AI technology is profoundly changing all walks of life, and in the application of the G-end (government side), the field of emergency management is particularly critical. From natural disasters to public safety incidents, the efficiency and accuracy of emergency management are directly related to the safety of people’s lives and property and social stability. This article will delve into the application direction of AI in emergency management, analyzing its development history, industry pain points, and how AI can solve practical problems through technological innovation.
Last timeRegulation and risk prevention and controlIn this direction, today we will talk about another application direction – emergency management; Interested friends, continue to watch!
The development history of emergency management
1. Initial stage (1949-1989): In the early days of the founding of the People’s Republic of China, emergency management was mainly based on fire safety, and the Fire Bureau of the Ministry of Public Security established in 1949 was responsible for disaster prevention and rescue. Emergency management at this stage mainly relies on government guidance and organization.
2. Legal construction stage (1990-2002): Since the 90s, our country has begun to establish a legal system for emergency management, and in 1991, the Fire Protection Law was promulgated to establish public security fire protection agencies. Subsequently, laws and regulations such as the Public Emergency Response Law were promulgated to provide legal support for emergency management.
3. Institutional improvement stage (2003-2012): In the face of public incidents such as SARS, the National Emergency Management Office was established in 2003 to coordinate emergency management responsibilities. At the same time, establish a multi-level and multi-departmental emergency management system and improve the management system.
4. Responsibility clarification stage (2012-2018): Further clarify the division of responsibilities for emergency management, and establish the National Work Safety Committee in 2012 to strengthen the preparation of plans, drills and rescues. Strengthen cooperation with social organizations, enterprises and institutions, and the public.
5. Scientific and technological innovation stage (2018-present): Use big data, cloud computing, artificial intelligence and other technologies to improve disaster monitoring and early warning, information reporting, command and dispatch capabilities. Actively participate in international exchanges and enhance the international influence of emergency management.
Emergency management industry analysis
Industry pain points
- The legal and regulatory system is not perfect: The provisions of the existing Emergency Response Law are highly principled, and the construction of supporting regulations and standard systems is lagging behind, making it difficult to fully cover new risks.
- Insufficient departmental coordination and linkage: The boundaries between the powers and responsibilities of the emergency management department and the industry supervision department are blurred, the information sharing mechanism is not perfect, and there is a multi-head command phenomenon in emergency response.
- The emergency response capacity at the grassroots level is weak: The material reserves of streets/communities are insufficient, the coverage rate of professional emergency teams is low, and the operability of emergency plans needs to be improved urgently.
- Lack of public awareness of emergency response: Insufficient investment in disaster prevention education, low participation rate in emergency drills, and weak self-rescue and mutual rescue ability of residents in the face of disasters.
- Insufficient scientific and technological support capacity: The risk monitoring and early warning system is incomplete, the integration of the emergency command information platform is low, and the application of intelligent auxiliary decision-making technology is insufficient.
- There are shortcomings in the performance of emergency rescue equipmentThe communication guarantee of “three breaks” (circuit break, network disconnection, and power outage) needs to be improved urgently, and there is a lack of interdisciplinary integration.
- Internal coordination is highly complexUnder the new system, emergency management coordination faces the challenges of highly complex internal coordination, insufficient authority of ministerial coordination, and insufficient authorization of cross-border coordination.
Impact on the industry
- Inefficient response: Poor coordination between departments leads to slow emergency response and difficulty in effectively responding to emergencies.
- Waste of resources: Repeated construction and unreasonable resource allocation, resulting in resource waste and inefficiency.
- Public security decreases: The public’s lack of emergency awareness leads to weak self-rescue ability in the event of a disaster and increases social instability.
- The application of science and technology lags behind: Insufficient scientific and technological support affects the intelligent and accurate level of emergency management.
Policy review of AI emergency management
On February 20, 2019, the Ministry of Emergency Management issued the “2019 Implementation Guidelines for Local Emergency Management Informatization”, which pointed out that it is necessary to comprehensively build a catalogue database including emergency management supervision matters, build and improve the comprehensive application of regulatory data, collaborative regulatory data push and feedback, regulatory complaints and reports, etc., accelerate the sharing and integration of regulatory data, gradually achieve full coverage of regulatory matters and full recording of regulatory processes, and continuously improve the standardization, accuracy and intelligence of supervision during and after the event.
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In October 2020, the Ministry of Emergency Management of the Ministry of Industry and Information Technology issued the “Industrial Internet + Safety Production” Action Plan (2021-2023), which should form a new capability system such as rapid perception, real-time monitoring, advanced early warning, linkage disposal, and system evaluation of “Industrial Internet + Safety Production”, and significantly improve the level of digital management, network collaboration, and intelligent control.
In 2024, the Work Safety Commission of the State Council issued the “Three-Year Action Parties for Tackling the Root Cause of Work Safety (2024-2026)”: it is required to accelerate the digital action of the safety production supervision model to pre-emptive prevention, accelerate the digital transformation of the safety production supervision model to pre-prevention, and promote the integrated development of artificial intelligence, big data, Internet of Things and other technologies with safety production.
AI emergency management application
Safety load and early warning
App content:By building a digital intelligence management platform for production safety, integrating the Internet of Things, big data and artificial intelligence technology, real-time monitoring and data collection of enterprises can be realized. The platform can access remote telecommunication instruments, surveillance cameras, sensors and other equipment to obtain key data in the production process of enterprises in real time, and analyze them through AI algorithms to detect early warnings and release early warning information in advance.
