CN110726850A - Railway crosswind early warning system based on wind direction decomposition and crosswind strength calculation method - Google Patents
Railway crosswind early warning system based on wind direction decomposition and crosswind strength calculation method Download PDFInfo
- Publication number
- CN110726850A CN110726850A CN201910952000.4A CN201910952000A CN110726850A CN 110726850 A CN110726850 A CN 110726850A CN 201910952000 A CN201910952000 A CN 201910952000A CN 110726850 A CN110726850 A CN 110726850A
- Authority
- CN
- China
- Prior art keywords
- data
- wind
- crosswind
- quality control
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P5/00—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P13/00—Indicating or recording presence, absence, or direction, of movement
- G01P13/02—Indicating direction only, e.g. by weather vane
- G01P13/025—Indicating direction only, e.g. by weather vane indicating air data, i.e. flight variables of an aircraft, e.g. angle of attack, side slip, shear, yaw
Abstract
The invention discloses a railway crosswind early warning system based on wind direction decomposition and a crosswind strength calculation method, wherein the system comprises a data acquisition unit, a quality control unit, a detection unit and an alarm unit, the data acquisition unit acquires wind speed and wind direction data, acquires the geographic position of a current station through a positioning module and calls a terrain element database, then the data is sent to the data quality control unit, a meteorological element numerical interval which appears in history of the station and meteorological data of adjacent stations are compared and analyzed, invalid data are deleted and error data are corrected, finally the data calculated by the data quality control unit are transmitted to the detection unit to be compared with a preset threshold value, and finally a control instruction is sent to the alarm unit. The system provided by the invention can assist in surveying and mapping meteorological historical data and improve crosswind early warning capability and accuracy.
Description
Technical Field
The invention belongs to traffic and transportation meteorological measurement and early warning, and particularly relates to a railway crosswind early warning system based on wind direction decomposition and a crosswind strength calculation method.
Background
In recent years, with the rapid increase of national economy, the transportation industry plays an important role, and the railway transportation industry as the life line of the national economy is rapidly developed. In order to construct a high-speed railway basic route map with five longitudinal lines, six transverse lines and eight connecting lines, the high-speed railway in China is also developed at a very high speed. Under the background of rapid development of high-speed railways, research on the safety of the high-speed railways is increasingly playing.
The high-speed railway can inevitably pass through some sections with complex terrains and severe natural environments along the railway, wherein wind-induced disasters are particularly serious. With the continuous increase of the speed of the high-speed railway train, in addition, under the strong crosswind environment, the aerodynamic characteristics of the train are rapidly deteriorated, and great threat is caused to the safe operation of the off-speed railway. Train accidents caused by strong crosswind occur at home and abroad, and great loss is caused to traffic and personnel safety.
Due to the fact that the high-speed train is high in speed and the train body is light in weight, casualties are serious when accidents happen. In order to guarantee the driving safety of the train in the wind area, a wind speed measuring point is arranged near the line, the change condition of the wind speed is monitored, and the wind speed of a remote incoming flow is reflected by the wind speed of the measuring point. However, when a train passes by at a high speed, the wind characteristics along the railway become extremely complex and change violently, and the strong wind in the environmental wind parallel to the train running direction has little influence on the train although the wind speed is fast, but is easy to report by mistake. Therefore, the wind characteristics along the railway are detected, the wind direction is decomposed and analyzed after the quality of the collected data is controlled, and the wind direction is in the cross wind direction perpendicular to the railway, so that the alarm accuracy can be effectively improved, and the safe operation of the train is guaranteed.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects of the transverse early warning analysis technology in the running process of the existing railway train and improving the running safety of the railway train, the invention aims to provide a railway crosswind early warning system based on wind direction decomposition, and aims to provide a crosswind strength calculation method for the railway crosswind early warning system
The technical scheme is as follows: a railway train cross wind early warning system based on data quality control comprises a data acquisition unit, a data quality control unit, a detection unit and an alarm unit, wherein the data acquisition unit acquires wind speed and wind direction data, acquires the geographic position and the terrain element information of a station through positioning, calls a terrain element database, and then sends the data to the data quality control unit, and the data quality control unit is used for checking whether the cross wind data is in the numerical range of the historical meteorological data of the station, rejecting invalid data and correcting error data; the data calculated by the data quality control unit is transmitted to the control unit for actual data verification, and finally a control instruction is issued to the alarm unit.
