CN108961688B - Geological disaster monitoring and early warning method under support of big data - Google Patents
Geological disaster monitoring and early warning method under support of big data Download PDFInfo
- Publication number
- CN108961688B CN108961688B CN201810771432.0A CN201810771432A CN108961688B CN 108961688 B CN108961688 B CN 108961688B CN 201810771432 A CN201810771432 A CN 201810771432A CN 108961688 B CN108961688 B CN 108961688B
- Authority
- CN
- China
- Prior art keywords
- geological disaster
- monitoring
- data
- disaster
- geological
- 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
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/10—Alarms for ensuring the safety of persons responsive to calamitous events, e.g. tornados or earthquakes
-
- 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"
-
- 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
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
Abstract
The invention provides a geological disaster monitoring and early warning method under the support of big data, which comprises the steps of generating a corresponding geological disaster monitoring model according to historical data of geological disaster points; carrying out similarity analysis on the monitored area and the geological disaster detection model, and setting the monitored area as a geological disaster hidden danger area if the similarity reaches a limit value; acquiring monitoring data of the geological disaster hidden danger area through real-time equipment; performing similarity analysis on each factor in the monitoring data and the data corresponding to the geological disaster monitoring model with the highest similarity to obtain a probability value of occurrence of a geological disaster; and if the probability value is greater than the limit value, carrying out real-time early warning on the geological disaster, and improving the accuracy of monitoring the disaster.
Description
Technical Field
The invention relates to a geological disaster monitoring and early warning method under the support of big data.
Background
Factors influencing geological disaster activities comprise weather, geography, geology and the like, are mutually interwoven, and natural and artificial factors are mutually superposed. The existing early warning model is determined by an expert in a mode of firstly assuming and then verifying, based on a statistical method, early warning criteria are established by coupling multiple factors such as geological environment change, rainfall parameters and the like, but the method does not fully play the role of big data, is controlled and influenced by factors such as selection of statistical samples, fineness of geological environment conditions, accurate matching of live rainfall data and the like to a certain extent, is difficult to update and perfect in real time, and a unified experience model cannot meet requirements on applicability and accuracy of geological disaster monitoring under different conditions.
Disclosure of Invention
The invention aims to provide a geological disaster monitoring and early warning method under the support of big data, and the accuracy of monitoring disasters is improved.
The invention is realized by the following steps: a geological disaster monitoring and early warning method under the support of big data comprises the following steps:
step 1, generating a corresponding geological disaster monitoring model according to historical data of geological disaster points;
step 2, carrying out similarity analysis on the monitored area and the geological disaster detection model, and setting the monitored area as a geological disaster hidden danger area if the similarity reaches a limit value;
4, performing similarity analysis on each data in the monitoring data and the data corresponding to the geological disaster monitoring model with the highest similarity to obtain a probability value of occurrence of geological disasters;
and 5, if the probability value is larger than the limit value, carrying out real-time early warning on the geological disaster.
Further, the step 1 is further specifically: historical disaster point data (disaster type, disaster level, disaster influence range), historical remote sensing image data, landforms, stratigraphic structures, vegetation coverage, climate, precipitation, water system distribution, dam construction and road construction.
Further, the step 4 is further specifically: performing similarity analysis on each data in the monitoring data and the data corresponding to the geological disaster monitoring model with the highest similarity, setting probability weight for each data, multiplying the value obtained by the similarity analysis by the corresponding probability weight, and adding the obtained values to obtain the probability value of occurrence of the geological disaster; the monitoring data includes: landform, stratum structure, vegetation coverage, water system distribution, remote sensing image data, terrain deformation data, meteorological data, dam construction and road construction.
The invention has the following advantages:
1) the disaster-causing inducement of the geological disaster is analyzed from the factors of nature, man-made, remote sensing and the like according to historical data, and the value of the multi-source historical big data is fully exerted.
2) By adopting an artificial intelligence technology and a continuous learning mode, experience is continuously accumulated, and multi-source data value is mined, so that the model is self-optimized, and the barrier that the experience model cannot be self-perfected is broken through.
