A vehicle early warning method for bridge collapse based on vehicle-bridge coupled vibration and vehicle-to-everything (V2X) network

Abstract

This invention discloses a bridge collapse vehicle early warning method based on vehicle-bridge coupled vibration and vehicle-to-everything (V2X) networking. The method includes: collecting historical vibration data of the bridge and historical operating parameters of vehicles traveling on the bridge; constructing a vehicle-bridge coupled motion model based on the historical vibration data and operating parameters; predicting whether the bridge will collapse and its location using the vehicle-bridge coupled motion model; and issuing early warnings to vehicles at different locations if a bridge collapse is predicted. This invention obtains vehicle-bridge coupled vibration values ​​for each vehicle on the bridge using the vehicle-bridge coupled motion model, and predicts whether the bridge will collapse and its location based on these values. It is unaffected by weather, visibility obstructions, or other factors, ensuring that drivers can receive timely warnings and react even in extreme weather or with obstructed visibility, thus preventing dangerous accidents such as bridge falls and rear-end collisions.

Description

Technical Field

[0001] This invention relates to the field of traffic safety, specifically to a method for early warning of bridge collapse vehicles based on vehicle-bridge coupled vibration and vehicle networking. Background Technology

[0002] Bridges are a common piece of infrastructure in the transportation sector, and countless traffic accidents are caused by their collapses. When a bridge collapses, drivers may not be able to react in time due to poor weather or obstructed visibility, leading to situations such as falling off the bridge or rear-end collisions. In addition, visual sensors may have difficulty detecting the location of the bridge collapse in time due to weather and visibility factors, thus failing to provide timely warnings.

[0003] In view of this, the present invention is proposed. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention proposes a bridge collapse vehicle early warning method based on vehicle-bridge coupled vibration and vehicle networking. The method obtains the vehicle-bridge coupled vibration values ​​of each vehicle on the bridge through a vehicle-bridge coupled motion model, and predicts whether the bridge will collapse and the location of the collapse based on the vehicle-bridge coupled vibration values. Once the bridge collapse is predicted, early warning can be given to vehicles on the bridge and vehicles about to pass, and evacuation work can be carried out.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] This invention proposes a bridge collapse vehicle early warning method based on vehicle-bridge coupled vibration and vehicle-to-everything (V2X) networking, comprising the following steps:

[0007] S1. Collect historical vibration data of the bridge and historical operating parameters of vehicles traveling on the bridge.

[0008] S2. Construct a vehicle-bridge coupled motion model based on historical bridge vibration data and historical operating parameters of vehicles.

[0009] S3. Predict whether the bridge will collapse and the location of the collapse using the vehicle-bridge coupled motion model; if the bridge collapse is predicted, proceed to step S4.

[0010] S4. Issue warnings for vehicles in different locations.

[0011] Specifically, S2 includes the following sub-steps:

[0012] S21. Construct a bridge model based on historical bridge vibration data;

[0013] S22. Construct a vehicle model based on the historical operating parameters of the vehicle.

[0014] S23. Construct a deformation coordination model between the traveling vehicle and the bridge based on the bridge model and the vehicle model, and determine the interaction force between the traveling vehicle and the bridge based on the deformation coordination model and the vehicle model.

[0015] S24. Based on the interaction forces between the vehicle and the bridge, and the vehicle model, construct a vehicle-bridge coupled motion model.

[0016] Specifically, in S2, the vehicle-axle coupled motion model is described by the following formula:

[0017]

[0018] This indicates the vibration acceleration of the vehicle on the bridge; This represents the interaction force between the bridge and the wheels, i.e., the external force acting on the car; It refers to the stiffness of the vehicle on the bridge; Indicates vehicle vibration displacement; This indicates the vibration velocity of the vehicle on the bridge; It is the vehicle's damping on the bridge; the mass matrix of the car Simplified to the mass of the vehicle on the bridge .

[0019] Specifically, the formula for calculating the vehicle-axle coupled vibration velocity is as follows:

[0020] Formula for calculating the vehicle-axle coupled vibration distance: .

[0021] It is the start time;

[0022] This is the current time.

[0023] Specifically, S3 specifically includes:

[0024] S31. Obtain real-time vibration data of the bridge and real-time operating parameters of vehicles traveling on the bridge, and obtain vehicle-bridge coupled vibration values ​​through the vehicle-bridge coupled motion model.

