CN111710165A - Bridge supervision and early warning method and system based on multi-source monitoring data fusion and sharing - Google Patents
Bridge supervision and early warning method and system based on multi-source monitoring data fusion and sharing Download PDFInfo
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- CN111710165A CN111710165A CN202010827320.XA CN202010827320A CN111710165A CN 111710165 A CN111710165 A CN 111710165A CN 202010827320 A CN202010827320 A CN 202010827320A CN 111710165 A CN111710165 A CN 111710165A
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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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
- G01G19/02—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles
- G01G19/03—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles for weighing during motion
- G01G19/035—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles for weighing during motion using electrical weight-sensitive devices
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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
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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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
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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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
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Abstract
The invention discloses a bridge supervision and early warning method and system based on multi-source monitoring data fusion and sharing. The method comprises 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. The method combines bridge safety and health monitoring with vehicle overload conditions, allows vehicles to enter a bridge main body under the condition of ensuring that the vehicles run on the bridge safely, is beneficial to ensuring the running safety of the vehicles, can not allow overloaded vehicles which seriously affect a bridge structure to go onto the bridge, can reduce the damage to the bridge structure, and improves the safety of bridge supervision.
Description
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 multi-source monitoring data fusion and sharing.
Background
The highway traffic infrastructure is the foundation and the life line of national economy development, wherein, the bridge is the weak link of highway infrastructure, influences the safety of whole highway network. In recent years, due to frequent bridge safety accidents caused by natural factors, human factors and the like, great economic loss and personal injury are caused. Therefore, the capability of safety monitoring, early warning and management and control of the reinforced bridge is an effective means for reducing loss.
However, the existing bridge safety and health monitoring system 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 needs to be solved by those skilled in the art at present.
Disclosure of Invention
In view of this, the invention aims to provide a bridge supervision and early warning method based on multi-source monitoring data fusion and sharing, which can improve the safety of bridge supervision. The invention also aims to provide a bridge supervision and early warning system based on multi-source monitoring data fusion and sharing, which can improve the safety of bridge supervision.
In order to achieve the purpose, the invention provides the following technical scheme:
a bridge supervision and early warning method based on multi-source monitoring data fusion and sharing comprises 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.
Preferably, the method further comprises the following steps:
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.
Preferably, the vehicle load information includes vehicle speed information, vehicle axle weight information, vehicle license plate number information, and vehicle change lane 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.
Preferably, the vehicle axle weight information is acquired by adopting a 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-evaluation information.
A bridge supervision and early warning system based on multi-source monitoring data fusion sharing comprises: 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 system;
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.
Preferably, the early warning module further comprises a bridge safety early warning unit, which is used for judging by using a preset bridge safety early warning model and the information on the bridge, and performing 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, the vehicle information collection sub-module including:
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.
Preferably, the vehicle axle weight information is acquired by adopting a bridge dynamic weighing technology.
Preferably, the monitoring module includes a bridge information collection submodule for monitoring the bridge structure information, and the bridge information collection submodule includes:
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.
The invention provides a bridge supervision and early warning method based on multi-source monitoring data fusion and sharing, which comprises 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.
According to the method, the multi-source data such as bridge information and vehicle information are collected, the bridge safety and health monitoring is combined with the vehicle overload condition, the vehicle is allowed to enter the bridge main body under the condition that the vehicle is ensured to run on the bridge safely, the running safety of the vehicle is ensured, the overload vehicle which seriously affects the bridge structure can not be allowed to go onto the bridge, the damage to the bridge structure can be reduced, and the bridge supervision safety is improved.
The bridge supervision and early warning system based on multi-source monitoring data fusion and sharing can improve the safety of bridge supervision.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a structural diagram of a bridge supervision and early warning system based on multi-source monitoring data fusion sharing provided in an embodiment of the present application;
fig. 2 is a first flowchart of a bridge supervision and early warning method based on multi-source monitoring data fusion sharing according to an embodiment of the present application;
fig. 3 is a second flowchart of a bridge supervision and early warning method based on multi-source monitoring data fusion sharing provided in the embodiment of the present application.
