CN111915128A - Post-disaster evaluation and rescue auxiliary system for secondary landslide induced by earthquake - Google Patents
Post-disaster evaluation and rescue auxiliary system for secondary landslide induced by earthquake Download PDFInfo
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Abstract
The invention discloses a post-disaster evaluation and rescue auxiliary system for secondary landslides induced by earthquakes, which comprises a special unmanned aerial vehicle construction module for constructing a special unmanned aerial vehicle for detecting and identifying post-earthquake landslide disasters, a post-earthquake landslide detection planning construction module for completing various preparation works 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 navigation system construction, cruise route setting and information acquisition and transmission, a landslide disaster analysis decision module for analyzing relevant attribute parameters such as secondary landslide disasters after earthquakes, the height of the trailing edge, the stability and the like, and a post-earthquake rescue planning evaluation module for planning rescue routes, making rescue schemes and evaluating disaster influence degrees. The method can meet the requirements of accuracy, instantaneity and high efficiency in the function of intelligent identification of secondary landslide disasters after earthquake.
Description
Technical Field
The invention belongs to the field of intelligent disaster relief, and particularly relates to an evaluation and rescue auxiliary system for secondary landslide induced by earthquake.
Background
The secondary landslide disaster caused by earthquake is a large-scale landslide phenomenon caused by the earthquake, and can be generated immediately along with the earthquake or within a period of time after the earthquake. The earthquake secondary landslide is large in scale, and has the characteristics of high sliding speed, long sliding distance and wide disaster range. Landslide disasters induced by earthquakes are serious, and sometimes loss caused by secondary landslide disasters is larger than loss directly caused by earthquakes. China has wide land areas and a plurality of mountainous regions, and the performance of secondary landslides induced by earthquakes in different areas is greatly different due to geological structure difference and the like. The earthquake secondary landslide rescue is a very difficult task all the time, the difficulty of the disaster rescue projects is time cost, and if an effective rescue scheme cannot be made in a short time, lives and properties of trapped people are threatened. The overall general situation of the disaster area can be rapidly known in real time, and the rescue route and the rescue scheme can be made with considerable help.
At present, for the implementation of rescue of earthquake-induced secondary landslide disasters, a rescue scheme is mostly developed by entering a site and surveying on the spot. In the method, the response is lacked in some emergency situations, for example, the rescue route is blocked by a secondary landslide disaster, the rescue route is temporarily changed, and the rescue time is greatly consumed; when a disaster site is entered for reconnaissance to make a rescue scheme, more casualties are easily caused due to the generation of secondary landslides; when the unmanned aerial vehicle is used for reconnaissance of the field situation, the video stream is shot or the picture is shot through manual observation to screen landslide scenes in a disaster area, 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 auxiliary system for post-disaster evaluation and rescue of secondary landslides induced by earthquakes, different detection models are set for the secondary landslide disasters of earthquakes in different areas, an artificial intelligent medium-depth convolution neural network image recognition technology is carried on an unmanned aerial vehicle, the function of intelligently recognizing the landslide disasters liberates manpower, the function of quickly recognizing and positioning ensures that a rescue army quickly masters the overall situation of a disaster area and plans a correct route to enter the scene, and real-time analysis of the scene landslide image provides guarantee for the formulation of a rescue scheme.