[0025] S32. Determine whether the bridge has collapsed based on the instantaneous change of the vehicle-bridge coupled vibration value. If the result is that the bridge has collapsed, determine the location of the bridge collapse based on the vehicle-bridge coupled vibration value.

[0026] Specifically, the vehicle-axle coupled vibration value is the vibration acceleration.

[0027] Specifically, in S32, the method for determining whether a bridge has collapsed is as follows: if the number of vehicles on the bridge whose vehicle-bridge coupled vibration values ​​change drastically accounts for more than 30% of the total number of vehicles on the bridge, then the bridge is determined to have collapsed.

[0028] Specifically, if the coupled vibration value of a vehicle on the bridge after the change is more than twice the coupled vibration value before the change, then the vehicle is considered to have a drastic change in the coupled vibration value between the vehicle and the bridge.

[0029] Specifically, in S32, the location of bridge collapse is determined based on the curve showing the change in vehicle-bridge coupled vibration values ​​with the bridge position.

[0030] Specifically, in S31, before obtaining the vehicle-bridge coupled vibration values ​​through the vehicle-bridge coupled motion model, the obtained real-time vibration data of the bridge and the real-time operating parameters of the vehicles traveling on the bridge are filtered and noise-reduced.

[0031] Specifically, S4 includes reminding the vehicle to take emergency braking measures when the distance between the vehicle and the collapse location is less than 30m; reminding the vehicle to brake quickly when the distance between the vehicle and the collapse location is less than 50m and less than 30m; and reminding the vehicle to brake slowly when the distance between the vehicle and the collapse location is greater than or equal to 50m.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] (1) The present invention constructs a vehicle-bridge coupled motion model based on the historical vibration data of the bridge and the historical operating parameters of the vehicle, and determines whether the bridge collapses and the location of the collapse based on the vehicle-bridge coupled motion model. The historical vibration data of the bridge and the historical operating parameters of the vehicle are all real values, making the prediction results of the constructed vehicle-bridge coupled motion model closer to the actual scene and with high accuracy.

[0034] (2) The present invention is based on the vehicle-bridge coupled motion model to determine whether the bridge has collapsed and the location of the collapse. Its prediction process and results are not affected by factors such as weather and visual obstruction. It can ensure that drivers can still receive early warnings in time and react in extreme weather or when there is visual obstruction, so as to avoid dangerous accidents such as falling off the bridge and rear-end collisions. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0036] Figure 1 This is a flowchart illustrating the process of predicting whether a bridge will collapse according to the present invention;

[0037] Figure 2 This is a flowchart of the bridge collapse early warning process of the present invention. Detailed Implementation

[0038] The embodiments of the present invention will be described in detail below with reference to examples. However, those skilled in the art will understand that the following examples are only for illustrating the present invention and should not be regarded as limiting the scope of the present invention.

[0039] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0040] Specific details are set forth in the following description to provide a full understanding of the invention. However, the invention can be practiced in many ways other than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0041] refer to Figure 1 This embodiment proposes a method for early warning of bridge collapse vehicles based on vehicle-bridge coupled vibration and vehicle-to-everything (V2X) networking, characterized by the following steps:

[0042] S1. Collect historical vibration data of the bridge and historical operating parameters of vehicles traveling on the bridge by sensors installed on both sides of the bridge;

[0043] S2. Construct a vehicle-bridge coupled motion model based on historical bridge vibration data and historical operating parameters of vehicles; specifically:

[0044] S21. Construct a bridge model based on historical bridge vibration data;

[0045] S22. Construct a vehicle model based on the historical operating parameters of the vehicle.

[0046] S23. Construct a deformation coordination model between the traveling vehicle and the bridge based on the bridge model and the vehicle model, and determine the interaction force between the traveling vehicle and the bridge based on the deformation coordination model and the vehicle model.

[0047] S24. Based on the interaction forces between the vehicle and the bridge, and the vehicle model, construct a vehicle-bridge coupled motion model;

[0048] Specifically, in S2, the vehicle-axle coupled motion model is described by the following formula:

[0049]

[0050] This indicates the vibration acceleration of a vehicle on a bridge; This represents the interaction force between the bridge and the wheels, i.e., the external force acting on the car. It refers to the stiffness of the vehicle on the bridge; Indicates vehicle vibration displacement; This indicates the vibration velocity of the vehicle on the bridge; It is the vehicle's damping on the bridge; the mass matrix of the car Simplified to the mass of the vehicle on the bridge ;

[0051] S3. Obtain real-time vibration data of the bridge and real-time operating parameters of vehicles traveling on the bridge. Obtain the vibration acceleration of the vehicle on the bridge through the vehicle-bridge coupled motion model. In this embodiment, the vehicle-bridge coupled vibration value is preferably the vibration acceleration of the vehicle on the bridge.

