CN106534166A - Digital library management system - Google Patents
Digital library management system Download PDFInfo
- Publication number
- CN106534166A CN106534166A CN201611105442.8A CN201611105442A CN106534166A CN 106534166 A CN106534166 A CN 106534166A CN 201611105442 A CN201611105442 A CN 201611105442A CN 106534166 A CN106534166 A CN 106534166A
- Authority
- CN
- China
- Prior art keywords
- data
- module
- resource
- server
- user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/08—Network architectures or network communication protocols for network security for authentication of entities
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/10—Network architectures or network communication protocols for network security for controlling access to devices or network resources
Landscapes
- Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Computer Security & Cryptography (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The present invention provides a digital library management system. The system comprises an authentication module, an authorization module and a resource access module. The resource access module comprises a data acquisition module, a data classification module, a classification-based detection module and a detection fusion module. The digital library management system has the beneficial effect of improving access security.
Description
Technical field
The present invention relates to digital library field, and in particular to a kind of digital library management system.
Background technology
At present, most of digital librarys are sold using simple parcel mode, and control certification conjunction by IP address
Method user.There is following drawback in this management mode of digital library：Clearly can not control and management service object, usually
Cause malice or excessive full text to download phenomenon, also, the service to all users in the range of the IP can only be closed to keep away
Exempt from malice or excessive full text download to result in greater loss, this is all caused to Digital Library Services business and its service object
Loss.
The content of the invention
For the problems referred to above, the present invention is intended to provide a kind of digital library management system.
The purpose of the present invention employs the following technical solutions to realize：
It is there is provided a kind of digital library management system, including authentication module, authorization module and resource access module, described
Resource access module includes data acquisition module, data categorization module, classification and Detection module and detection fusion module, certification mould
Block, accesses after resource to library server application for user, and library server verifies user and terminal interface；Authorize mould
Block, after being proved to be successful, authorization code is write library server the terminal interface of user, and is linked to resource business service
Device；Resource access module, after the double authentication for resource business server to authentication information with mandate access request, it is allowed to
User accesses, uses resource.
Beneficial effects of the present invention are：Improve access security.
Description of the drawings
Using accompanying drawing, the invention will be further described, but the embodiment in accompanying drawing does not constitute any limit to the present invention
System, for one of ordinary skill in the art, on the premise of not paying creative work, can be being obtained according to the following drawings
Other accompanying drawings.
Fig. 1 is the structure connection diagram of the present invention.
Reference：
Authentication module 1, authorization module 2, resource access module 3.
Specific embodiment
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of digital library management system of the present embodiment, including authentication module 1, authorization module 2 and money
Source access modules 3, the resource access module 3 include data acquisition module, data categorization module, classification and Detection module and detection
Fusion Module, authentication module 1 are accessed after resource to library server application for user, library server checking user
And terminal interface；Authorization module 2, after being proved to be successful, authorization code is write library server the terminal interface of user, and
It is linked to resource business's server；Resource access module 3, for resource business server is to authentication information and authorizes access request
Double authentication after, it is allowed to user accesses, uses resource, and the data acquisition module is used for gathering the number for needing to be detected
According to；The data categorization module for the data exported by data acquisition module are divided into view data and text data, and
Filtration treatment is carried out to sorted data；The classification and Detection module is for being analyzed detection to sorted data；Institute
Detection fusion module is stated for the view data according to needed for detection demand screening and text data.
Preferably, user and terminal interface are registered to library server in advance, with pre- in library server
Deposit the user and terminal interface identification information.
This preferred embodiment is easy to control and management service object.
Preferably, library server is to resource business's server registration, obtains library server code, and by library
In server, user and terminal interface log-on message are sent to resource business's server.
This preferred embodiment is easy to resource business's server to be managed library.
Preferably, the collection needs the data for being detected, including：
(1) data for needing to be detected in certain period of time are gathered, the data is carried out just by the filtering rule of setting
Step filtration treatment, the filtering rule of the setting include deleting comprising spcial character, promote related special Chinese character and webpage chain
The data of the content for connecing；
(2) time range of the certain period of time is set as [XB,XE], by [XB,XE] n is equally divided into sequentially in time
The individual sub- time period, the data in each sub- time period are carried out with importance degree assessment, assessment formula is defined as： In formula, UiFor the significance level of i-th sub- time period, UTiFor setting i-th sub- time period it is important
Degree value, GiFor the quantity of the data of i-th sub- time period, G is in [XB,XE] in data quantity；By each importance degree according to
Ascending to be ranked up, putting in order according to importance degree sends data successively to data categorization module.
