CN107024601B - A kind of the Along Railway wind measurement method and control system of control of intelligently being continued a journey based on unmanned aerial vehicle group - Google Patents
A kind of the Along Railway wind measurement method and control system of control of intelligently being continued a journey based on unmanned aerial vehicle group Download PDFInfo
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Abstract
The invention discloses a kind of based on the unmanned aerial vehicle group Along Railway wind measurement method that intelligently continuation of the journey controls and control system, this method is utilized by flexible and changeable unmanned aerial vehicle group continuation of the journey control method, realize to Along Railway a little so that multiple spot real-time measuring wind speed, time of measuring and target location can flexibly be selected according to task needs；By using the mobility of unmanned plane, the measurement range of measuring wind speed task has been obviously improved；Air monitoring large data center is assessed the state of the unmanned plane wind speed measuring device in task section before task starts, and incorporate the consideration influenceed on Along Railway wind direction on unmanned plane working condition, the unmanned plane wind speed measuring device progress task for being wherein most appropriate for task is found out, so as to further ensure the reliability of task；Air monitoring large data center remains the state estimation to unmanned plane wind speed measuring device to ensure being normally carried out for task during task progress.
Description
Technical Field
The invention belongs to the field of railway track monitoring, and particularly relates to a railway line wind speed measuring method and system based on unmanned aerial vehicle group intelligent endurance control.
Background
With the increasing importance of rail transit in national economy, the loss caused by natural disaster accidents becomes a topic of great concern. One particularly important aspect of this is the rollover accident caused by extreme weather, which has become a risk factor that threatens the proper operation of the railway system.
In order to reduce or even avoid railway safety accidents caused by strong winds, it is essential to monitor the wind speed along the track. In recent years, the railroads including China begin to pay attention to the development of a novel railway strong wind real-time monitoring and early warning system. For example, the System for monitoring and warning the strong wind is developed by the German national railway company (Deutsche Bahn AG), the System for train warning by Windas System designed by the EAST Japan railway company (JR-EAST), and the System for monitoring and warning the strong wind in Lanxin, Qinghai-Tibet and other strong wind lines developed by the original Ministry of railways in China. The early warning systems have similar working mechanisms, and are characterized in that a certain number of wind measuring stations are arranged in a strong wind area along a railway, wind speed information along the railway is collected in real time, road conditions (bridges/embankments/large curves) along the railway and train information (vehicle types/loads/driving speeds) are fused, a vehicle overturning stability model under severe strong wind environments is established, and high-speed trains of different types are dispatched and commanded in real time under different railway road conditions/areas and wind speed levels.
However, a wind station of such systems can only monitor a point or local area along the railway, and the spatial relationship between the collected data itself is discrete. Therefore, a wind speed monitoring blind area may exist in the established wind speed monitoring zone. The existence of blind areas is shown in the following two aspects: 1. the position beyond the measuring range of the wind measuring station can not be measured; 2. the position where the wind measuring station is difficult to erect cannot be measured. If the blind area is filled up in order to enhance the comprehensiveness of the collected data, a huge number of wind measuring stations are required to be built, and the cost and the maintenance cost are huge.
Disclosure of Invention
The invention provides a method and a system for measuring wind speed along a railway based on intelligent endurance control of an unmanned aerial vehicle group, aiming at obtaining real-time dynamic information of the wind speed along the railway by a railway dispatching part by introducing the unmanned aerial vehicle to measure the wind speed in real time and filling a blind area caused by discrete type and fixity of the existing measurement technical means.
