CN107054411B - A kind of Along Railway snow disaster unmanned plane snow depth intelligent measure and Forecasting Methodology and system - Google Patents

Abstract

The invention discloses a method and system for intelligently measuring and predicting the snow depth of a snow disaster along a railway using an unmanned aerial vehicle. The snow disaster monitoring big data center can continuously and dynamically monitor the operation of the train and the operating environment, thus filling the blind spots existing in the current monitoring methods; it makes great use of the flexibility of the UAV snow depth measurement device, with the help of unmanned Machine snow depth measurement devices, workstations, snow disaster monitoring big data center and ground train control center have established a monitoring network and historical database covering snow disaster dangerous areas; Stop moving before the snow accumulation position; by keeping the UAV snow depth measurement device and the train relatively still, the reliability and timeliness of the collected data are guaranteed, and the blind spot of monitoring the accumulation of ice and snow on the track is greatly avoided.

Description

A method and system for intelligent measurement and prediction of snow disaster UAV snow depth along the railway

technical field

The invention belongs to the field of railway track monitoring, and in particular relates to a method and system for intelligently measuring and predicting snow depth of a snow disaster along a railway by an unmanned aerial vehicle.

Background technique

With the increasing density of the railway network, the national economy's dependence on the railway is gradually increasing. However, my country's vast land, complex and diverse geographical and climatic environments have brought great challenges to the operation of railways. Among them, the unique snow accumulation in cold regions has brought a series of special problems to railway construction and operation. Northeast my country, northern Xinjiang, and parts of the Qinghai-Tibet Plateau have a cold winter climate and a large amount of snowfall. Affected by natural snowfall and wind blowing snow, thicker snow often forms on the ground, which has a greater impact on railway transportation. In particular, the construction of railway passenger dedicated lines in cold regions has put forward higher requirements for snow damage prevention and control. In order to reduce the impact of snow damage on railway transportation, it is necessary to correctly understand the characteristics of snow damage on railways in cold regions and take effective prevention and control measures.

There are two types of railway snow damage: wind blowing snow and avalanche. The existence of the wind blowing snow phenomenon makes the speed of snow accumulation greatly accelerated, and the suddenness and unpredictability of avalanches seriously affect the normal driving operations. Snowfall is abundant in the northern regions of my country, among which the snowfall in the northwest region accounts for about 40% of the total snowfall in the country, especially in Xinjiang, which accounts for about 33.9% of the total in the country, and most of the snowfall is concentrated in the northern Tianshan area. . Although the abundant snowfall provides abundant fresh water resources for the region, the wind blowing snow and avalanches caused by the massive snowfall and snow accumulation have a disastrous impact on the production and life in the region, especially the transportation.

In order to reduce the impact of snow damage along the railway on train operation and strengthen the disaster early warning capability of the train control system, various early warning and response systems have been developed in China. For wind blowing snow, my country has developed a snow depth monitoring and early warning system. The working principle of this type of system is similar: set monitoring points along the railway line to monitor the depth of snow in real time, and send an alarm when the snow depth exceeds the early warning threshold. . For avalanches, the current common method is to build barrier walls in avalanche-prone areas, which is a passive protection method.

Contents of the invention

The present invention proposes a method and system for intelligent measurement and prediction of snow depth by UAV along the railway. The purpose is that by introducing UAV for real-time monitoring, the railway dispatching part can obtain real-time dynamic information about the accumulation of ice and snow on the track , to fill in the previous lack of monitoring of track ice and snow accumulation.