The technical purpose of the invention is realized by the following scheme:
an evaluation and rescue auxiliary system for secondary landslide induced by earthquake after disaster, comprising:
the special unmanned aerial vehicle detection building module is used for building a special unmanned aerial vehicle for detecting and identifying landslide disasters after earthquake;
the post-earthquake landslide detection planning module is used for completing various preparation works required before disaster detection and identification on the unmanned aerial vehicle manufactured by the special unmanned aerial vehicle detection building module;
the post-disaster environment information acquisition module acquires a scene landslide disaster image and a position information coordinate by using the unmanned aerial vehicle set by the post-earthquake landslide detection preparation module;
the Beidou navigation positioning module is used for assisting in detecting a special unmanned aerial vehicle building module, a landslide after earthquake detection planning building module and a post-disaster environment information acquisition module in the aspects of navigation system building, cruise route setting and information acquisition and transmission;
the landslide hazard analysis decision module is used for analyzing landslide influence area, trailing edge height and stability related attribute parameters of secondary landslide hazard after earthquake by combining a landslide hazard image and position information coordinates obtained by a special unmanned aerial vehicle construction module for detection, a landslide detection planning preparation module after earthquake, a post-disaster environment information acquisition module and a Beidou navigation positioning module;
and the post-earthquake rescue planning evaluation module is used for providing a constructive suggestion for post-disaster rescue by combining the landslide disaster position and the related attribute parameters obtained by the landslide disaster analysis decision module.
The invention has the further improvement that the unmanned aerial vehicle special for detection is provided with a raspberry type microcomputer, a Beidou navigation and positioning device, a wide-angle camera and a Beidou information transmission device; the unmanned aerial vehicle special for detection building module divides the territory into four areas according to the area where landslide disasters are prone to occur or frequently occur, and loads the cascaded deep convolution neural network landslide disaster detection model corresponding to each area into a raspberry type microcomputer.
The method is further improved in that the cascaded 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 three-level structures, the three-level deep convolutional neural networks are connected in series, and a field shooting image to be detected is preprocessed to generate an image pyramid which is input into the cascaded deep convolutional neural network model for image recognition.
The method is further improved in that the post-earthquake landslide detection planning and preparation module selects a detection disaster relief area and a cruise route according to the earthquake center position and the earthquake grade, sets flight parameters relevant to the unmanned aerial vehicle special for detection, and sets an execution algorithm of assigning a microcomputer for the raspberry by a cascade deep convolution neural network landslide disaster detection model corresponding to the area according to the position of the detection rescue area.
The invention is further improved in that 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 or not, 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 further improves the unmanned aerial vehicle navigation and positioning module in an omnibearing auxiliary detection mode, and the unmanned aerial vehicle navigation and positioning module is used for carrying out omnibearing auxiliary detection on an unmanned aerial vehicle device building module, a landslide detection planning and preparation module after earthquake and a post-disaster environment information acquisition module.
The landslide hazard analysis and decision module comprises an information receiving unit for receiving and decoding packed data, a hazard display unit for comparing and displaying a field landslide hazard image and a 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 field landslide hazard image and position coordinate information.
The invention has the further improvement 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 artificial intelligence is carried on an 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 a disaster area can be mastered in a short time, the rescue execution time is shortened, and the response capability to emergency situations is improved to a certain extent;
3. the efficiency of making reasonable rescue scheme is improved, and more reasonable rescue scheme is helped to be made through carrying out data analysis and comparison on the disaster site.
4. The detection model that carries on has regional pertinence, detects advantages such as intelligent, and utilizes unmanned aerial vehicle as detection device, and is efficient, and danger is little, accuracy, real-time, the high efficiency in the functional requirement of satisfying earthquake disaster back landslide calamity discernment.
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FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic diagram of a landslide detection model of a cascaded deep convolutional neural network.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The present embodiment is only for explaining the present invention, and it is not limited to the present invention, and those skilled in the art can make modifications without inventive contribution to the present embodiment as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present invention.
According to the secondary landslide post-disaster assessment and rescue auxiliary system that figure 1 shows brings out earthquake, construct the module including being used for constructing the special unmanned aerial vehicle of detection of the special unmanned aerial vehicle of landslide calamity detection and identification after earthquake, a detection after earthquake landslide detection planning module for accomplishing required each item of preparation work before the calamity detection and identification, a post-disaster environmental information collection module for obtaining scene landslide calamity image and position information coordinate, a big dipper navigation orientation module for assisting detection special unmanned aerial vehicle and constructing the module, detection after earthquake landslide detection planning module, post-disaster environmental information collection module's big dipper navigation orientation module, a landslide calamity analysis decision-making module for analyzing the secondary landslide calamity relevant attribute parameter after earthquake, a rescue planning evaluation module after earthquake for providing the constructive suggestion for the post-disaster rescue.
