511 results about "Disaster monitoring" patented technology

The invention relates to a monitoring and prewarning system and a monitoring and prewarning method for geological disasters. The monitoring and prewarning system for the geological disasters comprises a plurality of global navigation satellite system (GNSS) monitoring stations 1, a continuous operational reference system (CORS) 2, a 3rd-generation (3G) wireless network or wireless bridge 3, a microwave communication system 4, a monitoring and prewarning server 5, a monitoring and prewarning central computer 6 and the like which are arranged in a monitored area. Each GNSS monitoring station 1 comprises a global positioning system (GPS) receiver 11, a 3G network transmission component 12, video monitoring equipment 13, a microwave communication transmission component 14, a power supply 15 and the like. In the method, the aims of forecasting and prewarning possibly initiated geological disasters automatically in real time can be fulfilled by setting the GNSS monitoring stations in areas liable to the geological disasters, monitoring ground surface displacement in real time by the CORS, performing real-time ground surface displacement monitoring on the geological disasters, such as collapse, landslide, debris flow, surface collapse, subsidence, ground fissure and the like, which can be caused by ground surface displacement, combining remote video monitoring, performing data processing on monitoring information, and automatically issuing prewarning information.

The invention discloses a grid meteorological disaster monitoring and early warning system which comprises a data collection subsystem, a data analysis subsystem, a data storage subsystem and a disaster weather comprehensive display subsystem, wherein the data collection subsystem is responsible for completing a collection task of original meteorological monitoring data. The original meteorological monitoring data is composed of automatic meteorological station data, Doppler radar data, meteorological satellite data and value forecasting data. The data analysis subsystem is responsible for analysis of original meteorological data, cutting a satellite cloud picture, and finally storing processed data into the data storage subsystem. The data storage subsystem is responsible for storing original data of data of various stages and types, including original data, a relational data, a vector data and a picture, the original data is received from a meteorological department, the relational data is analyzed by the data analysis subsystem, the vector data is analyzed by the data analysis subsystem, and the picture is processed by the data analysis subsystem. The disaster weather comprehensive display subsystem is responsible for system function display. The grid meteorological disaster monitoring and early warning system can accurately position grid facilities which are affected by disaster weather.

The invention relates to a rainstorm flood disaster monitoring pre-warning and emergency response system and method. The system comprises a wire/wireless network, a pre-warning emergency center master station, a pre-warning monitoring sub-station, a pre-warning emergency command center, a pre-warning emergency command sub-center, a mobile pre-warning emergency command sub-center and a mobile interaction terminal, the pre-warning emergency center master station is communicated with the pre-warning monitoring sub-station, the pre-warning emergency command center, the pre-warning emergency command sub-center, the mobile pre-warning emergency command sub-center and the mobile interaction terminal through the wire/wireless network, two-way communication is conducted on the pre-warning center master station and various kinds of pre-warning emergency terminals through the wire or wireless network, various data are analyzed and stored, and various command requests are processed. The rainstorm flood disaster monitoring pre-warning and emergency response system and method can support multiple kinds of communication networks, can achieve multiple types of data interaction, can support various types of terminals, and is high in extensibility and mobility.

The invention relates to a mine water bursting disaster monitoring and early-warning system. The system comprises an in-situ measurement subsystem, a data acquisition subsystem, a data transmission control subsystem and a data and warning release subsystem. The in-situ measurement subsystem is connected to the data acquisition subsystem by a communication system, data input and output ports of the data acquisition subsystem are connected with the data input and output ports of the data transmission control subsystem by the communication system to realize bidirectional data transmission, and the data transmission subsystem is connected with the data and warning release subsystem by a computer network. The invention also relates to a control method of the mine water bursting disaster monitoring and early-warning system.

