CN111858763A - Big data security protection monitored control system - Google Patents

Big data security protection monitored control system Download PDF

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Publication number
CN111858763A
CN111858763A CN202010712858.6A CN202010712858A CN111858763A CN 111858763 A CN111858763 A CN 111858763A CN 202010712858 A CN202010712858 A CN 202010712858A CN 111858763 A CN111858763 A CN 111858763A
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data
security monitoring
security
docker
monitoring data
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不公告发明人
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Li Wenwen
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Chen Yonghuan
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21Design, administration or maintenance of databases
    • G06F16/215Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60Protecting data
    • G06F21/602Providing cryptographic facilities or services
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60Protecting data
    • G06F21/64Protecting data integrity, e.g. using checksums, certificates or signatures
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for program control, e.g. control units
    • G06F9/06Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44Arrangements for executing specific programs
    • G06F9/455Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533Hypervisors; Virtual machine monitors
    • G06F9/45558Hypervisor-specific management and integration aspects
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for program control, e.g. control units
    • G06F9/06Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44Arrangements for executing specific programs
    • G06F9/455Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533Hypervisors; Virtual machine monitors
    • G06F9/45558Hypervisor-specific management and integration aspects
    • G06F2009/45562Creating, deleting, cloning virtual machine instances

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Computer Security & Cryptography (AREA)
  • Health & Medical Sciences (AREA)
  • Bioethics (AREA)
  • General Health & Medical Sciences (AREA)
  • Computer Hardware Design (AREA)
  • Data Mining & Analysis (AREA)
  • Quality & Reliability (AREA)
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Abstract

The invention discloses a big data security monitoring system, which comprises: a data acquisition module: the system comprises a monitoring server and a monitoring server, wherein the monitoring server is used for acquiring security monitoring data, and the acquired security monitoring data comprise video monitoring data, audio monitoring data and monitoring position positioning data; the data preprocessing module is used for preprocessing the acquired security monitoring data; rejecting abnormal bad point data existing in the security monitoring original data; smoothing or fitting the security monitoring data, and uploading the acquired security monitoring data to a cloud platform processing module and a block chain platform processing module through a gateway; the block chain platform processing module is used for encrypting and analyzing the security data; and the cloud platform processing module is used for performing distributed storage on the security data through a docker mirror image technology and forming mirror image persistent storage in a distributed file system of the docker warehouse. The invention combines the block chain and the docker mirror image technology, thereby realizing the simultaneous satisfaction of high processing efficiency and safety requirements.