The data that need not be detected are deleted, are reduced inspection by setting filtering rule by this preferred embodiment
Survey the data volume of subsequent treatment；Importance degree assessment is carried out by the data to each sub- time period, and it is suitable according to the arrangement of importance degree
Sequence, data are sent successively to data categorization module, enable follow-up module to anticipate the high data of significance level, are improved
The speed of detection.
Preferably, it is described that filtration treatment is carried out to sorted data, including：
A, extraction text data, carry out clustering processing, form the text data set of multiple classifications to this article notebook data；
B, calculate each classification text data concentrate data quantity, according to quantity by less to big order to multiple
Text data set is ranked up；Front 27% text data set is deleted, remaining text data set and view data are sent
To classification and Detection module.
This preferred embodiment further carries out clustering processing to text data, filters out the text data set of negligible amounts,
The data volume of subsequent detection is reduced, so as to further increase the speed of detection.
Preferably, it is described that clustering processing is carried out to this article notebook data, including：
Number K that a, determination cluster, including：The first of k-means clustering algorithms is set using method of equal intervals to this article notebook data
Beginning center, obtains cluster centre；Using the midpoint of adjacent cluster centre as the division points classified after cluster centre is obtained, will
Each object is added to closest apoplexy due to endogenous wind, so that it is determined that number K for clustering；
B, this article notebook data is divided into into n sample, vectorization is carried out to n sample, is calculated by included angle cosine function
All samples similarity between any two, obtains similarity matrix SIM：
SIM=[sim (ei,ej)]n×n, i, j=1 ..., n
C, the similarity sum for calculating each sample and other all samples, sum formula is：
In formula,For sample eiWith the similarity sum of other all samples, sim (ei,ej) represent sample ei,ejBetween similarity,
I, j=1 ..., n；
D, arrange in descending orderIfThe corresponding sample of front 4 values by arranging from big to small is
emax,emax-1,emax-2,emax-3, first initial center mi that clusters is determined according to the following equation：
Wherein, ωmax-μRepresent emax-μImportance degree weights；
It is e, rightIn the corresponding matrix of maximum in the element of row vector carry out ascending order arrangement, it is assumed that front k-1 is most
Little element is SIMpq, q=1 ..., k-1, k-1 minimum element S IM before selectingpqCorresponding sample is used as remaining k-
1 initial center that clusters；
Remaining sample is distributed to similarity most by f, the remaining sample of calculating and each initial similarity clustered between center
It is high to cluster, form the k after change and cluster；Calculate change after cluster in each sample average, after renewal
Cluster center replace update before the center that clusters；If the center that clusters before updating is identical with the center that clusters after renewal, or
Object function has reached minima, stops updating, and the object function is：
Wherein, ClL-th during expression k clusters clusters, exSample in clustering for l-th,For what is clustered for l-th
Center.
This preferred embodiment is prevented effectively from the single occasionality for taking arbitrary sampling method to be brought, and solves to text number
According to the problems of when k value and initialization cluster centre is chosen, improve cluster stability, enter when carrying out clustering processing
One step improves the precision for carrying out filtration treatment to text data.
