CN111710165A - Bridge supervision and early warning method and system based on fusion and sharing of multi-source monitoring data - Google Patents

Abstract

The invention discloses a bridge supervision and early warning method and system based on the fusion and sharing of multi-source monitoring data. The method includes: monitoring information on the bridge in real time, the information on the bridge includes vehicle load information and bridge structure information; using a preset overload warning model and the information on the bridge to make judgments, if the vehicle is overloaded, performing an overload vehicle warning and judging whether The overloaded vehicles are allowed to get on the bridge, and the vehicle traffic route is optimized for the overloaded vehicles allowed to get on the bridge, wherein the vehicle traffic route includes a lane. The method combines bridge safety and health monitoring with vehicle overloading, and allows vehicles to enter the main body of the bridge only when the vehicle is safe to drive on the bridge, which is beneficial to ensure the safety of vehicle driving. For overloaded vehicles that seriously affect the bridge structure, the Allowing the bridge can reduce the damage to the bridge structure and improve the safety of bridge supervision.

Description

Bridge supervision and early warning method and system based on fusion and sharing of multi-source monitoring data

technical field

The invention relates to the technical field of bridge monitoring, in particular to a bridge supervision and early warning method and system based on fusion and sharing of multi-source monitoring data.

Background technique

Highway transportation infrastructure is the foundation and lifeblood of national economic development. Among them, bridges are the weak link of highway infrastructure and affect the safety of the entire highway network. In recent years, bridge safety accidents have occurred frequently due to natural factors and human factors, resulting in large economic losses and personal injuries. Therefore, strengthening the safety monitoring, early warning and control capabilities of bridges is an effective means to reduce losses.

However, the existing bridge safety and health monitoring systems generally cannot be combined with vehicles for unified supervision, and the safety of bridge supervision is low.

Therefore, how to improve the safety of bridge supervision is a technical problem that those skilled in the art need to solve at present.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present invention is to provide a bridge supervision and early warning method based on the fusion and sharing of multi-source monitoring data, which can improve the safety of bridge supervision. Another object of the present invention is to provide a bridge supervision and early warning system based on the fusion and sharing of multi-source monitoring data, which can improve the safety of bridge supervision.

To achieve the above object, the present invention provides the following technical solutions:

A bridge supervision and early warning method based on fusion and sharing of multi-source monitoring data, comprising:

Real-time monitoring of bridge information, the bridge information includes vehicle load information and bridge structure information;

Use the preset overload warning model and the information on the bridge to judge, if the vehicle is overloaded, carry out the overload vehicle warning and judge whether the overloaded vehicle is allowed to go on the bridge, and optimize the vehicle passage route for the overloaded vehicle that is allowed to go on the bridge. The vehicle passing route includes a lane.

Preferably, it also includes:

Using the preset bridge safety early warning model and the information on the bridge to make judgments, after determining that the bridge is damaged, the bridge safety early warning is carried out.

Preferably, the vehicle load information includes vehicle speed information, vehicle axle load information, vehicle license plate number information, and vehicle lane change information; the vehicle lane change information includes the vehicle-to-vehicle distance, the number of lanes the vehicle travels, and The safety influence factor of the bridge structure when the vehicle travels on each lane of the bridge.

Preferably, the vehicle axle load information is obtained by using bridge dynamic weighing technology.

Preferably, the bridge structure information includes bridge acceleration information, bridge strain information, bridge dynamic and static displacement information, and health self-assessment information.

A bridge supervision and early warning system based on fusion and sharing of multi-source monitoring data, comprising: a monitoring module, an early warning module, a fusion sharing module and a management module, wherein the early warning module includes a vehicle overload warning unit;

The monitoring module is used to monitor information on the bridge in real time, and the information on the bridge includes vehicle load information and bridge structure information;

The vehicle overload warning unit is used to judge by using the preset overload warning model and the information on the bridge. If the vehicle is overloaded, it will perform an overload vehicle warning and judge whether the overloaded vehicle is allowed to go on the bridge, and the overloaded vehicle that is allowed to go on the bridge will be checked. Optimization of a traffic route, wherein the vehicle traffic route includes a lane;

The fusion sharing module is used to receive and save the data information transmitted by the monitoring module, the early warning module and the management system;

The management module is used to manage the normal operation, error debugging and hardware maintenance of the monitoring module, the early warning module and the fusion sharing module.

