CN115660424B - Disaster element analysis early warning system based on GIS - Google Patents
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Abstract
The invention relates to the technical field of geographic information analysis systems, in particular to a disaster element analysis and early warning system based on GIS, which comprises a GIS positioning analysis module, a mechanical inversion analysis module, an artificial intelligent prediction module, a safety evaluation calculation module, a disaster early warning decision module and a system maintenance management module. The invention realizes all-weather data real-time feedback, and improves the monitoring precision, the spatial resolution and the monitoring distance of the system by utilizing the GIS positioning analysis module; and a mechanical inversion analysis module and an artificial intelligence prediction module for deep learning are arranged, so that the data acquisition interval is shortened, and the land coding capacity aiming at different disaster factors is improved. Through setting up safe evaluation calculation module, through the high-level environment interference correction, carry out the whole evaluation to data, not influenced by environmental factor. Finally, a disaster early warning decision module is utilized to realize early warning decision, so that the disaster early warning decision-making system integrates system management, automatic control and data management.
Description
Technical Field
The invention relates to the technical field of geographic information analysis systems, in particular to a disaster element analysis and early warning system based on GIS.
Background
GIS is a geographic information system, sometimes referred to as a geoscience information system. It is a particular very important spatial information system. The system is a technical system for collecting, storing, managing, calculating, analyzing, displaying and describing the related geographic distribution data in the whole or part of the earth surface space under the support of a computer hardware and software system. China is a country with frequent natural disasters such as flood, earthquake, hurricane, drought and the like, and artificial disasters which directly threaten the life safety of human beings, such as dangerous goods diffusion, epidemic diseases and the like, cause great threat or loss to the life and property of the human beings. The geographic information system is important in aspects of disaster prediction, assessment, emergency response, daily maintenance and the like, and the acquisition and drawing analysis of geographic information are important.
The invention discloses a mobile terminal collaborative plotting method based on a power grid GIS, which comprises the following steps of: the method comprises the steps of collecting emergency information and determining the position of an emergency on a national network GIS map; classifying according to the need, judging the type and grade of the event, analyzing and judging the range of the influence of the emergency and the equipment and facilities of the power grid, and selecting an optimal path; respectively establishing an equipment image layer and an air image layer in a power grid GIS map according to positioning information of power equipment and a weather monitoring station, superposing the air image layer and the equipment image layer, intuitively drawing a situation map on the GIS map, and synchronizing plotting information to a central data end; the center data end synchronously transmits the summarized plotting information set to the mobile end of the collaborative plotting; setting a playing time scale, playing the plan data and checking any time situation, and carrying out dynamic map deduction. The invention displays the emergency resource situation completely, comprehensively, accurately and timely by using rich plotting languages such as graphics, symbolization and the like.
However, the above technical solution has the following drawbacks: the method is based on a national network GIS platform to collect and position emergency information, and then, the disaster is deduced and early-warned by combining information of power equipment and a meteorological monitoring station. The whole calculation process is finished by map plotting, and the occurrence probability of the later disasters is not researched and judged, and real-time early warning is carried out.
The invention with publication number CN108133578B discloses a dynamic early warning method and a refined hierarchical monitoring early warning method for dangerous cases of mountain floods, which are characterized in that a two-dimensional submarine wave equation is adopted to carry out numerical simulation on mountain floods along the journey on the basis of real-time data of a river basin investigation and monitoring system, the flood range and the flood depth of the mountain floods are predicted on the level of a DEM grid, and the information such as the degree of damage suffered by a disaster-bearing body, a safe evacuation line and the like is analyzed by overlapping disaster-bearing body data, so that the information is timely released to people in the river basin. The method is characterized in that monitoring information affecting elements of the mountain torrent disaster is obtained and analyzed in an omnibearing way, the whole mountain torrent forming, generating and evolving process is regarded as a highly dynamic process to be predicted and monitored, and the fine pre-warning of the mountain torrent disaster is gradually completed through four stages of pre-mountain torrent disaster prediction, disaster approaching pre-warning, disaster giving pre-warning and dangerous dynamic pre-warning. Compared with the existing one-step and one-index monitoring and early warning technology, the method fully combines dynamic characteristics and rules of the mountain torrent disasters, is more scientific and has higher disaster reduction practicality.
