A meteorological disaster early warning system based on BP neural network

Abstract

The present invention belongs to the field of meteorological disaster technology, and discloses a meteorological disaster early warning system based on BP neural network, including a meteorological early warning system: the meteorological early warning system includes a data acquisition module, a multi-layer BP neural network, a disaster early warning judgment module, an early warning information release module, a satellite remote sensing data collection submodule, a meteorological observation module, a radar data collection submodule, a historical meteorological data archiving submodule, a user interaction and visualization module, and a data storage and management module. The present invention, by utilizing the powerful learning and prediction capabilities of the multi-layer BP neural network, can more accurately analyze the complex nonlinear relationship between meteorological data and disasters. The system trains neural networks based on a large amount of historical data, can adapt to the characteristics of meteorological changes in different regions and seasons, can be applied in various meteorological environments around the world, and provide strong support for protecting the safety of people's lives and property in different regions and the stable development of social economy, which is conducive to practical application and operation.

Description

Technical Field

[0001] The invention relates to the technical field of meteorological disasters, and in particular to a meteorological disaster early warning system based on a BP neural network. Background Art

[0002] Meteorological disasters, as one of the major natural disasters facing the world, have had extremely serious impacts on the safety of life and property, agricultural production, infrastructure, ecological environment and many other aspects of human society. Heavy rains and floods can instantly destroy houses, submerge farmland and city streets, causing casualties and huge economic losses; typhoons, with their strong winds and destructive power of heavy rains, can uproot trees, overturn buildings, and destroy power and communication facilities; droughts pose a long-term threat to agricultural harvests, causing the land to dry up, crops to die, and cause food shortages and famine; cold waves can cause the temperature to drop sharply, posing a severe test to animal husbandry, energy supply and transportation, with livestock frostbite and freezing to death, water pipes freezing and cracking, and roads icing causing traffic accidents.

[0003] In the actual application of existing equipment, traditional meteorological disaster warning methods mainly rely on meteorological professionals to conduct manual analysis and empirical judgment of limited meteorological observation data, as well as some simple statistical models. However, with the intensification of global climate change, meteorological data presents massive, high-dimensional, complex, changeable and nonlinear characteristics. Traditional methods gradually expose many limitations when facing these complex data. It is difficult to fully explore the deep-level laws and hidden information in meteorological data, resulting in a significant reduction in the accuracy and timeliness of warnings. It cannot meet the urgent needs of modern society for accurate and timely warnings of meteorological disasters, which is not conducive to practical application and operation. Summary of the invention

[0004] One of the purposes of the present invention is to provide a meteorological disaster early warning system based on BP neural network.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is: a meteorological disaster early warning system based on BP neural network, including a meteorological early warning system: the meteorological early warning system includes a data acquisition module, a multi-layer BP neural network, a disaster early warning determination module, an early warning information release module, a satellite remote sensing data collection submodule, a meteorological observation module, a radar data collection submodule, a historical meteorological data archiving submodule, a user interaction and visualization module and a data storage and management module;

[0006] The tail of the data acquisition module is connected to the multi-layer BP neural network, the tail of the multi-layer BP neural network is connected to the disaster warning determination module, the disaster warning determination module and the user interaction and visualization module are interconnected, and the tail of the disaster warning determination module is connected to the warning information release module. By utilizing the powerful learning and prediction capabilities of the multi-layer BP neural network, the complex nonlinear relationship between meteorological data and disasters can be analyzed more accurately. Compared with traditional warning systems based on simple statistical models or empirical rules, the accuracy of meteorological disaster warnings is significantly improved, and the false alarm and missed alarm rates are reduced. The system trains neural networks based on a large amount of historical data, which can adapt to the characteristics of meteorological changes in different regions and seasons. It can be used in various meteorological environments around the world, providing strong support for protecting the safety of people's lives and property in different regions and the stable development of social economy.