Further, the data acquisition unit include main control unit, IO mouthful module, orientation module, transmission module, power module, LCD display, report wrong module and LAN module, main control unit is supplied power by power module, include and insert external memory or meteorological information collection equipment through IO mouthful module, include and acquire topographic data after acquireing website locating information through orientation module to with data transmission to remote server and realize the data sharing through LAN module through transmission module, main control unit shows the data information of gathering through the LCD display, reminds data transmission error warning and network error warning through reporting wrong module.
Furthermore, in the data acquisition unit, the meteorological information acquisition equipment comprises a wind speed sensor and a wind direction sensor; the positioning module is a GPS module; the transmission module is a GPRS module.
Furthermore, the data quality control unit performs comparative analysis on the data of the station, the data of the adjacent station and the historical data of the station on wind direction and wind speed, and the comparative analysis comprises internal consistency check, time consistency check, threshold value check and extreme value check.
Further, the data quality control unit checks and judges the following types:
(a) if the wind direction degree in the obtained adjacent station data or the historical data of the station is more than 360 degrees or less than 0 degree, marking the wind component as error data;
(b) if the wind direction degree is greater than 1 degree and the wind speed is 0, marking the wind component as error data;
(c) and setting a wind speed extreme value of the site, and marking the wind observation data larger than the extreme value as error observation.
Furthermore, the detection unit comprises a wind speed comparator, a wind direction comparator and an early warning control module, and the early warning control module controls the alarm unit.
The calculation method of the crosswind strength for implementing the early warning system comprises the following steps:
(1) acquiring two or more groups of wind speed and wind direction data of a station through a wind speed sensor, and establishing a coordinate system in the track traveling direction of the station;
(2) calculating a resultant speed according to the wind speeds in any two groups of different directions, and decomposing the resultant speed to an orthogonal coordinate axis established in the track direction;
the calculation expression of the resultant velocity is:
and then decomposing the resultant speed average wind speed in the vertical train running direction to obtain the crosswind average wind speed:
the total crosswind average wind speed can be obtained by adding the crosswind average wind speeds obtained by decomposing each wind direction:
wherein, the x direction meterThe track direction, the y direction represents the vertical track direction, theta is the included angle between the main wind direction and the train,
the speed component of the ambient wind speed in the vertical direction of train running is shown, and T is the measured average time interval;
and (3) comparing the crosswind average wind speed calculated in the step (2) with a preset value, and giving an alarm if the total crosswind speed exceeds a set value.
The data correction method of the early warning system is implemented, an inverse distance interpolation method is adopted for correcting abnormal data, and the method comprises the following calculation processes:
the weighting function expression is:
wherein p is an arbitrary positive real number, and is usually defined as p ═ 2;
distance h from discrete point to interpolated pointiComprises the following steps:
in the formula, (x, y) is an interpolation coordinate, and (xi, yi) is a discrete point coordinate;
wherein R is the distance from the interpolation point to the farthest discrete point; n is the total number of discrete points.
The data quality control unit corrects the error data by using the method, improves the accuracy of the data, and also improves the accuracy of the early warning system.
