanned item, profile, title, classification realize user manual modification,
It deletes, set-up function.After each end of scan, confirm whether number of articles, profile, title, classification are correct by user, not just
It is true then with manual modification and can preserve.
Space planning and display module 15, to show the optimal location suggestion in target luggage of each article.It uses
Dynamic programming algorithm and greedy algorithm provide two kinds of space planning results respectively.For the situation that gross space is inadequate, suggestion is provided
Give up list of articles.
With list of articles and not band article reminding must must be provided for store user with article reminding module 16.Complete
After manual rectification module, using Auto-matching algorithm it is automatic relatively scanned item with must be with list of articles, if in the presence of not sweeping
Article is retouched, prompting will be provided.
Network module 17, to the model of real-time synchronization service terminals system convolutional neural networks.
Service terminals system 2 includes taxonomy of goods convolutional neural networks module 21, data module 22.
Wherein, taxonomy of goods convolutional neural networks module 21, for training taxonomy of goods model.
Data module 22 is additionally operable to correction taxonomy of goods convolution god for storing the correct data after user corrects manually
Through network module.
User interacts with mobile terminal application subsystem 1, is safeguarded to service terminals system 1 by service team, moves
Moved end application subsystem 1 is communicated with service terminals system 2 by network.The target luggage of mobile terminal application subsystem 1 is swept
It retouches module 12 and waits for the scan module 13 of belongings, in order to reach optimum scanning effect and complete multiple articles with this
Scanning, article to be scanned please put according to Fig. 2, respectively from " front " " side " of article " above " take pictures, into
After row edge detection, classified to article using 2 trained neural network module 21 of service terminals system.
Classification results are corrected manually by user, and the result after correction is transferred to the data module of service terminals system 2
22 are preserved.Service terminals system 2 will be modified neural network using the correction data in data module 22.
Planning algorithm of the article in luggage uses dynamic programming and greedy algorithm, the calculated result of two methods equal
It shows, is voluntarily selected by user.
Dynamic programming algorithm is according to space characteristics, is several subproblems (dividing the stage) PROBLEM DECOMPOSITION, by problem
Present various objective circumstances are showed with different states and (are determined state and state variable) when developing to each stage.
Determine that decision-making technique and state transition equation (determine decision and write out shape according to the relationship between the state in two neighboring stage
State equation of transfer).(memorandum method) calculates optimal value in a manner of bottom-up or top-down memoryization.Most according to calculating
The information obtained when the figure of merit constructs the optimal solution of space characteristics.
Greedy algorithm fully considers the importance and volume of each product.The weight definition of each product is importance/body
Article is packed into luggage by product successively then according to the weight sequencing of article.
The matching of indispensable inventory.It, must be every on inventory with list of articles by user's manual confirmation before each vanning
The mark of one article is initialized as False.Each article is obtained with the title of article, program after being scanned through
Retrieval judges that the article whether in indispensable inventory, is, the mark of the necessity is set True, on the contrary then retrieve next
Part article, until scanned list of articles is judged completely.If when the end of scan, the mark that there is necessary list of articles is
False then gives user to prompt.
The present invention can solve under limited space conditions, provide a kind of tool of optimal location of the article in luggage.
Embodiment 2
Difference lies in target luggage scan module 12 can call camera, to reality for the present embodiment and above-described embodiment
The scanning recognition of the in-profile of existing luggage.Under camera picture clearly scene, to the inside of target luggage from each face
" preceding " " left side " "upper" three angle is taken pictures, and is repeated up to scanning and is completed all luggages, and is automatic using edge detection algorithm
Draw luggage in-profile figure.
The scan module 13 for waiting for belongings, can call camera, to realize the exterior contour for waiting for belongings.Such as figure
Shown in 2, under consistent light background, by mobile terminal camera to multiple articles to be carried for being arranged at oblique line from " preceding "
The angle of " left side " "upper" three is taken pictures, and the exterior contour figure of article is provided by edge detection algorithm, then utilizes convolutional Neural
Network carries out Item Title and classification.
Target luggage scan module 12 and item scan module 13 use edge detection algorithm to detect edge.
The present invention uses Sobel edge detection algorithms：After carrying out gray processing to image, pixel and its surrounding are utilized
The gray value of pixel calculate the gradient of the point.
Sobel warp factors are：
Make convolution by the above warp factor and former gray-scale map, respectively obtains the horizontal component and vertical component of gradient.I.e.：
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) indicates the gray value that pixel (a, b) is put.
The Grad of each pixel of image then by：
X represents xth row, and y represents y row, and f (a, b) represents the gray value of the pixel of a rows, b row, and G represents gradient,
Gx(x, y) represents coordinate as the horizontal gradient of the pixel of (x, y).
It is calculated, if gradient G is more than a certain predetermined threshold value, then it is assumed that the point (x, y) is marginal point.
Soble edge detection algorithms are easier, and efficiency is higher in practical application, can improve the speed of whole system
Rate.
Embodiment 3
Difference lies in wait for the scan module 13 of belongings, can call camera, use for the present embodiment and above-described embodiment
To realize the exterior contour for waiting for belongings.As shown in Fig. 2, under consistent light background, by mobile terminal camera at
Multiple articles to be carried that oblique line arranges are taken pictures from " preceding " " left side " "upper" three angle, and object is provided by edge detection algorithm
Then the exterior contour figure of product carries out Item Title and classification using convolutional neural networks.