However, the above technical solution has the following drawbacks: according to the method, a two-dimensional submarine wave equation is adopted to carry out numerical simulation on mountain torrent along-journey movement, and fine warning of mountain torrent disasters is completed by using a four-stage warning system. However, the method is limited to mountain torrent disaster research and judgment, does not combine a GIS map information system to perform full-kind natural disaster early warning analysis, and has a narrow application range.
Disclosure of Invention
Aiming at the technical problems in the background technology, the invention provides a disaster element analysis and early warning system based on GIS.
The technical scheme of the invention is as follows: a disaster element analysis and early warning system based on GIS comprises a GIS positioning analysis module, a mechanical inversion analysis module, an artificial intelligent prediction module, a safety evaluation calculation module, a disaster early warning decision module and a system maintenance management module.
The GIS positioning analysis module queries the position and geographic information in a specific map range based on a GIS system and performs statistical analysis. And the mechanical inversion analysis module analyzes the data monitored by the GIS positioning analysis module by adopting a numerical analysis method according to the character change monitored by the rock-soil body under the action of the actual engineering load, and analyzes the mechanical characteristics and the initial stress condition of the rock-soil body. The artificial intelligent prediction module is based on a deep learning model, and numerical simulation models are built by adopting finite element and discrete element numerical calculation software on the data constructed by the mechanical inversion analysis module. The safety evaluation calculation module performs comparison analysis of a preset basic model aiming at model data monitored by artificial intelligence and machine learning, and performs safety evaluation aiming at corresponding disaster elements. And the disaster early warning decision module carries out safety state decision by combining dynamic monitoring data according to the engineering mechanics model based on the real-time evaluation and early warning obtained by the safety evaluation calculation module. The system maintenance management module is provided with a customized security risk source monitoring solution, and performs visual output on system data and log recording on daily operation of the system.
Preferably, the GIS positioning analysis module comprises a geographic data informatization sub-module, an intelligent networking sub-module and a GIS data statistics sub-module.
Preferably, the mechanical inversion analysis module comprises a monitoring parameter establishment sub-module, a machine learning sub-module and a MARS algorithm sub-module.
Preferably, the artificial intelligence prediction module comprises a data acquisition sub-module, a network measurement sub-module and an interactive data summarization sub-module.
Preferably, the safety evaluation calculation module comprises a preset basic data sub-module, a data comparison analysis sub-module and a numerical simulation sub-module.
Preferably, the system maintenance management module comprises a data information management sub-module, a numerical value induction sub-module and a system self-checking sub-module.
Preferably, the disaster early warning decision module comprises a safety state diagnosis sub-module, an early warning decision sub-module and a monitoring scheme setting sub-module.
A disaster element analysis early warning system based on GIS, the operation method of the system comprises the following steps:
s1, the GIS analysis module acquires map information on line, combines weather real-time data, analyzes and counts geological data related to disaster elements, and sends the geological data to the wireless network data terminal.
S2, a mechanical inversion analysis module adopts a machine learning regression algorithm of a multivariable adaptive regression spline method, effectively cooperates with the BPNN neural network model, and calculates and outputs the immediate geographic position safety condition according to the input of the on-site real-time monitoring parameters.
S3, the artificial intelligent prediction module is combined with the data model analyzed by mechanical inversion and the historical parameters of the GIS analysis module to acquire key data elements, and is combined with network real-time data to perform measurement analysis, so that interactive data summarization is realized.
S4, the safety evaluation calculation module performs analysis and comparison according to preset disaster element basic data, performs numerical simulation and performs safety evaluation in combination with a specific environment.
S5, the disaster early warning decision module carries out data induction, early warning decision is finally carried out, and a record disaster monitoring scheme is selected in the database.
S6, the system maintenance management module generates log data, stores and summarizes data information generated by each module, and performs system self-inspection for a plurality of times in a preset period.
