CN111429471A - Geological disaster information management system and method - Google Patents
Geological disaster information management system and method Download PDFInfo
- Publication number
- CN111429471A CN111429471A CN202010215157.1A CN202010215157A CN111429471A CN 111429471 A CN111429471 A CN 111429471A CN 202010215157 A CN202010215157 A CN 202010215157A CN 111429471 A CN111429471 A CN 111429471A
- Authority
- CN
- China
- Prior art keywords
- information
- geological
- module
- disaster
- geological disaster
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000007726 management method Methods 0.000 claims abstract description 38
- 238000004458 analytical method Methods 0.000 claims abstract description 31
- 238000000605 extraction Methods 0.000 claims abstract description 15
- 238000013528 artificial neural network Methods 0.000 claims abstract description 11
- 238000012544 monitoring process Methods 0.000 claims abstract description 11
- 238000013500 data storage Methods 0.000 claims abstract description 10
- 238000012545 processing Methods 0.000 claims description 65
- 238000012549 training Methods 0.000 claims description 18
- 230000002087 whitening effect Effects 0.000 claims description 9
- 229910052500 inorganic mineral Inorganic materials 0.000 claims description 8
- 239000011707 mineral Substances 0.000 claims description 8
- 230000006378 damage Effects 0.000 claims description 6
- 230000004927 fusion Effects 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 4
- 230000008030 elimination Effects 0.000 claims description 3
- 238000003379 elimination reaction Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000004891 communication Methods 0.000 claims description 2
- 238000004590 computer program Methods 0.000 claims description 2
- 238000007596 consolidation process Methods 0.000 claims 1
- 230000007547 defect Effects 0.000 abstract description 2
- 241000282414 Homo sapiens Species 0.000 description 8
- 230000000694 effects Effects 0.000 description 7
- 239000002689 soil Substances 0.000 description 4
- 239000007787 solid Substances 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 239000011435 rock Substances 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 239000008186 active pharmaceutical agent Substances 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004162 soil erosion Methods 0.000 description 1
- 230000002269 spontaneous effect Effects 0.000 description 1
- CCEKAJIANROZEO-UHFFFAOYSA-N sulfluramid Chemical group CCNS(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F CCEKAJIANROZEO-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G06T5/77—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
Abstract
The invention belongs to the technical field of geological disaster information management, and discloses a geological disaster information management system and a geological disaster information management method, wherein the geological disaster information management system comprises: the geological disaster forecasting system comprises a geological information acquisition module, an image feature extraction module, a central control module, a feature information analysis module, an information editing module, an information updating module, an information publishing module, a geological disaster forecasting module, a data storage module, a terminal module and a display module. According to the method, the disaster forecasting module is used for building the forecasting model by adopting the RBF neural network, so that the corresponding geological disaster occurrence probability under the current condition can be calculated, a more accurate basis is provided for making a forecasting decision, and the defect that the traditional geological monitoring system can only collect data but cannot analyze the data is overcome; and a self-learning function is added, the problem of inaccurate prediction caused by the fact that the initial threshold value is not set according to the reality is corrected, and the accuracy of geological disaster prediction is improved.
Description
Technical Field
The invention belongs to the technical field of geological disaster information management, and particularly relates to a geological disaster information management system and method.
Background
Geological disasters refer to disastrous geological events caused by various geological actions during the development and evolution of the earth. The distribution change rule of geological disasters in time and space is not only limited by natural environment, but also related to human activities, and is often the result of interaction between human and the natural world. The ground disaster is a natural disaster with geological dynamic activity or abnormal change of geological environment as a main cause. Under the action of the internal power, the external power or the artificial geological power, the earth generates abnormal energy release, material movement, deformation and displacement of rock and soil bodies, abnormal change of the environment and the like, and the phenomena or processes of harming human lives and properties, living and economic activities or destroying resources and environments on which human beings live and develop are generated. Adverse geological phenomena are commonly called geological disasters, and refer to geological events that deteriorate geological environment, reduce environmental quality, directly or indirectly harm human safety, and cause losses for social and economic construction, caused by natural geological effects and human activities. Geological disasters are geological effects (phenomena) which are formed under the action of natural or human factors and damage and lose human lives, properties and environments. Such as collapse, landslide, debris flow, ground fissure, ground subsidence, rock burst, water burst in underground tunnel, mud burst, gas burst, spontaneous combustion of coal bed, loess collapse, rock-soil expansion, sandy soil liquefaction, land freeze-thaw, water loss and soil erosion, land desertification and swampiness, soil salinization, earthquake, volcano, geothermal damage, etc. However, the existing geological disaster information management system needs to spend a lot of time and effort on extracting geological map information; meanwhile, the error of disaster forecast is large.