Has the advantages that: compared with the prior art, the invention has the remarkable effects that: firstly, the data acquired by the collector is subjected to reasonable quality control, so that the accuracy of the data is enhanced, the accuracy of environmental wind assessment is improved, invalid data are removed, and the probability of false triggering alarm is reduced; secondly, the data after quality control can also be transmitted to other platforms or devices through a GPRS transmission module to carry out other aspects of research on environmental wind along the railway; thirdly, the acquisition device acquires the geographical position of a wind field through a GPS module, then inputs terrain data of the place into a main controller by calling a national terrain element database, and can better judge the influence of special terrain wind such as canyon wind, tunnel wind, sea land wind and the like on the running safety of trains along the railway by combining wind speed and wind direction data; fourthly, the data transmission part adopts GPRS wireless transmission, so that the cost is saved, and the rapidness, convenience, instantaneity and adaptability of data transmission are improved; fifthly, for the reliability of data transmission, the main acquisition device is provided with an error reporting module which is used for reminding workers to perform corresponding processing in time when data transmission errors or network problems occur; for data visualization, the main acquisition device is provided with an LCD display module for displaying corresponding information. In order to realize data sharing, the main collector is connected with a local area network through an RJ45 interface; finally, the method for decomposing the wind speed and the wind direction is added, the parallel wind which has small influence on the running safety of the railway train but is easy to trigger an alarm by mistake can be better eliminated, the crosswind which has large influence on the safety of the railway train can be better detected and an alarm can be given, and the occurrence of railway crosswind disasters can be reduced.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a diagram of a meteorological element data acquisition and transmission circuit according to the present invention;
FIG. 3 is a schematic exploded view of the wind direction of a railway
FIG. 4 is a data quality control flow diagram;
FIG. 5 is a flow chart of the system operation of the present invention
FIG. 6 is a schematic view showing daily variation of wind speed in the embodiment;
FIG. 7 is a diagram illustrating a wind direction rose in the embodiment.
Detailed Description
To further illustrate the technical solutions disclosed in the present invention, the following description is further made with reference to the drawings and specific examples.
The invention provides a railway traveling crosswind early warning system based on data quality control, which comprises a data acquisition unit, a data quality control unit, a detection unit and an alarm unit, wherein the data acquisition unit acquires wind speed and wind direction data, and calls national 1: and 5 ten thousand topographic elements database, then sending the data to a data quality control unit, wherein the data quality control unit is used for checking whether the meteorological element record exceeds the maximum and minimum values of the meteorological elements which have occurred in the station historically, deleting invalid data and correcting error data, transmitting the data calculated by the data quality control unit to a detection unit for actual data verification, and finally sending a control instruction to an alarm unit.
Specifically, the data acquisition unit and the data quality control unit transmit data through the GPRS wireless transmission module, and the transmitted data is processed by an algorithm carried by the data quality control unit to obtain more accurate data after quality control, including verification of the acquired data and verification of historical meteorological data acquired by the station. The data quality control unit and the detection unit also transmit data through the GPRS wireless transmission module. And transmitting the quality-controlled data to terminal equipment, decomposing and analyzing elements such as wind speed, wind direction and the like, and displaying the result through a display, so that the quality-controlled data can be conveniently analyzed and used by workers and dispatching and supervising personnel.
The system structure is shown in figure 1, the data acquisition unit is composed of a main acquisition device, the main acquisition device is meteorological data acquisition equipment and mainly comprises a main controller, an I/O port module, a GPS module, a GPRS module, a power supply module, an LCD display, an error reporting module, a local area network module and the like. The main controller is a central mechanism for receiving, processing and transmitting data by the acquisition device and coordinating the work of each device. The I/O port receives a wind speed sensor and a wind direction sensor. The acquisition of the topographic data is completed by matching with a GPS module. The GPS module is used for positioning the geographic position of the railway, and the national 1: a database of 5 ten thousand terrain features. 1, nationwide: the 5 ten thousand terrain vector element database is a database composed of major core terrain elements such as water systems, contour lines, borders, traffic, residential areas and the like, wherein the database comprises spatial relationships among the terrain elements and related attribute information, then the terrain data of the terrain are sent into the main acquisition device through the I/O port, the data received by the I/O port are transmitted to the main controller for storage, the terrain data can be used for better analyzing the characteristics of environmental wind, and for example, the influence of canyon wind and sea and land wind on the railway line can be judged by combining the terrain and the environmental wind. The GPRS module realizes wireless transmission, and the main controller uploads data to the remote data platform through the GPRS module, so that the adaptability and the rapidity of data transmission are enhanced. In order to realize resource sharing, the main acquisition device is connected with the local area network through an RJ45 interface. And the power supply module of the main collector provides power for the work of the main controller. In order to realize the visualization of data, the main acquisition device is connected with the LCD through an HDMI interface. The main controller is connected with the error reporting module through a CAN bus.