A method for intelligently measuring and predicting snow depth by UAV for snow disasters along railway lines, comprising the following steps:

Step 1: According to the historical ice and snow accumulation data on the track and the train operation accident data when the train is running along the railway track, select the continuous track section corresponding to the ice and snow accumulation on the track exceeding the safety value and the operation accident, and perform equalization on the selected track section. The distance is divided, and workstations are set up in each track section unit, and each workstation is equipped with two sets of UAV snow depth measurement devices;

The unmanned aerial vehicle snow depth measuring device comprises flying device and the ultrasonic snow depth measuring instrument, Kinect sensor, distance sensor and train speed measuring device loaded on the flying device;

The UAV snow depth measuring device communicates with the workstation, and the workstation, the snow disaster monitoring big data center and the ground control center communicate sequentially;

Step 2: Number each track section unit obtained in step 1, and record the start mileage, end mileage, train safety speed in the section unit, workstation number and UAV snow depth measurement device number of each track section unit;

Step 3: The target train enters the snow disaster dangerous track section, and starts the track snow depth monitoring task;

When the target train enters the snow disaster dangerous track section, the ground train control center will send the unit number of the monitored snow disaster dangerous track section unit number and the train number of the target controlled train to the snow disaster monitoring big data center, and the snow disaster monitoring big data center will send the information to the snow disaster dangerous track The workstation in the interval unit issues a measurement command to initialize the UAV track snow depth monitoring task;

The two sets of UAV snow depth measuring devices in the control station of the workstation take off synchronously and track the target train;

Step 4: and use the Kinect sensor on the UAV snow depth measurement device to collect the train number, send it to the snow disaster monitoring big data center and compare it with the target controlled train number sent by the ground train control center in advance. If the results are consistent, go to step 5, otherwise, the two sets of UAV snow depth measurement devices return to the workstation, stop tracking, and wait for the next command;

Step 5: Two sets of UAV snow depth measurement devices collect track snow depth data, train speed and relative distance from the train in real time, and transmit them to their workstations in real time;

Step 6: The snow disaster monitoring big data center compares the train speed and snow depth data with the pre-stored safety data in real time based on the messages to be received by the workstation, using the train number as the search key, and uses the real-time snow depth data to find Corresponding to the safe speed of the train, if the real-time speed of the train exceeds the safe speed corresponding to the real-time snow depth data, the snow disaster monitoring big data center will send out an alarm message;

When the ground train control center receives the alarm information, it will dispatch the train in real time;

Step 7: When the target train leaves the snow disaster dangerous track section, the ground train control center sends a signal of task completion to the snow disaster monitoring big data center, and the snow disaster monitoring big data center sends a signal of task completion to the workstation, and the workstation sends a signal to the two groups of drones The snow depth measuring device sends a signal that the task is completed, and the two sets of UAV snow depth measuring devices return to the workstation and enter the standby state.

When the two sets of UAV snow depth measurement devices are synchronously tracking the target train, one of the UAV snow depth measurement devices maintains a flying attitude and stops relatively still at the point above the head of the target controlled train, and the other group of UAV snow depth measurement devices The depth measurement device accelerates and flies in the direction of the target controlled train, and the distance between the head of the train and the train is _S0 ;

S ₀ represents the safe braking distance in snow disasters, S ₀ =S×K, S represents the train braking distance, K represents the safety factor, and the value range is 1.1-1.2; the train braking distance can be obtained according to the attributes of the train.

When the snow depth data exceeds the snow depth safety value corresponding to the safe operation of the train, the snow disaster monitoring big data center sends out an alarm message.

In the pre-stored safety data, the train safety speed decreases with the increase of track snow depth.

After the UAV snow depth measurement device moves forward synchronously with the target train, when the target travels to the next workstation, the UAV snow depth measurement device performs task handover, and the ground train control center issues instructions to the latest workstation entered by the target train, The two UAV snow depth measurement devices of the workstation take off and track the target train synchronously. The two UAV snow depth measurement devices that originally flew enter the workstation for charging and send the real-time speed and position of the target train to the ground train control center.

