CN115471144A - Debris flow monitoring and early warning method, device and medium based on multi-source data fusion - Google Patents
Debris flow monitoring and early warning method, device and medium based on multi-source data fusion Download PDFInfo
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Abstract
The application provides a debris flow monitoring and early warning method, device and medium based on multisource data fusion, through the space observation data, the scanning data that cruises and the earth's surface monitoring data that acquire monitoring area, the geomorphology model in monitoring mountain highway corridor area is found to carry out debris flow risk assessment to monitoring area, if monitoring area's debris flow risk index is higher than debris flow risk threshold value, carry out debris flow risk early warning. The method can integrate the technical means of space-based observation, space-based observation and foundation observation, construct a landform model for monitoring the mountain highway corridor area, and perform perfect risk assessment, thereby ensuring the accuracy of the risk assessment of debris flow in the mountain highway corridor area, being capable of early warning accurately, giving people more response time to arrange evacuation, and reducing the loss of lives and properties of people as much as possible.
Description
Technical Field
The application relates to the technical field of debris flow monitoring, in particular to a debris flow monitoring and early warning method, device and medium based on multi-source data fusion.
Background
The landform type of China is complex, the area of a hilly area accounts for about 2/3 of the total land area of the whole country, the hilly area is one of the most serious countries of mountain torrents/debris flow disasters in the world, serious economic loss and casualties can be caused every year, and great threats are caused to the social and economic development and the safety of human life and property.
The research and the start of the domestic debris flow disaster monitoring technical method are late, the traditional surface displacement monitoring means (such as a paint brushing method, a surface mounting method, a nail burying method and the like) are difficult to meet the functions of automatic real-time monitoring, real-time early warning and the like, and the method has certain hysteresis.
The debris flow generation mechanism is complex and generally comprises three stages of continuous rainfall in the early stage, debris flow starting and producing in the middle stage, debris flow converging in the later stage and the like. Debris flow production is divided into soil power and water power: the soil power type runoff generation refers to the phenomenon that a soil body collapses and slides under the influence of outside rainfall or runoff, and the power of the water power type runoff generation process is water. As the mud-rock flow starts the flow production stage, along with the sliding of a small part of mud-rock, an early warning signal is required to be sent out, and even so, the reaction time for people is not sufficient.
Therefore, if the debris flow monitoring can accurately early warn before the debris flow starts to produce, people can be evacuated in more response time, and the life and property loss of people is reduced as much as possible.
Disclosure of Invention
The embodiment of the application aims to provide a debris flow monitoring and early warning method, a device and a medium based on multi-source data fusion so as to monitor debris flow, accurately early warn in advance and reduce life and property losses of people as far as possible.
In order to achieve the above object, embodiments of the present application are implemented as follows:
in a first aspect, an embodiment of the present application provides a debris flow monitoring and early warning method based on multi-source data fusion, including: acquiring space observation data, cruise scanning data and earth surface monitoring data of a monitoring area; constructing a landform model for monitoring a mountain highway corridor area based on the space observation data and the cruise scanning data; performing debris flow risk assessment on the monitoring area based on the surface monitoring data and the landform model; and if the debris flow risk index of the monitoring area is higher than a debris flow risk threshold, carrying out debris flow risk early warning.
In the embodiment of the application, through the space observation data, the cruise scanning data and the earth's surface monitoring data who acquire monitoring area, the geomorphology model in monitoring mountain highway corridor area is found to carry out the debris flow risk assessment to monitoring area, if monitoring area's debris flow risk index is higher than debris flow risk threshold value, carry out debris flow risk early warning. The method can integrate the technical means of space-based observation, space-based observation and foundation observation, construct a landform model for monitoring the mountain highway corridor area, and perform perfect risk assessment, thereby ensuring the accuracy of the risk assessment of debris flow in the mountain highway corridor area, being capable of early warning accurately, giving people more response time to arrange evacuation, and reducing the loss of lives and properties of people as much as possible.
With reference to the first aspect, in a first possible implementation manner of the first aspect, the spatial observation data includes at least two SAR images of the monitored area, the cruise scan data includes cruise images taken by the unmanned aerial vehicle for the monitored area in a cruise manner and point cloud data acquired by an airborne LiDAR flight platform, and a geomorphologic model of the monitored mountain road corridor area is constructed based on the spatial observation data and the cruise scan data, and includes: performing interference processing on at least two SAR images by utilizing an InSAR technology to obtain a first image containing the height information of the monitoring area; processing the point cloud data to obtain DEM data of the monitoring area; and generating a geomorphic model of the monitoring mountain road corridor area based on the first image, the DEM data and the cruising image.
In the implementation mode, by using the mode, the first image containing the elevation information of the monitoring area can be obtained by using the InSAR technology, the DEM data (elevation data) of the monitoring area can be obtained by using the point cloud data, the calibration effect can be achieved, the complete and detailed cruising image of the monitoring area can be obtained by cruising, the defect of the first image is filled, and the construction of a perfect landform model for monitoring the mountain road corridor area is facilitated.
With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, the generating a geomorphic model of the monitored mountain highway corridor area based on the first image, the DEM data, and the cruise image includes: calibrating the elevation information in the monitoring area based on the first image and the DEM data to obtain a second image after the elevation information is calibrated; carrying out gradient identification on the second image to determine a monitoring area image for revealing gradient information in the monitoring area; dividing the monitoring area into a plurality of subareas based on the cruise image, and determining the vegetation coverage rate, the runoff generating substance type and the runoff generating substance distribution of each subarea, wherein the number of slope bodies in each subarea is not more than one; and registering and fusing the cruise image and the monitoring area image to obtain a landform model for revealing elevation information, gradient information, vegetation coverage information and runoff generating substance information in the monitoring area.
In the implementation mode, factors in multiple aspects such as elevation information, gradient information, vegetation coverage information and runoff substance information in the monitored area can be considered, and a landform model for monitoring the mountain road corridor area is constructed.