The special unmanned aerial vehicle building module for detection comprises the following steps:
step S1, dividing the territory into four areas according to the areas where landslide disasters are prone to occur or frequently occur, loading the cascaded deep convolution neural network landslide disaster detection models corresponding to each area into a raspberry-type microcomputer, and then loading the raspberry-type microcomputer on an unmanned aerial vehicle;
s2, carrying Beidou navigation positioning equipment capable of accurately positioning on the unmanned aerial vehicle, and obtaining position information of the detected unmanned aerial vehicle in real time;
s3, carrying the wide-angle camera on an unmanned aerial vehicle, and shooting and acquiring an image of the disaster area environment after the earthquake;
s4, carrying the Beidou information transmission device on an unmanned aerial vehicle, and packaging and transmitting data, images, information and the like processed by the raspberry serving microcomputer;
step S5, connecting the wide-angle camera, the raspberry microcomputer, the Beidou navigation and positioning equipment and the Beidou information transmission device to form a transmission path;
as shown in fig. 2, the landslide detection model of the cascaded deep convolutional neural network includes the following steps:
step S1, image preprocessing is carried out on the 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, then the size of the image is standardized through image cutting and zooming, and finally Gaussian filtering processing is utilized to ensure that the image is smoother and redundant noise is eliminated;
s2, generating an image pyramid of the image to be detected by an image pyramid module on the image to be detected obtained after image preprocessing; the image pyramid module is characterized in that a three-layer image pyramid is constructed by combining downsampling and Gaussian filtering operations;
s3, extracting a third layer of image to be detected from the pyramid of the image to be detected, and inputting the third layer of image to be detected into a first-level depth convolution neural network landslide detection model for image recognition; if the image is judged not to be the landslide image, the multilayer cascade detection model is ended, and a result is output; if the image is judged to be a landslide image, inputting a third layer of image to be detected which is judged to be a landslide image into an image pyramid for hierarchical conversion, and extracting a second layer of image of the image to be detected; the first-stage deep convolutional neural network landslide detection model consists of a convolutional layer and a maximum pooling layer;
s4, inputting the second layer of image to be detected into a second-level depth convolution neural network landslide detection model for image recognition; if the image is judged not to be the landslide image, the multilayer cascade detection model is ended, and a result is output; if the image is judged to be a landslide image, inputting a second layer of image to be detected which is judged to be a landslide image into an image pyramid for hierarchical conversion, and extracting a first layer of image of the image to be detected; 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 depth convolution neural network landslide detection model for image recognition; if the image is judged not to be the landslide image, the multilayer cascade detection model is ended, and a result is output; and if the image is judged to be the landslide image, outputting the result. The third-level deep convolutional neural network landslide detection model consists of a convolutional layer and a maximum pooling layer;
the post-earthquake landslide detection planning and preparation module comprises the following steps:
s1, defining the earthquake center position and the coverage range of the earthquake disaster, selecting a mountain area in the range of the earthquake disaster 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 the back-and-forth flight shooting of an unmanned aerial vehicle;
s2, setting the flight height of the unmanned aerial vehicle according to the average mountain height of the cruising mountain area, setting the flight speed and the interval shooting time according to the path, and ensuring that an image is shot at intervals of 20 meters;
s3, selecting a corresponding deep convolution neural network landslide detection model in a corresponding area according to the area where landslide disasters are prone to occur or frequently occur where the set cruise route is located, and using the deep convolution neural network landslide detection model as an execution algorithm of a raspberry-dispatching microcomputer;
the post-disaster environmental information acquisition module comprises: the system comprises an image shooting unit, a landslide disaster identification unit, a position coordinate acquisition unit and an information transmission unit;
the image shooting unit is used for shooting images of the lower mountain body once every 20 m by using a wide-angle camera carried by the unmanned aerial vehicle, and the back-and-forth cruising can ensure that the detection area is covered in a panoramic way;
the landslide hazard identification unit transmits the shot image to a raspberry microcomputer from a wide-angle camera, executes a deep convolution neural network landslide hazard detection model, and transmits the image identified as the landslide hazard to the information transmission unit after image preprocessing and three-level detection model identification; deleting images which are not landslide disasters;