In summary, the problems and disadvantages of the prior art are: the existing geological disaster information management system needs to spend a great deal of time and energy on extracting geological map information; meanwhile, the error of disaster forecast is large.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a geological disaster information management system and a geological disaster information management method.
The invention is realized in such a way, and the geological disaster information management method comprises the following steps:
acquiring geological image information through geological monitoring equipment; extracting the characteristics of the acquired geological image information through a characteristic extraction program: (1) extracting surface element information of the geological image through a feature extraction program, and sequentially carrying out boundary whitening treatment, color segmentation, communication body construction, small color spot elimination and white area restoration at the boundary on the geological map subjected to pretreatment;
(2) extracting line element information, including extracting region boundary line information and extracting non-region boundary line information;
(3) extracting point element information, namely generating a mineral point template, matching an image and positioning a mineral point sequentially on the preprocessed geological map;
(4) the three methods are methods for simultaneously performing the preprocessed geological maps respectively.
Step two, analyzing the extracted characteristic information of the geological image by a central processing unit control information analysis program: (I) acquiring characteristic information of a geological image to be analyzed, and acquiring coding characteristic information and processing nodes of the geological image to be analyzed according to the characteristic information;
(II) acquiring a processing path of the geological image according to the coding feature information, wherein the processing path comprises at least one processing node;
and (III) performing characteristic analysis by using an information analysis program based on the processing path of the geological image to obtain an analysis result of the characteristic information of the geological image to be analyzed.
Step three, editing the geological information and the analysis result through an editing program; updating the geological information through an updating program; and releasing geological disaster information through a release program.
And fourthly, forecasting the geological disaster information by utilizing a geological disaster forecasting model through a forecasting program: 1) establishing a geological disaster forecasting model based on neural network and multi-parameter information fusion through a forecasting program;
2) establishing a relation between the occurrence probability of the geological disaster and the grade of the geological disaster;
3) and (3) carrying out data acquisition by using a multi-parameter geological disaster monitoring system, and inputting the acquired data into the step 1) and the step 2) to realize the forecast of the geological disaster.
And step five, storing the collected geological information, extracting the information, analyzing the result, updating the information, releasing the information and forecasting the information through a cloud database server.
And step six, sending the geological disaster information data to the mobile terminal through the cloud database server, and performing remote control on the management system.
And seventhly, displaying the acquired geological information, extracting the information, analyzing the result, updating the information, releasing the information and forecasting the real-time data of the information through a display.
Further, in the step one, the boundary whitening processing specifically includes:
performing edge search on the geological map; performing primary coarsening processing on the boundary image; the original image and the boundary image are subtracted from each other, and the original image is subjected to whitening processing on all the boundaries.
Further, in step two, the obtaining of the processing path of the geological image according to the coding feature information in step (II) includes:
determining at least one target processing node corresponding to the coding characteristic information according to the corresponding relation between the coding characteristic information and the processing nodes;
and generating a processing path of the geological image according to the processing sequence of the at least one target processing node.
Further, the processing path includes information related to the processing node, and the information related to the processing node includes at least one of a client identifier of the processing node, provider information of the processing node, and processing time.
Further, in the fourth step, the process of establishing the geological disaster forecasting model based on the neural network and the multi-parameter information fusion through the forecasting program in the step 1) is as follows:
(a) training data arrangement and threshold setting;
(b) establishing a forecasting model based on a radial basis function neural network;
the forecasting model is divided into an input layer, a hidden layer and an output layer, the input of the forecasting model is multi-parameter and the occurrence probability of the corresponding geological disaster, and the weight parameters in the hidden layer are calculated through the given data of the input layer and the output layer, so that the following relation between the input and the output is obtained:
a{1}＝radbas(netprod(dist(net，IW{1,1}，p),net,b{1}))；
wherein, a is the occurrence probability of the output geological disaster, and p is the input training data to obtain net;
(c) substituting the relational expression obtained in the step (b) into a training model formula in an MAT L AB function library to obtain the following geological disaster forecasting model:
net＝newrb(p1,a1)；
the net is the obtained geological disaster forecast model, the a1 is the output of the occurrence probability of the current geological disaster, and the p1 is the input of real-time collected data.