With reference to the second possible implementation manner of the first aspect, in a third possible implementation manner of the first aspect, the performing debris flow risk assessment on the monitoring area based on the surface monitoring data and the geomorphic model includes: classifying each subarea in the monitoring area into a slope top subarea, a slope body subarea and a slope bottom subarea; aiming at each slope top subarea of the monitoring area, calculating a debris flow risk index of the slope top subarea based on rainfall monitoring data, soil property monitoring data, gradient information, vegetation coverage information and runoff generating material information of the slope top subarea; determining a slope top associated partition or a slope body associated partition adjacent to the upper part of the slope body partition aiming at each slope body partition of the monitoring area, and calculating a debris flow risk index of the slope body partition based on rainfall monitoring data, soil property monitoring data, gradient information, vegetation coverage information and flow production substance information of the slope body partition, and a debris flow risk index of the slope top associated partition or a debris flow risk index of the slope body associated partition; and determining adjacent slope body associated subareas above the slope bottom subareas aiming at each slope bottom subarea of the monitoring area, and calculating the debris flow risk index of the slope bottom subarea based on rainfall monitoring data, soil quality monitoring data, slope information, vegetation coverage information and runoff generating substance information of the slope bottom subarea and the debris flow risk index of the slope body associated subarea.
In the implementation mode, each subarea in the monitoring area is classified into a top subarea, a body subarea and a bottom subarea, different modes are adopted for calculating the debris flow risk index aiming at different types of subareas, the top subarea is used as the top, the influence of other subareas on the top subarea is not required to be considered, and the influence of the top subarea on other subareas is only required to be considered. The slope body subarea not only considers the factors of the slope body subarea to calculate the basic debris flow risk index, but also needs to consider the influence of the adjacent slope top subarea (or the slope body subarea) above the slope body subarea on the slope body subarea, accords with the reality, and can more comprehensively evaluate the debris flow risk index of the slope body subarea. The slope bottom subarea not only considers the factors of the slope bottom subarea to calculate the basic debris flow risk index, but also needs to consider the influence of the adjacent slope body subarea above the slope bottom subarea on the slope bottom subarea, so that the debris flow risk index of the slope body subarea can be comprehensively evaluated.
With reference to the third possible implementation manner of the first aspect, in a fourth possible implementation manner of the first aspect, the method for calculating the debris flow risk index of the slope top partition based on the rainfall monitoring data, the soil property monitoring data, the gradient information, the vegetation coverage information and the runoff generating material information of the slope top partition includes:
calculating the debris flow risk index of the slope top subarea by adopting the following formula:
wherein the content of the first and second substances,
is the debris flow risk index of the top of the slope subarea,
、
and
are all weighted and take values
，
Scoring the real-time precipitation of the top of the slope subarea,
the cumulative precipitation score for the topsides zone,
scoring the continuous precipitation duration of the top of the slope zone,
the length of the precipitation time of the slope top subarea,
the grade score of the slope top subarea is obtained,
、
and
are all weighted and take values
，
Scoring the soil quality of the slope top subarea,
scoring the vegetation in the top of the hill for zoning,
and scoring the runoff yield risk of the slope top subarea.
With reference to the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner of the first aspect, if a slope top associated partition is adjacent to the upper side of the slope body partition, calculating a debris flow risk index of the slope body partition based on rainfall monitoring data, soil property monitoring data, gradient information, vegetation coverage information and runoff producing substance information of the slope body partition and a debris flow risk index of the slope top associated partition, includes:
calculating the debris flow risk index of the slope body partition by adopting the following formula:
wherein the content of the first and second substances,
is divided into sections for the slope bodyThe basic index of debris flow risk of (a),
、
and
are all weighted and take on values
，
Scoring the real-time precipitation amount of the slope body subarea,
the accumulated precipitation score of the slope body subarea is obtained,
scoring the continuous precipitation duration of the slope body subarea,
the length of the precipitation time of the slope body subarea,
the grade of the slope body subarea is scored,
、
and
are all weighted and take values
，
The soil quality of the slope body subarea is scored,
scoring the vegetation in the sub-area of the slope,
the runoff producing risk score of the slope body subarea is obtained,
is the debris flow risk index of the slope body subarea,
the debris flow risk index of the slope top associated subarea,
is the debris flow risk threshold.
With reference to the fourth possible implementation manner of the first aspect, in a sixth possible implementation manner of the first aspect, if a slope-related partition is adjacent to the upper side of the slope body partition, calculating a debris flow risk index of the slope body partition based on rainfall monitoring data, soil property monitoring data, gradient information, vegetation coverage information, runoff producing material information of the slope body partition, and a debris flow risk index of the slope top-related partition, includes:
calculating the debris flow risk index of the slope body partition by adopting the following formula:
wherein, the first and the second end of the pipe are connected with each other,
is the debris flow risk basic index of the slope body subarea,
、
and
are all weighted and take values
，
Scoring the real-time precipitation of the slope body subarea,
the accumulated precipitation score of the slope body subarea is obtained,
scoring the continuous precipitation duration of the slope body subarea,
the length of the precipitation time of the slope body subarea,
the grade of the slope body subarea is scored,
、
and
are all weighted and take values
，
Scoring the soil quality of the slope body subarea,
scoring the vegetation of the slope body subarea,
the runoff producing risk score of the slope body subarea,
is the debris flow risk index of the slope body subarea,
the debris flow risk index of the slope body associated subareas,
is the debris flow risk threshold.
