CN117765689B - Geological disaster monitoring and early warning system - Google Patents
Geological disaster monitoring and early warning system Download PDFInfo
- Publication number
- CN117765689B CN117765689B CN202410196731.1A CN202410196731A CN117765689B CN 117765689 B CN117765689 B CN 117765689B CN 202410196731 A CN202410196731 A CN 202410196731A CN 117765689 B CN117765689 B CN 117765689B
- Authority
- CN
- China
- Prior art keywords
- mountain
- analysis
- disaster
- characteristic
- feature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 41
- 238000004458 analytical method Methods 0.000 claims abstract description 175
- 238000000034 method Methods 0.000 claims abstract description 57
- 230000008569 process Effects 0.000 claims abstract description 54
- 238000006073 displacement reaction Methods 0.000 claims abstract description 45
- 238000010191 image analysis Methods 0.000 claims abstract description 14
- 230000007613 environmental effect Effects 0.000 claims description 49
- 238000001556 precipitation Methods 0.000 claims description 48
- 230000008859 change Effects 0.000 claims description 31
- 238000005457 optimization Methods 0.000 claims description 8
- 239000003673 groundwater Substances 0.000 claims description 6
- 239000000284 extract Substances 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000011435 rock Substances 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011081 inoculation Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
Landscapes
- Alarm Systems (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention relates to a geological disaster monitoring and early warning system, in particular to the technical field of geological disaster monitoring, which comprises an information acquisition module, a data acquisition module and a data processing module, wherein the information acquisition module is used for periodically acquiring mountain information, environment information and mountain images of a mountain; the information storage module is used for storing mountain information, environment information, mountain images and mountain characteristics of the mountain; the mountain analysis module is used for analyzing mountain characteristics; the image analysis module is used for analyzing the vertical displacement and the mountain variation according to the mountain image; the feature analysis module is used for analyzing disaster features, adjusting the analysis process of the disaster features according to stored mountain features, and optimizing the adjustment process of the disaster features according to vertical displacement and mountain changes; and the disaster early warning module is used for early warning the geological disaster according to the disaster characteristics. The invention realizes accurate monitoring of the geological disasters and improves the accuracy of geological disaster early warning.
Description
Technical Field
The invention relates to the technical field of geological disaster monitoring, in particular to a geological disaster monitoring and early warning system.
Background
Geological disasters, such as landslide, mud-rock flow, earthquake and the like, bring huge life and property loss to people. Traditional monitoring means, such as simple collapse observation and macroscopic investigation, cannot early warn timely and accurately. Along with the progress of science and technology, modern geological disaster monitoring technology is gradually applied to disaster early warning, and powerful support is provided for reducing disaster loss.
Chinese patent publication No.: CN115424426a discloses a method for improving accuracy of regional geological disaster early warning and forecasting, which comprises the steps of combining monitoring early warning carried out by geological disaster monitoring points with geological disaster meteorological early warning carried out by regional scales, aiming at regularity of rainfall induced geological disaster inoculation and development, monitoring abrupt change of early warning level by monitoring points in a shorter time in early warning period, quantifying deformation evolution trend of slope rock-soil body, and correcting regional geological disaster meteorological early warning result. The method realizes the combined analysis of the meteorological information and the geological disasters, does not realize the comprehensive analysis of the environmental state of the mountain and the change of each area in the mountain, and has the problems of low monitoring efficiency and inaccurate early warning of the geological disasters.
Disclosure of Invention
Therefore, the invention provides a geological disaster monitoring and early warning system which is used for solving the problems of low geological disaster monitoring efficiency and inaccurate early warning in the prior art.
In order to achieve the above object, the present invention provides a geological disaster monitoring and early warning system, comprising:
the information acquisition module is used for periodically acquiring mountain information, environment information and mountain images of the mountain;
The information storage module is used for storing mountain information, environment information, mountain images and mountain characteristics of the mountain;
The mountain analysis module is used for analyzing mountain characteristics according to mountain information and environment information, analyzing mountain fluctuation according to stored mountain information, adjusting the analysis process of the mountain characteristics according to the mountain fluctuation, analyzing environment characteristics according to stored environment information, and optimizing the adjustment process of the mountain characteristics according to the environment characteristics;
The image analysis module is used for analyzing the vertical displacement according to the mountain image and analyzing the mountain change according to the stored mountain image;
The feature analysis module is used for analyzing disaster features according to mountain features and environmental information, adjusting the analysis process of the disaster features according to the stored mountain features, and optimizing the adjustment process of the disaster features according to vertical displacement and mountain changes;
And the disaster early warning module is used for early warning the geological disaster according to the disaster characteristics.
Further, the mountain analyzing module is provided with a mountain analyzing unit for analyzing mountain characteristics through a mountain characteristic analyzing formula according to an inclination angle, a ground stress and a reference distance, a ground water level, daily precipitation information, a ground surface temperature and an earthquake magnitude, and the mountain analyzing unit is provided with a mountain characteristic analyzing formula as follows:
F(i,j)={A,B,C,V}
A=|S1(i)-S1(i-1)|×D
B=|S2(i)-S2(i-1)|×D
C=|S3(i)-S3(i-1)|×D
D=eQ1(i)/Q1(i-1)-1+Q2(i)×ln[q2(i)+1]×lg|Q3(i)+1|
Wherein F (i, j) denotes a mountain characteristic, i denotes a period number, j denotes a characteristic number, a denotes an angle characteristic, B denotes a ground stress characteristic, C denotes a horizontal characteristic, V denotes a seismic magnitude, F (i, 1) =a, F (i, 2) =b, F (i, 3) =c, F (i, 4) =v, D denotes an influence parameter, S1 (i) denotes an inclination angle of a current period, S1 (i-1) denotes an inclination angle of a previous period, S2 (i) denotes a ground stress of a current period, S2 (i-1) denotes a ground stress of a previous period, S3 (i) denotes a reference distance of a current period, S3 (i-1) denotes a reference distance of a previous period, Q1 (i-1) denotes a ground water level of a previous period, Q2 (i) denotes a daily precipitation amount of a current period, Q3 (i) denotes a surface temperature of a current period, Q2 (i) denotes a rainfall intensity parameter of a current period, and when S2 (i-1) denotes a rainfall intensity parameter of a current period is no rainfall intensity, Q (i-1) =2, Q (i) is a rainfall intensity of Q-2, Q (Q-2) is a rainfall intensity of Q-2 when Q-2 (i) =2, Q-2 is a rainfall intensity of a value of a current period.