In the embodiment, the main controller adopts an AT91SAM9260B-CFU type chip, the wind speed sensor adopts an NRG #40 type wind speed sensor, the wind direction sensor adopts an NRG #200P type wind direction sensor, the GPS module adopts a Beidou EG12-BZ chip, and the GPRS module adopts an RDA8851 type GPRS chip; the data collected by each meteorological sensor is analog signals, and needs to be converted into digital signals through analog-to-digital conversion, and the digital signals are transmitted to the I/O port of the collector through the output module and then transmitted to the main controller, as shown in FIG. 2. When a transmission data error or a network problem occurs, the main controller CAN start an error reporting module through the CAN bus to remind a worker to process in time; the data processed by the main collector can be displayed through an LCD display to realize the visualization of the data; and the data acquired by the data acquisition unit is transmitted to a remote data platform through a GPRS module for quality control of the data.
The extreme value check in the data quality control unit checks whether the meteorological element record exceeds the maximum and minimum values of meteorological elements which have historically appeared at the station. Considering the possible quality problem of the historical data, when extreme parameters are determined, the average value and the standard deviation of each element of each station and each month in the historical data of the ground weather forecast are firstly calculated, and then the standard deviation of 2 times plus or minus the average value is used as the extreme value of each element of each station and each month, namely the traditional method. However, many of the new automatic observers do not have a long history of calculating the mean and standard deviation of their elements to determine their extreme values, so that such new stations can only use the weather extreme values of the neighboring stations for quality control. The internal consistency check refers to the check whether the observed values of the same meteorological element or different meteorological elements conform to certain physical relation. For checking the wind direction and the wind speed, the following modes are adopted:
if the wind direction degree is greater than 360 degrees or less than 0 degree, marking the wind component as error data; if the wind direction degree is greater than 1 degree and the wind speed is 0, marking the wind component as error data; an extreme value of the wind speed is set to 75m/s, and wind observations above this extreme value are considered erroneous observations. The change of data with time has a certain rule, and whether the change of data conforms to the rule is called as time consistency check. The data can not exceed the variation range within a certain time, and the data beyond the variation range is suspicious data. Such as: the wind speed change value in adjacent minutes is less than or equal to 5m/s, the wind speed change value in adjacent hours is less than or equal to 30m/s, and the average wind speed in 10 minutes is less than or equal to the maximum wind speed in day.
The data quality control unit can eliminate errors in the data acquisition or data transmission process, the data passing through the data quality control unit can be more accurate, more accurate data resources are provided for later crosswind assessment, and effectiveness and accuracy of the crosswind assessment are improved. The data after passing through the data quality control unit is transmitted to the detection unit through the GPRS module, and also transmitted to other equipment through the GPRS module for research in other aspects. The detection unit comprises the decomposition and analysis of wind speed and wind direction.
Further, the data acquisition unit comprises a function that all the evaluation indexes are displayed in the LCD through the HDMI, and therefore the data acquisition unit is convenient for workers to use.
The invention also provides a method for analyzing the wind direction and evaluating the cross wind strength, which comprises the following steps:
the average wind speed reflects the speed level of the ambient wind over a time span. And for the collected wind speed data, the collected wind speed data comprises the information of horizontal wind speed and wind direction. The anemometer measures the wind speed data at two angles to obtain the resultant velocity, and the wind speed is resolved to the orthogonal coordinate axis established in the direction of the track, as shown in fig. 3.
Wherein the x direction represents the track direction, the y direction represents the vertical track direction, theta is the included angle between the main wind direction and the train, and U1 is the component speed of the ambient wind speed in the vertical direction of train running. The length of the average time interval T determines the calculation result of the average wind speed, the average wind characteristic cannot be analyzed when the time interval is too long or too short, and the average time interval is 10 min. Studies have shown that the average of the difference between the maximum and minimum wind speeds increases with time, but becomes slower after 10min, indicating that the average of wind speeds obtained at 10min is representative. In the specific calculation, the calculation time interval T is 10min, and the resultant velocity average wind speed calculation formula is as follows:
and then decomposing the resultant speed average wind speed in the vertical train running direction to obtain the crosswind average wind speed:
the total crosswind average wind speed can be obtained by adding the crosswind average wind speeds obtained by decomposing each wind direction:
and comparing the total crosswind average wind speed with a preset value, and giving an alarm if the total crosswind speed exceeds the set value. And finishing the alarm of strong crosswind.
