21 October 2015, Shenzhen, China – ZTE Corporation (0763.HK / 000063.SZ), a major international provider of telecommunications, enterprise and consumer technology solutions for the Mobile Internet, recently launched the Emergency Management System V2.0 at GITEX 2015, a global event for communications electronics held in Dubai. Based on the existing emergency management system and big data analysis techniques, the ZTE Emergency Management System V2.0 optimises emergency prediction, early warning and decision-making, and develops innovative ideas of big data-based emergency management, significantly enhancing the command and control capabilities of emergency handling.
In recent years, frequent natural and man-made misfortunes, such as the explosion of hazardous chemicals in Tianjin and the overturning of the Eastern Star ferry in the Yangtze River, have not only threatened public safety and social stability, but also tested the emergency management capabilities of all levels of governments. It has become a necessary means for governments to establish emergency management systems based on information and communication technologies to improve their capabilities regarding emergency prediction, prevention, handling, and recovery.
A key aim of emergency management is to reduce the probability of emergencies through pre-event prediction. However, the current emergency management systems deployed by governments only implement after-event response and treatment, providing rapid actionwith the assistance of emergency communications systems after incidents occur, and taking control and rescue measures to the incident site to reduce casualties and property losses. The emergency prediction capabilities of these systems still a need to improve.
ZTE’s Emergency Management System V2.0 leverages bigdata-based emergency management processes in an innovative way. The system uses big data technologies to build emergency analysis models by learning from Chinese domestic experience in emergency data monitoring, data analysis, mining, prediction, and early warning.
These models can be used for in-depth correlation analysis of data from various sources such as sensor networks, social media and governmental informationto predict potential emergency incidents. For example, the emergency management capabilities could provide support for a chemical explosion by establishing databases for major fire-control units (such as hazardous chemical warehouses and gas stations), and the hazard source data in these databases can then be used to establish mathematical models for big data mining and analysis. When the model parameters vary with the storage status of hazardous chemicals, potential risks could be forecast through dynamic calculations, allowing people to take measures to prevent dangerous events from occurring.
In addition, ZTE's Emergency Management System V2.0 discovers event correlation by performing big data analysis on heterogeneous data sets, providing effective support for emergency decision-making.It also changes the decision-making process from the reactive procedure of "event occurrence->cause analysis->decision-making" into the predictive "data mining->correlation analysis->decision-making" model.
Mathematical models are available that can be used to simulate the accident process and calculate explosion ranges,estimate casualties and the impact on surrounding residential areas, schools, and hospitals, providing a test scenario to help improve the efficiency and accuracy of decision-making should an event occur.
As a world-leading provider of ICT solutions and services, ZTE has been committed to the government and enterprise business for many years. In the field of public safety, ZTE helps governments enhance response and management capabilities for emergency incidents globally.Its public safety solutions have been used in more than 40 countries and regions. The Emergency Management System V2.0, which is theresult of ZTE's years of experience in emergency management and big data technologies, will help governments witness a considerable improvement in emergency prediction and decision-making.