A snow disaster UAV snow depth intelligent measurement and prediction system along the railway, including:

The ground train control center is used to receive the processing data transmitted in real time from the snow disaster monitoring big data center, and dispatch the train, including a train dispatching module, an alarm information storage module and a first wireless communication module;

The snow disaster monitoring big data center is used to receive the real-time collected data sent from the workstation, and analyze and process the data, including the UAV scheduling module, task data storage module, central processing module and the second wireless communication module;

A workstation, including a UAV operation module, a UAV database, a third wireless communication module and at least two UAV snow depth measurement devices;

Wherein, the unmanned aerial vehicle snow depth measuring device 1 comprises a flight device and an ultrasonic snow depth measuring instrument installed thereon, a distance sensor, a Kinect sensor and a fourth wireless communication module;

UAV snow depth measuring device 2 comprises flight device and ultrasonic snow depth measuring instrument installed thereon, Kinect sensor, train speed measuring device, distance sensor and the fourth wireless communication module;

The UAV snow depth measurement device collects real-time track snow depth data under the current position in real time, and at the same time collects the train number through the Kinect sensor;

The workstation receives the messages collected by the UAV snow depth measurement device in real time, and transmits the messages to the snow disaster monitoring big data center, which analyzes and processes the messages;

The snow disaster monitoring big data center and the ground train control center dispatch and control the UAV snow depth measuring device and the train according to the above method, thereby realizing snow disaster monitoring and early warning.

Further, LED lights are also provided on the UAV snow depth measuring device.

Beneficial effect

The present invention provides a method and system for intelligent measurement and prediction of snow depth by UAVs along the railway. In front of the train, the real-time snow depth data at the track with a distance of S ₀ and the real-time snow depth data on the track at the front of the train, the real-time snow depth data at these two positions and the real-time train speed are returned to the snow disaster monitoring big data center together, so that The snow disaster monitoring big data center can continuously and dynamically monitor the operation of the train and the operating environment, thus filling the blind spots existing in the current monitoring methods; it makes great use of the flexibility of the UAV snow depth measurement device, with the help of unmanned Machine snow depth measurement devices, workstations, snow disaster monitoring big data center and ground train control center have established a monitoring network and historical database covering snow disaster dangerous areas; when the snow depth exceeds the safe snow depth, or the real-time speed of the train exceeds the safe operation When the speed is high, the snow disaster monitoring big data center sends an alarm message to the ground train control center, so that the ground train control center can dispatch the train in real time. The train can perform emergency braking in time after receiving the dispatch information, and stop before reaching the dangerous snow position; by keeping the UAV snow depth measurement device and the train relatively stationary, the reliability and timeliness of the collected data are ensured .

Description of drawings

Figure 1 is a schematic diagram of the position of the UAV snow depth measurement device and the train;

Fig. 2 is a schematic structural diagram of the system of the present invention.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

A method for intelligently measuring and predicting snow depth by UAV for snow disasters along railway lines, comprising the following steps:

Step 1: According to the historical ice and snow accumulation data on the track and the train operation accident data when the train is running along the railway track, select the continuous track section corresponding to the ice and snow accumulation on the track exceeding the safety value and the operation accident, and perform equalization on the selected track section. The distance is divided, and workstations are set up in each track section unit, and each workstation is equipped with two sets of UAV snow depth measurement devices;

The unmanned aerial vehicle snow depth measuring device comprises flying device and the ultrasonic snow depth measuring instrument, Kinect sensor, distance sensor and train speed measuring device loaded on the flying device;

The UAV snow depth measuring device communicates with the workstation, and the workstation, the snow disaster monitoring big data center and the ground control center communicate sequentially;

Step 2: Number each track section unit obtained in step 1, and record the start mileage, end mileage, train safety speed in the section unit, workstation number and UAV snow depth measurement device number of each track section unit;

Step 3: The target train enters the snow disaster dangerous track section, and starts the track snow depth monitoring task;