3) The grid personalized geological disaster monitoring model is established, the influence of the traditional model on disaster monitoring, which cannot realize universality, is broken, and the accuracy of geological disaster monitoring and early warning in local areas can be effectively improved.
Drawings
The invention will be further described with reference to the following examples with reference to the accompanying drawings.
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a flow chart of an embodiment of the present invention.
Detailed Description
As shown in fig. 1, the invention relates to a geological disaster monitoring and early warning method supported by big data, which comprises the following steps:
step 1, generating a corresponding geological disaster monitoring model through a neural network learning algorithm according to the historical data of geological disasters; the historical data of the geological disaster and the relevant influence factor data comprise: historical disaster point data (disaster type, disaster level and disaster influence range), historical remote sensing image data, landforms, stratum structures, vegetation coverage, climate, precipitation, water system distribution, dam construction and road construction;
step 2, carrying out similarity analysis on the monitored area and the geological disaster detection model, and setting the monitored area as a geological disaster hidden danger area if the similarity reaches a limit value;
and 5, if the probability value is larger than the limit value, carrying out real-time early warning on the geological disaster.
One specific embodiment of the present invention:
in order to solve the limitation problem of an empirical geological disaster monitoring model, the invention introduces the idea of a grid personalized model, adopts an artificial intelligence technology, combines multi-source and multi-type big data based on historical geological disaster point data, and analyzes the cause of the geological disaster from natural and man-made factors. Further, a geological disaster monitoring model with grid individuation and self-improvement capacity is established, fine monitoring and early warning of geological disasters are finally achieved, and the position, type and occurrence probability of the geological disasters are predicted. The method can give full play to the advantages of big data, deeply analyze the disaster law hidden behind the data, and avoid incompleteness of data analysis; meanwhile, dynamic self-improvement and optimization of the model can be realized, and the influence on disaster monitoring caused by untimely model improvement is reduced; moreover, the grid personalized model can improve the monitoring accuracy of local geological disasters.
As shown in fig. 2, the present invention includes:
1) selecting China as a service area, dividing the service area into a plurality of grids according to the topographic variation characteristics of the service area, and using the grids as basic units established by a geological disaster monitoring model.
2) On the basis of the grid in the step (1), firstly, based on different types of geological disasters such as landslides, debris flows and the like, taking historical geological disaster point data as a sample, and positioning the place, time and frequency of occurrence of the historical geological disasters and disaster grades; analyzing various factors before, when and after the geological disasters of different levels and different frequencies occur, wherein the factors comprise natural factors such as landforms, stratum structures, vegetation coverage, climate and precipitation, and man-made factors causing geological disaster hidden dangers such as dam construction and road construction, analyzing the change conditions of factors such as spectrum and texture in the geological disaster occurrence process by combining historical remote sensing image data of a historical geological disaster occurrence area and an influence range thereof, comprehensively analyzing the change conditions of various data in the formation and occurrence process, excavating a disaster incentive, and evaluating the influence degree of different factors on the disaster occurrence; and finally, establishing a corresponding geological disaster monitoring model according to the influence factors and corresponding weights of geological disaster occurrence of different types, different levels and different frequencies, and finally forming a grid personalized geological disaster monitoring model library covering all the geological disaster types.
3) And (3) carrying out similarity analysis on the whole service area and the historical geological disaster occurrence area based on the relevant factors and the geological disaster model mentioned in the step (2), and judging the area with the potential hazard of the geological disaster if the similarity is higher.
4) Based on real-time monitoring equipment such as an unmanned aerial vehicle and a micro-variation radar, the real-time change conditions of the displacement deformation data and the meteorological data of the geological disaster hidden danger area positioned in the step (3) are monitored in a key mode, and real-time remote sensing data are processed and analyzed in time.
5) And (3) accessing the real-time monitoring data into the geological disaster monitoring model obtained in the step (2) in real time, comparing all the influence factors, and performing similarity calculation to obtain the probability of occurrence of the geological disaster, thereby realizing the real-time early warning of the geological disaster.
6) And (3) transmitting the real-time monitoring data and the geological disaster early warning data as sample data to the step (2) for continuous learning and analysis, further analyzing and predicting the situations that the ground disaster possibly occurs and the ground disaster actually does not occur, and further adjusting the geological disaster monitoring model to realize the self-improvement function of the model.
Although specific embodiments of the invention have been described above, it will be understood by those skilled in the art that the specific embodiments described are illustrative only and are not limiting upon the scope of the invention, and that equivalent modifications and variations can be made by those skilled in the art without departing from the spirit of the invention, which is to be limited only by the appended claims.
Claims (1)
1. A geological disaster monitoring and early warning method under the support of big data is characterized in that: the method comprises the following steps:
step 1, dividing a service area into a plurality of grids according to the topographic variation characteristics of the service area, and taking the grids as basic units established by a geological disaster monitoring model; generating a corresponding geological disaster monitoring model according to historical data of geological disaster points, and forming a grid personalized geological disaster monitoring model library covering all geological disaster types;
step 2, carrying out similarity analysis on the monitored area and the geological disaster detection model, and setting the monitored area as a geological disaster hidden danger area if the similarity reaches a limit value;
step 3, acquiring monitoring data of the geological disaster hidden danger area through real-time equipment;
step 4, performing similarity analysis on each factor in the monitoring data and a factor corresponding to the geological disaster monitoring model with the highest similarity, setting probability weight for each factor, multiplying a value obtained by the similarity analysis by the corresponding probability weight, and adding the obtained values to obtain a probability value of occurrence of a geological disaster; the monitoring data includes: disaster type, disaster level, topography, stratigraphic construction, vegetation coverage, climate, precipitation, dam construction and road construction;
and 5, if the probability value is larger than the limit value, carrying out real-time early warning on the geological disaster.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810771432.0A CN108961688B (en) | 2018-07-13 | 2018-07-13 | Geological disaster monitoring and early warning method under support of big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810771432.0A CN108961688B (en) | 2018-07-13 | 2018-07-13 | Geological disaster monitoring and early warning method under support of big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108961688A CN108961688A (en) | 2018-12-07 |
| CN108961688B true CN108961688B (en) | 2020-11-03 |
Family
ID=64483293
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810771432.0A Active CN108961688B (en) | 2018-07-13 | 2018-07-13 | Geological disaster monitoring and early warning method under support of big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108961688B (en) |
Families Citing this family (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109584515B (en) * | 2018-12-21 | 2021-12-28 | 武汉万集信息技术有限公司 | Mountain disaster early warning method and device and readable storage medium |
| CN110264058A (en) * | 2019-06-11 | 2019-09-20 | 深圳市燃气集团股份有限公司 | A kind of method for early warning and system of the geological disaster based on gas ductwork |
| CN110633858A (en) * | 2019-09-18 | 2019-12-31 | 国网湖南省电力有限公司 | Clustering early warning method and system for rainstorm geological disasters of power transmission line |
| CN111340012B (en) * | 2020-05-19 | 2020-09-15 | 北京数字绿土科技有限公司 | Geological disaster interpretation method and device and terminal equipment |
| CN111932832A (en) * | 2020-08-07 | 2020-11-13 | 西南交通大学 | Construction engineering environment disaster accident monitoring and early warning method |
| CN111950993A (en) * | 2020-09-03 | 2020-11-17 | 深圳市不动产评估中心(深圳市地质环境监测中心) | Geological disaster prevention and control full-flow management system, method and storage medium |
| CN112382043A (en) * | 2020-10-23 | 2021-02-19 | 杭州翔毅科技有限公司 | Disaster early warning method, device, storage medium and device based on satellite monitoring |
| CN112215439B (en) * | 2020-11-09 | 2021-05-14 | 广东新禾道信息科技有限公司 | Geological disaster emergency command data processing method and system based on GIS |
| CN112946240B (en) * | 2021-02-01 | 2022-08-02 | 北京科技大学 | Landslide geological disaster gene identification and prediction system |
| 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 |
| CN113139020B (en) * | 2021-04-22 | 2024-03-26 | 成都智汇数援科技有限公司 | Self-adaptive migration method of landslide monitoring and early warning model |
| CN113762090B (en) * | 2021-08-16 | 2024-03-08 | 国网浙江省电力有限公司湖州供电公司 | Disaster monitoring and early warning method for ultra-high voltage dense transmission channel |
| CN113538861B (en) * | 2021-09-16 | 2021-12-07 | 山东金科星机电股份有限公司 | Geological disaster information management system based on mineral geological exploration |
| CN114093129B (en) * | 2021-10-15 | 2023-07-21 | 重庆地质矿产研究院 | Mine geological environment intelligent monitoring and early warning method based on 5G intelligent communication |
| CN114676907B (en) * | 2022-01-17 | 2022-09-20 | 中国地质大学（北京） | Regional geological disaster early warning method and device, storage medium and equipment |
| CN114782831B (en) * | 2022-06-23 | 2022-11-01 | 广东孺子牛地理信息科技有限公司 | Dynamic integrated natural disaster remote sensing monitoring method and system and cloud platform |
| CN116306223B (en) * | 2023-01-09 | 2023-09-22 | 浪潮智慧科技有限公司 | Hydraulic engineering monitoring method, equipment and medium |
| CN116976837B (en) * | 2023-09-25 | 2024-01-30 | 中国铁塔股份有限公司吉林省分公司 | Ground disaster situation analysis method and system applied to data sharing |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101379039B1 (en) * | 2014-01-06 | 2014-03-31 | (주)종성테크 | Flooding forecast alarm system |
| CN104751602A (en) * | 2015-03-05 | 2015-07-01 | 北京交通大学 | Big data concept based railway geological disaster early warning system |
| CN104881583A (en) * | 2015-06-04 | 2015-09-02 | 唐晓松 | Multi-means, dynamic and whole-process landslide prewarning method |
| CN106297198A (en) * | 2016-10-13 | 2017-01-04 | 成都亚联科科技有限公司 | A kind of high-speed railway Geological Disaster Warning System based on big data |
| CN107221129A (en) * | 2017-07-20 | 2017-09-29 | 杨柳 | Early warning system on a kind of line for geological disaster and weather warning |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6504478B1 (en) * | 2001-11-27 | 2003-01-07 | J. Y. Richard Yen | Earth stratum flush monitoring method and a system thereof |
| CN102682573B (en) * | 2012-03-31 | 2016-08-03 | 上海海洋大学 | A kind of storm surge disaster early warning system based on time series analysis |
| CN105741498A (en) * | 2016-04-28 | 2016-07-06 | 成都理工大学 | Method and device for monitoring and performing early warning on geological hazards |
| CN105956981A (en) * | 2016-05-06 | 2016-09-21 | 西安工业大学 | Disaster information cloud computing system and disaster information cloud computing method |
| CN105956151B (en) * | 2016-05-13 | 2019-03-26 | 中国有色金属长沙勘察设计研究院有限公司 | Aid decision-making method, Tailings Dam monitoring method and system based on prediction scheme |
| CN107610421A (en) * | 2017-09-19 | 2018-01-19 | 合肥英泽信息科技有限公司 | A kind of geo-hazard early-warning analysis system and method |
| CN108154651A (en) * | 2018-01-29 | 2018-06-12 | 西安工程大学 | A kind of device and method for landslide disaster Monitoring and forecasting system in real-time |
-
2018
- 2018-07-13 CN CN201810771432.0A patent/CN108961688B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101379039B1 (en) * | 2014-01-06 | 2014-03-31 | (주)종성테크 | Flooding forecast alarm system |
| CN104751602A (en) * | 2015-03-05 | 2015-07-01 | 北京交通大学 | Big data concept based railway geological disaster early warning system |
| CN104881583A (en) * | 2015-06-04 | 2015-09-02 | 唐晓松 | Multi-means, dynamic and whole-process landslide prewarning method |
| CN106297198A (en) * | 2016-10-13 | 2017-01-04 | 成都亚联科科技有限公司 | A kind of high-speed railway Geological Disaster Warning System based on big data |
| CN107221129A (en) * | 2017-07-20 | 2017-09-29 | 杨柳 | Early warning system on a kind of line for geological disaster and weather warning |
Non-Patent Citations (1)
| Title |
|---|
| 甘肃省气象科学数据共享平台及其应用研究;陈学君;《中国博士学位论文全文数据库》;20091231;49-54 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108961688A (en) | 2018-12-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108961688B (en) | Geological disaster monitoring and early warning method under support of big data | |
| CN111047099B (en) | Regional torrential flood risk prediction method and system | |
| KR102242977B1 (en) | Early warning method and system for landslide integrated with weather forecasting information | |
| KR101788104B1 (en) | System and method for real-time early warning for extreme rainfall-induced landslides | |
| KR20190057013A (en) | System for managing detailed weather prediction information in real time and method to use for impact forecasting of heat-wave and tropical night using the system | |
| Martinotti et al. | Landslides, floods and sinkholes in a karst environment: the 1–6 September 2014 Gargano event, southern Italy | |
| CN109615118A (en) | Based on big data hazards control Informatization Service integrated control system and method | |
| CN109543870B (en) | Power transmission line tower lightning stroke early warning method based on neighborhood preserving embedding algorithm | |
| CN111144656A (en) | Disaster evaluation analysis method based on GIS | |
| CN108090285B (en) | Microclimate observation point distribution method for wind disaster monitoring of power transmission line in complex terrain | |
| Fanos et al. | A novel hybrid machine learning-based model for rockfall source identification in presence of other landslide types using LiDAR and GIS | |
| CN114201922A (en) | Dynamic landslide sensitivity prediction method and system based on InSAR technology | |
| Hong et al. | Effects of land cover changes induced by large physical disturbances on hydrological responses in Central Taiwan | |
| Houston et al. | Thunderstorm Observation by Radar (ThOR): An algorithm to develop a climatology of thunderstorms | |
| Rijal et al. | Detection of gullies in Fort Riley military installation using LiDAR derived high resolution DEM | |
| CN111639865A (en) | High-speed rail line meteorological disaster occurrence risk analysis method | |
| CN112132341B (en) | Flood risk prediction method based on rainstorm process | |
| Maurizio et al. | A multi temporal kernel density estimation approach for new triggered landslides forecasting and susceptibility assessment | |
| Ru-Hua et al. | Modeling the potential distribution of shallow-seated landslides using the weights of evidence method and a logistic regression model: a case study of the Sabae Area, Japan | |
| CN110390149B (en) | Strong earthquake region debris flow activity prediction method based on source aggregation quantity | |
| CN115600047B (en) | Small watershed surface average rainfall measurement and calculation method and system based on grid analysis | |
| CN116502748A (en) | Landslide space risk early warning decision mechanism research method integrating multiple influencing factors | |
| Farina et al. | Development of an early warning system for shallow landslide hazard in the Grasberg area, Indonesia | |
| KR102307898B1 (en) | Groundwater potential mapping using integrated ensemble of three bivariate statistical models with random froest and logistic tree models | |
| JP7375915B2 (en) | Analytical equipment, analytical methods and programs |
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 | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: Room 107, Building 16, E District, Fuzhou Software Park, 89 Software Avenue, Gulou District, Fuzhou City, Fujian Province Applicant after: Telliver Information Technology Co.,Ltd. Address before: Room 107, Building 16, E District, Fuzhou Software Park, 89 Software Avenue, Gulou District, Fuzhou City, Fujian Province Applicant before: FUJIAN TELEWARE INFORMATION TECHNOLOGY Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |