Intelligent analysis helps security enter the era of big data
The important factors that drive the development of big data come from two aspects: First, the consumption field, such as the large amount of data generated by online shopping and social media applications; the other is from urban infrastructure construction, security is one of them. The process of building a safe city is accompanied by the production of large amounts of data, especially Digital Security Surveillance (DSS), which is primarily characterized by video surveillance. There are countless high-definition cameras in the cities in which we live. These include security monitoring, command communications, detection and detection, law enforcement, and social services. Video access scales range from a few thousand to hundreds of thousands. With security monitoring, HD, smart, and The requirements for networking are getting higher and higher, and the scale of data generated each day is growing at an alarming rate.
With the advent of the era of big data, industry reforms have only just begun and the future is promising. As far as the current development is concerned, the application fields of big data in the domestic security industry are still relatively limited, mainly in the public domain. At present, China's use of big data in the public security are mainly focused on intelligent transportation and judicial systems.
Intelligent Transportation: The Ministry of Transport issued a notice in July this year, which will provide financial support for the construction of public transportation information application systems, related support systems, and the construction of data resources and exchange systems. Under the favorable support of policies, we can dig up the smart transportation sector from the following three areas:
1 Engaged in the construction of urban transportation systems, highway information construction and other fields;
2 The necessary video surveillance equipment suppliers for intelligent transportation development;
3Provide content providers for navigation maps and GIS software.
Judicial system: Large-scale informationization and equipment investment in the public security market have produced massive amounts of unstructured data. The actual application of public security is an important application area of ​​big data. The big data companies in this area include electronic data forensics, electronic data authentication, network public opinion analysis, digital rights protection, notary cloud, search cloud, and forensic cloud services.
In the security industry, the types of data involved are many, and in terms of the type of data structure, it includes various types of unstructured, structured, and semi-structured information. Among them, unstructured data mainly includes video recording and picture recording, such as surveillance video recording, alarm recording, summary recording, vehicle mount picture, face capture picture, alarm capture picture, etc.; structured data includes alarm record, system log Records, operation and maintenance data records, abstract analysis of structured description records, and various related information databases such as population information, geographic data information, vehicle management information, etc. Semi-structured data such as face modeling data, fingerprint records, etc.
For IT big data, its characteristics are usually summarized in 4V (Velocity, Variety, Volume, Veracity), but for security big data, it has its own unique characteristics. First, security big data is dominated by unstructured video surveillance data, so it focuses more on the analysis, extraction, and processing of unstructured data. Second, on the data capacity, it is mainly based on video recording. Security big data has higher bandwidth and storage space requirements in the transmission, storage, and calculation processes; again, based on data volume comparisons, the value density of information in security big data is lower, and it is faster and faster from massive image information. It is more difficult to accurately detect or mine useful information. Finally, video surveillance data is continuously updated and accumulated 7×24 hours, which is more time-efficient.
There are two types of security big data processing and analysis tools: One is the processing and analysis tools for unstructured information such as video images, including video intelligent analysis tools, video summary tools, image clarity tools, video clarity tools, Video transcoding tools, video editing tools, and so on; the other is the big data analysis and processing tools for structured, semi-structured information, and the security of such processing and analysis tools draws on the IT community’s structure in dealing with big data. And experience, more popular frameworks such as Hadoop, Spark big data processing, and Mahout, R data mining tools to achieve fast and accurate data analysis and mining of structured and semi-structured data.
The application of big data in the security industry makes security more intelligent. Big data technologies are generally divided into data acquisition, storage, mining, and analysis technologies. Among them, intelligent analysis is at the core.
Intelligent analysis is the fundamental point of distinguishing security big data from IT big data. Only by using intelligent analysis technologies to convert unstructured data of security big data into structured data, can IT data mature technology systems be applied to security big data. To give full play to the role of security big data. For the analysis and processing of unstructured data, such as video images, it may be more to be attributed to the scope of smart analysis. Many of these technologies have been continuously improved and improved in the initial application, and many newer intelligent analysis technologies are still in place. During the R&D process, the analysis and processing of such data will also become the core value point of security big data. Increasingly rich intelligent algorithms will greatly increase the scope and value of video surveillance cameras. Intelligent video surveillance at the initial stage of application will also develop rapidly with the increasing abundance of intelligent algorithms. Digital processing chips, codec capabilities, and compression algorithms are important factors affecting image processing technology. The core of security intelligence is also embodied in VA (video analytics or image analysis), and VA requires the support of the underlying algorithm and the implementation of the unit, which can improve the efficiency of video analysis.