When the target train enters the snow disaster dangerous track section, the ground train control center will send the unit number of the monitored snow disaster dangerous track section unit number and the train number of the target controlled train to the snow disaster monitoring big data center, and the snow disaster monitoring big data center will send the information to the snow disaster dangerous track The workstation in the interval unit issues a measurement command to initialize the UAV track snow depth monitoring task;

The two sets of UAV snow depth measuring devices in the control station of the workstation take off synchronously and track the target train;

Step 4: and use the Kinect sensor on the UAV snow depth measurement device to collect the train number, send it to the snow disaster monitoring big data center and compare it with the target controlled train number sent by the ground train control center in advance. If the results are consistent, go to step 5, otherwise, the two sets of UAV snow depth measurement devices return to the workstation, stop tracking, and wait for the next command;

Step 5: Two sets of UAV snow depth measurement devices collect track snow depth data, train speed and relative distance from the train in real time, and transmit them to their workstations in real time;

Step 6: The snow disaster monitoring big data center compares the train speed and snow depth data with the pre-stored safety data in real time based on the messages to be received by the workstation, using the train number as the search key, and uses the real-time snow depth data to find Corresponding to the safe speed of the train, if the real-time speed of the train exceeds the safe speed corresponding to the real-time snow depth data, the snow disaster monitoring big data center will send out an alarm message;

When the ground train control center receives the alarm information, it will dispatch the train in real time;

Step 7: When the target train leaves the snow disaster dangerous track section, the ground train control center sends a signal of task completion to the snow disaster monitoring big data center, and the snow disaster monitoring big data center sends a signal of task completion to the workstation, and the workstation sends a signal to the two groups of drones The snow depth measuring device sends a signal that the task is completed, and the two sets of UAV snow depth measuring devices return to the workstation and enter the standby state.

As shown in Figure 1, when two sets of UAV snow depth measurement devices are synchronously tracking the target train, one of the UAV snow depth measurement devices maintains a flying posture and stops relatively still at the position above the head of the target controlled train. A group of unmanned aerial vehicle snow depth measurement devices are accelerated to fly in the direction of the target controlled train, and the distance between the head of the train is _S0 ;

S ₀ represents the safe braking distance in snow disasters, S ₀ =S×K, S represents the train braking distance, and K represents the safety factor; the train braking distance can be obtained according to the attributes of the train, and the reason for setting the safety factor K is that there is a radius of curvature of the track on the one hand ; On the other hand, because the friction coefficient of the track is different from that in the theoretical calculation, K can be 1.1-1.2.

When the snow depth data exceeds the snow depth safety value corresponding to the safe operation of the train, the snow disaster monitoring big data center sends out an alarm message.

In the pre-stored safety data, the train safety speed decreases with the increase of track snow depth.

The real-time snow depth data is sequentially transmitted to the workstation through the UAV snow depth measurement device 1/UAV snow depth measurement device 2 to the snow disaster monitoring big data center;

The real-time running speed of the train is sequentially transmitted to the snow disaster monitoring big data center through the UAV snow depth measurement device 2 → workstation;

The relative distance between the UAV snow depth measurement device 2 and the train passes through the UAV snow depth measurement device 2 → workstation → snow disaster monitoring big data center;

The measurement time of the train speed, track snow depth and the distance data between the UAV and the train is sequentially transmitted to the snow disaster monitoring big data center through the UAV snow depth measurement device 1/UAV snow depth measurement device 2→workstation.

After the UAV snow depth measurement device moves forward synchronously with the target train, when the target travels to the next workstation, the UAV snow depth measurement device performs task handover, and the ground train control center issues instructions to the latest workstation entered by the target train, The two UAV snow depth measurement devices of the workstation take off and track the target train synchronously. The two UAV snow depth measurement devices that originally flew enter the workstation for charging and send the real-time speed and position of the target train to the ground train control center.