Description

Big data security protection monitored control system
Technical Field
The invention belongs to the technical field of security and protection monitoring, and particularly relates to a big data security and protection monitoring system.
Background
The security video monitoring data has two typical characteristics, mass and unstructured. The video monitoring data size is huge, and with the trend of ultra-high-definition being strengthened, the video monitoring data size will grow at a faster exponential level. Unlike structured data, most of the data generated by video surveillance services is mainly unstructured video and audio data and picture data, which brings great challenges to traditional data management and use mechanisms.
Security protection monitoring data has 2 obvious characteristics: 1) data scale sea quantification: 2) the data type is unstructured. Data transmission and storage difficulties are caused by data scale and data type unstructured data brings great challenges to data utilization. At present, a cloud computing technology based on Hadoop is initially and successfully applied to the field of security and protection monitoring big data, the processing efficiency of the security and protection monitoring big data is greatly improved, but the cloud computing technology essentially belongs to a distributed processing mode, a great challenge is provided for mass data storage, the storage period is long for places such as finance and commerce, and once an accident occurs, video recording data is required to be guaranteed to be available, and the storage and backup of the video data are required to have high reliability. The traditional video storage utilizes DVR or NVR to store, and utilizes a storage backup server to carry out secondary backup, so that the system architecture is complex, and the video playback operation efficiency is low.
Disclosure of Invention
The invention aims to solve the problems and provides a big data security monitoring system which combines a block chain and a docker mirror image technology to simultaneously meet the requirements of high processing efficiency and safety.
In order to achieve the purpose, the invention adopts the following technical scheme:
a big data security monitoring system, comprising:
a data acquisition module: the system comprises a monitoring server and a monitoring server, wherein the monitoring server is used for acquiring security monitoring data, and the acquired security monitoring data comprise video monitoring data, audio monitoring data and monitoring position positioning data;
the data preprocessing module is used for preprocessing the acquired security monitoring data; preprocessing security monitoring data comprises extracting useful information in security original data and converting the useful information into a decimal format; rejecting abnormal bad point data existing in the security monitoring original data; removing the linear trend term; performing zero-equalization processing on the security monitoring original data; smoothing or fitting the security monitoring data, and uploading the acquired security monitoring data to a cloud platform processing module and a block chain platform processing module through a gateway;
the block chain platform processing module is used for encrypting and analyzing security data;
the cloud platform processing module performs distributed storage on the security data through a docker mirror image technology, and forms mirror image persistent storage in a distributed file system of the docker warehouse.
Further, the blockchain platform processing platform comprises:
the receiving unit is used for calling a write-in interface of the block chain platform to receive a write-in request of the security data;
the encryption module is used for generating a data fingerprint according to the received security monitoring data by the block chain platform and embedding the generated data fingerprint into the security monitoring data to prevent the data fingerprint from being maliciously attacked and tampered; then, after encrypting the security monitoring data embedded with the data fingerprint, converting the security monitoring data according to a corresponding preset conversion rule and writing the security monitoring data into a database;
and the processing and analyzing module is used for processing and analyzing the encrypted security monitoring data in real time to obtain specific information in the security monitoring data.
Further, the processing and analyzing module comprises a body type recognition module and a face recognition module, wherein the body type recognition module is used for dividing regional grids, comparing and measuring reference objects, analyzing and counting distances, calculating a body type and outputting statistics;
the face recognition module is used for recognizing human body features, selecting face images, recognizing and analyzing faces and conveniently comparing face information in a police database; and jointly confirming the data by utilizing the body type analysis data and the data analyzed by face recognition.
Furthermore, the processing and analyzing module further comprises a task label unit, a feature picture extracting unit and a feature text generating unit;
the task labeling unit is used for adding a label to the real-time data, wherein the label comprises license plate data, face data, behavior data and violation events; screening corresponding label data according to the tasks submitted by the client;
the characteristic picture extraction unit is used for carrying out corresponding picture characteristic extraction on real-time data of different labels, wherein the picture characteristic extraction includes license plate recognition, face recognition, position recognition, action recognition and traffic violation recognition, and characteristic pictures are obtained;
the characteristic text generation unit is used for generating character characteristic description and behavior characteristic description or receiving a manually-entered video abstract.
Further, the cloud platform comprises a Docker mirror server and a Docker warehouse, wherein the Docker warehouse comprises a distributed cache system and a distributed file system; the distributed cache system consists of a distributed cache system main node and all the Docker local cache nodes; the distributed file system is composed of a main storage node and a plurality of auxiliary storage nodes.
Further, the docker mirror image comprises a docker mirror image which is created according to uploading time of the security monitoring data, and each created docker mirror image of the security monitoring data is stored in the distributed cache system; the distributed cache system main node is used for registering the Docker mirror images stored in the distributed cache system and inquiring whether the required security and protection monitoring data Docker mirror images are stored in the distributed cache system, and the master node and the slave node of the distributed file system are used for persistently storing the security and protection monitoring data Docker mirror images.
Further, the distributed cache system is used for storing mirror images required by operating the Docker container; the master and slave nodes of the distributed file system are served by a storage server that does not run a Docker container for persistent storage container mirroring.
Further, the distributed file system master node is configured to register a Docker image stored in the distributed file system, and is configured to query whether a required Docker image is stored in the distributed file system.
