CN111709335A - Railway line safety early warning method based on satellite remote sensing - Google Patents
Railway line safety early warning method based on satellite remote sensing Download PDFInfo
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Abstract
The invention relates to the field of railway line safety early warning, in particular to a railway line safety early warning method based on satellite remote sensing, which comprises the following steps of continuously acquiring a remote sensing satellite image covering a railway line area; matching the remote sensing satellite image with a railway line map, and carrying out grid division on the remote sensing satellite image along the railway line based on the trend of the railway line map; step three, continuously comparing the image characteristic changes in each grid according to a set time interval for each divided grid; and step four, if the image characteristic change exceeds a specified threshold value, sending out an early warning signal. 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 running environment along the railway, and timely sends early warning signals according to monitoring abnormity.
Description
Technical Field
The invention relates to the field of railway line safety early warning, in particular to a railway line safety early warning method based on satellite remote sensing.
Background
The railway is a long and large linear engineering spanning different natural and geographic areas, needs to be tested under complex geological conditions, and is very easy to be attacked by natural disasters. The development of railroads requires not only an increase in the length of the line, but also an improvement in the safety and quality of transportation. A plurality of geological disasters such as debris flow ditches, large and medium landslides, collapse and the like are distributed along the railway in the existing mountainous area in China. At present, for the safety protection of the environment along the high-speed railway, means such as setting a fence, a warning board or manual patrol along the high-speed railway are mainly adopted. However, these methods are passive safeguards or are inefficient for patrolling. In addition, due to the wide distribution range of railway lines, the cost required by video monitoring or sensor monitoring is huge.
Disclosure of Invention
The invention aims to provide a railway line safety early warning system based on satellite remote sensing aiming at the defects of the prior art, which comprises a remote sensing satellite, a space and ground signal transmission unit and a ground monitoring center, wherein the remote sensing satellite is connected with the ground monitoring center through the space and ground signal transmission unit; the ground monitoring center comprises a remote sensing image acquisition unit, an image comparison analysis unit and an early warning signal release unit;
the remote sensing image acquisition unit 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 is connected with the image comparison analysis unit;
the remote sensing image comparison analysis unit is used for comparing the line remote sensing image data acquired at different times and sending the comparison result to the early warning signal issuing unit;
and the early warning signal issuing unit is used for issuing an early warning signal according to the comparison result.
Further, the early warning signal issuing unit comprises an alarm device, and the alarm device is a computer with a display and/or a loudspeaker.
Further, the remote sensing satellite is a high-spectrum optical remote sensing satellite or a microwave remote sensing satellite.
The invention also provides a railway line safety early warning method based on satellite remote sensing, which comprises the following steps:
continuously acquiring remote sensing satellite images covering a railway line area;
matching the remote sensing satellite image with a railway line map, and carrying out grid division on the remote sensing satellite image along the railway line based on the trend of the railway line map;
step three, continuously comparing the image characteristic changes in each grid according to a set time interval for each divided grid;
and step four, if the image characteristic change exceeds a specified threshold value, sending out an early warning signal.
Optionally, in the fourth step, if the image feature transformation does not exceed the specified threshold, outputting updated remote sensing satellite images covering the railway line area at preset intervals.
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, gridding a railway line area diagram along the line trend;
secondly, registering the remote sensing satellite image to the railway line area map of the same area;
and step two, extracting a grid overlapped by the remote sensing satellite image and the railway line area 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.
Further, the fourth step further comprises the step of judging the abnormal type of the abnormal grid image with the image characteristic change exceeding a specified threshold value by adopting a deep learning model, wherein the abnormal type comprises collapse, debris flow and foreign body invasion.
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 railway line safety early warning method based on satellite remote sensing provided by the invention continuously monitors the safety condition along the railway based on the satellite remote sensing image data, can timely and comprehensively master the running environment along the railway, 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 running safety of the railway line by adopting ground monitoring equipment, can realize the on-line monitoring of the whole-line running area of the railway according to the gridding monitoring of the railway line, can obviously reduce the accidents of derailment and the like caused by the geological disaster of the line in the running train, and provides the running safety of the high-speed railway.
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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 a diagram of a railway track safety pre-warning system based on satellite remote sensing in embodiment 1;
FIG. 2 is a physical connection diagram of the railway line safety warning system based on satellite remote sensing in embodiment 1;
FIG. 3 is a flow chart of a railway track 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 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 railway line database 34.
