Source: China Meteorological News
National Early Warning Center(hereafter referred to as the Center) was founded on February 26, 2015, a part of CMA Public Meteorological Service Center.
China Meteorological Administration (CMA) is responsible for the construction, operation and maintenance of the national early warning information dissemination system, which releases early warning information at the national, provincial, prefectural and county levels. Now it has 1 national, 31 provincial, and 358 prefecture-level dissemination management platforms and 2016 county-level early warning dissemination application terminals.
Wider outreach and new media reach
On May 10, 2019, the Center started comprehensive strategic cooperation with Jinritoutiao,Douyin together with early warning centers of all provinces, cities and counties across China.
The Center and Douyin have developed the early warning short video service, using artificial intelligence to automatically convert graphic early warnings into 15-second Douyin short videos, and then automatically match Douyin users of all levels of early warning centers according to the dissemination area. It takes only one minute for the early warning information from the center to the user.
The Center and Jinritoutiao, Douyin have recently launched a new disaster reporting platform.
When the alert level reaches red (the highest level), the platform can accurately push the alert information to Douyin users in the relevant geographical location (administrative city level). When users click the early warning message, they can read the details of the disaster, and learn how to protect themselves when they encounter the disaster. When disasters occur, users can also post their own safety information through the platform and upload videos of disasters with the geographical location.
Through cooperation with mobile phone manufacturers, the Center launches early warning information across the country based on mobile phone system-level channels or related software.
At present, the early warning information push reminder included the ordinary mode and the strong mode. When the normal reminder is issued, users can receive the real-time early warning information through the system push channel.When the strong reminder is issued, it ignores the lock screen of the user's mobile phone or the state of the phone, and directly sets the pop-up in the form of important system reminder, accompanied by early warning sound, to remind the user until it is shutdown. The strong alert mode is suitable for disseminating urgent and important warning information. The feature is now available on 40 million Xiaomi phones nationwide.
Readable early warning and AI smart short videos
Since 2019, the Centre has carried out profound cooperation with Baidu, and fully realized AI smart creation in nearly 3000 early warning dissemination platform accounts. It starts with the data from the Center and realizes automatic creation in seconds. Furthermore, it is equipped with professional videos with voice, cartoon, rolling into one.
With the assistance of AI smart writing of Baidu and information distribution system, national early warning dissemination platform account at Baidu has released over 1 million text, image, and video contents, and reached about 50 billion times of users. It covers four categories of emergency event early warning information like natural disasters, accidents and disasters, public health events, and social security events, which touches on 16 sectors of 77 categories.
In the meanwhile, the Center has organized Global Multi-Hazard Alert System in Asia (GMAS-A) Workshop each year. This meeting is based on the requirement of GMAS-A, and invited countries and territories from Asia and organizations around the world to carry out in-depth discussions from the perspective of management mechanism and technical innovation. The holding of this meeting has further deepened cross-nation and cross-sector cooperation, consolidated data sharing and coordination, and enhanced the capacity of those countries & territories in disaster preparedness and response to climate change.
Editor: Xu Nenyu, Liu Shuqiao