CN111784082B - GIS mountain torrent prevention early warning system based on big data - Google Patents
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Abstract
The invention provides a GIS (geographic information system) torrential flood prevention and early warning system based on big data, which is used for solving the problems of limited monitoring range, insufficient reference data and low prediction precision of the conventional torrential flood prediction system and comprises a cloud computing platform, an environment monitoring module, a terrain monitoring module, an image processing module, a neural network module, a data query module, a data storage module, an alarm driving module and an intelligent terminal module; the invention is provided with the neural network module, and the design takes the existing data as the basis to construct the prediction model, so that the overall prediction precision of the system can be improved; the mountain torrent early warning system is provided with the environment monitoring module, the terrain monitoring module and the image processing module, so that abundant data are provided for the mountain torrent early warning system, and the mountain torrent early warning precision of the mountain torrent early warning system is ensured; the data storage module is arranged in the cloud computing platform, and the design is used for making up the defect of the storage capacity of the cloud computing platform, so that the system is more complete and stable.
Description
Technical Field
The invention belongs to the technical field of mountain torrent disaster early warning, and particularly relates to a GIS mountain torrent prevention early warning system based on big data.
Background
The mountain torrents are sudden, rising and falling landmark runoff caused by rainfall in small watershed of a mountain area, and are characterized by violent coming, fast bearing, strong destructiveness and easy casualties; and the regionality is obvious, the susceptibility is strong, and the prediction and prevention difficulty is large. The dangerousness of the torrential flood is mainly manifested by paroxysmal property, concentrated water quantity, large flow velocity and strong scouring force, and a large amount of silt and stone and the like are often wrapped and carried to form a debris flow and a landslide, so that the torrential flood has strong destructiveness. Therefore, research on risk assessment and distribution rules of the torrential flood is urgently needed to be comprehensively developed, the torrential flood is prevented, and the influence degree of loss is reduced.
In the existing mountain torrent forecasting and early warning method, a rainfall critical value of an area where mountain torrents occur is mostly counted by historical rainfall at home, a dynamic critical rainfall value is mostly taken as a threshold value at foreign countries, namely, the rainfall occurring on a small watershed is calculated and analyzed through a hydrological model to obtain real-time soil mixing degree of the watershed, the rainfall required by the situation that the peak flow of the outlet section of the watershed reaches a preset early warning flow value is reversely deduced, and when the rainfall reaches the value, mountain torrent early warning is issued. However, the problems of fast mountain torrent speed, short forecast period, data shortage, different prediction model and conventional hydrological prediction thinking and the like still exist in the simulation process.
The scheme can achieve the effect of mountain torrents prediction to a certain extent, but the monitoring range is limited, the reference data is insufficient, and the prediction precision is not high, so that a place worthy of improvement still exists.
Disclosure of Invention
In order to solve the problems of the torrential flood early warning system, the invention provides a GIS (geographic information system) torrential flood prevention early warning system based on big data, which is provided with a neural network module, wherein the design takes the existing data as a basis to construct a prediction model, so that the overall prediction precision of the system can be improved; the environment monitoring module, the terrain monitoring module and the image processing module are arranged, the climate environment and the terrain environment of a monitored area are monitored through the environment monitoring module and the terrain monitoring module, the mountain torrent occurrence coefficient of the monitored area is calculated according to a formula, when the mountain torrent occurrence coefficient is larger than a set threshold value, the image processing module shoots an image of the monitored area and sends the image to the cloud computing platform to calculate the slope of a hillside and the depth of a canyon in the image, the risk level is determined, and the cloud computing platform generates a corresponding instruction to be sent to the intelligent terminal module and the alarm driving module, so that mountain torrent early warning of the monitored area can be efficiently completed; the alarm driving module is arranged, and the alarm is sent to the intelligent terminal after the alarm driving module receives the instruction sent by the cloud computing platform, so that the early warning function of the system is realized; the invention also provides a data storage module, and the design aims to make up the defect of the storage capacity of the cloud computing platform, so that the system is more complete and stable.