Example 1
Embodiment 1 provides a railway line safety early warning system based on satellite remote sensing, as shown in fig. 1-2, including a remote sensing satellite 1, a space and ground signal transmission unit 2 and a ground monitoring center 3, wherein the remote sensing satellite 1 and the ground monitoring center 3 are connected through the space and ground signal transmission unit 2; the ground monitoring center 3 comprises a remote sensing image acquisition unit 31, an image contrast analysis unit 32 and an early warning signal release unit 33;
the remote sensing image acquisition unit 31 is used for receiving the line remote sensing image data transmitted back by the remote sensing satellite 1, 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 32 is used for comparing the remote sensing image data along the line acquired at different times 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 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 server 32 is further connected to a railway line database 34, and the railway line database 34 stores a gridded railway line route map. Based on the railway line database, the remote sensing image contrast analysis server can realize image contrast analysis according to grids.
The remote sensing satellite 1 is a high-spectrum optical remote sensing satellite or a microwave remote sensing satellite.
The embodiment provides a railway line safety early warning method based on satellite remote sensing, as shown in fig. 3, including:
s100, continuously acquiring remote sensing satellite images covering a railway line area;
s200, matching the remote sensing satellite image with a railway line map, and carrying out grid division on the remote sensing satellite image along the railway line based on the trend of the railway line map;
s300, continuously comparing image characteristic changes in each grid according to a set time interval for each divided grid;
s400, if the image characteristic change exceeds a specified threshold value, an early warning signal is sent out. The remote sensing image feature comparison based on meshing simplifies the processing complexity of the remote sensing image, as the railway lines are distributed in a band shape, the ground feature information along the railway lines is relatively concise, the abnormal area can be rapidly identified based on the meshing image feature comparison of the railway lines, and the early warning efficiency is improved.
In step S400, if the image feature transformation does not exceed a specified threshold, outputting updated remote sensing satellite images covering railway line areas at intervals of a preset period
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, gridding a railway line area map along the line trend;
step S220, registering the remote sensing satellite image to the railway line area map of the same area;
and step S230, extracting a grid overlapped by the remote sensing satellite image and the railway line area 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. 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.
Step S400 further comprises the step of judging the abnormal type of the abnormal grid image with the image characteristic change exceeding a specified threshold value by adopting a deep learning model, wherein the abnormal type comprises collapse, debris flow and foreign body invasion.
The method comprises the steps of training a CNN (neural network) and other mainstream neural networks by using common collapse, debris flow and foreign matter invasion remote sensing image abnormal samples of the high-speed rail in advance, and then judging the type of an abnormal grid image by using 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 subsequent workers can conveniently and rapidly eliminate potential safety hazards 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 railway line safety early warning method based on satellite remote sensing is characterized by comprising the following steps:
continuously acquiring remote sensing satellite images covering a railway line area;
matching the remote sensing satellite image with a railway line map, and carrying out grid division on the remote sensing satellite image along the railway line based on the trend of the railway line map;
step three, continuously comparing the image characteristic changes in each grid according to a set time interval for each divided grid;
and step four, if the image characteristic change exceeds a specified threshold value, sending out an early warning signal.
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 feature transformation does not exceed a specified threshold, outputting updated remote sensing satellite images covering the railway line area every other preset period.
3. The method of claim 1, wherein: the second step specifically comprises:
step two, gridding a railway line area diagram along the line trend;
secondly, registering the remote sensing satellite image to the railway line area map of the same area;
and step two, extracting a grid overlapped by the remote sensing satellite image and the railway line area 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 with the image characteristic change exceeding a specified threshold value by adopting a deep learning model, wherein the abnormal type comprises collapse, debris flow and foreign body invasion.
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 |
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| CN115833924A (en) * | 2023-02-23 | 2023-03-21 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Satellite control method and device for railway remote sensing detection |
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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| Publication number | Priority date | Publication date | Assignee | Title |
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| 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 |
|---|---|---|---|---|
| CN115833924A (en) * | 2023-02-23 | 2023-03-21 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Satellite control method and device for railway remote sensing detection |
| CN115833924B (en) * | 2023-02-23 | 2023-04-25 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Satellite control method and device for railway remote sensing detection |
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