CN116863652A - Geological disaster early warning method based on Internet adding technology - Google Patents
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Abstract
The invention relates to the technical field of geological disaster early warning, and discloses a geological disaster early warning method based on an internet adding technology, which comprises the following steps: establishing a geological disaster monitoring system based on the internet of things, arranging sensors in a region prone to occurrence, monitoring geological disaster influence factors in real time, and transmitting monitoring data acquired by the sensors to a data center through a network; analyzing and processing the collected monitoring data, establishing a geological disaster assessment model, and judging the type, time and range of possible geological disasters by using machine learning and deep learning algorithms; distributing geological early warning information to government departments and emergency management departments through an internet technology according to the monitoring data and the model analysis result; establishing interaction platforms of government departments, emergency management departments and the public by utilizing an internet technology; the internet adding technology is utilized to carry out informatization upgrading in the whole process of geological disaster monitoring, early warning, rescue and reconstruction, so that more accurate and efficient geological disaster management is realized.
Description
Technical Field
The invention relates to the technical field of geological disaster early warning, in particular to a geological disaster early warning method based on internet adding technology.
Background
The China has carried out geological disaster investigation in more than 2000 county cities in the whole country, and carries out geological disaster easy (risk) division based on investigation results, and on the basis of the geological disaster easy (risk) division, the model criteria are utilized by combining rainfall critical values, so that geological disaster early warning can be carried out.
The existing geological disaster early warning method based on the Internet adding technology has the following defects: the method cannot carry out informatization upgrading in the whole process of geological disaster monitoring, early warning, rescue and reconstruction, is difficult to realize more accurate and efficient geological disaster management, has lower efficiency and accuracy of geological disaster assessment, is inconvenient to popularize and use, and needs to design a corresponding technical scheme to solve the problems.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a geological disaster early warning method based on the Internet adding technology, which solves the technical problems that informatization upgrading cannot be carried out in the whole process of geological disaster monitoring, early warning, rescue and reconstruction, more accurate and efficient geological disaster management is difficult to realize, the efficiency and accuracy of geological disaster assessment are lower, and popularization and use are inconvenient.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: a geological disaster early warning method based on Internet adding technology comprises the following steps:
s1, establishing a geological disaster monitoring system based on the Internet of things technology, arranging sensors in an easily-developed area, monitoring geological disaster influence factors in real time, and transmitting monitoring data acquired by the sensors to a data center through a network;
s2, analyzing and processing the collected monitoring data, establishing a geological disaster assessment model, and judging the type, time and range of possible geological disasters by using machine learning and deep learning algorithms;
s3, distributing geological disaster early warning information to government departments, emergency management departments and the public through an internet technology according to the monitoring data and the model analysis result;
s4, establishing interaction platforms of government departments, emergency management departments and the public by utilizing an internet technology;
s5, constructing a digital twin system of the geological disaster by utilizing cloud computing and big data technology, and predicting loss and evolution processes under different disaster conditions by the system through simulation technology;
s6, utilizing a 5G communication technology to ensure the speed and reliability of information transmission between the disaster site and the command center;
s7, developing a geological disaster emergency rescue system based on the Internet adding technology, positioning personnel by using a navigation technology, and optimizing rescue command by using voice recognition and AI digital assistant;
s8, assisting rescue workers in grasping disaster site information by using an AR/VR technology, and performing simulation training;
s9, establishing a donation management system based on the blockchain, and utilizing the decentralization and transparency of the blockchain;
s10, performing post-disaster reconstruction information release, collection of comments, resource coordination and other works by utilizing an internet technology, and performing data backup and recovery by utilizing cloud services to promote digitization and intellectualization of post-disaster reconstruction work.
Preferably, in step S1, the sensor includes one or more of a rainfall sensor, a soil humidity sensor for measuring a water content of soil, a water level sensor for measuring a water level of ground, and a displacement sensor for measuring vertical and horizontal displacements of ground.
