CN110505444A - Safety defense monitoring system based on big data - Google Patents
Safety defense monitoring system based on big data Download PDFInfo
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- CN110505444A CN110505444A CN201910622268.1A CN201910622268A CN110505444A CN 110505444 A CN110505444 A CN 110505444A CN 201910622268 A CN201910622268 A CN 201910622268A CN 110505444 A CN110505444 A CN 110505444A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/06—Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/231—Content storage operation, e.g. caching movies for short term storage, replicating data over plural servers, prioritizing data for deletion
- H04N21/23103—Content storage operation, e.g. caching movies for short term storage, replicating data over plural servers, prioritizing data for deletion using load balancing strategies, e.g. by placing or distributing content on different disks, different memories or different servers
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/234—Processing of video elementary streams, e.g. splicing of video streams, manipulating MPEG-4 scene graphs
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/234—Processing of video elementary streams, e.g. splicing of video streams, manipulating MPEG-4 scene graphs
- H04N21/23418—Processing of video elementary streams, e.g. splicing of video streams, manipulating MPEG-4 scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/27—Server based end-user applications
- H04N21/274—Storing end-user multimedia data in response to end-user request, e.g. network recorder
- H04N21/2743—Video hosting of uploaded data from client
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
- H04N21/83—Generation or processing of protective or descriptive data associated with content; Content structuring
- H04N21/845—Structuring of content, e.g. decomposing content into time segments
- H04N21/8456—Structuring of content, e.g. decomposing content into time segments by decomposing the content in the time domain, e.g. in time segments
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
- H04N21/85—Assembly of content; Generation of multimedia applications
- H04N21/854—Content authoring
- H04N21/8549—Creating video summaries, e.g. movie trailer
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
Abstract
The invention discloses a kind of safety defense monitoring systems based on big data, including data active layer, Hadoop cluster service layer；Real time data is sent to the Hadoop cluster service layer by access gateway by the data active layer；The Hadoop cluster service layer includes several clustered nodes and distributed computing unit；The real time data distribution is stored in clustered node；The real time data that the distributed computing unit is stored in different clustered nodes to distribution is decomposed, and is carried out parallel computation with nearby principle and is obtained data element, is sent to client.The present invention uses distributed storage architecture, utilizes Node distribution formula computing architecture, multitude of video analytical calculation can be carried out parallel utilizes Node distribution formula computing architecture, multitude of video analytical calculation can be carried out parallel, it is more efficient for the big data storage and processing of safety monitoring.
Description
Technical field
The present invention relates to security monitoring technology, in particular to a kind of safety defense monitoring system based on big data.
Background technique
Security protection video monitoring data is there are two typical feature --- magnanimity and unstructured.Video monitoring data gauge mould
It is huge, and with the intensifying trend in superelevation Qinghua, video monitoring data scale will be increased with faster index rank.With structure
Change that data are different, the data overwhelming majority that video monitoring service generates based on non-structured video and audio and picture category data,
This brings great challenge to traditional data management and employment mechanism.Presently, there are ask as follows for HD video safety monitoring
Topic:
1, mass data storage and scaling problem.High-definition video monitoring system video data flow is bigger, and tradition is concentrated and deposited
Under storage mode, needs because of the present circumstance and a period of time extension demand that looks to the future is disposed, it is such the result is that early investment
Vacant resource is more, and later period extension demand is indefinite, causes programming and planning difficult.
2, mass data intelligence computation and problem analysis.The validity of massive video data intellectual analysis will be following large-scale
The important indicator of safety defense monitoring system.For the public safeties monitoring system such as subway, airport and safe city, once thing occurs
Therefore thousands of video camera video recording needs to retrieve or play back, even if accident does not occur under normality, it is also desirable to video counts
According to being analyzed, extracted and information excavating.Under centrally stored and serial analysis the mode of tradition, efficiency is lower, it is time-consuming more and
It is unable to satisfy the demand that quickly investigation judges after the accident.
3, system high reliability and redundancy problem.Massive video storage, especially finance and other important applications, storage week
Phase is longer, and once accident occurs, it is desirable that video record data guarantee can be used, this, which requires the storage of video data to back up, has height
Reliability.Conventional video storage is stored using DVR or NVR, and carries out Secondary Backup, system using storage and backup service device
Framework is complicated, and playing back videos operating efficiency is lower.
Therefore, how to provide the safety defense monitoring system that a kind of system architecture based on big data is simple, computational efficiency is high is
The problem of those skilled in the art's urgent need to resolve.