The purpose of the invention can be realized by the following technical scheme: a GIS (geographic information system) torrential flood prevention and early warning system based on big data comprises a cloud computing platform, an environment monitoring module, a terrain monitoring module, an image processing module and a neural network module;
the environment monitoring module is used for acquiring the environmental information of a monitoring area, the environment monitoring module comprises a temperature monitoring node, a humidity monitoring node and a wind speed monitoring node, and the specific monitoring steps are as follows:
the method comprises the following steps: monitoring a monitoring area in real time through a temperature monitoring node, a humidity monitoring node and a wind speed monitoring node, acquiring rainfall prediction data of the monitoring area through a meteorological platform, and sending all monitoring data to a cloud computing platform;
step two: after the cloud computing platform receives the data of the environment monitoring module and the meteorological platform, the temperature, the humidity, the wind speed and the rainfall are respectively marked as Wt1、St1、Ft1And Yt1T1 is the monitoring time;
step three: obtaining environmental safety factor H of monitoring area through formulat1The calculation formula is
The system comprises a data storage module, a neural network module, a data storage module, a data acquisition module and a data processing module, wherein alpha, beta, gamma and delta are specific proportionality coefficients, monitoring data, rainfall, monitoring time and an environmental safety coefficient of the environmental monitoring module are sent to a K1 storage of the data storage module, and;
the terrain monitoring module is used for acquiring terrain information of a monitored area, the terrain information comprises a bare land area coefficient, a vegetation area coefficient and a lake area coefficient, and the specific acquisition steps are as follows:
s1: acquiring an image of a monitored area through an optical satellite outside the atmosphere, and sending the acquired image to a cloud computing platform;
s2: the method comprises the steps that when a cloud computing platform receives an image, a vector file of a monitoring area is imported, the image is cut through ArcGIS software to obtain the image of the monitoring area, and the image of the monitoring area is preprocessed, wherein the preprocessing comprises geometric correction, radiometric calibration, atmospheric correction and image fusion;
s3: identifying the image of the monitoring area by a pattern identification technology, and respectively calculating the total number L of pixel points of bare landt2Total number of pixels Z of vegetationt2Total number of pixels P in laket2And total pixel point number T of imaget2Where t2 is the time of image acquisition;
s4: by the formula
Obtaining terrain safety factor D of monitoring areat2Wherein epsilon, epsilon and theta are specific proportionality coefficients, the calculated total number of pixel points of bare land, the calculated total number of pixel points of vegetation, the calculated total number of pixel points of lakes, the calculated total number of pixel points of images, the image acquisition time and the calculated terrain safety coefficient are sent to a K2 memory of a data storage module for storage, and meanwhile, the data are sent to a neural network module;
s5: by the formula
Obtaining mountain torrent occurrence coefficient F of monitoring areatWherein t is the coefficient of mountain torrents generation FtThe time of (a) is,
is a specific proportionality coefficient;
s6: when mountain torrents occur coefficient FtWhen the mountain torrents are less than or equal to the set threshold value, the cloud computing platform generates a mountain torrent generation coefficient FtSending to K3 memory of data storage module, and generating coefficient F when torrential flood occurstWhen the mountain torrent generation coefficient is larger than the set threshold value, the cloud computing platform sends an instruction to the image processing module, and the mountain torrent generation coefficient F is converted into a mountain torrent generation coefficienttA K3 memory sent to the data storage module;
the image processing module is used for calculating the slope of a hillside and the depth of a canyon in an image of a monitored region and classifying the region in the image, and the specific processing steps are as follows:
SS 1: after receiving an instruction sent by a cloud computing platform, an image processing module acquires a multi-source satellite image of a monitoring area from a resource satellite application center, simultaneously imports a vector file of the monitoring area, cuts the image through ArcGIS software to acquire the image of the monitoring area, and preprocesses the cut image;
