CN111915128B - Post-disaster evaluation and rescue auxiliary system for secondary landslide induced by earthquake - Google Patents
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Abstract
The invention discloses a post-earthquake-induced secondary landslide disaster assessment and rescue auxiliary system, which comprises a special unmanned aerial vehicle construction module for constructing a post-earthquake landslide disaster detection and identification special unmanned aerial vehicle, a post-earthquake landslide detection planning and preparation module for completing preparation work before disaster detection and identification, a post-disaster environment information acquisition module for acquiring on-site landslide disaster images and position information coordinates, a Beidou navigation positioning module for assisting the three modules in the aspects of navigation system construction, cruising route setting and information acquisition and transmission, a landslide disaster analysis decision module for analyzing relevant attribute parameters such as post-earthquake secondary landslide disaster influence area, trailing edge height and stability, and a post-earthquake rescue planning and evaluation module for planning a rescue route, making a rescue scheme and evaluating disaster influence degree. The method can meet the accuracy, instantaneity and high efficiency in the intelligent identification function requirement of the secondary landslide hazard after earthquake.
Description
Technical Field
The invention belongs to the field of intelligent disaster relief, and particularly relates to an auxiliary system for post-disaster evaluation and rescue of secondary landslide induced by earthquake.
Background
The earthquake secondary landslide disaster is a large-scale landslide phenomenon caused by the occurrence of an earthquake, and the landslide disaster can be generated immediately along with the occurrence of the earthquake or in a period of time after the earthquake. The secondary landslide of the earthquake has the characteristics of large scale, high sliding speed, long sliding distance and wide disaster-affected range. Earthquake-induced landslide disasters are serious, and sometimes the loss caused by secondary landslide disasters is larger than the loss caused by earthquakes directly. The land area of China is wide, the mountain area is many, and the secondary landslide performance induced by earthquake is also greatly different in different areas due to geologic structure difference and other reasons. Earthquake secondary landslide rescue is a very difficult task all the time, and the difficulty of such disaster relief projects is time cost, and life and property of trapped people are threatened if an effective rescue scheme cannot be formulated in a short time. The whole overview of the disaster area is quickly and real-timely known, and the method has non-negligible help for making rescue routes and rescue schemes.
At present, the implementation of earthquake-induced secondary landslide disaster rescue mostly adopts entering sites and making rescue schemes by field investigation. When encountering some emergency, the method lacks coping ability, for example, the rescue route is blocked by a secondary landslide disaster, and the rescue route is temporarily changed, so that the rescue time is greatly consumed; when entering a disaster site to survey and make a rescue scheme, more casualties are easily caused by the generation of secondary landslide; when the unmanned aerial vehicle is used for reconnaissance of scene conditions, shooting video streams or manually observing shooting pictures are often adopted to screen landslide scenes in disaster areas, so that manpower and material resources are consumed, and the rescue requirements of intelligent detection and positioning cannot be met.
Disclosure of Invention
The invention aims to provide an earthquake-induced secondary landslide disaster post-evaluation and rescue auxiliary system, different detection models are set for earthquake secondary landslide disasters in different areas, an artificial intelligence medium-depth convolution neural network image recognition technology is carried on an unmanned plane, the function of intelligently recognizing landslide disasters liberates manpower, the function of quickly recognizing and positioning ensures that rescue forces quickly master the overall situation of disaster areas and plan correct routes to enter sites, and real-time analysis of site landslide images provides a guarantee for establishment of rescue schemes.