Preferably, in step S2, normalization processing is performed on all kinds of collected monitoring data, and a standardized data set is formed after abnormal values are removed;
inputting the standardized data set into deep learning models such as a convolutional neural network, a cyclic neural network and the like for training, and establishing evaluation models of different types of geological disasters;
adding a time sequence analysis module into the deep learning model, analyzing the time change rule of the monitoring data, and predicting the trend in a certain time in the future;
establishing an evaluation model of disaster types, time and space ranges by utilizing machine learning algorithms such as random forests, support vector machines and the like and combining geological disaster history statistical data;
the analysis of the real-time monitoring data by the deep learning model and the analysis result of the historical data by the machine learning model are fused, and multi-source heterogeneous data mining is carried out, so that the accurate prediction of geological disasters is realized;
the prediction results of disaster possibility, disaster type, occurrence time and space influence range output by the model are visually expressed, and an intuitive geological disaster early warning analysis report is generated;
and a plurality of models are called in real time by adopting cloud computing and distributed computing technologies, so that low-delay response to sudden disasters is realized.
Preferably, in step S3, the early warning information includes a possibility of occurrence of a geological disaster, a disaster-affected area, and a countermeasure proposal.
Preferably, in step S6, a 5G micro base station is established in the disaster site area to form a dedicated 5G communication network, so as to ensure that the key infrastructure has 5G network coverage;
the high-bandwidth and large-capacity transmission site real-time video of the 5G network is utilized to realize the visual command of the disaster site, and a commander can remotely observe the disaster site;
a network slicing technology in a 5G network is adopted to specially divide a network slice for on-site commanders, so as to carry out service isolation and ensure the quality of command service;
the on-site commander uses the 5G industrial grade router to realize high-reliability connection with the 5G base station, so as to ensure that the command terminal has optimal communication quality;
constructing networking of site commanders and rescue workers to realize temporary communication by using a direct communication mode D2D between 5G devices;
the 5G edge computing technology is applied, partial command computing and data analysis functions are deployed at edge nodes, and dependence on transmission bandwidth is reduced;
and intelligent scheduling coordination is carried out on command services by adopting a network slice scheduling technology of a 5G core network.
Preferably, in step S7, a UWB wideband accurate positioning technology, a WIFI fingerprint positioning technology, a database is built by collecting WIFI hotspot information of a specific area, a combination of a magnetic sensor array and inertial navigation is used, a visible light communication technology is used to perform high-precision positioning and communication through an LED lamp and a light receiver, and an acoustic guided positioning algorithm and a 5G millimeter wave technology are adopted.
Preferably, in step S8, the site rescue workers wear AR glasses through an AR technology, and the positions of key facilities and the distribution information of buried objects are displayed in a superimposed manner on the disaster site to grasp the complex environment;
building a simulation training scene by using a VR technology, and enabling rescue workers to experience different disaster sites in the simulation environment to train repeatedly;
the VR training system is combined with an artificial intelligence algorithm, so that the operation process of rescue workers can be evaluated, and optimization suggestions are given to realize intelligent training;
the video of the first visual angle is obtained through the AR/VR glasses and transmitted to the command center, so that a commander can be helped to remotely sense the disaster site environment;
developing an AR navigation system, enabling site personnel to wear AR equipment, receiving rescue route navigation information issued by a command center, and optimizing site movement organization;
the AR/VR is utilized to realize remote expert guidance, on-site personnel and rear experts perform real-time audio and video interaction, and the remote acquisition guidance improves rescue efficiency;
the AR/VR system supports multi-person collaboration, and information sharing and collaborative operation among on-site rescue teams can be achieved.
(III) beneficial effects
Compared with the prior art, the invention has the beneficial effects that: the internet technology is utilized to carry out informatization upgrading in the whole process of geological disaster monitoring, early warning, rescue and reconstruction, so that more accurate and efficient geological disaster management is realized, the machine learning and deep learning technologies can be fully utilized, intelligent analysis of mass monitoring data is realized, the efficiency and accuracy of geological disaster assessment are greatly improved, the refined early warning analysis result is output, and important social benefits and application prospects are realized.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a technical scheme that: a geological disaster early warning method based on Internet adding technology comprises the following steps:
s1, establishing a geological disaster monitoring system based on the Internet of things technology, arranging sensors in an easily-developed area, monitoring geological disaster influence factors in real time, and transmitting monitoring data acquired by the sensors to a data center through a network;
s2, analyzing and processing the collected monitoring data, establishing a geological disaster assessment model, and judging the type, time and range of possible geological disasters by using machine learning and deep learning algorithms;
s3, distributing geological disaster early warning information to government departments, emergency management departments and the public through an internet technology according to the monitoring data and the model analysis result;
s4, establishing interaction platforms of government departments, emergency management departments and the public by utilizing an internet technology; the public can feed back disaster information through the platform, departments can release disaster relief information, and information interaction efficiency is improved.