A method for measuring wind speed along a railway based on unmanned aerial vehicle group electric quantity endurance control comprises the following steps:
the method comprises the following steps:
step 1: the method comprises the following steps that workstations are arranged at equal intervals along a railway track line, and each workstation is provided with a plurality of unmanned aerial vehicle wind speed measuring devices;
the unmanned aerial vehicle wind speed measuring device is communicated with the workstation, and the workstation, the wind speed monitoring big data center and the ground control center are communicated in sequence;
step 2: when the ground train control center sends a measurement task starting instruction to the wind speed monitoring big data center, the wind speed monitoring big data center sends a task initialization instruction to a work station in the area where the measurement task is located;
and step 3: the large wind speed monitoring data center selects a wind speed measuring device of an unmanned aerial vehicle under the jurisdiction of a workstation from the interval of the measurement task to measure the railway wind speed;
judging whether the task position coordinates are located in a cross area of the radius of the workstation, if so, calculating the electric quantity consumption coefficient of each unmanned aerial vehicle wind speed measuring device under the jurisdiction of two workstations adjacent to the task positioniSelecting the power consumption coefficientThe wind speed measuring device of the small unmanned aerial vehicle, otherwise, the power consumption coefficient of each wind speed measuring device of the unmanned aerial vehicle administered in the workstation where the task position is located is calculatediSelecting an unmanned aerial vehicle wind speed measuring device with the minimum electric quantity consumption coefficient;
the electric quantity of the unmanned aerial vehicle wind speed measuring device participating in electric quantity consumption coefficient calculation is larger than the electric quantity required for completing the monitoring task;
wherein,ithe electric quantity consumption coefficient of the ith unmanned aerial vehicle wind speed measuring device is represented; i isiAnd ItiRespectively representing the electric quantity consumed by the ith unmanned aerial vehicle wind speed measuring device for completing the task and the current residual electric quantity;the wind direction influence factor S representing the wind speed measuring device of the ith unmanned aerial vehicle along the direction of the flight pathiThe linear distance between the workstation where the ith unmanned aerial vehicle wind speed measuring device is located and the position coordinate of the task is represented;
the wind direction influence factor is decomposed into the wind speed of a direction vector between the wind speed measuring device of the unmanned aerial vehicle and the task coordinate according to the real-time wind speed of a workstation where the wind speed measuring device of the unmanned aerial vehicle is located, and the wind speed is obtained from a pre-stored wind speed and wind direction influence factor corresponding table;
the wind speed has a direction, and when the flight direction of the unmanned aerial vehicle wind speed measuring device flying to the task position is on the same side with the wind speed direction, the wind direction influence factor is a downwind factor; when the wind direction is on different sides, the wind direction influence factor is an upwind factor;
the wind direction influence factor is determined by experiments, and the calculation formula is as follows:
wherein E is the power consumption in the flight process, SCis-transAnd SInverse directionThe distance that unmanned aerial vehicle wind speed measurement device walked under the downwind and upwind condition is shown.Andthe expression is respectively a downwind factor and an upwind factor, and the meaning is the electric quantity consumed when the wind speed measuring device of the unmanned aerial vehicle walks through a unit distance.
And 4, step 4: the selected unmanned aerial vehicle wind speed measuring device reaches a task position by utilizing a vehicle-mounted GPS (global positioning system), measures the real-time wind speed of the task position, and returns wind speed data, measuring time and the residual electric quantity of the device to a workstation in real time, and the workstation sends the data returned by the unmanned aerial vehicle wind speed measuring device to a wind speed monitoring big data center;
the wind speed monitoring big data center evaluates the unmanned aerial vehicle wind speed measuring device according to the obtained residual electric quantity information, when the residual electric quantity of the device is insufficient, the task is reinitialized, and the step 3 is repeated;
and 5: and the wind speed monitoring big data center receives and stores task information returned by the unmanned aerial vehicle wind speed measuring device. When a task is divided into a plurality of subtasks, the subtask information is stored in the task information in groups according to a time sequence;
step 6: the ground train control center receives and stores task information returned by the wind speed monitoring big data center, and schedules the train in real time according to the information content;
and 7: after the measurement task is completed, the wind speed monitoring big data center sends a task completion signal to the workstation, the workstation sends a task completion signal to the unmanned aerial vehicle wind speed measurement device which performs the task, and the unmanned aerial vehicle wind speed measurement device returns to the workstation to enter a standby state.
The wind speed measurement task position can be selected according to the requirement, and for the position which is difficult to monitor by the conventional means, the data can be collected by utilizing the method. These locations typically include a location on the interior wall surface of the tunnel (e.g., the roof, etc.) where it is difficult to erect the measuring device (e.g., the ramp, etc.).
Further, if the residual electric quantity of the selected unmanned aerial vehicle wind speed measuring device cannot complete the task, the wind speed monitoring big data center divides the original task into a plurality of groups of subtasks, sends a subtask instruction to the workstation, and reselects the unmanned aerial vehicle wind speed measuring device.