The technical aim of the invention is realized by the following scheme:
a post-earthquake-induced secondary landslide hazard assessment and rescue assistance system comprising:
the special unmanned aerial vehicle building module is used for building the special unmanned aerial vehicle for detecting and identifying landslide disasters after earthquake;
the post-earthquake landslide detection planning and raising module is used for completing various preparation works required before disaster detection and identification for the unmanned aerial vehicle manufactured by the unmanned aerial vehicle building module special for detection;
the post-disaster environment information acquisition module acquires an on-site landslide disaster image and position information coordinates by using the unmanned aerial vehicle set by the post-earthquake landslide detection and preparation module;
the Beidou navigation positioning module is used for assisting in detecting the special unmanned aerial vehicle building module, the post-earthquake landslide detection planning and raising module and the post-disaster environment information acquisition module in the aspects of navigation system building, cruising route setting and information acquisition and transmission;
the landslide disaster analysis decision module is used for analyzing related attribute parameters of landslide influence area, trailing edge height and stability of the post-earthquake secondary landslide disaster by combining the special unmanned aerial vehicle building module for detection, the post-earthquake landslide detection planning and raising module, the post-disaster environment information acquisition module and the Beidou navigation positioning module;
the post-earthquake rescue planning and evaluating module is used for providing constructive opinions for post-disaster rescue by combining the landslide disaster position and related attribute parameters obtained by the landslide disaster analysis and decision-making module.
The invention is further improved in that the detection special unmanned aerial vehicle is provided with a raspberry group microcomputer, beidou navigation positioning equipment, a wide-angle camera and a Beidou information transmission device; the special unmanned aerial vehicle detection building module divides homeland into four areas according to landslide disaster prone or multiple areas, and loads a cascade depth convolution neural network landslide disaster detection model corresponding to each area into a raspberry group microcomputer.
The invention further improves that the cascade deep convolutional neural network landslide disaster detection model utilizes a convolutional layer and a maximum pooling layer to build a deep convolutional neural network with different tertiary structures, the three-stage deep convolutional neural networks are connected in series, and a field shooting image to be detected is preprocessed and an image pyramid is generated and is input into the cascade deep convolutional neural network model for image recognition.
The invention is further improved in that the post-earthquake landslide detection planning and raising module selects a detection disaster relief area and a cruising route through the earthquake middle position and the earthquake grade, sets relevant flight parameters of the special unmanned aerial vehicle for detection, and sets a cascade depth convolution neural network landslide disaster detection model corresponding to the area as an execution algorithm of the raspberry group microcomputer according to the position of the detection rescue area.
The post-disaster environment information acquisition module comprises an image shooting unit for shooting a scene image, a landslide disaster identification unit for judging whether the acquired image is a landslide disaster, a position coordinate acquisition unit for acquiring position coordinate information of a secondary landslide disaster, and an information transmission unit for packaging and transmitting the scene landslide disaster image and the position coordinate information to the landslide disaster analysis decision module.
The invention is further improved in that the Beidou navigation positioning module comprehensively assists in detecting the unmanned aerial vehicle equipment building module, the post-earthquake landslide detection planning and raising module and the post-disaster environment information acquisition module, and the accurate positioning and real-time transmission functions of the Beidou navigation positioning module realize unmanned aerial vehicle cruise coordinate path presetting and position information coordinate acquisition.
The landslide disaster analysis decision module comprises an information receiving unit for receiving and decoding the packed data, a disaster display unit for comparing and displaying the on-site landslide disaster image with the remote sensing satellite image, a disaster analysis unit for analyzing and estimating landslide image area, trailing edge height and stability parameters, and a database unit for storing the on-site landslide disaster image and position coordinate information.
The invention further improves that the post-earthquake rescue planning and evaluating module comprises a rescue route planning unit for planning to enter a disaster site, a rescue scheme making unit for making a reasonable rescue scheme and a disaster influence degree evaluating unit for evaluating disaster grades and making subsequent work.
Compared with the prior art, the invention has at least the following advantages:
1. the deep convolutional neural network image recognition technology in the artificial intelligence is carried on the unmanned aerial vehicle, so that intelligent recognition is realized, the recognition accuracy is improved, and part of manpower is liberated;
2. the unmanned aerial vehicle is used for detection and identification, so that the overall disaster situation of the disaster area can be mastered in a short time, the rescue execution time is shortened, and the coping capability of emergency is improved to a certain extent;
3. the efficiency of establishing a reasonable rescue scheme is improved, and the more reasonable rescue scheme is established by carrying out data analysis and comparison on disaster sites.