Further, in step (a), the training data sorting and the setting of the threshold include:
the method comprises the steps of respectively counting the relations between debris flow factors, landslide factors and hazard degrees of areas with geological disasters, taking each parameter factor as training data, then selecting the maximum range of a corresponding parameter measuring sensor, grading the number of points between 0 and the maximum range for installing the training data, determining the initial relation distribution of each parameter and the occurrence probability of the geological disasters, and dividing the disaster probability into four ranges of 0-5%, 5-20%, 20-40% and 40-90%, thereby determining the threshold value of each parameter.
Further, in the fourth step, the establishing of the relationship between the occurrence probability of the geological disaster and the grade of the geological disaster in the step 2) includes:
according to the loss condition, the disaster probability and the damage degree which is easy to generate caused by the sudden occurrence of the disaster in the geological disaster, the grade of the geological disaster is divided into four grades: particularly, the severe sudden disaster is four-level, the occurrence probability of the geological disaster is 40-90%, the red early warning is adopted, the severe sudden disaster is three-level, the occurrence probability of the geological disaster is 20-40%, the orange early warning is adopted, the large sudden disaster is two-level, the occurrence probability of the geological disaster is 5-20%, the yellow early warning is adopted, the general sudden disaster is one-level, the occurrence probability of the geological disaster is 0-5%, and the blue early warning is adopted.
Another object of the present invention is to provide a geological disaster information management system using the geological disaster information management method, the geological disaster information management system including:
the geological disaster forecasting system comprises a geological information acquisition module, an image feature extraction module, a central control module, a feature information analysis module, an information editing module, an information updating module, an information publishing module, a geological disaster forecasting module, a data storage module, a terminal module and a display module.
The geological information acquisition module is connected with the central control module and is used for acquiring geological image information through geological monitoring equipment;
the image feature extraction module is connected with the central control module and used for extracting the features of the acquired geological image information through a feature extraction program;
the central control module is connected with the geological information acquisition module, the image feature extraction module, the feature information analysis module, the information editing module, the information updating module, the information publishing module, the geological disaster forecasting module, the data storage module, the terminal module and the display module and is used for controlling each module to normally work through the central processing unit;
the characteristic information analysis module is connected with the central control module and used for analyzing the extracted characteristic information of the geological image through an information analysis program;
the information editing module is connected with the central control module and is used for editing the geological information and the analysis result through an editing program;
the information updating module is connected with the central control module and used for updating the geological information through an updating program;
the information issuing module is connected with the central control module and used for issuing the geological disaster information through an issuing program;
the geological disaster forecasting module is connected with the central control module and used for forecasting geological disaster information by utilizing the geological disaster forecasting model through a forecasting program;
the data storage module is connected with the central control module and used for storing the collected geological information, the extracted information, the analysis result, the updated information, the issued information and the forecast information through the cloud database server;
the terminal module is connected with the central control module and used for sending the geological disaster information data to the mobile terminal through the cloud database server and performing remote control on the management system;
and the display module is connected with the central control module and used for displaying the acquired geological information, the extracted information, the analysis result, the updated information, the published information and the real-time data of the forecast information through the display.
It is another object of the present invention to provide a computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement the geological disaster information management method when executed on an electronic device.
Another object of the present invention is to provide a computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to perform the geological disaster information management method.
The invention has the advantages and positive effects that: the invention solves the difficult problem of the existing geological map information extraction through an information extraction module, namely, a semi-structured standard geological map is taken as a research object, geological map information is extracted as the aim, legend information is combined, main information related to mineral products, such as stratum, magma, fault, mineral points and the like, contained in the geological map is extracted, a thematic information map layer is constructed, and a set of technology for converting semi-structured data (images) into structural data is formed; the information extraction speed is greatly improved; meanwhile, the disaster forecasting module adopts the RBF neural network to build a forecasting model, so that the occurrence probability of the corresponding geological disaster under the current condition can be calculated, and the grade of the geological disaster can be calculated according to the occurrence probability, so that the occurrence of the disaster can be prevented and reduced by adopting measures of the corresponding grade; multiple factors influencing collapse landslide and debris flow geological disasters are fully considered, more accurate basis is provided for making a forecast decision, and the defect that a traditional geological monitoring system can only collect data but cannot analyze the data is overcome; and a self-learning function is added, the problem of inaccurate prediction caused by the fact that the initial threshold value is not set according to the reality is corrected, and the accuracy of geological disaster prediction is improved.