Compared with the prior art, the technical scheme provided by the invention has the following beneficial technical effects: by arranging the GIS positioning analysis module, the mechanical inversion analysis module, the artificial intelligent prediction module, the safety evaluation calculation module, the disaster early warning decision module and the system maintenance management module, all-weather data real-time feedback is realized, and the monitoring precision, the spatial resolution and the monitoring distance of the system are improved by utilizing the GIS positioning analysis module; and a mechanical inversion analysis module and an artificial intelligence prediction module for deep learning are arranged, so that the data acquisition interval is shortened, and the land coding capacity aiming at different disaster factors is improved. Through setting up safe evaluation calculation module, through the high-level environment interference correction, carry out the whole evaluation to data, not influenced by environmental factor. Finally, a disaster early warning decision module is utilized to realize early warning decision, so that the method integrates system management, automatic control and data management, and provides favorable support for monitoring and early warning of geological environment disasters by using various network communication modes. The method solves the technical problems that the whole calculation process of the existing early warning system is finished in map plotting, the later disaster occurrence probability is not researched and judged and early warned in real time, and the problem that the prior art is limited to mountain torrent disaster research and judgment, the GIS map information system is not combined to perform full-type natural disaster early warning analysis and the application range is narrow by expanding a preset database is solved.
Drawings
FIG. 1 is a schematic block diagram of an embodiment of the present invention.
Fig. 2 is a schematic diagram of a submodule of a GIS positioning analysis module according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of a sub-module of a mechanical inversion analysis module according to an embodiment of the invention.
FIG. 4 is a schematic diagram of a submodule of an artificial intelligence prediction module according to an embodiment of the present invention.
FIG. 5 is a schematic diagram of a submodule of the security assessment computation module according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of a submodule of a disaster early warning decision module according to an embodiment of the present invention.
FIG. 7 is a block diagram illustrating a system maintenance management module according to an embodiment of the present invention.
Detailed Description
Example 1
As shown in fig. 1, the disaster element analysis and early warning system based on the GIS provided in this embodiment includes a GIS positioning analysis module, a mechanical inversion analysis module, an artificial intelligence prediction module, a security evaluation calculation module, a disaster early warning decision module and a system maintenance management module.
The GIS positioning analysis module queries the position and geographic information in a specific map range based on a GIS system and performs statistical analysis. And the mechanical inversion analysis module analyzes the data monitored by the GIS positioning analysis module by adopting a numerical analysis method according to the character change monitored by the rock-soil body under the action of the actual engineering load, and analyzes the mechanical characteristics and the initial stress condition of the rock-soil body. The artificial intelligent prediction module is based on a deep learning model, and numerical simulation models are built by adopting finite element and discrete element numerical calculation software on the data constructed by the mechanical inversion analysis module. The safety evaluation calculation module performs comparison analysis of a preset basic model aiming at model data monitored by artificial intelligence and machine learning, and performs safety evaluation aiming at corresponding disaster elements. And the disaster early warning decision module carries out safety state decision by combining dynamic monitoring data according to the engineering mechanics model based on the real-time evaluation and early warning obtained by the safety evaluation calculation module. The system maintenance management module is provided with a customized security risk source monitoring solution, and performs visual output on system data and log recording on daily operation of the system.
In the embodiment, by arranging the GIS positioning analysis module, the mechanical inversion analysis module, the artificial intelligent prediction module, the safety evaluation calculation module, the disaster early warning decision module and the system maintenance management module, all-weather data real-time feedback is realized, and the monitoring precision, the spatial resolution and the monitoring distance of the system are improved by utilizing the GIS positioning analysis module; and a mechanical inversion analysis module and an artificial intelligence prediction module for deep learning are arranged, so that the data acquisition interval is shortened, and the land coding capacity aiming at different disaster factors is improved. Through setting up safe evaluation calculation module, through the high-level environment interference correction, carry out the whole evaluation to data, not influenced by environmental factor. Finally, a disaster early warning decision module is utilized to realize early warning decision, so that the method integrates system management, automatic control and data management, and provides favorable support for monitoring and early warning of geological environment disasters by using various network communication modes. The method solves the technical problems that the whole calculation process of the existing early warning system is finished in map plotting, the later disaster occurrence probability is not researched and judged and early warned in real time, and the problem that the prior art is limited to mountain torrent disaster research and judgment, the GIS map information system is not combined to perform full-type natural disaster early warning analysis and the application range is narrow by expanding a preset database is solved.