Further, the mountain analyzing module is further provided with an analyzing and adjusting unit, which is used for calculating mountain fluctuation according to a stored mountain fluctuation analyzing formula according to stored mountain information, and the analyzing and adjusting unit is provided with the mountain fluctuation analyzing formula as follows:
，
wherein a1 represents angular fluctuation, b1 represents ground stress fluctuation, c1 represents horizontal fluctuation, and i max represents the maximum value of the cycle number;
the analysis adjustment unit adjusts an analysis process of mountain characteristics according to mountain fluctuation, wherein:
When A1 < [1-S1 (i-1)/S1 (i) ], the analysis and adjustment unit judges that the angle fluctuation is large, adjusts the analysis process of the angle characteristic, wherein the adjusted angle characteristic is A1, and A1=AX (1+a1);
When B1 < [1-S2 (i-1)/S2 (i) ] the analysis adjustment unit determines that the ground stress fluctuation is large, adjusts the analysis process of the ground stress characteristic, and sets b1=b× (1+b1) for the adjusted ground stress characteristic as B1;
When C1 < [1-S3 (i-1)/S3 (i) ], the analysis adjustment unit determines that the level fluctuation is large, adjusts the analysis process of the level characteristic, and sets c1=c× (1+c1) for the adjusted level characteristic as C1;
When a1 is more than or equal to [1-S1 (i-1)/S1 (i) ] or b1 is more than or equal to [1-S2 (i-1)/S2 (i) ] or c1 is more than or equal to [1-S3 (i-1)/S3 (i) ], the analysis and adjustment unit judges that the mountain fluctuation is normal, and does not adjust the analysis process of the mountain characteristics.
Further, the mountain analysis module is further provided with an analysis optimizing unit for analyzing the environmental sample data according to the stored precipitation information and the surface temperature, wherein:
When Q2 (i) is more than 0, the analysis optimizing unit takes precipitation information and surface temperature of a period corresponding to the daily precipitation amount which is analyzed currently as environment sample data;
when Q2 (i) =0, the analysis optimizing unit does not analyze the environmental sample data;
The analysis optimizing unit calculates environmental characteristics according to the environmental sample data pair through an environmental characteristic analysis formula, and the analysis optimizing unit is provided with the environmental characteristic analysis formula as follows:
，
Wherein G represents an environmental characteristic, Q2 (k) represents a daily precipitation amount in the environmental sample data, k represents an environmental sample data number, Q2 (k) represents a daily precipitation intensity parameter in the environmental sample data, Q3 (k) represents a surface temperature in the environmental sample data, and k max represents a maximum value of the environmental sample data number;
The analysis optimizing unit optimizes the adjustment process of the mountain feature according to the environmental feature, the optimized angle feature is A2, A2=A1×Dx log D G is set, the optimized ground stress feature is B2, B2=B1×Dx log D G is set, the optimized horizontal feature is C2, and C2=C1×Dx log D G is set.
Further, the image analysis module is provided with a displacement analysis unit for calculating vertical displacement according to a stored height image through a vertical displacement analysis formula, and the displacement analysis unit is provided with a vertical displacement analysis formula as follows:
，
Wherein U (i) represents vertical displacement, X represents the number of pixels in the X-axis direction in the height image, Y represents the number of pixels in the Y-axis direction in the height image, L1 (i) (X, Y) represents the gray value of each pixel in the height image of the current analysis period, L1 (i-1) (X, Y) represents the gray value of each pixel in the height image of the previous analysis period, and U represents the height parameter of the height image.
Further, the image analysis module is further provided with a change analysis unit for calculating mountain change according to a stored relief image through a mountain change analysis formula, and the change analysis unit is provided with a mountain change analysis formula as follows:
，
Wherein P (i) represents a mountain change, and L2 (i) (x, y) represents a gray value of each pixel point in the relief image of the current analysis period.
Further, the feature analysis module is provided with a feature analysis unit for calculating disaster features according to mountain features and future rainfall information through a disaster feature analysis formula, and the feature analysis unit is provided with a disaster feature analysis formula as follows:
，
Wherein W represents disaster characteristics, R represents future precipitation amount, and R represents future precipitation intensity.
Further, the feature analysis module is further provided with a feature adjustment unit for adjusting the analysis process of the disaster feature according to the stored mountain feature, the feature adjustment unit extracts the mountain feature with V > 0 in the stored mountain feature as a mountain sample feature, adjusts the analysis process of the disaster feature according to the mountain sample feature, the adjusted disaster feature is W1, w1=wxα is set, α represents a disaster adjustment parameter, and is set
，
Where F (n, j) represents the mountain sample feature, n represents the number of the mountain sample feature, and n max represents the maximum value of the mountain sample feature number.
Further, the feature analysis module is further provided with a feature optimization unit for optimizing the adjustment process of disaster features according to vertical displacement and mountain variation, wherein:
when U (i) is not less than [ S3 (i) -S3 (i-1) ], the characteristic optimizing unit judges that the vertical displacement is large, optimizes the disaster characteristic adjusting process, and sets W2=W1×P (i) for the optimized disaster characteristic as W2;
when U (i) < [ S3 (i) -S3 (i-1) ] the feature optimization unit determines that the vertical displacement is normal, and does not optimize the adjustment process of the disaster feature.
Further, the disaster early warning module compares the disaster characteristics with characteristic threshold values and early warns geological disasters according to comparison results, wherein:
when W is less than W, the disaster early warning module judges that the disaster characteristics accord with a threshold value, and does not early warn the geological disaster;
When W is more than or equal to W, the disaster early warning module judges that the disaster characteristics do not accord with a threshold value, and early warning is carried out on geological disasters;
where w represents a feature threshold.
Compared with the prior art, the invention has the advantages that the information acquisition module is used for acquiring mountain information, environment information and mountain images of the mountain so as to improve the accuracy of information acquisition, thereby improving the monitoring efficiency of the system on geological disasters, improving the early warning accuracy, improving the accuracy of information storage by the information storage module, improving the monitoring efficiency of the system on the geological disasters by increasing the number of system analysis sample data, improving the early warning accuracy, analyzing the mountain information and the environment information by the mountain analysis module so as to analyze mountain characteristics, expressing the relation between each parameter change and the environment of the mountain by the mountain characteristics, improving the monitoring efficiency of the system on the geological disasters by the mountain characteristics, improving the early warning accuracy, analyzing the mountain images by the image analysis module so as to analyze vertical displacement and the mountain changes, expressing the height change of the mountain in the vertical direction by the vertical displacement, expressing the change of the mountain in the top view, improving the monitoring efficiency of the system on the geological disasters, improving the early warning efficiency by the system on the geological disasters by the mountain characteristics, analyzing the disaster characteristics by the future disaster characteristics by the mountain characteristics, and improving the disaster characteristics by the disaster analysis module, and improving the disaster characteristics by the disaster analysis efficiency by the future disaster analysis system, and the early warning accuracy is improved.
Drawings
FIG. 1 is a block diagram of a geological disaster monitoring and early warning system according to the present embodiment;
fig. 2 is a block diagram of the mountain analyzing module according to the present embodiment;
FIG. 3 is a block diagram showing an image analysis module according to the present embodiment;
Fig. 4 is a block diagram of the feature analysis module of the present embodiment.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, a geological disaster monitoring and early warning system according to the present embodiment includes:
The system comprises an information acquisition module, a control module and a control module, wherein the information acquisition module is used for periodically acquiring mountain information, environment information and mountain images of a mountain, the mountain information comprises an inclination angle, ground stress and a reference distance, the environment information comprises an underground water level, precipitation information, surface temperature and earthquake magnitude, the inclination angle is the degree of an included angle between the surface of the mountain and the horizontal direction, the acquisition mode is that the inclination angle is measured and uploaded through an inclinometer, the ground stress is the pressure acting in a mountain rock pore, the acquisition mode is that the pressure is measured and obtained through an underground stress monitoring device, the reference distance is the distance between two reference points set in a mountain area, the acquisition mode is that the distance is measured and uploaded through a laser range finder, the underground water level is the height of underground water, the acquisition mode is that the underground water resource information website is accessed, the precipitation information comprises daily precipitation information and future precipitation information, the daily precipitation information comprises daily precipitation and daily precipitation intensity, the daily precipitation is actual precipitation of each day, the future precipitation information is expected precipitation information within 48 hours in the future, the future precipitation information comprises future precipitation and future precipitation intensity, the precipitation information is obtained by visiting a weather website, the precipitation intensity comprises no rain, light rain, medium rain, heavy rain and heavy rain, the surface temperature is obtained by a temperature sensor, the earthquake magnitude is the Riemerald magnitude, the earthquake magnitude is obtained by visiting a national earthquake science data center website, the mountain image is a satellite remote sensing image, the mountain image comprises a height image and a relief image, the height image is a gray level image of the height of each region of the mountain in gray values, the gray scale is 0 to represent the altitude of 0, the geomorphic image is a top view of a mountain shot by a remote sensing satellite, the geomorphic image is a gray scale image, the gray scale value is used for representing the geomorphic color of the mountain, and the mountain image is obtained by a satellite remote sensing technology;
the information storage module is used for storing mountain information, environment information, mountain images and mountain characteristics of the mountain and is connected with the information acquisition module;
The mountain analysis module is used for analyzing mountain characteristics according to mountain information and environment information, analyzing mountain fluctuation according to stored mountain information, adjusting the analysis process of the mountain characteristics according to the mountain fluctuation, analyzing environment characteristics according to stored environment information, optimizing the adjustment process of the mountain characteristics according to the environment characteristics, and connecting the mountain analysis module with the information storage module;
The image analysis module is used for analyzing the vertical displacement according to the mountain image and analyzing the mountain change according to the stored mountain image, and is connected with the information acquisition module;
The feature analysis module is used for analyzing disaster features according to mountain features and environmental information, adjusting the analysis process of the disaster features according to stored mountain features, optimizing the adjustment process of the disaster features according to vertical displacement and mountain changes, and connecting the feature analysis module with the mountain analysis module and the image analysis module;
And the disaster early warning module is used for early warning the geological disaster according to the disaster characteristics and is connected with the characteristic analysis module.
Referring to fig. 2, the mountain analyzing module includes:
the mountain analyzing unit is used for analyzing mountain characteristics according to mountain information and environment information;
The analysis and adjustment unit is used for analyzing the mountain fluctuation according to the stored mountain information and adjusting the analysis process of the mountain characteristics according to the mountain fluctuation, and is connected with the mountain analysis unit, wherein the mountain fluctuation comprises angle fluctuation, ground stress fluctuation and horizontal fluctuation;
The analysis optimizing unit is used for analyzing the environmental characteristics according to the stored rainfall information and the ground surface temperature, optimizing the adjustment process of the mountain characteristics according to the environmental characteristics, and is connected with the analysis adjusting unit.
Referring to fig. 3, the image analysis module includes:
The displacement analysis unit is used for analyzing the vertical displacement according to the stored height image;
And the change analysis unit is used for analyzing the mountain change according to the stored geomorphic image and is connected with the displacement analysis unit.
Referring to fig. 4, the feature analysis module includes:
the characteristic analysis unit is used for analyzing disaster characteristics according to mountain characteristics and future rainfall information;
the characteristic adjustment unit is used for adjusting the analysis process of disaster characteristics according to the stored mountain characteristics and is connected with the characteristic analysis unit;
the characteristic optimization unit is used for optimizing the disaster characteristic adjustment process according to the vertical displacement and the mountain variation, and is connected with the characteristic adjustment unit.