4. The carried detection model has the advantages of regional pertinence, intelligent detection and the like, and the unmanned aerial vehicle is used as a detection device, so that the efficiency is high, the danger is low, and the accuracy, the instantaneity and the high efficiency in the function requirements of landslide disaster identification after earthquake disaster are met.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic diagram of a cascade deep convolutional neural network landslide detection model.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The present embodiment is merely illustrative of the present invention and is not intended to limit the present invention, and those skilled in the art, after having read the present specification, may make modifications to the present embodiment without creative contribution as required, but are protected by patent laws within the scope of the appended claims.
According to the post-earthquake-induced secondary landslide disaster assessment and rescue auxiliary system shown in fig. 1, the post-earthquake-induced secondary landslide disaster assessment and rescue auxiliary system comprises a special detection unmanned aerial vehicle construction module for constructing a post-earthquake landslide disaster detection and identification special unmanned aerial vehicle, a post-earthquake landslide detection planning preparation module for completing various preparation works required before disaster detection and identification, a post-disaster environment information acquisition module for acquiring on-site landslide disaster images and position information coordinates, a Beidou navigation positioning module for assisting the special detection unmanned aerial vehicle construction module, the post-earthquake landslide detection planning preparation module and the post-disaster environment information acquisition module, and a landslide disaster analysis decision module for analyzing relevant attribute parameters of the post-earthquake secondary landslide disaster, and a post-earthquake rescue planning and assessment module for providing constructive comments for post-earthquake rescue.
The unmanned aerial vehicle building module special for detection comprises the following steps:
step S1, dividing the homeland into four areas according to areas where landslide disasters are easy to occur or more, loading a cascade depth convolutional neural network landslide hazard detection model corresponding to each area into a raspberry group microcomputer, and loading the raspberry group microcomputer on an unmanned plane;
step S2, beidou navigation positioning equipment capable of being positioned accurately is mounted on the unmanned aerial vehicle and used for acquiring position information of the unmanned aerial vehicle in real time;
s3, carrying a wide-angle camera on the unmanned aerial vehicle for shooting and collecting disaster area environment images after earthquake;
step S4, mounting a Beidou information transmission device on the unmanned aerial vehicle, and packaging and transmitting data, images, information and the like processed by the raspberry group microcomputer;
step S5, connecting the wide-angle camera, the raspberry group microcomputer, the Beidou navigation positioning device and the Beidou information transmission device to form a transmission path;
as shown in fig. 2, the cascade deep convolutional neural network landslide detection model includes the following steps:
s1, performing image preprocessing on an image to be detected; the image preprocessing is characterized in that whether the size of an input image to be detected is qualified or not is judged, the image size is standardized through image clipping and scaling, and finally Gaussian filtering processing is utilized to ensure that the image is smoother and unnecessary noise is eliminated;
s2, generating an image pyramid of the image to be detected by an image pyramid module from the image to be detected obtained after the image preprocessing; wherein the image pyramid module is characterized by combining downsampling and gaussian filtering operations to construct a three-layer image pyramid;
s3, extracting a third layer of image to be detected from the image pyramid to be detected, and inputting the third layer of image to be detected into a first-stage depth convolution neural network landslide detection model to perform image recognition; if the image is judged not to be the landslide image, ending the multi-layer cascade detection model and outputting a result; if the image is judged to be the landslide image, inputting the third layer image to be detected which is judged to be the landslide image into an image pyramid for level conversion, and extracting a second layer image of the image to be detected from the image pyramid; the first-stage deep convolutional neural network landslide detection model consists of a convolutional layer and a maximum pooling layer;
s4, inputting a second layer of to-be-detected image into a second level depth convolution neural network landslide detection model to perform image recognition; if the image is judged not to be the landslide image, ending the multi-layer cascade detection model and outputting a result; if the image is judged to be the landslide image, inputting a second layer of image to be detected which is judged to be the landslide image into an image pyramid for level conversion, and extracting a first layer of image of the image to be detected from the image pyramid; the second-stage deep convolutional neural network landslide detection model consists of a convolutional layer and a maximum pooling layer;
s5, inputting the first layer of image to be detected into a third-level deep convolutional neural network landslide detection model, and performing image recognition; if the image is judged not to be the landslide image, ending the multi-layer cascade detection model and outputting a result; and if the image is judged to be the landslide image, outputting a result. The third-stage deep convolutional neural network landslide detection model consists of a convolutional layer and a maximum pooling layer;
the post-earthquake landslide detection planning and raising module comprises the following steps:
s1, defining a position in the earthquake and a range covered by the earthquake disaster, selecting a mountain area in the earthquake disaster range as a detection rescue area, setting a cruising route along a road as much as possible, setting Beidou navigation coordinates of a flight scanning path, and setting unmanned aerial vehicle to fly back and forth for shooting;
step S2, setting the flying height of the unmanned aerial vehicle according to the average mountain height of the cruising mountain area, and setting the flying speed and the interval shooting time according to the path so as to ensure that an image is shot every 20 meters;
s3, selecting a corresponding deep convolutional neural network landslide detection model in a corresponding region according to a landslide disaster prone or frequent region where a set cruising route is located, and taking the model as an execution algorithm of a raspberry group microcomputer;
the post-disaster environment information acquisition module comprises: the system comprises an image shooting unit, a landslide hazard identification unit, a position coordinate acquisition unit and an information transmission unit;
the image shooting unit shoots images of the lower mountain body at intervals of 20 meters by using a wide-angle camera carried by the unmanned aerial vehicle, and the panoramic coverage of a detection area can be ensured by cruising back and forth;
the landslide hazard identification unit transmits the shot image from the wide-angle camera to the raspberry-set microcomputer, a deep convolution neural network landslide hazard detection model is executed, and the image identified as the landslide hazard is transmitted to the information transmission unit after image preprocessing and identification of the three-level detection model; deleting images which are not landslide disasters;
the position coordinate acquisition unit triggers Beidou navigation positioning equipment when identifying landslide hazard images, and transmits position coordinate information of the current unmanned aerial vehicle to the information transmission unit;
the information transmission unit packages the landslide disaster image obtained by identification and the position coordinate information of the unmanned aerial vehicle, and transmits data to the landslide disaster analysis decision module through the Beidou information transmission device;
the system comprises a Beidou navigation positioning module, a special unmanned aerial vehicle building module for omnibearing auxiliary detection, a post-earthquake landslide detection planning and raising module and a post-disaster environment information acquisition module, wherein the Beidou navigation positioning module is used for accurately positioning and transmitting the information to a system in real time by relying on the Beidou navigation positioning module from the path preset of the cruise coordinates of the unmanned aerial vehicle to the acquisition of the position information coordinates of the unmanned aerial vehicle.
The landslide disaster analysis decision module comprises an information receiving unit, a disaster display unit, a disaster analysis unit and a database unit;
the information receiving unit receives data packaged and transmitted by the unmanned aerial vehicle through the wireless communication device, and decodes the data into an on-site landslide disaster image and position information coordinates;
the disaster display unit presents the scene landslide disaster image on a computer of a monitoring center through a Web end for analysis by staff; converting the site landslide disaster position information coordinates into remote sensing satellite image coordinates, and calling a remote sensing satellite image of a corresponding position shot in the latest time;
the disaster analysis unit compares an on-site landslide disaster image shot by the unmanned aerial vehicle with an on-site remote sensing satellite image before landslide does not occur, and draws out the general shape of the on-site landslide on a remote sensing satellite image according to parameters such as the relative distance of each characteristic point of the image shot by the unmanned aerial vehicle and the like according to the flight height of the unmanned aerial vehicle, estimates parameters such as the landslide influence area, the trailing edge height and the stability and the like, and takes the attribute parameters as one of guiding data of rescue planning;
the database unit reserves the on-site landslide disaster image and the corresponding position information coordinates thereof in the database for use in optimizing the landslide disaster detection model of the deep convolutional neural network;
the post-earthquake rescue planning evaluation module comprises a rescue route planning unit, a rescue scheme making unit and a disaster influence degree evaluation unit;
the rescue route planning unit marks the position of a secondary landslide disaster and the estimated landslide influence area in the range of the earthquake disaster on a remote sensing satellite map, selects roads which are not covered by the landslide disaster as a rescue main route, uses roads which are covered by the landslide disaster as rescue auxiliary routes, and plans rescue teams, rescue materials and the like to gradually approach the inside of a disaster-stricken mountain area along the routes with less safety obstruction;
the rescue scheme making unit expert makes reasonable rescue scheme by carrying out terrain analysis on the scene landslide disaster image and the remote sensing satellite image and combining the condition of the secondary landslide disaster on the residential houses of people and carrying out case comparison analysis on the similar condition in the past so as to ensure the life and property safety of people;
the disaster influence degree evaluation unit classifies the influence degree in the range according to the number of secondary landslide disasters after earthquake, the influence area of the disasters, the loss caused to economic property and the like, and delimits safe stopping places in the disaster area range for subsequent input of rescue manpower and material resources as reference advice.