The division principle is that the cruising ability of the unmanned aerial vehicle under the jurisdiction of the task section workstation is utilized to the maximum extent. The subtask instruction is identical in form to the original task instruction. And the wind speed monitoring big data center performs capacity evaluation based on the electric quantity consumption coefficient on the cruising state of the wind speed measuring device of the unmanned aerial vehicle under the jurisdiction of the workstation according to the subtasks, and the workstation sends the wind speed measuring device of the unmanned aerial vehicle meeting the lowest electric quantity consumption coefficient to a task position for executing a measuring task.
Furthermore, according to the sequence of the electric quantity consumption coefficients from small to large, the first N unmanned aerial vehicle wind speed measuring devices are selected to measure the wind speed of a section, and the number of N is 1< N < 5.
The multi-group unmanned aerial vehicle wind speed measuring devices measure the wind speed of the cross section, namely the multi-group unmanned aerial vehicle wind speed measuring devices are positioned in the same cross section, so that the aim of comprehensively measuring the wind speed of a certain position on the track is fulfilled, and the wind speed condition of a certain cross section on the track is comprehensively evaluated. When a plurality of groups of unmanned aerial vehicle wind speed measuring devices are assigned to measure wind speeds of a plurality of sections, a wind speed measuring cylinder is formed, the unmanned aerial vehicle wind speed measuring device group measures the real-time wind speed in a certain section of space on a track, and a measuring area is changed into a three-dimensional space from a two-dimensional plane.
The utility model provides a railway wind speed along line measurement control system based on control of unmanned aerial vehicle crowd's intelligence continuation of journey, includes:
the ground train control center comprises a train dispatching module, a wind speed information storage module and a first wireless communication module;
the wind speed monitoring big data center comprises an unmanned aerial vehicle scheduling module, a task data storage module, a central processor module and a second wireless communication module;
the workstation comprises an unmanned aerial vehicle operation module, an unmanned aerial vehicle database, a third wireless communication module, a wind speed measuring module and a plurality of unmanned aerial vehicle wind speed measuring devices;
each unmanned aerial vehicle wind speed measuring device comprises a flying device, and an ultrasonic anemoscope, a train speed measuring device, a distance sensor, a Kinect sensor, a fourth wireless communication module and a GPS module which are loaded on the flying device;
the unmanned aerial vehicle wind speed measuring device collects wind speed data of a task position in real time;
the work station receives a message acquired by the unmanned aerial vehicle wind speed measuring device in real time, and transmits the message to the wind speed monitoring big data center, and the wind speed monitoring big data center analyzes and processes the message and then sends the message to the ground train control center;
the wind speed monitoring big data center and the ground train control center control the unmanned aerial vehicle wind speed measuring device according to the method, so that the wind speed measurement of the specific position along the railway is realized.
Further, still be provided with the LED lamp on the unmanned aerial vehicle wind speed measurement device.
Advantageous effects
The invention provides a method and a system for measuring the wind speed along a railway based on the intelligent endurance control of an unmanned aerial vehicle group, which realize the real-time wind speed measurement of one point or even multiple points along the railway by flexible and changeable unmanned aerial vehicle group endurance control, and the measurement time and the target position can be flexibly selected according to the task requirements. By utilizing the maneuverability of the unmanned aerial vehicle, the measurement range of the wind speed measurement task is remarkably improved, and the method has good performance even in the aspect that the conventional technical means is difficult to realize, such as long-time measurement task in a place where a wired power grid is difficult to erect; the method has the advantages that the flexible and changeable task target selection and the working mode which can divide the task when necessary are far superior to the prior means in accuracy, reliability and coverage, and the prior blind area is greatly filled; before the task starts, the wind speed monitoring big data center evaluates the state of the wind speed measuring device of the unmanned aerial vehicle in the task interval, considers the influence of the wind direction along the railway on the working state of the unmanned aerial vehicle, and selects the wind speed measuring device of the unmanned aerial vehicle which is most suitable for carrying out the task to carry out the task, so that the reliability of the task is further ensured; the wind speed monitoring big data center always keeps the state evaluation of the unmanned aerial vehicle wind speed measuring device in the task carrying process so as to ensure the normal carrying of the task: when the task in the step 4 is reinitialized again and meets the evaluation, the unmanned aerial vehicle wind speed measuring device continues the task; when the evaluation is not met, sending out the unmanned aerial vehicle wind speed measuring device meeting the evaluation to take over the task, and the endurance control method ensures the normal running of the task, avoids the task failure caused by the accident to a great extent, and improves the reliability and the accuracy; the control system is simple in structure, a real-time wind speed data flow network in a large range along a railway is established through a wind speed measurement network which is covered comprehensively, so that all-around measurement of the wind speed data flow network along the railway and the nearby area is achieved, massive data are provided for establishing a more complex, accurate and stable wind speed model and processing large data related to wind speed research, and the data are far superior to the existing technical means in quantity, representativeness and reliability.