With reference to the fourth possible implementation manner of the first aspect, in a seventh possible implementation manner of the first aspect, the calculating a debris flow risk index of the slope bottom sub-area based on the rainfall monitoring data, the soil property monitoring data, the slope information, the vegetation coverage information, the runoff producing substance information of the slope bottom sub-area, and the debris flow risk index of the slope body associated sub-area includes:
calculating the debris flow risk index of the slope bottom subarea by adopting the following formula:
wherein, the first and the second end of the pipe are connected with each other,
is divided into slope bottomsA mud-rock flow risk basis index for a region,
、
and
are all weighted and take values
，
Scoring the real-time precipitation of the slope bottom subarea,
the cumulative precipitation score for the base of slope partition,
scoring the continuous precipitation duration of the slope bottom subarea,
the length of the precipitation time of the slope bottom subarea,
the grade of the slope base subarea is scored,
、
and
are all weighted and take on values
，
The soil quality of the slope bottom subarea is scored,
scoring the vegetation in the sub-area of the slope bottom,
the runoff producing risk score of the slope base partition,
is the debris flow risk index of the slope bottom subarea,
the debris flow risk index of the slope body associated subareas,
is the debris flow risk threshold.
In a second aspect, an embodiment of the present application provides a debris flow monitoring and early warning device based on air, space and ground integration, includes: the data acquisition unit is used for acquiring space observation data, cruise scanning data and earth surface monitoring data of a monitoring area; the model building unit is used for building a landform model for monitoring a mountain road corridor area based on the space observation data and the cruise scanning data; the risk assessment unit is used for performing debris flow risk assessment on the monitoring area based on the surface monitoring data and the landform model; and the risk early warning unit is used for carrying out early warning on the debris flow risk when the debris flow risk index of the monitoring area is higher than a debris flow risk threshold value.
In a third aspect, an embodiment of the present application provides a storage medium, where the storage medium includes a stored program, and when the program runs, a device where the storage medium is located is controlled to execute the method for monitoring and warning a debris flow based on multi-source data fusion according to any one of the first aspect or possible implementation manners of the first aspect.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic view of an aerospace-ground-based integrated debris flow monitoring and early warning system provided in an embodiment of the present application.
Fig. 2 is a flowchart of a debris flow monitoring and early warning method based on multi-source data fusion according to an embodiment of the present application.
Fig. 3 is a structural block diagram of a debris flow monitoring and early warning device based on air-space-ground integration provided in the embodiment of the present application.
Icon: 10-a debris flow monitoring and early warning system; 11-a server; 12-a satellite; 13-unmanned aerial vehicle; 14-a rainfall detector; 15-a soil property monitoring device; 20-debris flow monitoring and early warning device; 21-a data acquisition unit; 22-a model building unit; 23 a risk assessment unit; 24-risk early warning unit.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Referring to fig. 1, fig. 1 is a schematic view of an aerospace-ground-based integrated debris flow monitoring and early warning system 10 according to an embodiment of the present disclosure.
In this embodiment, the main body of the air-space-based integrated debris flow monitoring and early warning system 10 is a server 11 (e.g., a cloud server, a server cluster, a network server, etc.), and the server 11 is in communication with an external satellite 12, and is configured to acquire spatial observation data (e.g., multiple SAR images) obtained by monitoring a monitored area. The system further comprises an unmanned aerial vehicle 13, the unmanned aerial vehicle 13 is provided with a high-resolution CCD digital camera (used for acquiring the cruise images of the monitoring area), and the unmanned aerial vehicle 13 can be loaded with an airborne LiDAR (used for acquiring the point cloud data of the monitoring area), and the cruise images and the point cloud data of the monitoring area are collectively called as the cruise scanning data of the monitoring area. In addition, the air-ground-based integrated debris flow monitoring and early warning system 10 further includes various foundation monitoring communication devices arranged on the ground surface of the monitoring area, such as a plurality of rainfall detectors 14 (for acquiring rainfall monitoring data of the monitoring area), soil quality monitoring devices 15 (for acquiring soil quality monitoring data of the monitoring area), and the like distributed in the monitoring area.
In order to realize the debris flow risk early warning for the monitoring area, the server 11 is used as an execution main body in the embodiment, and a debris flow monitoring and early warning method based on multi-source data fusion is operated.
Referring to fig. 2, fig. 2 is a flowchart of a debris flow monitoring and early warning method based on multi-source data fusion according to an embodiment of the present disclosure. The debris flow monitoring and early warning method based on multi-source data fusion can comprise a step S10, a step S20, a step S30 and a step S40.
First, the server 11 may perform step S10.
Step S10: and acquiring space observation data, cruise scanning data and earth surface monitoring data of the monitoring area.
In this embodiment, the frequency of acquiring the surface monitoring data of the monitoring area may be based on the meteorological data of the monitoring area. For example, if the meteorological data of the monitored area shows that there is no rain in the current and next hours (or half an hour, or 2 hours, or 3 hours, etc.), and the rainfall monitoring data in the surface monitoring data shows that the current real-time rainfall is zero, the frequency of acquiring the surface monitoring data of the monitored area can be designed to be acquired every half an hour (or an hour, or 10 minutes, or 2 hours, etc.), so that the power consumption of the system is greatly reduced.
If the meteorological data of the monitored area shows that rain exists at present or rain exists in the next hour (or half hour, or 2 hours, 3 hours and the like), adjusting the frequency of acquiring the surface monitoring data of the monitored area to return to normal, and performing real-time monitoring (or acquiring data once at intervals of 10 seconds, 30 seconds, 1 minute, 3 minutes and the like, but the interval does not exceed 3 minutes).
The space observation data for acquiring the monitoring area may be a plurality of SAR images, and the cruise scan data may include cruise images taken by the drone 13 for the monitoring area (which may be taken in an oblique photography manner) and point cloud data collected by the airborne LiDAR flight platform. The surface monitoring data may include rainfall monitoring data and soil property monitoring data.
After acquiring the spatial observation data, the cruise scan data and the ground surface monitoring data of the monitoring area, the server 11 may execute step S20.
Step S20: and constructing a landform model of the monitored mountain highway corridor area based on the space observation data and the cruise scanning data.
In this embodiment, the server 11 may construct a geomorphic model for monitoring the mountainous road corridor area based on the spatial observation data and the cruise scan data.