SS 2: leading in a high-precision digital elevation model of the monitoring area, calculating the slope of the hillside and the depth of the canyon of the monitoring area by combining the image of the monitoring area and the high-precision digital elevation model, and marking the slope of the hillside as AiI is the number of hillsides, and every two hillsides are spaced by more than N meters, otherwise, the hillsides are combined into one hillside for calculation, and the canyon depth is marked as BjJ is the number of canyons, and every two canyons are spaced by more than N meters, otherwise, the canyons are combined into one canyon for calculation, and the slope A of the hillside is calculatediAnd canyon depth BjSending the data to a cloud computing module, wherein N is a set safety value;
SS 3: when slope A of a hilliAnd the depth B of the canyon in the square circle N meters by taking the center position of the hillside as the midpointjWhen the current value is less than the set threshold value, the cloud computing platform sends a low-risk instruction to the intelligent terminal module; when slope A of a hilliAnd the depth B of the canyon in the square circle N meters by taking the center position of the hillside as the midpointjWhen one of the risk commands is larger than a set threshold value, the cloud computing platform sends a medium risk command to the intelligent terminal module and the alarm driving module; when slope A of a hilliAnd the depth B of the canyon in the square circle N meters by taking the center position of the hillside as the midpointjWhen the current time is greater than the set threshold value, the cloud computing platform sends a high-risk instruction to the intelligent terminal module and the alarm driving module;
the neural network module trains by using monitoring data of the environment monitoring module and the terrain monitoring module to construct an auxiliary prediction model, and the specific construction steps are as follows:
SSS 1: the method comprises the steps that a neural network module receives environment monitoring data, rainfall prediction data, monitoring time, terrain monitoring data and image acquisition time as input data of model training, mountain torrent occurrence coefficients serve as output data of the model training, the output data are assigned to be 0 or 1, the model is trained, wherein 0 represents that the mountain torrent occurrence coefficients are smaller than or equal to a set threshold value, and 1 represents that the mountain torrent occurrence coefficients are larger than the set threshold value;
SSS 2: taking the nearest 12h monitoring data of the environment monitoring module and the terrain monitoring module as input data of the model, and when the output data of the model is 0, taking the nearest 12h monitoring data of the two modules as training data of the model to carry out secondary training on the model; when the output data of the model is 1, the monitoring data of the two modules in the last 12h are used as the training data of the model to perform secondary training on the model and send instructions to the cloud computing platform.
Preferably, the system further comprises a data query module, wherein the data query module is configured to query the monitoring data stored in the data storage module, and the specific query step is as follows:
ST 1: a user inputs a query keyword to a data query module through an intelligent terminal;
ST 2: after receiving the query keywords, the data query module searches the keywords in the data storage module through the query keywords and acquires corresponding data;
ST 3: and the data storage module sends all the data searched according to the keywords to an intelligent terminal of the user through the cloud computing platform, and the user uses the intelligent terminal to check the data.
Preferably, the system further comprises an alarm driving module, wherein the alarm driving module sends an alarm according to an instruction sent by the cloud computing platform and sends the alarm to the intelligent terminal.
Preferably, the system further comprises an intelligent terminal module, wherein the intelligent terminal module displays the early warning result of the system, and when the received instruction is low risk, the ArcGIS software is used for performing image superposition on the image of the monitoring area and marking the corresponding low risk area as green to be displayed on the intelligent terminal; when the medium risk is received, map-overlaying is carried out on the image of the monitoring area by using ArcGIS software, and the corresponding medium risk area is marked to be yellow and displayed on the intelligent terminal; when the high risk is displayed, map overlaying is carried out on the image of the monitoring area by using ArcGIS software, and the corresponding high risk area is marked to be red and displayed on the intelligent terminal; the intelligent terminal comprises an intelligent mobile phone, a notebook computer and an intelligent display.