Drawings
FIG. 1 is a schematic diagram of task interval division;
FIG. 2 is a flow chart of a primary wind speed measurement task;
fig. 3 is a schematic structural diagram of the system of the present invention.
Detailed Description
The invention will be further described with reference to the following figures and examples.
A method for measuring wind speed along a railway based on intelligent endurance control of an unmanned aerial vehicle group is shown as a flow of a primary wind speed measurement task in figure 2, and comprises the following steps:
step 1: the method comprises the following steps that workstations are arranged at equal intervals along a railway track line, and each workstation is provided with a plurality of unmanned aerial vehicle wind speed measuring devices;
the unmanned aerial vehicle wind speed measuring device is communicated with the workstation, and the workstation, the wind speed monitoring big data center and the ground control center are communicated in sequence;
step 2: when the ground train control center sends a measurement task starting instruction to the wind speed monitoring big data center, the wind speed monitoring big data center sends a task initialization instruction to a work station in the area where the measurement task is located;
and step 3: the large wind speed monitoring data center selects a wind speed measuring device of an unmanned aerial vehicle under the jurisdiction of a workstation from the interval of the measurement task to measure the railway wind speed;
judging whether the task position coordinates are located in a cross area of the radius of the workstation, if so, calculating the electric quantity consumption coefficient of each unmanned aerial vehicle wind speed measuring device under the jurisdiction of two workstations adjacent to the task positioniSelecting the unmanned aerial vehicle wind speed measuring device with the minimum electric quantity consumption coefficient, otherwise, calculating the electric quantity consumption coefficient of each unmanned aerial vehicle wind speed measuring device administered in the workstation where the task position is locatediSelecting an unmanned aerial vehicle wind speed measuring device with the minimum electric quantity consumption coefficient;
the electric quantity of the unmanned aerial vehicle wind speed measuring device participating in electric quantity consumption coefficient calculation is larger than the electric quantity required for completing the monitoring task;
wherein,ithe electric quantity consumption coefficient of the ith unmanned aerial vehicle wind speed measuring device is represented; i isiAnd ItiRespectively representing the electric quantity consumed by the ith unmanned aerial vehicle wind speed measuring device for completing the task and the current residual electric quantity;the wind direction influence factor S representing the wind speed measuring device of the ith unmanned aerial vehicle along the direction of the flight pathiThe linear distance between the workstation where the ith unmanned aerial vehicle wind speed measuring device is located and the position coordinate of the task is represented;
the wind direction influence factor is decomposed into the wind speed of a direction vector between the wind speed measuring device of the unmanned aerial vehicle and the task coordinate according to the real-time wind speed of a workstation where the wind speed measuring device of the unmanned aerial vehicle is located, and the wind speed is obtained from a pre-stored wind speed and wind direction influence factor corresponding table;
when no device can finish the task independently, the wind speed monitoring big data center divides the task according to the division principle, divides the original task into a plurality of groups of subtasks, and then repeats the steps;
and 4, step 4: the selected unmanned aerial vehicle wind speed measuring device reaches a task position by utilizing a vehicle-mounted GPS (global positioning system), measures the real-time wind speed of the task position, and returns wind speed data, measuring time and the residual electric quantity of the device to a workstation in real time, and the workstation sends the data returned by the unmanned aerial vehicle wind speed measuring device to a wind speed monitoring big data center;
the wind speed monitoring big data center evaluates the unmanned aerial vehicle wind speed measuring device according to the obtained residual electric quantity information, when the residual electric quantity of the device is insufficient, the task is reinitialized, and the step 3 is repeated;
and 5: and the wind speed monitoring big data center receives and stores task information returned by the unmanned aerial vehicle wind speed measuring device. When a task is divided into a plurality of subtasks, the subtask information is stored in the task information in groups according to a time sequence;
step 6: the ground train control center receives and stores task information returned by the wind speed monitoring big data center, and schedules the train in real time according to the information content;
and 7: after the measurement task is completed, the wind speed monitoring big data center sends a task completion signal to the workstation, the workstation sends a task completion signal to the unmanned aerial vehicle wind speed measurement device which performs the task, and the unmanned aerial vehicle wind speed measurement device returns to the workstation to enter a standby state.