S5, constructing a digital twin system of the geological disaster by utilizing cloud computing and big data technology, and predicting loss and evolution processes under different disaster conditions by the system through simulation technology; the scientificity of the decision is improved.
S6, utilizing a 5G communication technology to ensure the speed and reliability of information transmission between the disaster site and the command center;
s7, developing a geological disaster emergency rescue system based on the Internet adding technology, positioning personnel by using a navigation technology, and optimizing rescue command by using voice recognition and AI digital assistant;
s8, using an AR/VR technology to assist rescue workers in grasping disaster site information, performing simulation training, and improving actual combat rescue efficiency;
s9, establishing a donation management system based on the blockchain, and utilizing the decentralization and transparency of the blockchain;
s10, performing post-disaster reconstruction information release, collection of comments, resource coordination and other works by utilizing an internet technology, and performing data backup and recovery by utilizing cloud services to promote digitization and intellectualization of post-disaster reconstruction work.
Further more, in step S1, the sensor includes one or more of a rainfall sensor, a soil humidity sensor for measuring a water content of soil, a water level sensor for measuring a water level of ground, and a displacement sensor for measuring vertical and horizontal displacements of ground.
Further more, in step S2, normalization processing is performed on all kinds of collected monitoring data, and a standardized data set is formed after abnormal values are removed;
inputting the standardized data set into deep learning models such as a convolutional neural network, a cyclic neural network and the like for training, and establishing evaluation models of different types of geological disasters;
adding a time sequence analysis module into the deep learning model, analyzing the time change rule of the monitoring data, and predicting the trend in a certain time in the future;
establishing an evaluation model of disaster types, time and space ranges by utilizing machine learning algorithms such as random forests, support vector machines and the like and combining geological disaster history statistical data;
the analysis of the real-time monitoring data by the deep learning model and the analysis result of the historical data by the machine learning model are fused, and multi-source heterogeneous data mining is carried out, so that the accurate prediction of geological disasters is realized;
the prediction results of disaster possibility, disaster type, occurrence time and space influence range output by the model are visually expressed, and an intuitive geological disaster early warning analysis report is generated;
and a plurality of models are called in real time by adopting cloud computing and distributed computing technologies, so that low-delay response to sudden disasters is realized.
Further improved, in step S3, the early warning information includes the possibility of occurrence of a geological disaster, the disaster range, and countermeasure advice.
Further improved, in step S7, UWB wideband accurate positioning technology, WIFI fingerprint positioning technology, collecting WIFI hotspot information of a specific area, establishing a database, combining a magnetic sensor array and inertial navigation, performing high-precision positioning and communication by using visible light communication technology through LED lamps and light receivers, and adopting acoustic guided positioning algorithm and 5G millimeter wave technology.
Specifically, in step S8, the site rescue workers wear AR glasses through an AR technology, and the positions of key facilities and the distribution information of buried objects are displayed in a superimposed manner on the disaster site to master the complex environment; building a simulation training scene by using a VR technology, and enabling rescue workers to experience different disaster sites in the simulation environment to train repeatedly; the VR training system is combined with an artificial intelligence algorithm, so that the operation process of rescue workers can be evaluated, and optimization suggestions are given to realize intelligent training; the video of the first visual angle is obtained through the AR/VR glasses and transmitted to the command center, so that a commander can be helped to remotely sense the disaster site environment; developing an AR navigation system, enabling site personnel to wear AR equipment, receiving rescue route navigation information issued by a command center, and optimizing site movement organization; the AR/VR is utilized to realize remote expert guidance, on-site personnel and rear experts perform real-time audio and video interaction, and the remote acquisition guidance improves rescue efficiency; the AR/VR system supports multi-person collaboration, and information sharing and collaborative operation among on-site rescue teams can be achieved.
The intelligent analysis of mass monitoring data can be realized by fully utilizing machine learning and deep learning technologies, the efficiency and accuracy of geological disaster assessment are greatly improved, and a refined early warning analysis result is output.
Establishing a 5G micro base station in a disaster site area to form a special 5G communication network, and ensuring that a key infrastructure has 5G network coverage; the micro base station adopts simple and rapid deployment, and can rapidly recover communication.