Reference numerals:
the system comprises a monitoring module 1, a vehicle information acquisition submodule 11, a vehicle speed unit 111, a vehicle axle load unit 112, a vehicle license plate unit 113, a lane change unit 114, a bridge information acquisition submodule 12, an acceleration monitoring unit 121, a strain monitoring unit 122, a dynamic and static displacement monitoring unit 123, a health self-evaluation unit 124, an early warning module 2, a vehicle overload early warning unit 21, a bridge safety early warning unit 22, a fusion sharing module 3 and a management module 4.
Detailed Description
The technical solutions 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, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The core of the invention is to provide a bridge supervision and early warning method based on multi-source monitoring data fusion and sharing, which can improve the safety of bridge supervision. The other core of the invention is to provide a bridge supervision and early warning system based on multi-source monitoring data fusion and sharing, which can improve the safety of bridge supervision.
Unless defined otherwise, 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 terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The invention provides a bridge supervision and early warning method based on multi-source monitoring data fusion and sharing, which is applied to a bridge, wherein a detection area is arranged in a section of an end part of the bridge, and the bridge supervision and early warning method is implemented so as to detect vehicles going to the bridge after passing through the detection area. Meanwhile, the method is realized by a corresponding bridge supervision and early warning system based on multi-source monitoring data fusion sharing, and the system comprises 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 comprises the following steps:
s1: and monitoring on-bridge information in real time, wherein the on-bridge information comprises vehicle load information and bridge structure information. The vehicle load information is collected by a vehicle information collection submodule 11 in the monitoring module 1, and the bridge structure information is collected by a bridge information collection submodule 12 in the monitoring module 1.
Optionally, the vehicle load information includes the following four aspects:
(1.1) vehicle speed information is obtained through a Bridge dynamic weighing technology (BWIM), and in addition, the obtaining is an important step of further obtaining the axle weight of the vehicle through the Bridge dynamic weighing technology;
(1.2) acquiring vehicle axle weight information based on a bridge dynamic weighing technology, wherein the bridge dynamic weighing technology specifically comprises methods such as an equivalent shear method, a wavelet transform method, a virtual simply supported beam method, a support reaction method, an Independent Component Analysis (ICA) method and the like;
(1.3) vehicle license number information used for distinguishing each vehicle, optionally, the vehicle information collection submodule 11 includes a camera, the vehicle license number information is realized by image recognition of the shot vehicle, the function of the vehicle information collection submodule 11 for recognizing the vehicle license number can be realized by machine learning, the machine learning specifically includes methods of target detection, instance segmentation and the like, and certainly, the function of the vehicle information collection submodule 11 for recognizing other information of the image can also be realized by machine learning;
(1.4) vehicle lane change information, the vehicle information acquisition sub-module 11 acquires the information by acquiring vehicle images and performing image recognition, and the function of recognizing the vehicle lane change information is realized by machine learning.
Wherein the vehicle lane change information includes the following three aspects:
(1.4.1) acquiring the distance between the vehicles by carrying out image recognition on the vehicles through a camera arranged on the bridge;
(1.4.2) acquiring the number of lanes where the vehicle runs by carrying out image recognition on the vehicle through a camera installed on the bridge;
(1.4.3) determining the ratio of the axle weight of the vehicle to the maximum allowable bearing of each lane as the safety influence coefficient by combining the bridge structure information, for example, combining the axle weight and the bridge structure information of each lane on the bridge.
For a vehicle, the standard of whether the vehicle is overloaded or not can be determined based on the vehicle axle load and the bridge structure information, however, the vehicle cannot get on the bridge as long as the vehicle is overloaded, and the overload can be caused by the bridge as long as the set safety requirement is met by combining the real-time condition of the bridge. The information of the vehicle lane change is obtained, and the vehicle passing route can be better planned for the overloaded vehicle which is allowed to pass through after the verification of the bridge structure and the overloaded vehicle.
Optionally, the bridge structure information includes the following three aspects:
(2.1) bridge acceleration information is acquired by an acceleration sensor arranged on the bridge in the bridge information acquisition submodule 12 and can reflect bridge displacement;
(2.2) acquiring bridge strain information by using strain sensors arranged on the bridge in the bridge information acquisition submodule 12, preferably, the strain sensors comprise high-precision FBG strain sensors, and a high-precision strain measurement technology can be adopted to improve the precision and the sensitivity of bridge strain measurement;
(2.3) carrying out real-time monitoring and acquisition on the dynamic and static displacement information of the bridge by using a satellite positioning system in the bridge information acquisition submodule 12, wherein the satellite positioning system comprises a Beidou satellite positioning system and other mainstream positioning systems;
and (2.4) comparing the health self-evaluation information with the preset bridge health standard specifically by combining bridge acceleration information, bridge strain information and bridge dynamic and static displacement information, and performing self-evaluation on the bridge health safety level, wherein when the bridge health standard is exceeded, the health self-evaluation information is that the bridge is unhealthy, and the health self-evaluation information is that the bridge is healthy if the bridge is not healthy.