As shown in Figure 2, a snow disaster UAV snow depth intelligent measurement and prediction system along the railway, including:

The ground train control center is used to receive the processing data transmitted in real time from the snow disaster monitoring big data center, and dispatch the train, including a train dispatching module, an alarm information storage module and a first wireless communication module;

The snow disaster monitoring big data center is used to receive the real-time collected data sent from the workstation, and analyze and process the data, including the UAV scheduling module, task data storage module, central processing module and the second wireless communication module;

A workstation, including a UAV operation module, a UAV database, a third wireless communication module and at least two UAV snow depth measuring devices;

Wherein, the unmanned aerial vehicle snow depth measuring device 1 comprises a flight device and an ultrasonic snow depth measuring instrument installed thereon, a distance sensor, a Kinect sensor and a fourth wireless communication module;

UAV snow depth measuring device 2 comprises flight device and ultrasonic snow depth measuring instrument installed thereon, Kinect sensor, train speed measuring device, distance sensor and the fourth wireless communication module;

The UAV snow depth measurement device collects real-time track snow depth data under the current position in real time, and at the same time collects the train number through the Kinect sensor;

The workstation receives the messages collected by the UAV snow depth measurement device in real time, and transmits the messages to the snow disaster monitoring big data center, which analyzes and processes the messages;

The snow disaster monitoring big data center and the ground train control center dispatch and control the UAV snow depth measuring device and the train according to the above method, thereby realizing snow disaster monitoring and early warning.

LED lights are also provided on the UAV snow depth measurement device for auxiliary measurement when driving at night.

By using the scheme described in the present invention, the railway dispatching part no longer needs to install a large number of high-cost snow depth monitoring points along the railway, and directly avoids the problem of missing control of a certain running train and the snow depth along the railway from the technical solution. The blind spot problem of deep measurement has obviously improved the operational safety of trains in heavy snow weather.

Claims (7)

1. a kind of Along Railway snow disaster unmanned plane snow depth intelligent measure and Forecasting Methodology, it is characterised in that comprise the following steps：

Step 1：History ice and snow accumulation data and train operation thing when being run according to train along Along Railway track on track Therefore data, select ice and snow accumulation on track and exceed continuous orbit section corresponding to safety value and interruption of service, to selected track regions Between equidistantly divided, and each track section unit set work station, each two groups of unmanned plane snow depths of workstation configuration Measurement apparatus；

The unmanned plane snow depth measurement apparatus include flight instruments and be loaded on flight instruments ultrasonic snow depth measuring instrument, Kinect sensor, range sensor and locomotive velocity measuring device；

The unmanned plane snow depth measurement apparatus is communicated with the work station, the work station, snow disaster monitoring large data center Communicated successively with ground control centre；

Step 2：The each track section unit obtained to step 1 is numbered, and records the beginning of each track section unit Mileage, terminate mileage, train safe speed, station number and unmanned plane snow depth measurement apparatus numbering in the unit of section；

Step 3：Target train enters snow disaster danger track section, starts track snow depth monitoring task；

When target train enters snow disaster danger track section, snow disaster danger track regions that railway trains control centre will be monitored Between the train number of element number and the controlled train of target be sent to snow disaster monitoring large data center, snow disaster monitoring large data center Work station into the snow disaster danger track section unit sends measurement instruction, carries out the first of unmanned plane track snow depth monitoring task Beginningization；

Two groups of unmanned plane snow depth measurement apparatus in work station control station are synchronously taken off and track target train；

Step 4：And using the Kinect sensor collection train number in unmanned plane snow depth measurement apparatus, send it to snow disaster The controlled train number of target that monitoring large data center is sent out in advance with railway trains control centre is compared, if comparison result Unanimously, then into step 5, otherwise, 2 groups of unmanned plane snow depth measurement apparatus return to work station, stop tracking, and wait refers to next time Order；

Step 5：The real-time acquisition trajectory snow depth data of two groups of unmanned plane snow depth measurement apparatus, train speed and relative with train Distance, and real-time Transmission is to affiliated work station；