[0007] Preferably, the satellite remote sensing data collection submodule, meteorological observation module, radar data collection submodule and historical meteorological data archiving submodule are all connected to the data acquisition module, and multi-source meteorological data are collected through the satellite remote sensing data collection submodule, meteorological observation module, radar data collection submodule and historical meteorological data archiving submodule, integrating macro and micro, real-time and historical meteorological information, so that the system can have a more comprehensive understanding of meteorological conditions, thereby making more reliable predictions and assessments of meteorological disasters, and can cope with various types of meteorological disasters, such as rainstorms, floods, typhoons, droughts, cold waves, etc.

[0008] Preferably, the disaster warning determination module is connected to the data storage and management module, and the data storage and management module is connected to the historical meteorological data archiving sub-module. The data storage and management module provides a rich data foundation for meteorological research and long-term disaster prevention strategy optimization, and can promote the continuous development and improvement of meteorological disaster warning technology.

[0009] Compared with the prior art, the present invention has the following beneficial effects:

[0010] (1) By utilizing the powerful learning and prediction capabilities of the multi-layer BP neural network, the present invention can more accurately analyze the complex nonlinear relationship between meteorological data and disasters. Compared with the traditional early warning system based on simple statistical models or empirical rules, it significantly improves the accuracy of meteorological disaster early warnings and reduces the false alarm and missed alarm rates. The system trains neural networks based on a large amount of historical data and can adapt to the characteristics of meteorological changes in different regions and seasons. It can be applied in various meteorological environments around the world, providing strong support for protecting the safety of people's lives and property in different regions and the stable development of social economy.

[0011] (2) The present invention collects multi-source meteorological data through a satellite remote sensing data collection submodule, a meteorological observation module, a radar data collection submodule and a historical meteorological data archiving submodule, and integrates macro and micro, real-time and historical meteorological information, so that the system can have a more comprehensive understanding of meteorological conditions, thereby making more reliable predictions and assessments of meteorological disasters, and can cope with various types of meteorological disasters, such as rainstorms, floods, typhoons, droughts, cold waves, etc.

[0012] (3) The present invention facilitates meteorological staff to quickly and intuitively obtain warning information and related data through user interaction and visualization modules, which helps them make scientific and reasonable disaster prevention and mitigation decisions in a timely manner and improve the efficiency of responding to meteorological disasters. The data storage and management module provides a rich data foundation for meteorological research and long-term disaster prevention strategy optimization, which can promote the continuous development and improvement of meteorological disaster warning technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the overall structure of the weather warning system of the present invention. DETAILED DESCRIPTION

[0014] The present invention is further described below in conjunction with specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0015] In the description of the present invention, it should be noted that directional words, such as the terms "center", "lateral", "longitudinal", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", etc., indicating directions and positional relationships are based on the directions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and cannot be understood as limiting the specific scope of protection of the present invention.

[0016] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0017] One of the preferred embodiments of the present invention is as follows Figure 1 As shown, a meteorological disaster warning system based on BP neural network includes a meteorological warning system: the meteorological warning system includes a data acquisition module, a multi-layer BP neural network, a disaster warning determination module, a warning information release module, a satellite remote sensing data collection submodule, a meteorological observation module, a radar data collection submodule, a historical meteorological data archiving submodule, a user interaction and visualization module and a data storage and management module;

[0018] The tail of the data acquisition module is connected to the multi-layer BP neural network, the tail of the multi-layer BP neural network is connected to the disaster warning judgment module, the disaster warning judgment module and the user interaction and visualization module are interconnected, and the tail of the disaster warning judgment module is connected to the warning information release module. The data acquisition module collects various types of meteorological data through the satellite remote sensing data collection submodule, the meteorological observation module, the radar data collection submodule and the historical meteorological data archiving submodule, including real-time satellite cloud map information, basic meteorological elements observed by the meteorological station, precipitation and storm data detected by radar, and historical meteorological data. After these data are aggregated into the data acquisition module, they are transmitted to the multi-layer BP neural network after necessary sorting and preliminary processing. The multi-layer BP neural network is pre-trained based on a large amount of historical meteorological data and its corresponding disaster results. It can perform complex feature extraction and pattern recognition on the input meteorological data. Through the connection and calculation between the neuron nodes inside the neural network, the hidden laws and relationships related to the occurrence of disasters in the meteorological data are mined, so as to output relevant information about the possibility of meteorological disasters, disaster types and intensity.