Summary of the invention
The present invention in view of the shortcomings of the prior art, provides a kind of safety defense monitoring system based on big data, using distribution
Formula storage architecture, using Node distribution formula computing architecture, multitude of video analytical calculation can be carried out parallel and utilize Node distribution formula
Computing architecture can carry out multitude of video analytical calculation parallel.Concrete scheme is as follows:
A kind of safety defense monitoring system based on big data, including data active layer, Hadoop cluster service layer；
Real time data is sent to the Hadoop cluster service layer by access gateway by the data active layer；
The Hadoop cluster service layer includes several clustered nodes and distributed computing unit；The real time data
Distribution is stored in clustered node；The real time data that the distributed computing unit is stored in different clustered nodes to distribution carries out
It decomposes, parallel computation is carried out with nearby principle and obtains data element, is sent to client.
Preferably, the data active layer includes video camera and sensor, for acquiring real time flow medium data.
Preferably, the data active layer includes outbound data interface, for acquiring non-real-time streaming media data, including from
The media data that DVR, NVR or third party system import.
Preferably, the distributed computing unit is connected with HDFS memory node, and the distributed computing unit will calculate
Obtained data element is stored to the HDFS memory node.
Preferably, the distributed computing unit includes task label unit, feature image extraction unit, the life of feature text
At unit；
The task label unit be used for real time data add label, the label include license plate data, human face data,
Behavioral data, violation event；And corresponding label data is screened according to the task that the client is submitted；
The feature image extraction unit is used to carry out corresponding picture feature extraction, packet to the real time data of different labels
Car license recognition, recognition of face, position identification, action recognition, break in traffic rules and regulations identification are included, feature image is obtained；
The feature text generation unit is for generating character features description, behavior characteristic characterization, or reception record manually
The video frequency abstract entered.
Preferably, several clustered nodes are connected to name node, and the name node is for saving real time data
Store path.According to the real time data for each clustered node storage of task schedule that the client is submitted.
Preferably, the step of clustered node write-in data include:
Described issued by Web Server program to the name node uploads file request；
After the name node receives write request, cluster is distributed according to file size and back end memory state
Node；
Assigned clustered node receives the file uploaded by client；
Directory information of the name each file of node updates in clustered node.
Preferably, the clustered node is stored with the file of client upload in the form of data block.
Preferably, the distributed computing unit carries out intelligent view using Hadoop MapReduce distributed computing platform
Frequency analysis generates video metadata, and the characteristic information of video metadata is excavated by Hive, carries out secondary analysis application.
The present invention provides a kind of safety defense monitoring systems based on big data compared with the prior art to have the advantages that
1, based on the distributed system architecture of Hadoop, flexible expansion can be carried out according to later period demand to meet not same order
The demand of section, without largely being invested in the early stage, framework is simple and easy, and the addition and deletion of system node, node are appointed
The transfer of business is very flexible；
2, the deployment of clustered node in the cluster, can be flexibly carried out, clustered node can use the hardware of connection universal type,
Its reliability is improved by Distributed Storage and calculation, the mode using traditional high-side hardware is avoided, substantially reduces
Cost of investment；
3, data are uploaded and is stored in multiple nodes, using Node distribution formula computing architecture, multitude of video can be carried out parallel
Analytical calculation work will be broken down into many lesser tasks, calculate in multiple nodal parallels and then be exported again, the sea for being
It measures video data analysis and association mining is more efficient.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of invention for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is a kind of distributed system architecture schematic diagram of Hadoop of the present invention；
Fig. 2 is a kind of organization chart of the safety defense monitoring system based on big data of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Present embodiments provide a kind of safety defense monitoring system based on big data, including data active layer, Hadoop cluster clothes
Business layer；Real time data is sent to Hadoop cluster service layer by access gateway by data active layer；Data active layer includes video camera
And sensor, for acquiring real time flow medium data.It further include outbound data interface, for acquiring non-real-time streaming media data,
Including the media data imported from DVR, NVR or third party system.Hadoop cluster service layer include several clustered nodes with
And distributed computing unit；Real time data distribution is stored in clustered node, can according to circumstances carry out data backup data choosing
(3 parts of data of default backup) are selected, index of reference is established by HBase.It is needed at this time to the NVR and special purpose memory devices for passing suede
It is reconstructed, to bring into distributed type assemblies node architecture.