The invention discloses a hyper-spectral image classification method based on recurrent neural network with the object to solving the problems that in prior art, the input characteristic determination ability is weak and that the extraction of local spatial characteristics is not complete. The method comprises the following steps: 1) extracting the spatial texture characteristics and the sparse representation characteristics of a hyper-spectral image and piling and combining them as the low-level characteristics; 2) extracting from the low-level characteristics the sample local spatial sequence characteristics; 3) according to the local spatial sequence characteristics, creating a recurrent neural network model; and utilizing the training sample local spatial sequence characteristics to train the recurrent neural network model parameters; and 4) inputting the testing sample local spatial sequence characteristics into the well-trained recurrent neural network model; obtaining the highly abstract high-level semantic characteristics and obtaining the classification information of the testing sample. According to the deep learning method of the invention, the correct efficiency for hyper-spectral image classification is increased and the method can be used for vegetation investigation, disaster monitoring, map making and intelligence obtaining.

The invention relates to a method for inversing land surface temperature from MODIS data, which can be applied in meteorology, environmental monitoring, land management, agricultural condition monitoring, disaster monitoring and other remote sensing application departments. The method comprises four steps: the first step is to calculate the transmittance of 31st and 32nd wave bands of MODIS through water vapor content of atmosphere. The second step is to calculate the emissivity of the 31st and the 32nd wave bands of the MODIS sensor through vegetation index NDVI. The third step is to simplify a radiation transmission equation. The fourth step is to carry out inversion calculation on the MODIS data and obtain the land surface target object temperature and the emissivity distribution situation, so that the method can be used in meteorological forecasting, environmental monitoring, agricultural condition monitoring, disaster monitoring and other departments.

The invention relates to a coal mine gas disaster monitoring and pre-warning system and a pre-warning method. According to the system and the method, automatic monitoring, transmission, real-time acquisition, storage and dynamic sharing of coal mine gas disaster related safety information are firstly achieved through a gas disaster safety information acquisition platform; then, deep mining and analysis are conducted on the related safety information by utilizing the gas disaster safety information acquisition platform according to gas disaster monitoring and pre-warning indexes and rules established on the four aspects of objective dangerousness, precautionary measure defects, management measure defects and catastrophe control, gas disaster danger level and range are automatically judged, and pre-warning results of different levels are issued in sound, light, short message and network forms and the like; finally, emergency linkage control of a coal mine power monitoring system, an underground personnel location system, an underground broadcasting system, an online ventilation monitoring system and other coal mine monitoring systems is achieved by utilizing a pre-warning linkage control software system according to the issued pre-warning results, and finally the purposes of online monitoring, real-time analysis and intelligent pre-warning and linkage control of coal mine gas disasters are achieved.

The invention discloses a snow disaster remote sensing monitoring simulation evaluation method based on a disaster reduction small satellite, comprising the following steps: recognize accumulated snow according to the spectral characteristic of the accumulated snow and form accumulated snow remote sensing images data; create a snow disaster case database and a snow disaster statistical database, the snow disaster case database comprises a disaster time, a location hit by the disaster, an accumulated snow day number, a normalized difference vegetation index NDVI, a population number hit by the disaster and a accumulated snow area, the snow disaster statistical database comprises a total precipitation, a highest air temperature, a lowest air temperature, a average daily temperature, a wind speed and a surface temperature; carry out an accumulated snow depth inversion to a recognized accumulated snow, and respectively store the accumulated snow depth into the snow disaster case database and the snow disaster statistical database; carry out simulation evaluation to a snow disaster according to the data in the snow disaster case database and the snow disaster statistical database. A new and high applicability remote sensing data source is provided for snow disaster monitoring in the invention, and the snow disaster remote sensing monitoring simulation evaluation method has a great importance of disaster reduction and disaster relief works for China snow disaster.

The invention discloses a multi-parameter integrated monitoring and early-warning method for an excavation working face, and belongs to the field of coal mine dynamic disaster monitoring and early-warning. The method comprises the steps that 1, partitioned monitoring is performed on the excavation working face; 2, at least two of a coal bed stress monitoring system, a ground sound and microquake monitoring system and an anchor rod and anchor cable stress monitoring system are arranged in an excavation roadway; 4, certain weight is assigned to all monitoring and early-warning indexes, and accumulation is performed to obtain an integrated early-warning index. According to the multi-parameter integrated monitoring and early-warning method for the excavation working face, the thought of partitioned monitoring of the excavation working face is presented according to the excavation working face impact ground pressure occurrence mechanism, coupling is performed on the change conditions of all the monitoring indexes in the different partitions, and the multi-parameter integrated monitoring indexes serve as early-warning parameters to achieve integrated early-warning of the impact dangerousness of the excavation working face; the problem that in the prior art, the purpose of predicting the impact ground pressure dangerousness of the excavation working face only depending on a single-index monitoring method for the impact ground pressure of the excavation working face cannot be achieved is solved.

The invention discloses a debris flow disaster early monitoring system based on the internet of things. The debris flow disaster early monitoring system comprises an internet of things cloud server and a weather center connected with the cloud server through communication. The cloud server is connected with a wireless sensor network through communication. The wireless senor network comprises network nodes, a soil moisture sensor, a soil inner pressure sensor and a locating device. Data detected by the soil moisture sensor, data detected by the soil inner pressure sensor and data detected by the locating device are respectively transmitted to the cloud server though the network nodes. The wireless sensor network is laid in the zone with the requirements for debris flow disaster monitoring, soil is detected through the soil moisture sensor and the soil inner pressure sensor in the wireless sensor network, movement displacement of soil is detected by the locating device, weather data, collected by the weather center, of the surroundings are combined, calculation and simulation of the cloud processor are conducted, therefore, the purposes that a debris flow disaster is monitored in real time and the accuracy is high are achieved.

The invention discloses an automatic geometric correction and orthorectification method for multispectral remote sensing satellite images of a mountainous area. The method comprises the following steps of: selection and splicing of reference images; automatic selection of object points with same names; screening of the object points with same names, and geometric correction and orthorectification for correcting the images; precision evaluation; and output of the images. The multispectral remote sensing satellite images of the mountainous area, such as images of China domestic environment and disaster monitoring small satellite (with the model HJ-1A/B), have the characteristic of serious geometric distortion, but the automatic geometric correction and orthorectification method can be used for performing the orthorectification on the multispectral remote sensing satellite images of the mountainous area in mass and generating residual error reports of the coherent images by adopting a concept of the object points with same names to implement the geometric correction and orthorectification. The automatic geometric correction and orthorectification method has the advantages of high efficiency and precision, is very efficient when being used for processing the multispectral remote sensing satellite images with mass data of the mountainous area, can remarkably save manpower and material resources needed by the conventional remote sensing image processing process, and particularly is beneficial to the processing of the images of the China domestic environment and disaster monitoring small satellite.

In view of the characteristics of the high-resolution remote sensing images, the invention provides a practical intelligent method for classifying images, which comprises the following six steps: generating the image segmentation result of a full-color image; acquiring the segmentation result of a multispectral image by utilizing space mapping; determining whether the segmented regions of the full-colour image are insufficiently segmented; resegmenting the detected insufficiently-segmented region; generating the regional feature space; and classifying the image by using a classifier. The invention solves the problem that the insufficiently-segmented regions frequently influence the image classification precision in the image classification process. The method is suitable for high-resolution images from remote sensing satellites, such as IKONOS, QUICKBID and the like, and plays an important role in extracting application messages, such as target identification, resource environment survey, land utilization trends, disaster monitoring, disaster situation evaluation and the like.

The invention discloses a satellite and launch vehicle integrated aerospace system, and relates to a satellite and rocket integrated aerospace system. The system aims to solve the problems that a small satellite is difficult to flexibly adapt to the requirements of different loads of emergency disaster monitoring and that the maximum capability of a small space launch vehicle is restricted in terms of the orbit injection quality of the launched satellite. The system comprises a mission payload bay and a launch vehicle. A high-resolution camera and a data measurement and control module are arranged in the mission payload bay. The launch vehicle comprises a first-stage launch rocket, a second-stage launch rocket and a third-stage launch rocket. The system further comprises a shared service capsule and a controller area network (CAN) bus. A data acquisition and control instruction module, a propelling module, a posture and orbit control module, a power module, an apparatus mounting module and a thermal control module are arranged in the shared service capsule. The data acquisition and control instruction module controls the mission payload bay and the launch vehicle through the CAN bus. The system is applied in the field of aerospace.