In the data quality control, the correction of abnormal data adopts IDW (inverse distance interpolation), and the method comprises the following steps: weighting function:
wherein p is an arbitrary positive real number, and is usually defined as p ═ 2; h isiIs the distance from the discrete point to the interpolated point:
(x, y) are interpolated coordinates, (x)i,yi) Discrete point coordinates;
wherein R is the distance from the interpolated point to the farthest discrete point; n is the total number of discrete points;
the data quality control unit corrects the error data by using the method, improves the accuracy of the data, and also improves the accuracy of the early warning system.
In an implementation of the above system, a calculation of the crosswind intensity is included. As shown in fig. 4, the main controller transmits the collected data to the data quality control part through the GPRS wireless transmission module, and the data quality control part performs quality control on the transmitted data. The system labels data according to suspected error types of the data, and 4 quality control codes (0-3) are set: 0 indicates not checked, 1 indicates correct, 2 indicates suspect, and 3 indicates data error. The data quality control comprises threshold value check, extreme value check, internal consistency check and time consistency check, and in each check stage, a quality control code of the data is output, if the data is correct, the control code is 1, and if the data is incorrect, the control code is divided into 2 suspicious and 3 wrong. Checking the limit value, if the data is not in the limit value range, the data is wrong, and directly deleting the wrong data. And for extreme value check, internal consistency check and time consistency check, if the data does not meet the requirements, the data is suspicious data, because the data is possibly reasonable data generated due to abnormal weather, the data needs to be analyzed according to the weather condition of the day, if the data is reasonable data, the control code is changed into 1, if the data is abnormal data, the data can be corrected by using a reverse distance interpolation method, the corrected data is subjected to threshold value check, extreme value check, internal consistency check and time consistency check until the data is correct, the data is output, and the crosswind strength is evaluated. The quality control of the data can delete error data generated in the data acquisition or transmission process, improve the accuracy of the data and provide an accurate data base for strong crosswind alarm.
As shown in fig. 5, the quality-controlled data is transmitted to the detection unit through the GPRS wireless transmission module, and the detection unit analyzes the transmitted data, including the decomposition of wind speed and direction and the calculation and analysis of the final total crosswind average wind speed. And the data can also be transmitted to other equipment through the GPRS module for other researches. For example, the day/month change of the wind speed refers to the change of the wind speed within one day/month, and can reflect the change of the wind speed within one day/month. A typical day and a typical month are generally selected, the wind speed change from time to time of the typical day can reflect the general daily change rule of wind, and the wind speed change from day to day of the typical month can reflect the general monthly change rule of wind. As shown in fig. 6, a daily variation curve of wind speed is shown, and it can be seen from the graph that: the daily variation curve chart of the wind speed has a certain trend, the wind speed is smaller in the daytime, and the wind speed is larger at night. When the wind speed is from 3 hours to 17 hours, the wind speed is in a descending trend; from 17 hours to 3 days, the wind speed showed a clear rising trend. The statistical description of the wind direction is shown by a wind direction rose diagram. As shown in fig. 7, the wind direction rose graph represents the frequency of the wind direction, which is a percentage value of each wind direction and wind speed according to the average statistics of a certain region for many years, and is drawn according to a certain proportion, generally expressed by 8 or 16 compass directions, and is named because the shape thereof is very similar to a rose. The invention can provide reliable data after data quality control for the daily variation graph and the wind rose graph of the wind speed while providing strong crosswind alarm.
Claims (8)
1. The utility model provides a railway driving crosswind early warning system based on data quality control which characterized in that: the system comprises a data acquisition unit, a data quality control unit, a detection unit and an alarm unit, wherein the data acquisition unit acquires wind speed and wind direction data, a terrain element database is called by positioning geographic position and terrain element information of a station, and then the data is sent to the data quality control unit, and the data quality control unit is used for checking whether crosswind data is in a numerical range of meteorological data in history of the station, eliminating invalid data and correcting error data; the data calculated by the data quality control unit is transmitted to the detection unit for actual data verification, and finally a control instruction is issued to the alarm unit.