For example, the server 11 may perform an interferometric processing on at least two SAR images by using an InSAR technology to obtain a first image including elevation information of the monitored area. The InSAR technology generates medium-to-ultrahigh-resolution digital elevation models by using different SAR images obtained by monitoring the same monitoring area, can be used for displacement monitoring, settlement monitoring, geological disaster monitoring and the like, and can reach the millimeter level of precision. Here, interferometric processing is performed on at least two SAR images by using an InSAR technology, which is mainly used for obtaining a first image containing elevation information of a monitoring area, and is not described in detail.
Then, the server 11 may process the point cloud data to obtain DEM data of the monitored area.
Here, the unmanned aerial vehicle 13 uses the point cloud data obtained by the oblique photography technique, and can quickly obtain the three-dimensional model of the monitoring area, and at the same time, can also provide high-precision DSM (digital surface model) data and DOM (digital ortho image) data, and then converts the DSM data into DEM data.
Then, the server 11 may generate a geomorphic model for monitoring the mountainous area highway corridor area based on the first image, the DEM data and the cruise image. In the mode, the InSAR technology can be utilized to obtain the first image containing the elevation information of the monitored area, the point cloud data is utilized to obtain the DEM data (elevation data) of the monitored area, the calibration effect can be achieved, the complete and detailed cruising image of the monitored area can be obtained through cruising, the defects of the first image are filled, and the construction of a perfect landform model for monitoring the mountain road corridor area is facilitated.
Specifically, the server 11 may calibrate elevation information in the monitored area based on the first image and the DEM data, and obtain a second image after the elevation information is calibrated. The calibration process is mainly used for filling and calibrating elevation information of a non-signal part and a peripheral part in a first image, and for a part with a normal signal in the first image, the elevation information in the first image can be adopted, and an average value can be calculated with the elevation information in DEM data, so that a second image with the calibrated elevation information is obtained.
Then, the server 11 may perform slope recognition on the second image to determine a monitoring area image that reveals slope information in the monitoring area. For slope identification, the server 11 may calculate the slope by combining elevation information with the actual distance represented by the pixel in the second image, so as to obtain a monitoring area image that reveals the slope information in the monitoring area.
Then, the server 11 may divide the monitoring area into a plurality of partitions based on the cruise image, and determine the vegetation coverage, the runoff generating substance type and the runoff generating substance distribution of each partition, wherein no more than one slope body is in each partition.
Here, the server 11 may perform region division on the monitoring region in the cruise image to obtain a plurality of partitions, and the division may be performed by first performing slope body identification, and then dividing the slope body, for example, into a top partition, a body partition (which may be one or a plurality of partitions, and is divided according to the actual body amount of different slope bodies, for example, a slope body with a height of more than 200 meters, and at least 2 body partitions) and a bottom partition.
After the monitoring area is divided into a plurality of subareas, the server 11 can register and fuse the cruise image and the monitoring area image to obtain a landform model for revealing elevation information, gradient information, vegetation coverage information and runoff substance information in the monitoring area. Therefore, the geomorphic model for monitoring the mountain road corridor area can be constructed by considering various factors such as elevation information, gradient information, vegetation coverage information and runoff substance information (including runoff substance types and runoff substance distribution, the runoff substance types such as broken stones, mud cakes or other sundries easy to flow, the runoff substance distribution is used for revealing the coverage area of the runoff substances) in the monitoring area.
After constructing the geomorphic model for monitoring the mountain road corridor area, the server 11 may execute step S30.
Step S30: and carrying out debris flow risk assessment on the monitoring area based on the surface monitoring data and the landform model.
In this embodiment, the server 11 may perform debris flow risk assessment on the monitored area based on the surface monitoring data (including rainfall monitoring data and soil quality monitoring data, the rainfall monitoring data includes information such as real-time rainfall, accumulated rainfall, rainfall duration, and the soil quality monitoring data is used for revealing surface composition substances, porosity, and the like, and is used for assessing whether soil quality is easy to run off) and the landform model.
For example, the server 11 may classify each partition in the monitored area into a top partition, a body partition, and a bottom partition.
First, the server 11 may assign a score according to the real-time rainfall (the rainfall in unit time at the current time), the cumulative rainfall (cumulative calculation is performed at the beginning of continuous rainfall) and the continuous rainfall duration (all rains in each day are regarded as continuous rainfall, and the unit is day) in the rainfall monitoring data:
the higher the real-time precipitation is, the higher the real-time precipitation score is, for example, the 12-hour precipitation at the current time is less than 10 mm, the score is given as 1, the score is added by 1 every 10 mm of the 12-hour precipitation, and the total score is 10 at the maximum.
The higher the cumulative precipitation, the higher the cumulative precipitation score, e.g., the cumulative precipitation is below 10 mm, with an assigned score of 1, and for each 20 ml increase in cumulative precipitation, the assigned score is added by 1, with a total score of up to 10.
The longer the continuous rainfall duration is, the higher the continuous rainfall duration score is, the continuous rainfall duration is within 1 day, and 1-5 points are assigned (based on the rainfall duration determination, the value is assigned for 1 point, 3-4 hours, 2 points, 5-8 hours, 3 points, 9-12 hours, 4 points, more than 12 hours, and 5 points are assigned) for 1-5 points (based on the rainfall duration determination, the point is assigned for 1 point for 1 hour), and the total point is 10 points at the highest every 1 day of the continuous rainfall duration increase.
And the server 11 may score the slope size (a comprehensive slope is obtained by performing weighted average calculation based on the overall slope and the maximum slope), and then perform assignment scoring according to the range of the slope size to obtain a slope score. For example, a score of 1 is assigned for less than 10 °, a score of 1 is added for each 5 ° increase in grade, and a total score of 15 is maximized.
