CN111341101A - Large-wind driving monitoring and early warning system for large-span highway bridge - Google Patents
Large-wind driving monitoring and early warning system for large-span highway bridge Download PDFInfo
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- CN111341101A CN111341101A CN202010127811.3A CN202010127811A CN111341101A CN 111341101 A CN111341101 A CN 111341101A CN 202010127811 A CN202010127811 A CN 202010127811A CN 111341101 A CN111341101 A CN 111341101A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/048—Detecting movement of traffic to be counted or controlled with provision for compensation of environmental or other condition, e.g. snow, vehicle stopped at detector
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
Abstract
The invention discloses a large-wind driving monitoring and early warning system for a large-span highway bridge, and relates to the technical field of driving early warning of the large-span highway bridge; the early warning system consists of a data acquisition module, a central server and an early warning terminal, wherein the data acquisition module respectively acquires real-time wind speed data and meteorological information according to a field data acquisition station and meteorological software; the central server formulates a vehicle speed limit rule according to a wind tunnel test with a TombH scale and refined wind bridge coupling vibration analysis, predicts the wind speed through a high-precision prediction model, discriminates the road surface state by combining meteorological information and comprehensively judges the reasonable running speed of the vehicle under the predicted wind speed; the early warning terminal comprises a desktop program, a webpage, a mobile phone App and other platforms. The invention is beneficial to the management department to scientifically manage the traffic on the bridge according to the driving guidance suggestions and the dangerous case information sent by the early warning terminal or the central server, and effectively improves the safety of driving on the bridge.
Description
Technical Field
The invention belongs to the technical field of highway traffic early warning, and particularly relates to a large-wind driving monitoring and early warning system for a large-span highway bridge.
Background
With the rapid development of national economy, the expressway which is used as an artery for the development of national economy and society is also unprecedentedly developed, and plays a very important role in the field of transportation in China. Meanwhile, it should be appreciated that the highway traffic safety problem is increasingly prominent due to the relative lag of the traffic safety management and strategy research in China. Among them, strong wind is a relatively common adverse meteorological condition that is a serious hazard to traffic safety. The vehicle is easy to have the problems of lateral deviation, lateral slip, overturning and the like under the action of cross wind, so that serious traffic accidents are caused. Particularly, wind-induced driving safety accidents are frequent in coastal areas and complex mountainous areas, and a severe test is provided for traffic safety. The large-span bridge is used as a key node of the highway and is also a bottleneck point of the traffic safety of the highway. The wind field environment around the large-span bridge is changeable, and the driving environment is complicated. Particularly, when the road is in wet and slippery conditions such as rain, snow and the like, the occurrence probability of dangerous traffic situations is increased suddenly, the driving safety is seriously threatened, and a reliable highway bridge strong wind driving early warning system is indispensable.
At present, a strong wind early warning system mainly judges wind speed in advance by means of weather forecast or weather station information in a large area, and influences of local small terrains and small climates on early warning are ignored. Particularly in western mountainous areas, large-span bridges are mostly built in complex terrain environments and are affected by local terrains, and the actual wind speed of the bridge deck is often greatly different from that of conventional weather forecast or weather station forecast. In addition, most of the current strong wind monitoring and early warning systems are for railway systems, and the strong wind monitoring and early warning systems for highway bridges are almost blank. For a highway system, the driving safety under the action of crosswind is influenced by the difference of parameters such as vehicle types, road surface states and even lane positions, and the conventional early warning system is difficult to issue different early warnings aiming at various conditions.
In conclusion, a set of complete high wind driving safety early warning system needs to be developed to guarantee the high wind driving safety of the long-span bridge of the highway system.
Disclosure of Invention
The invention provides a long-span highway bridge strong wind driving monitoring and early warning system, which aims to ensure the safety of highway bridge strong wind driving and perfect a highway traffic early warning system.
A long-span highway bridge high wind driving monitoring and early warning system. The system comprises a data acquisition module, a central server and an early warning terminal. The data acquisition module comprises wind speed data acquisition and meteorological information acquisition; the central server comprises a data acquisition function module, a road surface state identification function module, a vehicle speed limit rule formulation function module, a wind speed prediction function module and a driving early warning function module; the early warning terminal consists of a desktop program, a webpage and a mobile phone App platform.
Furthermore, the wind speed data acquisition is completed by a field data acquisition station consisting of a wind measuring device and a data acquisition device, and the meteorological information acquisition is completed by means of third-party meteorological software.