Claims (4)
1. A post-earthquake-induced secondary landslide hazard assessment and rescue assistance system, comprising:
the special unmanned aerial vehicle building module is used for building the special unmanned aerial vehicle for detecting and identifying landslide disasters after earthquake; the detection special unmanned aerial vehicle is provided with a raspberry group microcomputer, beidou navigation positioning equipment, a wide-angle camera and a Beidou information transmission device; the special unmanned aerial vehicle detection building module divides homeland into four areas according to landslide disaster prone or multiple areas, and loads a cascade depth convolution neural network landslide disaster detection model corresponding to each area into a raspberry-style microcomputer; the cascade deep convolutional neural network landslide hazard detection model utilizes a convolutional layer and a maximum pooling layer to build deep convolutional neural networks with different tertiary structures, the three-level deep convolutional neural networks are connected in series, and a field shooting image to be detected is preprocessed and an image pyramid is generated and is input into the cascade deep convolutional neural network model for image recognition;
the post-earthquake landslide detection planning and raising module is used for completing various preparation works required before disaster detection and identification for the unmanned aerial vehicle manufactured by the unmanned aerial vehicle building module special for detection; the post-earthquake landslide detection planning and raising module selects a detection disaster relief area and a cruising route through the earthquake center position and the earthquake grade, sets relevant flight parameters of the special detection unmanned aerial vehicle, and sets a cascade depth convolution neural network landslide disaster detection model corresponding to the area as an execution algorithm of a raspberry group microcomputer according to the position of the detection rescue area;
the post-disaster environment information acquisition module acquires an on-site landslide disaster image and position information coordinates by using the unmanned aerial vehicle set by the post-earthquake landslide detection and preparation module; the post-disaster environment information acquisition module comprises an image shooting unit for shooting a scene image, a landslide disaster identification unit for judging whether the obtained image is a landslide disaster, a position coordinate acquisition unit for acquiring position coordinate information of a secondary landslide disaster, and an information transmission unit for packaging and transmitting the scene landslide disaster image and the position coordinate information to the landslide disaster analysis decision module;
the Beidou navigation positioning module is used for assisting in detecting the special unmanned aerial vehicle building module, the post-earthquake landslide detection planning and raising module and the post-disaster environment information acquisition module in the aspects of navigation system building, cruising route setting and information acquisition and transmission;
the landslide disaster analysis decision module is used for analyzing related attribute parameters of landslide influence area, trailing edge height and stability of the post-earthquake secondary landslide disaster by combining the special unmanned aerial vehicle building module for detection, the post-earthquake landslide detection planning and raising module, the post-disaster environment information acquisition module and the Beidou navigation positioning module;
the post-earthquake rescue planning and evaluating module is used for providing constructive opinions for post-disaster rescue by combining the landslide disaster position and related attribute parameters obtained by the landslide disaster analysis and decision-making module.