As shown in figure 3, the convolutional neural networks of taxonomy of goods give the specific knot for the neural network that taxonomy of goods uses
Structure.The neural network has 12 convolutional layers altogether, and each convolutional layer uses the convolution kernel of 3*3 sizes, pool/2 to represent the convolution
Immediately with pond layer after layer, pond is carried out using max-pooling modes.The number of each layer of convolution kernel is provided successively by scheming.
Activation primitive uses ReLU functions.
The concrete form of ReLU activation primitives is：
Neural network is trained by the way of backpropagation, and min-batch chooses 256, and step-length chooses 10e-3.
After the end of scan, the scanning figure of article is calculated the input as the neural network, then neural network,
The title of each article is provided in the output.
Embodiment 4
The present embodiment and above-described embodiment difference lies in, mobile terminal of the invention include mobile phone, tablet computer and
The equipment for installing this system with camera.The present invention is made full use of this system by existing equipment, can
Reasonably solve the problems, such as user's luggage insufficient space.
Embodiment 5
Difference lies in the present invention can be also used for a system such as space layout in express delivery box for the present embodiment and above-described embodiment
The planning problem of the row confined space.Limited space is reasonably planned for a long time, and then user can be improved to space
The good habit of planning.
The foregoing is merely presently preferred embodiments of the present invention, is merely illustrative for the purpose of the present invention, and not restrictive
's.Those skilled in the art understand that in the spirit and scope defined by the claims in the present invention many changes can be carried out to it,
It changes or even equivalent, but falls in protection scope of the present invention.
Claims (10)
1. a kind of luggage space planning system based on mobile terminal, which is characterized in that including：Mobile terminal application subsystem kimonos
Business terminal system, the mobile terminal application subsystem include：Log-in module for user's registration and logs in；
The scan module of target luggage, for realizing the scanning recognition of the in-profile of luggage；
The scan module for waiting for belongings, for realizing the exterior contour of belongings is waited for；
Manual rectification module, for the number of scanned item, profile, title, classification realize user manual modification, deletion,
Set-up function；
Space planning and display module, to show the optimal location suggestion in target luggage of each article；
With list of articles and not band article reminding must must be provided for store user with article reminding module；
Network module, for service terminals system information described in real-time synchronization；
The service terminals system includes：
Taxonomy of goods convolutional neural networks module, for training taxonomy of goods model；
Data module, for storing the correct data after user corrects manually.
2. a kind of luggage space planning system based on mobile terminal as described in claim 1, which is characterized in that the target
The scan module of luggage and wait for that the scan modules of belongings is scanned using the camera that intelligent terminal carries.
3. a kind of luggage space planning system based on mobile terminal as claimed in claim 2, which is characterized in that the target
The scan module of luggage is by the intelligent terminal camera to the inside of target luggage from " preceding " " left side " "upper" three in each face
A angle is taken pictures, and is repeated up to scanning and is completed all luggages.
4. a kind of luggage space planning system based on mobile terminal as claimed in claim 2, which is characterized in that described to wait taking
Scan module with article multiple is waited for by the intelligent terminal camera to what is arranged at oblique line under consistent light background
The article of carrying is taken pictures from " preceding " " left side " "upper" three angle.
5. a kind of luggage space planning system based on mobile terminal as described in claim 3 or 4, which is characterized in that described
Target luggage scan module and item scan module use edge detection algorithm to detect edge.
6. a kind of luggage space planning system based on mobile terminal as claimed in claim 5, which is characterized in that the edge
Detection algorithm be Sobel edge detection algorithms, the Sobel edge detection algorithms by image carry out gray processing after,
The gradient that the point is calculated using the pixel of described image and the gray value of surrounding pixel, is calculated, if gradient
More than a certain predetermined threshold value, then it is assumed that the point is marginal point.
7. a kind of luggage space planning system based on mobile terminal as claimed in claim 6, which is characterized in that the gray scale
The computational methods of value are：
Make convolution by warp factor and former gray-scale map, respectively obtains the horizontal component and vertical component of gradient.I.e.：
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) indicates that the gray value that pixel (a, b) is put, x represent xth row, and y represents y row, f (a, b) represent a rows,
The gray value of the pixel of b row, Gx(x, y) represents coordinate as the horizontal gradient of the pixel of (x, y).
8. a kind of luggage space planning system based on mobile terminal as claimed in claim 7, which is characterized in that described image
Each pixel Grad then by：
Wherein：G represents gradient, GxCoordinate is represented as the horizontal gradient of the pixel of x, GyCoordinate is represented as the horizontal ladder of the pixel of y
Degree.
9. a kind of luggage space planning system based on mobile terminal as described in claim 1, which is characterized in that the article
The convolutional neural networks module of classification carries out Item Title and classification using convolutional neural networks.
10. a kind of luggage space planning system based on mobile terminal as claimed in claim 9, which is characterized in that the volume
Using ReLU activation primitives, the ReLU activation primitives are the activation primitive of product neural network：
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Citations (4)
| 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 |
-
2018
- 2018-03-29 CN CN201810269291.2A patent/CN108510116B/en not_active Expired - Fee Related
Patent Citations (4)
| 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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| CN108510116B (en) | 2020-06-30 |
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