Cloud computing technology to large systems, big data for the most significant features, and security industry is a very typical big data scenarios, security industry bayonet monitoring system, video surveillance system consists of a large number of devices (including a large number of front-end Collection equipment, back-end platform and cloud computing server clusters, etc.), generating geometric growth-level data every day. With the successful success of large-scale smart city projects, the entire security platform presents a large amount of data, diverse data types, data processing logic Complexity, data cleaning, data sharing, data mining difficult to deal with high difficulty, the security vendors presented a huge challenge. cloud computing Security cloud computing core technology Among them, it is mainly manifested in the massive volume of traffic flow information in the field of intelligent transportation industry and snap shot pictures of bayonet cars and unstructured data such as massive video recording files in the field of smart city. The major users in the security industry are public security and traffic police. Massive image and video files for safe and effective data storage, high-performance parallel computing, intelligent data analysis and mining after the strong demand for actual combat, which are in line with the cloud computing features. At the same time of providing mass storage, how to locate multidimensional data quickly and effectively and mine the potential relationships among various types of data in multiple dimensions has always been a problem that we are devoted to solving. Cloud computing, big data and other technologies are slowly penetrating the security industry, with the maturity of these technologies will bring a revolutionary impact on the security industry. Large-scale hybrid computing If only a large number of video image data generated by the monitoring system are processed manually, the efficiency will be very low. With the video intelligent processing algorithm, some simple features can be obtained from video image data for comparison or pattern matching Alarm events, improve the efficiency of processing. The amount of data, the degree of data composition, the type of data, etc. that can be handled in this way are still low and can not cope with the massive data and the ever-increasing demand. The purpose of large-scale computing technology is to provide a unified data processing platform, which integrates various intelligent algorithms and computing models to comprehensively process massive monitoring data to obtain more valuable data faster. Uniform resource management technology The main data generated by the monitoring system is the video and image data. After the original data is processed, it will produce richer data and the way of processing will be greatly different. For example, historical video data can be processed in the background of the video data retrieval, license plate and face feature data for the bayonets need real-time cloth control, historical mount information needs to be done in real time retrieval. These data all need different computing frameworks to deal with. By introducing a unified resource management platform, different computing frameworks can be operated in the same resource pool to greatly improve the utilization of resources. At the same time, when resources are monopolized by a certain kind of business, Can maximize the performance of the system. Real-time retrieval technology The traditional structured data are stored in relational databases. Database clusters are formed by techniques such as RAC and accelerated by indexing. However, the core is still based on row storage and relational operations. In the face of massive records, they have encountered bottlenecks in all aspects . Real-time retrieval technology can deal with 100 billion levels of structured data by introducing technologies such as distributed database, columnar storage, memory computing, indexing engine and so on, which can greatly improve the storage capacity, scalability, retrieval speed and other aspects . The system has important research value and broad application prospect in the field of video surveillance such as intelligent transportation and criminal investigation. Complex event processing techniques With the development of the security industry, the business becomes more and more complicated. For example, in the field of intelligent transportation, the demand for vehicle points research, deck car analysis and same-car analysis has emerged. These requirements exist to produce the results depend on many conditions, the process of real-time requirements of high, the need to deal with a huge amount of data and so on. The traditional way is to use a relational database, through the combination of complex SQL statements, constantly check the way of comparison, it is difficult to meet the real-time requirements. Complex event processing By introducing streaming computing and other technologies, dynamic analysis of input data in real time, the processing speed can be provided substantially. Not meet the conditions of the data are discarded, the system only exists in the processing results or may be useful intermediate data, so the requirements of the storage becomes smaller, completely in memory for the whole process of analysis, real-time be guaranteed. Face search technology Face retrieval technology in a single server application has been more mature, can be used in identification, fugitives arrested, suspicious staff to investigate, ID check and other fields. Face detection process can be divided into the following stages: video or image decoding, face detection, feature extraction, feature comparison, the first three steps are each request corresponds to a calculation, the amount of control is relatively controllable, and the last one Compared with each request, the step feature needs to compare with the facial features of hundreds of millions of levels, which is a stage with the largest amount of computation. Some real-time application requests up to hundreds of requests per second up to millions of face alignment, the entire system needs to support 100 million face features per second comparison. Such a large-scale calculation, stand-alone can not be completed, the cluster must be completed. The feature library itself is small in size but large in comparison, which belongs to a typical compute-intensive cluster. The feature library can be all poured into memory and completed in memory. Massive video retrieval technology After the video data collected by the image sensor is saved to the back-end storage, the user can select multiple cameras in the target area at any time and submit the video data to the video retrieval cluster. All clusters corresponding to the characteristics of the target object are quickly searched to generate video data and find the target The appearance of the object features the video, and locate the exact point in time. Which mainly uses the intelligent technology to achieve the conversion of video data to object feature structured data, support for vehicle color, license plate, clothing color, face and other features. Based on a unified computing resource pool, parallel computing of intelligent algorithms can be implemented to improve the retrieval efficiency linearly. Structured data can be saved to the database, the next search can be directly through the structural data for secondary search, a substantial increase in search efficiency. Distributed Object Storage Technology Security cloud system architecture and design, give full consideration to the reality of hardware and software failures in large-scale cluster environment, the use of advanced management ideas and software systems to achieve a large number of common storage server storage space resources for virtual integration, hardware and software failure Highly fault-tolerant, build a highly stable and reliable storage cluster. 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