With the popularization of digital security technologies, monitoring technology is gradually becoming more and more high-tech and networked. What follows is a massive data storage problem. Mass data must have reliable, guaranteed efficiency, fast read and write, and responsiveness. The storage of capabilities. Storage devices have gradually moved from the marginalized position of the surveillance system to the center, and the proportion of the surveillance system has also been greatly improved with the increase of the centralization. The traditional storage method can no longer meet the needs of network storage, and cloud storage is a new type. Storage services came into being.
The so-called cloud storage refers to a system that integrates a large number of different types of storage devices in a network through application software, and provides external data storage and service access through cluster applications, grid technologies, and distributed file system functions. That is, cloud computing system with data storage and management as the core.
Cloud computing, cloud storage, and big data will bring changes in storage architecture, virtualization, security, and efficient processing to the video surveillance industry.
First, the characteristics of big data have skyrocketed in the overall demand for storage capacity, and mass storage models have evolved from traditional centralized storage architectures to distributed storage architectures, such distributed architectures, multi-replication, network RAID technology, and snapshot technology. Driven by the drive, high-reliability, large-scale concurrent capability of mass storage is realized, and the upgrade and transformation of storage from the equipment supply mode to the service mode are promoted.
Secondly, virtualization technology will continue to develop in the development of storage service capacity. The upgrade model will evolve from SCALE-UP to SCALE-OUT, which will be the scheduling and management of ubiquitous storage resources, online expansion of storage resources, and data continuity. Uninterrupted protection and storage services provide strong support. Virtualization, on the one hand, greatly simplifies the application process, saves customers' construction costs, and provides stronger storage and sharing functions. On the other hand, it solves the waste of storage space, can automatically redistribute data, and improves the utilization of storage space. With load balancing and fault redundancy.
Third, the security aspects of real-time computing and storage require higher and higher performance for storage devices, storage network performance, and simplified configuration of storage resources. In complex storage services, based on the hybrid storage system built by virtualization, the system's automatic tiering storage capability is particularly important. With the ever-decreasing cost of flash memory, the market is also seeing the emergence of all-flash array products, automated tiering of storage resources under virtualization, strategies for hierarchical storage of data, and migration strategies for real-time and security of big data. More indispensable.
Finally, in the face of structured data, unstructured data, and semi-structured metadata processing mechanisms, cloud storage management can be automated and intelligent. All storage resources are integrated and customers see a single storage space. The storage efficiency is improved; cloud storage can achieve scale effect and elastic expansion, reduce operating costs, and avoid resource waste. Limited by the characteristics of security video surveillance's own business, there may be differences between monitoring cloud storage and existing Internet cloud computing models. For example, security users prefer video information stored locally, government video surveillance applications are more sensitive, video information privacy issues, and video Monitor the problem of high network bandwidth consumption and other issues. The retrieval, directory service, and de-duplication of mass data storage will bring new development opportunities to the storage industry in storage applications that are driven by big data.
At present, the development of security monitoring is in full swing. At the same time, the application of cloud storage technology in security monitoring is becoming more and more common. As a trend of security storage development in the future, cloud storage vendors are now implementing various types of search, application technologies, and cloud storage. Combined, in order to provide users with a series of data services, however, the development of cloud storage is still limited by the following factors:
1 The limitation of network bandwidth: The real cloud storage system will be a huge public system distributed in many regions, all over the country, even all over the world. Users need to connect to cloud storage through broadband access devices such as adsl and ddn. Only if the broadband network is fully developed, can the user obtain sufficient data transmission bandwidth, realize the transmission of a large amount of capacity data, and truly enjoy the cloud storage service, otherwise it can only be empty talk.
2 Data Security: Since the birth of cloud computing, security has always been one of the top priorities for enterprises to implement cloud computing. Similarly, in terms of cloud storage, security is still the first issue considered. The security threats of cloud storage systems mainly include the following:
Cloud storage provides scalable data services, which cannot clearly define security boundaries and protect devices, making it difficult for cloud storage security protection measures.