In addition, by means of assigning scores, soil texture scores (for example, scores are given based on soil texture types, such as 1-5 points for type A soil texture, 6-10 points for type B soil texture, and 11-15 points for type C soil texture), and after soil texture is determined, scores can be specifically given according to characteristics of soil texture, and scores can be given according to numerical values of porosity) can be obtained, so that the soil texture scores are obtained. The vegetation score can be scored according to the coverage rate of the vegetation, the coverage rate of the vegetation is less than 4%, the value is assigned for 1, each time the value is increased by 4%, the score is increased by 1, and the maximum score is 20; and assigning scores according to the types and the distribution of the runoff producing substances, assigning 1 to 15 scores for a single type of runoff producing substances (such as crushed stones or soil blocks), determining a specific score according to the ratio of the coverage area of the distribution of the runoff producing substances to the total area of the subareas, wherein the score is assigned to 1 score when the percentage is less than 3%, and the score is added to 1 score when the percentage is increased by 3%, and the maximum score is 15. For many types of runoff producing materials (such as crushed stones and clods), the value is assigned from 1 to 20 points. The score is determined according to the ratio of the area of the coverage area of the distribution of the runoff producing material to the total area of the partition, less than 3%, the score is 1, every 3% increase, the score is 1, and the maximum score is 20.
This makes it possible to assign the index to each partition.
Then, for each top-of-slope partition of the monitoring area, the server 11 may calculate the debris flow risk index of the top-of-slope partition based on the rainfall monitoring data, the soil property monitoring data, the gradient information, the vegetation coverage information, and the runoff generating material information of the top-of-slope partition.
Specifically, the server 11 may first perform index scoring on the slope top partition according to a manner of assigning and scoring, and then calculate the debris flow risk index of the slope top partition by using the following formula:
wherein the content of the first and second substances,
is the debris flow risk index of the top of the slope subarea,
、
and
are all weighted and take values
，
Scoring the real-time precipitation amount of the slope top subarea,
the cumulative precipitation score for the topsides zone,
scoring the continuous precipitation duration of the hill top section,
the length of the precipitation time of the slope top subarea,
the grade score of the slope top subarea is obtained,
、
and
are all weighted and take on values
，
The soil quality of the slope top subarea is scored,
scoring the vegetation in the top of the hill for a division,
and scoring the runoff producing risk of the slope top subarea.
For each slope body partition of the monitoring area, the server 11 determines a slope top associated partition or a slope body associated partition adjacent to the upper side of the slope body partition, and calculates a debris flow risk index of the slope body partition based on rainfall monitoring data, soil property monitoring data, gradient information, vegetation coverage information and runoff producing substance information of the slope body partition, and a debris flow risk index of the slope top associated partition or a debris flow risk index of the slope body associated partition.
Specifically, the server 11 may determine the adjacent top-of-slope-related partition or the adjacent body-of-slope-related partition above the local body partition. And then, performing index assignment on the slope body partition according to an assignment and scoring mode, and calculating the debris flow risk basic index of the slope body partition by adopting the following formula:
wherein the content of the first and second substances,
is the debris flow risk basic index of the slope body subarea,
、
and
are all weighted and take values
，
Scoring the real-time precipitation of the slope body subarea,
the accumulated precipitation score of the slope body subarea is obtained,
scoring the continuous precipitation duration of the slope body subarea,
the length of the precipitation time of the slope body subarea,
the grade of the slope body subarea is scored,
、
and
are all weighted and take values
，
The soil quality of the slope body subarea is scored,
scoring the vegetation in the sub-area of the slope,
and scoring the runoff producing risk of the slope body subarea.
For the case that the adjacent zone above the slope body zone is the slope top associated zone, the server 11 may further calculate the debris flow risk index by using the following formula:
wherein the content of the first and second substances,
is the debris flow risk index of the slope body subarea,
the debris flow risk index of the slope top associated subarea,
is the debris flow risk threshold.
For the situation that the adjacent area above the slope body area is the slope body associated area, the server 11 may further calculate the debris flow risk index by using the following formula:
wherein, the first and the second end of the pipe are connected with each other,
is the debris flow risk index of the slope body subarea,
the debris flow risk index of the slope body associated subareas,
is the debris flow risk threshold.
For each slope base partition of the monitoring area, the server 11 may determine a slope body associated partition adjacent to the upper side of the slope base partition, and calculate a debris flow risk index of the slope base partition based on the rainfall monitoring data, the soil property monitoring data, the slope information, the vegetation coverage information, the runoff producing material information of the slope base partition, and the debris flow risk index of the slope body associated partition.
Specifically, the server 11 may determine the slope body associated partition adjacent to the top of the slope base partition. And then carrying out index assignment on the slope bottom subarea according to an assignment scoring mode, and calculating the debris flow risk basic index of the slope bottom subarea by adopting the following formula:
wherein, the first and the second end of the pipe are connected with each other,
is the debris flow risk basic index of the slope bottom subarea,
、
and
are all weighted and take on values
，
Scoring the real-time precipitation of the slope bottom subarea,
scoring the cumulative precipitation for the sub-area of the base of the slope,
scoring the continuous precipitation duration of the slope base section,
the length of the precipitation time of the slope bottom subarea,
the grade of the slope base subarea is scored,
、
and
are all weighted and take on values
，
Scoring the soil quality of the slope bottom subarea,
is divided into slope bottomsThe vegetation score of a region is determined,
and scoring the runoff yield risk of the slope base subarea.
The server 11 may then calculate the debris flow risk index for the present slope bottom sub-section based on the following formula:
wherein the content of the first and second substances,
is the debris flow risk index of the slope bottom subarea,
the debris flow risk index of the slope body associated subareas,
is the debris flow risk threshold.
Thus, the server 11 can calculate the debris flow risk index of each partition in the monitored area.
Classifying each subarea in the monitoring area into a top subarea, a body subarea and a bottom subarea, calculating the debris flow risk index by adopting different modes aiming at different types of subareas, taking the top subarea as the top, not considering the influence of other subareas on the top subarea, and only considering the influence of the top subarea on other subareas. The slope body subarea not only considers the debris flow risk index of the self factor calculation basis, but also needs to consider the influence of the adjacent slope top subarea (or the slope body subarea) above the slope body subarea on the slope body subarea, accords with the reality, and can more comprehensively evaluate the debris flow risk index of the slope body subarea. The slope bottom subarea not only considers the factors of the slope bottom subarea to calculate the basic debris flow risk index, but also needs to consider the influence of the adjacent slope body subarea above the slope bottom subarea on the slope bottom subarea, so that the debris flow risk index of the slope body subarea can be comprehensively evaluated.