The invention has the beneficial effects that: the combination of the block chain and the docker mirror image technology is utilized, so that the requirements on high processing efficiency and safety are met simultaneously; the invention not only ensures the processing efficiency of security monitoring big data; meanwhile, the monitoring-prevention data is delivered to the block chain platform for data processing, DDOS attack is prevented by using the characteristics of the block chain technology disclosure, transparency and non-falsification, DDOS attack is eliminated, meanwhile, the security data is delivered to the cloud platform, and potential safety hazards caused by distributed storage of the docker mirror image are used, so that the security data is safer. The invention can be widely applied to the field of security protection.
Drawings
FIG. 1 is a system block diagram of a security monitoring system of the present invention;
fig. 2 is a system block diagram of a blockchain platform processing module.
Detailed Description
The invention is further described with reference to the following figures and examples.
As shown in fig. 1, a big data security monitoring system includes:
a data acquisition module: the system comprises a monitoring server and a monitoring server, wherein the monitoring server is used for acquiring security monitoring data, and the acquired security monitoring data comprise video monitoring data, audio monitoring data and monitoring position positioning data;
the data preprocessing module is used for preprocessing the acquired security monitoring data; preprocessing security monitoring data comprises extracting useful information in security original data and converting the useful information into a decimal format; rejecting abnormal bad point data existing in the security monitoring original data; removing the linear trend term; performing zero-equalization processing on the security monitoring original data; smoothing or fitting the security monitoring data, and uploading the acquired security monitoring data to a cloud platform processing module and a block chain platform processing module through a gateway; the block chain platform processing module is used for encrypting and analyzing the security data; and the cloud platform processing module is used for performing distributed storage on the security data through a docker mirror image technology and forming mirror image persistent storage in a distributed file system of the docker warehouse.
As shown in fig. 2, the blockchain platform processing platform includes:
the receiving unit is used for calling a write-in interface of the block chain platform to receive a write-in request of the security data;
the encryption module is used for generating a data fingerprint according to the received security monitoring data by the block chain platform and embedding the generated data fingerprint into the security monitoring data to prevent the data fingerprint from being maliciously attacked and tampered; then, after encrypting the security monitoring data embedded with the data fingerprint, converting the security monitoring data according to a corresponding preset conversion rule and writing the security monitoring data into a database;
and the processing and analyzing module is used for processing and analyzing the encrypted security monitoring data in real time to obtain specific information in the security monitoring data.
The processing and analyzing module comprises a body type identifying module and a face identifying module, wherein the body type identifying module is used for dividing regional grids, comparing and measuring reference objects, analyzing and counting distances, calculating a body type metering algorithm and outputting statistics; the face recognition module is used for recognizing human body features, framing human face images, recognizing and analyzing human faces and conveniently comparing the human face information with face information in a police database; jointly confirming by utilizing body type analysis data and face recognition analysis data; the processing and analyzing module also comprises a task label unit, a feature picture extracting unit and a feature text generating unit; the task labeling unit is used for adding a label to the real-time data, wherein the label comprises license plate data, face data, behavior data and violation events; screening corresponding label data according to the tasks submitted by the client; the characteristic picture extraction unit is used for carrying out corresponding picture characteristic extraction on the real-time data of different labels, wherein the picture characteristic extraction includes license plate recognition, face recognition, position recognition, action recognition and traffic violation recognition, and a characteristic picture is obtained; the characteristic text generation unit is used for generating character characteristic description, behavior characteristic description or receiving a manually-entered video abstract.
The cloud platform comprises a Docker mirror server and a Docker warehouse, wherein the Docker warehouse comprises a distributed cache system and a distributed file system; the distributed cache system consists of a distributed cache system main node and all the Docker local cache nodes; the distributed file system is composed of a main storage node and a plurality of auxiliary storage nodes.
The docker mirror image comprises the steps of creating a docker mirror image of the security monitoring data according to uploading time of the security monitoring data, and storing each created docker mirror image of the security monitoring data into a distributed cache system; the distributed cache system main node is used for registering the Docker mirror images stored in the distributed cache system and inquiring whether the required security and protection monitoring data Docker mirror images are stored in the distributed cache system, and the master node and the slave node of the distributed file system are used for persistently storing the security and protection monitoring data Docker mirror images. The distributed cache system is used for storing mirror images required by operating the Docker container; the master node and the slave node of the distributed file system are used as storage servers which do not run the Docker container and used for persisting the storage container mirror image; and the distributed file system main node is used for registering the Docker mirror image stored in the distributed file system and inquiring whether the required Docker mirror image is stored in the distributed file system.
The method comprises the steps that a Docker daemon process receives a command of a Docker client, creates and deletes Docker images, starts a Docker container, a distributed cache system of a Docker warehouse caches formed security data images and registers the security data images in a main node of the distributed cache system, the storage position is a memory provided by a Docker host where the Docker daemon process is located, and the access speed of mirror image data can be improved by copying the memory; and then persistently storing the security data mirror image stored in the distributed cache system to the distributed file system.
The block chain platform can analyze security data in real time and can give an alarm in time when an accident occurs; the cloud platform adopts the Docker mirror image to carry out long-term storage to security protection data to in case relevant security protection monitored data can in time be transferred in the later stage.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (8)