The wind speed has a direction, when the flight direction of the unmanned aerial vehicle wind speed measuring device flying to the task position is at the same side with the wind speed direction, the wind direction influence factor is a downwind factorOn different sides, the wind direction influencing factor is an upwind factor
The wind direction influence factor is determined by experiments, and the calculation formula is as follows:
wherein E is the power consumption in the flight process, SCis-transAnd SInverse directionThe distance that unmanned aerial vehicle wind speed measurement device walked under the downwind and upwind condition is shown.Andthe expression is respectively a downwind factor and an upwind factor, and the meaning is the electric quantity consumed when the wind speed measuring device of the unmanned aerial vehicle walks through a unit distance.
The wind speed measurement task position can be selected according to the requirement, and for the position which is difficult to monitor by the conventional means, the data can be collected by utilizing the method. These locations typically include a location on the interior wall surface of the tunnel (e.g., the roof, etc.) where it is difficult to erect the measuring device (e.g., the ramp, etc.).
As shown in fig. 1, which is a schematic diagram of task interval division, for a section to be detected with a length of L, the section is divided into 4 intervals by using 5 groups of unmanned aerial vehicle wind speed measuring devices, a small circle in the diagram is a workstation set point, and a three-dimensional coordinate system is established with each workstation as an origin. Each set of drone anemometry devices has the same effective working radius, R in this sector. The length of one interval is a, and a ═ R. To completely cover the segment, it is guaranteed that 4R ≧ L.
For any section with the length of L0, if n groups of unmanned aerial vehicle wind speed measuring devices are adopted to cover the section, then (n-1) R is larger than or equal to L.
The division principle is that the cruising ability of the unmanned aerial vehicle under the jurisdiction of the task section workstation is utilized to the maximum extent. The subtask instruction is identical in form to the original task instruction. And the wind speed monitoring big data center performs capacity evaluation based on the electric quantity consumption coefficient on the cruising state of the wind speed measuring device of the unmanned aerial vehicle under the jurisdiction of the workstation according to the subtasks, and the workstation sends the wind speed measuring device of the unmanned aerial vehicle meeting the lowest electric quantity consumption coefficient to a task position for executing a measuring task.
The method can measure the wind speed of the cross section (namely, a plurality of groups of unmanned aerial vehicle wind speed measuring devices are positioned in the same cross section), and can achieve the aim of comprehensively measuring the wind speed of a certain position on the track, thereby comprehensively evaluating the wind speed condition of a certain cross section on the track. When a plurality of groups of unmanned aerial vehicle wind speed measuring devices are assigned to measure wind speeds of a plurality of sections, a wind speed measuring cylinder is formed, the unmanned aerial vehicle wind speed measuring device group measures the real-time wind speed in a certain section of space on a track, and a measuring area is changed into a three-dimensional space from a two-dimensional plane.
Real-time wind speed data, measuring time and residual electric quantity sequentially pass through an unmanned aerial vehicle wind speed measuring device → a workstation → a wind speed monitoring big data center.