Further, the data collection module is responsible for retrieving relevant data; the road surface state identification module comprehensively judges the accurate states of dry, wet, snow and ice on the bridge surface on the basis of meteorological information; the vehicle limit rule making module makes speed limit rules under different vehicle types, road surface states and lane positions (different traffic conditions) according to wind tunnel experiments of the Todar scale and refined windmill bridge coupling analysis; the wind speed prediction module selects a reasonable prediction model to carry out numerical analysis on the original data to obtain the predicted wind speed, and provides a model optimization function to improve the wind speed prediction precision; the driving early warning module analyzes the maximum safe driving speed of different types of vehicles at the next moment according to the road surface state, the vehicle speed limit rule and the predicted wind speed, and immediately issues early warning information to the early warning terminal and the early warning object in real time once the value is lower than the expected given vehicle driving speed.
The early warning terminal is responsible for displaying important information such as current wind speed, predicted wind speed and bridge traffic state in real time, and system visualization is enhanced.
The invention has the beneficial effects that:
1. wind speed is predicted one or more steps ahead based on-bridge field wind speed data. According to the vehicle speed limit rule formulated by the wind tunnel experiment of the Todar scale and the refined windmill bridge coupling analysis, early warning information is respectively given according to different traffic working conditions. The method comprehensively considers the local wind field of the bridge deck and various traffic working conditions, forms a set of complete highway bridge strong wind driving safety early warning system, and fills the blank of highway traffic early warning, especially the technical field of highway bridge traffic;
2. and a wind tunnel experiment according to a large scale and a refined windmill bridge coupling analysis are provided to establish a perfect vehicle speed limit rule. In order to truly reflect the structural characteristics of the local wind field of the bridge deck, the aerodynamic coefficient of the vehicle and the bridge in the windmill bridge coupling system is obtained by adopting a large-scale segmental wind tunnel experiment, then a windmill bridge coupling system model is established by a numerical analysis means, and the vehicle speed limit values under different traffic conditions are calculated and finally given by adopting a separation iteration method.
3. The early warning system has strong adaptability, can be compatible with wind measuring devices of all models, and supports self-adaptive selection of a wind speed prediction model, advance prediction of the step number and increase, deletion and modification of vehicle speed limit rules. In addition, the method can release early warning information on multiple platforms, and is widely applicable.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
FIG. 2 is a flow chart of the system early warning method of the present invention.
Fig. 3 is a flow chart of speed limit rule making.
FIG. 4 is a schematic view a of a large scale segment wind tunnel experiment.
Fig. 5 is a schematic view b of a large scale segment wind tunnel experiment.
FIG. 6 is a schematic view c of a large scale segment wind tunnel experiment.
FIG. 7 is a schematic view d of a large scale segment wind tunnel experiment.
FIG. 8 is a schematic diagram of a two-axis vehicle dynamics model. In the figure: zvIndicating vertical displacement of the vehicle body, YvIndicating a vehicle body side (lateral) displacement,
indicating a body side-tipping displacement, thetavIndicating the pitch displacement of the vehicle body, ZsiIndicating the vertical displacement of the i-th wheel, YsiDenotes the lateral (transverse) displacement of the i-th wheel, KuziRepresenting the vertical stiffness, C, of the upper elastic element of the i-th wheeluziRepresents the vertical damping value, K, of the upper damper of the ith wheellziRepresenting the vertical stiffness, C, of the lower elastic element of the i-th wheellziRepresents the lower damper lateral damping value, K, of the ith wheeluyiRepresenting the transverse stiffness, C, of the upper elastic element of the i-th wheeluyiRepresents the upper damper lateral damping value, K, of the i-th wheellyiRepresenting the transverse stiffness, C, of the lower elastic element of the i-th wheellyiThe lower damper lateral damping value of the i-th wheel is indicated.
FIG. 9 is a schematic diagram of wind speed prediction in advance by one step and multiple steps. In the figure: xiRepresenting the ith wind speed data value.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely a few embodiments of the invention and are not to be taken as the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1 and 2, the large-wind driving safety early warning system for the large-span highway bridge consists of a data acquisition module, a central server and an early warning terminal. The main working process is as follows: the data acquisition module adopts a field data acquisition station and meteorological software to respectively acquire real-time wind speed data and weather data and transmit the real-time wind speed data and the weather data to the central server. The central server predicts the wind speed based on a reasonable prediction model by analyzing original data, discriminates the road surface state by combining meteorological information according to the driving rule obtained by a wind tunnel test with a large scale and a refined windmill bridge coupling vibration analysis, comprehensively judges the reasonable driving speed of the vehicle under the predicted wind speed, and sends a warning message to a warning object before a dangerous case occurs. The early warning object can also obtain related information at a multi-platform early warning terminal composed of a desktop program, a webpage, an App and the like. The data acquisition module comprises wind speed data acquisition and meteorological information acquisition. The wind speed data acquisition is completed by a data acquisition station consisting of a field wind measuring device and a data acquisition unit. The ultrasonic three-dimensional anemoscope and the propeller mechanical anemoscope are jointly responsible for collecting wind signals of the same station and are transmitted to a data collector in a bridge span through a special cable. The two instruments work together and check each other, so that the validity of wind measurement data is guaranteed. The data continues to be transmitted back to the central server through a feasible communication means. In this example, the data is transmitted back through wired optical fiber by using the existing optical fiber communication place on the bridge. The central server is in butt joint with meteorological software to directly acquire meteorological data at the bridge site to finish meteorological information acquisition.
The central server comprises main functional modules of data acquisition, pavement state recognition, vehicle speed limit rule formulation, wind speed prediction, driving early warning and the like.
The data acquisition module acquires return data in real time and is compatible with data of any type of wind speed instrument.
The road surface state identification module comprehensively judges whether the bridge surface belongs to the exact states of dry, wet, snow and ice through the mutual relation of rainfall and temperature on the basis of real-time meteorological data.
The vehicle speed limit rule making module is based on a wind tunnel experiment with a large scale and refined windmill bridge coupling analysis, and the main flow is shown in fig. 3. When passing through the bridge surface, an adhesion layer is formed in a certain height area of the bridge surface, and the vehicles are positioned above the bridge surface in the circumfluence, the local wind field of the bridge surface has direct influence on the pneumatic response of the vehicles. Is composed ofThe structural characteristics of the local wind field of the bridge are truly reflected, and the aerodynamic coefficient of the vehicle and the bridge in the windmill bridge coupling system is obtained by adopting a segment wind tunnel experiment with a large scale, as shown in figures 4-7. Considering the aerodynamic characteristic difference caused by different gravity center positions of different vehicle types and different aerodynamic cross sections, different wind fields of inner and outer lanes and different road surface states, different vehicle models with typical aerodynamic shapes, such as MIRA models (automobile models established by the United kingdom automotive industry research union) and the like, are selected, and five-component coefficients of lift force, lateral force, pitching force, yawing moment, rollover moment and the like of the vehicle models on different lanes are measured. In the wind wheel bridge coupling analysis, the dynamic model simulation of the vehicle needs to be focused. Taking a two-axis vehicle as an example, a vehicle body, an axle and wheels are regarded as rigid bodies, and the rigid bodies are connected with each other through an elastic element and a damper. For a two-axis vehicle, the entire vehicle may be divided into 5 rigid bodies: 1 vehicle body and 4 wheels. Vehicle body taking into account vertical displacement ZvLateral displacement YvSide turning displacement phivYaw displacement
And pitch displacement θvIn total 5 degrees of freedom, the wheel takes into account the vertical displacement ZsiAnd a lateral displacement Y si2 degrees of freedom each. Therefore, the two-axle four-wheel vehicle has 13 degrees of freedom, the dynamic analysis model is shown in fig. 8, and the vehicle degree of freedom displacement vector is expressed as follows:
in the coupling analysis, the gravity of a main beam of the bridge, the static wind force expressed based on the three-component coefficient constant, the vibration shaking force expressed by the Scanlan calibration constant after the correction of the pneumatic admittance function and the self-exciting force expressed by the impulse response function in a non-constant way are considered, and the static wind force and the vibration shaking force influenced by the three-dimensional effect of the pneumatic appearance are considered for the automobile based on the gravity and the five-component coefficient. Therefore, the motion equation of the windmill bridge coupling system can be expressed as follows:
in the formula: subscripts b, v denote bridge and vehicle, respectively; m, C, K represent the mass, damping and stiffness matrices, respectively; f. ofvb、fbvRepresenting the interaction force between the axle systems; f. ofbg、fvgRespectively representing the dead weights of the bridge and the vehicle; f. ofstb、fbub、fsebRespectively representing static wind force, shaking force and self-excitation force acting on the bridge; f. ofstv、fbuvRespectively representing the static wind force and the shaking force acting on the vehicle.
Furthermore, the excitation effect of the road surface irregularity is considered to be simulated in a one-dimensional random process with respect to the distance.
And respectively and independently solving the wind-bridge subsystem and the vehicle-bridge subsystem for each step of integral by adopting a separation iteration method, and carrying out balance iteration according to the coupling relation so as to calculate the dynamic contact force (namely wheel pressure) between the wheels and the road surface under the action of crosswind. The sideslip stability and the side-rolling stability which represent the driving safety of the vehicle are measured by taking the wheel pressure as a standard, and vehicle speed allowable values of different vehicle types under different road surface conditions and lanes under different wind speeds are obtained. The vehicle speed limit rule is established accordingly. The vehicle speed limit rule making module can import any speed limit rule according to actual needs.