Example two
The disaster element analysis and early warning system based on the GIS comprises a GIS positioning analysis module, a mechanical inversion analysis module, an artificial intelligent prediction module, a safety evaluation calculation module, a disaster early warning decision module and a system maintenance management module.
As shown in fig. 1-2, the GIS location analysis module queries for location and geographic information within a specific map range based on a GIS system and performs statistical analysis. And the mechanical inversion analysis module analyzes the data monitored by the GIS positioning analysis module by adopting a numerical analysis method according to the character change monitored by the rock-soil body under the action of the actual engineering load, and analyzes the mechanical characteristics and the initial stress condition of the rock-soil body. The artificial intelligent prediction module is based on a deep learning model, and numerical simulation models are built by adopting finite element and discrete element numerical calculation software on the data constructed by the mechanical inversion analysis module. The safety evaluation calculation module performs comparison analysis of a preset basic model aiming at model data monitored by artificial intelligence and machine learning, and performs safety evaluation aiming at corresponding disaster elements. And the disaster early warning decision module carries out safety state decision by combining dynamic monitoring data according to the engineering mechanics model based on the real-time evaluation and early warning obtained by the safety evaluation calculation module. The system maintenance management module is provided with a customized security risk source monitoring solution, and performs visual output on system data and log recording on daily operation of the system.
In the embodiment, by arranging the GIS positioning analysis module, the mechanical inversion analysis module, the artificial intelligent prediction module, the safety evaluation calculation module, the disaster early warning decision module and the system maintenance management module, all-weather data real-time feedback is realized, and the monitoring precision, the spatial resolution and the monitoring distance of the system are improved by utilizing the GIS positioning analysis module; and a mechanical inversion analysis module and an artificial intelligence prediction module for deep learning are arranged, so that the data acquisition interval is shortened, and the land coding capacity aiming at different disaster factors is improved. Through setting up safe evaluation calculation module, through the high-level environment interference correction, carry out the whole evaluation to data, not influenced by environmental factor. Finally, a disaster early warning decision module is utilized to realize early warning decision, so that the method integrates system management, automatic control and data management, and provides favorable support for monitoring and early warning of geological environment disasters by using various network communication modes. The method solves the technical problems that the whole calculation process of the existing early warning system is finished in map plotting, the later disaster occurrence probability is not researched and judged and early warned in real time, and the problem that the prior art is limited to mountain torrent disaster research and judgment, the GIS map information system is not combined to perform full-type natural disaster early warning analysis and the application range is narrow by expanding a preset database is solved.
Further, the GIS positioning analysis module comprises a geographic data informatization sub-module, an intelligent networking sub-module and a GIS data statistics sub-module.
Example III
The disaster element analysis and early warning system based on the GIS comprises a GIS positioning analysis module, a mechanical inversion analysis module, an artificial intelligent prediction module, a safety evaluation calculation module, a disaster early warning decision module and a system maintenance management module.
As shown in fig. 1-3, the GIS location analysis module queries for location and geographic information within a specific map range based on a GIS system and performs statistical analysis. And the mechanical inversion analysis module analyzes the data monitored by the GIS positioning analysis module by adopting a numerical analysis method according to the character change monitored by the rock-soil body under the action of the actual engineering load, and analyzes the mechanical characteristics and the initial stress condition of the rock-soil body. The artificial intelligent prediction module is based on a deep learning model, and numerical simulation models are built by adopting finite element and discrete element numerical calculation software on the data constructed by the mechanical inversion analysis module. The safety evaluation calculation module performs comparison analysis of a preset basic model aiming at model data monitored by artificial intelligence and machine learning, and performs safety evaluation aiming at corresponding disaster elements. And the disaster early warning decision module carries out safety state decision by combining dynamic monitoring data according to the engineering mechanics model based on the real-time evaluation and early warning obtained by the safety evaluation calculation module. The system maintenance management module is provided with a customized security risk source monitoring solution, and performs visual output on system data and log recording on daily operation of the system.