Distributed computing unit is stored in the real time datas of different clustered nodes to distribution and decomposes, with nearby principle into
Row parallel computation obtains data element, is sent to client.Distributed computing unit uses Hadoop MapReduce distribution meter
It calculates model and carries out intelligent video analysis, generate video metadata, the characteristic information of video metadata is excavated by Hive, carry out two
Secondary analysis application.
Distributed computing unit includes task label unit, feature image extraction unit, feature text generation unit；
Task label unit is used to add label to real time data, and label includes license plate data, human face data, behavior number
According to, violation event；And corresponding label data is screened according to the task that client is submitted；
Feature image extraction unit is used to carry out corresponding picture feature extraction, including vehicle to the real time data of different labels
Board identification, recognition of face, position identification, action recognition, break in traffic rules and regulations identification, obtain feature image；
Feature text generation unit is for generating character features description, behavior characteristic characterization, or receives and be manually entered
Video frequency abstract.Video frequency abstract reacts the main contents of a camera lens.
Several clustered nodes are connected to name node, and name node is for saving real-time data memory path.According to visitor
The real time data of each clustered node storage of the task schedule that family end is submitted.Clustered node is stored with client in the form of data block
Hold the file uploaded.For video data, since video is unstructured data, compression coding mode will increase in video frame with
Relevance between frame.Clustered node be written data the step of include:
It is issued by Web Server program to name node and uploads file request；
After name node receives write request, cluster section is distributed according to file size and back end memory state
Point；
Assigned clustered node receives the file uploaded by client；
Name each file of node updates in the directory information of clustered node.
Include: to the step of clustered node reading data
It is issued by Web Server program to name node and obtains file request；
Node checks respective file information is named, and file content is sent to by client by clustered node；
The blocks of files of client downloads transmission, and blocks of files is merged into a file.
In one embodiment, distributed computing unit is connected with HDFS memory node, and distributed computing unit will calculate
Obtained data element is stored to HDFS memory node.Client can transfer historical data from HDFS memory node in real time.
A kind of safety defense monitoring system based on big data provided by the present invention is described in detail above, herein
Apply that a specific example illustrates the principle and implementation of the invention, the explanation of above example is only intended to help
Understand method and its core concept of the invention；At the same time, for those skilled in the art, according to the thought of the present invention,
There will be changes in the specific implementation manner and application range, in conclusion the content of the present specification should not be construed as to this
The limitation of invention.
Herein, relational terms such as first and second and the like be used merely to by an entity or operation with it is another
One entity or operation distinguish, and without necessarily requiring or implying between these entities or operation, there are any this reality
Relationship or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to the packet of nonexcludability
Contain, so that the process, method, article or equipment for including a series of elements not only includes those elements, but also including
Other elements that are not explicitly listed, or further include for elements inherent to such a process, method, article, or device.
In the absence of more restrictions, the element limited by sentence " including one ... ", it is not excluded that including described want
There is also other identical elements in the process, method, article or equipment of element.
Claims (9)
1. a kind of safety defense monitoring system based on big data, which is characterized in that including data active layer, Hadoop cluster service layer；
Real time data is sent to the Hadoop cluster service layer by access gateway by the data active layer；
The Hadoop cluster service layer includes several clustered nodes and distributed computing unit；The real time data distribution
It is stored in clustered node；The real time data that the distributed computing unit is stored in different clustered nodes to distribution is divided
Solution carries out parallel computation with nearby principle and obtains data element, is sent to client.
2. the safety defense monitoring system according to claim 1 based on big data, which is characterized in that the data active layer includes
Video camera and sensor, for acquiring real time flow medium data.
3. the safety defense monitoring system according to claim 1 based on big data, which is characterized in that the data active layer includes
Outbound data interface, for acquiring non-real-time streaming media data, including the media number imported from DVR, NVR or third party system
According to.
4. the safety defense monitoring system according to claim 1 based on big data, which is characterized in that the distributed computing list
Member is connected with HDFS memory node, and the data element being calculated is stored to the HDFS to store and be saved by the distributed computing unit
Point.
5. the safety defense monitoring system according to claim 1 based on big data, which is characterized in that the distributed computing list
Member includes task label unit, feature image extraction unit, feature text generation unit；
The task label unit is used to add label to real time data, and the label includes license plate data, human face data, behavior
Data, violation event；And corresponding label data is screened according to the task that the client is submitted；
The feature image extraction unit is used to carry out corresponding picture feature extraction, including vehicle to the real time data of different labels
Board identification, recognition of face, position identification, action recognition, break in traffic rules and regulations identification, obtain feature image；
The feature text generation unit is for generating character features description, behavior characteristic characterization, or receives and be manually entered
Video frequency abstract.