[0019] The satellite remote sensing data collection submodule, meteorological observation module, radar data collection submodule and historical meteorological data archiving submodule are all connected to the data acquisition module. The disaster warning judgment module receives the output results of the multi-layer BP neural network and compares it with the preset warning threshold. If the result exceeds the threshold, it is determined that a meteorological disaster may occur and triggers subsequent actions. On the one hand, the relevant warning information is transmitted to the warning information release module so that warnings can be issued to the public and relevant departments in a timely manner; on the other hand, the relevant data of the warning judgment is transmitted to the user interaction and visualization module for meteorological staff to view and analyze.

[0020] The disaster warning determination module is connected to the data storage and management module, which is connected to the historical meteorological data archiving submodule. The warning data is stored in the data storage and management module to facilitate subsequent tracing and statistical analysis.

[0021] Working principle:

[0022] When in use, the data acquisition module collects a wide range of meteorological data through the satellite remote sensing data collection submodule, the meteorological observation module, the radar data collection submodule and the historical meteorological data archiving submodule, including real-time satellite cloud map information, basic meteorological elements observed by the weather station, precipitation and storm data detected by the radar, and historical meteorological data. After these data are aggregated into the data acquisition module, they are transmitted to the multi-layer BP neural network after necessary sorting and preliminary processing. The multi-layer BP neural network is pre-trained based on a large amount of historical meteorological data and its corresponding disaster results. It can perform complex feature extraction and pattern recognition on the input meteorological data. Through the connection and calculation between the neuron nodes within the neural network, the hidden laws and relationships related to the occurrence of disasters in the meteorological data are excavated, thereby outputting relevant information such as the possibility of the occurrence of meteorological disasters, the type of disasters, and the intensity of the disasters;

[0023] The disaster warning judgment module receives the output results of the multi-layer BP neural network and compares it with the preset warning threshold. If the result exceeds the threshold, it is determined that a meteorological disaster may occur and triggers subsequent actions. On the one hand, the relevant warning information is transmitted to the warning information release module so that warnings can be issued to the public and relevant departments in a timely manner; on the other hand, the relevant data of the warning judgment is transmitted to the user interaction and visualization module for meteorological staff to view and analyze, and the warning data is stored in the data storage and management module for subsequent tracing and statistical analysis.

[0024] The above describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention to be protected. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A meteorological disaster early warning system based on BP neural network, characterized in that: It includes a meteorological early warning system: the meteorological early warning system includes a data acquisition module, a multi-layer BP neural network, a disaster early warning determination module, an early warning information release module, a satellite remote sensing data collection submodule, a meteorological observation module, a radar data collection submodule, a historical meteorological data archiving submodule, a user interaction and visualization module, and a data storage and management module; The tail of the data acquisition module is connected to the multi-layer BP neural network, the tail of the multi-layer BP neural network is connected to the disaster warning determination module, the disaster warning determination module and the user interaction and visualization module are interconnected, and the tail of the disaster warning determination module is connected to the warning information release module.

2. A meteorological disaster early warning system based on BP neural network as claimed in claim 1, characterized in that: The satellite remote sensing data collection submodule, the meteorological observation module, the radar data collection submodule and the historical meteorological data archiving submodule are all connected to the data acquisition module.

3. A meteorological disaster early warning system based on BP neural network as claimed in claim 1, characterized in that: The disaster warning determination module is connected to the data storage and management module, and the data storage and management module is connected to the historical meteorological data archiving submodule.

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