As shown in fig. 3, a railway line wind speed measurement control system based on unmanned aerial vehicle crowd intelligent endurance control includes:
the ground train control center comprises a train dispatching module, a wind speed information storage module and a first wireless communication module;
the wind speed monitoring big data center comprises an unmanned aerial vehicle scheduling module, a task data storage module, a central processor module and a second wireless communication module;
the workstation comprises an unmanned aerial vehicle operation module, an unmanned aerial vehicle database, a third wireless communication module, a wind speed measuring module and a plurality of unmanned aerial vehicle wind speed measuring devices;
each unmanned aerial vehicle wind speed measuring device comprises a flying device, and an ultrasonic anemoscope, a train speed measuring device, a distance sensor, a Kinect sensor, a fourth wireless communication module and a GPS module which are loaded on the flying device;
the unmanned aerial vehicle wind speed measuring device collects wind speed data of a task position in real time;
the work station receives a message acquired by the unmanned aerial vehicle wind speed measuring device in real time, and transmits the message to the wind speed monitoring big data center, and the wind speed monitoring big data center analyzes and processes the message and then sends the message to the ground train control center;
the wind speed monitoring big data center and the ground train control center control the unmanned aerial vehicle wind speed measuring device according to the method, so that the wind speed measurement of the specific position along the railway is realized.
And the unmanned aerial vehicle wind speed measuring device is also provided with an LED lamp.
By applying the scheme of the invention, the railway dispatching part does not need to install a large number of high-cost wind speed monitoring points along the railway, and the blind area caused by the discreteness and the fixity of the existing measuring means is filled.
The above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (5)
1. A railway line wind speed measurement method based on unmanned aerial vehicle group intelligent endurance control is characterized by comprising the following steps:
step 1: the method comprises the following steps that workstations are arranged at equal intervals along a railway track line, and each workstation is provided with a plurality of unmanned aerial vehicle wind speed measuring devices;
the unmanned aerial vehicle wind speed measuring device is communicated with the workstation, and the workstation, the wind speed monitoring big data center and the ground control center are communicated in sequence;
step 2: when the ground train control center sends a measurement task starting instruction to the wind speed monitoring big data center, the wind speed monitoring big data center sends a task initialization instruction to a work station in the area where the measurement task is located;
and step 3: the large wind speed monitoring data center selects a wind speed measuring device of an unmanned aerial vehicle under the jurisdiction of a workstation from the interval of the measurement task to measure the railway wind speed;
judging whether the task position coordinates are located in a cross area of the radius of the workstation, if so, calculating the electric quantity consumption coefficient of each unmanned aerial vehicle wind speed measuring device under the jurisdiction of two workstations adjacent to the task positioniSelecting the unmanned aerial vehicle wind speed measuring device with the minimum electric quantity consumption coefficient, otherwise, calculating the electric quantity consumption coefficient of each unmanned aerial vehicle wind speed measuring device administered in the workstation where the task position is locatediSelecting an unmanned aerial vehicle wind speed measuring device with the minimum electric quantity consumption coefficient;
the electric quantity of the unmanned aerial vehicle wind speed measuring device participating in electric quantity consumption coefficient calculation is larger than the electric quantity required for completing the monitoring task;
wherein,ithe electric quantity consumption coefficient of the ith unmanned aerial vehicle wind speed measuring device is represented; i isiAnd ItiRespectively representing the electric quantity consumed by the ith unmanned aerial vehicle wind speed measuring device for completing the task and the current residual electric quantity;the wind direction influence factor S representing the wind speed measuring device of the ith unmanned aerial vehicle along the direction of the flight pathiThe linear distance between the workstation where the ith unmanned aerial vehicle wind speed measuring device is located and the position coordinate of the task is represented;
the wind direction influence factor is decomposed into the wind speed of a direction vector between the wind speed measuring device of the unmanned aerial vehicle and the task coordinate according to the real-time wind speed of a workstation where the wind speed measuring device of the unmanned aerial vehicle is located, and the wind speed is obtained from a pre-stored wind speed and wind direction influence factor corresponding table;
and 4, step 4: the selected unmanned aerial vehicle wind speed measuring device reaches a task position by utilizing a vehicle-mounted GPS (global positioning system), measures the real-time wind speed of the task position, and returns wind speed data, measuring time and the residual electric quantity of the device to a workstation in real time, and the workstation sends the data returned by the unmanned aerial vehicle wind speed measuring device to a wind speed monitoring big data center;
the wind speed monitoring big data center evaluates the unmanned aerial vehicle wind speed measuring device according to the obtained residual electric quantity information, when the residual electric quantity of the device is insufficient, the task is reinitialized, and the step 3 is repeated;
and 5: and the wind speed monitoring big data center receives and stores task information returned by the unmanned aerial vehicle wind speed measuring device. When a task is divided into a plurality of subtasks, the subtask information is stored in the task information in groups according to a time sequence;
step 6: the ground train control center receives and stores task information returned by the wind speed monitoring big data center, and schedules the train in real time according to the information content;
and 7: after the measurement task is completed, the wind speed monitoring big data center sends a task completion signal to the workstation, the workstation sends a task completion signal to the unmanned aerial vehicle wind speed measurement device which performs the task, and the unmanned aerial vehicle wind speed measurement device returns to the workstation to enter a standby state.