The high-bandwidth and large-capacity transmission site real-time video of the 5G network is utilized to realize the visual command of the disaster site, and a commander can remotely observe the disaster site; a network slicing technology in a 5G network is adopted to specially divide a network slice for on-site commanders, so as to carry out service isolation and ensure the quality of command service; the on-site commander uses the 5G industrial grade router to realize high-reliability connection with the 5G base station, so as to ensure that the command terminal has optimal communication quality; constructing networking of site commanders and rescue workers to realize temporary communication by using a direct communication mode D2D between 5G devices; the 5G edge computing technology is applied, partial command computing and data analysis functions are deployed at edge nodes, and dependence on transmission bandwidth is reduced; and the network slice scheduling technology of the 5G core network is adopted to conduct intelligent scheduling coordination aiming at command service, so that the transmission reliability is further improved.
In conclusion, the 5G communication technology can provide high-speed and reliable information transmission guarantee for communication between the disaster site and the command center, and emergency command efficiency is greatly improved.
In the step S7, an UWB broadband accurate positioning technology is adopted to realize indoor and outdoor seamless navigation positioning, and the accuracy reaches the level of 10 cm; by adopting a WIFI fingerprint positioning technology, a database is established by collecting WIFI hotspot information of a specific area, so that indoor accurate positioning is realized; the combination of the magnetic sensor array and inertial navigation is utilized to realize indoor and outdoor hybrid navigation positioning; the visible light communication technology is applied, and high-precision positioning and communication are carried out through the LED lamp and the light receiver; positioning and navigating by adopting an acoustic guiding positioning algorithm and utilizing a sound source signal; by combining the 5G millimeter wave (millimeter wave) technology, high-precision indoor positioning can be realized.
Through an AR technology, on-site rescue workers wear AR glasses, positions of key facilities and distribution information of buried objects are displayed in a superposition mode on a disaster site, and a complex environment is mastered; building a simulation training scene by using a VR technology, and enabling rescue workers to experience different disaster sites in the simulation environment to train repeatedly; the simulation environment can adjust parameters such as disaster scenes, meteorological conditions, time and the like according to the needs. The VR training system is combined with an artificial intelligence algorithm, so that the operation process of rescue workers can be evaluated, and optimization suggestions are given to realize intelligent training; the video of the first visual angle is obtained through the AR/VR glasses and transmitted to the command center, so that a commander can be helped to remotely sense the disaster site environment; developing an AR navigation system, enabling site personnel to wear AR equipment, receiving rescue route navigation information issued by a command center, and optimizing site movement organization; the AR/VR is utilized to realize remote expert guidance, on-site personnel and rear experts perform real-time audio and video interaction, and the remote acquisition guidance improves rescue efficiency; the AR/VR system supports multi-person collaboration, and information sharing and collaborative operation among on-site rescue teams can be achieved.
In conclusion, the AR/VR technology can comprehensively improve the emergency response capability and training effect of rescue workers, so that rescue is more efficient and accurate.
While the fundamental and principal features of the invention and advantages of the invention have been shown and described, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (7)
1. The geological disaster early warning method based on the Internet adding technology is characterized by comprising the following steps of:
s1, establishing a geological disaster monitoring system based on the Internet of things technology, arranging sensors in an easily-developed area, monitoring geological disaster influence factors in real time, and transmitting monitoring data acquired by the sensors to a data center through a network;
s2, analyzing and processing the collected monitoring data, establishing a geological disaster assessment model, and judging the type, time and range of possible geological disasters by using machine learning and deep learning algorithms;
s3, distributing geological disaster early warning information to government departments, emergency management departments and the public through an internet technology according to the monitoring data and the model analysis result;
s4, establishing interaction platforms of government departments, emergency management departments and the public by utilizing an internet technology;
s5, constructing a digital twin system of the geological disaster by utilizing cloud computing and big data technology, and predicting loss and evolution processes under different disaster conditions by the system through simulation technology;
s6, utilizing a 5G communication technology to ensure the speed and reliability of information transmission between the disaster site and the command center;
s7, developing a geological disaster emergency rescue system based on the Internet adding technology, positioning personnel by using a navigation technology, and optimizing rescue command by using voice recognition and AI digital assistant;
s8, assisting rescue workers in grasping disaster site information by using an AR/VR technology, and performing simulation training;
s9, establishing a donation management system based on the blockchain, and utilizing the decentralization and transparency of the blockchain;
s10, performing post-disaster reconstruction information release, collection of comments, resource coordination and other works by utilizing an internet technology, and performing data backup and recovery by utilizing cloud services to promote digitization and intellectualization of post-disaster reconstruction work.