After obtaining the debris flow risk index of each sub-area in the monitored area, the server 11 may execute step S40.
Step S40: and if the debris flow risk index of the monitoring area is higher than a debris flow risk threshold, carrying out debris flow risk early warning.
In this embodiment, the server 11 may make the determination: and judging whether the debris flow risk index of each subarea in the monitoring area is higher than a debris flow risk threshold value or not.
If the debris flow risk index of any partition is not higher than the debris flow risk threshold, the server 11 may determine that the debris flow risk index of the monitoring area is not higher than the debris flow risk threshold. If the debris flow risk index of any partition is higher than the debris flow risk threshold, the server 11 may determine that the debris flow risk index of the monitoring area is higher than the debris flow risk threshold, so as to perform debris flow risk early warning, prompt the staff that the debris flow risk index of the target partition (i.e., the partition where the debris flow risk index is higher than the debris flow risk threshold) is higher than the debris flow risk threshold, and perform evacuation preparation at any time by performing close observation.
Referring to fig. 3, based on the same inventive concept, an embodiment of the present application further provides a debris flow monitoring and early warning device 20 based on air, space and ground integration, including:
and the data acquisition unit 21 is used for acquiring space observation data, cruise scanning data and ground surface monitoring data of the monitoring area.
And the model construction unit 22 is used for constructing a landform model of the monitoring mountain road corridor area based on the space observation data and the cruise scanning data.
And the risk evaluation unit 23 is used for carrying out debris flow risk evaluation on the monitoring area based on the surface monitoring data and the landform model.
And the risk early warning unit 24 is configured to perform early warning on the debris flow risk when the debris flow risk index of the monitored area is higher than a debris flow risk threshold.
In this embodiment, the spatial observation data includes at least two SAR images of the monitoring area, the cruise scan data includes a cruise image taken by the unmanned aerial vehicle 13 in a cruise manner on the monitoring area and point cloud data acquired by an airborne LiDAR flight platform, and the model construction unit 22 is specifically configured to: performing interference processing on at least two SAR images by utilizing an InSAR technology to obtain a first image containing elevation information of the monitoring area; processing the point cloud data to obtain DEM data of the monitoring area; and generating a geomorphic model of the monitoring mountain road corridor area based on the first image, the DEM data and the cruising image.
In this embodiment, the model building unit 22 is specifically configured to: calibrating the elevation information in the monitoring area based on the first image and the DEM data to obtain a second image after the elevation information is calibrated; carrying out gradient identification on the second image, and determining a monitoring area image for revealing gradient information in the monitoring area; dividing the monitoring area into a plurality of subareas based on the cruise image, and determining the vegetation coverage rate, the runoff generating substance type and the runoff generating substance distribution of each subarea, wherein the number of slope bodies in each subarea is not more than one; and registering and fusing the cruise image and the monitoring area image to obtain a landform model for revealing elevation information, gradient information, vegetation coverage information and runoff generating substance information in the monitoring area.
In this embodiment, the surface monitoring data includes rainfall monitoring data and soil property monitoring data, and the risk assessment unit 23 is specifically configured to: classifying each subarea in the monitoring area into a slope top subarea, a slope body subarea and a slope bottom subarea; aiming at each slope top subarea of the monitoring area, calculating a debris flow risk index of the slope top subarea based on rainfall monitoring data, soil property monitoring data, gradient information, vegetation coverage information and runoff generating material information of the slope top subarea; determining a slope top associated partition or a slope body associated partition adjacent to the upper side of the slope body partition aiming at each slope body partition of the monitoring area, and calculating a debris flow risk index of the slope body partition based on rainfall monitoring data, soil property monitoring data, gradient information, vegetation coverage information and runoff producing substance information of the slope body partition, and a debris flow risk index of the slope top associated partition or a debris flow risk index of the slope body associated partition; and determining adjacent slope body associated subareas above the slope bottom subareas aiming at each slope bottom subarea of the monitoring area, and calculating the debris flow risk index of the slope bottom subareas based on rainfall monitoring data, soil quality monitoring data, slope information, vegetation coverage information and runoff generating substance information of the slope bottom subareas and the debris flow risk index of the slope body associated subareas.
In this embodiment, the risk assessment unit 23 is specifically configured to: calculating the debris flow risk index of the slope top subarea by adopting the following formula:
wherein the content of the first and second substances,
is the debris flow risk index of the top of the slope subarea,
、
and
are all weighted and take on values
，
Scoring the real-time precipitation of the top of the slope subarea,
the cumulative precipitation score for the topsides zone,
is a slopeThe continuous precipitation duration of the top partition is scored,
the length of the precipitation time of the slope top subarea,
the grade score of the slope top subarea is obtained,
、
and
are all weighted and take values
，
The soil quality of the slope top subarea is scored,
scoring the vegetation in the top of the hill for zoning,
and scoring the runoff yield risk of the slope top subarea.
In this embodiment, if the adjacent section above the slope body section is a slope top associated section, the risk assessment unit 23 is specifically configured to: calculating the debris flow risk index of the slope body partition by adopting the following formula:
wherein the content of the first and second substances,
is the debris flow risk basic index of the slope body subarea,
、
and
are all weighted and take values
，
Scoring the real-time precipitation of the slope body subarea,
the accumulated precipitation score of the slope body subarea is obtained,
scoring the continuous precipitation duration of the slope body subarea,
the length of the precipitation time of the slope body subarea,
the grade of the slope body subarea is scored,
、
and
are all the weight values of the weight values,and all values are taken
，
The soil quality of the slope body subarea is scored,
scoring the vegetation of the slope body subarea,
the runoff producing risk score of the slope body subarea,
is the debris flow risk index of the slope body subarea,
the debris flow risk index of the associated sub-area of the top of the slope,
is the debris flow risk threshold.