The bridge dynamic and static displacement real-time monitoring technology based on the acceleration sensor and the bridge dynamic and static displacement monitoring technology based on the Beidou satellite positioning system are utilized to simultaneously monitor the bridge displacement in real time, the concept of multi-source data fusion and sharing can be embodied, and the reliability of the monitoring result is ensured.
S2: 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. Wherein, S2 is realized by the early warning module 2 and the fusion sharing module 3.
The fusion sharing module 3 receives multi-source data, and specifically comprises data transmitted by the monitoring module 1, the early warning module 2 and the management module 4. Meanwhile, the fusion sharing module 3 stores all the data information received by the fusion sharing module 3, and in addition, the preset overload early warning model and the preset safety early warning model are also pre-stored in the fusion sharing module 3. By constructing a fusion sharing platform of multi-source data, real-time updating, comparative analysis and quick calling of the data can be maintained.
The early warning module 2 includes a vehicle overload early warning unit 21. The early warning module 2 transfers data from the fusion sharing module 3, and then processes the data to supervise the vehicle. The vehicle overload early warning unit 21 can send out information of early warning of an overloaded vehicle, and specifically sends out the information to a driver, optionally, the vehicle overload early warning unit 21 includes an LED display screen arranged above the bridge, and the information of early warning of the overloaded vehicle is displayed on the LED display screen.
For the supervision of the vehicle, the following steps S21-S24 are specifically included:
s21: and judging whether the vehicle is an overloaded vehicle or not by utilizing a preset overload early warning model and the on-bridge information, and if so, entering S22.
Optionally, the preset overload early warning model includes overload standards corresponding to bridges under various bridge structure information, the overload standard of the current bridge can be determined based on comparison between the bridge structure information obtained in real time and the preset overload early warning model, and then the axle load information of the current vehicle is compared with the overload standard of the current bridge, so that whether the vehicle is overloaded on the bridge can be determined.
S22: and (4) carrying out early warning on the overloaded vehicle, judging whether the overloaded vehicle is allowed to get on the bridge or not, if so, entering S23, and otherwise, entering S24.
Specifically, if it is determined that the vehicle is an overloaded vehicle, the vehicle overload warning unit 21 further checks the bridge structure and the overloaded vehicle to determine whether the overloaded vehicle is allowed to get on the bridge. Optionally, the vehicle overload warning unit 21 retrieves important data such as dynamic and static displacement (deflection) and strain magnitude of the bridge measured by an acceleration sensor, a strain sensor and the like, compares the data with a pre-stored bridge safety limit value or models the data, and if the overloaded vehicle is on the bridge and the data is still in the limit range, meets the set safety requirement, the vehicle can be on the bridge. In addition, when determining whether the overloaded vehicle allows getting on the axle, specifically, the determination may be made only by the result of the checking of the vehicle overload warning unit 21, or may be made in combination with the result of the checking and the response of the worker.
S23: optimizing a vehicle passing line for overloaded vehicles that are allowed to get on the bridge, wherein the vehicle passing line comprises lanes.
Specifically, the optimal lane can be selected by optimizing the vehicle passing route after the overloaded vehicle gets on the bridge and specifically combining the information of the vehicle changing lane. Optionally, the actual ratio of the axle load of the current actual vehicle to the bridge bearing capacity of the current bridge structure information, the axle load of the actual vehicle after the simulated vehicle changes lane and the virtual ratio (which may be one or at least two) of the bridge bearing capacity of the simulated bridge structure information are compared, in each ratio, when the actual ratio is the minimum value, the current lane is the optimal lane, if the virtual ratio is smaller than the actual ratio, the lane with the minimum virtual ratio is the optimal lane, and the vehicle is guided to the optimal lane.