[0052] The instantaneous changes are used to determine whether the bridge has collapsed. If the result indicates that the bridge has collapsed, the location of the collapse is determined based on the curve of the vehicle-bridge coupled vibration value changing with the bridge position, and then proceeds to S4;

[0053] S4. Issue warnings for vehicles in different locations.

[0054] Specifically, in S3, sensors installed on both sides of the bridge collect real-time vibration data of the bridge and real-time operating parameters of vehicles traveling on the bridge, and transmit the data to the data acquisition equipment on the vehicles. The data acquisition equipment performs noise reduction and filtering on the received data, and then transmits it to the server through the vehicle network. The server processes the received data and obtains the vibration acceleration of the vehicles on the bridge through the vehicle-bridge coupled motion model. Based on the vibration acceleration of the vehicles on the bridge, it determines whether the bridge has collapsed. If the value of the vibration acceleration of the vehicle on the bridge after the change is more than twice the value before the change, the vehicle is considered to be a vehicle with drastic changes in vehicle-bridge coupled vibration. Considering factors such as speed bumps and potholes, if more than 30% of the vehicles on the bridge have drastic changes in vehicle-bridge coupled vibration, the bridge is judged to have collapsed; if less than 30%, the drastic changes are considered to be caused by special reasons such as speed bumps and potholes.

[0055] After confirming the bridge collapse, the server cleaned the data, excluding vibration values ​​from special vehicles such as trucks and buses. The location of the bridge collapse was then inferred based on the changes in values ​​from other common sedans and SUVs. Given that a typical sedan weighs approximately 1.5 tons and travels at 60-80 km / h on the bridge, the typical vehicle-car coupling vibration acceleration is between 0.1 g and 0.3 g, with a dynamic load of approximately 2 kN to 6 kN. Between KN, the vibration velocity is usually between a few centimeters per second (cm / s) and tens of centimeters per second (cm / s). When the bridge collapses, the vibration acceleration increases significantly, reaching several times or even tens of times the normal level. Other values ​​also change drastically. The closer to the bridge collapse location, the more obvious the changes. Based on the vehicle position and the transmitted numerical change information, the vehicle-bridge coupled vibration acceleration above the bridge shows the most drastic change at the collapse location. It is a convex function graph that decreases from the collapse location to both sides. Therefore, on the curve of vehicle-bridge coupled vibration value changing with the bridge position, the location corresponding to the convex point is determined as the bridge collapse location. The degree of change of vehicle-bridge coupled vibration acceleration value is the most drastic within 30 meters of the collapse location. The overall convex function gradually slows down the rate of decrease to both sides. The vehicle-bridge coupled vibration acceleration value farthest from the collapse location is closer to the value before the transformation.

[0056] Before the change in vehicle-axle coupled vibration acceleration:

[0057] After the axle-vehicle coupled vibration acceleration changes:

[0058] This indicates the vehicle-axle coupled vibration acceleration;

[0059] Indicates vehicle vibration acceleration;

[0060] This indicates the vibration acceleration of the bridge.

[0061] It is a coefficient that represents the multiple of the impact on vehicles when a bridge collapses. Vehicles closer to the point of bridge collapse are more affected, and the larger the value of m is; vehicles farther away are less affected by the collapse, and the smaller the value of m is.

[0062] Coefficient m:

[0063] This represents a reference distance, which is selected based on the type of bridge.

[0064] d represents the distance between the vehicle and the point where the bridge collapsed.

[0065] In this embodiment S4, under normal circumstances, the speed of vehicles traveling on the bridge does not exceed 80 km / h. Therefore, when the distance between the vehicle and the collapse site is <30m, the vehicle warning system reminds the vehicle to take emergency braking measures; when the distance between the vehicle and the collapse site is 30m≤ and <50m, the vehicle warning system reminds the vehicle to brake quickly; when the distance between the vehicle and the collapse site is ≥50m, the vehicle warning system reminds the vehicle to brake slowly. Furthermore, the server is connected to hospitals and traffic management departments. When a bridge collapse is detected, it sends signals to hospitals and traffic management departments requesting support so that they can respond quickly and take appropriate action.