1. The utility model provides a big data security protection monitored control system which characterized in that includes:
a data acquisition module: the system comprises a monitoring server and a monitoring server, wherein the monitoring server is used for acquiring security monitoring data, and the acquired security monitoring data comprise video monitoring data, audio monitoring data and monitoring position positioning data;
the data preprocessing module is used for preprocessing the acquired security monitoring data; preprocessing security monitoring data comprises extracting useful information in security original data and converting the useful information into a decimal format; rejecting abnormal bad point data existing in the security monitoring original data; removing the linear trend term; performing zero-equalization processing on the security monitoring original data; smoothing or fitting the security monitoring data, and uploading the acquired security monitoring data to a cloud platform processing module and a block chain platform processing module through a gateway;
the block chain platform processing module is used for encrypting and analyzing security data;
the cloud platform processing module performs distributed storage on the security data through a docker mirror image technology, and forms mirror image persistent storage in a distributed file system of the docker warehouse.
2. The big data security monitoring system of claim 1, wherein the blockchain platform processing module comprises:
the receiving unit is used for calling a write-in interface of the block chain platform to receive a write-in request of the security data;
the encryption module is used for generating a data fingerprint according to the received security monitoring data by the block chain platform and embedding the generated data fingerprint into the security monitoring data to prevent the data fingerprint from being maliciously attacked and tampered; then, after encrypting the security monitoring data embedded with the data fingerprint, converting the security monitoring data according to a corresponding preset conversion rule and writing the security monitoring data into a database;
and the processing and analyzing module is used for processing and analyzing the encrypted security monitoring data in real time to obtain specific information in the security monitoring data.
3. The big data security monitoring system according to claim 1, wherein the processing and analyzing module comprises a body type recognition module and a face recognition module, and the body type recognition module is used for dividing regional grids, comparing and measuring reference objects, analyzing and counting distances, calculating a body type and outputting statistics;
the face recognition module is used for recognizing human body features, selecting face images, recognizing and analyzing faces and conveniently comparing face information in a police database; and jointly confirming the data by utilizing the body type analysis data and the data analyzed by face recognition.
4. The big data security monitoring system according to claim 1, wherein the processing and analyzing module further comprises a task label unit, a feature picture extracting unit, and a feature text generating unit;
the task labeling unit is used for adding a label to the real-time data, wherein the label comprises license plate data, face data, behavior data and violation events; screening corresponding label data according to the tasks submitted by the client;
the characteristic picture extraction unit is used for carrying out corresponding picture characteristic extraction on real-time data of different labels, wherein the picture characteristic extraction includes license plate recognition, face recognition, position recognition, action recognition and traffic violation recognition, and characteristic pictures are obtained;
the characteristic text generation unit is used for generating character characteristic description and behavior characteristic description or receiving a manually-entered video abstract.
5. The big data security monitoring system of claim 1, wherein the cloud platform comprises a Docker mirror server and a Docker warehouse, and the Docker warehouse comprises a distributed cache system and a distributed file system; the distributed cache system consists of a distributed cache system main node and all the Docker local cache nodes; the distributed file system is composed of a main storage node and a plurality of auxiliary storage nodes.
6. The big data security monitoring system according to claim 5, wherein the docker image comprises docker images of security monitoring data created according to uploading time of the security monitoring data, and each created docker image of security monitoring data is stored in the distributed cache system; the distributed cache system main node is used for registering the Docker mirror images stored in the distributed cache system and inquiring whether the required security and protection monitoring data Docker mirror images are stored in the distributed cache system, and the master node and the slave node of the distributed file system are used for persistently storing the security and protection monitoring data Docker mirror images.
7. The big data security monitoring system according to claim 5, wherein the distributed cache system is used for storing a mirror image required for operating a Docker container; the master and slave nodes of the distributed file system are served by a storage server that does not run a Docker container for persistent storage container mirroring.
8. The big data security monitoring system of claim 5, wherein the distributed file system master node is configured to register a Docker image stored in the distributed file system, and is configured to query whether the distributed file system stores a required Docker image.
CN202010712858.6A 2020-07-22 2020-07-22 Big data security protection monitored control system Pending CN111858763A (en)

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