Taking the first lane and the second lane arranged on the bridge as an example, when the overloaded vehicle runs on the first lane, the first bridge structure information acquired by the bridge information acquisition submodule 12 includes a first bridge acceleration, a first strain, a first dynamic and static displacement and the like. When the overloaded vehicle runs on the second lane, the second bridge structure information acquired by the bridge information acquisition submodule 12 includes second bridge acceleration, second strain, second dynamic and static displacement and the like. When the overloaded vehicle runs in the second lane at present, the safety check calculation of the bridge structure is carried out through the data acquired by the bridge information acquisition submodule 12, and whether the passing is allowed or not is judged. If the vehicle is in the checking and calculating qualified range, a route with small influence on the safety of the bridge is planned to pass the overloaded vehicle, for example, if the influence on the safety of the bridge caused by the vehicle always running in the second lane is smaller than that in the first lane, the driver is reminded to run on the second lane through the LED display screen.
S24: and guiding the vehicle to drive away from the bridge. The guide can be manually guided or can be guided by sending guide information through an LED display screen.
Further, the method further comprises:
s3: 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. Preferably, S3 is performed before the determination of whether to allow the overloaded vehicle to get on the bridge in S2, and the result of S3 can be used as one of the criteria for determining whether to allow the overloaded vehicle to get on the bridge, and to avoid the vehicle from entering the bridge with potential safety hazard.
Specifically, the early warning module 2 includes a bridge safety early warning unit 22, and the bridge safety early warning unit 22 supervises the bridge and sends out bridge safety early warning, specifically to the staff.
For bridge supervision, specifically, the bridge safety early warning unit 22 detects and judges the safety condition of the bridge in real time based on the data transmitted by the monitoring module 1, for example, the current health self-evaluation information of the bridge. After the bridge is determined to be damaged and potential safety hazards exist, early warning is carried out through the bridge safety early warning unit 22, workers are reminded to respond to the potential safety hazards of the bridge, and the response made by the workers comprises various bridge maintenance and maintenance measures. Because except the influence that the bearing vehicle can produce bridge structures, natural factors such as wind load, debris flow, earthquake and the like, and artificial uncontrollable factors such as ship impact and the like can all influence the bridge structures, the bridge corrosion, peeling, holes, weathering and other damages caused by the influence are caused, and the monitoring module 1 can also comprise equipment such as an earthquake detection device and the like, and can directly monitor the natural environment which can influence the safety of the bridge.
In the embodiment, if the vehicles are allowed to pass, an optimized passing route is planned so as to minimize damage to the bridge structure; and when the vehicles are not allowed to pass, the vehicles are guided to drive away from the bridge in time.
In addition to the bridge supervision and early warning method, the invention also provides a bridge supervision and early warning system based on multi-source monitoring data fusion sharing, which applies the method, and the beneficial effects can be correspondingly referred to the above embodiments. The system specifically comprises a monitoring module 1, an early warning module 2, a fusion sharing module 3 and a management module 4.
The early warning module 2 comprises a vehicle overload early warning unit 21;
the monitoring module 1 is used for monitoring on-bridge information in real time, wherein the on-bridge information comprises vehicle load information and bridge structure information;
the vehicle overload early warning unit 21 is configured to perform judgment by using a preset overload early warning model and the on-bridge information, perform overload vehicle early warning and judge whether to allow an overloaded vehicle to go on a bridge if the vehicle is overloaded, and perform optimization of a vehicle passing route for the overloaded vehicle that is allowed to go on the bridge, where the vehicle passing route includes a lane;
the fusion sharing module 3 is used for receiving and storing 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 for managing 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 perform judgment by using a preset bridge safety early warning model and the on-bridge information, and perform bridge safety early warning after determining that the bridge is damaged.
Further, the monitoring module 1 includes a vehicle information collecting sub-module 11 for monitoring the vehicle load information, and the vehicle information collecting sub-module 11 includes:
the vehicle speed size unit 111 is used for acquiring vehicle speed information;
a vehicle axle weight unit 112, configured to collect vehicle axle weight information;
a vehicle license plate unit 113 for collecting vehicle license plate number information;
and the lane change unit 114 is used for collecting vehicle lane change information, wherein the vehicle lane change information comprises the 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.
Further, the vehicle axle weight information is obtained by adopting a bridge dynamic weighing technology.