The invention discloses an urban flood disaster early warning system and method. The urban flood disaster early warning system comprises a water level measuring module, a data collecting module, a transmission module and an early warning module. The water level measuring module is used for measuring water level data of rivers, lakes and inspection wells, the data collecting module is used for collecting the water level data measured by the water level measuring module, the transmission module is used for transmitting the water level data collected by the data collecting module to the early warning module, and the early warning module is used for analyzing the water level data and publishing early warning information. The urban flood disaster early warning system is simple in structure, high in automation degree, simple in implementation process, good in expansibility and capable of effectively realizing flood disaster monitoring and control automation.

The invention discloses an intelligent landslide monitoring device and method based on an LSTM long and short term memory network, and relates to the field of landslide monitoring early warning and artificial intelligence. The method comprises the following steps of obtaining different input characteristic values of landslide historical monitoring data, such as surface deformation, deep displacement, underground water level, pore water pressure, seepage flow velocity and landslide instability, adding a data label, and dividing the monitoring data into a training set, a verification set and a test set according to a certain proportion; carrying out gradient descent training on the training set under an LSTM network to obtain a training model; using the verification set and the test set operate in the training model to obtain an accuracy result, determining the model and the data set adjustment direction through result analysis, and obtaining a monitoring model with high accuracy throughcontinuous debugging. By combining an artificial intelligence optimization algorithm, the accuracy of landslide disaster prediction is improved, the cost of the landslide disaster monitoring device is reduced, and the landslide disaster monitoring device is popularized to monitoring of other types of landslides in a large range after being combined with transfer learning.

The invention discloses a mountain disaster monitoring, forecasting and early warning system which is characterized by comprising a data acquisition system, a data transmission system, a data processing center and an early warning system. The data acquisition system is used for collecting relevant data information and transmitting the relevant data information to the data processing center through the data transmission system. The data processing center analyzes and processes the transmitted data and stores the data. The early warning system judges whether to carry out an early warning operation or not according to the processing result of the data processing center. The system belongs to early warning and an early warning method based on a disaster forming mechanism and a disaster forming process, the precision is high, the false alarm rate is low, the construction cost of the whole set of system is low, the system is easy to realize and is promoted to hydropower, transportation, and city monitoring and early warning practices through experiments, and significant disaster reduction benefit and social and economic benefit are obtained.

The invention provides a remote sensing extraction method of agricultural disaster information based on vegetation index time-space statistical characteristics and belongs to the technical field of agricultural disaster information acquisition, aiming at solving the problems that existing agricultural disaster remote sensing monitoring cannot be used for monitoring and evaluating a large-scale region or a long time sequence in the region and a monitoring method has no universality. On the basis of considering that vegetation indexes of different phonological regions, crops and growth phases have difference, an NDVI (Normalized Difference Vegetation Index) of each pixel in the phonological regions with known diseases and an average value NDVIm of the NDVI and a standard difference STD of the same crops are extracted; a relation between the parameters is analyzed by utilizing the statistical characteristics according to NDVI gradation histogram characteristics before and after the disasters and a disaster monitoring model is established; the agricultural disasters are extracted. According to the method provided by the invention, interference factors caused by the growth regions, different crops and growth phases are considered and the precision of a monitoring result is improved. The remote sensing extraction method provided by the invention is used for monitoring the agricultural disasters.

A multipoint coal and rock mass stress real-time monitoring device. The device is composed of a plurality of capsule pressure sensors (1), connection rods (2), three-way valves (3), a multichannel monitor (4), a multichannel control valve (5), first high-pressure oil pipes (6), second high-pressure oil pipes (7), third high-pressure oil pipes (8), a four high-pressure oil pipe (9), a high-pressure oil pump (10) and monitoring substations (11). The present invention also relates to a multipoint coal and rock mass stress real-time monitoring method. Oil is injected into each capsule pressure sensor (1) that is arranged in a drilled hole through the multi-channel control valve (5), so that oil pressure inside each capsule pressure sensor (1) reaches the preset pressure, and the capsule pressure sensors and the coal and rock mass are well coupled; monitoring signals are synchronously collected, converted, stored and displayed by the multichannel monitor (4) or are transferred to a remote monitoring center through the monitoring substations (11). The device and method can be used for coal and rock dynamic disaster monitoring early warning such as coal and rock mass stress monitoring, mine pressure analysis, rock burst and coal and gas rush, and the stress and changes of the stress of multiple measuring points can be measured at the same time in the multiple drilled holes of the coal and rock mass.