And the wind speed prediction module selects a reasonable prediction model to calculate the original data to obtain the predicted wind speed. The module supports selection of an appropriate wind speed prediction model, the number of steps of advance prediction, the type of prediction and the like according to the characteristics of the wind field of the predicted station. The prediction types include a maximum value and an average value of the wind speed within a selected time interval. In addition, the module also provides an optimization function of the model so as to continuously improve the wind speed prediction accuracy. The optimization of the model comprises automatic optimization and manual optimization. And the automatic optimization automatically updates the model weight by using the newly acquired data according to the optimization frequency set by the user. In manual optimization, a user can use data in a certain self-defined time interval to perform an optimization model. In the present embodiment, the principle of wind speed prediction using one or more steps ahead is shown in fig. 9. Firstly, a certain amount of original data is selected to form a sample of an input-output structure, and a prediction model is trained according to the sample. And comparing the predicted value and the true value of the model, and correcting the model according to the error. When the advanced one-step prediction is carried out, the existing data is taken as input to obtain a predicted value; when two-step prediction is carried out in advance, the prediction result of one step in advance is used as one part of input to form new input, and a prediction value is obtained; when three-step prediction is carried out in advance, the results of the one-step and two-step prediction are used as part of new input to obtain a predicted value. The model optimization means that the model is continuously corrected according to the error between the real-time wind speed predicted value and the real wind speed so as to continuously improve the prediction precision.
The driving early warning module obtains the maximum safe driving speed of different vehicles at the next moment based on the road condition, the vehicle speed limit rule and the predicted wind speed analysis, and once the value is lower than the expected given vehicle driving speed, early warning information is issued to the early warning object in real time.
The early warning terminal is composed of a desktop program, a webpage, an App and other platforms. The early warning terminal receives the data of the central server and completes the functions of real-time wind speed time-course display, future time wind speed forecast, weather condition broadcast, vehicle speed limit suggestion and the like, and the management department can make corresponding traffic control accordingly.
Claims (3)
1. A large-wind driving monitoring and early warning system for a large-span highway bridge is characterized by comprising a data acquisition module, a central server and an early warning terminal;
the data acquisition module comprises wind speed data acquisition and meteorological information acquisition;
the central server comprises a data acquisition function module, a road surface state identification function module, a vehicle speed limit rule formulation function module, a wind speed prediction function module and a driving early warning function module;
the early warning terminal is composed of a desktop program, a webpage and a mobile phone App platform.
2. The large wind driving monitoring and early warning system for the large-span highway bridge according to claim 1, wherein the wind speed data acquisition is completed by a field data acquisition station consisting of a wind measuring device and a data acquisition device, and the meteorological information acquisition is completed by means of third-party meteorological software.
3. The large wind driving monitoring and early warning system for the large-span highway bridge according to claim 1, wherein the data acquisition module is responsible for retrieving relevant data; the pavement state identification module comprehensively judges the exact state of the bridge floor on the basis of meteorological information; the vehicle limit rule making module makes limit rules under different vehicle types, road surface states and lane positions according to wind tunnel experiments of the Todar scale and refined windmill bridge coupling analysis; the wind speed prediction module selects a prediction model to carry out numerical analysis on the original data to obtain the predicted wind speed, and provides a model optimization function to improve the wind speed prediction precision; the driving early warning module analyzes the maximum safe driving speed of different vehicles at the next moment according to the road surface state, the vehicle speed limit rule and the predicted wind speed, and immediately issues early warning information to the early warning terminal and the early warning object once the value is lower than the expected given vehicle driving speed.
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| CN114187752A (en) * | 2022-02-14 | 2022-03-15 | 西南交通大学 | Early warning system and method for dangerous chemical vehicle in cross-sea bridge transportation |
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| CN111931277B (en) * | 2020-07-27 | 2023-11-03 | 中铁大桥勘测设计院集团有限公司 | Design method for safety wind prevention of long-span railway bridge driving and wind reduction device |
| CN114187752A (en) * | 2022-02-14 | 2022-03-15 | 西南交通大学 | Early warning system and method for dangerous chemical vehicle in cross-sea bridge transportation |
| CN114187752B (en) * | 2022-02-14 | 2022-04-15 | 西南交通大学 | Early warning system and method for dangerous chemical vehicle in cross-sea bridge transportation |
| CN115223367A (en) * | 2022-07-28 | 2022-10-21 | 福建工程学院 | Road crosswind early warning system based on Internet of things |
| CN115223367B (en) * | 2022-07-28 | 2023-11-03 | 福建工程学院 | Road crosswind early warning system based on Internet of things |
| CN116434539A (en) * | 2023-02-28 | 2023-07-14 | 东南大学 | Expressway speed early warning method based on digital twinning under extreme rainwater weather |
| CN116434539B (en) * | 2023-02-28 | 2024-03-15 | 东南大学 | Expressway speed early warning method based on digital twinning under extreme rainwater weather |
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