Further, the monitoring module 1 includes a bridge information collecting sub-module 12 for monitoring the bridge structure information, and the bridge information collecting sub-module 12 includes:
the acceleration monitoring unit 121 is used for acquiring bridge acceleration information;
the strain monitoring unit 122 is used for acquiring bridge strain information;
the dynamic and static displacement monitoring unit 123 is used for acquiring dynamic and static displacement information of the bridge;
and the health self-evaluation unit 124 is used for collecting health self-evaluation information of the bridge.
The invention builds a multi-source data fusion sharing platform by utilizing the internet technology, completes high-speed access and analysis of sensor data of multiple types, multiple sources and multiple manufacturers, is convenient to compare and analyze various data, constructs a multi-dimensional early warning model and an early warning service system of road bridge vehicle load and structure safety based on big data, realizes reasonable and rapid judgment of road bridge overload vehicle right of passage and optimization of passage lines, solves the problems of difficult bridge cluster monitoring, low data sharing rate and low data value mining rate, and greatly reduces hardware cost and analysis cost.
In the several embodiments provided in the present application, it should be understood that the disclosed system may be implemented in other ways. For example, the above-described embodiments are merely illustrative, and for example, a division of modules is merely a division of logical functions, and an actual implementation may have another division, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, and the indirect coupling or communication connection of the modules may be in an electrical, mechanical or other form.
Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The bridge supervision and early warning method and system provided by the invention are introduced in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the 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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| CN202010827320.XA CN111710165B (en) | 2020-08-17 | 2020-08-17 | Bridge supervision and early warning method and system based on multi-source monitoring data fusion and sharing |
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| CN202010827320.XA CN111710165B (en) | 2020-08-17 | 2020-08-17 | Bridge supervision and early warning method and system based on multi-source monitoring data fusion and sharing |
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112419725A (en) * | 2020-11-19 | 2021-02-26 | 重庆同枥信息技术有限公司 | Overweight vehicle driving state risk early warning method and system based on structure monitoring |
| CN112507588A (en) * | 2020-12-03 | 2021-03-16 | 宁波朗达工程科技有限公司 | Method and system for evaluating influence of overloaded vehicle on bridge and computer equipment |
| CN112562278A (en) * | 2020-11-26 | 2021-03-26 | 浙江浙大中控信息技术有限公司 | Method for realizing bridge load early warning monitoring based on traffic big data |
| CN112816046A (en) * | 2020-12-31 | 2021-05-18 | 深圳市天健工程技术有限公司 | Bridge abnormal data trend judgment device based on Beidou positioning system |
| CN113567027A (en) * | 2021-08-10 | 2021-10-29 | 尹恒 | Road overload grade working condition monitoring method and system |
| CN113610442A (en) * | 2021-08-25 | 2021-11-05 | 浙江大华技术股份有限公司 | Vehicle on-axle management method and device |
| CN113804258A (en) * | 2021-09-27 | 2021-12-17 | 中铁第四勘察设计院集团有限公司 | Intelligent monitoring system, method, equipment and storage medium for rail bridge |
| CN113984110A (en) * | 2021-09-29 | 2022-01-28 | 合肥市正茂科技有限公司 | Bridge safety and health monitoring system and method |
| CN114512004A (en) * | 2022-01-18 | 2022-05-17 | 元准智能科技(苏州)有限公司 | Anti-overturn system for actively guiding and dispatching vehicles by bridge and dispatching method thereof |
| CN115171375A (en) * | 2022-06-27 | 2022-10-11 | 襄阳达安汽车检测中心有限公司 | Bridge traffic control method, device, equipment and storage medium |
| CN117291350A (en) * | 2023-11-24 | 2023-12-26 | 深圳市城市交通规划设计研究中心股份有限公司 | Urban bridge group safety state evaluation system and evaluation method |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101615341A (en) * | 2009-07-17 | 2009-12-30 | 重庆交通大学 | A kind of bridge intelligent control method based on traffic flow control |
| US20150316426A1 (en) * | 2012-12-13 | 2015-11-05 | Universität Wien | Method for Measuring a Moving Vehicle |
| CN105719478A (en) * | 2014-12-02 | 2016-06-29 | 西安众智惠泽光电科技有限公司 | Real-time on-line monitoring system for overloaded truck passing bridge |
| CN106017556A (en) * | 2016-05-20 | 2016-10-12 | 清华大学合肥公共安全研究院 | Bridge safety operation monitoring and management system |
| CN106908129A (en) * | 2017-03-29 | 2017-06-30 | 华南理工大学 | A kind of intelligent region vehicle overload detection, early warning system and method |
| EP3187838A1 (en) * | 2015-12-29 | 2017-07-05 | Neostrain spolka z ograniczona odpowiedzialnoscia | System for vehicles weight preselection and evaluation of the technical state of road infrastructure |
| WO2017164796A1 (en) * | 2016-03-22 | 2017-09-28 | Motus Weighing Ab | Weighing scale and methods thereof |
| CN107687914A (en) * | 2017-09-18 | 2018-02-13 | 核工业西南勘察设计研究院有限公司 | A kind of bridge management system and bridge management method |
| CN207082223U (en) * | 2017-08-22 | 2018-03-09 | 湖南大学 | A kind of system that Vehicle Axles and speed are travelled on identification bridge |
| CN207752355U (en) * | 2017-12-28 | 2018-08-21 | 南京工程学院 | A kind of overweight alarm interceptor control system based on PLC |
| CN109992827A (en) * | 2019-02-20 | 2019-07-09 | 深圳高速工程顾问有限公司 | Bridge structure method for early warning, device, computer equipment and storage medium |
| CN110672192A (en) * | 2019-10-10 | 2020-01-10 | 英华达（南京）科技有限公司 | Road surface load monitoring method, device, system, electronic equipment and storage medium |
| CN110827539A (en) * | 2019-10-30 | 2020-02-21 | 张家嘉 | Bridge overload management device and method |
| CN211042444U (en) * | 2019-12-27 | 2020-07-17 | 北京智宸天驰科技有限公司 | Full-bridge deck vehicle positioning and load distribution monitoring system |
| DE102019000401A1 (en) * | 2019-01-22 | 2020-07-23 | Daimler Ag | Process for animating movements in a vehicle |
| CN111460551A (en) * | 2020-03-27 | 2020-07-28 | 华南理工大学 | Simulation, classification and compilation method of two-dimensional vehicle overrun load spectrum |
| CN111489559A (en) * | 2020-04-03 | 2020-08-04 | 河海大学 | System and method for monitoring and early warning overload of viaduct |
-
2020
- 2020-08-17 CN CN202010827320.XA patent/CN111710165B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101615341A (en) * | 2009-07-17 | 2009-12-30 | 重庆交通大学 | A kind of bridge intelligent control method based on traffic flow control |
| US20150316426A1 (en) * | 2012-12-13 | 2015-11-05 | Universität Wien | Method for Measuring a Moving Vehicle |
| CN105719478A (en) * | 2014-12-02 | 2016-06-29 | 西安众智惠泽光电科技有限公司 | Real-time on-line monitoring system for overloaded truck passing bridge |
| EP3187838A1 (en) * | 2015-12-29 | 2017-07-05 | Neostrain spolka z ograniczona odpowiedzialnoscia | System for vehicles weight preselection and evaluation of the technical state of road infrastructure |
| WO2017164796A1 (en) * | 2016-03-22 | 2017-09-28 | Motus Weighing Ab | Weighing scale and methods thereof |
| CN106017556A (en) * | 2016-05-20 | 2016-10-12 | 清华大学合肥公共安全研究院 | Bridge safety operation monitoring and management system |
| CN106908129A (en) * | 2017-03-29 | 2017-06-30 | 华南理工大学 | A kind of intelligent region vehicle overload detection, early warning system and method |
| CN207082223U (en) * | 2017-08-22 | 2018-03-09 | 湖南大学 | A kind of system that Vehicle Axles and speed are travelled on identification bridge |
| CN107687914A (en) * | 2017-09-18 | 2018-02-13 | 核工业西南勘察设计研究院有限公司 | A kind of bridge management system and bridge management method |
| CN207752355U (en) * | 2017-12-28 | 2018-08-21 | 南京工程学院 | A kind of overweight alarm interceptor control system based on PLC |
| DE102019000401A1 (en) * | 2019-01-22 | 2020-07-23 | Daimler Ag | Process for animating movements in a vehicle |
| CN109992827A (en) * | 2019-02-20 | 2019-07-09 | 深圳高速工程顾问有限公司 | Bridge structure method for early warning, device, computer equipment and storage medium |
| CN110672192A (en) * | 2019-10-10 | 2020-01-10 | 英华达（南京）科技有限公司 | Road surface load monitoring method, device, system, electronic equipment and storage medium |
| CN110827539A (en) * | 2019-10-30 | 2020-02-21 | 张家嘉 | Bridge overload management device and method |
| CN211042444U (en) * | 2019-12-27 | 2020-07-17 | 北京智宸天驰科技有限公司 | Full-bridge deck vehicle positioning and load distribution monitoring system |
| CN111460551A (en) * | 2020-03-27 | 2020-07-28 | 华南理工大学 | Simulation, classification and compilation method of two-dimensional vehicle overrun load spectrum |
| CN111489559A (en) * | 2020-04-03 | 2020-08-04 | 河海大学 | System and method for monitoring and early warning overload of viaduct |
Non-Patent Citations (1)
| Title |
|---|
| 刘静 等: "超重车辆对公路桥梁的破坏作用及过桥管理措施", 《公路交通科技（应用技术版）》 * |
Cited By (18)
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|---|---|---|---|---|
| CN112419725B (en) * | 2020-11-19 | 2021-08-27 | 重庆同枥信息技术有限公司 | Overweight vehicle driving state risk early warning method and system based on structure monitoring |
| CN112419725A (en) * | 2020-11-19 | 2021-02-26 | 重庆同枥信息技术有限公司 | Overweight vehicle driving state risk early warning method and system based on structure monitoring |
| CN112562278B (en) * | 2020-11-26 | 2022-06-24 | 浙江中控信息产业股份有限公司 | Method for realizing bridge load early warning monitoring based on traffic big data |
| CN112562278A (en) * | 2020-11-26 | 2021-03-26 | 浙江浙大中控信息技术有限公司 | Method for realizing bridge load early warning monitoring based on traffic big data |
| CN112507588A (en) * | 2020-12-03 | 2021-03-16 | 宁波朗达工程科技有限公司 | Method and system for evaluating influence of overloaded vehicle on bridge and computer equipment |
| CN112507588B (en) * | 2020-12-03 | 2023-12-29 | 宁波朗达工程科技有限公司 | Method, system and computer equipment for evaluating influence of overloaded vehicle on bridge |
| CN112816046A (en) * | 2020-12-31 | 2021-05-18 | 深圳市天健工程技术有限公司 | Bridge abnormal data trend judgment device based on Beidou positioning system |
| CN113567027A (en) * | 2021-08-10 | 2021-10-29 | 尹恒 | Road overload grade working condition monitoring method and system |
| CN113610442A (en) * | 2021-08-25 | 2021-11-05 | 浙江大华技术股份有限公司 | Vehicle on-axle management method and device |
| CN113610442B (en) * | 2021-08-25 | 2024-04-26 | 浙江大华技术股份有限公司 | Vehicle upper axle management method and device |
| CN113804258B (en) * | 2021-09-27 | 2023-08-04 | 中铁第四勘察设计院集团有限公司 | Intelligent monitoring system, method, equipment and storage medium for track bridge |
| CN113804258A (en) * | 2021-09-27 | 2021-12-17 | 中铁第四勘察设计院集团有限公司 | Intelligent monitoring system, method, equipment and storage medium for rail bridge |
| CN113984110A (en) * | 2021-09-29 | 2022-01-28 | 合肥市正茂科技有限公司 | Bridge safety and health monitoring system and method |
| CN114512004A (en) * | 2022-01-18 | 2022-05-17 | 元准智能科技(苏州)有限公司 | Anti-overturn system for actively guiding and dispatching vehicles by bridge and dispatching method thereof |
| CN114512004B (en) * | 2022-01-18 | 2023-12-15 | 元准智能科技(苏州)有限公司 | Anti-overturning system for actively guiding and dispatching vehicles for bridge and dispatching method thereof |
| CN115171375A (en) * | 2022-06-27 | 2022-10-11 | 襄阳达安汽车检测中心有限公司 | Bridge traffic control method, device, equipment and storage medium |
| CN117291350A (en) * | 2023-11-24 | 2023-12-26 | 深圳市城市交通规划设计研究中心股份有限公司 | Urban bridge group safety state evaluation system and evaluation method |
| CN117291350B (en) * | 2023-11-24 | 2024-03-26 | 深圳市城市交通规划设计研究中心股份有限公司 | Urban bridge group safety state evaluation system and evaluation method |
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