In the embodiment, by arranging the GIS positioning analysis module, the mechanical inversion analysis module, the artificial intelligent prediction module, the safety evaluation calculation module, the disaster early warning decision module and the system maintenance management module, all-weather data real-time feedback is realized, and the monitoring precision, the spatial resolution and the monitoring distance of the system are improved by utilizing the GIS positioning analysis module; and a mechanical inversion analysis module and an artificial intelligence prediction module for deep learning are arranged, so that the data acquisition interval is shortened, and the land coding capacity aiming at different disaster factors is improved. Through setting up safe evaluation calculation module, through the high-level environment interference correction, carry out the whole evaluation to data, not influenced by environmental factor. Finally, a disaster early warning decision module is utilized to realize early warning decision, so that the method integrates system management, automatic control and data management, and provides favorable support for monitoring and early warning of geological environment disasters by using various network communication modes. The method solves the technical problems that the whole calculation process of the existing early warning system is finished in map plotting, the later disaster occurrence probability is not researched and judged and early warned in real time, and the problem that the prior art is limited to mountain torrent disaster research and judgment, the GIS map information system is not combined to perform full-type natural disaster early warning analysis and the application range is narrow by expanding a preset database is solved.
Further, the mechanical inversion analysis module comprises a monitoring parameter establishment sub-module, a machine learning sub-module and a MARS algorithm sub-module.
Example IV
The disaster element analysis and early warning system based on the GIS comprises a GIS positioning analysis module, a mechanical inversion analysis module, an artificial intelligent prediction module, a safety evaluation calculation module, a disaster early warning decision module and a system maintenance management module.
As shown in fig. 1-4, the GIS location analysis module queries for location and geographic information within a specific map range based on a GIS system and performs statistical analysis. And the mechanical inversion analysis module analyzes the data monitored by the GIS positioning analysis module by adopting a numerical analysis method according to the character change monitored by the rock-soil body under the action of the actual engineering load, and analyzes the mechanical characteristics and the initial stress condition of the rock-soil body. The artificial intelligent prediction module is based on a deep learning model, and numerical simulation models are built by adopting finite element and discrete element numerical calculation software on the data constructed by the mechanical inversion analysis module. The safety evaluation calculation module performs comparison analysis of a preset basic model aiming at model data monitored by artificial intelligence and machine learning, and performs safety evaluation aiming at corresponding disaster elements. And the disaster early warning decision module carries out safety state decision by combining dynamic monitoring data according to the engineering mechanics model based on the real-time evaluation and early warning obtained by the safety evaluation calculation module. The system maintenance management module is provided with a customized security risk source monitoring solution, and performs visual output on system data and log recording on daily operation of the system.
In the embodiment, by arranging the GIS positioning analysis module, the mechanical inversion analysis module, the artificial intelligent prediction module, the safety evaluation calculation module, the disaster early warning decision module and the system maintenance management module, all-weather data real-time feedback is realized, and the monitoring precision, the spatial resolution and the monitoring distance of the system are improved by utilizing the GIS positioning analysis module; and a mechanical inversion analysis module and an artificial intelligence prediction module for deep learning are arranged, so that the data acquisition interval is shortened, and the land coding capacity aiming at different disaster factors is improved. Through setting up safe evaluation calculation module, through the high-level environment interference correction, carry out the whole evaluation to data, not influenced by environmental factor. Finally, a disaster early warning decision module is utilized to realize early warning decision, so that the method integrates system management, automatic control and data management, and provides favorable support for monitoring and early warning of geological environment disasters by using various network communication modes. The method solves the technical problems that the whole calculation process of the existing early warning system is finished in map plotting, the later disaster occurrence probability is not researched and judged and early warned in real time, and the problem that the prior art is limited to mountain torrent disaster research and judgment, the GIS map information system is not combined to perform full-type natural disaster early warning analysis and the application range is narrow by expanding a preset database is solved.
Further, the artificial intelligent prediction module comprises a data acquisition sub-module, a network measurement sub-module and an interactive data summarization sub-module.
Example five
The disaster element analysis and early warning system based on the GIS comprises a GIS positioning analysis module, a mechanical inversion analysis module, an artificial intelligent prediction module, a safety evaluation calculation module, a disaster early warning decision module and a system maintenance management module.
As shown in fig. 1-5, the GIS location analysis module queries for location and geographic information within a specific map range based on a GIS system and performs statistical analysis. And the mechanical inversion analysis module analyzes the data monitored by the GIS positioning analysis module by adopting a numerical analysis method according to the character change monitored by the rock-soil body under the action of the actual engineering load, and analyzes the mechanical characteristics and the initial stress condition of the rock-soil body. The artificial intelligent prediction module is based on a deep learning model, and numerical simulation models are built by adopting finite element and discrete element numerical calculation software on the data constructed by the mechanical inversion analysis module. The safety evaluation calculation module performs comparison analysis of a preset basic model aiming at model data monitored by artificial intelligence and machine learning, and performs safety evaluation aiming at corresponding disaster elements. And the disaster early warning decision module carries out safety state decision by combining dynamic monitoring data according to the engineering mechanics model based on the real-time evaluation and early warning obtained by the safety evaluation calculation module. The system maintenance management module is provided with a customized security risk source monitoring solution, and performs visual output on system data and log recording on daily operation of the system.
In the embodiment, by arranging the GIS positioning analysis module, the mechanical inversion analysis module, the artificial intelligent prediction module, the safety evaluation calculation module, the disaster early warning decision module and the system maintenance management module, all-weather data real-time feedback is realized, and the monitoring precision, the spatial resolution and the monitoring distance of the system are improved by utilizing the GIS positioning analysis module; and a mechanical inversion analysis module and an artificial intelligence prediction module for deep learning are arranged, so that the data acquisition interval is shortened, and the land coding capacity aiming at different disaster factors is improved. Through setting up safe evaluation calculation module, through the high-level environment interference correction, carry out the whole evaluation to data, not influenced by environmental factor. Finally, a disaster early warning decision module is utilized to realize early warning decision, so that the method integrates system management, automatic control and data management, and provides favorable support for monitoring and early warning of geological environment disasters by using various network communication modes. The method solves the technical problems that the whole calculation process of the existing early warning system is finished in map plotting, the later disaster occurrence probability is not researched and judged and early warned in real time, and the problem that the prior art is limited to mountain torrent disaster research and judgment, the GIS map information system is not combined to perform full-type natural disaster early warning analysis and the application range is narrow by expanding a preset database is solved.
Further, the safety evaluation calculation module comprises a preset basic data sub-module, a data comparison analysis sub-module and a numerical simulation sub-module.
Example six
The disaster element analysis and early warning system based on the GIS comprises a GIS positioning analysis module, a mechanical inversion analysis module, an artificial intelligent prediction module, a safety evaluation calculation module, a disaster early warning decision module and a system maintenance management module.
As shown in fig. 1-6, the GIS location analysis module queries for location and geographic information within a specific map range based on a GIS system and performs statistical analysis. And the mechanical inversion analysis module analyzes the data monitored by the GIS positioning analysis module by adopting a numerical analysis method according to the character change monitored by the rock-soil body under the action of the actual engineering load, and analyzes the mechanical characteristics and the initial stress condition of the rock-soil body. The artificial intelligent prediction module is based on a deep learning model, and numerical simulation models are built by adopting finite element and discrete element numerical calculation software on the data constructed by the mechanical inversion analysis module. The safety evaluation calculation module performs comparison analysis of a preset basic model aiming at model data monitored by artificial intelligence and machine learning, and performs safety evaluation aiming at corresponding disaster elements. And the disaster early warning decision module carries out safety state decision by combining dynamic monitoring data according to the engineering mechanics model based on the real-time evaluation and early warning obtained by the safety evaluation calculation module. The system maintenance management module is provided with a customized security risk source monitoring solution, and performs visual output on system data and log recording on daily operation of the system.
In the embodiment, by arranging the GIS positioning analysis module, the mechanical inversion analysis module, the artificial intelligent prediction module, the safety evaluation calculation module, the disaster early warning decision module and the system maintenance management module, all-weather data real-time feedback is realized, and the monitoring precision, the spatial resolution and the monitoring distance of the system are improved by utilizing the GIS positioning analysis module; and a mechanical inversion analysis module and an artificial intelligence prediction module for deep learning are arranged, so that the data acquisition interval is shortened, and the land coding capacity aiming at different disaster factors is improved. Through setting up safe evaluation calculation module, through the high-level environment interference correction, carry out the whole evaluation to data, not influenced by environmental factor. Finally, a disaster early warning decision module is utilized to realize early warning decision, so that the method integrates system management, automatic control and data management, and provides favorable support for monitoring and early warning of geological environment disasters by using various network communication modes. The method solves the technical problems that the whole calculation process of the existing early warning system is finished in map plotting, the later disaster occurrence probability is not researched and judged and early warned in real time, and the problem that the prior art is limited to mountain torrent disaster research and judgment, the GIS map information system is not combined to perform full-type natural disaster early warning analysis and the application range is narrow by expanding a preset database is solved.
Further, the system maintenance management module comprises a data information management sub-module, a numerical value induction sub-module and a system self-checking sub-module.
Example seven
The disaster element analysis and early warning system based on the GIS comprises a GIS positioning analysis module, a mechanical inversion analysis module, an artificial intelligent prediction module, a safety evaluation calculation module, a disaster early warning decision module and a system maintenance management module.
As shown in fig. 1-7, the GIS location analysis module queries for location and geographic information within a specific map range based on a GIS system and performs statistical analysis. And the mechanical inversion analysis module analyzes the data monitored by the GIS positioning analysis module by adopting a numerical analysis method according to the character change monitored by the rock-soil body under the action of the actual engineering load, and analyzes the mechanical characteristics and the initial stress condition of the rock-soil body. The artificial intelligent prediction module is based on a deep learning model, and numerical simulation models are built by adopting finite element and discrete element numerical calculation software on the data constructed by the mechanical inversion analysis module. The safety evaluation calculation module performs comparison analysis of a preset basic model aiming at model data monitored by artificial intelligence and machine learning, and performs safety evaluation aiming at corresponding disaster elements. And the disaster early warning decision module carries out safety state decision by combining dynamic monitoring data according to the engineering mechanics model based on the real-time evaluation and early warning obtained by the safety evaluation calculation module. The system maintenance management module is provided with a customized security risk source monitoring solution, and performs visual output on system data and log recording on daily operation of the system.
In the embodiment, by arranging the GIS positioning analysis module, the mechanical inversion analysis module, the artificial intelligent prediction module, the safety evaluation calculation module, the disaster early warning decision module and the system maintenance management module, all-weather data real-time feedback is realized, and the monitoring precision, the spatial resolution and the monitoring distance of the system are improved by utilizing the GIS positioning analysis module; and a mechanical inversion analysis module and an artificial intelligence prediction module for deep learning are arranged, so that the data acquisition interval is shortened, and the land coding capacity aiming at different disaster factors is improved. Through setting up safe evaluation calculation module, through the high-level environment interference correction, carry out the whole evaluation to data, not influenced by environmental factor. Finally, a disaster early warning decision module is utilized to realize early warning decision, so that the method integrates system management, automatic control and data management, and provides favorable support for monitoring and early warning of geological environment disasters by using various network communication modes. The method solves the technical problems that the whole calculation process of the existing early warning system is finished in map plotting, the later disaster occurrence probability is not researched and judged and early warned in real time, and the problem that the prior art is limited to mountain torrent disaster research and judgment, the GIS map information system is not combined to perform full-type natural disaster early warning analysis and the application range is narrow by expanding a preset database is solved.
Further, the disaster early warning decision module comprises a safety state diagnosis sub-module, an early warning decision sub-module and a monitoring scheme setting sub-module.
A disaster element analysis early warning system based on GIS, the operation method of the system comprises the following steps:
s1, the GIS analysis module acquires map information on line, combines weather real-time data, analyzes and counts geological data related to disaster elements, and sends the geological data to the wireless network data terminal.
S2, a mechanical inversion analysis module adopts a machine learning regression algorithm of a multivariable adaptive regression spline method, effectively cooperates with the BPNN neural network model, and calculates and outputs the immediate geographic position safety condition according to the input of the on-site real-time monitoring parameters.
S3, the artificial intelligent prediction module is combined with the data model analyzed by mechanical inversion and the historical parameters of the GIS analysis module to acquire key data elements, and is combined with network real-time data to perform measurement analysis, so that interactive data summarization is realized.
S4, the safety evaluation calculation module performs analysis and comparison according to preset disaster element basic data, performs numerical simulation and performs safety evaluation in combination with a specific environment.
S5, the disaster early warning decision module carries out data induction, early warning decision is finally carried out, and a record disaster monitoring scheme is selected in the database.
S6, the system maintenance management module generates log data, stores and summarizes data information generated by each module, and performs system self-inspection for a plurality of times in a preset period.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explanation of the principles of the present invention and are in no way limiting of the invention. Accordingly, any modification, equivalent replacement, improvement, etc. made without departing from the spirit and scope of the present invention should be included in the scope of the present invention. Furthermore, the appended claims are intended to cover all such changes and modifications that fall within the scope and boundary of the appended claims, or equivalents of such scope and boundary.
Claims (8)
1. The disaster element analysis and early warning system based on the GIS is characterized by comprising a GIS positioning analysis module, a mechanical inversion analysis module, an artificial intelligent prediction module, a safety evaluation calculation module, a disaster early warning decision module and a system maintenance management module;
the GIS positioning analysis module queries the position and geographic information in a specific map range based on a GIS system and performs statistical analysis;
the mechanical inversion analysis module analyzes the data monitored by the GIS positioning analysis module by adopting a numerical analysis method according to the character change monitored by the rock-soil body under the action of the actual engineering load, and analyzes the mechanical characteristics and the initial stress condition of the rock-soil body;
the artificial intelligent prediction module is based on a deep learning model, and adopts finite element and discrete element numerical calculation software to the data constructed by the mechanical inversion analysis module to establish a numerical simulation model;
the safety evaluation calculation module performs comparison analysis of a preset basic model aiming at model data monitored by artificial intelligence and machine learning, and performs safety evaluation aiming at corresponding disaster elements;
the disaster early warning decision module carries out safety state decision by combining dynamic monitoring data according to the engineering mechanical model based on the real-time evaluation and early warning obtained by the safety evaluation calculation module;
the system maintenance management module is provided with a customized security risk source monitoring solution, and performs visual output on system data and log recording on daily operation of the system.
2. The disaster element analysis and early warning system based on GIS according to claim 1, wherein the GIS positioning analysis module comprises a geographic data informatization sub-module, an intelligent networking sub-module and a GIS data statistics sub-module.
3. The GIS-based disaster element analysis and early warning system according to claim 1, wherein the mechanical inversion analysis module comprises a monitoring parameter establishment sub-module, a machine learning sub-module and a MARS algorithm sub-module.
4. The GIS-based disaster element analysis and early warning system according to claim 1, wherein the artificial intelligence prediction module comprises a data acquisition sub-module, a network measurement sub-module and an interactive data summarization sub-module.
5. The disaster element analysis and early warning system based on GIS according to claim 1, wherein the safety evaluation calculation module comprises a preset basic data sub-module, a data comparison analysis sub-module and a numerical simulation sub-module.
6. The GIS-based disaster element analysis and early warning system according to claim 1, wherein the system maintenance management module comprises a data information management sub-module, a numerical induction sub-module and a system self-checking sub-module.
7. The system of claim 1, wherein the disaster warning decision module comprises a security status diagnosis sub-module, a warning decision sub-module and a monitoring scheme setting sub-module.
8. A GIS-based disaster element analysis and early warning system according to any one of claims 1 to 7, the operation method of the system comprising the steps of:
s1, a GIS analysis module acquires map information on line, combines weather real-time data, analyzes and counts geological data related to disaster elements and sends the geological data to a wireless network data end;
s2, a mechanical inversion analysis module adopts a machine learning regression algorithm of a multivariable adaptive regression spline method, effectively cooperates with the BPNN neural network model, and calculates and outputs the immediate geographic position safety condition according to the input of the on-site real-time monitoring parameters;
s3, the artificial intelligent prediction module is combined with the data model analyzed by mechanical inversion and the historical parameters of the GIS analysis module to acquire key data elements, and is combined with network real-time data to perform measurement analysis, so that interactive data summarization is realized;
s4, the safety evaluation calculation module analyzes and compares the basic data of the preset disaster factors, performs numerical simulation and performs safety evaluation in combination with a specific environment;
s5, carrying out data induction by a disaster early warning decision module, finally carrying out early warning decision, and selecting a remembering disaster monitoring scheme in a database;
s6, the system maintenance management module generates log data, stores and summarizes data information generated by each module, and performs system self-inspection for a plurality of times in a preset period.
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