Preferably, the classification and Detection module includes view data detector unit and text data detector unit；The figure
As data detecting unit is detected to view data based on semantic feature, specially：Using the method for wavelet transformation to image
Split, region low-level feature is extracted, structural features matrix is reapplied Non-negative Matrix Factorization training algorithm construction language
Adopted space, projects image onto the space to obtain image, semantic feature；The text data detector unit includes text data
Modeling subelement, text data classification subelement, detection sub-unit, specially：
(1) text data modeling subelement, for using constituting the lexical item of document expressing the semanteme of document, which is by n pieces
Document t1,t2,…,tnEvery document representation into m dimensional feature vector v1,v2,…,vm, constitute the document-eigenmatrix of n × m：
Wherein, m is the quantity of the lexical item for constituting document；
Wherein, h (ti,vj) represent lexical item vjIn document tiIn shared weight, f (ti,vj) represent lexical item vjIn document tiIn go out
Existing number of times, f (vj) represent lexical item vjThe number of times summation occurred in all documents；
(2) text data classification subelement, for classifying to the text document after modeling, specifically includes：
A, by the document Random Maps in text set to a two dimensional surface mesh space, one can only be projected in each grid
Piece document, meanwhile, a number of Formica fusca is placed on two dimensional surface；
B, every Formica fusca are moved in two-dimensional grid space at random, select a document to pick up, and carry it in two-dimensional grid
Space random movement, it is often mobile once, Formica fusca calculates document entrained by it or institute document within a grid and surrounding
Swarm similarity, decides whether to pick up or put down the document, using each grid as two-dimensional grid spatial spreading value, if Formica fusca
Position is p, and the swarm similarity of its place environment is defined as：
In formula, ti∈ p (a × a) represent document tiThe neighborhood of length of side a of p × a in position, r (ti,tj) represent two documents between text
This distance, σ represent the similarity factor, and the span of σ is [1,2],Formula
In, m represents lexical item quantity in document；
C, pick up and put down, if Formica fusca does not carry any document movement, then it will pick up and surrounding colony
The relatively low document of similarity；If Formica fusca is carrying a document movement, then when Formica fusca is in abortive haul lattice, and this is literary
When shelves are higher with the swarm similarity of surrounding, it will put down this document, pick up probability Pj(ti) and put down probability Pf(ti)
It is defined as：In formula, T1And T2For constant threshold,
T1=0.14, T2=0.16；
D, return perform b and c, and through a period of time, the high document of similarity will be collected at the same area.
This preferred embodiment carries out classification and Detection to data, can make full use of different types of data feature, using correspondence
Method detected, improve the specific aim of detection；Document is modeled, non-structured text data is converted into can
The structural data of calculating, while be easy to subsequently classify document；Text data classification subelement improves detection efficiency,
Detection time is saved.
Digital book pipe Management System Data testing result of the present invention is as shown in the table：
| Digital book bibliography | Data Detection speed | Data examine side accuracy rate |
| 50000 | 0.22s | 96% |
| 60000 | 0.26s | 94% |
| 70000 | 0.28s | 93% |
Finally it should be noted that above example is only illustrating technical scheme, rather than to present invention guarantor
The restriction of shield scope, although having made to explain to the present invention with reference to preferred embodiment, one of ordinary skill in the art should
Work as understanding, technical scheme can be modified or equivalent, without deviating from the reality of technical solution of the present invention
Matter and scope.
Claims (4)
1. a kind of digital library management system, is characterized in that, including authentication module, authorization module and resource access module, institute
Stating resource access module includes data acquisition module, data categorization module, classification and Detection module and detection fusion module, certification mould
Block, accesses after resource to library server application for user, and library server verifies user and terminal interface；Authorize mould
Block, after being proved to be successful, authorization code is write library server the terminal interface of user, and is linked to resource business service
Device；Resource access module, after the double authentication for resource business server to authentication information with mandate access request, it is allowed to
User accesses, uses resource.
2. a kind of digital library management system according to claim 1, is characterized in that, user and terminal interface in advance to
Library server is registered, with the user and the terminal interface identification information of prestoring in library server.
3. a kind of digital library management system according to claim 2, is characterized in that, library server is to resource business
Server registration, obtains library server code, and user in library server and terminal interface log-on message is transmitted
To resource business's server.