2. The geological disaster early warning method based on the internet adding technology according to claim 1, wherein the geological disaster early warning method is characterized in that: in step S1, the sensor includes one or more of a rainfall sensor, a soil humidity sensor for measuring the water content of the soil, a water level sensor for measuring the groundwater level, and a displacement sensor for measuring the vertical and horizontal displacement of the ground.
3. The geological disaster early warning method based on the internet adding technology according to claim 1, wherein the geological disaster early warning method is characterized in that: in the step S2, various collected monitoring data are subjected to normalization processing, and a standardized data set is formed after abnormal values are removed;
inputting the standardized data set into deep learning models such as a convolutional neural network, a cyclic neural network and the like for training, and establishing evaluation models of different types of geological disasters;
adding a time sequence analysis module into the deep learning model, analyzing the time change rule of the monitoring data, and predicting the trend in a certain time in the future;
establishing an evaluation model of disaster types, time and space ranges by utilizing machine learning algorithms such as random forests, support vector machines and the like and combining geological disaster history statistical data;
the analysis of the real-time monitoring data by the deep learning model and the analysis result of the historical data by the machine learning model are fused, and multi-source heterogeneous data mining is carried out, so that the accurate prediction of geological disasters is realized;
the prediction results of disaster possibility, disaster type, occurrence time and space influence range output by the model are visually expressed, and an intuitive geological disaster early warning analysis report is generated;
and a plurality of models are called in real time by adopting cloud computing and distributed computing technologies, so that low-delay response to sudden disasters is realized.
4. The geological disaster early warning method based on the internet adding technology according to claim 1, wherein the geological disaster early warning method is characterized in that: in step S3, the early warning information includes the possibility of occurrence of a geological disaster, the disaster range and countermeasure suggestions.
5. The geological disaster early warning method based on the internet adding technology according to claim 1, wherein the geological disaster early warning method is characterized in that: in step S6, a 5G micro base station is established in a disaster site area to form a special 5G communication network, so that the key infrastructure is ensured to have 5G network coverage;
the high-bandwidth and large-capacity transmission site real-time video of the 5G network is utilized to realize the visual command of the disaster site, and a commander can remotely observe the disaster site;
a network slicing technology in a 5G network is adopted to specially divide a network slice for on-site commanders, so as to carry out service isolation and ensure the quality of command service;
the on-site commander uses the 5G industrial grade router to realize high-reliability connection with the 5G base station, so as to ensure that the command terminal has optimal communication quality;
constructing networking of site commanders and rescue workers to realize temporary communication by using a direct communication mode D2D between 5G devices;
the 5G edge computing technology is applied, partial command computing and data analysis functions are deployed at edge nodes, and dependence on transmission bandwidth is reduced;
and intelligent scheduling coordination is carried out on command services by adopting a network slice scheduling technology of a 5G core network.
6. The geological disaster early warning method based on the internet adding technology according to claim 1, wherein the geological disaster early warning method is characterized in that: in step S7, a UWB wideband accurate positioning technology, a WIFI fingerprint positioning technology, a database built by collecting WIFI hotspot information of a specific area, a combination of a magnetic sensor array and inertial navigation, a visible light communication technology, an LED lamp and a light receiver for high-precision positioning and communication, an acoustic guided positioning algorithm and a 5G millimeter wave technology are adopted.
7. The geological disaster early warning method based on the internet adding technology according to claim 1, wherein the geological disaster early warning method is characterized in that: in step S8, on-site rescue workers wear AR glasses through an AR technology, positions of key facilities and distribution information of buried objects are displayed in a superposition mode on a disaster site, and a complex environment is mastered;
building a simulation training scene by using a VR technology, and enabling rescue workers to experience different disaster sites in the simulation environment to train repeatedly;
the VR training system is combined with an artificial intelligence algorithm, so that the operation process of rescue workers can be evaluated, and optimization suggestions are given to realize intelligent training;
the video of the first visual angle is obtained through the AR/VR glasses and transmitted to the command center, so that a commander can be helped to remotely sense the disaster site environment;
developing an AR navigation system, enabling site personnel to wear AR equipment, receiving rescue route navigation information issued by a command center, and optimizing site movement organization;
remote expert guidance is realized by using AR/VR, real-time audio and video interaction is carried out between field personnel and rear experts, and guidance is obtained remotely;
the AR/VR system supports multi-person collaboration, and information sharing and collaborative operation among on-site rescue teams can be achieved.
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