2. The post-earthquake-induced secondary landslide disaster assessment and rescue auxiliary system according to claim 1, wherein the Beidou navigation positioning module comprehensively assists in detecting unmanned aerial vehicle equipment building modules, post-earthquake landslide detection planning and raising modules and post-disaster environment information acquisition modules, and the accurate positioning and real-time transmission functions of the post-earthquake-induced secondary landslide disaster assessment and rescue auxiliary system achieve unmanned aerial vehicle cruise coordinate path presetting and position information coordinate acquisition.
3. The post-earthquake-induced secondary landslide hazard assessment and rescue assistance system of claim 1, wherein the landslide hazard analysis decision module comprises an information receiving unit for receiving and decoding the packed data, a hazard display unit for comparing and displaying the on-site landslide hazard image with the remote sensing satellite image, a hazard analysis unit for analyzing and estimating landslide image area, trailing edge height and stability parameters, and a database unit for storing the on-site landslide hazard image and position coordinate information.
4. The post-earthquake-induced secondary landslide hazard assessment and rescue assistance system of claim 1, wherein the post-earthquake rescue planning and assessment module comprises a rescue route planning unit for planning a disaster site, a rescue plan making unit for making a reasonable rescue plan, and a disaster influence degree assessment unit for assessing disaster grades and making follow-up work.
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| CN113344327A (en) * | 2021-05-06 | 2021-09-03 | 河南省科达水利勘测设计有限公司 | Flood control influence evaluation system |
| CN114443883B (en) * | 2022-02-10 | 2023-07-21 | 广州分布式软件有限责任公司 | Data processing method, system and medium based on big data and cloud computing |
| CN114927002B (en) * | 2022-04-28 | 2023-03-14 | 浙江中裕通信技术有限公司 | Road induction method and equipment for post-disaster rescue |
| CN115963764B (en) * | 2023-01-12 | 2023-06-02 | 中国地质调查局水文地质环境地质调查中心 | Method and device for collecting monitoring data, electronic equipment and storage medium |
| CN116167594B (en) * | 2023-04-21 | 2023-10-17 | 中国地质大学（北京） | Unmanned aerial vehicle platform for detecting vital signs of human body under landslide body and detection method |
| CN117115644A (en) * | 2023-08-08 | 2023-11-24 | 江苏省地质调查研究院 | Disaster analysis method and device based on image data |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104299365A (en) * | 2014-08-06 | 2015-01-21 | 江苏恒创软件有限公司 | Method for monitoring mountain landslide and debris flow in mountainous areas based on unmanned aerial vehicle |
| CN106251032A (en) * | 2016-06-28 | 2016-12-21 | 中国人民解放军后勤工程学院 | A kind of Earthquake-landslide disaster Disaster rapid evaluation method |
| CN110542708A (en) * | 2019-09-29 | 2019-12-06 | 长江勘测规划设计研究有限责任公司 | landslide early warning system and method |
| CN111006593A (en) * | 2019-12-13 | 2020-04-14 | 武汉纵横天地空间信息技术有限公司 | Method and system for monitoring mountain landform and predicting landslide by using unmanned aerial vehicle |
| CN111126183A (en) * | 2019-12-09 | 2020-05-08 | 武汉大学 | Method for detecting damage of building after earthquake based on near-ground image data |
-
2020
- 2020-06-17 CN CN202010556130.9A patent/CN111915128B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104299365A (en) * | 2014-08-06 | 2015-01-21 | 江苏恒创软件有限公司 | Method for monitoring mountain landslide and debris flow in mountainous areas based on unmanned aerial vehicle |
| CN106251032A (en) * | 2016-06-28 | 2016-12-21 | 中国人民解放军后勤工程学院 | A kind of Earthquake-landslide disaster Disaster rapid evaluation method |
| CN110542708A (en) * | 2019-09-29 | 2019-12-06 | 长江勘测规划设计研究有限责任公司 | landslide early warning system and method |
| CN111126183A (en) * | 2019-12-09 | 2020-05-08 | 武汉大学 | Method for detecting damage of building after earthquake based on near-ground image data |
| CN111006593A (en) * | 2019-12-13 | 2020-04-14 | 武汉纵横天地空间信息技术有限公司 | Method and system for monitoring mountain landform and predicting landslide by using unmanned aerial vehicle |
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