Preferably, the early warning module further includes a bridge safety early warning unit, which is configured to use a preset bridge safety early warning model and information on the bridge to make judgments, and to perform bridge safety early warning after determining that the bridge is damaged.

Preferably, the monitoring module includes a vehicle information collection sub-module for monitoring the vehicle load information, and the vehicle information collection sub-module includes:

Vehicle speed size unit, used to collect vehicle speed information;

Vehicle axle load unit, used to collect vehicle axle load information;

Vehicle license plate unit, used to collect vehicle license plate number information;

The lane changing unit is used for collecting vehicle lane changing information, where the vehicle changing lane information includes the vehicle-to-vehicle distance, the number of lanes the vehicle travels, and the safety influence coefficient on the bridge structure when the vehicle travels on each lane of the bridge.

Preferably, the vehicle axle load information is obtained by using bridge dynamic weighing technology.

Preferably, the monitoring module includes a bridge information collection sub-module for monitoring the bridge structure information, and the bridge information collection sub-module includes:

Acceleration monitoring unit, used to collect bridge acceleration information;

Strain monitoring unit for collecting bridge strain information;

Dynamic and static displacement monitoring unit, used to collect bridge dynamic and static displacement information;

The health self-assessment unit is used to collect the health self-assessment information of the bridge.

The bridge supervision and early warning method based on the fusion and sharing of multi-source monitoring data provided by the present invention includes: monitoring information on the bridge in real time, where the information on the bridge includes vehicle load information and bridge structure information; using a preset overload early warning model and the bridge The above information is used to judge, if the vehicle is overloaded, the overloaded vehicle warning is carried out and it is judged whether the overloaded vehicle is allowed to go on the bridge, and the vehicle traffic route is optimized for the overloaded vehicle allowed to go on the bridge, wherein the vehicle traffic route includes the lane.

The method collects multi-source data such as bridge information and vehicle information, combines bridge safety and health monitoring with vehicle overloading, and allows vehicles to enter the main body of the bridge only when the vehicle is safe to drive on the bridge, which is conducive to ensuring the safety of vehicles. For overloaded vehicles that seriously affect the bridge structure, it is not allowed to go on the bridge, which can reduce the damage to the bridge structure and improve the safety of bridge supervision.

The bridge supervision and early warning system based on the fusion and sharing of multi-source monitoring data provided by the invention can improve the safety of bridge supervision.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only It is an embodiment of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the provided drawings without creative work.

1 is a structural diagram of a bridge supervision and early warning system based on the fusion and sharing of multi-source monitoring data provided by the embodiment of the present application;

2 is a first flowchart of a bridge supervision and early warning method based on multi-source monitoring data fusion and sharing provided by an embodiment of the present application;

FIG. 3 is a second flowchart of the bridge supervision and early warning method based on the fusion and sharing of multi-source monitoring data provided by the embodiment of the present application.

Reference number:

Monitoring module 1, vehicle information collection sub-module 11, vehicle speed size unit 111, vehicle axle load unit 112, vehicle license plate unit 113, lane change unit 114, bridge information collection sub-module 12, acceleration monitoring unit 121, strain monitoring unit 122, dynamic and static Displacement monitoring unit 123 , health self-assessment unit 124 , warning module 2 , vehicle overload warning unit 21 , bridge safety warning unit 22 , fusion sharing module 3 , management module 4 .