when the position coordinate acquisition unit identifies a landslide disaster image, the Beidou navigation positioning equipment is triggered, and the position coordinate information of the current unmanned aerial vehicle is transmitted to the information transmission unit;
the information transmission unit packages the landslide hazard image and unmanned aerial vehicle position coordinate information obtained through recognition, and transmits data to the landslide hazard analysis decision module through the Beidou information transmission device;
the unmanned aerial vehicle special for omnibearing auxiliary detection of the Beidou navigation positioning module builds a module, a landslide detection planning and preparation module after earthquake and a post-disaster environment information acquisition module, the acquisition of the position information coordinate where the unmanned aerial vehicle is located is preset from the path of the unmanned aerial vehicle cruise coordinate and depends on the Beidou navigation positioning module, and the accurate positioning and real-time transmission of the unmanned aerial vehicle provide guarantee for reasonable operation of the system.
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 a scene landslide disaster image and a position information coordinate;
the disaster display unit displays the site landslide disaster image on a monitoring center computer through a Web end for analysis of workers; converting the site landslide disaster position information coordinates into remote sensing satellite image coordinates, and calling a corresponding position remote sensing satellite image shot at the latest time;
the disaster analysis unit compares a scene landslide disaster image shot by the unmanned aerial vehicle with a scene remote sensing satellite image before landslide occurs, outlines the approximate shape of the scene landslide on a remote sensing satellite image according to parameters such as the flight height of the unmanned aerial vehicle and the relative distance of each characteristic point of the image shot by the unmanned aerial vehicle, estimates parameters such as landslide influence area, trailing edge height and stability, and takes the attribute parameters as one of guiding data of rescue planning;
the database unit is used for storing the scene landslide hazard image and the corresponding position information coordinates in a database for use when a deep convolution neural network landslide hazard detection model is optimized;
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 in the earthquake disaster range and the estimated landslide influence area on a remote sensing satellite map, selects a road where the landslide disaster does not arrive as a main rescue route, and a road where the landslide disaster reaches a smaller area as a secondary rescue route, and plans a rescue team, disaster relief materials and the like to gradually approach the interior of a disaster-affected mountain area along a route with less safety barrier;
the rescue scheme making unit expert makes a reasonable rescue scheme by analyzing the terrain of a scene landslide disaster image and a remote sensing satellite image, combining the spreading condition of a secondary landslide disaster to residences of people and comparing and analyzing the cases of the previous similar conditions, so that the life and property safety of people is ensured;
and the disaster influence degree evaluation unit grades the influence degree in the range according to the number of secondary landslide disasters after earthquake, the influence area of the disasters, the losses caused to economic properties and the like, and defines safe parking places in the disaster area range for subsequent rescue manpower and material resources as referential suggestions.
Claims (8)
1. An evaluation and rescue auxiliary system for secondary landslide induced by earthquake after disaster is characterized by comprising:
the special unmanned aerial vehicle detection building module is used for building a special unmanned aerial vehicle for detecting and identifying landslide disasters after earthquake;
the post-earthquake landslide detection planning module is used for completing various preparation works required before disaster detection and identification on the unmanned aerial vehicle manufactured by the special unmanned aerial vehicle detection building module;
the post-disaster environment information acquisition module acquires a scene landslide disaster image and a position information coordinate by using the unmanned aerial vehicle set by the post-earthquake landslide detection preparation module;
the Beidou navigation positioning module is used for assisting in detecting a special unmanned aerial vehicle building module, a landslide after earthquake detection planning building module and a post-disaster environment information acquisition module in the aspects of navigation system building, cruise route setting and information acquisition and transmission;
the landslide hazard analysis decision module is used for analyzing landslide influence area, trailing edge height and stability related attribute parameters of secondary landslide hazard after earthquake by combining a landslide hazard image and position information coordinates obtained by a special unmanned aerial vehicle construction module for detection, a landslide detection planning preparation module after earthquake, a post-disaster environment information acquisition module and a Beidou navigation positioning module;
and the post-earthquake rescue planning evaluation module is used for providing a constructive suggestion for post-disaster rescue by combining the landslide disaster position and the related attribute parameters obtained by the landslide disaster analysis decision module.