Drawings
Fig. 1 is a flowchart of a geological disaster information management method according to an embodiment of the present invention.
FIG. 2 is a block diagram of a geological disaster information management system according to an embodiment of the present invention;
in the figure: 1. a geological information acquisition module; 2. an image feature extraction module; 3. a central control module; 4. a characteristic information analysis module; 5. an information editing module; 6. an information updating module; 7. an information release module; 8. a geological disaster forecasting module; 9. a data storage module; 10. a terminal module; 11. and a display module.
Fig. 3 is a flowchart of a method for extracting features of acquired geological image information through a feature extraction program according to an embodiment of the present invention.
Fig. 4 is a flowchart of a method for analyzing extracted feature information of a geological image by controlling an information analysis program through a central processing unit according to an embodiment of the present invention.
Fig. 5 is a flowchart of a method for forecasting geological disaster information by a forecasting program using a geological disaster forecasting model according to an embodiment of the present invention.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, a geological disaster information management method provided by an embodiment of the present invention includes the following steps:
s101, collecting geological image information through geological monitoring equipment; and extracting the characteristics of the acquired geological image information through a characteristic extraction program.
S102, controlling each module to normally work through a central processing unit; and analyzing the extracted characteristic information of the geological image through an information analysis program.
S103, editing geological information and analysis results through an editing program; and updating the geological information through an updating program.
S104, releasing geological disaster information through a release program; and forecasting the geological disaster information by utilizing a geological disaster forecasting model through a forecasting program.
And S105, storing the collected geological information, the extracted information, the analysis result, the updated information, the release information and the forecast information through a cloud database server.
And S106, sending the geological disaster information data to the mobile terminal through the cloud database server, and performing remote control on the management system.
And S107, displaying the acquired geological information, the extracted information, the analysis result, the updated information, the published information and the real-time data of the forecast information through a display.
As shown in fig. 2, a geological disaster information management system according to an embodiment of the present invention includes: the geological disaster forecasting system comprises a geological information acquisition module 1, an image feature extraction module 2, a central control module 3, a feature information analysis module 4, an information editing module 5, an information updating module 6, an information publishing module 7, a geological disaster forecasting module 8, a data storage module 9, a terminal module 10 and a display module 11.
The geological information acquisition module 1 is connected with the central control module 3 and is used for acquiring geological image information through geological monitoring equipment;
the image feature extraction module 2 is connected with the central control module 3 and used for extracting the features of the acquired geological image information through a feature extraction program;
the central control module 3 is connected with the geological information acquisition module 1, the image feature extraction module 2, the feature information analysis module 4, the information editing module 5, the information updating module 6, the information publishing module 7, the geological disaster forecasting module 8, the data storage module 9, the terminal module 10 and the display module 11, and is used for controlling each module to normally work through a central processing unit;
the characteristic information analysis module 4 is connected with the central control module 3 and is used for analyzing the extracted characteristic information of the geological image through an information analysis program;
the information editing module 5 is connected with the central control module 3 and is used for editing the geological information and the analysis result through an editing program;
the information updating module 6 is connected with the central control module 3 and used for updating the geological information through an updating program;
the information release module 7 is connected with the central control module 3 and used for releasing the geological disaster information through a release program;
the geological disaster forecasting module 8 is connected with the central control module 3 and is used for forecasting geological disaster information by utilizing a geological disaster forecasting model through a forecasting program;
the data storage module 9 is connected with the central control module 3 and used for storing the collected geological information, the extracted information, the analysis result, the updated information, the issued information and the forecast information through a cloud database server;
the terminal module 10 is connected with the central control module 3 and used for sending geological disaster information data to the mobile terminal through the cloud database server and performing remote control on the management system;
and the display module 11 is connected with the central control module 3 and is used for displaying the acquired geological information, the extracted information, the analysis result, the updated information, the published information and the real-time data of the forecast information through a display.
The invention is further described with reference to specific examples.
Example 1
As shown in fig. 1 and fig. 3, the method for managing geological disaster information according to the embodiment of the present invention for extracting features of acquired geological image information by a feature extraction program includes:
s201, extracting surface element information of the geological image through a feature extraction program, and sequentially carrying out boundary whitening treatment, color segmentation, communicating body construction, small color spot elimination and white area restoration at the boundary on the geological image after preprocessing.
S202, line element information is extracted, including extraction area boundary line information and extraction non-area boundary line information.