2. The railway train crosswind early warning system based on data quality control according to claim 1, wherein: the data acquisition unit comprises a main controller, an I/O port module, a positioning module, a transmission module, a power supply module, an LCD (liquid crystal display) display, an error reporting module and a local area network module, wherein the main controller is powered by the power supply module, and comprises an external memory or meteorological information acquisition device which is accessed through the I/O port module, topographic data is acquired after site positioning information is acquired through the positioning module, data is transmitted to a remote server through the transmission module, and data sharing among all sites is realized through the local area network module, the main controller displays acquired data information through the LCD display, and data transmission error warning and network error warning are reminded through the error reporting module.
3. The railway train crosswind early warning system based on data quality control according to claim 2, wherein: in the data acquisition unit, the meteorological information acquisition equipment comprises a wind speed sensor and a wind direction sensor; the positioning module is a GPS module; the transmission module is a GPRS module.
4. The railway train crosswind early warning system based on data quality control according to claim 1, wherein: and the data quality control unit compares and analyzes the data of the station with the data of the adjacent stations and the historical data of the station in terms of wind direction and wind speed, and comprises internal consistency check, time consistency check, threshold check and extreme value check.
5. The railway train crosswind early warning system based on data quality control according to claim 4, wherein: the data quality control unit checks and judges the following types:
(a) if the wind direction degree in the obtained adjacent station data or the historical data of the station is more than 360 degrees or less than 0 degree, marking the wind component as error data;
(b) if the wind direction degree is greater than 1 degree and the wind speed is 0, marking the wind component as error data;
(c) and setting a wind speed extreme value of the site, and marking the wind observation data larger than the extreme value as error observation.
6. The railway train crosswind early warning system based on data quality control according to claim 1, wherein: the detection unit comprises a wind speed comparator, a wind direction comparator and an early warning control module, and the early warning control module controls the warning unit.
7. A crosswind intensity calculation method for implementing the early warning system of claim 1, wherein: the method comprises the following steps:
(1) acquiring two or more groups of wind speed and wind direction data of a station through a wind speed sensor, and establishing a coordinate system in the track traveling direction of the station;
(2) calculating a resultant speed according to the wind speeds in any two groups of different directions, and decomposing the resultant speed to an orthogonal coordinate axis established in the track direction;
the calculation expression of the resultant velocity is:
and then decomposing the resultant speed average wind speed in the vertical train running direction to obtain the crosswind average wind speed:
and adding the crosswind average wind speeds obtained by decomposing each wind direction to obtain the total crosswind average wind speed:
wherein the x direction represents the direction along the track, the y direction represents the direction perpendicular to the track, theta is the angle between the main wind direction and the train,
the speed component of the ambient wind speed in the vertical direction of train running is shown, and T is the measured average time interval.
8. A data correction method for implementing the warning system of claim 1, characterized in that: the abnormal data is corrected by adopting an inverse distance interpolation method, which comprises the following calculation processes:
the weighting function expression is:
wherein p is an arbitrary positive real number, and is usually defined as p ═ 2;
distance h from discrete point to interpolated pointiComprises the following steps:
in the formula, (x, y) is an interpolation coordinate, and (xi, yi) is a discrete point coordinate;