2. The post-disaster evaluation and rescue auxiliary system for the secondary landslides induced by the earthquake as claimed in claim 1, wherein the unmanned aerial vehicle special for detection is provided with a raspberry-type microcomputer, a Beidou navigation positioning device, a wide-angle camera and a Beidou information transmission device; the unmanned aerial vehicle special for detection building module divides the territory into four areas according to the area where landslide disasters are prone to occur or frequently occur, and loads the cascaded deep convolution neural network landslide disaster detection model corresponding to each area into a raspberry type microcomputer.
3. The post-disaster evaluation and rescue auxiliary system for secondary landslides induced by earthquakes according to claim 2 is characterized in that a cascaded deep convolutional neural network landslide hazard detection model builds deep convolutional neural networks with different three-level structures by utilizing convolutional layers and maximum pooling layers, the three-level deep convolutional neural networks are connected in series, and a field shot image to be detected is preprocessed to generate an image pyramid which is input into the cascaded deep convolutional neural network model for image recognition.
4. The post-disaster evaluation and rescue auxiliary system for secondary landslides induced by earthquakes as claimed in claim 2, wherein the post-earthquake landslide detection planning and preparation module selects a detection disaster relief area and a cruise route according to the epicenter position and the earthquake grade, sets flight parameters related to the unmanned aerial vehicle special for detection, and sets an execution algorithm of a microcomputer assigned to the raspberry by a cascade deep convolutional neural network landslide disaster detection model corresponding to the area according to the position of the detection rescue area.
5. The post-disaster evaluation and rescue auxiliary system for secondary landslides induced by earthquakes as claimed in claim 2, wherein the post-disaster environment information acquisition module comprises an image shooting unit for shooting a scene image, a landslide disaster recognition unit for judging whether the obtained image is a landslide disaster, a position coordinate acquisition unit for acquiring position coordinate information of the 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.
6. The secondary landslide post-disaster assessment and rescue auxiliary system for earthquake induced secondary landslide according to claim 1, wherein a Beidou navigation positioning module is used for omnibearing auxiliary detection of an unmanned aerial vehicle equipment building module, a post-earthquake landslide detection planning building module and a post-disaster environment information acquisition module, and the unmanned aerial vehicle cruise coordinate path presetting and the position information coordinate obtaining are realized through the accurate positioning and real-time transmission functions of the unmanned aerial vehicle cruise coordinate path presetting and the post-disaster environment information acquisition module.
7. The post-disaster evaluation and rescue assisting system for secondary landslides induced by earthquakes as claimed in claim 1, wherein the landslide hazard analysis decision module comprises an information receiving unit for receiving and decoding packed data, a disaster display unit for displaying a scene landslide hazard image and a remote sensing satellite image in a contrasting manner, a disaster analysis unit for analyzing and estimating parameters of the area, the height and the stability of the landslide image, and a database unit for storing the scene landslide hazard image and position coordinate information.
8. The post-disaster evaluation and rescue assisting system for secondary landslides induced by earthquakes according to claim 1, wherein the post-earthquake rescue planning and evaluation 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 evaluation unit for evaluating disaster grades and making subsequent 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 |
| CN114443883A (en) * | 2022-02-10 | 2022-05-06 | 北京永利信达科技有限公司 | Data processing method, system and medium based on big data and cloud computing |
| CN114927002A (en) * | 2022-04-28 | 2022-08-19 | 浙江中裕通信技术有限公司 | Road induction method and device for post-disaster rescue |
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