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Developing a Digital System for Disaster Prevention Planning: An Analysis Based on Local Government Policy Texts

Bin Jiang

Bin Jiang

  • School of Emergency Technology and Management, North China Institute of Science and TechnologyHebei, China
  • Multi-scene water chain accident wisdom emergency technology innovation center of HebeiHebei, China
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Jiang, Bin
, 
Bo Dai

Bo Dai

  • School of Emergency Technology and Management, North China Institute of Science and TechnologyHebei, China
  • Multi-scene water chain accident wisdom emergency technology innovation center of HebeiHebei, China
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Dai, Bo
, 
Rui Zhang

Rui Zhang

  • School of Emergency Technology and Management, North China Institute of Science and TechnologyHebei, China
  • Multi-scene water chain accident wisdom emergency technology innovation center of HebeiHebei, China
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Zhang, Rui
, 
Bingfen Cheng

Bingfen Cheng

  • School of Emergency Technology and Management, North China Institute of Science and TechnologyHebei, China
  • Multi-scene water chain accident wisdom emergency technology innovation center of HebeiHebei, China
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Cheng, Bingfen
, 
Bo Yang

Bo Yang

  • School of Emergency Technology and Management, North China Institute of Science and TechnologyHebei, China
  • Multi-scene water chain accident wisdom emergency technology innovation center of HebeiHebei, China
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Yang, Bo
, 
Jian Li

Jian Li

  • School of Emergency Technology and Management, North China Institute of Science and TechnologyHebei, China
  • Multi-scene water chain accident wisdom emergency technology innovation center of HebeiHebei, China
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Li, Jian
 et 
Huiqing Lian

Huiqing Lian

  • School of Emergency Technology and Management, North China Institute of Science and TechnologyHebei, China
  • Multi-scene water chain accident wisdom emergency technology innovation center of HebeiHebei, China
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Lian, Huiqing
  
27 févr. 2025
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Publié en ligne: 27 févr. 2025
Reçu: 10 oct. 2024
Accepté: 25 janv. 2025
DOI: https://doi.org/10.2478/amns-2025-0108
Mots clés
© 2025 Bin Jiang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Proportion of needs analysed by datamodelling system
Proportion of needs analysed by datamodelling system

Figure 2.

Patterns of local government policy responses to disasters
Patterns of local government policy responses to disasters

Figure 3.

Machine learning logic
Machine learning logic

Figure 4.

Occurrence of different disasters in the last 10 years
Occurrence of different disasters in the last 10 years

Figure 5.

Trends in the number of policies formulated by the local government in the past 10 years
Trends in the number of policies formulated by the local government in the past 10 years

Figure 6.

Trend of k-value in the last 10 years
Trend of k-value in the last 10 years

Figure 7.

Predicted 3-year results vs. actual policy increases
Predicted 3-year results vs. actual policy increases

Figure 8.

Satisfaction survey on the use of data systems
Satisfaction survey on the use of data systems

Data sources and number of data analysed by the data system

Number
Local legislature 164
The government report 32
Natural disaster news 361
Related website 163

Statistics on the use of digital systems for different groups of people

Project Number of man Proportion Number of woman Proportion
Never used 67 67% 78 78%
Occasionally use 21 21% 17 17%
Frequently use 12 12% 5 5%
Total 100 100

Relative energy consumption (W) of traditional computing model and data system

Model Hot power generation Electric refrigeration Waste heat recovery
1 9.34 5.23 6.43
2 4.43 6.57 7.87
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