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The core of the invention is to provide a bridge supervision and early warning method based on the fusion and sharing of multi-source monitoring data, which can improve the safety of bridge supervision. Another core of the present invention is to provide a bridge supervision and early warning system based on the fusion and sharing of multi-source monitoring data, which can improve the safety of bridge supervision.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terms used herein in the description of the present invention are for the purpose of describing specific embodiments only, and are not intended to limit the present invention.

The bridge supervision and early warning method based on the fusion and sharing of multi-source monitoring data provided by the present invention, when applied to a bridge, a detection area is set up in a section of the end of the bridge, and the bridge supervision and early warning method is implemented, so that the detection area needs to pass the detection area. Vehicles on the rear axle are detected. At the same time, the method is implemented by a corresponding bridge supervision and early warning system based on the fusion and sharing of multi-source monitoring data. The system includes a monitoring module 1 , an early warning module 2 , a fusion sharing module 3 and a management module 4 .

The bridge supervision and early warning method includes the following steps:

S1: Real-time monitoring of information on the bridge, where the information on the bridge includes vehicle load information and bridge structure information. The vehicle load information is collected by the vehicle information collection sub-module 11 in the monitoring module 1 , and the bridge structure information is collected by the bridge information collection sub-module 12 in the monitoring module 1 .

Optionally, the vehicle load information includes the following four aspects:

(1.1) Vehicle speed information is obtained through bridge dynamic weighing technology (BWIM, Bridge Weigh-in-motion). In addition, its acquisition is an important step to further obtain vehicle axle load through bridge dynamic weighing technology;

(1.2) Vehicle axle load information is obtained based on bridge dynamic weighing technology, which includes equivalent shear force method, wavelet transform method, virtual simply supported beam method, bearing reaction force method, independent component analysis method ( ICA) and other methods;

(1.3) Vehicle license plate number information, which is used to distinguish each vehicle. Optionally, the vehicle information collection sub-module 11 includes a camera, and the vehicle license plate number information is realized by performing image recognition on the captured vehicle, and the vehicle information collection sub-module 11 recognizes the vehicle. The function of the license plate number can be realized through machine learning, and machine learning specifically includes methods such as target detection and instance segmentation. Of course, other information recognition functions of the image by the vehicle information collection sub-module 11 can also be realized through machine learning;

(1.4) Vehicle lane change information, the vehicle information collection sub-module 11 obtains the information by acquiring vehicle images and performing image recognition, and the function of recognizing vehicle lane change information is realized through machine learning.

Among them, the vehicle lane change information includes the following three aspects:

(1.4.1) The vehicle-to-vehicle distance is obtained through image recognition of the vehicle by the camera installed on the bridge;

(1.4.2) The number of lanes the vehicle travels, obtained through image recognition of the vehicle by the camera installed on the bridge;

(1.4.3) The safety influence coefficient on the bridge structure when the vehicle travels on each lane of the bridge, which is used to reflect the influence on the safety of the bridge structure before and after the vehicle changes lanes. Based on the bridge structure information of each lane, the ratio of the vehicle axle load to the maximum allowable load-bearing capacity of each lane is determined as the safety influence factor.

For vehicles, there is a standard for overloading or not. Based on the axle load of the vehicle and the bridge structure information, it can be determined whether it is an overloaded vehicle. However, it is not that the vehicle cannot go on the bridge as long as it is overloaded. Combined with the real-time situation of the bridge, as long as the set safety is satisfied Requirements, the overload can also be on the bridge. Obtaining the information of vehicle lane change can better plan the vehicle traffic route for overloaded vehicles that have been overloaded but are allowed to pass after checking the bridge structure and overloaded vehicles.