Step 6：Snow disaster monitoring large data center according to work station by the message of reception, it is real using train number as search key When to train speed, snow depth data compared with the secure data prestored, utilize real-time snow depth number According to the safe speed for finding corresponding train, if the real-time speed of train corresponds to safe speed more than real-time snow depth data When, then snow disaster monitoring large data center sends a warning；

Step 7：After target train rolls snow disaster danger track section away from, railway trains control centre is into snow disaster monitoring big data The heart sends the signal that task is completed, and snow disaster monitoring large data center sends the signal that task completes to work station, and work station is to two Group unmanned plane snow depth measurement apparatus sends the signal that task is completed, and two groups of unmanned plane snow depth measurement apparatus return work stations, which enter, to be treated Machine state.

2. according to the method for claim 1, it is characterised in that two groups of unmanned plane snow depth measurement apparatus synchronized tracking target columns Che Shi, one of which unmanned plane snow depth measurement apparatus rest against the controlled train head of target with keeping the posture geo-stationary of flight Top position point, another group of unmanned plane snow depth measurement apparatus accelerates flight on the controlled train direct of travel of target, and and train Distance is S between head₀；

S₀Represent snow disaster safe stopping distance, S₀=S × K, S represent train braking distance, and K represents safety coefficient, and span is 1.1-1.2。

3. according to the method for claim 2, it is characterised in that when snow depth data exceed corresponding to safe train operation During snow depth safety value, snow disaster monitoring large data center sends a warning.

4. according to the method for claim 3, it is characterised in that in the secure data prestored, train safety speed Degree reduces with the increase of track snow depth thickness.

5. according to the method described in claim any one of 1-4, it is characterised in that when unmanned plane snow depth measurement apparatus and target column After car synchronously moves ahead, in target travel to next work station, unmanned plane snow depth measurement apparatus carries out task handing-over, railway trains Control centre sends instruction to the newest work station that target train enters so that two unmanned plane snow depths measurement dress of the work station The synchronized tracking target train that takes off is put, the two unmanned plane snow depth measurement apparatus originally flown enter the work station, are charged, And the real-time speed of target train and position are sent to railway trains control centre.

6. a kind of Along Railway snow disaster unmanned plane snow depth measurement and forecasting system, it is characterised in that including：

Railway trains control centre, for receiving the processing data from snow disaster monitoring large data center real-time Transmission, and to train It is scheduled, including train scheduling module, warning information memory module and the first wireless communication module；

Snow disaster monitoring large data center, for receiving the real-time data collection sent from work station, and data are carried out at analysis Reason, including unmanned plane scheduler module, task data memory module, CPU module and the second wireless communication module；

Work station, including unmanned plane operation module, Unmanned Aerial Vehicle Data storehouse, the 3rd wireless communication module and at least two unmanned planes Snow depth measurement apparatus；

Wherein, a unmanned plane snow depth measurement apparatus includes flight instruments and the ultrasonic snow depth measuring instrument being installed on it, distance Sensor, Kinect sensor and the 4th wireless communication module；

Another unmanned plane snow depth measurement apparatus includes flight instruments and the ultrasonic snow depth measuring instrument being installed on it, Kinect are passed Sensor, locomotive velocity measuring device, range sensor and the 4th wireless communication module；

Collection is presently in the real-time track snow depth data below position to unmanned plane snow depth measurement apparatus in real time, passes through simultaneously Kinect sensor gathers train number；

Work station receives the message that unmanned plane snow depth measurement apparatus gathers in real time, and transfers a message in snow disaster monitoring big data The heart, snow disaster monitoring large data center analyze and process to message；

The method pair of the snow disaster monitoring large data center and railway trains control centre described according to claim any one of 1-5 Unmanned plane snow depth measurement apparatus and train are scheduled control, so as to realize snow disaster monitoring and early warning.

7. system according to claim 6, it is characterised in that be additionally provided with LED in the unmanned plane snow depth measurement apparatus Lamp.