Specifically, the embodiment is applied to monitoring and early warning of mountain geological disasters in a multi-rainwater weather area, the mountain environment and mountain information change are analyzed to realize monitoring and early warning of mountain geological disasters, the mountain information, the environment information and the mountain images of the mountain are acquired by the information acquisition module to improve accuracy of information acquisition, so that monitoring efficiency of the system on the geological disasters is improved, early warning accuracy is improved, the information storage module is used for storing mountain information, the environment information, mountain images and mountain characteristics to improve accuracy of information storage, the system analysis sample data quantity is increased, so that monitoring efficiency of the system on the geological disasters is improved, early warning accuracy is improved, mountain characteristics are analyzed by the mountain analysis module, the relation between each parameter change of the mountain and the environment is represented by the mountain characteristics, so that monitoring efficiency of the system on the geological disasters is improved, the accuracy is improved, the mountain image analysis of the mountain images is improved, the vertical displacement and the mountain changes are analyzed to show that the mountain changes are represented by the vertical displacement, the mountain changes are represented by the mountain characteristics of the mountain in the vertical displacement, the mountain characteristics are analyzed by the mountain changes in the aspect of the system, the mountain characteristics are analyzed by the mountain characteristics of the mountain characteristics are analyzed by the mountain analysis module to realize the improvement of the mountain characteristics, the future disasters, the disaster characteristics are analyzed by the mountain characteristics are improved, and the system analysis of the mountain characteristics are improved, the mountain characteristics are analyzed to realize the disaster changes, and the mountain characteristics are analyzed by the mountain characteristics are improved, the method and the system have the advantages that the address disasters are early-warned, and the geological disasters are early-warned when the disaster characteristics are large, so that the monitoring efficiency of the system on the geological disasters is improved, and the early-warning accuracy is improved.
Specifically, in this embodiment, the mountain analyzing unit analyzes the mountain characteristics through a mountain characteristic analysis formula according to the inclination angle, the ground stress and the reference distance, the ground water level, the daily precipitation information, the surface temperature and the seismic magnitude, and the mountain analyzing unit is provided with a mountain characteristic analysis formula as follows:
F(i,j)={A,B,C,V}
A=|S1(i)-S1(i-1)|×D
B=|S2(i)-S2(i-1)|×D
C=|S3(i)-S3(i-1)|×D
D=eQ1(i)/Q1(i-1)-1+Q2(i)×ln[q2(i)+1]×lg|Q3(i)+1|
Wherein F (i, j) denotes a mountain characteristic, i denotes a period number, j denotes a characteristic number, a denotes an angle characteristic, B denotes a ground stress characteristic, C denotes a horizontal characteristic, V denotes a seismic magnitude, F (i, 1) =a, F (i, 2) =b, F (i, 3) =c, F (i, 4) =v, D denotes an influence parameter, S1 (i) denotes an inclination angle of a current period, S1 (i-1) denotes an inclination angle of a previous period, S2 (i) denotes a ground stress of a current period, S2 (i-1) denotes a ground stress of a previous period, S3 (i) denotes a reference distance of a current period, S3 (i-1) denotes a reference distance of a previous period, Q1 (i-1) denotes a ground water level of a previous period, Q2 (i) denotes a daily precipitation amount of a current period, Q3 (i) denotes a surface temperature of a current period, Q2 (i) denotes a rainfall intensity parameter of a current period, and when S2 (i-1) denotes a rainfall intensity parameter of a current period is no rainfall intensity, Q (i-1) =2, Q (i) is a rainfall intensity of Q-2, Q (Q-2) is a rainfall intensity of Q-2 when Q-2 (i) =2, Q-2 is a rainfall intensity of a value of a current period.
Specifically, in this embodiment, the mountain analysis unit analyzes the mountain information and the environmental information to analyze the mountain characteristics, and the mountain characteristics are used to represent the relationship between the change of each parameter of the mountain and the environmental information, so as to increase the analysis diversity of the system, thereby improving the monitoring efficiency of the system on the geological disasters and the early warning accuracy.
Specifically, in this embodiment, the analysis adjustment unit calculates the mountain fluctuation by a mountain fluctuation analysis formula according to the stored mountain information pair, and the analysis adjustment unit is provided with the mountain fluctuation analysis formula as follows:
，
Wherein a1 represents angular fluctuation, b1 represents ground stress fluctuation, c1 represents horizontal fluctuation, and i max represents the maximum value of the cycle number.
Specifically, the analysis adjustment unit in this embodiment adjusts an analysis process of mountain characteristics according to mountain fluctuation, in which:
When A1 < [1-S1 (i-1)/S1 (i) ], the analysis and adjustment unit judges that the angle fluctuation is large, adjusts the analysis process of the angle characteristic, wherein the adjusted angle characteristic is A1, and A1=AX (1+a1);
When B1 < [1-S2 (i-1)/S2 (i) ] the analysis adjustment unit determines that the ground stress fluctuation is large, adjusts the analysis process of the ground stress characteristic, and sets b1=b× (1+b1) for the adjusted ground stress characteristic as B1;
When C1 < [1-S3 (i-1)/S3 (i) ], the analysis adjustment unit determines that the level fluctuation is large, adjusts the analysis process of the level characteristic, and sets c1=c× (1+c1) for the adjusted level characteristic as C1;
When a1 is more than or equal to [1-S1 (i-1)/S1 (i) ] or b1 is more than or equal to [1-S2 (i-1)/S2 (i) ] or c1 is more than or equal to [1-S3 (i-1)/S3 (i) ], the analysis and adjustment unit judges that the mountain fluctuation is normal, and does not adjust the analysis process of the mountain characteristics.
Specifically, in this embodiment, the stored mountain information is analyzed by the analysis adjustment unit to analyze the mountain fluctuation, and the fluctuation size of the mountain information change in each period is represented by the mountain fluctuation, so that the monitoring efficiency of the system on the geological disasters is improved, the early warning accuracy is improved, the analysis of the mountain fluctuation by the analysis adjustment unit is performed to adjust the analysis process of the mountain characteristics, so that the mountain characteristics are related to the mountain fluctuation, and when the current analysis period and the previous period of the mountain information change are greater than the mountain fluctuation, the mountain characteristics are increased, the influence of fluctuation abnormality on the mountain characteristics is realized, so that the monitoring efficiency of the system on the geological disasters is improved, and the early warning accuracy is improved.
Specifically, the analysis optimizing unit in this embodiment analyzes the environmental sample data according to the stored precipitation information and the surface temperature, where:
When Q2 (i) is more than 0, the analysis optimizing unit takes precipitation information and surface temperature of a period corresponding to the daily precipitation amount which is analyzed currently as environment sample data;
when Q2 (i) =0, the analysis optimizing unit does not analyze the environmental sample data.
Specifically, the analysis optimizing unit in this embodiment calculates the environmental characteristics according to the environmental sample data pair through an environmental characteristic analysis formula, and the analysis optimizing unit is provided with the environmental characteristic analysis formula as follows:
，
Where G represents an environmental characteristic, Q2 (k) represents a daily precipitation amount in the environmental sample data, k represents an environmental sample data number, Q2 (k) represents a daily precipitation intensity parameter in the environmental sample data, Q3 (k) represents a surface temperature in the environmental sample data, and k max represents a maximum value of the environmental sample data number.
Specifically, in this embodiment, the analysis optimizing unit optimizes the adjustment process of the mountain feature according to the environmental feature, the optimized angle feature is A2, a2=a1×d×log D G is set, the optimized ground stress feature is B2, b2=b1×d×log D G is set, the optimized level feature is C2, and c2=c1×d×log D G is set.
Specifically, in this embodiment, the analysis optimizing unit analyzes the stored precipitation information and the surface temperature to analyze the environmental characteristics, and the environmental characteristics are used to represent the relationship between the precipitation information and the temperature in the period in which precipitation exists, so as to increase the diversity of system analysis, thereby improving the monitoring efficiency of the system on geological disasters and the early warning accuracy.
Specifically, in this embodiment, the image analysis module uses a pixel point at the lower left corner of the mountain image as an origin of coordinates, uses two sides adjacent to the origin of coordinates as an x axis and a y axis, establishes a rectangular plane coordinate system, increases the x axis from left to right in sequence, increases the y axis from bottom to top in sequence, and uses the coordinate points to represent the positions of the pixels in the mountain image.
Specifically, in this embodiment, the displacement analysis unit calculates the vertical displacement from the stored height image by a vertical displacement analysis formula provided with the following:
，
Wherein U (i) represents vertical displacement, X represents the number of pixels in the X-axis direction in the height image, Y represents the number of pixels in the Y-axis direction in the height image, L1 (i) (X, Y) represents the gray value of each pixel in the height image of the current analysis period, L1 (i-1) (X, Y) represents the gray value of each pixel in the height image of the previous analysis period, U represents the height parameter of the height image, and U is more than or equal to 10 and less than or equal to 100. It can be understood that the value of the height parameter is not specifically limited in this embodiment, and a person skilled in the art can freely set the height parameter only by meeting the analysis of vertical displacement, the setting of the height parameter is related to the monitored mountain height, and the height parameter represents the ratio of the mountain height to the gray value.
Specifically, in this embodiment, the change analysis unit calculates a mountain change from the stored relief image by a mountain change analysis formula provided with the following:
，
Wherein P (i) represents a mountain change, and L2 (i) (x, y) represents a gray value of each pixel point in the relief image of the current analysis period.
Specifically, in this embodiment, the feature analysis unit calculates disaster features according to a disaster feature analysis formula according to mountain features and future precipitation information, and the feature analysis unit is provided with a disaster feature analysis formula as follows:
，
Wherein W represents disaster characteristics, R represents future precipitation amount, and R represents future precipitation intensity.
Specifically, in this embodiment, the feature analysis unit analyzes the mountain feature and the future precipitation information to analyze the disaster feature, and the disaster feature is used to represent the influence of the future weather low mountain feature change, so as to improve the monitoring efficiency of the system on the geological disaster and improve the early warning accuracy.
Specifically, the feature adjustment unit in this embodiment adjusts the analysis process of the disaster feature according to the stored mountain feature, the feature adjustment unit extracts the mountain feature with V > 0 in the stored mountain feature as the mountain sample feature, adjusts the analysis process of the disaster feature according to the mountain sample feature, and sets w1=wxα, where α represents the disaster adjustment parameter, and sets
，
Where F (n, j) represents the mountain sample feature, n represents the number of the mountain sample feature, and n max represents the maximum value of the mountain sample feature number.
Specifically, in this embodiment, the stored mountain features are analyzed by the feature adjustment unit to extract mountain sample features, so as to adjust the analysis process of disaster features, improve the analysis precision of disaster features due to the fact that the analysis data of earthquake have larger fluctuation, and improve the monitoring efficiency of the system on geological disasters and the early warning accuracy.
Specifically, the feature optimization unit in this embodiment optimizes the adjustment process of disaster features according to the vertical displacement and the mountain variation, where:
when U (i) is not less than [ S3 (i) -S3 (i-1) ], the characteristic optimizing unit judges that the vertical displacement is large, optimizes the disaster characteristic adjusting process, and sets W2=W1×P (i) for the optimized disaster characteristic as W2;
when U (i) < [ S3 (i) -S3 (i-1) ] the feature optimization unit determines that the vertical displacement is normal, and does not optimize the adjustment process of the disaster feature.
Specifically, in this embodiment, the feature optimization unit is used to analyze the vertical displacement and the mountain variation, so as to optimize the adjustment process of the disaster feature, which is that the disaster feature is related to the mountain vertical displacement and the landform variation, so as to increase the diversity of system analysis, thereby improving the monitoring efficiency of the system on the geological disaster and the early warning accuracy.