6. the safety defense monitoring system according to claim 1 based on big data, which is characterized in that several clustered nodes
It is connected to name node, the name node is for saving real-time data memory path.Appointed according to what the client was submitted
The real time data of each clustered node storage is dispatched in business.
7. the safety defense monitoring system according to claim 6 based on big data, which is characterized in that the clustered node write-in
The step of data includes:
Described issued by Web Server program to the name node uploads file request；
After the name node receives write request, cluster section is distributed according to file size and back end memory state
Point；
Assigned clustered node receives the file uploaded by client；
Directory information of the name each file of node updates in clustered node.
8. the safety defense monitoring system according to claim 1 based on big data, which is characterized in that the clustered node is with number
The file of client upload is stored with according to the form of block.
9. the safety defense monitoring system according to claim 1 based on big data, which is characterized in that the distributed computing list
Member carries out intelligent video analysis using Hadoop MapReduce distributed computing platform, generates video metadata, passes through Hive
The characteristic information of video metadata is excavated, secondary analysis application is carried out.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111246168A (en) * | 2020-01-15 | 2020-06-05 | 深圳市网新新思软件有限公司 | Video content batch supervision cluster system and method |
| CN111858763A (en) * | 2020-07-22 | 2020-10-30 | 陈永欢 | Big data security protection monitored control system |
| CN112396016A (en) * | 2020-11-26 | 2021-02-23 | 武汉宏数信息技术有限责任公司 | Face recognition system based on big data technology |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2387240A2 (en) * | 2010-05-13 | 2011-11-16 | Honeywell International, Inc. | Surveillance system with direct database server storage |
| CN102638456A (en) * | 2012-03-19 | 2012-08-15 | 杭州海康威视系统技术有限公司 | Intelligent analysis method for mass real-time video code stream based on cloud computation and system thereof |
| CN105045820A (en) * | 2015-06-25 | 2015-11-11 | 浙江立元通信技术股份有限公司 | Method for processing video image information of mass data and database system |
| CN106534344A (en) * | 2016-12-07 | 2017-03-22 | 高新兴科技集团股份有限公司 | Cloud platform video processing system and application method thereof |
| CN108205468A (en) * | 2016-12-16 | 2018-06-26 | 上海仪电（集团）有限公司中央研究院 | A kind of distributed system and implementation method towards massive video image |
| CN109922315A (en) * | 2019-03-01 | 2019-06-21 | 西安电子科技大学 | A kind of real-time video behavior analysis method based on Spark and OpenCV |
-
2019
- 2019-07-10 CN CN201910622268.1A patent/CN110505444A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2387240A2 (en) * | 2010-05-13 | 2011-11-16 | Honeywell International, Inc. | Surveillance system with direct database server storage |
| CN102638456A (en) * | 2012-03-19 | 2012-08-15 | 杭州海康威视系统技术有限公司 | Intelligent analysis method for mass real-time video code stream based on cloud computation and system thereof |
| CN105045820A (en) * | 2015-06-25 | 2015-11-11 | 浙江立元通信技术股份有限公司 | Method for processing video image information of mass data and database system |
| CN106534344A (en) * | 2016-12-07 | 2017-03-22 | 高新兴科技集团股份有限公司 | Cloud platform video processing system and application method thereof |
| CN108205468A (en) * | 2016-12-16 | 2018-06-26 | 上海仪电（集团）有限公司中央研究院 | A kind of distributed system and implementation method towards massive video image |
| CN109922315A (en) * | 2019-03-01 | 2019-06-21 | 西安电子科技大学 | A kind of real-time video behavior analysis method based on Spark and OpenCV |
Non-Patent Citations (1)
| Title |
|---|
| 徐鹏: "《Hadoop 2.X HDFS源码剖析》", 31 March 2016 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111246168A (en) * | 2020-01-15 | 2020-06-05 | 深圳市网新新思软件有限公司 | Video content batch supervision cluster system and method |
| CN111858763A (en) * | 2020-07-22 | 2020-10-30 | 陈永欢 | Big data security protection monitored control system |
| CN112396016A (en) * | 2020-11-26 | 2021-02-23 | 武汉宏数信息技术有限责任公司 | Face recognition system based on big data technology |
| CN112396016B (en) * | 2020-11-26 | 2021-07-23 | 武汉宏数信息技术有限责任公司 | Face recognition system based on big data technology |
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