2. The method of claim 1, wherein if the selected wind speed measurement device of the unmanned aerial vehicle cannot complete the task, the wind speed monitoring big data center divides the original task into a plurality of sub-tasks, sends sub-task instructions to the workstation, and reselects the wind speed measurement device of the unmanned aerial vehicle.
3. The method according to claim 2, wherein the first N unmanned aerial vehicle wind speed measurement devices are selected to measure the wind speed of a cross section according to the sequence of the power consumption coefficients from small to large, wherein 1< N < 5.
4. The utility model provides a railway wind speed along line measurement control system based on control of unmanned aerial vehicle crowd's intelligence continuation of journey, its characterized in that includes:
the ground train control center comprises a train dispatching module, a wind speed information storage module and a first wireless communication module;
the wind speed monitoring big data center comprises an unmanned aerial vehicle scheduling module, a task data storage module, a central processor module and a second wireless communication module;
the workstation comprises an unmanned aerial vehicle operation module, an unmanned aerial vehicle database, a third wireless communication module, a wind speed measuring module and a plurality of unmanned aerial vehicle wind speed measuring devices;
each unmanned aerial vehicle wind speed measuring device comprises a flying device, and an ultrasonic anemoscope, a train speed measuring device, a distance sensor, a Kinect sensor, a fourth wireless communication module and a GPS module which are loaded on the flying device;
the unmanned aerial vehicle wind speed measuring device collects wind speed data of a task position in real time;
the work station receives a message acquired by the unmanned aerial vehicle wind speed measuring device in real time, and transmits the message to the wind speed monitoring big data center, and the wind speed monitoring big data center analyzes and processes the message and then sends the message to the ground train control center;
the wind speed monitoring big data center and the ground train control center control the unmanned aerial vehicle wind speed measuring device according to the method of any one of the claims 1 to 3, so that the wind speed measurement of a specific position along a railway is realized.
5. The system of claim 4, wherein the unmanned aerial vehicle wind speed measurement device is further provided with an LED lamp.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710298265.8A CN107024601B (en) | 2017-04-30 | 2017-04-30 | A kind of the Along Railway wind measurement method and control system of control of intelligently being continued a journey based on unmanned aerial vehicle group |
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| CN201710298265.8A CN107024601B (en) | 2017-04-30 | 2017-04-30 | A kind of the Along Railway wind measurement method and control system of control of intelligently being continued a journey based on unmanned aerial vehicle group |
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| CN108510737B (en) * | 2018-04-12 | 2020-04-10 | 中南大学 | Unmanned vehicle power supply real-time monitoring method and device integrating wind environment |
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| CN110286390A (en) * | 2019-06-11 | 2019-09-27 | 中国科学院合肥物质科学研究院 | A kind of specified path wind measurement method, device and windfinding radar scaling method |
| CN111999522B (en) * | 2020-07-17 | 2022-09-06 | 山西省交通建设工程质量检测中心（有限公司） | High-precision tunnel wind speed detection system and method thereof |
| CN112730881B (en) * | 2020-12-15 | 2023-11-10 | 苏州西热节能环保技术有限公司 | Method for detecting air flow distribution state in inlet flue of dust remover |
| CN114252646A (en) * | 2021-11-11 | 2022-03-29 | 浙江三一装备有限公司 | Wind speed early warning method and device and operation machine |
| CN115290923B (en) * | 2022-10-08 | 2023-01-20 | 广东电网有限责任公司中山供电局 | Method, system, equipment and medium for monitoring wind speed along power transmission line |
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