Accurate disaster early warning rescue system and method thereof

Abstract

The invention provides a precise disaster early warning and rescue system and a method thereof, and relates to the technical field of disaster early warning and rescue. The system comprises a disaster monitoring module, an early warning analysis module, a rescue decision module and a communication coordination module. The method comprises the steps of monitoring disaster data in real time, analyzing a disaster development trend, formulating a rescue scheme and implementing rescue actions. According to the invention, accurate early warning and effective rescue of disasters can be realized, the timeliness and accuracy of disaster response are improved, and the loss caused by disasters is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster warning and rescue, in particular to a system and method capable of achieving precise warning and rescue. Background Art

[0002] In the context of frequent natural and man-made disasters, existing disaster warning and rescue systems often have problems such as inaccurate warning and slow rescue response, resulting in low disaster response efficiency and unnecessary casualties and property losses. Summary of the Invention

[0003] The purpose of the present invention is to provide a precise disaster warning and rescue system and method to improve the accuracy of disaster warning and the timeliness of rescue.

[0004] To achieve the above purpose, the present invention provides the following technical solutions:

[0005] A precise disaster warning and rescue system includes four parts: a disaster monitoring module, a warning analysis module, a rescue decision-making module, and a communication coordination module.

[0006] The disaster monitoring module is used to collect and analyze disaster-related data in real time, including meteorological, geographical, environmental and other information. Its innovations include:

[0007] Integrating a multi-source sensor network, including satellite positioning devices, micro-deformation monitoring sensors, and environmental parameter sensors, combined with urban infrastructure monitoring terminals and UAV three-dimensional modeling systems to achieve integrated sky-earth-space monitoring;

[0008] Preprocessing data through edge computing nodes to reduce transmission delay.

[0009] The warning analysis module, based on the collected data, uses data analysis and machine learning technologies to predict the development trend of disasters and generate warning information. Its innovations include:

[0010] Setting up an adaptive adjustment mechanism for warning levels to dynamically correct warning levels according to the disaster diffusion speed and population density.

[0011] The rescue decision-making module, according to the warning information, combines the geographic information system (GIS) and the expert system to formulate the optimal rescue plan. Its innovations include:

[0012] Integrating a multi-objective optimization algorithm, comprehensively considering the distribution of rescue resources, the status of the transportation network, and the priority of the affected area;

[0013] Building a built-in disaster case library, using an incremental learning algorithm to dynamically optimize the model and implement a terrain-adaptive rescue strategy;

[0014] Introduce an AR-assisted decision-making system to improve the efficiency of on-site rescue command through visualization technology.

[0015] The communication and coordination module transmits early warning information and rescue plans to rescue personnel and relevant departments in real time through a wireless communication network. Its innovative points include:

[0016] Adopt multi-network fusion communication technology to integrate 5G, satellite communication, and low-power wide-area network to ensure communication reliability in disaster environments.

[0017] Through the above improvements, the present invention further enhances the comprehensiveness of disaster monitoring, the dynamics of early warning analysis, the scientific nature of rescue decision-making, and the robustness of communication coordination, significantly improves the accuracy of disaster early warning and rescue efficiency, and reduces the losses caused by disasters. Brief Description of the Drawings

[0018] Figure 1 It is a schematic flowchart of the precise disaster early warning and rescue system of the present invention. Detailed Embodiment

[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] The following is in conjunction with Figure 1 Describe the precise disaster early warning and rescue system and its method of the present invention.

[0021] Figure 1 It is a schematic flowchart of the precise disaster early warning and rescue system provided by the present invention. As Figure 1 shown, the method of the precise disaster early warning and rescue system includes the following steps:

[0022] Step 110: Construct an integrated space-air-ground monitoring network, and the integrated space-air-ground monitoring network includes a sky-end monitoring network and a ground-end monitoring network.

[0023] In this embodiment, deploy a three-dimensional perception network composed of a satellite remote sensing monitoring station, a ground-based micro-deformation sensor, and an unmanned aerial vehicle (UAV) oblique camera. Among them:

[0024] The satellite node accesses the data of Fengyun-4 meteorological satellites and high-resolution remote sensing satellites to achieve surface deformation monitoring updated every hour;

[0025] The ground sensors are arranged in a grid of 50m×50m in high-risk areas, integrating multi-parameter perception functions such as soil moisture content, groundwater level, and tilt angle;

[0026] The UAV fleet is equipped with LiDAR and thermal imagers, and a three-dimensional real scene model is generated every 15 minutes during a disaster. After the point cloud data is compressed (compression rate ≥ 85%) by the edge computing node, it is transmitted back.

[0027] Step 120: Run the multi-modal early warning decision-making engine. The multi-modal includes a data fusion layer, a model calculation layer, and a dynamic early warning layer.

[0028] In this embodiment, a three-layer multi-modal engine is deployed, where:

[0029] The data fusion layer establishes a hybrid storage architecture including a historical disaster case database, a real-time monitoring stream database, and a social and economic disaster-bearing body database;

[0030] The model calculation layer deploys a disaster chain deduction algorithm based on an LSTM-GRU hybrid neural network. The input dimension includes 28 types of feature variables such as 72-hour weather forecast data, geological structure parameters, and population heat maps;

[0031] The dynamic early warning layer sets four levels of early warning thresholds of red / orange / yellow / blue. When the predicted loss exceeds 0.3‰ of the GDP, the red early warning is automatically triggered, and the disaster evolution path is simulated through the digital twin platform.

[0032] Step 130: Real-time and intelligent scheduling of rescue resources. The rescue resources include multi-modal rescue resources such as land, sea, and air.

[0033] In this embodiment, a plan matching engine and an adaptive learning mechanism are used, where:

[0034] A vector database of professional rescue teams and material reserves is established, and the Dijkstra-A* fusion algorithm is used to plan the optimal rescue path;

[0035] The disaster case database is incrementally updated weekly. When the recognition accuracy of new disaster types is lower than 85%, the model retraining process is triggered.

[0036] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A precise disaster warning and rescue system and method, characterized in that, It consists of four parts: a disaster monitoring module, a warning analysis module, a rescue decision-making module, and a communication coordination module.

2. The precise disaster warning and rescue system and method according to claim 1, characterized in that: The disaster monitoring module is used to collect and analyze disaster-related data in real time, including meteorological, geographical, environmental and other information.

3. The precise disaster warning and rescue system and method according to claim 1, characterized in that: The warning analysis module, based on the collected data, uses data analysis and machine learning technologies to predict the development trend of disasters and generate warning information.

4. The precise disaster warning and rescue system and method according to claim 1, characterized in that: The rescue decision-making module formulates the optimal rescue plan according to the warning information, combining the Geographic Information System (GIS) and the expert system.

5. The precise disaster warning and rescue system and method according to claim 1, characterized in that: The communication coordination module transmits the warning information and the rescue plan to the rescue personnel and relevant departments in real time through the wireless communication network.

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