The invention relates to a method for estimating the temperature of near-surface air near from MODIS data, which can be used for remote sensing application departments, i.e. weather, environmental monitoring, land management, agricultural monitoring, disaster monitoring, and the like. The method comprises three steps: step1: utilizing the near-surface temperature and the emission rate of each pixel of a MODIS data product and the water vapour content value of atmosphere as priori knowledge and input parameters of atmosphere radiation transmission module simulation software MODTRAN4, carrying out forward direction simulation in different areas and seasons aiming at a 29th thermal infrared band, a 31st thermal infrared band and a 32nd thermal infrared band of each pixel of the obtained remote sensing data MODIS and establishing a training and testing database; step2: utilizing a neural network to repeatedly training and testing a training and testing dataset; step3: carrying out back calculation for practical image data of the MODIS to obtain the temperature distribution condition of the air near the earth's surface of an objective area of the earth's surface. The method can be usedfor weather prognosis, the environmental monitoring, the agricultural monitoring, the disaster monitoring, and the like.

The invention relates to a static and dynamic combined intelligent pre-warning anchor rod. The intelligent pre-warning anchor rod consists of a hollow grouting rod body (1) and a solid rod body (3) which are sleeved together through a sleeve (2) and are bonded and anchored to wall rock of a hole wall, wherein the sleeve (2) is not bonded to the wall rock of the hole wall; the solid rod body (3) can penetrate through a large internal diameter section of the sleeve (2), but part of the rod body cannot penetrate through a small internal diameter section of the sleeve (2); a displacement sensor (10) is fixed in the sleeve, and is connected with an intelligent pre-warning device (12) outside an anchor hole by penetrating through a communication wire (11) of the hollow grouting rod body (1); and the displacement sensor (10) can be a mechanical wheel, an ultrasonic range finder or an infrared range finder and the like. On the basis of the original static and dynamic combined anchor rod, the static and dynamic combined intelligent pre-warning anchor rod is provided with functions of intelligent measurement and pre-warning, can realize continuous and reliable supporting, safe monitoring and pre-warning in the field of surrounding rock dynamic disaster monitoring and control, and is suitable for market popularization and application.

The invention discloses a treelets-based method for detecting remote sensing image changes and belongs to the field of remote sensing image analysis and processing, aiming to solve the problem that a traditional method for detecting remote sensing image changes has plenty of false change information. The method comprises the steps of: (1) carrying out Treelets filter to an input time-phase remotesensing image 1; (2) carrying out the Treelets cross filter to an input time-phase remote sensing image 2; (3) calculating difference values of corresponding pixel point gray values of the two time-phase remote sensing images subjected to filter to obtain a difference image; (4) carrying out the Treelets filter to the difference image once more to obtain a new difference image; (5) dividing the new difference image into a changed type or an unchanged type by adopting K-means clustering to obtain a final change detection result graph. The method invention can effectively reduce the impacts of unsatisfactory radiation correction and uneven illumination to detection results, improve the precision of the change detection, and can be used for disaster monitoring, land utilization and agricultural investigation.

The present invention discloses a landslide body change detection method based on a laser radar. According to the method, in a landslide disaster, especially for the change detection of a landslide body at a complex terrain, the change of position and attitude of the landslide body at a certain time period is detected automatically, once the change of a potential landslide body is detected and a change range is outlined, the field staff is reminded to pay attention and take safety measures or leave a danger zone. The method has the advantages of low cost, easy operation, fast computing, high accuracy and convenient detection, a scientific basis is provided for landslide disaster monitoring and evaluation, the security is provided for natural disaster rescue, and economic and social benefits are produced.