Cloud storage transmits data over IP networks. Therefore, security threats on traditional networks also exist on cloud storage systems, such as data destruction, data theft, data tampering, and denial of service, affecting the secure storage of data.
Data storage security includes static storage security and dynamic storage security. Static storage security is to ensure the storage of the final stored data on the cloud storage system. Dynamic storage security is to ensure the integrity and confidentiality of data transmission.
Cloud storage needs to ensure the fault-tolerance, recoverability and integrity of data, and how to avoid data service interruption and data loss when a disaster occurs.
As a public data center, the cloud storage system has the characteristics of multi-client connection, high interactivity, and high data security requirements. It is sensitive to intrusions, attacks, viruses, and malicious software. It is necessary to actively conduct data flow in cloud storage in real time. Detection and defense.
3 The development of application storage: Cloud storage is not just storage, but more applications. Application storage is a kind of storage device that integrates application software functions in storage devices. It not only has data storage functions but also application software functions, which can be seen as a collection of servers and storage devices. The development of application storage technology can greatly reduce the number of servers in cloud storage, thereby reducing system construction costs, reducing single point of failure and performance bottlenecks caused by servers in the system, reducing data transmission links, providing system performance and efficiency, and ensuring the efficiency of the entire system. Stable operation.
Under the impact of the wave of informatization, people have now stepped into the era of the Internet and cloud computing. The entire security industry has gradually shifted from product sales and platform construction to system operation and data analysis. The IT industry in the security industry is an unavoidable trend.
In the field of video surveillance, along with the tide of high-definition surveillance, more and more massive video data has been produced. However, a large amount of video data is still independent and fragmented. The video recording data is scattered in various industries and units in an independent system. It has not reached the level of networking and sharing. The industry has not formed a common method for data mining and utilization. The core technology is still under study and no major breakthrough has yet been achieved.
At present, a large number of video surveillance data are used in the field of security, but mainly based on artificial search. There are still few applications for cross-police, cross-departmental, and cross-regional networking sharing among governments, not to mention applications for the common people and society. Has not started yet. If we can open up these video resources and serve the people, not just for security and criminal cases, we can promote the development of new data services through information disclosure, data sharing, and data mining. This will be a boon to society.
Security industry from a single product to system integration, from analog monitoring to network monitoring, from closed system to big data, cloud platform, is not a simple video transmission, but IT system solutions involving data collection, communication, processing, feedback . At the product level, the storage, transmission and server technologies of the monitoring system platform are more dependent on IT technology. Future intelligent analysis based on big data processing also needs IT technology support.
What security companies need to do is to actively strengthen internal strength, increase research and development capabilities, strengthen technology reserves, and respond to the impact of greater data volume. Late security manufacturers will differentiate, and some traditional security manufacturers will focus more on the field of fixed security and continue to deepen their cultivation, focusing on products and technologies. Some security manufacturers will shift to Daan Security Integrated Platform, focusing on business integration and data analysis and processing.
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Preparation of stainless steel seamless pipe of the production process a. bar; B. heating; C. hot rolling perforation; D. cutting head; e. pickling; F. grinding; g. lubrication; h. cold processing; I. degreasing; J. solid solution heat treatment; K. straightening; L. pipe cutting; M. pickling; n. the finished product inspection.
classification
Stainless steel pipe material for ordinary carbon steel, quality carbon structural steel, alloy structural pipe, alloy steel and bearing steel pipe, stainless steel pipe and to save precious metals and meet the special requirements of double metal composite pipe, coating and coating tube. Stainless steel tube of a wide variety of uses, its technical requirements are different, the production method is also different. The current production of steel pipe diameter range 0.1 - 4500mm, wall thickness range of 0.01 ~ 250mm. In order to distinguish the characteristics, it is usually classified as follows.
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Photo of our Stainless Pipe:
Production process
Production technology of stainless steel welded pipe:
The raw materials - welding pipe - end repair - polishing -- Inspection (printing) - Packaging - shipment (warehousing) (decorative pipe)
Raw material points - welding pipe heat treatment -- correction - straightening straight - xiu end - pickling and hydraulic pressure test and test (printing) - Packaging - shipment (12.5) (welded pipe industry piping pipe)
Hardness
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Vickers hardness
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Hardness test
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