Work something out:Traditional safety management information lags behind and data collection is not obtained in time, resulting in potential safety hazards cannot be discovered in time and accurate early warning cannot be achieved.
Accident command and rescue
App content:By building a command accident platform, integrating regions to form pre-accident cases and accident resources, “accident map”, find accident materials, expert accidents, accident teams and other information, so as to quickly allocate resources. The platform has a built-in emergency response process to standardize command and rescue work and improve the efficiency of accident elimination.
Work something out:Poor information sharing between various departments, insufficient good combat capabilities, and slow emergency response speed, resulting in low efficiency.
Systematization of risk management and control
App content:Through the customized platform, enterprises pass the enterprise information to the AI assistant, and the platform gives reference suggestions to the customs. At the same time, the platform connects IoT sensors to analyze data in real time, dynamically adjust risk levels, and control tasks. This dynamic risk assessment system can effectively solve the problems of risk and control, and improve the accuracy of risk management. The regulatory authorities keep abreast of the implementation of enterprise risk management and control through the platform.
Work something out:Risk depends on experience, and the implementation of control measures is difficult to track and achieve dynamic adjustment.
Exhaust gas investigation and treatment
App content:Using AI technology, it realizes the rapid identification of on-site scenes, and takes advantage of the compatibility of information processes, closed-loop tracking of scenes, and realizes responsibility to people; Through the intelligent AI module, it automatically identifies the unsafe behaviors of on-site personnel (such as not wearing safety helmets, illegal operations, etc.) and the unsafe state of equipment and environment (such as flame smoke, equipment overtemperature, etc.), and provides real-time early warning information. Through the reporting of blind investigation results on the platform, the regulatory authorities track the degree of rectification in real time, and automatically issue rectification notices to enterprises that fail to rectify as required to ensure closed-loop management of blind people.
Work something out:Traditional manual investigation methods are inefficient, difficult to cover comprehensively, and easy to miss poverty, making it difficult to close the loop of emission control.
Safety supervision and law enforcement
App content:Improve the accuracy and efficiency of law enforcement through standardized means. The platform uses artificial intelligence technology to analyze the security data of enterprises, automatically identify violations of laws and regulations, and generate law enforcement recommendations. At the same time, the platform supports the digital management of law enforcement records, ensures that the law enforcement process is transparent and fair, and promotes enterprises to implement the main responsibility for safe production.
Work something out:The limited number of grassroots supervisors makes it difficult to cope with the growing regulatory tasks, especially the weak links in the supervision of small and medium-sized enterprises and high-risk industries.
Safety education and training
App content:By providing rich safety education resources for enterprises and industry personnel, it covers multiple dimensions such as laws and regulations, technical specifications, and accident cases. Use waste time to simplify complex training and standardize boring processes. Students answer the test online and give timely feedback on the training effect.
Work something out:Traditional training methods are boring, employee participation is low, and the training effect is difficult to quantify.
Special operation management
App content:Through the construction of the platform, the special operation process robot is realized, and the government grasps the special operation situation of the enterprise, connects with the government database, automatically scans the number certificate, and completes the authenticity verification and the most reminder in 10. At the same time, the platform supports real-time monitoring of special work sites through camera + AI image recognition, and automatically completes violations.
Work something out:The operation process of special operations is cumbersome, on-site monitoring is difficult, and the problem of qualification fraud occurs from time to time.
Standardized safety management
App content:By transforming the platform, a good safety management model for humans and machines is built. The platform supports intelligent risk identification and early warning, analyzes production data in real time through AI algorithms, and detects vulnerable groups in advance and warns of early warning information. At the same time, the platform supports intelligent decision support systems to provide scientific management suggestions for regulatory authorities and enterprises.
Work something out:Traditional safety management relies on manual labor, is inefficient, and can easily lead to turnover, making it difficult to adapt to complex and changing safety production needs.
The future development trend of AI emergency management
- Engage in the transformation from post-response to pre-warning: With the development of AI technology, emergency management is changing from the traditional “post-response” model to the “pre-warning” model. AI technology can automatically analyze complex data through machine learning and deep learning algorithms, realize automated risk warning and decision support, and predict and prevent potential risks in advance.
- Multi-source data integration and intelligent analysis: AI emergency management information system can predict the probability and scope of natural disasters and accidents in advance by integrating meteorological, geological and hydrological data, using big data analysis and machine learning algorithms. For example, based on the combination of meteorological data and high-precision remote sensing data, the system can dynamically predict the accumulation of water and release flood warning information in a timely manner.
- Smart fire protection and smart emergency: In specific scenarios such as airports, smart fire protection and smart emergency construction are becoming important development directions. Policy promotion and technological progress enable smart fire protection systems to monitor and analyze multiple risk factors in real time, automatically adjust risk levels and distribute early warning information in real time, and improve emergency response capabilities.
- The application of AI in specific scenarios: The application of AI technology in specific scenarios is also deepening. For example, in mine supervision, AI products such as Huawei’s Pangu model have achieved practical results, solving the problems of low accuracy and poor real-time performance of manual inspection. In addition, AI can also be used for pipeline inspections, complex area rescue, etc., to improve the efficiency of safety supervision and emergency response.
- Technical and policy support: Policy support and technological progress provide a strong guarantee for the development of AI emergency management. A number of policy documents clearly propose to improve the level of emergency management, improve the emergency work system, strengthen emergency response capabilities, and promote the integration of emergency management into the local emergency rescue system. These policies provide a broad space and clear direction for the application of AI technology in emergency management.