wherein R is the distance from the interpolation point to the farthest discrete point; n is the total number of discrete points.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910952000.4A CN110726850A (en) | 2019-10-08 | 2019-10-08 | Railway crosswind early warning system based on wind direction decomposition and crosswind strength calculation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910952000.4A CN110726850A (en) | 2019-10-08 | 2019-10-08 | Railway crosswind early warning system based on wind direction decomposition and crosswind strength calculation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110726850A true CN110726850A (en) | 2020-01-24 |
Family
ID=69220835
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910952000.4A Pending CN110726850A (en) | 2019-10-08 | 2019-10-08 | Railway crosswind early warning system based on wind direction decomposition and crosswind strength calculation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110726850A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111239857A (en) * | 2020-02-18 | 2020-06-05 | 潘新民 | Strong wind forecasting method for special terrain |
| CN112382039A (en) * | 2020-11-11 | 2021-02-19 | 海云创数字科技(南京)有限公司 | Wisdom family cognitive control thing allies oneself with system |
| CN113592360A (en) * | 2021-08-20 | 2021-11-02 | 国网福建省电力有限公司 | Electric power high-altitude operation strong wind early warning method and system |
| CN113740931A (en) * | 2020-05-29 | 2021-12-03 | 新疆金风科技股份有限公司 | Gust detection method and device for wind generating set |
| CN115525637A (en) * | 2022-10-13 | 2022-12-27 | 华润电力技术研究院有限公司 | Wind vector observation data quality control and processing method, system and equipment |
| CN115525637B (en) * | 2022-10-13 | 2024-04-26 | 华润电力技术研究院有限公司 | Quality control and processing method, system and equipment for wind vector observation data |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102323441A (en) * | 2011-06-09 | 2012-01-18 | 东南大学 | A kind of signal processing method of wireless anemoscope |
| CN104112167A (en) * | 2014-06-06 | 2014-10-22 | 国家电网公司 | Method for obtaining distribution of wind resources capable of power generation |
| CN104635281A (en) * | 2015-02-17 | 2015-05-20 | 南京信息工程大学 | Method for controlling data quality of automatic meteorological station based on strong weather process correction |
| CN104724143A (en) * | 2015-03-13 | 2015-06-24 | 南京理工大学 | High speed railway crosswind information collection and early warning method and system |
| JP2017170921A (en) * | 2016-03-18 | 2017-09-28 | スズキ株式会社 | Crosswind prediction notification system of vehicle and crosswind prediction notification method |
| CN206649653U (en) * | 2017-04-05 | 2017-11-17 | 南京信息工程大学 | A kind of wind field data collecting system |
| CN108545077A (en) * | 2018-05-07 | 2018-09-18 | 河南工程学院 | A kind of automobile side aweather automatic checkout system and method |
| CN108909774A (en) * | 2018-07-02 | 2018-11-30 | 南京理工大学 | A kind of light rail train beam wind information collection and method for early warning and system |
| CN109164269A (en) * | 2018-09-19 | 2019-01-08 | 中国飞行试验研究院 | The method and apparatus of runway crosswind measurement |
| CN110197442A (en) * | 2019-06-10 | 2019-09-03 | 南京信息工程大学 | A kind of wind-resources assessment system of combination Quality Control |
-
2019
- 2019-10-08 CN CN201910952000.4A patent/CN110726850A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102323441A (en) * | 2011-06-09 | 2012-01-18 | 东南大学 | A kind of signal processing method of wireless anemoscope |
| CN104112167A (en) * | 2014-06-06 | 2014-10-22 | 国家电网公司 | Method for obtaining distribution of wind resources capable of power generation |
| CN104635281A (en) * | 2015-02-17 | 2015-05-20 | 南京信息工程大学 | Method for controlling data quality of automatic meteorological station based on strong weather process correction |
| CN104724143A (en) * | 2015-03-13 | 2015-06-24 | 南京理工大学 | High speed railway crosswind information collection and early warning method and system |
| JP2017170921A (en) * | 2016-03-18 | 2017-09-28 | スズキ株式会社 | Crosswind prediction notification system of vehicle and crosswind prediction notification method |
| CN206649653U (en) * | 2017-04-05 | 2017-11-17 | 南京信息工程大学 | A kind of wind field data collecting system |
| CN108545077A (en) * | 2018-05-07 | 2018-09-18 | 河南工程学院 | A kind of automobile side aweather automatic checkout system and method |
| CN108909774A (en) * | 2018-07-02 | 2018-11-30 | 南京理工大学 | A kind of light rail train beam wind information collection and method for early warning and system |