[0066] In the above embodiments, alarm systems can also be installed at both ends of the bridge. The server can send bridge collapse information to the alarm system in a synchronized manner. When the bridge collapse is detected at the first moment, the alarms on both sides will start to sound an alarm, reminding cars at the entrances at both ends of the bridge to stop moving forward.

[0067] Although the present invention has been described in detail in this specification with general description and specific embodiments, some modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the present invention are within the scope of protection claimed by the present invention.

Claims

1. A bridge collapse vehicle early warning method based on vehicle axle coupling vibration and Internet of Vehicles, characterized in that, The method comprises the following steps: S1, collecting historical vibration data of the bridge and historical running parameters of vehicles running on the bridge; S2, constructing a vehicle-bridge coupling motion model based on the historical vibration data of the bridge and the historical running parameters of the vehicles; S3, predicting whether the bridge collapses and the collapse position of the bridge through the vehicle-bridge coupling motion model; If it is predicted that the bridge collapses, step S4 is entered; S4, warning vehicles at different positions; S3 specifically comprises: S31, obtaining real-time vibration data of the bridge and real-time running parameters of vehicles running on the bridge, and obtaining vehicle-bridge coupling vibration values through the vehicle-bridge coupling motion model; S32, judging whether the bridge collapses according to the instantaneous change of the vehicle-bridge coupling vibration values, and if the result of the judgment is that the bridge collapses, determining the collapse position of the bridge based on the vehicle-bridge coupling vibration values; Specifically, in S32, the specific method for judging whether the bridge collapses is that if the vehicles with dramatic changes in vehicle-bridge coupling vibration values account for more than 30% of the total vehicles on the bridge, it is determined that the bridge has collapsed; if the coupling vibration values of the vehicles on the bridge after the change are more than twice the coupling vibration values before the change, the vehicles are considered to be the vehicles with dramatic changes in vehicle-bridge coupling vibration values; Specifically, in S32, the collapse position of the bridge is determined according to the curve of the vehicle-bridge coupling vibration values changing with the position of the bridge; S4 specifically comprises: when the distance between the vehicle and the collapse position is less than 30m, reminding the vehicle to take emergency braking measures; when the distance between the vehicle and the collapse position is 30m to less than 50m, reminding the vehicle to take rapid braking; and when the distance between the vehicle and the collapse position is greater than or equal to 50m, reminding the vehicle to take slow deceleration braking. 2.The bridge collapse vehicle early warning method based on vehicle axle coupled vibration and Internet of Vehicles according to claim 1, characterized in that, S2 comprises the following sub-steps: S21, constructing a bridge model based on the historical vibration data of the bridge; S22, constructing a vehicle model based on the historical running parameters of the vehicles; S23, constructing a deformation coordination model between the running vehicles and the bridge based on the bridge model and the vehicle model, and determining the interaction force between the passing vehicles and the bridge according to the deformation coordination model and the vehicle model; S24, constructing a vehicle-bridge coupling motion model based on the interaction force between the running vehicles and the bridge and the vehicle model. 3.The bridge collapse vehicle early warning method based on vehicle axle coupled vibration and Internet of Vehicles according to claim 1, characterized in that, In S2, the vehicle-bridge coupling motion model is described by the following formula: denotes the vibration acceleration of the vehicle on the bridge; F represents the interaction force between the bridge and the wheel, i.e. the external force on the car; k vb is the stiffness of the vehicle on the bridge; represents the vehicle vibration displacement; is the damping of the vehicle on the bridge; The mass matrix of the car is simplified to the mass of the car on the bridge ;​ represents the vibration velocity of the vehicle on the bridge. 4.The bridge collapse vehicle early warning method based on vehicle axle coupled vibration and Internet of Vehicles according to claim 1, characterized in that, The vehicle-bridge coupling vibration values are vibration accelerations. 5.The bridge collapse vehicle early warning method based on vehicle axle coupled vibration and Internet of Vehicles according to claim 1, wherein, In S31, before obtaining the vehicle-bridge coupling vibration values through the vehicle-bridge coupling motion model, the obtained real-time vibration data of the bridge and the real-time running parameters of the vehicles running on the bridge are filtered and denoised.

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