And S203, extracting point element information, namely generating a mineral point template, matching an image and positioning a mineral point for the preprocessed geological map in sequence.
And S204, respectively and simultaneously carrying out the three methods on the preprocessed geological map.
The boundary whitening treatment provided by the embodiment of the invention specifically comprises the following steps: performing edge search on the geological map; performing primary coarsening processing on the boundary image; the original image and the boundary image are subtracted from each other, and the original image is subjected to whitening processing on all the boundaries.
Example 2
As shown in fig. 1 and fig. 4, the method for managing geological disaster information according to the embodiment of the present invention for analyzing extracted feature information of a geological image by controlling an information analysis program through a central processing unit includes:
s301, acquiring the characteristic information of the geological image to be analyzed, and acquiring the coding characteristic information and the processing node of the geological image to be analyzed according to the characteristic information.
S302, acquiring a processing path of the geological image according to the coding feature information, wherein the processing path comprises at least one processing node.
And S303, performing characteristic analysis by using an information analysis program based on the processing path of the geological image to obtain an analysis result of the geological image characteristic information to be analyzed.
The processing path for acquiring the geological image according to the coding feature information in step S302 provided by the embodiment of the present invention includes:
determining at least one target processing node corresponding to the coding characteristic information according to the corresponding relation between the coding characteristic information and the processing nodes;
and generating a processing path of the geological image according to the processing sequence of the at least one target processing node.
The processing path provided by the embodiment of the invention comprises the relevant information of the processing node, and the relevant information of the processing node comprises at least one of a client identifier of the processing node, provider information of the processing node and processing time.
Example 3
As shown in fig. 1, and as a preferred embodiment, as shown in fig. 5, a method for forecasting geological disaster information by using a geological disaster forecasting model through a forecasting program according to an embodiment of the present invention includes:
s401, establishing a geological disaster forecasting model based on neural network and multi-parameter information fusion through a forecasting program.
S402, establishing a relation between the geological disaster occurrence probability and the geological disaster grade.
And S403, performing data acquisition by using the multi-parameter geological disaster monitoring system, and inputting the acquired data into S401 and S402 to realize the forecasting of the geological disaster.
The process of establishing a geological disaster forecasting model based on neural network and multi-parameter information fusion through a forecasting program in step S401 provided by the embodiment of the present invention is as follows:
(a) training data arrangement and threshold setting;
(b) establishing a forecasting model based on a radial basis function neural network;
the forecasting model is divided into an input layer, a hidden layer and an output layer, the input of the forecasting model is multi-parameter and the occurrence probability of the corresponding geological disaster, and the weight parameters in the hidden layer are calculated through the given data of the input layer and the output layer, so that the following relation between the input and the output is obtained:
a{1}＝radbas(netprod(dist(net，IW{1,1}，p),net,b{1}))；
wherein, a is the occurrence probability of the output geological disaster, and p is the input training data to obtain net;
(c) substituting the relational expression obtained in the step (b) into a training model formula in an MAT L AB function library to obtain the following geological disaster forecasting model:
net＝newrb(p1,a1)；
the net is the obtained geological disaster forecast model, the a1 is the output of the occurrence probability of the current geological disaster, and the p1 is the input of real-time collected data.
In step (a) provided in the embodiment of the present invention, the training data sorting and the setting of the threshold include:
the method comprises the steps of respectively counting the relations between debris flow factors, landslide factors and hazard degrees of areas with geological disasters, taking each parameter factor as training data, then selecting the maximum range of a corresponding parameter measuring sensor, grading the number of points between 0 and the maximum range for installing the training data, determining the initial relation distribution of each parameter and the occurrence probability of the geological disasters, and dividing the disaster probability into four ranges of 0-5%, 5-20%, 20-40% and 40-90%, thereby determining the threshold value of each parameter.
The establishing of the relationship between the occurrence probability of the geological disaster and the grade of the geological disaster in step S402 provided by the embodiment of the present invention includes:
according to the loss condition, the disaster probability and the damage degree which is easy to generate caused by the sudden occurrence of the disaster in the geological disaster, the grade of the geological disaster is divided into four grades: particularly, the severe sudden disaster is four-level, the occurrence probability of the geological disaster is 40-90%, the red early warning is adopted, the severe sudden disaster is three-level, the occurrence probability of the geological disaster is 20-40%, the orange early warning is adopted, the large sudden disaster is two-level, the occurrence probability of the geological disaster is 5-20%, the yellow early warning is adopted, the general sudden disaster is one-level, the occurrence probability of the geological disaster is 0-5%, and the blue early warning is adopted.