Preferably, the data storage module comprises a K1 memory, a K2 memory, a K3 memory and a K4 memory, wherein the K1 memory is used for storing environment monitoring data, rainfall prediction data and monitoring time, the K2 memory is used for storing terrain monitoring data, image acquisition time and terrain safety factor, the K3 memory is used for storing a torrential flood occurrence coefficient, and the K4 memory is used for storing other data in the working process of the system, and the other data are temporary data generated during the running of the system.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the GIS torrent prevention and early warning system based on the big data, the neural network module is added, and the design takes the existing data as the basis to construct the prediction model, so that the overall prediction precision of the system can be improved;
2. the environment monitoring module, the terrain monitoring module and the image processing module are arranged, the climate environment and the terrain environment of a monitored area are monitored through the environment monitoring module and the terrain monitoring module, the mountain torrent occurrence coefficient of the monitored area is calculated according to a formula, when the mountain torrent occurrence coefficient is larger than a set threshold value, the image processing module shoots an image of the monitored area and sends the image to the cloud computing platform to calculate the slope of a hillside and the depth of a valley in the image, the risk grade is determined, and the cloud computing platform generates a corresponding instruction to be sent to the intelligent terminal module and the alarm driving module, so that the mountain torrent early warning of the monitored area can be efficiently completed;
3. the intelligent terminal is also provided with an alarm driving module, and the alarm driving module sends an alarm to the intelligent terminal after receiving the instruction sent by the cloud computing platform, so that the early warning function of the system is realized; the invention also provides a data storage module, and the design aims to make up the defect of the storage capacity of the cloud computing platform, so that the system is more complete and stable.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a big data-based GIS flood prevention and early warning system includes a cloud computing platform, an environment monitoring module, a terrain monitoring module, an image processing module, a neural network module, an alarm driving module, an intelligent terminal module, a data query module, a data storage module, and a biometric module;
the environment monitoring module is used for acquiring the environmental information of a monitoring area, the environment monitoring module comprises a temperature monitoring node, a humidity monitoring node and a wind speed monitoring node, and the specific monitoring steps are as follows:
the method comprises the following steps: monitoring a monitoring area in real time through a temperature monitoring node, a humidity monitoring node and a wind speed monitoring node, acquiring rainfall prediction data of the monitoring area through a meteorological platform, and sending all monitoring data to a cloud computing platform;
step two: after the cloud computing platform receives the data of the environment monitoring module and the meteorological platform, the temperature, the humidity, the wind speed and the rainfall are respectively marked as Wt1、St1、Ft1And Yt1T1 is the monitoring time;
step three: by the formula to obtainEnvironmental safety factor H of monitored areatThe calculation formula is
The system comprises a data storage module, a neural network module, a data storage module, a data acquisition module and a data processing module, wherein alpha, beta, gamma and delta are specific proportionality coefficients, monitoring data, rainfall, monitoring time and an environmental safety coefficient of the environmental monitoring module are sent to a K1 storage of the data storage module, and;
the terrain monitoring module is used for acquiring terrain information of a monitored area, the terrain information comprises a bare land area coefficient, a vegetation area coefficient and a lake area coefficient, and the specific acquisition steps are as follows:
s1: acquiring an image of a monitored area through an optical satellite outside the atmosphere, and sending the acquired image to a cloud computing platform;
s2: the method comprises the steps that when a cloud computing platform receives an image, a vector file of a monitoring area is imported, the image is cut through ArcGIS software to obtain the image of the monitoring area, and the image of the monitoring area is preprocessed, wherein the preprocessing comprises geometric correction, radiometric calibration, atmospheric correction and image fusion;
s3: identifying the image of the monitoring area by a pattern identification technology, and respectively calculating the total number L of pixel points of bare landt2Total number of pixels Z of vegetationt2Total number of pixels P in laket2And total pixel point number T of imaget2Where t2 is the time of image acquisition;
s4: by the formula
Obtaining terrain safety factor D of monitoring areat2Wherein epsilon, epsilon and theta are specific proportionality coefficients, and the calculated total number of pixel points of bare land, the calculated total number of pixel points of vegetation, the calculated total number of pixel points of lakes, the calculated total number of pixel points of images, the image acquisition time and the calculated terrain safety coefficient are sent to a K2 memory of a data storage module to be storedStoring the data in a row and sending the data to a neural network module;