4. a kind of digital library management system according to claim 3, is characterized in that, the collection needs are detected
Data, including：
(1) data for needing to be detected in certain period of time are gathered, tentatively mistake is carried out to the data by the filtering rule of setting
Filter is processed, and the filtering rule of the setting includes deleting comprising spcial character, the special Chinese character of popularization correlation and web page interlinkage
The data of content；
(2) time range of the certain period of time is set as [XB,XE], by [XB,XE] n is equally divided into sequentially in time
The sub- time period, the data in each sub- time period are carried out with importance degree assessment, assessment formula is defined as： In formula, UiFor the significance level of i-th sub- time period, UTiFor setting i-th sub- time period it is important
Degree value, GiFor the quantity of the data of i-th sub- time period, G is in [XB,XE] in data quantity；By each importance degree according to
Ascending to be ranked up, putting in order according to importance degree sends data successively to data categorization module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611105442.8A CN106534166A (en) | 2016-12-05 | 2016-12-05 | Digital library management system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201611105442.8A CN106534166A (en) | 2016-12-05 | 2016-12-05 | Digital library management system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106534166A true CN106534166A (en) | 2017-03-22 |
Family
ID=58354744
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201611105442.8A Pending CN106534166A (en) | 2016-12-05 | 2016-12-05 | Digital library management system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106534166A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050071650A1 (en) * | 2003-09-29 | 2005-03-31 | Jo Su Hyung | Method and apparatus for security engine management in network nodes |
| CN101887549A (en) * | 2010-07-16 | 2010-11-17 | 谢天红 | Book acquiring system of digital library |
| CN102930439A (en) * | 2011-08-12 | 2013-02-13 | 江苏大学 | Digital library management system |
| CN103473281A (en) * | 2013-08-29 | 2013-12-25 | 南京斯谱蓝自动化科技有限公司 | Digital audio and video library system |
| CN104715024A (en) * | 2015-03-03 | 2015-06-17 | 湖北光谷天下传媒股份有限公司 | Multimedia hotspot analysis method |
-
2016
- 2016-12-05 CN CN201611105442.8A patent/CN106534166A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050071650A1 (en) * | 2003-09-29 | 2005-03-31 | Jo Su Hyung | Method and apparatus for security engine management in network nodes |
| CN101887549A (en) * | 2010-07-16 | 2010-11-17 | 谢天红 | Book acquiring system of digital library |
| CN102930439A (en) * | 2011-08-12 | 2013-02-13 | 江苏大学 | Digital library management system |
| CN103473281A (en) * | 2013-08-29 | 2013-12-25 | 南京斯谱蓝自动化科技有限公司 | Digital audio and video library system |
| CN104715024A (en) * | 2015-03-03 | 2015-06-17 | 湖北光谷天下传媒股份有限公司 | Multimedia hotspot analysis method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20210182611A1 (en) | Training data acquisition method and device, server and storage medium | |
| CN107577688B (en) | Original article influence analysis system based on media information acquisition | |
| CN105608179B (en) | The method and apparatus for determining the relevance of user identifier | |
| CN107862022B (en) | Culture resource recommendation system | |
| CN105095223B (en) | File classification method and server | |
| CN111461553A (en) | System and method for monitoring and analyzing public sentiment in scenic spot | |
| CN103970752B (en) | Independent access person's quantity survey (surveying) method and system | |
| WO2017097231A1 (en) | Topic processing method and device | |
| CN106506528A (en) | A kind of Network Safety Analysis system under big data environment | |
| CN106446124B (en) | A kind of Website classification method based on cyberrelationship figure | |
| CN106095939B (en) | The acquisition methods and device of account authority | |
| CN107436877B (en) | Hot topic pushing method and device | |
| CN107741958A (en) | A kind of data processing method and system | |
| CN109241392A (en) | Recognition methods, device, system and the storage medium of target word | |
| CN115941322A (en) | Attack detection method, device, equipment and storage medium based on artificial intelligence | |
| CN111738341A (en) | Distributed large-scale face clustering method and device | |
| CN106528870B (en) | A kind of big data intelligent analysis system | |
| CN104966109A (en) | Medical laboratory report image classification method and apparatus | |
| CN108959289B (en) | Website category acquisition method and device | |
| CN111538859B (en) | Method and device for dynamically updating video tag and electronic equipment | |
| CN107025567A (en) | A kind of data processing method and device | |
| CN109992960B (en) | Counterfeit parameter detection method and device, electronic equipment and storage medium | |
| CN108520012B (en) | Mobile internet user comment mining method based on machine learning | |
| CN106534166A (en) | Digital library management system | |
| Washha et al. | Information quality in social networks: Predicting spammy naming patterns for retrieving twitter spam accounts |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170322 |