| CN109164269A (en) * | 2018-09-19 | 2019-01-08 | 中国飞行试验研究院 | The method and apparatus of runway crosswind measurement |
| CN110197442A (en) * | 2019-06-10 | 2019-09-03 | 南京信息工程大学 | A kind of wind-resources assessment system of combination Quality Control |
Non-Patent Citations (3)
| Title |
|---|
| 姜海等: "高速铁路监测系统大风预警功能方案研究", 《电气化铁道》 * |
| 徐影等: "东南沿海高速铁路沿线环境风特性研究", 《高速铁路技术》 * |
| 梁金强等: "《天然气水合物成矿预测技术》", 30 August 2017, 地质出版社 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111239857A (en) * | 2020-02-18 | 2020-06-05 | 潘新民 | Strong wind forecasting method for special terrain |
| CN111239857B (en) * | 2020-02-18 | 2020-09-11 | 潘新民 | Strong wind forecasting method for special terrain |
| CN113740931A (en) * | 2020-05-29 | 2021-12-03 | 新疆金风科技股份有限公司 | Gust detection method and device for wind generating set |
| CN113740931B (en) * | 2020-05-29 | 2023-12-22 | 金风科技股份有限公司 | Wind array detection method and device for wind generating set |
| CN112382039A (en) * | 2020-11-11 | 2021-02-19 | 海云创数字科技(南京)有限公司 | Wisdom family cognitive control thing allies oneself with system |
| CN113592360A (en) * | 2021-08-20 | 2021-11-02 | 国网福建省电力有限公司 | Electric power high-altitude operation strong wind early warning method and system |
| CN113592360B (en) * | 2021-08-20 | 2023-04-07 | 国网福建省电力有限公司 | Electric power high-altitude operation strong wind early warning method and system |
| CN115525637A (en) * | 2022-10-13 | 2022-12-27 | 华润电力技术研究院有限公司 | Wind vector observation data quality control and processing method, system and equipment |
| CN115525637B (en) * | 2022-10-13 | 2024-04-26 | 华润电力技术研究院有限公司 | Quality control and processing method, system and equipment for wind vector observation data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110726850A (en) | Railway crosswind early warning system based on wind direction decomposition and crosswind strength calculation method | |
| CN106679625B (en) | Wide scope electric power tower high-precision deformation monitoring method based on dipper system | |
| CN109579909B (en) | Iron tower on-line monitoring system based on multi-source information | |
| CN109407128A (en) | A kind of electric power line pole tower system for monitoring displacement and method | |
| CN106950614A (en) | A kind of region automatic weather station hour rainfall data method of quality control | |
| CN105206057B (en) | Detection method and system based on Floating Car resident trip hot spot region | |
| CN104992254B (en) | Mountain fire based on power grid GIS and the meteorological trend pre-judging method combined | |
| CN106846700B (en) | Method and system for monitoring forest fire of power transmission line | |
| CN105204092A (en) | Thunder and lightening prewarning system with honeycomb layout | |
| CN106710314B (en) | Bridge anti-collision monitoring management system and monitoring management method based on GIS | |
| CN113298295B (en) | Meteorological forecast system for power production | |
| CN109118039B (en) | Inland water area oriented ship self-sinking important risk identification, prevention and control device and method | |
| CN111428916B (en) | Navigation path planning method for rescue vessel at sea | |
| CN112802004A (en) | Portable intelligent video detection device for health of transmission line and tower | |
| CN106601032A (en) | Multi-path terrain integrity detection method based on downward-looking sensor | |
| CN109764244B (en) | Oil and gas pipeline disaster emergency treatment method based on mobile phone signal LBS and big data | |
| CN207851308U (en) | Object space variation monitoring system based on satellite positioning | |
| CN114814888B (en) | Handheld terminal equipment based on big dipper No. three | |
| CN110969798A (en) | Power transmission line forest fire early warning method and system | |
| CN213634824U (en) | NB-LOT network-based ancient and famous tree safety monitoring and early warning device | |
| CN115854853A (en) | Seawall micro-deformation monitoring method based on Beidou foundation enhancement station | |
| CN106779184B (en) | A kind of marine ships method for predicting modeled based on big data | |
| CN113074694B (en) | Automatic monitoring device for tunnel section deformation | |
| CN114384554A (en) | Power transmission line galloping monitoring system based on Beidou differential positioning | |
| CN102721652A (en) | High-reflectivity marine oil spill detection method based on optical remote sensing data of MODIS (moderate resolution imaging spectrometer) |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200124 |