s5: by the formula
Obtaining mountain torrent occurrence coefficient F of monitoring areatWherein t is the coefficient of mountain torrents generation FtThe time of (a) is,
is a specific proportionality coefficient;
s6: when mountain torrents occur coefficient FtWhen the mountain torrents are less than or equal to the set threshold value, the cloud computing platform generates a mountain torrent generation coefficient FtSending to K3 memory of data storage module, and generating coefficient F when torrential flood occurstWhen the mountain torrent generation coefficient is larger than the set threshold value, the cloud computing platform sends an instruction to the image processing module, and the mountain torrent generation coefficient F is converted into a mountain torrent generation coefficienttA K3 memory sent to the data storage module;
the image processing module is used for calculating the slope of the hillside and the depth of the canyon in the monitored region image and classifying the region in the image, and the specific processing steps are as follows:
SS 1: after receiving an instruction sent by a cloud computing platform, an image processing module acquires a multi-source satellite image of a monitoring area from a resource satellite application center, simultaneously imports a vector file of the monitoring area, cuts the image through ArcGIS software to acquire the image of the monitoring area, and preprocesses the cut image;
SS 2: leading in a high-precision digital elevation model of the monitoring area, calculating the slope of the hillside and the depth of the canyon of the monitoring area by combining the image of the monitoring area and the high-precision digital elevation model, and marking the slope of the hillside as AiI is the number of hillsides, and every two hillsides are spaced by more than N meters, otherwise, the hillsides are combined into one hillside for calculation, and the canyon depth is marked as BjJ is the number of canyons, and every two canyons are spaced by more than N meters, otherwise, the canyons are combined into one canyon for calculation, and the slope A of the hillside is calculatediAnd canyon depth BjSending the data to a cloud computing module, wherein N is a set safety value;
SS 3: when slope A of a hilliAnd the depth B of the canyon in the square circle N meters by taking the center position of the hillside as the midpointjWhen the current value is less than the set threshold value, the cloud computing platform sends a low-risk instruction to the intelligent terminal module; when slope A of a hilliAnd the depth B of the canyon in the square circle N meters by taking the center position of the hillside as the midpointjWhen one of the risk commands is larger than a set threshold value, the cloud computing platform sends a medium risk command to the intelligent terminal module and the alarm driving module; when slope A of a hilliAnd the depth B of the canyon in the square circle N meters by taking the center position of the hillside as the midpointjWhen the current time is greater than the set threshold value, the cloud computing platform sends a high-risk instruction to the intelligent terminal module and the alarm driving module;
the neural network module trains by utilizing the monitoring data of the environment monitoring module and the terrain monitoring module to construct an auxiliary prediction model, and the specific construction steps are as follows:
SSS 1: the method comprises the steps that a neural network module receives environment monitoring data, rainfall prediction data, monitoring time, terrain monitoring data and image acquisition time as input data of model training, mountain torrent occurrence coefficients serve as output data of the model training, the output data are assigned to be 0 or 1, the model is trained, wherein 0 represents that the mountain torrent occurrence coefficients are smaller than or equal to a set threshold value, and 1 represents that the mountain torrent occurrence coefficients are larger than the set threshold value;
SSS 2: taking the nearest 12h monitoring data of the environment monitoring module and the terrain monitoring module as input data of the model, and when the output data of the model is 0, taking the nearest 12h monitoring data of the two modules as training data of the model to carry out secondary training on the model; when the output data of the model is 1, the monitoring data of the two modules in the last 12h are used as the training data of the model to perform secondary training on the model and send instructions to the cloud computing platform.
The data query module is used for querying the monitoring data stored by the data storage module, and the specific query steps are as follows:
ST 1: a user inputs a query keyword to a data query module through an intelligent terminal;
ST 2: after receiving the query keywords, the data query module searches the keywords in the data storage module through the query keywords and acquires corresponding data;
ST 3: and the data storage module sends all the data searched according to the keywords to an intelligent terminal of the user through the cloud computing platform, and the user uses the intelligent terminal to check the data.
And the alarm driving module sends out an alarm according to the instruction sent by the cloud computing platform and sends the alarm to the intelligent terminal.
The intelligent terminal module displays the early warning result of the system, and when the received instruction is low risk, the ArcGIS software is used for performing image superposition on the image of the monitoring area and marking the corresponding low risk area as green to be displayed on the intelligent terminal; when the medium risk is received, map-overlaying is carried out on the image of the monitoring area by using ArcGIS software, and the corresponding medium risk area is marked to be yellow and displayed on the intelligent terminal; when the high risk is displayed, map overlaying is carried out on the image of the monitoring area by using ArcGIS software, and the corresponding high risk area is marked to be red and displayed on the intelligent terminal; the intelligent terminal comprises an intelligent mobile phone, a notebook computer and an intelligent display.
The data storage module comprises a K1 memory, a K2 memory, a K3 memory and a K4 memory, wherein the K1 memory is used for storing environment monitoring data, rainfall prediction data and monitoring time, the K2 memory is used for storing terrain monitoring data, image acquisition time and terrain safety factor, the K3 memory is used for storing a mountain torrent generation coefficient, the K4 memory is used for storing other data in the working process of the system, and the other data are temporary data generated during the operation of the system.
The biological statistics module is used for counting the number of times that the biology appears in 1 hour in the monitoring area, and the biological statistics module includes that the image shoots node, pattern recognition node, and the biology includes human and animal, and concrete statistics step is:
a1: dividing a monitoring area into a plurality of blocks, and marking each block as i, i is 1, 2, … … and n; each version block is internally provided with an image shooting node, each image shooting node can completely cover the corresponding monitoring version block, and each image shooting node shoots a picture every 10 minutes and sends the picture to the mode identification nodeThe pattern recognition node counts the number of times a living being appears in the image and marks it as Aij，j＝1、2、……、6；
A2: by the formula
Acquiring the total number of the number of times of the organisms appearing in 1 hour in the ith module; by the formula
Acquiring the total times of the organisms appearing in the monitoring area all day;
a3: the biological statistic module is used for monitoring the total number G of all-day organisms in the areahSending the data to a cloud computing platform;
a4: when G ishWhen the value is less than the set threshold value L1, G is sent to the cloud computing platformhSending to the intelligent terminal module, and displaying G by the intelligent terminal modulehValue, and color their background green; when G ishLess than the set threshold L2 and greater than L1, the cloud computing platform will GhSending to the intelligent terminal module, and displaying G by the intelligent terminal modulehValues and their background color is colored yellow; when G ishWhen the value is greater than the set threshold value L2, the cloud computing platform will GhSending to the intelligent terminal module, and displaying G by the intelligent terminal modulehValues and their background color is colored red, where 0. ltoreq. L1. ltoreq. L2;
a5: cloud computing platform send GhThe value to the data storage module.
The above formulas are all quantitative calculation, the formula is a formula obtained by acquiring a large amount of data and performing software simulation to obtain the latest real situation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The working principle of the invention is as follows: the method comprises the steps that an environment monitoring module and a terrain monitoring module monitor the climate environment and the terrain environment of a monitored area and send the climate environment and the terrain environment to a cloud computing platform for processing, a torrential flood occurrence coefficient is calculated according to a formula, when the torrential flood occurrence coefficient is larger than a set threshold value, an image processing module shoots an image of the monitored area and sends the image to the cloud computing platform to calculate the slope of a hillside and the depth of a canyon in the image, the risk level is determined, and a corresponding instruction is generated through the cloud computing platform and sent to an intelligent terminal module and an alarm driving module;
the alarm driving module sends out an alarm according to the instruction sent by the cloud computing platform; when the intelligent terminal module receives the instruction, the low risk is detected, the ArcGIS software is used for performing image superposition on the image of the monitoring area, and the corresponding area is marked as green and displayed on the intelligent terminal; when the risks are received, the ArcGIS software is used for performing image superposition on the image of the monitoring area, and the corresponding area is marked as yellow to be displayed on the intelligent terminal; and when the high risk is displayed, the ArcGIS software is used for overlaying the image of the monitoring area and marking the corresponding area as red to be displayed on the intelligent terminal.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (5)