Optionally, the bridge structure information includes the following three aspects:

(2.1) Bridge acceleration information, obtained by using the acceleration sensor installed on the bridge in the bridge information acquisition sub-module 12, which can reflect the bridge displacement;

(2.2) The bridge strain information is obtained by using the strain sensor installed on the bridge in the bridge information acquisition sub-module 12. Preferably, the strain sensor includes a high-precision FBG strain sensor, and a high-precision strain measurement technology can be used to improve the bridge strain. Measurement accuracy and sensitivity;

(2.3) The dynamic and static displacement information of the bridge is obtained by real-time monitoring using the satellite positioning system in the bridge information acquisition sub-module 12. The satellite positioning system includes mainstream positioning systems such as the Beidou satellite positioning system;

(2.4) Health self-assessment information, specifically comparing the bridge acceleration information, bridge strain information, bridge dynamic and static displacement information with the preset bridge health standards, and conduct self-assessment of the bridge health and safety level. When the preset bridge health standards are exceeded, the health self-assessment The assessment information is that the bridge is unhealthy, and if it is not, the health self-assessment information is that the bridge is healthy.

Using the real-time monitoring technology of bridge dynamic and static displacement based on acceleration sensor and the bridge dynamic and static displacement monitoring technology based on Beidou satellite positioning system to simultaneously monitor bridge displacement in real time, it can reflect the concept of multi-source data fusion and sharing, and ensure the reliability of monitoring results.

S2: Use the preset overload warning model and the information on the bridge to judge, if the vehicle is overloaded, carry out the overload vehicle warning and judge whether to allow the overloaded vehicle to go on the bridge, and optimize the vehicle passage route for the overloaded vehicle that is allowed to go on the bridge, wherein , the vehicle passing route includes a lane. Among them, S2 is realized by the early warning module 2 and the fusion sharing module 3.

Among them, the fusion sharing module 3 receives multi-source data, which specifically includes the data transmitted by the monitoring module 1 , the early warning module 2 , and the management module 4 . At the same time, the fusion sharing module 3 saves all the data information it receives. In addition, the preset overload warning model and the preset safety warning model are also pre-stored in the fusion sharing module 3 . By building a multi-source data fusion and sharing platform, data can be updated in real time, compared and analyzed, and called quickly.

The early warning module 2 includes a vehicle overload warning unit 21 . The early warning module 2 retrieves data from the fusion sharing module 3, and then processes the data to supervise the vehicle. The vehicle overload warning unit 21 can issue overload vehicle warning information, specifically to the driver. Optionally, the vehicle overload warning unit 21 includes an LED display screen arranged above the bridge, and the overload vehicle warning information is displayed on the LED display screen. .

The supervision of vehicles specifically includes the following steps S21-S24:

S21: Use the preset overload warning model and the information on the bridge to determine whether the vehicle is an overloaded vehicle, and if so, go to S22.

Optionally, the preset overload early warning model includes various bridge structural information and corresponding overload standards of the bridge. Based on the comparison between the bridge structure information obtained in real time and the preset overload early warning model, the current overload standard of the bridge can be determined, and then the current overload standard can be determined. The axle load information of the vehicle is compared with the current overload standard of the bridge to determine whether the vehicle is overloaded on the bridge.

S22: Carry out an overloaded vehicle warning, and judge whether the overloaded vehicle is allowed to go on the bridge, if so, go to S23, otherwise, go to S24.

Specifically, if it is determined that the vehicle is an overloaded vehicle, the vehicle overloaded warning unit 21 further checks the bridge structure and the overloaded vehicle to determine whether the overloaded vehicle is allowed to ride on the bridge. Optionally, the vehicle overload warning unit 21 obtains important data such as the dynamic and static displacement (deflection) and strain of the bridge measured by acceleration sensors, strain sensors, etc. The overloaded vehicle goes on the bridge so that the data is still within the limit, and if it meets the set safety requirements, it can go on the bridge. In addition, when judging whether the overloaded vehicle is allowed to go on the bridge, specifically, it may only rely on the checking result of the vehicle overload warning unit 21, or may combine the checking result and the staff's response to make the judgment.

S23: Optimizing the vehicle passage route for the overloaded vehicles allowed to get on the bridge, wherein the vehicle passage route includes a lane.

Specifically, by optimizing the vehicle passing route after the overloaded vehicle gets on the bridge, specifically, the optimal lane can be selected in combination with the vehicle lane change information. Wherein, the bridge includes at least two lanes, the vehicle travels in different lanes, the information collected by the bridge information collection sub-module 12 is different, and the impact on the safety of the bridge structure is different. Optionally, the current actual vehicle axle load and the current bridge The actual ratio of the bridge load-bearing capacity under the structural information, the actual vehicle axle load after the simulated vehicle lane change and the virtual ratio of the bridge load-bearing capacity under the simulated bridge structural information (the virtual ratio may be one or at least two) are compared. When the ratio is the minimum value, the current lane is the optimal lane. If there is a virtual ratio smaller than the actual ratio, the lane with the smallest virtual ratio is the optimal lane, and the vehicle is guided to the optimal lane.

Taking the setting of the first lane and the second lane on the bridge as an example, when the overloaded vehicle travels in the first lane, the first bridge structure information collected by the bridge information collection sub-module 12 includes the first bridge acceleration, the first strain, the first Movement and static displacement, etc. When the overloaded vehicle travels in the second lane, the second bridge structure information collected by the bridge information collection sub-module 12 includes the second bridge acceleration, the second strain, the second dynamic and static displacement, and the like. When the overloaded vehicle is currently driving in the second lane, the bridge structure safety check calculation is carried out through the data collected by the bridge information collection sub-module 12 to determine whether the vehicle is allowed to pass. If it is within the qualified range of the verification calculation, plan a route with less impact on the bridge safety for the overloaded vehicles to pass. For example, driving in the second lane all the time has less impact on the bridge safety than the first lane, and remind the driver to drive through the LED display. in the second lane.

S24: Guide the vehicle away from the bridge. Specifically, it can be guided manually or by sending out guidance information through the LED display.

Further, the method also includes:

S3: Use the preset bridge safety early warning model and the information on the bridge to make judgment, and perform bridge safety early warning after determining that the bridge is damaged. Preferably, S3 is performed before judging whether overloaded vehicles are allowed to go on the bridge in S2, and the result of S3 can be used as one of the basis for judging whether to allow overloaded vehicles to go on the bridge, so as to avoid vehicles from entering the bridge with potential safety hazards in time.

Specifically, the early warning module 2 includes a bridge safety early warning unit 22, which supervises the bridge and issues a bridge safety early warning, specifically to the staff.

For bridge supervision, specifically, the bridge safety early warning unit 22 performs real-time detection and judgment on the safety of the bridge based on the data transmitted by the monitoring module 1, for example, the current health self-assessment information of the bridge. After it is determined that the bridge is damaged and there are potential safety hazards, the bridge safety early warning unit 22 is used for early warning to remind the staff to respond to the potential safety hazards of the bridge, and the responses made by the staff include various bridge maintenance and maintenance measures. In addition to the impact of carrying vehicles on the bridge structure, natural factors such as wind loads, debris flows, earthquakes, and human uncontrollable factors such as ship strikes will affect the bridge structure, resulting in bridge corrosion, spalling, holes, weathering and other damages. The monitoring module 1 may also include equipment such as an earthquake detection device to directly monitor the natural environment that can affect the safety of the bridge.

In this embodiment, if the vehicle is allowed to pass, an optimized travel route is planned to minimize the damage to the bridge structure; when the vehicle is not allowed to pass, the vehicle is guided away from the bridge in time.

In addition to the above bridge supervision and early warning method, the present invention also provides a bridge supervision and early warning system based on the fusion and sharing of multi-source monitoring data using the method, and the beneficial effects may refer to the above embodiments accordingly. The system specifically includes a monitoring module 1 , an early warning module 2 , a fusion sharing module 3 and a management module 4 .

The early warning module 2 includes a vehicle overload warning unit 21;

The monitoring module 1 is used to monitor information on the bridge in real time, and the information on the bridge includes vehicle load information and bridge structure information;

The vehicle overload warning unit 21 is used to judge by using the preset overload warning model and the information on the bridge. If the vehicle is overloaded, carry out the overload vehicle warning and judge whether the overloaded vehicle is allowed to go on the bridge, and the overloaded vehicle that is allowed to go on the bridge is carried out. Optimization of vehicle traffic routes, wherein the vehicle traffic routes include lanes;

The fusion sharing module 3 is used to receive and save the data information transmitted by the monitoring module 1, the early warning module 2, and the management module 4;

The management module 4 is used to manage the normal operation, error debugging and hardware maintenance of the monitoring module 1 , the early warning module 2 , and the fusion sharing module 3 .

Further, the early warning module 2 further includes a bridge safety early warning unit 22, which is configured to use a preset bridge safety early warning model and the information on the bridge to make judgments, and to perform bridge safety early warning after it is determined that the bridge is damaged.

Further, the monitoring module 1 includes a vehicle information collection sub-module 11 for monitoring the vehicle load information, and the vehicle information collection sub-module 11 includes:

a vehicle speed size unit 111, used to collect vehicle speed information;

a vehicle axle load unit 112 for collecting vehicle axle load information;

The vehicle license plate unit 113 is used to collect vehicle license plate number information;

The lane change unit 114 is configured to collect vehicle lane change information, where the vehicle lane change information includes the vehicle-to-vehicle distance, the number of lanes the vehicle travels on, and the safety influence coefficient on the bridge structure when the vehicle travels on each lane of the bridge .

Further, the vehicle axle load information is obtained by using bridge dynamic weighing technology.

Further, the monitoring module 1 includes a bridge information collection sub-module 12 for monitoring the bridge structure information, and the bridge information collection sub-module 12 includes:

The acceleration monitoring unit 121 is used to collect bridge acceleration information;

The strain monitoring unit 122 is used for collecting bridge strain information;

The dynamic and static displacement monitoring unit 123 is used to collect bridge dynamic and static displacement information;

The health self-assessment unit 124 is used to collect the health self-assessment information of the bridge.

The invention uses the Internet technology to build a multi-source data fusion and sharing platform, completes high-speed access and analysis of multi-type, multi-source and multi-manufacturer sensor data, facilitates comparison and analysis of various types of data, and builds a system based on big data. The multi-dimensional early warning model and early warning service system for vehicle load and structural safety of highway bridges realizes the reasonable and rapid identification of the right of way for overloaded vehicles on highway bridges and the optimization of the passage route, which solves the difficulty of bridge cluster monitoring, low data sharing rate, and data value. The problem of low mining rate also greatly reduces hardware costs and analysis costs.

In the several embodiments provided in this application, it should be understood that the disclosed system may be implemented in other manners. For example, the above-described embodiments are only illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into Another system, or some features can be ignored, or not implemented. On the other hand, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of modules may be in electrical, mechanical or other forms.

Modules described as separate components may or may not be physically separated, and components shown as modules may or may not be physical modules, that is, they may be located in one place, or may be distributed to multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically alone, or two or more modules may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware, and can also be implemented in the form of software function modules.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments can be referred to each other.

The bridge supervision and early warning method and system provided by the present invention are described in detail above. The principles and implementations of the present invention are described herein by using specific examples, and the descriptions of the above embodiments are only used to help understand the method and the core idea of the present invention. It should be pointed out that for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can also be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims (10)

1. A bridge supervision and early warning method based on multi-source monitoring data fusion and sharing is characterized by comprising the following steps:

monitoring on-bridge information in real time, wherein the on-bridge information comprises vehicle load information and bridge structure information;

and judging by utilizing a preset overload early warning model and the on-bridge information, if the vehicle is overloaded, carrying out early warning on the overloaded vehicle, judging whether the overloaded vehicle is allowed to go on the bridge or not, and optimizing a vehicle passing line for the overloaded vehicle which is allowed to go on the bridge, wherein the vehicle passing line comprises a lane.

2. The bridge supervision and early warning method based on multi-source monitoring data fusion sharing according to claim 1, further comprising:

and judging by using a preset bridge safety early warning model and the information on the bridge, and carrying out bridge safety early warning after the bridge is determined to be damaged.

3. The multi-source monitoring data fusion sharing-based bridge supervision and early warning method according to claim 1, wherein the vehicle load information comprises vehicle speed information, vehicle axle weight information, vehicle license plate number information and vehicle lane change information; the vehicle lane change information comprises the inter-vehicle distance between the vehicles, the number of lanes on which the vehicles run and the safety influence coefficient on the bridge structure when the vehicles run on each lane of the bridge.

4. The bridge supervision and early warning method based on multi-source monitoring data fusion and sharing of claim 3, wherein the vehicle axle weight information is obtained by adopting a bridge dynamic weighing technology.

5. The bridge supervision and early warning method based on multi-source monitoring data fusion and sharing of claim 1, wherein the bridge structure information comprises bridge acceleration information, bridge strain information, bridge dynamic and static displacement information and health self-assessment information.

6. The utility model provides a bridge supervision, early warning system based on multisource monitoring data fuses sharing which characterized in that includes: the system comprises a monitoring module, an early warning module, a fusion sharing module and a management module, wherein the early warning module comprises a vehicle overload early warning unit;

the monitoring module is used for monitoring on-bridge information in real time, and the on-bridge information comprises vehicle load information and bridge structure information;

the vehicle overload early warning unit is used for judging by utilizing a preset overload early warning model and the on-bridge information, if the vehicle is overloaded, carrying out overload vehicle early warning and judging whether the overloaded vehicle is allowed to be on the bridge or not, and optimizing a vehicle passing line for the overloaded vehicle which is allowed to be on the bridge, wherein the vehicle passing line comprises a lane;

the fusion sharing module is used for receiving and storing the data information transmitted by the monitoring module, the early warning module and the management module;

the management module is used for managing the normal operation, error debugging and hardware maintenance of the monitoring module, the early warning module and the fusion sharing module.

7. The bridge supervision and early warning system based on multi-source monitoring data fusion and sharing of claim 6, wherein the early warning module further comprises a bridge safety early warning unit for judging by using a preset bridge safety early warning model and the on-bridge information and carrying out bridge safety early warning after determining that the bridge is damaged.

8. The multi-source monitoring data fusion sharing-based bridge supervision and early warning system according to claim 6, wherein the monitoring module comprises a vehicle information acquisition sub-module for monitoring the vehicle load information, the vehicle information acquisition sub-module comprising:

the vehicle speed unit is used for acquiring vehicle speed information;

the vehicle axle load unit is used for acquiring vehicle axle load information;

the vehicle license plate unit is used for acquiring the number information of the vehicle license plate;

and the lane change unit is used for collecting vehicle lane change information, wherein the vehicle lane change information comprises the distance between vehicles, the number of lanes on which the vehicles run and the safety influence coefficient on the bridge structure when the vehicles run on each lane of the bridge.

9. The multi-source monitoring data fusion sharing-based bridge supervision and early warning system according to claim 8, wherein the vehicle axle weight information is obtained by adopting a bridge dynamic weighing technology.

10. The multi-source monitoring data fusion sharing-based bridge supervision and early warning system according to claim 6, wherein the monitoring module comprises a bridge information acquisition sub-module for monitoring the bridge structure information, the bridge information acquisition sub-module comprising:

the acceleration monitoring unit is used for acquiring bridge acceleration information;

the strain monitoring unit is used for acquiring bridge strain information;

the dynamic and static displacement monitoring unit is used for acquiring dynamic and static displacement information of the bridge;

and the health self-evaluation unit is used for acquiring health self-evaluation information of the bridge.

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