CN111709334A - Subway safety early warning method based on satellite remote sensing - Google Patents
Subway safety early warning method based on satellite remote sensing Download PDFInfo
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Abstract
The invention relates to the field of subway safety early warning, in particular to a subway safety early warning method based on satellite remote sensing, which comprises the following steps of continuously acquiring a remote sensing satellite image covering an urban subway line region; matching the remote sensing satellite image with a subway map, and carrying out grid division on the remote sensing satellite image along the subway based on the urban subway line trend; step three, continuously comparing the image characteristic changes in each grid according to a set time interval for each divided grid; step four, if the image characteristic change exceeds a specified threshold value, judging the abnormal type of the image characteristic abnormal area; and fifthly, sending out an early warning signal according to the abnormal type. The method is used for continuously monitoring the safety condition along the railway based on the satellite remote sensing image data, can timely and comprehensively master the safety environment along the subway, and timely sends early warning signals according to monitoring abnormity.
Description
Technical Field
The invention relates to the field of subway safety early warning, in particular to a subway safety early warning system and method based on satellite remote sensing.
Background
The subway is mostly located in urban areas with dense population, important buildings and underground pipe networks are usually arranged around the subway, the ground surface above the subway is usually displaced, settled and deformed, or high-load engineering construction operation is carried out, and the factors seriously threaten the operation safety of urban underground railways and the construction safety of the subway under construction. The existing scheme is that a plurality of ground surface deformation monitoring devices are arranged above a subway tunnel to realize real-time monitoring of ground settlement. However, if complex load changes occur above the tunnel or the deformation monitoring device fails, it is difficult to provide effective early warning time; and for urban dense areas, the arrangement density of the deformation monitoring devices is limited, and the accuracy of subway safety early warning is also restricted.
Disclosure of Invention
The invention aims to provide a subway safety early warning method based on satellite remote sensing aiming at the defects of the prior art, which comprises the following steps:
continuously acquiring remote sensing satellite images covering urban subway line areas;
matching the remote sensing satellite image with a subway map, and carrying out grid division on the remote sensing satellite image along the subway based on urban subway line distribution;
step three, continuously comparing the image characteristic changes in each grid according to a set time interval for each divided grid;
step four, if the image characteristic change exceeds a specified threshold value, judging the abnormal type of the image characteristic abnormal area;
fifthly, sending out an early warning signal according to the abnormal type; the early warning signal comprises an abnormal position and an abnormal type.
Further, the remote sensing satellite image is an optical remote sensing image or a microwave remote sensing image.
Further, the second step specifically includes:
step two, moving a gridding subway region map along a line;
secondly, registering the remote sensing satellite image to the subway regional map of the same area;
and step two, extracting a grid of the remote sensing satellite image which is overlapped with the subway region map.
Optionally, the image is an optical remote sensing image, the image characteristic is image gray distribution, and if the variation of the image gray distribution in the grid in the previous and subsequent sampling exceeds a specified threshold, an early warning signal is sent.
Further, calculating the variation of the image gray distribution in two previous and next samples means: and calculating the gray gravity center and/or the average gray of the optical remote sensing images at the previous time and the next time, and calculating the deviation value of the gray gravity center and/or the change value of the average gray.
Optionally, the image is a microwave remote sensing image, the image characteristic is an image deformation characteristic, and if the variation of the image deformation characteristic in the grid in the previous sampling and the subsequent sampling exceeds a specified threshold, an early warning signal is sent.
Further, calculating the variation of the image deformation characteristic in two previous and subsequent samples refers to: and calculating the accumulated amount of pixel elevation change and/or the maximum amount of elevation change of the microwave remote sensing images at the previous and next times.
The invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the computer program.
The present invention also provides a computer-readable storage medium storing a computer program for executing the above-described security early warning method.
The beneficial effect that this technical scheme brought:
the subway safety early warning method based on satellite remote sensing provided by the invention continuously monitors the safety condition along the subway based on the satellite remote sensing image data, and timely sends early warning signals according to the image characteristic difference reflected in the remote sensing image. The method replaces the traditional mode of monitoring the safety of the subway by adopting ground deformation monitoring equipment, can realize on-line monitoring of the subway area according to the gridding monitoring of the subway, and can obviously reduce the influence of ground surface abnormity on the safety of the subway.
Drawings
The foregoing and following detailed description of the invention will be apparent when read in conjunction with the following drawings, in which:
FIG. 1 is the structure diagram of the subway safety pre-warning system based on satellite remote sensing in embodiment 1;
FIG. 2 is a physical connection diagram of the subway safety warning system based on satellite remote sensing in embodiment 1;
FIG. 3 is a flow chart of a subway safety pre-warning method based on satellite remote sensing in embodiment 1;
the following are marked in the figure: the system comprises a remote sensing satellite 1, a space and ground signal transmission unit 2, a ground subway monitoring center 3, a remote sensing image acquisition unit 31, a remote sensing image comparison analysis unit 32, an early warning signal release unit 33 and a subway line database 34.
Example 1
the remote sensing image acquisition unit 31 is used for receiving remote sensing image data along the line transmitted back by a remote sensing satellite, and the output end of the remote sensing image acquisition unit 31 is connected with the image contrast analysis unit 32;
the remote sensing image comparison analysis unit is used for comparing the remote sensing image data collected at different times along the line and sending the comparison result to the early warning signal issuing unit 33;
the early warning signal issuing unit 33 is configured to issue an early warning signal according to the comparison result.
The ground subway monitoring center is arranged in the railway dispatching center.
The early warning signal issuing unit 33 includes an alarm device which is a computer having a display and/or a speaker.
The remote sensing image contrast analysis unit 32 is further connected to a subway database 34, and the subway database 34 stores a gridded subway walking map. Based on the subway database, the remote sensing image contrast analysis server 32 can perform image contrast analysis according to a grid.
The remote sensing satellite 1 is a high-spectrum optical remote sensing satellite or a microwave remote sensing satellite.
The embodiment provides a subway safety early warning method based on satellite remote sensing, as shown in fig. 3, including:
step S100, continuously acquiring remote sensing satellite images covering urban subway line areas;
step S200, matching the remote sensing satellite image with a subway map, and carrying out grid division on the remote sensing satellite image along the subway based on the trend of the subway map;
step S300, continuously comparing image characteristic changes in each grid according to a set time interval for each divided grid;
step S400, if the image characteristic change exceeds a specified threshold, judging the abnormal type of the image characteristic abnormal area;
and S500, sending out an early warning signal according to the abnormal type. The remote sensing image feature comparison based on meshing simplifies the processing complexity of the remote sensing image, and the meshing image feature comparison based on the subway can quickly locate abnormal areas and improve the early warning efficiency because the subway is in mesh-type distribution.
Further, the remote sensing satellite image is an optical remote sensing image or a microwave remote sensing image.
Further, the step S200 specifically includes:
step S210, a gridding subway region map is run along a line;
step S220, registering the remote sensing satellite image to the subway regional map of the same area;
and step S230, extracting a grid overlapped by the remote sensing satellite image and the subway region map.
Optionally, the image is an optical remote sensing image, the image characteristic is image gray distribution, and if the variation of the image gray distribution in the grid in the previous and subsequent sampling exceeds a specified threshold, an early warning signal is sent.
Further, calculating the variation of the image gray distribution in two previous and next samples means: and calculating the gray gravity center and/or the average gray of the optical remote sensing images at the previous time and the next time, and calculating the deviation value of the gray gravity center and/or the change value of the average gray. When geological disasters such as collapse occur along the railway, the geological disasters can be identified from the gray scale characteristics of the remote sensing image. The gray scale gravity center method is to regard the gray scale value at each pixel position in a single grid area as the "quality" of the point, and find the gray scale gravity center in the grid by the following formula:
optionally, the image is a microwave remote sensing image, the image characteristic is an image deformation characteristic, and if the variation of the image deformation characteristic in the grid in the previous sampling and the subsequent sampling exceeds a specified threshold, an early warning signal is sent.
Calculating the variation of the image deformation characteristic in two sampling before and after means that: and calculating the accumulated amount of pixel elevation change and/or the maximum amount of elevation change of the microwave remote sensing images at the previous and next times.
And fourthly, judging the abnormal type of the abnormal grid image by adopting a deep learning model, wherein the abnormal type comprises surface subsidence, surface subsidence and surface engineering construction. The method comprises the steps of training mainstream neural networks such as CNN (neural network) by adopting common surface common remote sensing image abnormal samples common to subways in advance, and then judging the types of abnormal grid images by adopting the trained CNN. The abnormal type is judged based on the deep learning model, automatic safety early warning can be achieved, the abnormal type is provided, and potential safety hazards can be conveniently eliminated by follow-up workers in a targeted mode.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A subway safety early warning method based on satellite remote sensing is characterized by comprising the following steps:
continuously acquiring remote sensing satellite images covering urban subway line areas;
matching the remote sensing satellite image with a subway line map, and carrying out grid division on the remote sensing satellite image along the subway line based on urban subway line distribution;
step three, continuously comparing the image characteristic changes in each grid according to a set time interval for each divided grid;
step four, if the image characteristic change exceeds a specified threshold value, judging the abnormal type of the image characteristic abnormal area;
fifthly, sending out an early warning signal according to the abnormal type; the early warning signal comprises an abnormal position and an abnormal type.
2. The method of claim 1, wherein: the remote sensing satellite image is an optical remote sensing image or a microwave remote sensing image; and in the fourth step, if the image characteristic transformation does not exceed a specified threshold, outputting an updated remote sensing satellite image covering the urban subway line area every other preset period.
3. The method of claim 1, wherein: the second step specifically comprises:
step two, moving a gridding subway region map along a line;
secondly, registering the remote sensing satellite image to the subway regional map of the same area;
and step two, extracting a grid of the remote sensing satellite image which is overlapped with the subway region map.
4. The method of claim 3, wherein: the image is an optical remote sensing image, the image characteristic is image gray distribution, and if the variation of the image gray distribution in the grid in the previous sampling and the next sampling exceeds a specified threshold, an early warning signal is sent.
5. The method of claim 4, wherein: calculating the variation of the image gray distribution in two sampling processes, namely: and calculating the gray gravity center and/or the average gray of the optical remote sensing images at the previous time and the next time, and calculating the deviation value of the gray gravity center and/or the change value of the average gray.
6. The method of claim 3, wherein: the image is a microwave remote sensing image, the image characteristic is an image deformation characteristic, and if the variation of the image deformation characteristic in the grid in the previous sampling and the next sampling exceeds a specified threshold, an early warning signal is sent.
7. The method of claim 6, wherein: calculating the variation of the image deformation characteristic in two sampling before and after means that: and calculating the accumulated amount of pixel elevation change and/or the maximum amount of elevation change of the microwave remote sensing images at the previous and next times.
8. The method according to any one of claims 1 to 7, wherein: and fourthly, judging the abnormal type of the abnormal grid image by adopting a deep learning model, wherein the abnormal type comprises surface subsidence, surface subsidence and surface engineering construction.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the safety precaution method of any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium storing a computer program for executing the safety warning method according to any one of claims 1 to 8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114757238A (en) * | 2022-06-15 | 2022-07-15 | 武汉地铁集团有限公司 | Method and system for monitoring deformation of subway protection area, electronic equipment and storage medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109239734A (en) * | 2018-08-24 | 2019-01-18 | 河南东网信息技术有限公司 | A kind of Along Railway environmental safety monitor and control early warning system |
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Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109239734A (en) * | 2018-08-24 | 2019-01-18 | 河南东网信息技术有限公司 | A kind of Along Railway environmental safety monitor and control early warning system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114757238A (en) * | 2022-06-15 | 2022-07-15 | 武汉地铁集团有限公司 | Method and system for monitoring deformation of subway protection area, electronic equipment and storage medium |
| CN114757238B (en) * | 2022-06-15 | 2022-09-20 | 武汉地铁集团有限公司 | Method and system for monitoring deformation of subway protection area, electronic equipment and storage medium |
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