1. A GIS (geographic information system) torrential flood prevention and early warning system based on big data is characterized by comprising a cloud computing platform, an environment monitoring module, a terrain monitoring module, an image processing module and a neural network module;
the environment monitoring module is used for acquiring the environmental information of a monitoring area, the environment monitoring module comprises a temperature monitoring node, a humidity monitoring node and a wind speed monitoring node, and the specific monitoring steps are as follows:
the method comprises the following steps: monitoring a monitoring area in real time through a temperature monitoring node, a humidity monitoring node and a wind speed monitoring node, acquiring rainfall prediction data of the monitoring area through a meteorological platform, and sending all monitoring data to a cloud computing platform, wherein the monitoring data comprise temperature, humidity and wind speed;
step two: after the cloud computing platform receives the data of the environment monitoring module and the meteorological platform, the temperature, the humidity, the wind speed and the rainfall are respectively marked as Wt1、St1、Ft1And Yt1T1 is the corresponding monitoring time;
step three: obtaining environmental safety factor H of monitoring area through formulatThe calculation formula is
The system comprises a data storage module, a neural network module, a data storage module, a data acquisition module and a data processing module, wherein alpha, beta, gamma and delta are specific proportionality coefficients, monitoring data, rainfall, monitoring time and an environmental safety coefficient of the environmental monitoring module are sent to a K1 storage of the data storage module, and;
the terrain monitoring module is used for acquiring terrain information of a monitored area, the terrain information comprises a bare land area coefficient, a vegetation area coefficient and a lake area coefficient, and the specific acquisition steps are as follows:
s1: acquiring an image of a monitored area through an optical satellite outside the atmosphere, and sending the acquired image to a cloud computing platform;
s2: the method comprises the steps that when a cloud computing platform receives an image, a vector file of a monitoring area is imported, the image is cut through ArcGIS software to obtain the image of the monitoring area, and the image of the monitoring area is preprocessed;
s3: identifying the image of the monitoring area by a pattern identification technology, and respectively calculating the total number L of pixel points of bare landt2Total number of pixels Z of vegetationt2Total number of pixels P in laket2And total pixel point number T of imaget2Where t2 is the time of image acquisition;
s4: by the formula
Obtaining terrain safety factor D of monitoring areat2Wherein epsilon, epsilon and theta are specific proportionality coefficients, the calculated total number of pixel points of bare land, the calculated total number of pixel points of vegetation, the calculated total number of pixel points of lakes, the calculated total number of pixel points of images, the image acquisition time and the calculated terrain safety coefficient are sent to a K2 memory of a data storage module for storage, and meanwhile, the data are sent to a neural network module;
s5: by the formula
Obtaining mountain torrent occurrence coefficient F of monitoring areatWherein t is the coefficient of mountain torrents generation FtThe time of (a) is,
is a specific proportionality coefficient;
s6: when mountain torrents occur coefficient FtWhen the mountain torrents are less than or equal to the set threshold value, the cloud computing platform generates a mountain torrent generation coefficient FtSending to K3 memory of data storage module, and generating coefficient F when torrential flood occurstWhen the mountain torrent generation coefficient is larger than the set threshold value, the cloud computing platform sends an instruction to the image processing module, and the mountain torrent generation coefficient F is converted into a mountain torrent generation coefficienttA K3 memory sent to the data storage module;
the image processing module is used for calculating the slope of a hillside and the depth of a canyon in an image of a monitored region and classifying the region in the image, and the specific processing steps are as follows:
SS 1: after receiving an instruction sent by a cloud computing platform, an image processing module acquires a multi-source satellite image of a monitoring area from a resource satellite application center, simultaneously imports a vector file of the monitoring area, cuts the image through ArcGIS software to acquire the image of the monitoring area, and preprocesses the cut image;
SS 2: leading in a high-precision digital elevation model of the monitoring area, calculating the slope of the hillside and the depth of the canyon of the monitoring area by combining the image of the monitoring area and the high-precision digital elevation model, and marking the slope of the hillside as AiI is the number of hillsides, and every two hillsides are spaced by more than N meters, otherwise, the hillsides are combined into one hillside for calculation, and the canyon depth is marked as BjJ is the number of canyons, and every two canyons are spaced by more than N meters, otherwise, the canyons are combined into one canyon for calculation, and the slope A of the hillside is calculatediAnd canyon depth BjSending the data to a cloud computing module, wherein N is a set safety value;
SS 3: when slope A of a hilliAnd the depth B of the canyon in the square circle N meters by taking the center position of the hillside as the midpointjWhen the current value is less than the set threshold value, the cloud computing platform sends a low-risk instruction to the intelligent terminal module; when slope A of a hilliAnd the depth B of the canyon in the square circle N meters by taking the center position of the hillside as the midpointjWhen one of the risk commands is larger than a set threshold value, the cloud computing platform sends a medium risk command to the intelligent terminal module and the alarm driving module; when slope A of a hilliAnd the depth B of the canyon in the square circle N meters by taking the center position of the hillside as the midpointjWhen the current time is greater than the set threshold value, the cloud computing platform sends a high-risk instruction to the intelligent terminal module and the alarm driving module;
the neural network module trains by using monitoring data of the environment monitoring module and the terrain monitoring module to construct an auxiliary prediction model, and the specific construction steps are as follows:
SSS 1: the method comprises the steps that a neural network module receives environment monitoring data, rainfall prediction data, monitoring time, terrain monitoring data and image acquisition time as input data of model training, mountain torrent occurrence coefficients serve as output data of the model training, the output data are assigned to be 0 or 1, the model is trained, wherein 0 represents that the mountain torrent occurrence coefficients are smaller than or equal to a set threshold value, and 1 represents that the mountain torrent occurrence coefficients are larger than the set threshold value;
SSS 2: taking the nearest 12h monitoring data of the environment monitoring module and the terrain monitoring module as input data of the model, and when the output data of the model is 0, taking the nearest 12h monitoring data of the two modules as training data of the model to carry out secondary training on the model; when the output data of the model is 1, the monitoring data of the two modules in the last 12h are used as the training data of the model to perform secondary training on the model and send instructions to the cloud computing platform.
2. The big-data-based GIS mountain torrent prevention and early warning system according to claim 1, further comprising a data query module, wherein the data query module is used for querying the monitoring data stored by the data storage module, and the specific query steps are as follows:
ST 1: a user inputs a query keyword to a data query module through an intelligent terminal;
ST 2: after receiving the query keywords, the data query module searches the keywords in the data storage module through the query keywords and acquires corresponding data;
ST 3: and the data storage module sends all the data searched according to the keywords to an intelligent terminal of the user through the cloud computing platform, and the user uses the intelligent terminal to check the data.
3. The GIS torrential flood prevention and early warning system based on big data as claimed in claim 1, further comprising an alarm driving module, wherein the alarm driving module sends out an alarm according to an instruction sent by the cloud computing platform and sends the alarm to the intelligent terminal.
4. The GIS mountain torrent prevention and early warning system based on big data of claim 1, characterized in that the system further comprises an intelligent terminal module, the intelligent terminal module displays the early warning result of the system, when the received instruction is low risk, the ArcGIS software is used for overlaying the image of the monitoring area and the corresponding low risk area is marked as green and displayed on the intelligent terminal; when the medium risk is received, map-overlaying is carried out on the image of the monitoring area by using ArcGIS software, and the corresponding medium risk area is marked to be yellow and displayed on the intelligent terminal; when the high risk is displayed, map overlaying is carried out on the image of the monitoring area by using ArcGIS software, and the corresponding high risk area is marked to be red and displayed on the intelligent terminal; the intelligent terminal comprises an intelligent mobile phone, a notebook computer and an intelligent display.
5. The big data-based GIS torrential flood prevention and early warning system as claimed in claim 1, wherein the data storage module comprises a K1 memory, a K2 memory, a K3 memory and a K4 memory, the K1 memory is used for storing environment monitoring data, rainfall prediction data and monitoring time, the K2 memory is used for storing terrain monitoring data, image acquisition time and terrain safety factor, the K3 memory is used for storing torrential flood occurrence coefficient, the K4 memory is used for storing other data in the working process of the system, and the other data is temporary data generated when the system runs.
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