Specifically, in this embodiment, the disaster early warning module compares the disaster characteristics with the characteristic threshold, and early warns the geological disaster according to the comparison result, where:
when W is less than W, the disaster early warning module judges that the disaster characteristics accord with a threshold value, and does not early warn the geological disaster;
When W is more than or equal to W, the disaster early warning module judges that the disaster characteristics do not accord with a threshold value, and early warning is carried out on geological disasters;
wherein w represents a characteristic threshold value, and w is more than or equal to 0.4 and less than or equal to 0.7. It can be understood that, in this embodiment, the value of the feature threshold is not specifically limited, and a person skilled in the art can freely set the value of the feature threshold only by meeting the early warning of geological disasters, where the optimal value of the feature threshold is: w=0.5.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
Claims (5)
1. A geological disaster monitoring and early warning system, comprising:
the information acquisition module is used for periodically acquiring mountain information, environment information and mountain images of the mountain;
The information storage module is used for storing mountain information, environment information, mountain images and mountain characteristics of the mountain;
The mountain analysis module is used for analyzing mountain characteristics according to mountain information and environment information, analyzing mountain fluctuation according to stored mountain information, adjusting the analysis process of the mountain characteristics according to the mountain fluctuation, analyzing environment characteristics according to stored environment information, and optimizing the adjustment process of the mountain characteristics according to the environment characteristics;
The image analysis module is used for analyzing the vertical displacement according to the mountain image and analyzing the mountain change according to the stored mountain image;
The feature analysis module is used for analyzing disaster features according to mountain features and environmental information, adjusting the analysis process of the disaster features according to the stored mountain features, and optimizing the adjustment process of the disaster features according to vertical displacement and mountain changes;
The disaster early warning module is used for early warning the geological disaster according to the disaster characteristics;
The image analysis module is provided with a displacement analysis unit which is used for calculating vertical displacement according to a stored height image through a vertical displacement analysis formula, and the displacement analysis unit is provided with the following vertical displacement analysis formula:
；
wherein U (i) represents vertical displacement, X represents the number of pixels in the X-axis direction in the height image, Y represents the number of pixels in the Y-axis direction in the height image, L1 (i) (X, Y) represents the gray value of each pixel in the height image of the current analysis period, L1 (i-1) (X, Y) represents the gray value of each pixel in the height image of the previous analysis period, U represents the height parameter of the height image, and i represents the period number;
The image analysis module is also provided with a change analysis unit which is used for calculating mountain change according to a stored landform image through a mountain change analysis formula, and the change analysis unit is provided with the following mountain change analysis formula:
；
wherein P (i) represents mountain variation, L2 (i) (x, y) represents gray values of each pixel point in the relief image of the current analysis period;
the feature analysis module is provided with a feature analysis unit which is used for calculating disaster features according to mountain features and future rainfall information through a disaster feature analysis formula, and the feature analysis unit is provided with a disaster feature analysis formula as follows:
；
Wherein W represents disaster characteristics, R represents future precipitation amount, R represents future precipitation intensity, A represents angle characteristics, B represents ground stress characteristics, C represents horizontal characteristics, S1 (i) represents inclination angle of the current period, S2 (i) represents ground stress of the current period, and S3 (i) represents reference distance of the current period;
The characteristic analysis module is also provided with a characteristic adjustment unit for adjusting the analysis process of the disaster characteristic according to the stored mountain characteristic, the characteristic adjustment unit extracts the mountain characteristic with V more than 0 in the stored mountain characteristic as a mountain sample characteristic, adjusts the analysis process of the disaster characteristic according to the mountain sample characteristic, the adjusted disaster characteristic is W1, W1=Wxalpha, alpha represents a disaster adjustment parameter, and sets ；
Wherein F (n, j) represents the mountain sample feature, n represents the number of the mountain sample feature, and n max represents the maximum value of the mountain sample feature number;
The feature analysis module is also provided with a feature optimization unit which is used for optimizing the adjustment process of disaster features according to vertical displacement and mountain variation, wherein:
when U (i) is not less than [ S3 (i) -S3 (i-1) ], the characteristic optimizing unit judges that the vertical displacement is large, optimizes the disaster characteristic adjusting process, and sets W2=W1×P (i) for the optimized disaster characteristic as W2;
When U (i) < [ S3 (i) -S3 (i-1) ] the feature optimization unit determines that the vertical displacement is normal, the adjustment process of the disaster feature is not optimized, and S3 (i-1) represents the reference distance of the last period.
2. The geological disaster monitoring and early warning system according to claim 1, wherein the mountain analyzing module is provided with a mountain analyzing unit for analyzing mountain characteristics by a mountain characteristic analyzing formula according to an inclination angle, a ground stress and a reference distance, a ground water level, daily precipitation information, a surface temperature and a seismic magnitude, the mountain analyzing unit is provided with a mountain characteristic analyzing formula as follows:
F(i,j)={A,B,C,V}
A=|S1(i)-S1(i-1)|×D
B=|S2(i)-S2(i-1)|×D
C=|S3(i)-S3(i-1)|×D
D=eQ1(i)/Q1(i-1)-1+Q2(i)×ln[q2(i)+1]×lg|Q3(i)+1|
Wherein F (i, j) denotes a mountain characteristic, i denotes a period number, j denotes a characteristic number, a denotes an angle characteristic, B denotes a ground stress characteristic, C denotes a horizontal characteristic, V denotes a seismic magnitude, F (i, 1) =a, F (i, 2) =b, F (i, 3) =c, F (i, 4) =v, D denotes an influence parameter, S1 (i) denotes an inclination angle of a current period, S1 (i-1) denotes an inclination angle of a previous period, S2 (i) denotes a ground stress of a current period, S2 (i-1) denotes a ground stress of a previous period, S3 (i) denotes a reference distance of a current period, S3 (i-1) denotes a reference distance of a previous period, Q1 (i-1) denotes a ground water level of a previous period, Q2 (i) denotes a daily precipitation amount of a current period, Q3 (i) denotes a surface temperature of a current period, Q2 (i) denotes a rainfall intensity parameter of a current period, and when S2 (i-1) denotes a rainfall intensity parameter of a current period is no rainfall intensity, Q (i-1) =2, Q (i) is a rainfall intensity of Q-2, Q (Q-2) is a rainfall intensity of Q-2 when Q-2 (i) =2, Q-2 is a rainfall intensity of a value of a current period.
3. The geological disaster monitoring and early warning system according to claim 2, wherein the mountain analyzing module is further provided with an analyzing and adjusting unit for calculating mountain fluctuation from a stored mountain fluctuation analysis formula according to the stored mountain information, the analyzing and adjusting unit being provided with a mountain fluctuation analysis formula as follows:
；
wherein a1 represents angular fluctuation, b1 represents ground stress fluctuation, c1 represents horizontal fluctuation, and i max represents the maximum value of the cycle number;
the analysis adjustment unit adjusts an analysis process of mountain characteristics according to mountain fluctuation, wherein:
When A1 < [1-S1 (i-1)/S1 (i) ], the analysis and adjustment unit judges that the angle fluctuation is large, adjusts the analysis process of the angle characteristic, wherein the adjusted angle characteristic is A1, and A1=AX (1+a1);
When B1 < [1-S2 (i-1)/S2 (i) ] the analysis adjustment unit determines that the ground stress fluctuation is large, adjusts the analysis process of the ground stress characteristic, and sets b1=b× (1+b1) for the adjusted ground stress characteristic as B1;
When C1 < [1-S3 (i-1)/S3 (i) ], the analysis adjustment unit determines that the level fluctuation is large, adjusts the analysis process of the level characteristic, and sets c1=c× (1+c1) for the adjusted level characteristic as C1;
When a1 is more than or equal to [1-S1 (i-1)/S1 (i) ] or b1 is more than or equal to [1-S2 (i-1)/S2 (i) ] or c1 is more than or equal to [1-S3 (i-1)/S3 (i) ], the analysis and adjustment unit judges that the mountain fluctuation is normal, and does not adjust the analysis process of the mountain characteristics.
4. A geological disaster monitoring and early warning system according to claim 3, wherein the mountain analysis module is further provided with an analysis optimizing unit for analyzing the environmental sample data based on the stored precipitation information and the surface temperature, wherein:
When Q2 (i) is more than 0, the analysis optimizing unit takes precipitation information and surface temperature of a period corresponding to the daily precipitation amount which is analyzed currently as environment sample data;
when Q2 (i) =0, the analysis optimizing unit does not analyze the environmental sample data;
The analysis optimizing unit calculates environmental characteristics according to the environmental sample data pair through an environmental characteristic analysis formula, and the analysis optimizing unit is provided with the environmental characteristic analysis formula as follows:
；
Wherein G represents an environmental characteristic, Q2 (k) represents a daily precipitation amount in the environmental sample data, k represents an environmental sample data number, Q2 (k) represents a daily precipitation intensity parameter in the environmental sample data, Q3 (k) represents a surface temperature in the environmental sample data, and k max represents a maximum value of the environmental sample data number;
The analysis optimizing unit optimizes the adjustment process of the mountain feature according to the environmental feature, the optimized angle feature is A2, A2=A1×Dx log D G is set, the optimized ground stress feature is B2, B2=B1×Dx log D G is set, the optimized horizontal feature is C2, and C2=C1×Dx log D G is set.
5. The geological disaster monitoring and early warning system of claim 1, wherein the disaster early warning module compares disaster characteristics with characteristic thresholds and early warns of geological disasters according to comparison results, wherein:
when W is less than W, the disaster early warning module judges that the disaster characteristics accord with a threshold value, and does not early warn the geological disaster;
When W is more than or equal to W, the disaster early warning module judges that the disaster characteristics do not accord with a threshold value, and early warning is carried out on geological disasters;
where w represents a feature threshold.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410196731.1A CN117765689B (en) | 2024-02-22 | 2024-02-22 | Geological disaster monitoring and early warning system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410196731.1A CN117765689B (en) | 2024-02-22 | 2024-02-22 | Geological disaster monitoring and early warning system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117765689A CN117765689A (en) | 2024-03-26 |
| CN117765689B true CN117765689B (en) | 2024-05-10 |
Family
ID=90322253
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410196731.1A Active CN117765689B (en) | 2024-02-22 | 2024-02-22 | Geological disaster monitoring and early warning system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117765689B (en) |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102890290A (en) * | 2012-09-25 | 2013-01-23 | 中国石油天然气股份有限公司 | Prestack depth migration method under condition of undulating surface |
| WO2013077478A1 (en) * | 2011-11-23 | 2013-05-30 | 대민산업개발(주) | Monitoring system for debris barrier disaster management |
| CN104913726A (en) * | 2015-06-11 | 2015-09-16 | 重庆交通大学 | LED-based slope model surface displacement monitoring system and detection method |
| CN109872509A (en) * | 2019-04-02 | 2019-06-11 | 西安邮电大学 | Massif Geological Hazards Monitoring and early warning system and method based on the twin driving of number |
| CN110176130A (en) * | 2019-05-31 | 2019-08-27 | 国家电网有限公司 | A kind of hydroenergy storage station Geological Disaster Warning System |
| CN112364802A (en) * | 2020-11-19 | 2021-02-12 | 中国地质调查局水文地质环境地质调查中心 | Deformation monitoring method for collapse landslide disaster body |
| CN113012398A (en) * | 2021-02-20 | 2021-06-22 | 中煤航测遥感集团有限公司 | Geological disaster monitoring and early warning method and device, computer equipment and storage medium |
| KR102305468B1 (en) * | 2021-05-12 | 2021-09-30 | 씨티씨 주식회사 | Landslide distributed detection system based on deep learning |
| KR102305467B1 (en) * | 2021-05-12 | 2021-09-30 | 씨티씨 주식회사 | Landslide distributed detecting method based on deep learning |
| JP2022047348A (en) * | 2020-09-11 | 2022-03-24 | 日本信号株式会社 | System for estimating occurrence possibility of landslide disaster at specific point |
| CN114399888A (en) * | 2022-01-04 | 2022-04-26 | 四川爱联科技股份有限公司 | Mountain disaster early warning system |
| CN115936948A (en) * | 2022-12-06 | 2023-04-07 | 应急管理部国家自然灾害防治研究院 | Composite disaster risk prediction method and system |
| CN116011828A (en) * | 2023-03-24 | 2023-04-25 | 江西省气象服务中心(江西省专业气象台、江西省气象宣传与科普中心) | Geological disaster forecasting and early warning method, system, readable storage medium and equipment |
| CN116597616A (en) * | 2023-05-23 | 2023-08-15 | 中国建筑材料工业地质勘查中心四川总队 | Intelligent monitoring and early warning system for geological disasters in mining area |
| CN116975576A (en) * | 2023-09-19 | 2023-10-31 | 四川省公路规划勘察设计研究院有限公司 | Mountain road geological disaster risk evaluation method based on key information statistics |
| CN117058845A (en) * | 2023-08-07 | 2023-11-14 | 河南交院工程技术集团有限公司 | Highway high slope landslide hazard early warning method based on evaluation function training |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103460073A (en) * | 2010-12-17 | 2013-12-18 | 地震预警系统公司 | Earthquake warning system |
| US11402525B2 (en) * | 2019-11-10 | 2022-08-02 | Kenneth H Sheeks | Earthquake detector |
| CN114330168B (en) * | 2021-12-30 | 2022-06-21 | 中国科学院力学研究所 | Method for dynamically evaluating slope safety |
-
2024
- 2024-02-22 CN CN202410196731.1A patent/CN117765689B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013077478A1 (en) * | 2011-11-23 | 2013-05-30 | 대민산업개발(주) | Monitoring system for debris barrier disaster management |
| CN102890290A (en) * | 2012-09-25 | 2013-01-23 | 中国石油天然气股份有限公司 | Prestack depth migration method under condition of undulating surface |
| CN104913726A (en) * | 2015-06-11 | 2015-09-16 | 重庆交通大学 | LED-based slope model surface displacement monitoring system and detection method |
| CN109872509A (en) * | 2019-04-02 | 2019-06-11 | 西安邮电大学 | Massif Geological Hazards Monitoring and early warning system and method based on the twin driving of number |
| CN110176130A (en) * | 2019-05-31 | 2019-08-27 | 国家电网有限公司 | A kind of hydroenergy storage station Geological Disaster Warning System |
| JP2022047348A (en) * | 2020-09-11 | 2022-03-24 | 日本信号株式会社 | System for estimating occurrence possibility of landslide disaster at specific point |
| CN112364802A (en) * | 2020-11-19 | 2021-02-12 | 中国地质调查局水文地质环境地质调查中心 | Deformation monitoring method for collapse landslide disaster body |
| CN113012398A (en) * | 2021-02-20 | 2021-06-22 | 中煤航测遥感集团有限公司 | Geological disaster monitoring and early warning method and device, computer equipment and storage medium |
| KR102305467B1 (en) * | 2021-05-12 | 2021-09-30 | 씨티씨 주식회사 | Landslide distributed detecting method based on deep learning |
| KR102305468B1 (en) * | 2021-05-12 | 2021-09-30 | 씨티씨 주식회사 | Landslide distributed detection system based on deep learning |
| CN114399888A (en) * | 2022-01-04 | 2022-04-26 | 四川爱联科技股份有限公司 | Mountain disaster early warning system |
| CN115936948A (en) * | 2022-12-06 | 2023-04-07 | 应急管理部国家自然灾害防治研究院 | Composite disaster risk prediction method and system |
| CN116011828A (en) * | 2023-03-24 | 2023-04-25 | 江西省气象服务中心(江西省专业气象台、江西省气象宣传与科普中心) | Geological disaster forecasting and early warning method, system, readable storage medium and equipment |
| CN116597616A (en) * | 2023-05-23 | 2023-08-15 | 中国建筑材料工业地质勘查中心四川总队 | Intelligent monitoring and early warning system for geological disasters in mining area |
| CN117058845A (en) * | 2023-08-07 | 2023-11-14 | 河南交院工程技术集团有限公司 | Highway high slope landslide hazard early warning method based on evaluation function training |
| CN116975576A (en) * | 2023-09-19 | 2023-10-31 | 四川省公路规划勘察设计研究院有限公司 | Mountain road geological disaster risk evaluation method based on key information statistics |
Non-Patent Citations (2)
| Title |
|---|
| 基于时序InSAR的青海同仁市滑坡隐患早期识别与特征解析;李宗仁;科学技术工程;20231218;第35卷(第23期);15158-15170 * |
| 山地地震资料处理方法研究及应用;胡冰;2010年国际石油地球物理技术交流会会议专刊;20100723;45-48 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117765689A (en) | 2024-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gruszczyński et al. | Comparison of low-altitude UAV photogrammetry with terrestrial laser scanning as data-source methods for terrain covered in low vegetation | |
| Holland et al. | Practical use of video imagery in nearshore oceanographic field studies | |
| KR20180094702A (en) | Apparatus and method for extraction of tidal creeks in tidal flat zones using drone | |
| CN108550174A (en) | A kind of coastline Super-resolution Mapping and system based on half global optimization | |
| CN114627087B (en) | Automatic detection method and system for ground feature change of multi-temporal satellite remote sensing image | |
| CN111445522B (en) | Passive night vision intelligent lightning detection system and intelligent lightning detection method | |
| CN111473776A (en) | Landslide crack monitoring method based on single-image close-range photogrammetry | |
| CN112285710A (en) | Multi-source remote sensing reservoir water storage capacity estimation method and device | |
| CN114819737B (en) | Method, system and storage medium for estimating carbon reserves of highway road vegetation | |
| Gonçalves et al. | Monitoring Local Shoreline Changes by Integrating UASs, Airborne LiDAR, Historical Images and Orthophotos. | |
| CN115829918A (en) | Machine vision-based monitoring method for deformation of large facility structure | |
| CN110428013B (en) | Crop remote sensing classification method and system | |
| CN211477203U (en) | Refined monitoring equipment system based on high-resolution remote sensing image | |
| CN117765689B (en) | Geological disaster monitoring and early warning system | |
| CN113139994B (en) | High-resolution optical remote sensing satellite image building height monitoring method based on angular points | |
| CN112924967A (en) | Remote sensing monitoring method for crop lodging based on radar and optical data combination characteristics and application | |
| CN107576399A (en) | Towards bright the temperature Forecasting Methodology and system of MODIS forest fire detections | |
| CN116612080A (en) | Variation detection method based on hyperspectral image spectrum curve | |
| Lingua et al. | Iterative Refraction-Correction Method on Mvs-Sfm for Shallow Stream Bathymetry | |
| CN114494039A (en) | Underwater hyperspectral push-broom image geometric correction method | |
| CN111121781A (en) | Sun positioning and sunlight interference discrimination method based on foundation cloud chart | |
| CN106530326A (en) | Change detection method based on image texture features and DSM | |
| CN116358500B (en) | Method and system for detecting grain pile deformation based on image analysis | |
| CN115856890B (en) | PS-InSAR deformation observation method for plateau railway line | |
| CN117687111A (en) | Dynamic threshold snow accumulation coverage judging method for high-resolution optical satellite |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |