CN111866453A - Barrier dam emergency monitoring and early warning method based on artificial intelligence and BIM - Google Patents
Barrier dam emergency monitoring and early warning method based on artificial intelligence and BIM Download PDFInfo
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Abstract
The invention discloses an artificial intelligence and BIM-based emergency monitoring and early warning method for a damming dam, which utilizes a liquid level meter, an inclination angle sensor and an artificial intelligence-based video monitoring device to carry out real-time monitoring; the remote sensing unmanned aerial vehicle directionally acquires data by oblique photography and laser ranging; combining the data collected by the former with the data collected by InSAR and GNSS and guiding the data into a GIS + BIM data processing platform; continuously training the collected data in advance, carrying out contrastive analysis on the model after deep learning, deriving a visual actual model, evaluating the disaster grade after dam break, positioning and displaying the three-dimensional position of the dam defect, simulating the submergence range after dam break, and facilitating quick emergency response and subsequent control of the dam. According to the invention, through the integrated monitoring network of sky, air and ground, the automation, visualization, accuracy and integrated monitoring are realized, the guarantee is provided for the monitoring and early warning of the barrage dam, and the manpower and material resources are saved.
Description
Technical Field
The invention relates to an emergency disposal monitoring method for a damming dam formed by landslide, in particular to an emergency monitoring and early warning method for the damming dam based on artificial intelligence and Building Information Modeling (BIM).
Background
Large landslide often forms a damming dam (lake), so that not only are great economic losses, casualties and environmental damages caused, but also great hidden dangers are possibly caused to a downstream cascade reservoir, and further the occurrence of downstream cascade collapse is caused.
Once the dangerous case of the barrage dam occurs, various monitoring methods and equipment are combined to collect relevant data in a short time, the cause of the disaster is analyzed, and emergency disposal measures for avoiding the disaster and emergency plans for personnel evacuation and rescue support after the disaster occur are formulated.
However, due to the complex formation mechanism and process of the landslide and damming dam, the emergency treatment, monitoring and early warning after the landslide and damming dam is in a dangerous situation becomes very difficult. On one hand, because the emergency disposal monitoring and early warning of the damming dam needs to invest a large amount of manpower and material resources, the government financial expenditure is greatly increased; on the other hand, the monitoring and early warning means is relatively single, the field arrangement is difficult, the requirements of platformization and systematization are not met, the efficiency is low, and meanwhile, the monitoring accuracy is difficult to guarantee, so that the monitoring and early warning of the current emergency disposal of the barrage dam is still in a primary stage.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides an artificial intelligence and BIM-based emergency monitoring and early warning method for a damming dam, which aims to solve the technical problems that the existing damming dam emergency disposal monitoring and early warning and field monitoring arrangement consumes a large amount of material and labor, is low in efficiency and unreliable in monitoring result.
The technical scheme is as follows: the invention relates to an artificial intelligence and BIM-based emergency monitoring and early warning method for a damming dam, which comprises the following steps:
(1) monitoring the damming dam in real time by using a liquid level meter, an inclination wireless sensor and an artificial intelligence-based video monitoring device; collecting pressure borne by the dam body, horizontal angle data of the dam body and video information sent by a video monitoring device, and outputting the data;
(2) oblique photography and laser ranging are used for directionally acquiring data; shooting from a vertical angle and different inclination angles by oblique photography, and carrying out manual or automatic processing on the obtained image data to obtain three-dimensional model data; measuring the linear distance between the monitoring camera and the damming dam through the laser ranging;
(3) combining the collected data with data collected by InSAR and GNSS and importing the data into a Geographic Information System (GIS) (geographic Information System) and a BIM data processing platform; the GIS data platform analyzes and processes spatial information, maps and analyzes phenomena and events existing on the earth, and integrates the visualization effect and the geographic analysis function of a GIS technical map with general database operation, such as query and statistical analysis, and focuses on overall data management; BIM is a digital expression of physical and functional characteristics of an engineering project, a BIM database is dynamically changed, and is continuously updated, enriched and enriched in the application process, and the precise expression of local monomers is emphasized; the GIS and the BIM data platform are combined, and the organic fusion of the spatial geographic data can be realized. The GIS and BIM data processing platform converts the imported data to generate vector data which can be displayed in the system.
(4) And (3) continuously training the collected data in advance, carrying out contrastive analysis on the data and the deeply-learned model, deriving a visual actual model, and applying the visual actual model to assessment of disaster grade after dam break, positioning and displaying of dam defect three-dimensional positions and simulation of submergence range after dam break.
In the step (1), the liquid level meter and the inclination wireless sensor are arranged in a dam body of a to-be-monitored barrage dam, collected data and video information are sent back to the data processing center through a wireless sensor network, and the data processing center gives early warning feedback if abnormal data are found.
And extracting each frame of picture of the video by using the video information sent by the video monitoring device, inputting the frame of picture into a training model based on the damming monitoring after deep learning, grading the credibility of the monitoring result, and finally outputting the damming monitoring data.
In the step (3), continuously training the collected data in advance, carrying out comparative analysis on the deeply-learned model, and deriving a visual actual model; and processing the monitored data through a GIS and a BIM data processing platform to obtain indexes including the dam height, the dam width and the reservoir capacity of the damming dam as stability evaluation factors of the damming dam, quantizing and analyzing the stability evaluation factors of the damming dam by combining a fuzzy grey clustering algorithm, dividing danger grades for the damming dam, automatically evaluating and judging disaster grades, and making an emergency response.
Has the advantages that: compared with the prior art, the invention has the following advantages:
(1) the method is based on the GIS + BIM technology and real-time monitoring data, combines a fuzzy gray scale clustering algorithm, automatically evaluates and judges the disaster grade, quickly acquires the three-dimensional defect position and the surrounding environment related information of the damming dam on a GIS + BIM system platform, and provides decision support for the damming dam emergency response scheme.
(3) GIS focuses on overall data management, BIM focuses on fine expression of local monomers, and the GIS and the BIM are combined to realize organic fusion of spatial geographic data. Through establishing the integrated monitoring network system of sky + ground, the monitoring network system that remote sensing + unmanned aerial vehicle + artificial intelligence video monitoring, liquid level, inclination wireless sensor constitute promptly reaches each other of each item technique and complements each other, has improved damming dam monitoring early warning reliability and high efficiency.
(4) The invention realizes automatic monitoring, visual monitoring, accuracy monitoring and integrated monitoring.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a flow chart of video monitoring based on artificial intelligence according to the present invention.
Detailed Description
As shown in fig. 1, the emergency monitoring and early warning method for the damming dam based on artificial intelligence and BIM of the present invention includes the following steps:
(1) Monitoring the damming dam in real time by using a liquid level meter, an inclination wireless sensor and an artificial intelligence-based video monitoring device; the method comprises the following steps of placing a liquid level meter and an inclination angle sensor in a dam body of a damming dam to be monitored, collecting pressure borne by the dam body, horizontal angle data of the dam body and video information sent by a plurality of fixed-point unattended video monitoring points, and outputting the data; the acquired data is sent back to the data processing center through the wireless sensor network, and the data processing center links the corresponding part to give early warning feedback when finding abnormal data;
(2) the remote sensing unmanned aerial vehicle is used for acquiring data in an oblique photography and laser ranging orientation mode, and meanwhile, the remote sensing unmanned aerial vehicle is used as a mobile node in a wireless sensing network, and the communication range is expanded, the communication quality is improved, and the data transmission efficiency is improved through cooperative cooperation of an unmanned aerial vehicle cluster;
(3) and the data collected by the former is combined with the data collected by InSAR and GNSS and is led into a GIS + BIM data processing platform.
(4) Continuously training the collected data in advance, carrying out contrastive analysis on the data and a deeply learned model, deriving a visual actual BIM model, and applying the visual actual BIM model to assessment of disaster grade after dam break, positioning and displaying of dam defect three-dimensional positions and simulation of submergence range after dam break; based on the GIS + BIM technology and real-time monitoring data, the danger grade is divided by combining a fuzzy grey clustering algorithm, automatic evaluation and judgment are carried out on the disaster grade, and quick emergency response is carried out.
(5) Emergency evacuation and later treatment are carried out.
In the step (1), the artificial intelligent video monitoring device comprises an acquisition module, a monitoring module and an output module; and extracting each frame of picture of the video by using the video information sent by the video monitoring device, inputting the frame of picture into a training model based on the damming monitoring after deep learning, grading the credibility of the monitoring result, and finally outputting the damming monitoring data.
In the step (2), oblique photography is carried out from 1 vertical angle and four different oblique angles, and the obtained image data is processed manually or automatically to obtain three-dimensional model data; and measuring the linear distance between the monitoring camera and the damming dam through laser ranging.
In the step (3), continuously training the collected data in advance, carrying out comparative analysis on the deeply-learned model, and deriving a visual actual model; the method comprises the steps that monitored data are processed through a GIS and a BIM data processing platform, the GIS and the BIM data processing platform convert the imported data to generate vector data which can be displayed in a system, indexes including dam height, dam width and reservoir capacity of the damming dam are obtained and serve as stability evaluation factors of the damming dam, fuzzy grey clustering algorithm is combined, the stability evaluation factors of the damming dam are quantified and analyzed, danger grades are divided for the damming dam, automatic evaluation and judgment are conducted on the disaster grades, and emergency response is conducted.
Claims (7)
1. An emergency monitoring and early warning method for a damming dam based on artificial intelligence and BIM is characterized by comprising the following steps: the method comprises the following steps:
(1) monitoring the damming dam in real time by using a liquid level meter, an inclination wireless sensor and an artificial intelligence-based video monitoring device; collecting pressure borne by the dam body, horizontal angle data of the dam body and video information sent by a video monitoring device, and outputting the data;
(2) oblique photography and laser ranging are used for directionally acquiring data;
(3) the collected data is combined with data collected by InSAR and GNSS and is guided into a GIS and BIM data processing platform;
(4) and (3) continuously training the collected data in advance, carrying out contrastive analysis on the data and the deeply-learned model, deriving a visual actual model, and applying the visual actual model to assessment of disaster grade after dam break, positioning and displaying of dam defect three-dimensional positions and simulation of submergence range after dam break.
2. The dam emergency monitoring and early warning method based on artificial intelligence and BIM as claimed in claim 1, wherein: in the step (1), the liquid level meter and the inclination wireless sensor are arranged in a dam body of a to-be-monitored barrage dam, collected data and video information are sent back to the data processing center through a wireless sensor network, and the data processing center gives early warning feedback if abnormal data are found.
3. The dam emergency monitoring and early warning method based on artificial intelligence and BIM as claimed in claim 1, wherein: and extracting each frame of picture of the video by using the video information sent by the video monitoring device, inputting the frame of picture into a training model based on the damming dam monitoring after deep learning, grading the credibility of the monitoring result, and finally outputting the damming dam monitoring data.
4. The dam emergency monitoring and early warning method based on artificial intelligence and BIM as claimed in claim 1, wherein: and shooting from a vertical angle and different inclination angles by oblique photography, and carrying out manual or automatic processing on the obtained image data to obtain three-dimensional model data.
5. The dam emergency monitoring and early warning method based on artificial intelligence and BIM as claimed in claim 1, wherein: and measuring the linear distance between the monitoring camera and the damming dam through the laser ranging.
6. The dam emergency monitoring and early warning method based on artificial intelligence and BIM as claimed in claim 1, wherein: and (3) continuously training the collected data in advance, carrying out comparative analysis on the deeply-learned model, and deriving a visual actual model.
7. The artificial intelligence and BIM based emergency monitoring and early warning method for the barrage as claimed in any one of claims 1 to 6, wherein: and dividing the monitored data into danger grades by combining a fuzzy grey clustering algorithm based on a GIS and a BIM method, automatically evaluating and judging the disaster grades, and making an emergency response.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112669571A (en) * | 2020-12-16 | 2021-04-16 | 中国地质大学（北京） | Real-time landslide prediction early warning system based on three-dimensional GIS |
| CN112882446A (en) * | 2021-01-12 | 2021-06-01 | 中国十七冶集团有限公司 | BIM platform-based large-scale space steel structure visual construction monitoring system |
| CN116385680A (en) * | 2023-03-30 | 2023-07-04 | 宁波市水利水电规划设计研究院有限公司 | Three-dimensional immersive dam safety monitoring method and system based on UE technology |
| CN116385690A (en) * | 2023-06-05 | 2023-07-04 | 四川云控交通科技有限责任公司 | BIM model-based three-dimensional operation and maintenance management and control platform and management and control method thereof |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112669571A (en) * | 2020-12-16 | 2021-04-16 | 中国地质大学（北京） | Real-time landslide prediction early warning system based on three-dimensional GIS |
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Application publication date: 20201030 |