In this embodiment, if the adjacent section above the slope body section is a slope body-related section, the risk assessment unit 23 is specifically configured to: calculating the debris flow risk index of the slope body partition by adopting the following formula:
wherein, the first and the second end of the pipe are connected with each other,
is the debris flow risk basic index of the slope body subarea,
、
and
are all weighted and take values
，
Scoring the real-time precipitation of the slope body subarea,
the accumulated precipitation score of the slope body subarea is obtained,
scoring the continuous precipitation duration of the slope body subarea,
the length of the precipitation time of the slope body subarea,
the grade of the slope body subarea is scored,
、
and
are all weighted and take on values
，
Scoring the soil quality of the slope body subarea,
scoring the vegetation of the slope body subarea,
the runoff producing risk score of the slope body subarea,
is the debris flow risk index of the slope body subarea,
the debris flow risk index of the slope body associated subareas,
is the debris flow risk threshold.
In this embodiment, the risk assessment unit 23 is specifically configured to: calculating the debris flow risk index of the slope bottom subarea by adopting the following formula:
wherein, the first and the second end of the pipe are connected with each other,
is the debris flow risk basic index of the slope bottom subarea,
、
and
are all weighted and take values
，
Scoring the real-time precipitation of the slope bottom subarea,
scoring the cumulative precipitation for the sub-area of the base of the slope,
scoring the continuous precipitation duration of the slope base section,
the length of the precipitation time of the slope bottom subarea,
the grade of the slope base subarea is scored,
、
and
are all weighted and take values
，
The soil quality of the slope bottom subarea is scored,
scoring the vegetation in the sub-area of the slope bottom,
the runoff producing risk score of the slope base partition,
is the debris flow risk index of the slope bottom subarea,
the debris flow risk index of the slope body associated subareas,
is the debris flow risk threshold.
The embodiment of the application also provides a storage medium, wherein the storage medium comprises a stored program, and when the program runs, the equipment where the storage medium is located is controlled to execute the debris flow monitoring and early warning method based on the multi-source data fusion in the embodiment.
To sum up, the embodiment of the application provides a debris flow monitoring and early warning method, device and medium based on multisource data fusion, through the space observation data, the cruise scanning data and the earth surface monitoring data that acquire monitoring area, constructs the landform model of monitoring mountain highway corridor area to carry out debris flow risk assessment to monitoring area, if the debris flow risk index of monitoring area is higher than debris flow risk threshold value, carry out debris flow risk early warning. The method can synthesize the technical means of space-based observation, space-based observation and foundation observation, construct and monitor the landform model of the mountain highway corridor area, and perform perfect risk assessment, thereby ensuring the accuracy of risk assessment of debris flow in the mountain highway corridor area, accurately early warning in advance, arranging evacuation of more reaction time for people, and reducing the loss of lives and properties of people as much as possible.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another apparatus, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some communication interfaces, indirect coupling or communication connection between devices or units, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A debris flow monitoring and early warning method based on multi-source data fusion is characterized by comprising the following steps:
acquiring space observation data, cruise scanning data and earth surface monitoring data of a monitoring area;
constructing a landform model for monitoring a mountain highway corridor area based on the space observation data and the cruise scanning data;
performing debris flow risk assessment on the monitoring area based on the surface monitoring data and the landform model;
and if the debris flow risk index of the monitoring area is higher than a debris flow risk threshold, carrying out debris flow risk early warning.
2. The debris flow monitoring and early warning method based on multi-source data fusion of claim 1, wherein the spatial observation data comprises at least two SAR images of the monitoring area, the cruise scanning data comprises cruise images taken by the unmanned aerial vehicle for the monitoring area and point cloud data acquired by an onboard LiDAR flight platform, and a landform model for monitoring a mountain road corridor area is constructed based on the spatial observation data and the cruise scanning data, and the method comprises the following steps:
performing interference processing on at least two SAR images by utilizing an InSAR technology to obtain a first image containing the height information of the monitoring area;
processing the point cloud data to obtain DEM data of the monitoring area;
and generating a geomorphic model of the monitoring mountain road corridor area based on the first image, the DEM data and the cruising image.
3. The debris flow monitoring and early warning method based on multi-source data fusion of claim 2, wherein the generating of the geomorphic model of the monitoring mountainous area highway corridor area based on the first image, the DEM data and the cruising image comprises:
calibrating the elevation information in the monitoring area based on the first image and the DEM data to obtain a second image after the elevation information is calibrated;
carrying out gradient identification on the second image to determine a monitoring area image for revealing gradient information in the monitoring area;
dividing the monitoring area into a plurality of subareas based on the cruise image, and determining the vegetation coverage rate, the runoff generating substance type and the runoff generating substance distribution of each subarea, wherein the number of slope bodies in each subarea is not more than one;
and registering and fusing the cruise image and the monitoring area image to obtain a landform model for revealing elevation information, gradient information, vegetation coverage information and runoff generating substance information in the monitoring area.
4. The debris flow monitoring and early warning method based on multi-source data fusion of claim 3, wherein the surface monitoring data comprises rainfall monitoring data and soil property monitoring data, and debris flow risk assessment is performed on the monitoring area based on the surface monitoring data and the landform model, and the method comprises the following steps:
classifying each subarea in the monitoring area into a slope top subarea, a slope body subarea and a slope bottom subarea;
aiming at each slope top subarea of the monitoring area, calculating a debris flow risk index of the slope top subarea based on rainfall monitoring data, soil property monitoring data, gradient information, vegetation coverage information and runoff generating material information of the slope top subarea;
determining a slope top associated partition or a slope body associated partition adjacent to the upper side of the slope body partition aiming at each slope body partition of the monitoring area, and calculating a debris flow risk index of the slope body partition based on rainfall monitoring data, soil property monitoring data, gradient information, vegetation coverage information and runoff producing substance information of the slope body partition, and a debris flow risk index of the slope top associated partition or a debris flow risk index of the slope body associated partition;
and determining adjacent slope body associated subareas above the slope bottom subareas aiming at each slope bottom subarea of the monitoring area, and calculating the debris flow risk index of the slope bottom subareas based on rainfall monitoring data, soil quality monitoring data, slope information, vegetation coverage information and runoff generating substance information of the slope bottom subareas and the debris flow risk index of the slope body associated subareas.
5. The debris flow monitoring and early warning method based on multi-source data fusion of claim 4, wherein the debris flow risk index of the slope top partition is calculated based on rainfall monitoring data, soil property monitoring data, gradient information, vegetation coverage information and runoff producing material information of the slope top partition, and the method comprises the following steps:
calculating the debris flow risk index of the slope top subarea by adopting the following formula:
wherein, the first and the second end of the pipe are connected with each other,
is the debris flow risk index of the top of the slope subarea,
、
and
are all weighted and take on values
，
Scoring the real-time precipitation amount of the slope top subarea,
the cumulative precipitation score for the topsides zone,
scoring the continuous precipitation duration of the top of the slope zone,
the length of the precipitation time of the slope top subarea,
the grade score of the slope top subarea is obtained,
、
and
are all weighted and take on values
，
Scoring the soil quality of the slope top subarea,
scoring the vegetation in the top of the hill for a division,
and scoring the runoff producing risk of the slope top subarea.
6. The debris flow monitoring and early warning method based on multi-source data fusion of claim 5, wherein if the upper part of the slope body partition is adjacent to a slope top associated partition, the debris flow risk index of the slope body partition is calculated based on rainfall monitoring data, soil property monitoring data, gradient information, vegetation coverage information and flow production material information of the slope body partition and the debris flow risk index of the slope top associated partition, and the method comprises the following steps:
calculating the debris flow risk index of the slope body partition by adopting the following formula:
wherein, the first and the second end of the pipe are connected with each other,
is the debris flow risk basic index of the slope body subarea,
、
and
are all weighted and take on values
，
Scoring the real-time precipitation amount of the slope body subarea,
the accumulated precipitation score of the slope body subarea is obtained,
scoring the continuous precipitation duration of the slope body subarea,
the length of the precipitation time of the slope body subarea,
the grade of the slope body subarea is scored,
、
and
are all weighted and take on values
，
The soil quality of the slope body subarea is scored,
scoring the vegetation of the slope body subarea,
the runoff producing risk score of the slope body subarea,
is the debris flow risk index of the slope body subarea,
the debris flow risk index of the associated sub-area of the top of the slope,
is the debris flow risk threshold.
7. The debris flow monitoring and early warning method based on multi-source data fusion of claim 5, wherein if the upper part of the slope body partition is adjacent to a slope body associated partition, the debris flow risk index of the slope body partition is calculated based on rainfall monitoring data, soil property monitoring data, gradient information, vegetation coverage information and runoff producing material information of the slope body partition and the debris flow risk index of the slope top associated partition, and the method comprises the following steps:
calculating the debris flow risk index of the slope body partition by adopting the following formula:
wherein, the first and the second end of the pipe are connected with each other,
is the debris flow risk basic index of the slope body subarea,
、
and
are all weighted and take values
，
Scoring the real-time precipitation of the slope body subarea,
the accumulated precipitation score of the slope body subarea is obtained,
scoring the continuous precipitation duration of the slope body subarea,
the length of the precipitation time of the slope body subarea,
the grade of the slope body subarea is scored,
、
and
are all weighted and take values
，
The soil quality of the slope body subarea is scored,
scoring the vegetation in the sub-area of the slope,
the runoff producing risk score of the slope body subarea,
is the debris flow risk index of the slope body subarea,
the debris flow risk index of the slope body associated subareas,
is the debris flow risk threshold.
8. The debris flow monitoring and early warning method based on multi-source data fusion according to claim 5, wherein the debris flow risk index of the slope bottom partition is calculated based on rainfall monitoring data, soil property monitoring data, slope information, vegetation coverage information and flow production material information of the slope bottom partition and the debris flow risk index of the slope body association partition, and the method comprises the following steps:
calculating the debris flow risk index of the slope bottom subarea by adopting the following formula:
wherein, the first and the second end of the pipe are connected with each other,
is the debris flow risk basic index of the slope bottom subarea,
、
and
are all weighted and take values
，
Scoring the real-time precipitation of the slope bottom subarea,
scoring the cumulative precipitation for the sub-area of the base of the slope,
scoring the continuous precipitation duration of the slope bottom subarea,
the length of the precipitation time of the slope bottom subarea,
the grade of the slope base subarea is scored,
、
and
are all weighted and take values
，
The soil quality of the slope bottom subarea is scored,
scoring the vegetation in the sub-area of the slope bottom,
the runoff producing risk score of the slope base partition,
is the debris flow risk index of the slope bottom subarea,
the debris flow risk index of the slope body associated subareas,
is the debris flow risk threshold.
9. The utility model provides a mud-rock flow monitoring and early warning device based on sky ground integration which characterized in that includes:
the data acquisition unit is used for acquiring space observation data, cruise scanning data and earth surface monitoring data of a monitoring area;
the model building unit is used for building a landform model for monitoring a mountain road corridor area based on the space observation data and the cruise scanning data;
the risk assessment unit is used for carrying out debris flow risk assessment on the monitoring area based on the earth surface monitoring data and the landform model;
and the risk early warning unit is used for carrying out early warning on the debris flow risk when the debris flow risk index of the monitoring area is higher than a debris flow risk threshold value.
10. A storage medium, comprising a stored program, wherein when the program runs, a device where the storage medium is located is controlled to execute the debris flow monitoring and early warning method based on multi-source data fusion according to any one of claims 1 to 8.
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