The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, e.g., from one website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DS L) or wireless (e.g., infrared, wireless, microwave, etc.) means to another website site, computer, server, or data center via a solid state storage medium, such as a solid state storage medium, such as a DVD, a solid state disk, a solid state storage medium, a digital versatile disk, a digital video disk.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A geological disaster information management method is characterized by comprising the following steps:
acquiring geological image information through geological monitoring equipment; extracting the characteristics of the acquired geological image information through a characteristic extraction program: (1) extracting surface element information of the geological image through a feature extraction program, and sequentially carrying out boundary whitening treatment, color segmentation, communication body construction, small color spot elimination and white area restoration at the boundary on the geological map subjected to pretreatment;
(2) extracting line element information, including extracting region boundary line information and extracting non-region boundary line information;
(3) extracting point element information, namely generating a mineral point template, matching an image and positioning a mineral point sequentially on the preprocessed geological map;
(4) the three methods are methods for respectively and simultaneously carrying out the preprocessed geological maps;
step two, analyzing the extracted characteristic information of the geological image by a central processing unit control information analysis program: (I) acquiring characteristic information of a geological image to be analyzed, and acquiring coding characteristic information and processing nodes of the geological image to be analyzed according to the characteristic information;
(II) acquiring a processing path of the geological image according to the coding feature information, wherein the processing path comprises at least one processing node;
(III) carrying out characteristic analysis by using an information analysis program based on the processing path of the geological image to obtain an analysis result of the characteristic information of the geological image to be analyzed;
step three, editing the geological information and the analysis result through an editing program; updating the geological information through an updating program; releasing geological disaster information through a release program;
and fourthly, forecasting the geological disaster information by utilizing a geological disaster forecasting model through a forecasting program: 1) establishing a geological disaster forecasting model based on neural network and multi-parameter information fusion through a forecasting program;
2) establishing a relation between the occurrence probability of the geological disaster and the grade of the geological disaster;
3) carrying out data acquisition by using a multi-parameter geological disaster monitoring system, and inputting the acquired data into the step 1) and the step 2) to realize the forecast of geological disasters;
step five, storing the collected geological information, the extracted information, the analysis result, the updated information, the release information and the forecast information through a cloud database server;
step six, sending the geological disaster information data to the mobile terminal through the cloud database server, and performing remote control on the management system;
and seventhly, displaying the acquired geological information, extracting the information, analyzing the result, updating the information, releasing the information and forecasting the real-time data of the information through a display.
2. The geological disaster information management method according to claim 1, wherein in the first step, the boundary whitening process is specifically:
performing edge search on the geological map; performing primary coarsening processing on the boundary image; the original image and the boundary image are subtracted from each other, and the original image is subjected to whitening processing on all the boundaries.
3. The geological disaster information management method according to claim 1, wherein in the second step, the process of obtaining the geological image based on the coded feature information in the step (II) comprises:
determining at least one target processing node corresponding to the coding characteristic information according to the corresponding relation between the coding characteristic information and the processing nodes;
and generating a processing path of the geological image according to the processing sequence of the at least one target processing node.
4. A geological disaster information management method according to claim 3, wherein said processing path comprises information related to said processing node, said information related to said processing node comprising at least one of client identification of processing node, provider information of processing node, processing time.
5. The geological disaster information management method as claimed in claim 1, wherein in step four, the process of establishing a geological disaster forecast model based on neural network and multi-parameter information fusion through a forecast program in step 1) is:
(a) training data arrangement and threshold setting;
(b) establishing a forecasting model based on a radial basis function neural network;
the forecasting model is divided into an input layer, a hidden layer and an output layer, the input of the forecasting model is multi-parameter and the occurrence probability of the corresponding geological disaster, and the weight parameters in the hidden layer are calculated through the given data of the input layer and the output layer, so that the following relation between the input and the output is obtained:
a{1}＝radbas(netprod(dist(net，IW{1,1}，p),net,b{1}))；
wherein, a is the occurrence probability of the output geological disaster, and p is the input training data to obtain net;
(c) substituting the relational expression obtained in the step (b) into a training model formula in an MAT L AB function library to obtain the following geological disaster forecasting model:
net＝newrb(p1,a1)；
the net is the obtained geological disaster forecast model, the a1 is the output of the occurrence probability of the current geological disaster, and the p1 is the input of real-time collected data.
6. The geological disaster information management method according to claim 5, wherein the training data consolidation and threshold setting in step (a) comprise:
the method comprises the steps of respectively counting the relations between debris flow factors, landslide factors and hazard degrees of areas with geological disasters, taking each parameter factor as training data, then selecting the maximum range of a corresponding parameter measuring sensor, grading the number of points between 0 and the maximum range for installing the training data, determining the initial relation distribution of each parameter and the occurrence probability of the geological disasters, and dividing the disaster probability into four ranges of 0-5%, 5-20%, 20-40% and 40-90%, thereby determining the threshold value of each parameter.
7. The geological disaster information management method according to claim 1, wherein in step four, the establishing of the relationship between the occurrence probability of the geological disaster and the grade of the geological disaster in step 2) comprises:
according to the loss condition, the disaster probability and the damage degree which is easy to generate caused by the sudden occurrence of the disaster in the geological disaster, the grade of the geological disaster is divided into four grades: particularly, the severe sudden disaster is four-level, the occurrence probability of the geological disaster is 40-90%, the red early warning is adopted, the severe sudden disaster is three-level, the occurrence probability of the geological disaster is 20-40%, the orange early warning is adopted, the large sudden disaster is two-level, the occurrence probability of the geological disaster is 5-20%, the yellow early warning is adopted, the general sudden disaster is one-level, the occurrence probability of the geological disaster is 0-5%, and the blue early warning is adopted.
8. A geological disaster information management system to which the geological disaster information management method according to any one of claims 1 to 7 is applied, characterized by comprising:
the geological information acquisition module is connected with the central control module and is used for acquiring geological image information through geological monitoring equipment;
the image feature extraction module is connected with the central control module and used for extracting the features of the acquired geological image information through a feature extraction program;
the central control module is connected with the geological information acquisition module, the image feature extraction module, the feature information analysis module, the information editing module, the information updating module, the information publishing module, the geological disaster forecasting module, the data storage module, the terminal module and the display module and is used for controlling each module to normally work through the central processing unit;
the characteristic information analysis module is connected with the central control module and used for analyzing the extracted characteristic information of the geological image through an information analysis program;
the information editing module is connected with the central control module and is used for editing the geological information and the analysis result through an editing program;
the information updating module is connected with the central control module and used for updating the geological information through an updating program;
the information issuing module is connected with the central control module and used for issuing the geological disaster information through an issuing program;
the geological disaster forecasting module is connected with the central control module and used for forecasting geological disaster information by utilizing the geological disaster forecasting model through a forecasting program;
the data storage module is connected with the central control module and used for storing the collected geological information, the extracted information, the analysis result, the updated information, the issued information and the forecast information through the cloud database server;
the terminal module is connected with the central control module and used for sending the geological disaster information data to the mobile terminal through the cloud database server and performing remote control on the management system;
and the display module is connected with the central control module and used for displaying the acquired geological information, the extracted information, the analysis result, the updated information, the published information and the real-time data of the forecast information through the display.
9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for implementing a geological disaster information management method as claimed in any one of claims 1 to 7 when executed on an electronic device.
10. A computer readable storage medium storing instructions which, when executed on a computer, cause the computer to perform a geological disaster information management method as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010215157.1A CN111429471A (en) | 2020-03-24 | 2020-03-24 | Geological disaster information management system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010215157.1A CN111429471A (en) | 2020-03-24 | 2020-03-24 | Geological disaster information management system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111429471A true CN111429471A (en) | 2020-07-17 |
Family
ID=71549404
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010215157.1A Pending CN111429471A (en) | 2020-03-24 | 2020-03-24 | Geological disaster information management system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111429471A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111738439A (en) * | 2020-07-21 | 2020-10-02 | 电子科技大学 | Artificial intelligence processing method and processor supporting online learning |
| CN112464726A (en) * | 2020-10-30 | 2021-03-09 | 长威信息科技发展股份有限公司 | Disaster prevention and reduction early warning system based on satellite remote sensing big data |
| CN113178082A (en) * | 2021-04-14 | 2021-07-27 | 河北锐驰交通工程咨询有限公司 | Intelligent identification method and system for safety risks of expressway |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105931295A (en) * | 2016-07-13 | 2016-09-07 | 中国地质大学（北京） | Geological map thematic information extraction method |
| CN106128035A (en) * | 2016-06-30 | 2016-11-16 | 西安工程大学 | The geological disaster forecasting method merged based on neutral net and multi-parameter information |
| CN109214474A (en) * | 2017-06-30 | 2019-01-15 | 阿里巴巴集团控股有限公司 | Behavioural analysis, information coding risk analysis method and device based on information coding |
| CN110264672A (en) * | 2019-06-25 | 2019-09-20 | 广州市城市规划勘测设计研究院 | A kind of early warning system of geological disaster |
-
2020
- 2020-03-24 CN CN202010215157.1A patent/CN111429471A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106128035A (en) * | 2016-06-30 | 2016-11-16 | 西安工程大学 | The geological disaster forecasting method merged based on neutral net and multi-parameter information |
| CN105931295A (en) * | 2016-07-13 | 2016-09-07 | 中国地质大学（北京） | Geological map thematic information extraction method |
| CN109214474A (en) * | 2017-06-30 | 2019-01-15 | 阿里巴巴集团控股有限公司 | Behavioural analysis, information coding risk analysis method and device based on information coding |
| CN110264672A (en) * | 2019-06-25 | 2019-09-20 | 广州市城市规划勘测设计研究院 | A kind of early warning system of geological disaster |
Non-Patent Citations (1)
| Title |
|---|
| 陶志刚 等: "滑坡监测多源系统云服务平台架构及工程应用", 《岩石力学与工程学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111738439A (en) * | 2020-07-21 | 2020-10-02 | 电子科技大学 | Artificial intelligence processing method and processor supporting online learning |
| CN111738439B (en) * | 2020-07-21 | 2020-12-29 | 电子科技大学 | Artificial intelligence processing method and processor supporting online learning |
| CN112464726A (en) * | 2020-10-30 | 2021-03-09 | 长威信息科技发展股份有限公司 | Disaster prevention and reduction early warning system based on satellite remote sensing big data |
| CN113178082A (en) * | 2021-04-14 | 2021-07-27 | 河北锐驰交通工程咨询有限公司 | Intelligent identification method and system for safety risks of expressway |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Huang et al. | BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives | |
| Wang et al. | Machine learning-based regional scale intelligent modeling of building information for natural hazard risk management | |
| CN107829452B (en) | Deep foundation pit construction monitoring and early warning technology integrating multiple sensors and foundation SAR | |
| CN113611085B (en) | Monitoring and early warning method and system for geological disasters in geotechnical engineering | |
| CN111429471A (en) | Geological disaster information management system and method | |
| CN110858334A (en) | Road safety assessment method and device and road safety early warning system | |
| CN103491351A (en) | Intelligent video monitoring method for illegal buildings | |
| CN109615118A (en) | Based on big data hazards control Informatization Service integrated control system and method | |
| CN111241611B (en) | Method for assisting foundation pit implementation | |
| CN110927821A (en) | BIM + GIS-based advanced geological forecast information system for tunnel construction | |
| Horspool et al. | ShakeMapNZ: Informing post‐event decision making | |
| CN116486025A (en) | Urban geological data processing platform based on big data cloud computing technology | |
| CN114565313A (en) | Geological disaster hidden danger identification and analysis evaluation method and system | |
| CN114898212A (en) | High-resolution remote sensing image multi-terrain change information extraction method | |
| CN116994156B (en) | Landslide hidden danger comprehensive remote sensing identification method, system, equipment and medium | |
| Pradhan et al. | Optimized rule sets for automatic landslide characteristic detection in a highly vegetated forests | |
| Gao et al. | Exploring spatio-temporal correlation and complexity of safety monitoring data by complex networks | |
| CN117371949A (en) | Three-dimensional visual model-based power transmission line construction safety monitoring method and system | |
| CN112907567B (en) | SAR image ordered artificial structure extraction method based on spatial reasoning method | |
| CN112802307B (en) | Geological monitoring and early warning method and system for geological investigation | |
| Wen et al. | Hybrid optimized RF model of seismic resilience of buildings in mountainous region based on hyperparameter tuning and SMOTE | |
| CN112668477A (en) | High-risk area feature detection and identification method and intelligent identification system | |
| Zhou | Application of artificial intelligence in geography | |
| Firoozi et al. | Application of Machine Learning in Geotechnical Engineering for Risk Assessment | |
| Li et al. | Study on road damage assessment based on RS and GIS |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200717 |
|
| RJ01 | Rejection of invention patent application after publication |